UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Nutrient intakes of elite Canadian athletes with a spinal cord injury Krempien, Jennifer Luella 2010

You don't seem to have a PDF reader installed, try download the pdf

Item Metadata

Download

Media
ubc_2010_fall_krempien_jennifer.pdf [ 2.85MB ]
[if-you-see-this-DO-NOT-CLICK]
Metadata
JSON: 1.0071064.json
JSON-LD: 1.0071064+ld.json
RDF/XML (Pretty): 1.0071064.xml
RDF/JSON: 1.0071064+rdf.json
Turtle: 1.0071064+rdf-turtle.txt
N-Triples: 1.0071064+rdf-ntriples.txt
Original Record: 1.0071064 +original-record.json
Full Text
1.0071064.txt
Citation
1.0071064.ris

Full Text

NUTRIENT INTAKES OF ELITE CANADIAN ATHLETES  WITH A SPINAL CORD INJURY  by  Jennifer Luella Krempien B.Sc., University of Alberta, 1998  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Human Nutrition)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  July 2010 © Jennifer Luella Krempien, 2010  ii Abstract  Energy intakes of adults with spinal cord injury (SCI) have been reported to be relatively low with many micronutrients below recommended amounts but very little is known about the diets of athletes with SCI. This cross-sectional, observational study assessed energy intakes and estimated the prevalence of dietary inadequacy in a sample of elite Canadian athletes with SCI (n=32). Three-day self-reported food diaries completed at home and training camp were analyzed for energy (kcal), macronutrients, vitamins and elements and compared to the Dietary Reference Intakes (DRIs). Energy intakes were 2156 ± 431 kcal for men and 1991 ± 510 kcal for women and the macronutrient intakes as a percentage of energy were within the Acceptable Macronutrient Distribution Ranges for both men (55.6% carbohydrate, 17.9% protein, 28.1% fat) and women (53.3% carbohydrate, 17.9% protein, 28.9% fat).  While at training camp, greater than 25% of men had mean intakes below the Estimated Average Requirement (EAR) for magnesium, zinc, riboflavin, folate and vitamin B12. At home, prevalence of inadequacy decreased for magnesium, zinc and riboflavin but not for folate.  At home, men had greater intakes of vitamin D (160.1 ± 133.4 IU vs. 38.5 ± 78.3 IU, p<0.05) and calcium (856 ± 330 mg vs. 693 ± 204 mg, p<0.05).  The proportion of women with intakes below the EAR was greater while at training camp for magnesium, niacin and folate.  No significant differences in the mean intake of any nutrients were detected between home or training camp for women. Cognitive dietary restraint scores were higher than expected for men with relatively low scores for disinhibition and hunger.   These results demonstrate that athletes with SCI are at risk of several nutrient inadequacies relative to the DRIs, despite a diet with an appropriate macronutrient balance.  A higher prevalence of nutrient inadequacy was observed in men especially while at training camp.  Women were able to better maintain nutrient adequacy in both situations. This highlights an opportunity for coaches, administrators, sport scientists and dietitians working with these athletes to improve the access to better food choices and to educate athletes in making more balanced food choices.      iii Preface  This Master of Science thesis was prepared according to requirements as detailed by the Faculty of Graduate Studies at the University of British Columbia1.  For Chapters 2 and 3, I conceived the study design and methods, identified the study objectives, recruited the participants, collected and managed all data, planned and conducted the data analyses, presented the findings and wrote the manuscript.  My thesis supervisor (Dr. Susan Barr) was the Principal Investigator for the study and contributed continuously to all aspects of the study design, data management and analyses, interpretation and presentation of the results and key editorial contributions. My committee members (Dr. William Sheel and Dr. Andrei Krassioukov) made significant contributions by stimulating discussion pertaining to methodological choices, statistical analyses and provided editorial input to this manuscript Ethics approval to conducted this research was obtained by the University of British Columbia - Behavioural Research Ethics Board (BREB) on April 14, 2007 (UBC BREB number: H07-00011) with renewals on March 31, 2008 and February 3, 2009.  Ethics approval was terminated on November 19, 2009.                                                      1 The University of British Columbia Faculty of Graduate Studies. Masters and Doctoral Thesis Preparation and Submission. Available at http://www.grad.ubc.ca/current-students/dissertation-thesis-preparation/structure-masters-thesis. iv  Table of contents  Abstract ........… ......................................................................................................................... ii Preface............. ........................................................................................................................ iii Table of contents ..................................................................................................................... iv List of tables ............................................................................................................................ vii List of figures . . ........................................................................................................................ ix List of abbreviations ................................................................................................................. x Acknowledgements ................................................................................................................ xii  Chapter 1:   Introduction ......................................................................................................... 1 1.1 Background ....................................................................................................... 2 1.2 Literature review ............................................................................................... 2 1.2.1 Overview .............................................................................................. 2 1.2.2 Spinal cord injury ................................................................................. 3 1.2.3 Metabolic changes associated with spinal cord injury ....................... 5 1.2.4 Autonomic function following spinal cord injury ................................ 8 1.2.5 Body composition changes associated with spinal cord injury ......... 10 1.2.6 Body composition of athletes with spinal cord injury....................... 12 1.2.7 Energy expenditure ........................................................................... 14 1.2.8 Assessment of nutrient adequacy ..................................................... 17 1.2.9 Nutrient intakes in community living individuals with spinal cord injury .................................................................................................. 20 1.2.10 Nutrient intakes in athletes with spinal cord injury .......................... 27 1.3 Limits to current knowledge ........................................................................... 29 1.4 Rationale ......................................................................................................... 30 1.4.1 Research objectives ........................................................................... 30 1.5 References ...................................................................................................... 32  Chapter 2:   Elite Canadian Athletes with Spinal Cord Injury Are At Risk of Nutrient Inadequacies While at Home and Team Training Events ............................. 43 2.1 Introduction .................................................................................................... 44 2.2 Methods .......................................................................................................... 46 2.2.1 Overview of study design .................................................................. 46 2.2.2 Participant recruitment ..................................................................... 47 2.2.3 Participant characteristics ................................................................. 48 2.2.4 Description of spinal cord injury ....................................................... 48 2.2.5 Anthropometry and body composition ............................................. 49 2.2.6 Physical activity assessment .............................................................. 50 2.2.7 Self-reported food diary .................................................................... 51 2.2.8 Dietary analysis .................................................................................. 52 2.2.9 Prediction of energy expenditure ..................................................... 52 v  2.2.10 Nutrition knowledge.......................................................................... 53 2.2.11 Statistical analysis .............................................................................. 53 2.3 Results ............................................................................................................. 54 2.3.1 Participant characteristics ................................................................. 54 2.3.2 Dietary analysis from food sources only ........................................... 56 2.3.3 Dietary analysis incorporating supplemental vitamin and minerals 65 2.3.4 Nutrition knowledge.......................................................................... 70 2.3.5 Physical activity ................................................................................. 71 2.4 Discussion........................................................................................................ 73 2.4.1 Strengths and limitations .................................................................. 78 2.4.2 Conclusions ........................................................................................ 79 2.5 References ...................................................................................................... 81  Chapter 3:   Eating Attitudes and Behaviours in Elite Canadian Athletes with Spinal Cord Injury ...................................................................................................... 86 3.1 Introduction .................................................................................................... 87 3.2 Methods .......................................................................................................... 88 3.2.1 Overview of study design .................................................................. 88 3.2.2 Participant recruitment ..................................................................... 89 3.2.3 Participant characteristics ................................................................. 90 3.2.4 Dietary intake .................................................................................... 92 3.2.5 Three-Factor Eating Questionnaire ................................................... 92 3.2.6 Yale Eating Patterns Questionnaire ................................................... 93 3.2.7 Statistical analysis .............................................................................. 93 3.3 Results ............................................................................................................. 94 3.3.1 Participant characteristics ................................................................. 94 3.3.2 Dietary intake .................................................................................... 96 3.3.3 Three-Factor Eating Questionnaire ................................................... 97 3.3.4 Yale Eating Patterns Questionnaire ................................................... 98 3.3.5 High versus low cognitive dietary restraint ....................................... 99 3.3.6 Correlation between TFEQ and YEPQ subscales with anthropometrics and selected nutrients ..................................................................... 102 3.4 Discussion...................................................................................................... 104 3.4.1 Strengths and limitations ................................................................ 106 3.4.2 Future directions ............................................................................. 107 3.4.3 Conclusions ...................................................................................... 108 3.5 References .................................................................................................... 109       vi   Chapter 4:   Conclusion ....................................................................................................... 115 4.1 General discussion ........................................................................................ 116 4.2 Summary of the current state of knowledge ................................................ 116 4.3 General conclusions ...................................................................................... 118 4.4 Strengths and limitations .............................................................................. 124 4.5 Future directions ........................................................................................... 125 4.6 Applications................................................................................................... 126 4.7 References .................................................................................................... 129   Appendix 1:   University of British Columbia Behavioural Research Board ethics approval certificates ..................................................................................... 134 Appendix 2:   Consent form ................................................................................................. 138 Appendix 3:   Letter of invitation ......................................................................................... 145 Appendix 4:   Details of recruitment ................................................................................... 148 Appendix 5:   Participant questionnaire .............................................................................. 151 Appendix 6:   Physical activity log with rate of perceived exertion scale ........................... 155 Appendix 7:   Physical activity scale for individuals with a physical disability .................... 158 Appendix 8:    Food diary with instructions ......................................................................... 163 Appendix 9:    Nutrition Knowledge Questionnaire ............................................................ 167 Appendix 10:   Canadian Community Health Survey – summary tables.............................. 174 Appendix 11:  Three-Factor Eating Questionnaire............................................................... 178 Appendix 12:  Yale Eating Patterns Questionnaire .............................................................. 181 Appendix 13:  Summary of results for participants ............................................................. 184  vii  List of tables   Table 1.1   Segmental spinal cord level and function ......................................................... 5 Table 1.2  Explanation of terminology from Dietary Reference Intake framework ........ 19 Table 1.3   Summary of studies on dietary adequacy in adults with spinal cord injury ... 26 Table 1.4  Energy intakes of athletes with SCI: measured and predicted intakes .......... 29  Table 2.1        Participant background characteristics with group and subgroup analyses based on gender ............................................................................................. 55 Table 2.2 Group participant characteristics with comparisons made within the group based on level of injury (paraplegic vs. tetraplegia) and motor function of injury (complete vs. incomplete) .................................................................... 56 Table 2.3  Reported and predicted energy intakes categorized by gender, sport and spinal cord injury ............................................................................................. 58 Table 2.4   Usual intakes from food sources only compared to Adequate Intake of selected nutrients for men and women ......................................................... 60 Table 2.5   Comparison of the mean intakes for men from food alone for selected nutrients while at home and training camp with reference to the Estimated Average Requirement ..................................................................................... 63 Table 2.6   Comparison of the mean intakes for women from food alone for nutrients while at home and training camp with reference to the Estimated Average Requirement ................................................................................................... 65 Table 2.7          Percentage of subjects with mean intakes above the Adequate Intake for six-day average intake from food sources only and food sources with additional supplements……………………………………………………………………………………………….66 Table 2.8  Comparison of six-day mean intakes of calcium, vitamin D from food sources alone and food sources with additional supplements ................................... 67 Table 2.9  Percentage of men with six-day mean intakes from food and food plus supplements below Estimated Average Requirement ................................... 68 Table 2.10  Percentage of women with six-day mean intakes from food and food plus supplements below Estimated Average Requirement ................................... 69 Table 2.11  Comparison of six-day mean intakes of selected nutrients from food sources alone and food sources with additional supplements for nutrients .............. 70 Table 2.12  Nutrition knowledge scores with subgroup analyses based on gender ......... 71  viii     Table 3.1  Participant anthropometrics with subgroup analyses based on gender ....... 95 Table 3.2   Participant anthropometrics with subgroup analyses based on level of spinal cord injury ....................................................................................................... 95 Table 3.3   Average nutrient intakes with subgroup analyses based on gender ............. 96 Table 3.4  Average nutrient intakes with subgroup analyses based on level of spinal cord injury ....................................................................................................... 96 Table 3.5  Mean scores from the Three-Factor Eating Questionnaire for the group with subgroup analyses based on gender and level of spinal cord injury .............. 97 Table 3.6  Mean scores from the Yale Eating Patterns Questionnaire for the group with subgroup analyses based on gender and level of spinal cord injury .............. 98 Table 3.7  Percentage of participants categorized as either low or high restraint on the basis of cognitive dietary restraint score with subgroup analyses based on gender, spinal cord injury level and function ................................................. 99 Table 3.8   Comparison of anthropometrics, dietary intake and eating patterns scores between those with low and high cognitive dietary restraint scores .......... 101 Table 3.9   Association of cognitive restraint, disinhibition and hunger with anthropometric and dietary intake .............................................................. 102 Table 3.10 Correlation of selected scales from Yale Eating Patterns Questionnaire ..... 103  Table 4.1  Summary of key results with regards to specific objectives ......................... 121  ix  List of figures  Figure 1.1   Schematic of Dietary Reference Intake requirement distribution ................. 19  Figure 2.1   Percentage of energy from macronutrients with reference to the Acceptable Macronutrient Distribution Range .................................................................. 59 Figure 2.2   Percentage of men with mean intakes from food sources only below Estimated Average Requirements (EAR) ........................................................ 62 Figure 2.3   Percentage of women with mean intakes from food sources only below  Estimated Average Requirements (EAR) ........................................................ 64  x  List of abbreviations AB able-bodied AI Adequate Intake AIS  American Spinal Injury Association impairment scale AIS-A American Spinal Injury Association impairment scale – A indicating complete injury  AIS-B to D American Spinal Injury Association impairment scale – B to D indicating incomplete injury AMDR Acceptable Macronutrient Distribution Range ASIA American Spinal Injury Association BMI body mass index (kg/m2) BMR basal metabolic rate BREB Behavioural Research Ethics Board C cervical spine C4 cervical spine, 4th spinal cord segment cm centimetre CVD cardiovascular disease d day DRI(s) Dietary Reference Intake(s) DXA or DEXA dual energy X-ray absorptiometry  EAR Estimated Average Requirement EER Estimated Energy Requirement F female FFQ food frequency questionnaire g gram H home HDL high density lipoprotein cholesterol IU  International Units kcal kilocalorie kg kilogram kJ kilojoule L lumbar spine LDL low density lipoprotein cholesterol M male m metre mcg microgram mol/L micromole per litre MET hr/d calculated metabolic equivalent hours per day MET(s) metabolic equivalent(s) mg milligram mg/dL milligram per decilitre xi  mm millimetre mmol millimole mmol/L millimole per litre n sample size NR  not reported P paraplegia PASIPD Physical Activity Scale for Individuals with a Physical Disability RDA Recommended Dietary Allowance RE retinol equivalents REE resting energy expenditure RMR resting metabolic rate RPE rate of perceived exertion S sacral spine SCI spinal cord injury SD  standard deviation SHAPE-SCI Study of Health and Activity in People with Spinal Cord Injury SPSS Statistical Package for Social Sciences  T tetraplegia T thoracic spine T1 thoracic spine, 1st spinal cord segment TC training camp TEE total energy expenditure TEF thermic effect of feeding TFEQ Three-Factor Eating Questionnaire TG triglycerides UK United Kingdom USDA United States Department of Agriculture vs. versus YEPQ Yale Eating Pattern Questionnaire  xii  Acknowledgements  I would like to acknowledge the support and guidance of many individuals, without whom, this graduate degree would not have been possible.  First and foremost, I would like to thank my supervisor Dr. Susan Barr.  Susan, your endless patience, support and guidance has made this journey not only memorable, but also enjoyable.  Thank you for allowing me the freedom to explore and pursue the possibilities but always keeping me on track and moving forward.  It has been a privilege and true honour to work with you during this process.    To my committee members, Dr. William Sheel and Dr. Andrei Krassioukov I would like to thank you for sharing with me your excitement and enthusiasm for research and your keen insights on how to improve this study.  I would like to extend a tremendous thank you to all the athletes who participated in this study.  Your willing participation, with thoughtful and complete responses greatly enhanced the results of this study.      To my late Nana Krempien, thank you for inspiring me to dream this dream.  I grew up staring at the leather bound version of your own Masters thesis on our bookshelf and knew someday that I wanted to have my name in gold letters on the spine of a leather bound book.    For my parents who have unconditionally encouraged, loved, supported and celebrated with me throughout this degree and all aspects of my life, thank you simply feels inadequate.  I love you both and truly appreciate all that you have taught me.  To my kindred-spirit, Mike, thank you for your love.  Without your encouragement,  support, patience, selflessness and unconditional love this degree simply would not have been finished.  I love you and each day with you is a gift.1  Chapter 1:  Introduction 2  1.1 Background   Spinal cord injury (SCI) occurs when the spinal cord is damaged by trauma or disease and results in a loss of function (1).  The spinal cord does not need to be completely severed for a loss of function to occur and in many cases moderate damage to the spinal cord is sufficient to result in a loss of functioning (1, 2).  In 2008, the Canadian incidence rate for SCI was estimated at 1,200 per year, with an estimated 41,000 Canadians living with SCI (3). Advancements in the immediate treatment of SCI, medical management of chronic medical issues and improved rehabilitation over the past five decades have improved outcomes for individuals living with SCI (4). Most individuals with SCI continue to enjoy a high quality of life with many choosing competitive sport as a venue for challenging the barriers and obstacles created by SCI (5). Competitive sport opportunities for individuals with a disability have blossomed over the past 60 years culminating in an exhibition of athletic excellence at the winter and summer Paralympic Games.  The competitive nature of the Paralympic Games demands athletes to optimize training and performance strategies to attain competition goals.  Olympic and able-bodied athletes have benefited from decades of research with the aim to improve training and monitoring protocols, recovery techniques and nutrition strategies.  However, the research to support the training and performance strategies of athletes with a disability is still in its infancy. While a long-term goal would be the establishment of specific evidence-based performance nutrition recommendations directed towards athletes with a SCI, it is important to first better understand what elite athletes with an SCI are consuming and explore what factors are influencing those nutrition choices.   1.2 Literature review  1.2.1 Overview This review of the current research regarding athletes with SCI and their nutrition choices is organized into three sections.  To provide background information on how SCI could affect dietary choices, the first section provides a brief description of SCI and the 3  relevant metabolic differences associated with it.  The physiological adaptations and autonomic functional changes are also addressed as they pertain to the physical and cardiovascular capacity following SCI.  Body composition is influenced by physical activity, spinal cord injury and can also be influenced by nutrition. Accordingly, this review then shifts to illustrate some of the body composition changes that occur following SCI with a comparative look at body composition differences between athletes and non-athletes with SCI.  Energy expenditure including the energy expenditure associated with physical activity is then examined including reports of predicted versus actual intakes and some of the difficulties in using predictive equations in this population.  This review then offers an in depth discussion of the nutrient intakes and dietary adequacy of community dwelling individuals with SCI, finishing with a review of the few reported studies of dietary intakes in athletes with SCI.    1.2.2 Spinal cord injury The nervous system consists of an intricate and large neural communication network of which the primary role is to establish connections between the brain and the rest of the body.  When trauma, disease or congenital defects damage the spinal cord, a change in motor or sensory function occurs.  SCI can be divided into two types of injury based on motor and sensory function - complete or incomplete. A complete injury means that there is no function below the level of the injury; no sensation and no voluntary movement (6).  An incomplete injury means that there is some motor or sensory function below the primary level of the injury (6).   Spinal cord functions differ by level and structure (Table 1.1), with injury or disease resulting in varying types and degrees of dysfunction depending on the specific neural structures affected (7, 8). Typically, if the injury is higher in the spinal cord the impact of the injury with loss of motor and sensory function is more pronounced (9, 10).  Cervical injuries usually result in tetraplegia and injuries above the C4 spinal cord segment and may require ventilator support for the person to breathe. Those with a C5 injury often retain shoulder and biceps control, but limited or absent wrist or hand control. Those with C6 injury 4  generally retain wrist control, but no or limited hand function. Individuals with C7 or T1 injury can straighten their arms but still may have dexterity problems with the hand and fingers. Injuries of the upper thoracic level T1 – T8 result in poor trunk control as the result of lack of abdominal muscle control but full function of the shoulders, arms and hands remain.  With injury to the lower thoracic level T9 – T12, good truck control and good abdominal muscle control remains with lower limbs affected with loss of function. Lumbar and sacral injuries yield decreasing control of the hip flexors and legs.  SCI can be classified based on the level of injury and remaining function.  Those with injury to the cervical spinal cord, are categorized as tetraplegic, or more simply all four limbs are affected with reduced function.  Those with injury to the thoracic, lumbar or sacral spinal cord are categorized as paraplegic, as the lower limbs are affected with reduced function.    5  Table 1.1   Segmental spinal cord level and function Spinal Cord Level Function C1-C6 Neck flexors   C1-T1 Neck extensors C3, C4, C5 Supply of diaphragm C5, C6 Shoulder movement (arm raise, elbow flexion, C6 externally rotates arm) C6, C7, C8 Triceps and wrist extensors, pronates wrist C7, C8, T1 Wrist flexion C8, T1 Small muscles of hands T1 -T6 Intercostals and trunk control above waist T7-L1 Abdominal muscles L1, L2, L3, L4 Thigh flexion L2, L3, L4 Thigh adduction L4, L5, S1 Thigh abduction L5, S1 S2 Extension of leg at the hip (gluteus maximus) L2, L3, L4 Extension of the leg at the knee (quadriceps femoris) L4, L5, S1, S2 Flexion of the leg at the knee (hamstrings) L4, L5, S1 Dorsiflexion of foot (tibialis anterior) L4, L5, S1 Extension of toes L5, S1, S2 Plantar flexion of foot L5, S1, S2 Nexion of toes Adapted from Tortora & Derrickson, 2006. (10)  Abbreviations: C, Cervical Spine; T, Thoracic Spine; L, Lumbar Spine; S, Sacral Spine 1.2.3 Metabolic changes associated with spinal cord injury Metabolic syndrome Metabolic syndrome is defined as a clustering of anthropometric, biochemical and clinical symptoms known to increase an individual’s risk of cardiovascular disease (11). The National Cholesterol Education Program’s Adult Treatment Panel III report identified abdominal obesity, dyslipidemia, elevated blood pressure, insulin resistance with or without glucose intolerance, a proinflammatory state and a prothrombotic state to be key components of metabolic syndrome as it relates to cardiovascular disease (CVD) risk (12).   Individuals with SCI exhibit many of these traits common in metabolic syndrome including increased abdominal obesity (13-16), dyslipidemia (17-20),  hypertension (21, 22), insulin resistance (18, 23, 24) and a proinflammatory state (25, 26).  While the exact cause of these 6  observed abnormalities is not fully understood, it is most likely a combination of the direct hormonal, biochemical or autonomic changes associated with SCI and secondary factors including decreased muscle mass, increased adiposity and reduced physical activity related to the SCI. Given this pattern of the presence of metabolic syndrome characteristics, the observation that CVD is the leading cause of death in SCI (27), and that there are greater rates of CVD related morbidity in SCI compared to able-bodied populations, it would be reasonable to assume that, when compared to able-bodied populations, those with SCI are at an increased risk for metabolic syndrome and ultimately CVD (28).  However, the picture is not that clear.    A cross-sectional study of 185 men with SCI matched with able-bodied controls measured anthropometric and biochemical indices associated with metabolic syndrome.  Those with SCI did not appear to have increased prevalence of metabolic syndrome (29).  While a difference in the prevalence of metabolic syndrome per se was not detected, differences in the metabolic profile were observed with the SCI group having higher total cholesterol, low density lipoprotein cholesterol (LDL), triglycerides (TG), and glucose concentrations when adjusted for waist circumference, education, household income and smoking status as compared to their matched controls.  In a similar cross-sectional study of adults with SCI, Finnie et al. found a 2.0 to 5.4 times lower prevalence of metabolic syndrome compared to the general population depending on the criteria and definition of metabolic syndrome (30).  The authors postulated that current definitions of metabolic syndrome and screening tools such as Framingham Risk Scoring underestimate prevalence of metabolic syndrome in those with SCI.  For example, elevated fasting glucose levels are a criterion for metabolic syndrome, but in SCI, fasting levels may be normal whereas glucose tolerance is impaired (30).   Thus, in those with SCI, different criteria may be more appropriate, and it may prove to be beneficial to include markers of inflammation such as C-reactive protein in the determination of metabolic syndrome prevalence (25, 31).    7  Carbohydrate metabolism   Glucose intolerance is more frequent in those with SCI (18, 20, 32, 33).  Most individuals with SCI who have abnormal glucose metabolism show resistance to the action of insulin in mediating glucose uptake by peripheral tissues (28, 33, 34).  In general, fasting glucose levels in SCI are not abnormally elevated but glucose concentrations following a glucose load are significantly higher at the 60, 90 and 120 minute post-load measurements (23, 24, 35).  Abnormal glucose metabolism is multi-factorial with genetic predisposition, impaired insulin action, pancreatic beta cell function and modifiable risk factors all contributing to overall risk of developing type 2 diabetes (36).  The level of neurological impairment is associated with the degree to which glucose metabolism is affected.  For example, those with tetraplegia demonstrated hyperinsulinemia more frequently than those with paraplegia (53% vs. 37%, p<0.05), and those with complete tetraplegia had higher values for serum glucose concentration with an oral glucose tolerance test than incomplete tetraplegic or paraplegic subjects (23).  Thus SCI contributes another dimension to the list of risk factors for altered glucose metabolism.    Lipid metabolism   Low serum concentrations of high density lipoprotein (HDL) cholesterol and high concentrations of low density lipoprotein (LDL) cholesterol play an important role in the development of atherosclerotic plaque formation and coronary heart disease (12).  Higher concentrations of HDL cholesterol have been shown to be protective against atherosclerotic plaque formation while an elevated serum LDL cholesterol concentration is an independent CVD risk factor (12).  Individuals with SCI have been studied as a model for accelerated and premature coronary heart disease as they tend to have an increased odds ratio of death due to CVD (Odds Ratio 1.86, 95% confidence interval 0.86 – 4.05) (27).  There is a general consensus among researchers that the concentration of HDL cholesterol is lower in persons with SCI compared to able-bodied populations (35, 37-39).  Bauman et al. (39) found serum HDL cholesterol levels below 35 mg/dL (0.9 mmol/L) in 40% of subjects with SCI and a strong inverse correlation between serum triglyceride and HDL cholesterol concentrations.  As with glucose, there is a strong association between the level of neurological defect and 8  lipid metabolism as those with complete tetraplegia tend to have a more abnormal lipid profile in comparison to those with incomplete tetraplegia, complete and incomplete paraplegia (20, 40).  Although HDL cholesterol concentrations are generally depressed in SCI, improvement of cardiopulmonary fitness and increased levels of physical activity have been shown to improve HDL cholesterol levels (38, 41-44).  LDL cholesterol concentrations measured in SCI populations tend to be similar to those observed among able-bodied populations with approximately 25% of individuals having an elevated serum LDL cholesterol concentration (35, 42).  1.2.4 Autonomic function following spinal cord injury A healthy, intact spinal cord is responsible for transmitting motor and sensory neural signals within the somatic nervous system and an array of signals for the autonomic nervous system.  The autonomic nervous system is a key regulator for a myriad of essential physiological functions including cardiovascular control through heart rate determination, stroke volume, vascular resistance, arterial blood pressure and cardiac output (45).  The physiological and biochemical regulation of the autonomic nervous system involve complex processes and are beyond the scope of this review.  For a detailed and informative review, the reader is directed to an article by Krassioukov & Claydon (46). Following SCI, many autonomic disturbances or clinical symptoms can be experienced.  Those with a higher level of SCI (cervical or high thoracic injury) may experience low blood pressure and episodes of orthostatic hypotension (47). To prevent episodes of orthostatic hypotension, some clinicians recommend increasing plasma volume through hydration and increased salt intake in combination with postural changes (48).  The cardiovascular effects of autonomic dysfunction are likely the most profound symptoms but the alterations in autonomic nervous system function extend to impact thermoregulation (49, 50), sweat rate (51) and control of epinephrine release in response to exercise or stress (45).  In those with cervical SCI, resting supine plasma adrenaline and noradrenaline concentrations are low (52, 53).  Whereas resting supine plasma adrenaline and noradrenaline concentrations were normal in those with thoracic SCI (52).  Those with 9  cervical SCI have less ability to increase catecholamine concentrations in response to orthostatic challenge (52) or in response to maximal exercise (54).  Physiological adaptations in athletes with spinal cord injury  Additional physiological adaptations occur in response to exercise in those with SCI.  Alteration in cardiac structure and function are common among those with SCI with circulatory dysregulation and hypotension occurring in those with injuries to the cervical spine (55).  A low mean arterial pressure challenges the ability to regulate systemic blood pressure during orthostatic challenge and physical activity (56). As well, cardiac ventricular size and function are diminished (57). In paraplegia, blood pressure control, left ventricular mass and resting cardiac output remain normal (58). However, those with paraplegia have lower stroke volume because of a decreased venous return from the immobile lower extremities (59). There is strong support of a direct relationship among the level of injury, peak workload, and peak oxygen uptake attained during arm crank ergometry (60-63). Those with a higher level of injury, especially those with injury above the level of sympathetic outflow to the heart have significantly lower resting stroke volumes and higher resting heart rates than able-bodied individuals (64). In summary, SCI is associated with a number of metabolic and physiological changes.  Observed trends of dyslipidemia, glucose intolerance and hypertension in the context of high rates of mortality and increased morbidity from CVD may influence the diets and nutritional choices of those with SCI.  Often the first treatment recommended is therapeutic lifestyle modification including modification of dietary fats, decreased sodium intake, increased intake of whole-grains, increased intake of vegetables and fruit and increased physical activity (65). These recommendations may influence the types and quantities of foods selected by individuals with SCI in an attempt to decrease some of the modifiable risk factors associated with CVD.   10  1.2.5 Body composition changes associated with spinal cord injury Body fat Following SCI, body fat appears to increase in comparison to pre-injury body composition or compared to able-bodied controls.  Suggested ranges for ideal body fat percentage (body fat relative to body weight) for able-bodied men are 13 – 18% and 28 – 32% for women (66).  Although international standards for evaluating body fat percentages do not currently exist, it has been proposed that men with body fat percentages greater than 25% can be considered obese as are women with body fat percentages greater than 38% (66).  In 1988, Nuhlicek et al. reported body fat percentages of men with SCI to be significantly higher than able-bodied controls (67). When level and completeness of SCI injury were considered, body fat percentage increased along with the severity of the injury with tetraplegics having 35% body fat compared to paraplegics with 30% body fat.   Spungen et al. (68) studied the body composition of 133 males with SCI (66 men with tetraplegia, 67 men with paraplegia) with an age-, height-, weight- and ethnicity- matched control group of 100 subjects.  Using dual energy X-ray absorptiometry (DXA), Spungen et al. found the SCI group was 13 ± 1% (mean ± standard error) fatter per unit of body mass index (kg/m2) (p<0.0001). Tetraplegic and paraplegic groups had significantly higher total fat mass (24.11 ± 1.34 kg; 23.86 ± 1.42 kg respectively) compared to the control group (18.74 ± 1.08 kg), as well as a 50% higher percentage total body fat (33% vs. 22%).  Interestingly, when regional distribution of fat mass was considered a significantly greater amount of arm fat mass in both paraplegics (3.23 ± 0.02 kg) and tetraplegics (3.16 ± 0.02 kg) existed compared to the control group (1.63 ± 0.01 kg).  Within the paraplegic and tetraplegic groups, those with complete injury had higher absolute fat mass but this did not remain significant once adjusted for body size.  In this well designed cross-sectional study (68), those with SCI are clearly shown to have much higher total fat mass and percent body fat, including a higher regional percent body fat of the arms.    11  Muscle mass Altered body composition following SCI is characterized by increased fat mass and relative body fat percentage but perhaps more clinically important is the reduction in lean tissue or fat-free mass and muscle stores.   A small sample (n=5) of adult males with paraplegia compared with ten age- and height-matched controls demonstrated significant body composition changes assessed by dual energy x-ray absorptiometry (DXA) (69).  The SCI group had a lean tissue mass of 48.7 ± 6.7 kg (p<0.01), fat mass of 24.0 ± 13.6 kg (p<0.05) and percent body fat of 30.1 ± 9.0% (p<0.01), in comparison to the control group body composition of 57.9 ± 3.7 kg lean tissue, 12.6 ± 4.9 kg fat mass, and 16.6 ± 5.0% body fat respectively.  Consistent with these observations, Maggioni et al. (70) also reported that a group of 13 subjects with SCI had a significantly lower percentage of total body fat-free mass (62.2 ± 8.9% vs. 73.5 ± 6.4%, p<0.05).  When these authors assessed the regional distribution of fat-free mass, they found that the percentage of fat-free mass was significantly higher in the upper limbs of SCI compared to the control (10.6 ± 2.3% vs. 8.7 ± 1.0%, p<0.05), whereas lower percentages were observed in total body, trunk and lower body regions. The authors presented body composition data as a percentage of body weight of fat mass, fat-free mass and bone mineral density.  The overall pattern of percent lean and percent fat is similar to the results found by Spungen et al. (68) with values of approximately 63% of body weight as lean mass in those with SCI compared to 73% in controls and 31% of body weight as fat mass in those with SCI compared to 21% in controls.    Cardus and McTaggart (71) estimated total body protein in adults with SCI using a crude estimation method subtracting total body fat and total body water from the weight of bone mineral free body and reported a 24 to 30% reduction in absolute amount of total body protein. An interesting study by Spungen et al. (72) investigated body composition of monozygotic twins discordant for spinal cord injury.  Their data from eight pairs of male twins, one having a SCI (T6 – L1, motor complete lesion) demonstrate some unique body composition changes following SCI.  Generally, the SCI group weighed less; had less total body, lower body and trunk lean tissue mass; and had similar arm lean tissue mass.  On average, the difference in total body weight was 10.1 ± 11.5 kg which was predominantly 12  from the loss of lean tissue in the lower body (-10.0 ± 4.1 kg).  As the time from injury increased, the loss of lean tissue was more pronounced (72).   Bone mineral density and content  Several studies have documented the phenomenon of decreased bone mass following SCI (69, 73-75). Post injury demineralization of bone occurs in areas below the neurological lesion and predominantly in the long bones of the lower limbs increasing fracture risk at the distal femur and proximal tibia (72, 75-77).  A cross-sectional study by Dauty et al. (78) of 11 tetraplegics and 20 paraplegics more than one year post injury demonstrated a significant demineralization of the distal femur (-52%) and the proximal tibia (-70%) compared to age-matched controls.  The bone mineral density and bone mineral content is clearly reduced in regions below the level of injury in paraplegics compared to controls (78).  In tetraplegia, the regions of reduced bone mineral density and content extend to include the upper extremities (78).   It is thought that the demobilization and loss of mechanical force on the bone contributes to the decrease in bone mineralization (79) although this remains somewhat controversial.  It is interesting to note that the use of body weight supported treadmill training in a small number of subjects with tetraplegia shortly after injury did not prevent or slow the rate of bone loss (80, 81).  The benefits of physical activity on bone density were investigated comparing male paraplegic basketball players to male sedentary paraplegics (82). Both groups had decreased bone densities in the lower body (trochanters and femoral necks), whereas densities of lumbar and radial regions were slightly increased in both groups.  The radial density was significantly higher in the athletes compared with the sedentary group suggesting participation in sport may be beneficial to preserve bone density in upper extremities.   1.2.6 Body composition of athletes with spinal cord injury Relatively few studies have specifically considered the potential impact of high levels of physical activity in those with SCI.  Ide et al. (83) were one of the first groups to present data on the anthropometric characteristics of a large sample of wheelchair marathoners.  Anthropometric data from 2,677 competitors were collected over a ten year period from 13  1983 to 1992 as a part of a health check program associated with the race.  The competitors from each year were classified as fine racers if they had completed the full marathon race (n=710) or classified as poor racers if they did not complete the half marathon (n=99). The remaining participants (n=1868) were excluded from the analysis if they completed the half marathon or failed to complete the full marathon.  Statistical analysis was performed using a best versus worst performance approach with data from the 10 years pooled.  The comparison between the two groups showed the fine racers to have greater upper arm muscle power and a greater lung vital capacity.  The measures of body composition of the fine versus poor racers were not statistically different until the last year of data collection which showed the fine racer group had a lower body fat percentage (18.7 ± 4.3% vs. 23.7 ± 8.8%).  The girth measurements of chest and upper arm were significantly larger in the fine racers group which is likely related to increased muscle mass as measures of strength were also higher.  As the years progressed, the scores for strength measures improved in the fine racer group indicating a training effect.  One limitation of this interesting study is that the same athletes often compete in the race for multiple years which may have biased the statistical analysis. Bulbulian et al. (84) compared athletes with paraplegia to a heterogeneous sample of able-bodied subjects classified as having either mesomorph or ectomorph physiques.  Skinfold measurements of pectoral, tricep, subscapular, abdomen, thigh and calf sites in the athletes with SCI were all significantly larger than both mesomorph and ectomorph control groups.  Olle et al. (85) compared a sedentary and active SCI group found that the increased level of activity had a positive effect with lower estimates of percentage of fat mass and higher estimates of percentage of fat-free mass in the active group as measured by total body electrical conductivity. However, there was only a trend of reduced skinfold thicknesses in the athlete group.  A more recent study by Goosey-Tolfrey (86)  monitored the British men’s national wheelchair basketball team for the two years leading up to the 2000 Paralympic Games.  Measures of fitness (VO2 peak) improved but body composition, as measured by the sum of skinfolds from 4 sites, did not statistically improve.  Mojtahedi and colleagues (87) compared the body composition (measured by DXA) of 14 athletes with 14  SCI with a control group of 17 age- and BMI-matched able-bodied sedentary controls.  Despite matching for BMI, the SCI group weighed less (57.6 ± 11.0 kg vs. 70.5 ± 12.5 kg, p<0.05) and were shorter in stature (161.6 ± 11.1 cm vs. 172.1 ± 11.4 cm, p<0.05).  Statistically significant differences were also detected in body composition with SCI athletes having less fat mass, lean tissue and trunk fat mass but there was no difference in the overall measure of percentage of body fat (25.1 ± 7.0% vs. 26.5 ± 7.2%).   Although the data are limited and not completely consistent, the few studies that have reported on body composition in athletes with SCI suggest beneficial effects of strength and cardiovascular training in lowering the percentage of body fat.  Although somewhat inconclusive, it appears that the effects of training on body composition are not systemic but rather affect regions above the level of injury.    1.2.7 Energy expenditure Energy expenditure is often described as total energy expenditure (TEE) which is comprised of three components, resting energy expenditure (REE), thermic effect of food (TEF) and energy expenditure of physical activity.  TEE has been found to be lower in individuals with SCI compared to able-bodied populations (88). The three components of TEE and the impact of SCI are described in the following sections.  Resting energy expenditure in spinal cord injury REE is largely determined by body size and the amount of fat-free mass.  Fat-free mass consists of all residual lipid-free chemicals and tissues including water, muscle, bone, connective tissue and internal organs (89) and is described as the metabolically active component at the molecular and cellular level of body composition.  In normally active individuals, REE generally accounts for approximately 65% of TEE. REE is determined by extrapolating the resting metabolic rate (RMR) which is typically measured when the subject is at rest, at least 4 hours post-prandial and in ambient temperatures to reflect energy expenditure at rest for a 24 hour period. The RMR is typically 10% higher than the basal metabolic rate (BMR) which is measured under more stringent conditions where the 15  subject is fasted overnight, in a supine position, in a thermo-neutral environment and shortly after waking (90).        Absolute measured REE has been found to be considerably less in individuals with SCI compared with able-bodied individuals (16, 88, 91-93). The decrease in REE in SCI can be explained by a decrease in fat-free mass (88, 91, 93-97).  Fat-free mass accounts for 25 to 85% variation in REE (94, 98, 99) with fat-free mass the single best predictor of RMR in those with paraplegia (r2=0.70, p<0.0001) (91).  Adjusting REE for fat-free mass eliminates the differences between SCI and able-bodied groups in most studies (16, 91, 92).  Monroe et al. (88) found that 24 hour energy expenditure was lower in those with SCI (-180 kcal/day, p<0.01) compared to able-bodied controls after adjusting for fat-free mass, fat mass and age. Monroe et al. measured 24 hour energy expenditure, BMR, sleeping metabolic rate, spontaneous physical activity, TEF and the 24 hour respiratory quotient while subjects were housed in a respiratory chamber.  While the respiratory chamber is not the ideal condition for detecting differences between groups as the subjects are restricted to a small area, the observation of differences in energy expenditure after correcting for differences in body composition adds strength to the body of evidence that energy expenditure is less in those with SCI.  In free-living conditions, it would be expected that the differences in physical activity levels between the two groups would be more obvious as studies have shown that those with SCI tend to be more sedentary than able-bodied populations (88, 93).  Thermic effect of feeding in spinal cord injury Thermic effect of feeding (TEF) accounts for approximately 10% of total energy expenditure.  Three studies (88, 91, 100) have considered the potential impact of TEF in evaluating the energy expenditure of SCI.  One study found that TEF, expressed as a percentage of energy intake in male SCI subjects was lower than that of able-bodied controls (12.1 ± 2.7 vs. 15.3 ± 4.4, p<0.05) (88).   Conversely, in the other two studies, no differences in TEF were observed:  paraplegia had no apparent effect on TEF (91), and SCI 16  compared to able-bodied groups had similar TEF when expressed as a percentage of either energy intake or RMR (100).  Energy expenditure of physical activity in spinal cord injury Physical activity is the third component of total energy expenditure and on average, contributes 25 to 30% of TEE.  As previously discussed, it is unlikely that expenditure of metabolically active fat-free mass or the thermic effect of food is significantly different between SCI and able-bodied populations.  Differences in physical activity levels, in combination with a reduction in fat-free mass between the groups would account for the differences in TEE.  The physical activity levels of adults with SCI were measured by heart rate monitoring and activity records (93).  Over half (55.6%) of the subjects participated in structured physical activity at least one time during the three day observation period.  However, TEE was low; indicating that structured activity was not of sufficient frequency or intensity to offset the sedentary nature of daily living for this population.  Monroe et al. (88) measured energy expenditure and physical activity of SCI and controls in a respiratory chamber setting.  The amount of spontaneous physical activity was significantly lower in the SCI group (4.6 ± 1.9% vs. 6.5 ± 2.0% per 24 hours, p<0.05).  However, this was not an ideal measurement circumstance to reproduce energy expenditure in free-living individuals as the subjects were restricted to the chamber.  When men with SCI were monitored using heart rate telemetry, total daily energy expenditure on ‘inactive’ days was significantly lower than able-bodied controls (101).  On ‘active’ days the total energy expenditure was not significantly different between the SCI and able-bodied groups.  Individuals with SCI are at an increased risk of leading a sedentary lifestyle.  The impact of high intensity, high frequency physical activity and exercise on the total energy expenditure in athletes with SCI has not been measured.    Predictive equations Energy expenditure may be measured using indirect calorimetry or doubly labeled water methods.  When it is not feasible to measure REE, predictive equations have been 17  developed for a variety of healthy and clinical populations to predict energy requirements (90, 99, 102, 103).  The majority of such equations were developed using able-bodied individuals as the reference population and these predictive equations have been shown to over predict measured REE in individuals with SCI by 5 – 32% (16, 88, 91, 96). Mojtahedi and Evans (104) measured resting metabolic rate (indirect calorimetry) and body composition (DXA) in college-aged athletes with SCI and compared the measured values with estimates from commonly used predictive equations. Measured resting metabolic rate for males was 1598 ± 187 kcal/day and 1120 ± 129 kcal/day for women.  For the group, the Harris-Benedict equation (103) significantly over predicted RMR (1501 ± 236 kcal/day vs. 1337 ± 291 kcal/day, p=0.001).  Interestingly, when measured RMR was compared with the predicted RMR using the equation developed by Buchholz et al. (91) in a population of adults with paraplegia as the reference group, predicted RMR was still greater than measured (1530 ± 223 kcal/day vs. 1337 ± 291 kcal/day, p<0.001). They found the predictive equations overestimate resting metabolic rate in highly active people with SCI.  There are currently no validated equations or methods to predict energy requirements in this population.  However, for the purposes of this study and literature review, comparisons of reported energy intakes and the values predicted by the EER developed by the Institute of Medicine (90) have been made.  It is understood that these predictive equations were developed with a reference population of able-bodied adults and the interpretation of these comparisons should be done with the utmost of caution.     1.2.8 Assessment of nutrient adequacy This section describes the framework of how nutrient adequacy is assessed and how the prevalence of nutrient inadequacy in a group is determined. Before commenting on the available literature that reports nutrient intakes of individuals with SCI, it is important to describe the current framework established with the Dietary Reference Intakes (DRIs) (105, 106) and provide some explanation of how dietary adequacy was evaluated prior to the DRIs.  The terminology used in the DRI model is defined in Table 1.2 and a graphical schematic is provided in Figure 1.1 as a reference for the reader.   18  The DRIs provided us with reference data for nutrient intakes and established a novel framework for the assessment of the prevalence of nutrient inadequacy in a group (104). For nutrients with both an Estimated Average Requirement (EAR) and a Recommended Dietary Allowance (RDA), the prevalence of inadequacy can be estimated as the proportion of the group with usual intakes below the EAR.  For other nutrients, the data were not sufficiently robust to allow an EAR to be identified and for these nutrients, Adequate Intakes (AIs) were established.  The AI represents an intake level thought to meet or exceed the requirements of almost all members of a group, should it have been possible to determine the requirement distribution.  Inferences regarding nutrient adequacy for nutrients with an AI are limited:  if the group mean intake meets or exceeds the AI, it is probable that the prevalence of inadequacy in the group is low.   Prior to the introduction of the DRIs, the RDA was the only available reference standard, and it was not always based on knowledge of the requirement distribution.  Frequently, authors compared mean group intakes to the RDA (or in some cases to a percentage of the RDA, such as 67%), and assessed them as “adequate” if mean intake exceeded the RDA and “inadequate” if below the RDA.  While it is very likely that some proportion of the group would have inadequate intakes when mean intake falls below the RDA, this system did not permit insight into whether a small versus a large proportion of the group had inadequate intakes.  Furthermore, because the variability of intake distributions greatly exceeds the variability of requirement distributions, some prevalence of inadequacy will occur in a group even when mean intakes meet or slightly exceed the RDA.  Thus, comparing literature published before and after the introduction of the DRIs is challenging.  Nevertheless, when older literature reports that intakes were “inadequate” (based on the group’s mean intake falling below the RDA), it can be inferred that intakes of at least some proportion of the group did not meet their requirements.    19  Table 1.2  Explanation of terminology from Dietary Reference Intake framework Estimated Average Requirement (EAR):  The average daily nutrient intake level that is estimated to meet the requirements of half of the healthy individuals in a particular life stage and gender group. i.e., intake that meets the requirements of 50% of an age/sex group (mean intake) Recommended Dietary Allowance (RDA): The average daily dietary nutrient intake level that is sufficient to meet the nutrient requirements of nearly all (97 – 98 percent) of healthy individuals in a particular life stage and gender group. i.e., intake that meets the requirements of almost all members of an age/sex group (mean intake + 2 standard deviations) Adequate Intake (AI): The recommended average daily intake level based on observed or experimentally determined approximations or estimates of nutrient intake by a group (or groups) of apparently healthy people that are assumed to be adequate; used when an RDA cannot be determined. Prevalence of Nutrient Inadequacy in a Group:  An estimate of the proportion of the group with intakes that are below their estimated requirements. Tolerable Upper Intake Level (UL): The highest average daily nutrient intake level that is likely to pose no risk of adverse health effects to almost all individuals in the general population.  As intake increases about the UL, the potential risk of adverse effects may increase. Adapted from Institute of Medicine, Dietary Reference Intakes 2000 (106)     Figure 1.1 Schematic of Dietary Reference Intake requirement distribution   20  1.2.9 Nutrient intakes in community living individuals with spinal cord injury The body of literature which comprehensively evaluates energy intakes along with both macronutrient composition and micronutrient adequacy is limited with only four published reports of community-living adults with SCI and an additional two reports documenting the intakes of athletes with SCI.  Several other studies have measured and reported energy intake as a covariate of one of the study variables, or reported on one or two components of dietary intake but overall the evaluation of the full diet has been omitted from the published results.  The key findings of these studies are summarized in Table 1.3. The first report of dietary intakes of individuals with SCI was published in 1992 by Levine and colleagues (107). The dietary intake of 33 adults with SCI (21 with tetraplegia, 11 with paraplegia, 1 did not describe SCI) was self-reported for 7 days with average intake compared to the Recommended Dietary Allowance (RDA) (108). Mean intakes for each nutrient were calculated and reported as a percentage of the RDA. The energy intake was reported to be 1682 ± 429 kcal for men and 1282 ± 418 kcal for women.  For men, the percentage of energy from macronutrients was 46% carbohydrate, 38% fat and 17% protein.  Women had slightly higher carbohydrate intake at 52% of calories, slightly lower fat intake at 32% of calories and similar protein intake at 17%.  Fibre intake for both men (12.2 ± 4.7 g; range of 6.0 – 22.7 g) and women (14.3 ± 8.8 g; range of 4.4 – 27.6 g) was below the recommended intake and it is interesting that women had a slightly greater fibre intake while consuming fewer calories.  Intakes of several micronutrients were below the RDA for men including:  vitamin A (815 ± 455 RE, 82% RDA), thiamin (1.32 ± 0.43 mg, 88% RDA), riboflavin (1.46 ± 0.45 mg, 86% RDA), vitamin B6 (1.51 ± 0.49 mg, 76% RDA), vitamin E (7.8 ± 4.3 mg TE, 78% RDA), calcium (550 ± 268 mg, 69% RDA), magnesium (217 ± 65 mg, 62% RDA) and zinc (10.2 ± 4.1 mg, 68% RDA).  Women reported intakes below the RDA for calcium (525 ± 263 mg, 66% RDA), iron (13.5 ± 8.33 mg, 90% RDA) magnesium (242 ± 123 mg, 86% RDA) and zinc (9.3 ± 5.8 mg, 78% RDA).  Overall, the diets of this group were found to be low in energy intake, low in fibre with several micronutrients at risk of inadequate intake.  Women tended to 21  have a diet with fewer micronutrient inadequacies despite a very low reported caloric intake.   The results of this study are limited as the weight and height of subjects were not reported.  As body composition directly impacts energy requirements, it would have been helpful to know if this population was under or overweight as the reported energy intakes are quite a bit lower than in later reports. The authors comprehensively reported on the mean intakes with standard deviation and ranges for all macro and micronutrients with comparison to the RDA.  This study provides a good example of the difficulties in the assessment of the prevalence of inadequacy in a group because the tools required to estimate the prevalence of inadequacy in a group were not available.  As a result, the authors compared mean intakes to the RDA or some proportion of the RDA and assessed intakes as inadequate if the group mean fell below that standard.  However this does not mean that everyone in the group had inadequate intakes.  Moreover, even when a mean intake of a group equals or is slightly above the RDA, it is possible that some proportion of the group would have inadequate intakes (106).  However, even with these limitations, this report provided a comprehensive initial description of the dietary adequacy of community living SCI population. Tomey and colleagues evaluated the nutritional status of 95 men with paraplegia using a seven-day semi-quantitative food frequency questionnaire to measure dietary intakes (15).  Dietary adequacy was assessed by reporting the proportion of participants with mean intakes below the EAR for iron, vitamin C and folate or the proportion of participants meeting 67% of the AI for fibre, calcium and vitamin D.  Energy intake was reported at 9479 ± 3117 kJ (2264 ± 745 kcal) with 49.3 ± 8.8% of kcal from carbohydrate, 36.2 ± 7.1% of kcal from fat and 14.5 ± 3.0% of kcal from protein.  Fibre intake was reported at 17.1 ± 7.3 g per day with only 12% of participants exceeding the cut-point of 25 g of fibre (67% of AI).  Almost no participants had intakes below the EAR for iron, while approximately 25% of individuals had intakes of vitamin C below the EAR and 33% of individuals had intakes of folate below the EAR.  The mean calcium intake was 755 ± 373 mg with only 57% of participants reporting intakes above 670 mg (67% of the AI).  The strengths of this study 22  include the sample size and relative homogeneity of the participants and the method of collecting dietary intake reflected usual intake patterns and decreased the chance of an unusual or abnormal dietary intake. Dietary supplement usage was not reported which may have decreased the frequency of inadequacy. While the DRIs were used as the reference cut-points, the adjustment of the cut-point for nutrients with an AI to 67% may overestimate those with adequate intakes for a particular nutrient (106).   Groah and colleagues studied nutrient intakes in a sample of 73 adults (61 men and 12 women) with SCI (109).  Participants completed a four-day food record including at least one weekend day but excluded supplement intake.  Reported energy intake was statistically different with men consuming 2049 kcal and women reporting 1662 kcal (p=0.04).  There was no statistical difference in energy intake between the men with paraplegia (2088 kcal) and men with tetraplegia (2012 kcal).  Subgroup analyses were not completed for women as there was only one woman with tetraplegia.  The percentage of energy from macronutrients varied based on gender and SCI with the general trend of 44.3 – 52.5% kcal from carbohydrate, 31.7 – 36.6% kcal from fat and 14.2 – 18.5% kcal from protein.  Fibre intake was quite low with intakes ranging from 12.7 g to 14.5 g per day.  Vitamin and mineral intakes were reported as mean intakes for each of the four subgroups.  Statistical differences in mean intakes of vitamin D (3.53 mcg vs. 2.22 mcg) and vitamin B6 (1.73 mg vs. 1.32 mg) were detected as men with paraplegia reported a higher intake compared to men with tetraplegia. No other significant differences were detected.  Of note, calcium intake for men ranged from 649 mg (male-paraplegia) to 779 mg (male-tetraplegia) excluding the value for the one woman with tetraplegia who consumed 856 mg of calcium.   Unfortunately, there were no reports of the proportion of subjects with median intakes above the AI or the proportion of subjects with intakes below the EAR which makes it challenging to interpret or assess the probability of adequacy.  Data were only presented as means without standard deviation or ranges so it was difficult to interpret variability of the anthropometrics and nutrient intakes. One of the more complete studies reporting on the dietary intakes and adequacy population of adults with SCI was recently published by Walters and colleagues (110) as a 23  subset of data collected for the Study of Health and Activity in People with Spinal Cord Injury (SHAPE-SCI) (111).  Dietary intake was collected by interviewer administered  multiple-pass 24 hour recalls (n=77) with a repeat measure in six months time (n=68).  Mean usual intakes of macronutrients were compared to the Acceptable Macronutrient Distribution Range (AMDR) while inadequacy of micronutrients was determined by the percentages of participants with mean intakes below the EAR for a given nutrient.  For nutrients with an AI as the reference value, if the median intake of the nutrient met or exceeded the AI it was assumed that there was a low risk of inadequacy.  If the median intake was below the AI, no assessment of inadequacy was made.   For men the mean usual energy intake was 2096 ± 420 kcal per day with 52% of energy from carbohydrate, 30% from fat and 16% from protein.  Women reported intakes of 1711 ± 152 kcal per day with 53% of energy from carbohydrate, 28% from fat and 17% from protein.  The median intakes for vitamin D, calcium, potassium and fibre for all age groups and both genders were below the AI.  All intakes of sodium were well above the respective AI for the age and gender group. Nutrient inadequacies, reported as percentage of participants with mean intake below EAR were detected for several nutrients for both men and women.  Greater than 50% of men had intakes below the EAR for vitamin A (92%), magnesium (89%), folate (75%), zinc (71%) and vitamin C (52%).  Greater than 50% of women had inadequate intakes of folate (79%), magnesium (71%) and vitamin A (57%). Overall, the women had a decreased prevalence of mean intakes below the EAR as compared to the men.  The only statistically significant difference based on SCI was that those with a complete SCI consumed more calcium than those with an incomplete SCI (947 ± 449 mg vs. 758 ± 348 mg, p<0.05).  Usage of vitamin and mineral supplements was reported at 53% of participants consuming a calcium, multivitamin, vitamin C or vitamin D supplement. This study was well designed with several strengths.  The method of dietary intake data collection used reduced much of the potential participant bias by conducting the interview in person, in home and with graduated food models to estimate portion in a standardized manner (106).  As well the data collection was repeated at a six month interval 24  with 88% of participants providing a second report of dietary intake.  Dietary intakes were adjusted to remove intra-individual variation to provide a more precise estimate of the usual intake distribution before comparison to the DRIs was made.  A minor limitation of this study was that dietary supplements were not incorporated into dietary analysis to determine if the supplemental vitamins and minerals may have decreased the prevalence of inadequacies. Although vitamin and mineral supplements were not directly incorporated into the dietary analysis of the Walters et al. study, the pattern of supplement usage from the SHAPE-SCI group over a period of 18 months was recently reported (112).  In a study of 77 adults with SCI (from SHAPE-SCI), 24% of participants consumed a multivitamin, 20% consumed a calcium supplement and 16% consumed supplemental vitamin D.  Intake was measured at three time points (0, 6 and 18 months) and 71.4% of participants reported taking a supplement at least once during the 18 month period while 50.6% of participants were consistent supplement users (reported supplement usage at least two out of three reporting periods).   Measurements of serum vitamin concentrations support the findings of suboptimal dietary intakes in adults with SCI (113, 114).  Plasma concentrations of vitamin C were measured in a sample of 23 adults with physical disabilities of which SCI was included and compared to a control group (n=50) (113).  The group with disabilities had a lower plasma concentration of vitamin C as compared to the control group (62.1 ± 28.5 mol/L vs. 77.9 ± 24.5 mol/L, p=0.02).  Average daily intake of citrus fruits was also lower in the group with disabilities (0.25 ± 0.30 servings vs. 0.65 ± 0.66 servings, p=0.008) as was the dietary intake of fruits and vegetables (excluding potatoes) (1.02 ± 0.68 servings vs. 2.29 ± 1.2 servings, p=0.02). Moussavi and colleagues (114) measured serum concentrations of vitamin A, C and E in 110 adults with SCI.  They reported 16.4% of subjects had a serum concentration of vitamin A below the lower limit of the reference range, 37.3% of subjects had a serum concentration of vitamin C below the reference range and 30.0% of subjects were below the reference range for vitamin E.  In a sample of 22 individuals with physical disabilities, Burri and Neidlinger (115) measured dietary vitamin A, vitamin E and total carotenoids along with 25  plasma concentrations of retinol, -tocopherol and total carotenoids.  Compared to a control group (n=35), significantly lower plasma concentrations of -tocopherol (23 ± 7 mol/L vs. 28 ± 8 mol/L, p=0.004) and total carotenoids (1.0 ± 0.3 mol/L vs. 1.5 ± 0.5 mol/L, p=0.002) were detected.  The only significant difference in dietary intake between the groups was the number servings of fruits and vegetables (except potatoes) reported was lower in the subjects with disabilities compared to the control group (1.5 ± 1.0 servings vs. 2.2 ± 1.1 servings, p=0.01).  Interpretation and comparison of the data presented to date on dietary adequacy in the SCI population is difficult due to selection of cutpoints, reliance on reporting of mean intakes and a shift in the framework from Recommended Dietary Allowances to the concept of Estimated Average Requirements.  Until the most recent study by Walters et al., the concept of reporting prevalence of individuals with mean intakes below a cut-point was not well established.   However, in spite of these methodological shifts and differences in the data reported, trends have emerged from the limited research which has attempted to quantify and assess dietary intakes of adults with SCI.  Reported energy intakes are generally below what is estimated using a variety of predictive equations.  The distribution of macronutrients within the reduced energy intake follows the recommendations set by the AMDR except for consistently low fibre intakes.  The intake of micronutrients is concerning with several micronutrients identified as being at risk for suboptimal intakes including folate, vitamin C, magnesium and zinc.  The mean intakes of calcium and vitamin D are consistently below the recommended intake levels for healthy adults.  Those with SCI are at extremely high risk of developing osteoporosis below their injury (78, 116) and a general recommendation of increased calcium and vitamin D intakes to optimize bone health following SCI has been suggested by physiatrists (117, 118). 26  Table 1.3   Summary of studies on dietary adequacy in adults with spinal cord injury Reference Sample Size Sample  Method of Diet Data Collection BMI (kg·m-2) Energy Intake (kcal·day-1) Nutrients at risk of inadequacy  Levine et al. (1992)  (107) n=33 M: 24 F: 9  P: 11  T: 21 7 day food record and FFQ not reported M:1682 ± 429 F: 1282 ± 418 M: vitamin A, thiamin , riboflavin, pyridoxine, vitamin E, calcium, magnesium, zinc, fibre F: calcium,  iron, magnesium, zinc, fibre Potvin et al. (1996) (119) n=10 (athletes) M:10 3 day food record (weighed intake) M: 20.5 M: 2138 ± 473  M: vitamin E, zinc Tomey et al. (2005) (15) n=95 M: 95  P: 95 7 day food record and FFQ M: 26.2±6.5 M: 2264 ± 745 M: fibre, folate, calcium, vitamin C Groah et al. (2009) (109) n=73 M: 61 F: 12  P: 48 (11 F, 37 M) T:25 (1 F, 24 M) 4 day food record M-P: 25.2 M-T:24.2 F-P:21.2 F-T: 46.0  M-P: 2012 M-T: 2088 F-P: 1662 F-T: 2685 M: vitamin D, folate, calcium , zinc, fibre F: vitamin D, folate, calcium, fibre Walters et al. (2009) (110) n=77 M: 63 F: 14  P: 38 T: 39 multiple pass 24 hr recall, repeat 6 month interval M: 26±5    F: 26±7 M: 2096 ± 492 F:  1711 ± 152 M: calcium, vitamin D, potassium, fibre, vitamin A, vitamin C, folate, magnesium, zinc F: calcium, vitamin D, potassium, fibre, vitamin A, folate, vitamin B12, magnesium, zinc Abbreviations: BMI, Body Mass Index; M, Male; F, Female; P, Paraplegia; T, Tetraplegia; FFQ, food frequency questionnaire;  n, sample size 27  1.2.10 Nutrient intakes in athletes with spinal cord injury To date, only two articles and one abstract have been published which report on nutrient intakes in athletes with SCI (119-121).  The study by Potvin et al. (119) reported on the dietary intakes of ten Canadian male elite wheelchair marathoners.  The researchers weighed and measured all food consumed by these athletes for three days during a team training event.  Caloric intakes were reported to be 2138 ± 473 kcal per day (34.5 kcal per kg of body weight) with a macronutrient breakdown of 47.9% energy from carbohydrate, 32.1% energy from fat and 19.5% energy from protein.  Based on the Dietary Reference Intakes for Estimated Energy Requirements (EER) (90) the predicted energy intake for this group was 2300 kcal per day based on a sedentary physical activity coefficient and 2512 kcal per day based on a low active physical activity coefficient (Table 1.4).  Potvin and colleagues also reported on vitamin and mineral intakes (119).  Mean intakes were compared to Recommended Nutrient Intake (RNI) with a cut-point of 67% of the RNI.  Mean intakes of vitamins and minerals were all above the cut-point with the exception of vitamin E (six athletes had intakes below) and zinc (three athletes had intakes below).  Interestingly, in this study the athletes consumed 1154 ± 416 mg of calcium and 6.26 ± 4.00 g of vitamin D which are the highest reported intakes of both of these nutrients in the literature.   However, these results might not reflect true intakes of the athletes as food was provided for athletes and intake was closely monitored and weighed for the three days of data collection potentially biasing food choices of the athletes. Unfortunately, this report did not fully describe the level of injury or motor function of SCI for the subjects so it is difficult generalize the results.  It is evident however, that in this small sample of wheelchair marathoners, the energy intakes were below what one may expect or predict especially considering the probable high physical activity levels of these individuals.  Results presented in abstract form from Lally et al. (120) found similar results when investigating the dietary characteristics of sixteen American and twelve Japanese marathoners.  Based on a 24 hour recall, energy intake for the American marathoners was 1909 kcal/day and 1627 kcal/day for the Japanese marathoners.  The macronutrient 28  composition of the diet was not different between the groups and was 53% of energy from carbohydrate, 26% of energy from fat and 20% of energy from protein.  The American athletes were also reported to have a higher weekly training volume (379 vs. 296 minutes). A study by Ribeiro et al. (121) described the nutritional status and dietary intakes of 60 Brazilian male wheelchair basketball athletes.  The subjects’ disability was categorized as either SCI (n=28) or poliomyelitis sequels in the legs (n=32).  Ribeiro et al. used three consecutive 24 hour dietary recalls to capture dietary intakes combined with laboratory and body composition analysis.  Similar to the study by Potvin et al. (119), this group reported energy intakes which were considerably less than expected when predictive equations accounted for estimated fat-free mass.  The equations predicted 41 kcal/kg body weight using the predictive equation using fat-free mass developed by Cunningham et al. (99), whereas energy intakes of 25 kcal/kg body weight were observed.  The anthropometric data of the subjects reported by Ribeiro et al. can be used to estimate energy requirements using the DRIs (90).  The equations predict an estimated energy requirement of 2745 – 3180 kcal per day which is similar to the predictions for the subjects in the Potvin et al. study (119). The macronutrient distribution (50% of kcal from carbohydrate, 37% kcal from fat, 20% kcal from protein) for the subjects in the Ribeiro et al. study (121) was within the AMDR for carbohydrate and protein but slightly above recommendations for fat.  The results reported for micronutrients were limited to calcium with an average intake of 701 ± 392 mg.  It is unfortunate that mean intakes of key micronutrients and assessment of the prevalence of dietary inadequacy were not reported from the dietary intakes of these athletes. Of the published reports, it is clear that the observed energy intakes are lower than the predicted values for sedentary individuals.  The predictive methods used in the studies (90, 99, 108) are based on data gathered from able-bodied individuals and cannot be generalized for use with individuals with SCI.  It is also disappointing that all the subjects were male.  Female athletes have been reported to restrict energy intakes (122) and it would be interesting to know if this trend also holds true among female athletes with SCI.     29  Table 1.4  Energy intakes of athletes with SCI: measured and predicted intakes Study Subjects Measured Energy Intakes Predicted Energy Intakesa mean age, height, weight kcal/day kcal/kg kcal/day kcal/kg Potvin et al. (39)  30.7 years, 1.74 m, 62.2 kg 2138 ± 473 34.5 2300 – 2512a 37 – 40 Ribeiro et al.  (41)  18 – 40 years,  1.68 m, 62.1 kg NR (estimated at 1552 kcal using mean weight) 25 2266 – 2474a  ~2540b 37– 40  ~41b Lally et al.  (40)  NR 1909 kcal (American) 1627 kcal (Japanese) NR NR NR a Predicted energy intakes based on estimated energy requirements for age, gender, height and weight adjusted for estimated activity levels (90). Range presented is based on sedentary to low active physical activity coefficient. b  Predicted energy intakes using Cunningham equation (99) Abbreviations: NR, not reported  1.3 Limits to current knowledge     Individuals with SCI and in particular highly trained athletes with SCI provide a unique model to investigate.  Significant changes to body composition including a decrease in fat-free mass and increased adiposity (especially below the level of injury) contribute to a number of undesirable metabolic abnormalities.  While an increased incidence of dyslipidemia and glucose intolerance has been observed, the reasons why individuals with SCI are experiencing these metabolic patterns are not fully understood. Current theories relate to the impact of decreased lean muscle or increased adipose tissue, the potential impact of high rates of obesity in this population, the effect of a more sedentary lifestyle or most likely a combination of all these factors.  As the level of physical activity increases, those with SCI tend to have a more ‘normal’ metabolic profile.  Nutrition therapy, along with increased physical activity and reduction of obesity rates could offer better outcomes of CVD related morbidity and mortality post-SCI.    On the other side of the SCI spectrum of physical activity, athletes are challenging their bodies to adapt to the physical limitations of SCI and maximize the physical systems 30  that remain intact.  Nutrition as a performance and recovery strategy remains largely unstudied.  It is unknown what the specific energy and macronutrient requirements are for athletes with SCI to optimize glycogen replenishment or promote muscle recovery.  At a more basic level of understanding, it is unclear if these athletes are consuming a diet which meets the recommendations for all Canadians or if there are macronutrients in excess or micronutrient intakes which are suboptimal. 1.4 Rationale Elite athletes with a spinal cord injury are placing extreme athletic demands on their bodies with the expectation of excellence within their sport.  Advances in equipment, competitive opportunities and training protocols have supported these athletes in surpassing what was once believed to the ceiling of physical performance.  The physiological alterations and adaptations associated with SCI in combination with the high physical demands of training and competition create a unique situation of energy intakes and demands.  For these athletes to optimize nutrition as a performance enhancing strategy they must negotiate the macronutrient and energy balance associated with the energy expenditure of training and competition within the reduced energy expenditure (16, 88, 91-93) associated with decreased lean muscle mass and SCI (68).  This presents a novel and interesting population in which to explore and describe dietary intakes and practices. 1.4.1 Research objectives The primary purpose of this study was to explore and describe the current dietary intakes of elite Canadian athletes with a spinal cord injury. As this study was designed to be exploratory in nature, to better understand what elite athletes with SCI are consuming and what factors are influencing those dietary choices, specific hypotheses were not established.  Rather, specific objectives were established to guide the study and data analyses.      31  Objectives for chapter 2 1. Evaluate the dietary adequacy of macronutrient and micronutrient intakes of elite athletes with a spinal cord injury using the Dietary Reference Intakes for macronutrients (90), vitamins and elements (123-127) as comparative tools.  2. Assess the usage of supplemental vitamins and minerals and evaluate the impact on dietary adequacy.  3. Compare and contrast the energy and nutrient intakes of athletes while training at home and at a national team event.  4. Assess athletes’ understanding of nutrition recommendations using a nutrition knowledge questionnaire (128).  5. Compare and contrast the energy and nutrient intakes of elite athletes with a spinal cord injury to the Canadian population (129).   Objectives for chapter 3 1. Describe food related attitudes and behaviours as assessed by the Three-Factor Eating (130) and Yale Eating Patterns Questionnaires (131).  2. Explore cognitive dietary restraint scores and associations with dietary intake, anthropometrics and SCI characteristics. 32  1.5 References  1. Claus-Walker J, Halstead LS. Metabolic and endocrine changes in spinal cord injury (4 part series). Arch Phys Med Rehabil 1981-1982;62-3:569-80,595-601,628-638.  2. Bunten DC, Warner AL, Brunnemann SR, Segal JL. Heart rate variability is altered following spinal cord injury. Clin Auton Res 1998;8:329-34.  3. Spinal Cord Injury Solutions Network. About SCI. Version current 2008. Internet: http://www.rickhanseninstitute.org/index.php/en/information-and-resources/about-sci (accessed 2/15/2010 2010).  4. Liverman CT, Altevogt BM, Joy JE, Johnson RT, eds. Spinal Cord Injury: Progress, Promise, and Priorities. Washington, D.C.: Institute of Medicine of the National Academies, 2005.  5. Wu SK, Williams T. Factors influencing sport participation among athletes with spinal cord injury. Med Sci Sports Exerc 2001;33:177-82.  6. Marino RJ, Ditunno JF Jr, Donovan WH, Maynard F Jr. Neurologic recovery after traumatic spinal cord injury: data from the Model Spinal Cord Injury Systems. Arch Phys Med Rehabil 1999;80:1391-6.  7. Marino RJ, Barros T, Biering-Sorensen F, et al. International standards for neurological classification of spinal cord injury. J Spinal Cord Med 2003;26:S50-6.  8. Jacobs PL, Nash MS. Exercise recommendations for individuals with spinal cord injury. Sports Med 2004;34:727-51.  9. Ditunno JF Jr, Graziani V, Tessler A. Neurological assessment in spinal cord injury. Adv Neurol 1997;72:325-33.  10. Tortora GJ, Derrickson B. The spinal cord and spinal nerves. In: Tortora, GJ and Derrickson B, eds. Principles of anatomy and physiology. New Jersey: John Wiley & Sons, Inc., 2006:439-471.  11. Alberti KG. Eckel RH. Grundy SM, et al.  International Diabetes Federation Task Force on Epidemiology and Prevention. National Heart, Lung and Blood Institute. American Heart Association. World Heart Federation. International Atherosclerosis Society. International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-5.  33  12. National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).  Circulation 2002;106:3143-421.  13. Jones LM, Legge M, Goulding A. Healthy body mass index values often underestimate body fat in men with spinal cord injury. Arch Phys Med Rehabil 2003;84:1068-71.  14. Chen Y, Henson S, Jackson AB, Richards JS. Obesity intervention in persons with spinal cord injury. Spinal Cord 2006;44:82-91.  15. Tomey KM, Chen DM, Wang X, Braunschweig CL. Dietary intake and nutritional status of urban community-dwelling men with paraplegia. Arch Phys Med Rehabil 2005;86:664-71.  16. Jeon JY, Steadward RD, Wheeler GD, Bell G, McCargar L, Harber V. Intact sympathetic nervous system is required for leptin effects on resting metabolic rate in people with spinal cord injury. J Clin Endocrinol Metab 2003;88:402-7.  17. Bauman WA, Kahn NN, Grimm DR, Spungen AM. Risk factors for atherogenesis and cardiovascular autonomic function in persons with spinal cord injury. Spinal Cord 1999;37:601-16.  18. Bauman WA, Spungen AM. Disorders of carbohydrate and lipid metabolism in veterans with paraplegia or quadriplegia: a model of premature aging. Metabolism 1994;43:749-56.  19. de Groot S, Dallmeijer AJ, Post MW, Angenot EL, van den Berg-Emons RJ, van der Woude LH. Prospective analysis of lipid profiles in persons with a spinal cord injury during and 1 year after inpatient rehabilitation. Arch Phys Med Rehabil 2008;89:531-7.  20. Bauman WA, Adkins RH, Spungen AM, Kemp BJ, Waters RL. The effect of residual neurological deficit on serum lipoproteins in individuals with chronic spinal cord injury. Spinal Cord 1998;36:13-7.  21. Lee MY, Myers J, Abella J, Froelicher VF, Perkash I, Kiratli BJ. Homocysteine and hypertension in persons with spinal cord injury. Spinal Cord 2006;44:474-9.  22. Weaver FM, Collins EG, Kurichi J, et al. Prevalence of obesity and high blood pressure in veterans with spinal cord injuries and disorders: a retrospective review. Am J Phys Med Rehabil 2007;86:22-9.  23. Bauman WA, Adkins RH, Spungen AM, Waters RL. The effect of residual neurological deficit on oral glucose tolerance in persons with chronic spinal cord injury. Spinal Cord 1999;37:765-71.  34  24. Elder CP, Apple DF, Bickel CS, Meyer RA, Dudley GA. Intramuscular fat and glucose tolerance after spinal cord injury-a cross-sectional study. Spinal Cord 2004;42:711-6.  25. Gibson AE, Buchholz AC, Martin Ginis KA, et al. C-reactive protein in adults with chronic spinal cord injury: increased chronic inflammation in tetraplegia vs paraplegia. Spinal Cord 2008;46:616-21.  26. Huang CC, Liu CW, Weng MC, Chen TW, Huang MH. Association of C-reactive protein and insulin resistance in patients with chronic spinal cord injury. J Rehabil Med 2008;40:819-22.  27. DeVivo MJ, Krause JS, Lammertse DP. Recent trends in mortality and causes of death among persons with spinal cord injury. Arch Phys Med Rehabil 1999;80:1411-9.  28. Bauman WA, Spungen AM. Carbohydrate and lipid metabolism in chronic spinal cord injury. J Spinal Cord Med 2001;24:266-77.  29. Liang H, Chen D, Wang Y, Rimmer JH, Braunschweig CL. Different risk factor patterns for metabolic syndrome in men with spinal cord injury compared with able-bodied men despite similar prevalence rates. Arch Phys Med Rehabil 2007;88:1198-204.  30. Finnie AK, Buchholz AC, Martin Ginis KA, et al. Current coronary heart disease risk assessment tools may underestimate risk in community-dwelling persons with chronic spinal cord injury. Spinal Cord 2008;46:608-15.  31. Liang H, Mojtahedi MC, Chen D, Braunschweig CL. Elevated C-reactive protein associated with decreased high-density lipoprotein cholesterol in men with spinal cord injury. Arch Phys Med Rehabil 2008;89:36-41.  32. Bauman WA, Adkins RH, Spungen AM, Maloney P, Gambino R, Waters RL. Ethnicity effect on the serum lipid profile in persons with spinal cord injury. Arch Phys Med Rehabil 1998;79:176-80.  33. Duckworth WC, Solomon SS, Jallepalli P, Heckemeyer C, Finnern J, Powers A. Glucose intolerance due to insulin resistance in patients with spinal cord injuries. Diabetes 1980;29:906-10.  34. Bauman WA, Spungen AM. Metabolic changes in persons after spinal cord injury. Phys Med Rehabil Clin N Am 2000;11:109-40.  35. Jones LM, Legge M, Goulding A. Factor analysis of the metabolic syndrome in spinal cord-injured men. Metab Clin Exper 2004;53:1372-7.  35  36. DeFronzo RA. The triumvirate: beta-cell, muscle, liver. A collusion responsible for NIDDM. Diabetes 1988;37:667-87.  37. Hooker SP, Wells CL. Effects of low- and moderate-intensity training in spinal cord-injured persons. Med Sci Sports Exerc 1989;21:18-22.  38. Brenes G, Dearwater S, Shapera R, LaPorte RE, Collins E. High density lipoprotein cholesterol concentrations in physically active and sedentary spinal cord injured patients. Arch Phys Med Rehabil 1986;67:445-50.  39. Bauman WA, Spungen AM, Zhong YG, Rothstein JL, Petry C, Gordon SK. Depressed serum high density lipoprotein cholesterol levels in veterans with spinal cord injury. Paraplegia 1992;30:697-703.  40. Schmid A, Knoebber J, Vogt S, et al. Lipid profiles of persons with paraplegia and tetraplegia: sex differences. J Spinal Cord Med 2008;31:285-9.  41. de Groot S, Dallmeijer AJ, Post MW, Angenot EL, van der Woude LH. The longitudinal relationship between lipid profile and physical capacity in persons with a recent spinal cord injury. Spinal Cord 2008;46:344-51.  42. El-Sayed MS, Younesian A. Lipid profiles are influenced by arm cranking exercise and training in individuals with spinal cord injury. Spinal Cord 2005;43:299-305.  43. Manns PJ, McCubbin JA, Williams DP. Fitness, inflammation and the metabolic syndrome in men with paraplegia. Arch Phys Med Rehabil 2005;86:1176-81.  44. Bostom AG, Toner MM, McArdle WD, Montelione T, Brown CD, Stein RA. Lipid and lipoprotein profiles relate to peak aerobic power in spinal cord injured men. Med Sci Sports Exerc 1991;23:409-14.  45. Garstang SV, Miller-Smith SA. Autonomic nervous system dysfunction after spinal cord injury. Phys Med Rehabil Clin N Am 2007;18:275-96.  46. Krassioukov A, Claydon VE. The clinical problems in cardiovascular control following spinal cord injury: an overview. Prog Brain Res 2006;152:223-9.  47. Mathias CJ. Orthostatic hypotension and paroxysmal hypertension in humans with high spinal cord injury. Prog Brain Res 2006;152:231-43.  48. Oldenburg O, Kribben A, Baumgart D, Philipp T, Erbel R, Cohen MV. Treatment of orthostatic hypotension. Curr Opin Pharmacol 2002;2:740-7.  36  49. Price MJ, Campbell IG. Effects of spinal cord lesion level upon thermoregulation during exercise in the heat. Med Sci Sports Exerc 2003;35:1100-7.  50. Price MJ, Campbell IG. Thermoregulatory responses of spinal cord injured and able-bodied athletes to prolonged upper body exercise and recovery. Spinal Cord 1999;37:772-9.  51. Yaggie JA, Niemi TJ, Buono MJ. Adaptive sweat gland response after spinal cord injury. Arch Phys Med Rehabil 2002;83:802-5.  52. Claydon VE, Krassioukov AV. Orthostatic hypotension and autonomic pathways after spinal cord injury. J Neurotrauma 2006;23:1713-25.  53. Mathias CJ, Frankel HL. Autonomic disturbances in spinal cord lesions. In: Mathias CJ and Bannister R., eds. Autonomic Failure: A Textbook of Clinical Disorders of the Autonomic Nervous System, 4th edition. Oxford: Oxford University Press, 2002:494-513.  54. Schmid A, Huonker M, Barturen JM, et al. Catecholamines, heart rate and oxygen uptake during exercise in persons with spinal cord injury. J Appl Physiol 1998;85:635-41.  55. King ML, Lichtman SW, Pellicone JT, Close RJ, Lisanti P. Exertional hypotension in spinal cord injury. Chest 1994;106:1166-71.  56. Lopes P, Figoni SF, Perkash I. Upper limb exercise effect on tilt tolerance during orthostatic training of patients with spinal cord injury. Arch Phys Med Rehabil 1984;65:251-3.  57. Kessler KM, Pina I, Green B, et al. Cardiovascular findings in quadriplegic and paraplegic patients and in normal subjects. Am J Cardiol 1986;58:525-30.  58. Raymond J, Davis GM, Clarke J, Bryant G. Cardiovascular responses during arm exercise and orthostatic challenge in individuals with paraplegia. Eur J Appl Physiol 2001;85:89-95.  59. Davis GM. Exercise capacity of individuals with paraplegia. Med Sci Sports Exerc 1993;25:423-32.  60. Hooker SP, Greenwood JD, Hatae DT, Husson RP, Matthiesen TL, Waters AR. Oxygen uptake and heart rate relationship in persons with spinal cord injury. Med Sci Sports Exerc 1993;25:1115-9.  61. Groothuis JT, Boot CR, Houtman S, van Langen H, Hopman MT. Leg vascular resistance increases during head-up tilt in paraplegics. Eur J Appl Physiol 2005;94:408-14.  62. Hopman MT, Oeseburg B, Binkhorst RA. Cardiovascular responses in paraplegic subjects during arm exercise. Eur J Appl Physiol 1992;65:73-8.  37  63. Hopman MT, Dueck C, Monroe M, Philips WT, Skinner JS. Limits to maximal performance in individuals with spinal cord injury. Int J Sports Med 1998;19:98-103.  64. Van Loan MD, McCluer S, Loftin JM, Boileau RA. Comparison of physiological responses to maximal arm exercise among able-bodied, paraplegics and quadriplegics. Paraplegia 1987;25:397-405.  65. Myers J, Lee M, Kiratli J. Cardiovascular disease in spinal cord injury: an overview of prevalence, risk, evaluation, and management. Am J Phys Med Rehabil 2007;86:142-52.  66. Lohman TG, Houtkooper L, Going SB. Body fat measurement goes high-tech: Not all are created equal. ACSM Health Fitness J 1997;7:30-5.  67. Nuhlicek DN, Spurr GB, Barboriak JJ, Rooney CB, el Ghatit AZ, Bongard RD. Body composition of patients with spinal cord injury. Eur J Clin Nutr 1988;42:765-73.  68. Spungen AM, Adkins RH, Stewart CA, et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol 2003;95:2398-407.  69. Jones LM, Goulding A, Gerrard DF. DEXA: a practical and accurate tool to demonstrate total and regional bone loss, lean tissue loss and fat mass gain in paraplegia. Spinal Cord 1998;36:637-40.  70. Maggioni M, Bertoli S, Margonato V, Merati G, Veicsteinas A, Testolin G. Body composition assessment in spinal cord injury subjects. Acta Diabetol 2003;40:S183-6.  71. Cardus D, McTaggart WG. Body composition in spinal cord injury. Arch Phys Med Rehabil 1985;66:257-9.  72. Spungen AM, Wang J, Pierson RN Jr, Bauman WA. Soft tissue body composition differences in monozygotic twins discordant for spinal cord injury. J Appl Physiol 2000;88:1310-5.  73. de Bruin ED, Dietz V, Dambacher MA, Stussi E. Longitudinal changes in bone in men with spinal cord injury. Clin Rehabil 2000;14:145-52.  74. Kiratli BJ, Smith AE, Nauenberg T, Kallfelz CF, Perkash I. Bone mineral and geometric changes through the femur with immobilization due to spinal cord injury. J Rehabil Res Dev 2000;37:225-33.  75. Garland DE, Stewart CA, Adkins RH, et al. Osteoporosis after spinal cord injury. J Orthop Res 1992;10:371-8.  38  76. Maimoun L, Couret I, Mariano-Goulart D, et al. Changes in osteoprotegerin/RANKL system, bone mineral density and bone biochemicals markers in patients with recent spinal cord injury. Calcif Tissue Int 2005;76:404-11.  77. Maimoun L, Couret I, Micallef JP, et al. Use of bone biochemical markers with dual-energy x-ray absorptiometry for early determination of bone loss in persons with spinal cord injury. Metab Clin Exper 2002;51:958-63.  78. Dauty M, Perrouin Verbe B, Maugars Y, Dubois C, Mathe JF. Supralesional and sublesional bone mineral density in spinal cord-injured patients. Bone 2000;27:305-9.  79. Vico L, Collet P, Guignandon A, et al. Effects of long-term microgravity exposure on cancellous and cortical weight-bearing bones of cosmonauts. Lancet 2000;355:1607-11.  80. Giangregorio LM, Craven BC, Webber CE. Musculoskeletal changes in women with spinal cord injury: a twin study. J Clin Densitom 2005;8:347-51.  81. Giangregorio LM, Hicks AL, Webber CE, et al. Body weight supported treadmill training in acute spinal cord injury: impact on muscle and bone. Spinal Cord 2005;43:649-57.  82. Goktepe AS, Yilmaz B, Alaca R, Yazicioglu K, Mohur H, Gunduz S. Bone density loss after spinal cord injury: elite paraplegic basketball players vs. paraplegic sedentary persons. American Journal of Physical Medicine & Rehabilitation 2004;83:279-83.  83. Ide M, Ogata H, Kobayashi M, Tajima F, Hatada K.  Anthropometric features of wheelchair marathon race competitors with spinal cord injuries. Paraplegia 1994;32:174-9.  84. Bulbulian R, Johnson RE, Gruber JJ, Darabos B. Body composition in paraplegic male athletes. Med Sci Sports Exerc 1987;19:195-201.  85. Olle MM, Pivarnik JM, Klish WJ, Morrow JR Jr. Body composition of sedentary and physically active spinal cord injured individuals estimated from total body electrical conductivity. Arch Phys Med Rehabil 1993;74:706-10.  86. Goosey-Tolfrey VL. Physiological profiles of elite wheelchair basketball players in preparation for the 2000 Paralympic Games. Adapt Phys Activity Q 2005;22:57-66.  87. Mojtahedi MC, Valentine RJ, Arngrimsson SA, Wilund KR, Evans EM. The association between regional body composition and metabolic outcomes in athletes with spinal cord injury. Spinal Cord 2008;46:192-7.  88. Monroe MB, Tataranni PA, Pratley R, Manore MM, Skinner JS, Ravussin E. Lower daily energy expenditure as measured by a respiratory chamber in subjects with spinal cord injury compared with control subjects. Am J Clin Nutr 1998;68:1223-7.  39  89. Heyward VH, Wagner DR. Applied body composition assessment. 2nd ed. Champaign: Human Kinetics, 2004.  90. Institute of Medicine. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein and Amino Acids. 1st ed. Washington DC: The National Academies Press, 2005.  91. Buchholz AC, McGillivray CF, Pencharz PB. Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition. Am J Clin Nutr 2003;77:371-8.  92. Bauman WA, Spungen AM, Wang J, Pierson RN Jr. The relationship between energy expenditure and lean tissue in monozygotic twins discordant for spinal cord injury. J Rehabil Res Dev 2004;41:1-8.  93. Buchholz AC, McGillivray CF, Pencharz PB. Physical activity levels are low in free-living adults with chronic paraplegia. Obes Res 2003;11:563-70.  94. Spungen AM, Bauman WA, Wang J, Pierson RN Jr. The relationship between total body potassium and resting energy expenditure in individuals with paraplegia. Arch Phys Med Rehabil 1993;74:965-8.  95. Buchholz AC, Bartok C, Schoeller DA. The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract 2004;19:433-46.  96. Buchholz AC, Pencharz PB. Energy expenditure in chronic spinal cord injury. Curr Opin Clin Nutr Metab Care 2004;7:635-9.  97. Sedlock DA, Laventure SJ. Body composition and resting energy expenditure in long term spinal cord injury. Paraplegia 1990;28:448-54.  98. Buchholz AC, Rafii M, Pencharz PB. Is resting metabolic rate different between men and women? Br J Nutr 2001;86:641-6.  99. Cunningham JJ. A reanalysis of the factors influencing basal metabolic rate in normal adults. Am J Clin Nutr 1980;33:2372-4.  100. Aksnes AK, Brundin T, Hjeitnes N, Maehlum S, Wahren J. Meal-induced rise in resting energy expenditure in patients with complete cervical spinal cord lesion. Paraplegia 1993;31:462-72.  101. Yamasaki M, Irizawa M, Komura T, et al. Daily energy expenditure in active and inactive persons with spinal cord injury. J Hum Ergol (Tokyo) 1992;21:125-33.  40  102. Frankenfield DC, Muth ER, Rowe WA. The Harris-Benedict studies of human basal metabolism: history and limitations. J Am Diet Assoc 1998;98:439-45.  103. Harris JA, Benedict FG. A biometric study of the basal metabolism in man. Carnegie Institution of Washington 1919;Publication No. 279:Washington, D.C.  104. Mojtahedi MC, Evans EM. Predication equations overestimate resting metabolic rate of spinal cord injured athletes. Med Sci Sports Exerc 2005;37:S437(Abstract).  105. Barr SI. Introduction to dietary reference intakes. Appl Physiol Nutr Metab 2006;31:61-5.  106. Institute of Medicine. Dietary Reference Intakes. Applications in Dietary Assessment. 1st ed. Washington DC: The National Academies Press, 2000.  107. Levine AM, Nash MS, Green BA, Shea JD, Aronica MJ. An examination of dietary intakes and nutritional status of chronic healthy spinal cord injured individuals. Paraplegia 1992;30:880-9.  108. National Research Council, Food and Nutrition Board, Committee on Dietary Allowances, ed. Recommended Dietary Allowances. Washington DC: National Academies of Science, 1989.  109. Groah SL, Nash MS, Ljungberg IH, et al. Nutrient intake and body habitus after spinal cord injury: an analysis by sex and level of injury. J Spinal Cord Med 2009;32:25-33.  110. Walters JL, Buchholz AC, Martin Ginis KA. Evidence of dietary inadequacy in adults with chronic spinal cord injury. Spinal Cord 2009;47:318-22.  111. Martin Ginis KA, Latimer AE, Buchholz AC, et al. Establishing evidence-based physical activity guidelines: methods for the Study of Health and Activity in People with Spinal Cord Injury (SHAPE SCI). Spinal Cord 2008;46:216-21.  112. Opperman EA, Buchholz AC, Darlington GA, Martin Ginis ,K.A. Dietary supplement use in the spinal cord injury population. Spinal Cord 2010;48:60-4.  113. Cahill KM, Burri BJ, Sucher K. Dietary intakes and plasma concentrations of vitamin C are lowered in healthy people with chronic, nonprogressive physical disabilities. J Am Diet Assoc 2000;100:1065-7.  114. Moussavi RM, Garza HM, Eisele SG, Rodriguez G, Rintala DH. Serum levels of vitamins A, C, and E in persons with chronic spinal cord injury living in the community. Arch Phys Med Rehabil 2003;84:1061-7.  41  115. Burri BJ, Neidlinger TR. Dietary intakes and serum concentrations of vitamin E and total carotenoids of healthy adults with severe physical disabilities are lower than matched controls. J Am Diet Assoc 2002;102:1804-6.  116. Eser P, Frotzler A, Zehnder Y, et al. Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals. Bone 2004;34:869-80.  117. Ashe MC, Eng JJ, Krassioukov A. Physiatrists' opinions and practice patterns for bone health after SCI. Spinal Cord 2009;47:242-8.  118. Morse LR, Giangregorio L, Battaglino RA, et al. VA-based survey of osteoporosis management in spinal cord injury. PM R 2009;1:240-4.  119. Potvin A, Nadon R, Royer D, Farrar D. The diet of the disabled athlete. Sci Sports 1996;11:152-6.  120. Lally DA, Wang JH, Goebert DA, Quigley RD, Hartung GH. Performance training and dietary characteristics of American and Japanese wheelchair marathoners. Med Sci Sports Exerc 1991;23:S101(Abstract).  121. Ribeiro SM, Da Silva RC, De Castro IA, Tirapegui J. Assessment of nutritional status of active handicapped individuals. Nutr Res 2005;25:239-49.  122. Cupisti A, D'Alessandro C, Castrogiovanni S, Barale A, Morelli E. Nutrition knowledge and dietary composition in Italian adolescent female athletes and non-athletes. Int J Sport Nutr 2002;12:207-19.  123. Institute of Medicine. Dietary Reference Intakes: Water, Potassium, Sodium, Chloride and Sulfate. 1st ed. Washington DC: The National Academies Press, 2005.  124. Institute of Medicine. Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium and Zinc. 1st ed. Washington DC: The National Academies Press, 2001.  125. Institute of Medicine. Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium and Carotenoids. 1st ed. Washington DC: The National Academies Press, 2000.  126. Institute of Medicine. Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin and Choline. 1st ed. Washington DC: The National Academies Press, 1998.  127. Institute of Medicine. Dietary Reference Intakes for Calcium, Phosphorous, Magnesium, Vitamin D and Fluoride. 1st ed. Washington DC: The National Academies Press, 1997.  42  128. Parmenter K, Wardle J. Development of a general nutrition knowledge questionnaire for adults. Eur J Clin Nutr 1999;53:298-308.  129. Health Canada, Statistics Canada. Canadian Community Health Survey, Cycle 2.2, Nutrient Intakes from Food Provincial, Regional and National Summary Data Tables, Volume 1,2 and 3. 1st ed. Ottawa, ON: Her Majesty the Queen in Right of Canada, 2004.  130. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res 1985;29:71-83.  131. Kristeller JL, Rodin J. Yale Eating Patterns Questionnaire. Addict Behav 1989;14:631-42.    43  Chapter 2: Elite Canadian Athletes with Spinal Cord Injury Are At Risk of Nutrient Inadequacies While at Home and Team Training Events                        44  2.1 Introduction  Canadian athletes with a spinal cord injury (SCI) are training at intensities, durations and frequencies that rival those of their Olympic colleagues.  The competitive nature of the Paralympic Games demands athletes optimize training and performance strategies to attain competition goals.  Knowledge of sport-specific nutritional requirements of athletes with SCI is almost nonexistent while there is a wealth of knowledge to guide and support performance nutrition practices for able-bodied athletes (1).  However, before the sport-specific nutritional needs of athletes with SCI are investigated it is important to understand what dietary choices these athletes are making and to assess the nutrient adequacy of those choices.   There is evidence that individuals with SCI are at greater risk than able-bodied individuals for a number of metabolic abnormalities including dyslipidemia (2, 3), hyperinsulinemia (4), obesity (5-7), hypertension (8) and diabetes mellitus (9).  Those with SCI comprise a unique population with physiological differences which dramatically impact metabolism, cardiovascular control (10, 11) and body composition (7).  Sedentary individuals with SCI have been investigated as a model for premature aging as those with SCI have a higher risk of cardiovascular disease, impaired glucose metabolism and obesity (12, 13).  Physical activity is able to attenuate, but not completely eliminate, some of these negative metabolic effects in the SCI population (14-16).    Three studies have investigated dietary adequacy in the SCI population (17-19). The studies were consistent in reporting energy intakes of approximately 2100 – 2250 kcal per day for men and approximately 1700 kcal per day for women with a similar pattern of energy from macronutrients (46 – 53% kcal from carbohydrate, 28 – 36% kcal from fat and 15 – 17% kcal from protein).  Using multiple-pass 24-hour recalls repeated at six months, Walters et al. (17) reported that a group of 77 community-living Canadians with SCI had mean usual intakes of calcium, vitamin D, potassium and fibre below the Adequate Intake (AI) for their age and gender.  Greater than 50% of individuals in this sample had median intakes below the Estimated Average Requirements (EAR) for vitamin A, vitamin C, folate, magnesium and zinc and greater than 20% of individuals had mean intakes below the EAR 45  for thiamin, vitamin B6 and vitamin B12.  Groah et al. (18) reported the dietary intake of 73 individuals with SCI using four days of self-reported food records and found a similar pattern of intakes below the recommended level for calcium, vitamin D, fibre, folate and zinc.  Tomey et al. (19) reported on the dietary intakes of 95 men with paraplegia using a 7-day semi-quantitative food frequency questionnaire and found low intakes of calcium, vitamin C, folate and fibre.  The studies which targeted athletes with a disability are less robust with outcomes focused on energy intakes and percentage of energy from macronutrients (20-22).  Riberio et al. (20) reported on dietary intakes of 28 wheelchair basketball athletes with paraplegia and found energy intakes of approximately 1550 kcal (25 kcal per kg) with 50% of kcal from carbohydrate, 37% kcal from fat and 20% kcal from protein.  The only micronutrient reported was a mean calcium intake of 701 ± 392 mg.   Potvin et al. (21) reported energy intakes of 2138 kcal per day (48% carbohydrate, 32% fat, 20% protein) and macronutrients for ten wheelchair marathoners.  In this small sample, athletes were assessed to be at risk of suboptimal nutrient intakes for zinc (n=3) and vitamin E (n=6) as the mean intake was below a cut-point of 67% of the Recommended Dietary Allowance (RDA) (23).     The physiological alterations and adaptations associated with SCI in combination with the physical demands of training and competition create a unique situation of energy intakes and demands.  This presents a novel and interesting population in which to explore and describe dietary intakes and adequacy.  Accordingly, the primary objectives of this study were to assess the energy intakes and dietary adequacy of elite Canadian athletes with a SCI by comparing dietary intakes to the Dietary Reference Intakes (DRIs) and the reported dietary intakes of Canadians.  Secondary objectives included comparing intakes between home and national team training environments and an assessment of the athletes’ understanding of nutrition principles.  46  2.2 Methods 2.2.1 Overview of study design This exploratory cross-sectional study was designed to assess energy, macronutrient and micronutrient intakes of elite Canadian athletes with SCI.  Participants were enrolled between May 2007 and March 2009.  An investigator attended a national team training event to recruit participants, explain study procedures, collect questionnaire data, measure anthropometrics and assist participants with the completion of three-day food and activity records.  Athletes were instructed on how to accurately complete the three-day food records including methods to estimate portion sizes, recording strategies and the importance of including condiments and beverages.  In addition, an investigator was present during meal times to provide support with completing the food diary and answer any questions the participants had regarding how to record certain foods.   A questionnaire was self administered to record demographic data such as age, number of years on the national team, usual training practices, nutrition related goals, medications and details related to spinal cord injury including self-reporting of the level and completeness of injury.  The motor and sensory function of the participant’s SCI was assessed using a modified assessment of neurological function based on the American Spinal Injury Association (ASIA) standard system for classification of spinal cord injury (24, 25).  Participants also completed a questionnaire-based assessment of nutrition knowledge (26), the Three-Factor Eating questionnaire (27) and the Yale Eating Patterns questionnaire (28, 29).  Results of the Three-Factor Eating and Yale Eating Patterns questionnaires are presented elsewhere (Chapter 3).  For three days at the national team event, participants also recorded their time spent engaged in physical activity and rated the intensity of the activity using a rating of perceived exertion scale (30, 31).  On the last day of the national team event, the questionnaire data, three-day food records and three-day activity records were collected and participants were provided with the necessary forms to repeat the three-day food and activity records at home, along with a self-addressed stamped envelope to return these forms to the investigator.  Instructions were given to the participants to 47  complete the home food and activity records for three consecutive days, including one weekend day and return the complete records within two weeks.  Upon receipt of the records, if data were unclear or details missing, the investigator contacted the participant for clarification.  The study protocol was approved by the Behavioural Research Ethics Board at The University of British Columbia (Appendix 1) and participants provided informed written consent (Appendix 2).   2.2.2 Participant recruitment Recruitment and identification of potential athletes was conducted by an initial letter of invitation (Appendix 3) sent to all national team head coaches associated with the Canadian Paralympic Committee.  At the time of this study, Canada had athlete representation in 21 Paralympic sports and 11 of the sports met the eligibility requirements of providing competitive opportunities for athletes with SCI, with an expected minimum of 12 hours of physical training per week.  Coaches from all 11 eligible sports (4 winter and 7 summer) received an invitation to participate via email and each coach was contacted to discuss the study and arrange for an investigator to attend a national training camp or event.  Head coaches were requested to provide athletes with information pertaining to the study and alert the athletes that a researcher (Jennifer Krempien) would be at a national team event to collect data, should they choose to participate. Detailed information regarding the recruitment procedures is outlined in Appendix 4.  It is estimated that between 75 and 80 athletes within Canada met the inclusion criteria during the recruitment period. A total of 41 athletes were approached in person to participate in the study and a total of 34 athletes consented to participate with 32 athletes completing all aspects of the study. To be eligible, athletes had a SCI resulting in paraplegia or tetraplegia, with either complete or incomplete impairment of motor function.  Elite athletes were targeted for this study.  Each athlete met a minimum of one of the following criteria: member of a senior national team, received senior level funding from the Sport Canada Athlete’s Assistance Program, ranked in the top three for his or her sport within Canada or ranked in the top five in the world for his or her sport discipline.  For athletes participating in a team sport, the 48  last international ranking for the team was used to establish the athlete’s international ranking.  For example, the wheelchair rugby team finished third at the 2006 World championships so all athletes on that roster were ranked as third and thus met the inclusion criteria.  If an athlete was not a member of the national team, their ranking was based on their provincial team performance at Canadian Nationals.  All athletes participated in ongoing physical training for a minimum of 12 hours per week.  Athletes were 19 years of age or older and understood written and spoken English.   2.2.3 Participant characteristics Data pertaining to participant characteristics were self-reported by participants using a questionnaire developed for this purpose.  Demographics such as age, sport information including years on the national team, international ranking, training information and nutrition related goals were included.  Details of the participant’s SCI were also recorded including year of injury, level of SCI, cause and description of SCI.  Additional medical conditions or prescription medications were listed.  A copy of the demographic questionnaire is included in Appendix 5. 2.2.4 Description of spinal cord injury The American Spinal Injury Association (ASIA) developed a standard system for the classification and description of spinal cord injury tool designed for physiatrists to test the remaining motor and sensory function following SCI (24, 25, 32, 33).  The full assessment is quite burdensome for the subject as the sensory examination requires light touch and pin prick testing to each of 28 dermatomes on the right and left sides of the body and a strength evaluation of ten muscle groups.  For the purpose of this study, the ASIA Impairment Scale (AIS) was limited to classification of the neurological completeness of the injury. A score of AIS-A indicated no motor or sensory function preserved in the sacral segments S4-S5 indicating a complete injury while AIS-B through D combined to indicate incomplete SCI.  Each athlete was also categorized as either tetraplegic if they self-reported an injury to their cervical spine or paraplegic if they self-reported thoracic injury.    49  2.2.5 Anthropometry and body composition Weight (kg) was measured to the nearest 0.1 kg with athletes wearing light indoor clothing without shoes using a portable digital scale with remote display (Universal Weight Enterprise Company Ltd., Model AMP-150, Taipei, Taiwan).  The scale was modified with a larger seating platform and non-ambulatory participants sat directly on the scale for measurement.  Length (m) was measured with participants in a supine position on a firm surface with the soles of the participant’s feet against a wall. The subject’s length was marked on the surface and then measured.  The measured length was verbally reported to the subject. If the measurement was greater than 2 cm different than what the subject believed his or her height to be, the measurement procedure was repeated. All skinfold measurements were taken according to standardized procedures (34) using Harpenden skin calipers.  All measurements were taken on the right side of the body at triceps, biceps, subscapular and iliac crest sites.  Measurements were taken with subjects seated, with triplicate measurements taken in rotation to the nearest 0.2 mm, with the mean of the results used for calculations.  Additional measurements were taken if a measurement was different by greater than 10% from the other measurements from the same site (example: 20 ± 2 mm for iliac crest skinfold) and the closest three values averaged.   As reliable and validated predictive equations for body composition do not yet exist for SCI populations, many researchers have used the sum of skinfolds as a gross indicator of body fat (35-41).  While the sum of skinfolds is not able to accurately predict body density (or ultimately body fat percentage) in those with SCI (42), it can provide a crude body composition assessment of the individuals tested based on the assumptions that a relationship exists between the subcutaneous adipose tissue and total body fat and that the sum of several skinfold thicknesses can be used to estimate total body fat. However, the relationship between subcutaneous and visceral adipose tissue in those with SCI may be different than able-bodied individuals.  A comparison of abdominal visceral adipose tissue to abdominal subcutaneous adipose tissue found those with SCI had a significantly greater ratio of visceral to subcutaneous adipose tissue (6).  It is not understood if this observation 50  extends to other regions of the body but as the skinfold measurements are not attempting to predict body fatness, the sum of skinfold measurements remains the most appropriate field method of anthropometric testing.   2.2.6 Physical activity assessment Three methods were used to assess the frequency and intensity of physical activity for this study (copies of the instruments used are included in Appendices 5 - 7).  An athlete recorded the number of hours per week he or she typically engages in physical training, with the total number of training hours delineated by the number of hours spent in sport specific training, strength and aerobic training.  This self-reported global assessment of training assisted in describing the training patterns of athletes and for comparison of general training regimes between sports. Athletes were instructed to keep a three-day activity log for the same three-day periods in which food diaries were recorded.  For each training or sport related activity, athletes were requested to record the time spent participating in the activity, a description of the activity and also rate perceived exertion of the activity using the Borg scale for rating of perceived exertion (RPE) (30). The Borg scale uses a numerical system to rate perceived exertion on a scale of 6 (no exertion at all) to 20 (maximal exertion).  The scale was designed for use in able-bodied athletes to predict heart rate during activity (the rating approximates heart rate divided by 10 – for example,  an RPE of 20 reflects a heart rate of approximately 200 beats per minute in a young adult) and has been shown to have good validity and reliability, especially among athletes (31).  Unfortunately, the Borg scale has not been validated for predicting heart rate from perceived exertion in athletes with spinal cord injury as these athletes have altered heart rate and cardiovascular control during exercise (43, 44).  The utility of the RPE scale in this study is as a standard scale of exercise exertion to aid individual athletes when assessing exercise intensity.   The third method of assessment was a self-administered Physical Activity Scale for Individuals with a Physical Disability (PASIPD) (45).  This tool was developed to evaluate leisure time, household, and occupational activity in community dwelling individuals with a physical or mobility impairment.  Athletes were asked to recall the number of days in a 51  week they participated in an activity or category of activities and rank as never, seldom (1 – 2 days per week), sometimes (3 – 4 days per week), or often (5 – 7 days per week).  Athletes were also asked to estimate on average how many hours per day were spent in the activity as less than 1 hour, 1 to 2 hours, 2 – 4 hours or greater than 4 hours.  All PASIPD questionnaires were scored according to the instructions provided by the authors with the total score used in analyses. The total score was calculated by using the average number of hours per day for an activity and multiplying that time by a metabolic equivalent (MET) value.  This calculated a value for metabolic equivalent hours per day (MET hr/d) for each activity and the metabolic equivalent hours were then summed for all 13 items. METs reflect multiples of the resting metabolic rate (RMR):  For example, 20 MET hr/d could reflect 5 hours of activity at 4 METs (4 times RMR) or 2 hours of activity at 10 METs.  PASIPD was tested for construct validity in a sample of 227 men and 145 women with disabilities and was found to have low-to-moderate internal consistency within factors (Cronbach alpha ranging from 0.37 to 0.65).  The PASIPD has not yet been tested with additional populations, so the external validity of this instrument is unknown.  As this tool was developed for a population with low to moderate activity levels, it may not fully capture the activity frequency or intensity of elite level athletes.  Thus, activity logs in combination with the Borg scale of rate of perceived exertion were indicated. 2.2.7 Self-reported food diary  Nutrient intake data were collected using a self-reported three day food diary method (Appendix 8).  The recording period was three consecutive days during the training camp with a three day follow-up period once the athlete had returned to his or her home environment.  The food diary completed at home for three consecutive days (two weekdays and one weekend day) reflected the athletes’ typical dietary choices and intakes in a less artificial environment.  Participants were also instructed to record the brand name and amount consumed for all vitamin, mineral and herbal supplements. For athletes, a three to seven day diet monitoring period is believed to provide a reasonably accurate and precise estimation of habitual energy and macronutrient consumption in both individuals and groups (46).  The issue of under and over reporting is 52  of concern with most collection techniques for dietary intakes (47).  In this study, the issue of inaccurate reporting was addressed by providing a tutorial to the athletes prior to recording of intakes, and by having an investigator available to the athletes during meals to assist with data recording.  2.2.8 Dietary analysis Food record data were entered into Food Processor for Windows, version 9.0.0 (database version December 2007, ESHA Research, Salem, Oregon).  The Canadian Nutrient File (2007) database (48) was the primary nutrient reference database used.  If food values were not available from the Canadian Nutrient File database, the nutritional content for equivalent items from the USDA Standard Reference database (49) or values provided by the manufacturer were used.  Because of a software technical issue, nutrient values for vitamin D were calculated manually and vitamin A values were omitted.  The three-day food intakes from both training camp and home food records were averaged separately to compute mean nutrient intakes of energy (kcal); carbohydrate, fat, protein in both grams and percentage of calories; dietary fibre (g); elements and vitamins.   2.2.9 Prediction of energy expenditure For each athlete, energy expenditure was predicted using the Estimated Energy Requirement (EER) equations developed by the Institute of Medicine (50). The EER is defined as “the average dietary energy intake that is predicted to maintain energy balance in a healthy adult of a defined age, gender, weight, height and level of physical activity consistent with good health” (50).  Predictive equations were developed using a population of normal weight individuals with total daily energy expenditure measured by the doubly labeled water technique.  The EER equations have not been validated for individuals with SCI, and it is challenging to derive estimates of energy expenditure associated with physical activity because of the relatively smaller amounts of muscle mass in the SCI population. To date, validated equations to predict energy expenditure in those with SCI have not been established. For this reason, and to be most conservative, sedentary and low active physical activity levels were used to develop a range of predicted values for energy expenditure.   53  2.2.10 Nutrition knowledge General nutrition knowledge was assessed using a nutrition knowledge assessment tool (Appendix 9) developed for use in adults in the United Kingdom (UK) (26).  This assessment tool has been tested previously and has been shown to have acceptable internal consistency (Cronbach’s alpha of 0.7 – 0.97), test-retest reliability (Pearson’s correlation 0.8 – 0.98) and construct validity.  Slight modifications were necessary to substitute foods used in the questions but not commonly consumed in Canada.  For example; kippers was substituted with deli meat, mackerel was substituted with salmon, wholemeal was substituted with wholegrain and biscuit was substituted with cookie.  The assessment of nutrition knowledge consisted of four subscales measuring dietary recommendations, sources of nutrients, choosing everyday foods and the diet-disease relationship.  This tool was validated using an adult population in the UK but has not been validated specifically for athletes or those with a SCI.  2.2.11 Statistical analysis  All statistical analyses were completed using SPSS version 17.0.  Descriptive statistics are presented as means, standard deviations, medians and ranges.  A p-value of <0.05 was considered to be a statistically significant difference.  Independent t-tests were used to compare means between groups based on SCI (complete versus incomplete lesion, paraplegic versus tetraplegic), gender or sport.  Paired sample t-tests were used to detect differences in mean nutrient intakes between home and training camp. Independent t-tests were performed to compare mean training times between groups with paired sample t-tests to detect differences in activity times between home and training camps.   Three-day averages for home and training camp were calculated for energy (kcal), grams of carbohydrate, fat and protein which allowed for the contribution to total energy for each macronutrient.  Percentage of energy from carbohydrate, fat and protein was then compared to the Acceptable Macronutrient Distribution Range (AMDR) with the proportion of the group with intakes falling outside of the AMDR reported as excessive or inadequate.  Dietary micronutrient intakes were compared to the Dietary Reference Intakes (DRIs) to 54  assess adequacy.  Specifically, for nutrients with an Estimated Average Requirement (EAR), the prevalence of inadequate intakes was estimated as the proportion of individuals with mean intakes below the EAR (51).   The proportion of individuals with three day average intake below the EAR at home and training camp environments was compared using crosstabs and Pearson chi-square.  The proportion of individuals with six day average intakes from food alone below the EAR was compared to the proportion of individuals with combined intakes from food and supplements was compared using the McNemar test as the populations were related.  For nutrients with an Adequate Intake (AI) a different approach was used.  If the median intake for the population was at or above the AI, it was assumed that the group’s usual intake was adequate with a relatively low risk of inadequacy.  If the median intake fell below the AI, no assessment of adequacy for that nutrient can be determined (51). In addition, the prevalence of inadequacy and a comparison of mean intakes from food alone versus intakes from the combination of food and vitamin/mineral supplements were compared using six day averages.   2.3 Results 2.3.1 Participant characteristics A total of 32 athletes met the inclusion criteria, consented to participate and completed all components of the study. Most subjects were on the wheelchair rugby team (n=20) with the remainder of athletes competing in wheelchair basketball (n=7), para-alpine skiing (n=3) and wheelchair athletics (n=2). Background characteristics including age (years), height (m), weight (kg), BMI (kg·m-2) and description of spinal cord injury (SCI) are presented in Table 2.1.  Twenty of the participants had a complete SCI while 12 had an incomplete SCI.  Based on level of SCI, 20 had injuries resulting in tetraplegia and 12 had injuries resulting in paraplegia.  There were no differences between men and women pertaining to age or years since spinal cord injury.  As expected, the men were taller, weighed more and had a higher BMI as compared to the women.  There was no difference in the sum of the skinfolds or the triceps, subscapular or iliac crest sites but a statistically significant difference (p=0.025) was detected at the biceps site with women having a 3 mm larger skinfold.  When the 55  population was stratified by spinal cord injury (Table 2.2), no significant differences in anthropometrics were detected between those with paraplegia or tetraplegia.  When stratified by complete or incomplete injury, those with incomplete injury had a greater BMI compared to those with complete injury.    Table 2.1        Participant background characteristics with group and subgroup analyses based on gender  Group (N=32) Men (n=24) Women (n=8) Characteristic     Age (year) 30.6 ± 6.2a 30.5 ± 6.7 30.6 ± 4.7 Anthropometrics     Length (cm) 177.2 ± 8.9 179.2 ± 9.0b 171.1 ± 5.8c  Weight (kg) 67.0 ± 13.7 70.9 ± 13.3b 55.4 ± 6.0c  BMI (kg·m-2) 21.26 ± 3.4 22.1 ± 3.5b 18.9 ±  1.9c  Sum of skinfolds (mm) 51.1 ± 20.6 50.9 ± 22.8 51.5 ± 13.4  Triceps (mm) 12.1 ± 5.6 11.7 ± 6.2 13.2 ± 3.1  Biceps (mm) 7.6 ± 3.6 6.8 ± 3.4b 10.1 ± 3.2c  Subscapular (mm) 14.7 ± 6.1 15.2 ± 6.6 13.2 ± 4.2  Iliac crest (mm) 16.1 ± 7.9 17.2 ± 8.6 15.0 ± 5.5 Spinal Cord Injury     Years since SCI 13.4 ± 6.5 13.8 ± 6.8 12.5 ± 5.9  Paraplegic 12 (37.5%) 7 (29.2%) 5 (62.5%)  Tetraplegic 20 (62.5%) 17 (70.8%) 3 (37.5%)  Complete (ASIA A) 20 (62.5%) 15 (62.5%) 5 (62.5%)   Incomplete (ASIA B - D) 12 (37.5%) 9 (37.5%) 3 (37.5%) Abbreviations: BMI, Body mass index; ASIA, American Spinal Injury Association (a classification system for spinal cord injury); SCI, spinal cord injury a mean ± standard deviation b,c pair with a statistically significant difference p <0.05     56  Table 2.2 Group participant characteristics with comparisons made within the group based on level of injury (paraplegic vs. tetraplegia) and motor function of injury (complete vs. incomplete)    Group (N=32) Paraplegic (n=12) Tetraplegic (n=20) Complete (n=20) Incomplete (n=12) Characteristic      Age (year) 29.3 ± 5.1a 31.4 ± 6.7 31.5 ± 6.6 29.0 ± 5.3 Anthropometrics      Length (m) 174.1 ± 8.6 179.0 ± 8.8 178.6 ± 8.4 174.8 ± 9.6  Weight (kg) 64.6 ± 13.0 68.4 ± 14.2 64.0 ± 12.0 72.0 ± 15.3  BMI ( kg·m-2) 21.3 ± 3.7 21.2 ± 3.4 20.0 ± 2.9b 23.4 ± 3.3c  Sum of Skinfolds (mm) 50.2 ± 18.0 51.6 ± 22.5 45.4 ± 21.4b 60.5 ± 15.9c  Triceps (mm) 11.0 ± 4.0 12.7 ± 6.4 11.1 ± 6.3 13.3 ± 6.1  Biceps (mm) 8.6 ± 4.1 7.1 ± 3.3 7.0 ± 3.9 8.8 ± 3.0  Subscapular (mm) 14.6 ± 4.3 14.8 ± 7.1 13.3 ± 6.1 17.1 ± 5.6  Iliac crest (mm) 16.1 ± 7.3 16.9 ± 8.4 14.1 ± 7.2b 20.9 ± 7.4c Spinal Cord Injury      Years since SCI 14.0 ± 7.4 13.1 ±  6.1 12.8 ± 6.1 14.5 ± 7.3  Complete (ASIA A) 6 (50%) 14 (70%) 20 (100%) 0 (0%)   Incomplete (ASIA B - D) 6 (50%) 6 (30%) 0 (0%) 12 (100%) Abbreviations: BMI, Body mass index; ASIA, American Spinal Injury Association (a classification system for spinal cord injury); SCI, spinal cord injury a mean ± standard deviation b,c pair (within paraplegic versus tetraplegic or within completed versus incomplete injury) with a statistically significant difference p <0.05   2.3.2 Dietary analysis from food sources only Three-day food diaries were completed at the national team event and then repeated when the athlete returned home.  Average intake for each of the three day periods and an average for all six days are shown in Table 2.3. Predicted energy expenditure using the Institute of Medicine regression equations that reflect gender, age, height, weight and physical activity level of either sedentary or low active are also shown in Table 2.3 (50).    Comparison of the energy intakes between the home and training environment for the complete group showed an increased energy intake during the training camp.  When stratified by gender, sport, level of SCI or motor function of SCI, statistically higher energy 57  intakes at training camp versus home environments were detected for rugby athletes and those with incomplete SCI.   A comparison of the six day average energy intakes, with subgroup analyses by gender, sport, and SCI to the predicted Estimated Energy Requirement (EER) indicates the energy intake of men was significantly less than the predicted value for both sedentary and low active physical activity coefficient.  Women had energy intakes comparable to the EER for the sedentary and low active category with no statistically significant differences detected.  Subgroup analyses for sport showed rugby athletes had energy intakes below the sedentary EER and low active EER estimates whereas the energy intakes of basketball athletes were within the EER estimated range.  Those with tetraplegia had intakes below both sedentary and low active estimates whereas those with paraplegia had intakes comparable to the sedentary EER but intakes were significantly less than the low active EER.  Those with incomplete SCI had intakes significantly less than low active and sedentary EER whereas those with complete SCI had energy intakes similar to the sedentary EER values but less than the low active EER.       58   Table 2.3  Reported and predicted energy intakes categorized by gender, sport and spinal cord injury   Based on six-day mean usual intakes, all participants had macronutrient distributions that were within the AMDR (Figure 2.1).  No significant differences were detected between men and women or between home and training camp environments for the proportion of energy from the macronutrients.  The mean intakes representing the three days from home indicate only one male had a carbohydrate intake (% of kcal from carbohydrate) which was below the AMDR.  During the training camp, the number of men with the percentage of kcal   Training Camp Home  Six-day Average Predicted  EER Predicted  EERa Group (N=32) n (kcal) (kcal) (kcal) (Sedentary)c (Low Active)b Gender        Men 24 2285 ± 540d 2028 ± 528 2156 ± 431 2465 ± 226† 2695 ± 251*  Women 8 2056 ± 458 1927 ± 510 1991 ± 383 1903 ± 112 2115 ± 122 Sport        Rugby 20 2213 ± 556e 1899 ± 566f 2056 ± 453 2421 ± 265* 2650 ± 288*  Basketball 7 2398 ± 570 2263 ± 339 2330 ± 378 2256 ± 409 2486 ± 432  Other 5 2044 ± 265 2055 ± 452 2049 ± 272 2034 ± 212 2243 ± 213 Level of Injury        Paraplegic 12 2318 ± 509 2161 ± 479 2239 ± 414 2231 ± 307 2452 ± 347†  Tetraplegic 20 2173 ± 537 1908 ± 527 2040 ± 416 2381 ± 307* 2899 ± 361* Motor Function        Complete  20 2196 ± 556 2058 ± 535 2127 ± 428 2278 ± 288 2500 ± 306*  Incomplete 12 2279 ± 481e 1910 ± 494f 2095 ± 424 2402 ± 365† 2634 ± 390* Abbreviations: EER, Estimated Energy Requirement; kcal, kilocalorie; SCI, spinal cord injury Notes:  a Based on Dietary Reference Intakes: Estimated Energy Requirements for Adults (50) b Low Active indicates physical activity coefficient of 1.11 for males and 1.12 for females c Sedentary indicates physical activity coefficient of 1.00 for males and 1.00 females d  All values presented as mean ± standard deviation e,f  indicates pair with a statistically significant difference p < 0.05  †predicted EER (low active or sedentary) compared to six day average kcal intake with a statistical significance of p<0.05 (independent sample t-test) *predicted EER (low active and sedentary) compared to six day average kcal intake with a statistical significance of p<0.01 59  56.7 54.527.4 28.8 18.317.552.8 53.728.5 29.218.5 17.3Carbohydrate (H)                   (45 - 65%)Carbohydrate  (TC)                    (45 - 65%)Fat (H)                               (20 - 35%)Fat (TC)                            (20 - 35%)Protein  (H)                         (10 - 35%)Protein  (TC)                        (10 - 35%)Percentage of Energy IntakeMacronutrient Intake at Home (H) compared to Training Camp (TC) with reference to the Acceptable Macronutrient Distribution Range WomenMenfrom carbohydrate outside of the AMDR increased with one male reporting carbohydrate intake that fell below the AMDR and three reporting intakes that were greater than the AMDR.  While at home, all women reported carbohydrate intakes that fell within the AMDR. During training camp, all women exceeded the lower end of the AMDR for percentage of energy from carbohydrate with two women consuming slightly more than the 65% upper end of the range.  All participants had carbohydrate intakes that were well above the EAR of 100 g.   Figure 2.1   Percentage of energy from macronutrients with reference to the Acceptable Macronutrient Distribution Range    60   Results for nutrients with an Adequate Intake (AI) as the reference value are presented in Table 2.4.  All participants had dietary fibre intakes well below the recommended amount with no differences observed based on gender or training environment.  While both men and women reported intakes of vitamin D which were below the AI of 200 IU, women typically consumed more vitamin D than men in both environments while consuming fewer calories.  Men reported a drastic reduction in vitamin D intake while at training camp.  Vitamin D intake for men at training camp was not normally distributed (skewness score of 3.77 and kurtosis score of 15.39).  Statistical significance remained with a non-parametric test (Wilcoxon signed ranks test) of the difference between the vitamin D intake at home and training camp (p<0.001). Women consumed more calcium compared to men and managed to maintain their mean intakes above the AI in both environments.  Men consumed less potassium and more sodium while at training camp compared to when at home.  Mean intake of sodium in all cases was above both the AI of 1500 mg and the Upper Tolerable Limit Level (UL) of 2300 mg.   Table 2.4   Usual intakes from food sources only compared to Adequate Intake of selected nutrients for men and women     Men  (n=24)  Women  (n=8) AI Home Training Camp AI Home Training Camp Fibre (g) 38      20.6 ± 6.0a 19.1 ± 4.0 25 19.1 ± 4.0 18.8 ± 4.7 Vitamin D (IU) 200 160.1 ± 133.4b 38.5 ± 78.3c 200 179.9 ± 197.1 151.3 ± 131.3 Calcium (mg) 1000 856 ± 330b 693 ± 204c 1000 1077 ± 481 1102 ± 433 Potassium (mg) 4700 3201 ± 741b 2872 ± 648c 4700 3478 ± 1272 3014 ± 849 Sodium (mg) 1500 3582 ± 1016b 4702 ± 1302c 1500 3353 ± 1145 3383 ± 1024 Abbreviations:  AI, Adequate Intake; IU, International Units; g, gram; mg, milligram a mean ± standard deviation b,c pair with a statistically significant difference p <0.05  61  Using median intakes from all six days of food records, the proportion of women reporting intakes greater than the AI were as follows:  fibre (25%, n=2), vitamin D (12.5%, n=1), calcium 50%, n=4), and potassium (12.5%, n=1).  For men, the proportion reporting intakes greater than the AI were as follows: fibre (0%, n=0), vitamin D (17%, n=4), calcium (21%, n=5), and potassium (0%, n=0).  The median intake of sodium for both men (4252 mg) and women (3106 mg) was well above both the AI and the UL Level of 2300 mg. With the exception of one woman (results from at home and training camp), no athletes had mean intakes above the AI for potassium.  The AI for men and women is 4700 mg:  mean intakes ranged from 2872 ± 647 mg (men at training camp) to 3478 ± 1273 mg (women at home).    For the remaining nutrients, the prevalence of inadequate intakes was approximated by determining the proportion of individuals with usual mean intakes below the EAR.  The proportion of men with mean intakes from food sources exclusively below the EAR was greatest while at training camp with greater than 25% of men with reported intakes below EAR for riboflavin, folate, vitamin B12, magnesium and zinc (Figure 2.2).  While at home, greater than 25% of men reported intakes below the EAR for folate, magnesium and zinc.  Using Pearson chi-square to detect differences in the proportions of men with intakes below the EAR, statistically significant differences were observed for thiamin and riboflavin with a greater proportion of men below the EAR while at training camp for both nutrients. Paired t-tests comparing the mean intakes from food sources alone for men, detected statistically significantly higher intakes of riboflavin and thiamin while at home (Table 2.5).  The men reported greater intakes of protein while at training camp (1.43 ± 0.39 g/kg/d versus 1.28 ± 0.38 g/kg/d, p=0.028) (data not shown).   62  Figure 2.2   Percentage of men with mean intakes from food sources only below Estimated Average Requirements (EAR)  Notes:  1. Three nutrients were omitted from this figure as all men had intakes above the EAR while at home and training camp.  There were no observed instances of nutrient inadequacy in men for niacin, iron and phosphorous.  2.  Difference in proportion of men with intakes below EAR between home and training camp detected with Pearson chi-square test.         4.212.512.550.016.78.354.237.520.829.212.537.529.24.266.745.80.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0Thiamin (mg)Riboflavin (mg)Vitamin B6 (mg)Folate (mcg)Vitamin B12 (mcg)Vitamin C (mg)Magnesium (mg)Zinc (mg)Percent of men with reported intakes below EAR, n=24Men (Training Camp)Men (Home) * * * p <0.05 63  Table 2.5   Comparison of the mean intakes for men from food alone for selected nutrients while at home and training camp with reference to the Estimated Average Requirement    The proportion of women reporting usual mean intakes from food sources alone (Figure 2.3) below the EAR was considerably different than the men.  While at training camp, greater than 25% of women reported intakes below the EAR for niacin, folate and magnesium.  While at home, greater than 25% of women reported intakes below the EAR for magnesium. Using Pearson chi-square, a statistically significant difference in the proportion of women with intakes below the EAR at home compared to training camp was not detected for any of the nutrients. When comparing mean intakes between training camp and home environments for women, no statistical differences were detected (Table 2.6).   (n=24) EAR Home Training Camp p-value Thiamin (mg) 1.0 mg 1.7 ± 0.6a 1.4 ± 0.4 0.007 Riboflavin (mg) 1.1 mg 1.8 ± 0.6 1.4 ± 0.5 0.001 Niacin (mg) 12.0 mg 20.1 ± 7.7 18.8 ± 7.5 0.417 Vitamin B6 (mg) 1.1 mg 1.9 ± 0.7 1.7 ± 0.7 0.249 Folate (mcg) 320.0 mg 339.3 ± 118.9 340.8 ± 94.0 0.954 Vitamin B12 (mcg) 2.0 mcg 3.8 ± 1.9 3.4 ± 2.5 0.260 Vitamin C (mg) 75.0 mg 164.5 ± 73.1 173.8 ± 79.7 0.679 Iron (mg) 6.0 mg 14.5 ± 4.2 15.1 ± 4.3 0.599 Magnesium (mg) 350.0 mg 336.2 ± 84.1 322.8 ± 77.6 0.483 Phosphorous (mg) 580 mg 1373 ± 416 1278 ± 363 0.222 Zinc (mg) 9.4 mg 10.3 ± 3.6 9.5 ± 2.9 0.350 Abbreviations:  EAR, Estimated Average Requirement a data presented as mean ± standard deviation Paired t-test used to compare mean intakes.   Statistically significant difference when p-value <0.05  64  Figure 2.3   Percentage of women with mean intakes from food sources only below  Estimated Average Requirements (EAR)   Notes:  1.  Two nutrients were omitted from this figure as all women had intakes above the EAR while at home and training camp.  There were no observed instances of nutrient inadequacy in women for vitamin B12 or phosphorous. 2.  No significant differences in the proportion of women with intakes below EAR between home and training camp were detected using Pearson chi-square test.      12.50.012.512.525.012.512.512.525.00.012.525.012.537.50.00.037.512.50.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0Thiamin (mg)Riboflavin (mg)Niacin (mg)Vitamin B6 (mg)Folate (mcg)Vitamin C (mg)Iron (mg)Magnesium (mg)Zinc (mg)Percent of women with reported intakes below EAR,  n=8Women (Training Camp)Women (Home)65  Table 2.6   Comparison of the mean intakes for women from food alone for nutrients while at home and training camp with reference to the Estimated Average Requirement  (n=8) EAR Home Training Camp p-value Thiamin (mg) 0.9 mg 1.7 ± 0.8a 1.8 ± 0.9 0.616 Riboflavin (mg) 0.9 mg 1.9 ± 0.5 2.1 ± 0.7 0.569 Niacin (mg) 11.0 mg 14.5 ± 3.9 16.6 ± 6.3 0.496 Vitamin B6 (mg) 1.1 mg 1.8 ± 0.7 2.8 ± 2.3 0.241 Folate (mcg) 320.0 mg 390 ± 92 332 ± 113 0.316 Vitamin B12 (mcg) 2.0 mcg 8.3 ± 13.4 9.6 ± 13.3 0.130 Vitamin C (mg) 60.0 mg 195.1 ± 95.1 151.1 ± 44.4 0.355 Iron (mg) 8.1 mg 15.2 ± 7.1 17.0 ± 5.7 0.241 Magnesium (mg) 265.0 mg 372 ± 174 328 ± 114 0.483 Phosphorous (mg) 580 mg 1563 ± 687 1500 ± 502 0.798 Zinc (mg) 6.8 mg 9.5 ± 4.2 11.1 ± 3.5 0.386 Abbreviations:  EAR, Estimated Average Requirement a mean ± standard deviation Paired t-test used to compare mean intakes.  Statistically significant difference when p-value <0.05. No statistically significant differences were detected.   2.3.3 Dietary analysis incorporating supplemental vitamin and minerals Participants were instructed to record all vitamin, mineral and herbal supplements they consumed as a part of their food diaries.   While at home, 44% of participants (n=14; n=9 men and n=5 women) reported consuming a nutritional supplement of some sort. Twelve athletes reported taking a daily multivitamin, four athletes reported taking calcium with vitamin D, and one athlete consumed supplemental iron.  Four athletes reported taking a variety of other supplements such as B complex, omega 3 fatty acids or vitamin C.  While at training camp, the number of participants who reported consuming a supplement was slightly lower at 34% (n=11) with only two women reporting supplement consumption.  Seven participants reported taking a daily multivitamin, one reported taking calcium with vitamin D, and two reported taking iron.  Six participants reported taking a variety of other products including vitamin C, ginseng and garlic.   66  The dosage of supplemental micronutrients was incorporated into the data from the food diaries and the analyses repeated to evaluate the impact of vitamin and mineral supplementation on dietary adequacy.  Mean intakes from all six days of reported food intake were compared to mean intakes from all six days of reported food intakes with the additional micronutrients from reported dietary supplements incorporated.  The additional calcium increased the proportion of men and women with intakes above the AI slightly but the marginal increase did not reach statistical significance using the McNemar test (Table 2.7). The proportion of men with vitamin D intakes above the AI increased significantly from 4.2% to 29.2% while the proportion of women with vitamin D intakes above the AI only slightly increased.  The mean intakes of vitamin D were significantly increased with the supplemental nutrients for men but not women as shown in Table 2.8.   The mean intakes of calcium were not statistically different for either men or women.     Table 2.7  Percentage of subjects with mean intakes above the Adequate Intake for six-day average intake from food sources only and food sources with additional supplements   Food Sources Only Food and Supplements  AI n % n % p-value Men (n=24)        Calcium 1000 mg 5 20.8 7 29.2 0.500  Vitamin D 200 IU 1 4.2 7 29.2 0.031 Women (n=8)        Calcium 1000 mg 4 50 5 62.5 1.000  Vitamin D 200 IU 2 25 3 37.5 1.000 Abbreviations: AI, Adequate Intake; mg, milligrams; IU, International Units  Statistically significant difference (p < 0.05) using McNemar test.       67  Table 2.8  Comparison of six-day mean intakes of calcium, vitamin D from food sources alone and food sources with additional supplements   AI Food Sources   Only Food and  Supplements p-value Men (n=24)      Calcium (mg) 1000 mg 775 ± 206a 853 ± 283 0.054  Vitamin D (IU) 200 IU 86.9 ± 65.5 176.8 ± 200.1 0.018 Women (n=8)      Calcium (mg) 1000 mg 1089 ± 419 1118 ± 428 0.170  Vitamin D (IU) 200 IU 165.6 ± 130.1 215.6 ± 173.0 0.090 Abbreviations:  mg, milligram; IU, International Unit  a mean ± standard deviation p-value of <0.05 considered a statistically significant difference.  (Paired t-test)   For nutrients with an EAR, the additional nutrients provided by the consumption of vitamin and mineral supplements decreased the proportion of men with intakes below the EAR for riboflavin, folate, vitamin B12, magnesium and zinc; however, none of the changes were significant (Table 2.9).  For women, the additional nutrients did not improve the percentage of women with intakes below the EAR (Table 2.10).  A slight improvement for folate was observed with the percentage of women with intakes below the EAR decreasing from 37.5% (n=3) to 25% (n=2), while the percentage of women with intakes below the EAR for other nutrients remained unchanged.            68  Table 2.9  Percentage of men with six-day mean intakes from food and food plus supplements below Estimated Average Requirement (n=24)  Food Sources Only Food and Supplements  EAR n % n % p-value† Thiamin (mg) 1.0 mg 2 8.3 2 8.3 1.000  Riboflavin (mg) 1.1 mg 5 20.8 4 16.7 1.000 Niacin (mg) 12.0 mg 2 8.3 2 8.3 1.000 Vitamin B6 (mg) 1.1 mg 1 4.2 1 4.2 1.000 Folate (mcg) 320.0 mcg 11 45.8 7 29.2 0.125 Vitamin B12 (mcg) 2.0 mcg 4 16.7 3 12.5 1.000 Vitamin C (mg) 75.0 mg 0 0.0 0 0.0 not calculated Iron (mg) 6.0 mg 0 0.0 0 0.0 not calculated Magnesium (mg) 350.0 mg 12 50.0 10 41.7 0.500 Phosphorous (mg) 580.0 mg 0 0.0 0 0.0 not calculated Zinc (mg) 9.4 mg 11 45.8 6 25.0 0.063 Abbreviations:  EAR, Estimated Average Requirement; mg, milligram, mcg, microgram Statistically significant difference (p < 0.05) using McNemar test.  †No statistically significant differences were detected as all p-values were above 0.05.    69  Table 2.10  Percentage of women with six-day mean intakes from food and food plus supplements below Estimated Average Requirement   (n=8)  Food Sources Only Food and Supplements  EAR n % n % p-value† Thiamin (mg) 0.9 mg 0 0.0 0 0.0 not calculated  Riboflavin (mg) 0.9 mg 0 0.0 0 0.0 not calculated Niacin (mg) 11.0 mg 1 12.5 1 12.5 1.000 Vitamin B6 (mg) 1.1 mg 0 0.0 0 0.0 not calculated Folate (mcg) 320.0 mcg 3 37.5 2 25.0 1.000 Vitamin B12 (mcg) 2.0 mcg 0 0.0 0 0.0 not calculated Vitamin C (mg) 60.0 mg 0 0.0 0 0.0 not calculated Iron (mg) 8.1 mg 0 0.0 0 0.0 not calculated Magnesium (mg) 265.0 mg 1 12.5 1 12.5 1.000 Phosphorous (mg) 580.0 mg 0 0.0 0 0.0 not calculated Zinc (mg) 6.8 mg 1 12.5 1 12.5 1.000 Abbreviations:  EAR, Estimated Average Requirement; mg, milligram; mcg, microgram Statistically significant difference (p < 0.05) using McNemar test. †No statistically significant differences were detected as all p-values were above 0.05.   A comparison of the six-day average intakes from food alone and food with the additional supplements incorporated is presented in Table 2.11. The mean intakes for men improved with the addition of the supplements with a statistically significant improvement in most micronutrients with an EAR reference value with the exception of vitamin B12.   The mean intakes for women were not observed to be statistically different with the supplemental vitamins and minerals.    70  Table 2.11  Comparison of six-day mean intakes of selected nutrients from food sources alone and food sources with additional supplements for nutrients    2.3.4 Nutrition knowledge  All participants completed a general nutrition knowledge questionnaire modified from the questionnaire developed by Parmenter and Wardle (26).  Nutrition knowledge scores for the group with subgroup analyses based on gender are presented in Table 2.12.  Women scored higher than men on the aggregate score out of a possible 110 points.  A statistically significant difference in score was detected for only one category with women scoring higher than men on the food sources of nutrients subscale.  Subgroup analyses among sports, between level of SCI and motor function of SCI showed no significant differences in either the total score or subscales (data not shown).       Men     (n=24) Women     (n=8) Food Only Food and Supplements p-value Food Only Food and Supplements p-value Thiamin (mg) 1.5 ± 0.5 2.1 ± 1.5 0.038 1.8 ± 0.8 2.0 ± 1.2 0.170  Riboflavin (mg) 1.6 ± 0.5 2.6 ± 1.7 0.017 2.0 ± 0.4 2.4 ± 1.0 0.170 Niacin (mg) 19.5 ± 6.7 22.1 ± 7.5 0.001 15.6 ± 3.1 17.4 ± 5.0 0.170 Vitamin B6 (mg) 1.8 ± 0.6 3.6 ± 2.6 0.001 2.3 ± 1.2 3.6 ± 3.5 0.170 Folate (mcg) 340 ± 84 454 ± 178 0.001 361 ± 69 480 ± 192 0.084 Vitamin B12 (mcg) 3.6 ± 2.1 34.4 ± 124.0 0.235 9.0 ± 13.3 12.1 ± 13.4 0.170 Vitamin C (mg) 169 ± 54 262 ± 174 0.012 173 ± 39 204 ± 80 0.170 Iron (mg) 14.8 ± 3.4 16.3 ± 4.3 0.002 16.1 ± 6.1 21.6 ± 12.4 0.227 Magnesium (mg) 330 ± 66 352 ± 78 0.003 350 ± 121 363 ± 132 0.170 Zinc (mg) 9.9 ± 2.5 13.0 ± 5.3 0.003 10.3 ±2.9 12.2 ± 5.6 0.170 Abbreviations:  mg, milligram; mcg, microgram p-value of <0.05 considered a statistically significant difference.  (Paired t-test) 71  Table 2.12  Nutrition knowledge scores with subgroup analyses based on gender  Maximum Score Total Group Men Women  (N=32) (n=24) (n=8) p-value Total Score  110 69.0 ± 10.9a 66.7 ± 10.8 76.1 ± 7.9 0.031 (42 - 86)b (42 - 86) (57 - 82) Dietary Recommendations 11 7.9 ± 1.3 7.8 ± 1.3 8.4 ± 1.1 0.237 (5 - 10) (5 - 10) (7 - 10) Food Sources of Nutrients 69 48.1 ±  7.8 46.5 ± 7.6 52.9 ± 6.8 0.043 (30 - 59) (30 - 59) (37 - 59) Choosing Everyday Foods 10 6.0 ± 1.6 5.9 ±  1.7 6.3 ± 1.2 0.573 (3 - 9) (3 - 9) (5 - 8) Diet Disease Relationship 20 7.1 ± 2.7 6.6 ± 2.8 8.6 ± 1.9 0.062 (0 - 13) (0 - 13) (6 - 12) a mean standard deviation b range of scores Nutrition knowledge questionnaire adapted from Parmenter & Wardle (26) p-value of <0.05 considered statistically significant comparison with subgroup analyses based on gender  2.3.5 Physical activity  Participants self-reported their usual training regime reporting the total number of training hours per week and the number of hours per week spent in aerobic, strength and sport specific training.  As a group, the mean number of total training hours per week was 14.7 ± 4.5 hours with a range of 8 – 25 hours.  Athletes reported 4.8 ± 2.5 hours engaged in aerobic training, 3.7 ± 1.7 hours engaged in strength training and 6.2 ± 3.7 hours engaged in sport specific training.  Differences were detected between basketball and rugby athletes with basketball athletes reporting 8.0 ± 3.5 hours of strength training compared to 5.2 ± 2.6 hours for rugby (p=0.035).  Athletes with paraplegia also reported more hours of strength training compared to athletes with tetraplegia (4.7 ± 1.6 hours vs. 3.2 ± 1.4 hours, p=0.009).  The similarity between these findings on strength training likely reflects that athletes with tetraplegia typically play rugby and those with paraplegia typically play basketball.   72  Athletes recorded the number of minutes they participated in physical activity for the same six days they recorded food intake during training camp and while they were at home.  During training camp, the group reported an average total training time of 492 ± 266 minutes over three days with a range of 123 – 1170 minutes.  Differences in total training time over the three day period were detected between sports with basketball athletes reporting 739 ± 276 minutes and rugby athletes reporting 410 ± 238 minutes (p=0.006).  The basketball athletes also reported more minutes described as “hard exertion” compared to the rugby athletes (454 ± 199 minutes versus 145 ± 115 minutes, p<0.001)  The self-reported training time for the three-day period at home period was 366 ± 172 minutes for the group which is less than the reported time while at training camp (p <0.05).  When total training time was analyzed using subgroups, no differences were detected.  Compared to the time at training camp, while at home, athletes tended to spend less time engaged in activity with a RPE of 6 – 10 or “light exertion” (50 ± 54 minutes vs. 87 ± 78 minutes, p <0.05) and less time engaged in activity with a RPE of 15 – 17 or “hard exertion” (127 ± 115 minutes vs. 221 ± 181 minutes, p <0.05). The reported time spent in activities of “moderate exertion” or RPE of 11 – 14 was similar with 120 ± 98 minutes at training camp versus 130 ± 128 minutes at home, p=0.728. Usual participation in recreational, household and occupational activity was assessed using the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD) (45).  The group scored 25.7 ± 15.5 metabolic equivalent hours per day (MET hr/day) with a range of 7.3 to 89.6 MET hr/day.  No statistical differences were detected based on gender, sport or SCI.   73  2.4 Discussion  This study is the first to report on usual dietary intakes of athletes with a spinal cord injury while comparing intakes to the established standards of the Dietary Reference Intakes.  This study found athletes reported energy intakes that were typically less than the conservative estimates of either sedentary or low active physical activity levels, while their reported physical activity was generally quite high at a total of 492 minutes over the 3 days at training camp (an average of 2.7 hours per day) and a total of 366 minutes over the 3 days at home (an average of 2 hours per day).  Although the macronutrient composition for all athletes was within the AMDR range with 53% of energy from carbohydrate, 29% from fat and 18% from protein, intakes of several micronutrients were below the recommended amount.  Male athletes in particular did not consume adequate amounts of vitamin D, calcium, potassium, folate, magnesium or zinc.  Compared to intakes at home, while at training camp the proportion of men with intakes below the EAR increased for thiamin, vitamin B12, magnesium and zinc, despite slightly greater energy intakes.  Women reported dietary intakes with few micronutrient inadequacies while consuming fewer calories than the men.  As a group, the women did have marginal intakes of folate and zinc while at home and marginal intakes of niacin, folate and magnesium at training camp.  On average, women consumed adequate amounts of calcium with the mean intakes greater than the AI while their vitamin D intake was less than the AI.   The average energy intakes of 2156 ± 431 kcal per day for men and 1927 ± 510 kcal per day for women in this study can be compared to other reported energy intakes for athletes and non-athletes with SCI as well as the values predicted by the EER.  The energy intakes are similar to the 2138 ± 473 kcal per day reported for male marathoners by Potvin et al. (21) in spite of the observation that the men in this study were slightly taller and weighed slightly more.  Lally et al. reported energy intakes which were considerably lower at 1909 kcal for American marathoners and 1627 kcal for Japanese marathoners (22). Food intake was measured by diet recall for one 24 hour period which may not accurately reflect usual intake and the day to day variation in dietary intake.   While Lally et al. did not report 74  height, weight or gender for their participants, the lower reported energy intakes compared to the results of this study may be explained by the difference in reported training times.  Japanese marathoners reported training 296 minutes per week (42 minutes per day) while American marathoners reported training 379 minutes per week (54 minutes per day).  This is in contrast to the average reported training practices in this study of 366 minutes over 3 days (122 minutes per day) while at home and 492 minutes over 3 days (164 minutes per day) while at training camp.   The results by Ribeiro et al. are more difficult to interpret as energy intake was reported as 25 ± 21 kcal/kg and when multiplied by the average weight of subjects (62 kg) the daily energy intake was estimated at 1545 kcal (20).  The difference in energy intakes may be due, at least in part, to differences in body size as the Brazilian wheelchair basketball athletes were shorter (168 cm versus 179 cm) and weighed less (62 kg versus 71 kg) than the men in this study.  The energy intakes of female athletes with SCI and wheelchair rugby athletes have not yet been reported on and provide novel information to the body of research pertaining to the dietary intakes of athletes with SCI.     Energy intakes of community-living adults with SCI are typically reported in the range of 2000 – 2100 kcal per day (6, 17, 18)  with Tomey et al. reporting a slightly higher energy intake of 2265 kcal per day (19).   It is likely that the energy intakes of that population of men with paraplegia was greater than the requirements given the high prevalence of overweight with 57% reported as having a BMI greater than 25 kg·m-2 and 19% of subjects having a BMI greater than 30 kg·m-2 as compared to the male athletes in this study with a BMI of 22.2 kg·m-2.   The male participants in the study by Walters et al. also had a high BMI of 26 ± 5 kg·m-2 with a mean weight of 79.5 kg.    The energy intakes reported by men in this study are comparable to the results of marathoners in the study by Potvin and colleagues yet greater than the athletes in the studies by Lally et al. and Ribeiro et al.  The differences in the energy intakes can most likely be accounted for by differences in the training practices and body size.  Typically, the athletes were of an appropriate weight for height with BMI less than 25 kg·m-2.  However, an optimal BMI range for ideal weight in adults with SCI has not been clearly established but there is some evidence that a BMI above 22 kg·m-2 (rather than 25 kg·m-2) is a better 75  predictor of overweight and obesity in SCI (52).  It is interesting that the reports of energy intakes among community living men with SCI are equal to or greater than the athletes with SCI.  The combination of a greater prevalence of overweight individuals, reduced physical activity and excessive energy intakes contributing to the weight gains likely accounts for the increased reported energy intakes in community living men with SCI.  It can be hypothesized that while athletes are consuming about the same number of calories per day, they are expending a similar amount of energy with physical activity and maintaining their weight to height ratio.  Females with SCI who are not athletes have reported energy intakes of 1663 kcal per day (18) and 1771 kcal per day (17). Based on these two studies, the female athletes in this study consume between 200 and 300 additional calories per day (1991 ± 383 kcal per day).  The women in the study by Groah et al. (18) had a slightly greater BMI and considerably less energy intake compared to the athletes in this study. The women in the study by Walters et al. (17) had a considerably higher BMI and less energy intake compared to the athletes in this study.  One hypothesis is that the athletes are strength training thereby increasing the amount of lean mass, in addition to aerobic and sport specific training.  The physical activity levels of the athletes are likely quite a bit higher than the community living women and this increased physical activity could account for the additional energy intakes.   The predictive equations developed by the Institute of Medicine (50) were inappropriate for predicting the estimated energy requirements for athletes with SCI.   Women, basketball athletes, those with paraplegia and those with a complete SCI had energy intakes that were between the energy intakes predicted for sedentary and low active physical activity levels. In contrast, men had reported energy intakes that were below the sedentary level of physical activity for men despite average training times of over two hours per day.  In some instances the predictive equations considerably overestimated apparent energy needs as reflected by reported energy intakes while in other instances, reported energy intakes were comparable to the sedentary or low active EERs.  These equations have not been validated in those with SCI and the physical activity coefficients developed for able-bodied individuals do not accurately predict energy needs of sedentary 76  or active individuals with SCI.  Accurate and reliable tools for estimating energy needs in both active and non-active individuals with SCI are greatly needed to predict energy needs of these athletes.   Distinct differences in dietary adequacy were observed between home and training environments.  While energy intakes were statistically greater for the group and rugby athletes at training camp, the trend was for all subgroups except for “other sports” to have greater energy intakes at training camp.  These higher energy intakes, however, were not associated with improved nutrient adequacy.  At training camps, men consumed on average 1200 mg more of sodium and the proportion of men with vitamin or mineral intakes below the EAR increased for thiamin, riboflavin, niacin, vitamin B12, magnesium and zinc.  The women in this study maintained more consistency in their diets while at training camp with very few statistical differences between the two environments detected.   In the assessment of the athletes’ understanding of nutrition knowledge, women scored better than men in both the overall score and in food sources of key nutrients.  Two studies have evaluated the nutrition knowledge of adults with SCI (19, 53). Tomey et al. (19) assessed the nutrition knowledge of a group of community living men with paraplegia using a modified, shortened version of the Parmenter & Wardle (26) nutrition questionnaire. Unfortunately, the authors did not describe what components of the nutrition knowledge instrument they selected for the shortened version making a comparison between groups based on nutrition knowledge difficult, if not impossible. However, the men in their study scored 18.8 ± 5.5 out of a possible 28 (a score of approximately 67%) compared to the men in this study who scored approximately 61% while the women scored 69%.  The second study (53) measured nutrition knowledge of athletes with physical disabilities (primarily SCI and amputees) at a pre- and post-test following a nutrition education intervention.  The questionnaire used was compiled from a variety of sources with additional questions related to nutrition issues important to athletes with disabilities and the results, as presented, do not lend themselves to a quantitative assessment of nutrition knowledge.  A Canadian sample of able-bodied adults recently completed the Parmenter & Wardle questionnaire and scored 71/110 for the total score, 7.8/11 for dietary recommendations, 77  47.2/69 for food sources of nutrients, 6.7/10 for choosing everyday foods, 9.4/20 for diet disease relationships (54).  A significant difference was detected with women scoring higher than men in the food sources of nutrients category as well as the total score.  The results from the able-bodied population were comparable to the results of this study including the observation of women scoring higher in the overall score and subscale of food sources of nutrients.  These differences in nutrition knowledge may at least partially explain why women were able to maintain dietary adequacy while away from home.   A  comparison of the population in this study to the results from the Canadian Community Health Survey (55) (summary tables in Appendix 10) confirms a disparity in energy intakes of men, with the able-bodied men consuming almost 600 kcal more in a day (2737 kcal vs. 2156 kcal).  Interestingly, the women with SCI in this study reported similar energy intakes as Canadian women aged 19 – 30 years (1991 kcal vs. 1902 kcal).  There were many similarities in the proportion of individuals with intakes below the EAR or above the AI between this population and Canadians in general for riboflavin, thiamin, vitamin B12, vitamin C, vitamin D, calcium, phosphorus, magnesium and sodium.  Differences were observed as athletes with SCI had a greater proportion of individuals with intakes below the EAR for folate (men only), niacin and zinc compared to Canadians.  Overall, the patterns of dietary inadequacies of micronutrients were similar between the athletes with SCI and Canadians across the country.   The consumption of vitamin and mineral supplements was reported by 44% of athletes while at home and 34% of athletes while at training camp.  This is slightly less than the 50.6% of individuals with SCI who classify themselves as consistent supplement users (56).  However, when compared to vitamin and mineral supplement consumption rates in other populations of elite athletes this is similar to multivitamin usage reported by Division I varsity athletes at 47.3% (57) but almost double consumption rates by high performance Canadian athletes, among whom 20% reported taking a vitamin or mineral supplement (58).  The consumption of vitamin and mineral supplements by men in this study improved mean intakes of most nutrients but did not improve the proportion of men with intakes below the EAR. The consumption of supplemental nutrients made little difference to the mean intakes 78  or proportion of women with intakes below the EAR for women.  The overall quality of the diets of female athletes was adequate in meeting the estimated requirements for most of the micronutrients.  These women could benefit from supplemental vitamin D, zinc and magnesium.  The men would benefit from improving their food choices to increase the nutrient density before adding vitamin and mineral supplements.    2.4.1 Strengths and limitations The strengths of this study include the repeated measurements of activity and food records.  The consumption and usage of vitamin and mineral supplements was reported daily which allowed for the additional vitamins and minerals to be incorporated into dietary analysis for an additional assessment of dietary adequacy.  Comparison of dietary intakes to the standards established by the Institute of Medicine allowed for a more comprehensive analysis of dietary adequacy.  The intent of this study was to explore the dietary intakes of this unique population and while the study was not specifically powered to detect differences between subgroups, recruitment was sufficient to detect some statistical differences between groups.  A wide variety of data was collected on the athletes, in addition to food intake which allowed for a complete description of this population. One of the major limitations with this study was the reliance on self-reporting of dietary intake and activity and the risk of under- or over-reporting.  However, many steps were taken to minimize these risks including a) providing a detailed explanation of how to accurately record food intake, b) having a researcher with the athletes for the initial 3 day period to assist with recording food records, c) clarification if any of the recorded information was unclear and d) discreet observation of athletes’ food choices.  Furthermore, it is the author’s impression that the athletes were motivated to record their data as accurately as possible based on the level of detail in the reporting of food intake on most food diaries and the high return rate for the second set of food diaries (94% return). Another limitation of this study was the imbalance in the distribution of sports with rugby athletes being the largest overall group.   As athletes with tetraplegia typically play wheelchair rugby, this population had a disproportionate number subjects with tetraplegia.  This could have skewed the results as these athletes tended to have the lowest energy 79  intakes.  As well, this study may not have accurately assessed if athletes were intentionally trying to alter their dietary intakes to manage aspects of their SCI such as bowel or bladder management, sodium or fluid status for orthostatic blood pressure control or to manage other aspects of autonomic dysfunction.   This sample is not representative of elite athletes competing within Canada as the number of athletes competing in wheelchair athletics was under represented.  Those competing in athletics tend to have demanding training schedules which may have impacted energy intakes.  Ideally, a minimum number of 15 to 20 athletes per sport (rugby, basketball, athletics) with additional athletes from sports with less participation (alpine and cross-country skiing, tennis, rowing) is necessary to make comparisons between sports.  As well, the limited number of women made statistical comparisons difficult.   2.4.2 Conclusions This study provides preliminary evidence of dietary intakes and inadequacies in elite Canadian athletes with a spinal cord injury.  This study demonstrates that these athletes are typically consuming energy intakes at, or below, the lower range of predicted energy intakes and the energy intakes of elite male and female athletes with SCI are relatively low in comparison to their able-bodied colleagues.  While the macronutrient distribution was within the recommended ranges, intakes of several vitamins and minerals were below the recommended amount.  Within the limited energy intakes, it is even more important for these athletes to optimize their dietary choices to ensure they are consuming adequate amounts of micronutrients to reduce the risk of suboptimal nutrient intakes.  It may be necessary for many of the athletes, especially those with energy intakes below 1800 kcal per day to supplement their diets with additional vitamins and minerals.   Dietary inadequacies were more pronounced at national team events highlighting an opportunity for coaches, administrators, sport scientists and Registered Dietitians working with these athletes to improve the access to better food choices and to educate athletes in making more balanced food choices.  As the nutrient density of food choices needs to be optimal for these athletes to meet their recommended vitamin and mineral intakes, a closer evaluation of the dietary choices available to athletes at national team events is warranted.  80  Restaurant meals may not be appropriate to provide the complement of vitamins and minerals within a reduced energy intake and customized lower fat meals emphasizing whole grains, vegetables and fruits, low fat milk and milk products should be considered to provide the most appropriate food choices for athletes.  While the prevalence of dietary inadequacies was typically less when the athletes were at home, several improvements could still be made to optimize the athlete’s daily training diet.  Ongoing nutrition education and individualized micronutrient supplementation recommendations provided by Registered Dietitians with expertise in the area of sport nutrition would be a valuable asset to the integrated support team for all athletes with SCI. This study was designed to quantify what athletes with SCI typically ate and evaluate the dietary adequacy of those diets but many questions remain.  It is unknown if the macronutrient, vitamin or mineral requirement reference values established by the Institute of Medicine based on healthy adult populations are applicable to those with SCI. Many physiological and metabolic differences are observed in SCI and it is unknown if these differences may increase or decrease the requirements for specific nutrients. Accordingly, more research is required to assess whether nutrient requirements differ among those with SCI, and if an increased level of physical training alters those requirements.    81  2.5 References  1. Rodriguez NR, DiMarco NM, Langley S, American Dietetic Association, Dietitians of Canada, American College of Sports Medicine. Position of the American Dietetic Association, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and athletic performance. J Am Diet Assoc 2009;109:509-27.  2. de Groot S, Dallmeijer AJ, Post MW, Angenot EL, van den Berg-Emons RJ, van der Woude LH. Prospective analysis of lipid profiles in persons with a spinal cord injury during and 1 year after inpatient rehabilitation. Arch Phys Med Rehabil 2008;89:531-7.  3. Schmid A, Knoebber J, Vogt S, et al. Lipid profiles of persons with paraplegia and tetraplegia: sex differences. J Spinal Cord Med 2008;31:285-9.  4. Bauman WA, Adkins RH, Spungen AM, Waters RL. The effect of residual neurological deficit on oral glucose tolerance in persons with chronic spinal cord injury. Spinal Cord 1999;37:765-71.  5. Gupta N, White KT, Sandford PR. Body mass index in spinal cord injury – a retrospective study. Spinal Cord 2006;44:92-4.  6. Edwards LA, Bugaresti JM, Buchholz AC. Visceral adipose tissue and the ratio of visceral to subcutaneous adipose tissue are greater in adults with than in those without spinal cord injury, despite matching waist circumferences. Am J Clin Nutr 2008;87:600-7.  7. Spungen AM, Adkins RH, Stewart CA, et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol 2003;95:2398-407.  8. Weaver FM, Collins EG, Kurichi J, et al. Prevalence of obesity and high blood pressure in veterans with spinal cord injuries and disorders: a retrospective review. Am J Phys Med Rehabil 2007;86:22-9.  9. Bauman WA, Kahn NN, Grimm DR, Spungen AM. Risk factors for atherogenesis and cardiovascular autonomic function in persons with spinal cord injury. Spinal Cord 1999;37:601-16.  10. Teasell RW, Arnold JM, Krassioukov A, Delaney GA. Cardiovascular consequences of loss of supraspinal control of the sympathetic nervous system after spinal cord injury. Arch Phys Med Rehabil 2000;81:506-16.  11. Krassioukov A, Claydon VE. The clinical problems in cardiovascular control following spinal cord injury: an overview. Prog Brain Res 2006;152:223-9.  82  12. Bauman WA, Spungen AM. Disorders of carbohydrate and lipid metabolism in veterans with paraplegia or quadriplegia: a model of premature aging. Metabolism 1994;43:749-56.  13. DeVivo MJ, Krause JS, Lammertse DP. Recent trends in mortality and causes of death among persons with spinal cord injury. Arch Phys Med Rehabil 1999;80:1411-9.  14. de Groot S, Dallmeijer AJ, Post MW, Angenot EL, van der Woude LH. The longitudinal relationship between lipid profile and physical capacity in persons with a recent spinal cord injury. Spinal Cord 2008;46:344-51.  15. Manns PJ, McCubbin JA, Williams DP. Fitness, inflammation, and the metabolic syndrome in men with paraplegia. Arch Phys Med Rehabil 2005;86:1176-81.  16. El-Sayed MS, Younesian A. Lipid profiles are influenced by arm cranking exercise and training in individuals with spinal cord injury. Spinal Cord 2005;43:299-305.  17. Walters JL, Buchholz AC, Martin Ginis KA. Evidence of dietary inadequacy in adults with chronic spinal cord injury. Spinal Cord 2009;47:318-22.  18. Groah SL, Nash MS, Ljungberg IH, et al. Nutrient intake and body habitus after spinal cord injury: an analysis by sex and level of injury. J Spinal Cord Med 2009;32:25-33.  19. Tomey KM, Chen DM, Wang X, Braunschweig CL. Dietary intake and nutritional status of urban community-dwelling men with paraplegia. Arch Phys Med Rehabil 2005;86:664-71.  20. Ribeiro SM, Da Silva RC, De Castro IA, Tirapegui J. Assessment of nutritional status of active handicapped individuals. Nutr Res 2005;25:239-49.  21. Potvin A, Nadon R, Royer D, Farrar D. The diet of the disabled athlete. Sci Sports 1996;11:152-6.  22. Lally DA, Wang JH, Goebert DA, Quigley RD, Hartung GH. Performance training and dietary characteristics of American and Japanese wheelchair marathoners. Med Sci Sports Exerc 1991;23:S101(Abstract).  23. Health Canada. Nutrition Recommendations: The report of the scientific review committee. 1st ed. Ottawa ON: PWGSC, 1990.  24. Marino RJ, Barros T, Biering-Sorensen F, et al. International standards for neurological classification of spinal cord injury. J Spinal Cord Med 2003;26:S50-6.  25. Graves DE, Frankiewicz RG, Donovan WH. Construct validity and dimensional structure of the ASIA motor scale. J Spinal Cord Med 2006;29:39-45.  83  26. Parmenter K, Wardle J. Development of a general nutrition knowledge questionnaire for adults. Eur J Clin Nutr 1999;53:298-307.  27. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res 1985;29:71-83.  28. Kristeller JL, Rodin J. Yale Eating Patterns Questionnaire. Addict Behav 1989;14:631-42.  29. Appleton KM, Conner MT. Body weight, body-weight concerns and eating styles in habitual heavy users and non-users of artificially sweetened beverages. Appetite 2001;37:225-30.  30. Borg G. Borg's perceived exertion and pain scales. 1st ed. Champaign Illinois: Human Kinetics, 1998.  31. Chen MJ, Fan X, Moe ST. Criterion-related validity of the Borg ratings of perceived exertion scale in healthy individuals: a meta-analysis. J Sports Sci 2002;20:873-99.  32. Maynard FM Jr, Bracken MB, Creasey G, et al. International Standards for Neurological and Functional Classification of Spinal Cord Injury. American Spinal Injury Association. Spinal Cord 1997;35:266-74.  33. Curt A, Schwab ME, Dietz V. Providing the clinical basis for new interventional therapies: refined diagnosis and assessment of recovery after spinal cord injury. Spinal Cord 2004;42:1-6.  34. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Abridged edition. Champaign, Ill.; United States: Human Kinetics Publishers, 1991.  35. Price MJ, Campbell IG. Thermoregulatory responses of spinal cord injured and able-bodied athletes to prolonged upper body exercise and recovery. Spinal Cord 1999;37:772-9.  36. Olle MM, Pivarnik JM, Klish WJ, Morrow JR Jr. Body composition of sedentary and physically active spinal cord injured individuals estimated from total body electrical conductivity. Arch Phys Med Rehabil 1993;74:706-10.  37. Goosey-Tolfrey VL. Physiological profiles of elite wheelchair basketball players in preparation for the 2000 Paralympic Games. Adapt Phys Activity Q 2005;22:57-66.  38. George CM, Wells CL, Dugan NL. Validity of hydrodensitometry for determination of body composition in spinal injured subjects. Hum Biol 1988;60:771-80.  84  39. Janssen TW, van Oers CA, Hollander AP, Veeger HE, van der Woude LH. Isometric strength, sprint power, and aerobic power in individuals with a spinal cord injury. Med Sci Sports Exerc 1993;25:863-70.  40. Wells CL, Hooker SP. The spinal injured athlete. Adapt Phys Activity Q 1990;7:265-85.  41. Laskin JJ, James SA, Cantwell BM. A fitness and wellness program for people with spinal cord injury. Top Spinal Cord Inj Rehabil 1997;3:16-33.  42. Mojtahedi MC, Valentine RJ, Evans EM. Body composition assessment in athletes with spinal cord injury: comparison of field methods with dual-energy X-ray absorptiometry. Spinal Cord 2009;47:698-704.  43. Bhambhani YN. Physiology of wheelchair racing in athletes with spinal cord injury. Sports Med 2002;32:23-51.  44. Dec KL, Sparrow KJ, McKeag DB. The physically-challenged athlete: medical issues and assessment. Sports Med 2000;29:245-58.  45. Washburn RA, Zhu W, McAuley E, Frogley M, Figoni SF. The physical activity scale for individuals with physical disabilities: development and evaluation. Arch Phys Med Rehabil 2002;83:193-200.  46. Magkos F. Methodology of dietary assessment in athletes: concepts and pitfalls. Curr Opin Clin Nutr Metab Care 2003;6:539-49. 47. Livingstone MB, Black AE. Markers of the validity of reported energy intake. J Nutr 2003;133:S895-920.  48. Nutrition Research Division and Biostatistics and Computer Application Division, Health Protection Branch. Canadian Nutrient File, Compilation of Canadian Food Composition Data. Health Canada 2007. Internet: http://www.hc-sc.gc.ca/fn-an/nutrition/fiche-nutri-data/cnf_aboutus-aproposdenous_fcen-eng.php (accessed 03/14/2010).  49. United States Department of Agriculture, Agricultural Research Service. USDA National Nutrient Database for Standard Reference. Version current Release 20, 2007. Internet: http://www.nal.usda.gov/fnic/foodcomp (accessed 03/14/2010).  50. Institute of Medicine. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein and Amino Acids. 1st ed. Washington DC: The National Academies Press, 2005.  51. Institute of Medicine. Dietary Reference Intakes. Applications in Dietary Assessment. 1st ed. Washington DC: The National Academies Press, 2000.  85  52. Laughton GE, Buchholz AC, Martin Ginis KA, Goy RE. Lowering body mass index cutoffs better identifies obese persons with spinal cord injury. Spinal Cord 2009;47:757-62.  53. Rastmanesh R, Taleban FA, Kimiagar M, Mehrabi Y, Salehi M. Nutritional knowledge and attitudes in athletes with physical disabilities. J Athlet Train 2007;42:99-105.  54. Gottschall-Pass K, Reyno L, Maclellan D, Spidel M. What do adults in Prince Edward Island know about nutrition? Can J Diet Pract Res 2007;68:123-30.  55. Health Canada, Statistics Canada. Canadian Community Health Survey, Cycle 2.2, Nutrient Intakes from Food Provincial, Regional and National Summary Data Tables, Volume 1,2 and 3. 1st ed. Ottawa, ON: Her Majesty the Queen in Right of Canada, 2004.  56. Opperman EA, Buchholz AC, Darlington GA, Martin Ginis KA. Dietary supplement use in the spinal cord injury population. Spinal Cord 2010;48:60-4.  57. Froiland K, Koszewski W, Hingst J, Kopecky L. Nutritional supplement use among college athletes and their sources of information. Int J Sport Nutr Exerc Metab 2004;14:104-20.  58. Erdman KA, Fung TS, Doyle-Baker P, Verhoef MJ, Reimer RA. Dietary supplementation of high-performance Canadian athletes by age and gender. Clin J Sport Med 2007;17:458-64.    86  Chapter 3: Eating Attitudes and Behaviours in Elite Canadian Athletes with Spinal Cord Injury 87  3.1  Introduction Spinal cord injury (SCI) is associated with a number of significant metabolic changes including glucose intolerance (1), dyslipidemia (2-4) and differences in hormone concentrations (5, 6).   Body composition changes occur following SCI and often include increased adiposity (7-9), especially below the level of injury (10) and decreased lean tissue (10, 11).  Related to these changes in body composition, energy requirements are relatively low (12) and are not accurately predicted with equations developed for able-bodied populations (12, 13).  Despite demanding training regimes, athletes with SCI appear to have relatively modest energy requirements (14-16). Similar to able-bodied athletes, it is important for athletes with SCI to maintain favourable body composition to support optimal performance.  The literature suggests that, with this link between body composition and athletic performance, some able-bodied athletes may be susceptible to subclinical eating disorders (17).  There is virtually no information regarding the eating attitudes and behaviours of athletes with SCI, yet with their relatively modest energy requirements, it is possible that these athletes may be at risk for disordered eating attitudes. The Three-Factor Eating Questionnaire (18) was developed to as a method to assess three aspects of eating attitudes and food related behaviours.  Cognitive dietary restraint or simply dietary restraint is described as the purposeful monitoring and attempt to control or limit food intake by an individual in order to achieve or maintain a desired body weight or composition (18). This is in contrast to eating in response to physiological or hormonal hunger and satiety cues.  Disinhibition reflects a tendency toward overeating and eating opportunistically, or the feeling that once a person begins to eat, it is very difficult to stop (18, 19).  The trait of hunger is related to eating in response to innate physiological and hormonal cues.   Cognitive dietary restraint has been studied extensively in women and high levels of restraint have been associated with several physiological and metabolic changes including elevated urinary and salivary cortisol excretion (20-22), lower bone mineral density (23) and ovulatory disturbances (22, 24-26). Higher levels of restraint is 88  associated with greater frequency of exercise (20, 27). Women with a stronger tendency towards cognitive dietary restraint also tend to have a stronger expression of disinhibition (26, 28).  In comparison to women, men consistently score lower on the cognitive dietary restraint scale (28-31).  While there is a considerably smaller body of literature which assessed eating behaviours in men with the TFEQ, there is some evidence that overweight men have higher scores for disinhibition and hunger but not for restraint (32, 33).    Differences in reported dietary intakes have also been detected between those discordant for cognitive dietary restraint.  Women with higher levels of restraint typically report consuming fewer calories (20, 34-39) with reduced carbohydrate and fat intakes and increased protein intake (35, 40, 41).  Those with higher cognitive restraint often select diets that include more reduced calorie and reduced fat products (42), consume less red meat, desserts, soft drinks and alcohol  (39, 43, 44).   There is also a greater likelihood that women with higher restraint scores will choose a vegetarian diet (26).   To our knowledge, the psychometric traits related to eating behaviours have not been reported in a population of adults with SCI. Accordingly, the objectives of this study were to describe food related attitudes and behaviours among elite athletes with SCI and explore associations with dietary intake and anthropometric variables.   3.2 Methods 3.2.1 Overview of study design This cross-sectional observational study was designed to explore eating attitudes and behaviours in a group of elite Canadian athletes with SCI.  Participants were enrolled between May 2007 and May 2009.  An investigator attended a national team training event to recruit participants, collect questionnaire data, measure anthropometrics and provide instruction on the completion of the three-day self-reported food records.  Participants repeated three-day self-reported food records 89  when they returned home, providing a total of six days of dietary intake.  A general questionnaire was self-administered to capture demographic information such as age, sport and training information and spinal cord injury details. The Three-Factor Eating Questionnaire (TFEQ) (18) and Yale Eating Patterns Questionnaire (YEPQ) (45) were self-administered to assess eating attitudes and behaviours.     The data and results presented in this chapter comprise a subset of the data collected for this thesis research project.  This chapter will focus on the results pertaining to eating behaviours and attitudes with summary data on participant characteristics, anthropometrics and SCI used for subgroup and comparative analyses.  Detailed participant characteristics and the analyses of dietary adequacy are presented in Chapter 2 of this thesis.   The study protocol was approved by the Behavioural Research Ethics Board at The University of British Columbia (Appendix 1) and participants provided informed written consent (Appendix 2).   3.2.2 Participant recruitment Recruitment and identification of potential athletes was conducted by an initial letter of invitation (Appendix 3) sent to all national team head coaches associated with the Canadian Paralympic Committee.  At the time of this study, Canada had athlete representation in 21 Paralympic sports and 11 of the sports met the eligibility requirements of providing competitive opportunities for athletes with SCI, with an expected minimum of 12 hours of physical training per week.  Coaches from all 11 eligible sports (4 winter and 7 summer) received an invitation to participate via email and each coach was contacted to discuss the study and arrange for an investigator to attend a national training camp or event.  Head coaches were requested to provide athletes with information pertaining to the study and alert the athletes that the graduate student researcher (Jennifer Krempien) would be at a national team event to collect data, should they choose to participate. Detailed information on the recruitment procedures is outlined in Appendix 4.  It is estimated that between 75 and 80 athletes within Canada met the inclusion criteria during the recruitment period. A total of 41 90  athletes were approached in person to participate in the study and a total of 34 athletes consented to participate with 32 athletes completing all aspects of the study. To be eligible, athletes had a SCI resulting in paraplegia or tetraplegia, with either complete or incomplete impairment of motor function.  Elite athletes were targeted for this study. Each athlete met a minimum of one of the following criteria: member of a senior national team, received senior level funding from the Sport Canada Athlete’s Assistance Program, ranked in the top three for his or her sport within Canada or ranked in the top five in the world for his or her sport discipline.  For athletes participating in a team sport, the last international ranking for the team was used to establish the athlete’s international ranking.  For example, the rugby team finished third at the 2006 World championships so all athletes on that roster were ranked as third and thus met the inclusion criteria.  If an athlete was not a member of the national team, their ranking was based on their provincial team performance at Canadian Nationals.  All athletes participated in ongoing physical training for a minimum of 12 hours per week.  Athletes were 19 years of age or older and understood written and spoken English.   3.2.3 Participant characteristics Data pertaining to participant characteristics were self-reported by participants using a questionnaire developed for this purpose.  Detailed demographic information including age, gender, sport information, years on the national team, international ranking, training information, description of SCI and additional medical conditions was collected.  A copy of the demographic questionnaire is included in Appendix 5.   Description of spinal cord injury The American Spinal Injury Association (ASIA) developed a standard system for the classification and description of spinal cord injury tool designed for physiatrists to assess and quantify the remaining motor and sensory function following SCI (46-49).  The full assessment is quite burdensome for the subject as the sensory examination requires light touch and pin prick testing to each of 28 dermatomes on the right and left sides of the body and a strength evaluation of ten muscle groups.  For the purpose of 91  this study, the ASIA Impairment Scale (AIS) was limited to classification of the neurological completeness of the injury. A score of AIS-A indicated no motor or sensory function preserved in the sacral segments S4-S5 indicating a complete injury while AIS-B through D were combined to indicate incomplete SCI.  Each athlete self-reported his or her level of injury (e.g., injury to spinal cord segment T7 or C6). Athletes with a cervical injury were classified as tetraplegic and those with a thoracic or lumbar injury were classified as paraplegic for the purposes of subgroup analyses.  Anthropometrics Weight (kg) was measured to the nearest 0.1 kg with athletes wearing light indoor clothing without shoes using a portable digital scale with remote display (Universal Weight Enterprise Company Ltd., Model AMP-150, Taipei, Taiwan).  The scale was modified with a larger seating platform and non-ambulatory participants sat directly on the scale for measurement.  Length (m) was measured with participants in a supine position on a firm surface with the soles of the participant’s feet against a wall. The subject’s length was marked on the surface and then measured.  The measured length was verbally reported to the subject. If the measurement was greater than 2 cm different than what the subject believed his or her height to be, the measurement procedure was repeated. All skinfold measurements were taken according to standardized procedures (50) using Harpenden skin calipers.  All measurements were taken on the right side of the body at triceps, biceps, subscapular and iliac crest sites.  Measurements were taken with subjects seated, with triplicate measurements taken in rotation to the nearest 0.2 mm, with the mean used for calculations.  Additional measurements were taken if a measurement was different by greater than 10% from the other measurements from the same site (example: 20 ± 2 mm for iliac crest skinfold) and the closest three values averaged.  The sum of skinfolds was calculated from these measurements.  92  3.2.4 Dietary intake Dietary intake data were collected using a three-day self-reported food diary (Appendix 7).  The recording period was three consecutive days during the training camp with a three day follow-up period once the athlete returned home.  The food diary completed at home for three consecutive days (two weekdays and one weekend day) reflected the athletes’ typical dietary choices and intakes in a less artificial environment.  For athletes, a three to seven day diet monitoring period is believed to provide a reasonably accurate and precise estimation of habitual energy and macronutrient consumption in both individuals and groups (51).  The issue of under- and over-reporting is of concern with most collection techniques for dietary intakes (52).  In this study, the issue of inaccurate reporting was addressed by providing a tutorial to the athletes prior to recording of intakes and by having an investigator available to the athletes during meals to assist with data recording.  Food diary entries were reviewed and the participant was contacted if clarification was required. Food record data were entered into Food Processor for Windows, version 9.0.0 (database version December 2007, ESHA Research, Salem, Oregon).  The Canadian Nutrient File (2007) database (53)  was the primary nutrient reference database used.  If food values were not available from the Canadian Nutrient File database, the nutritional content for equivalent items from the USDA Standard Reference database (54) or values provided by the manufacturer were used. Six days of reported food intake from training camp and home food records were averaged to compute average energy intake (kcal), carbohydrate (g), fat (g) and protein (g). 3.2.5 Three-Factor Eating Questionnaire The Three-Factor Eating Questionnaire (TFEQ) measured three aspects of human eating behaviour (18).  This 51 item scale assessed cognitive dietary restraint, disinhibition and hunger and has been shown to have good test-retest reliability (18, 55). The TFEQ was administered and scored as outlined by the authors, with minor adaptations as suggested by Guest and Barr (22).  For example, the first question was 93  modified from “When I smell a sizzling steak…” to “When I smell my favourite food…” to improve question suitability for those who may not consume meat.  The questionnaire was scored as outlined by the authors. Each scale was scored separately with a higher score indicating a greater tendency toward the measured trait.  A copy of the modified questionnaire is included in Appendix 11. 3.2.6 Yale Eating Patterns Questionnaire The Yale Eating Patterns Questionnaire (YEPQ) was developed as a taxonomy of eating behaviour for the general population (45).  This questionnaire measured characteristics related to the emotionality of eating, satiation cues, attitudes towards dieting, and weight history along with typical snacking and food intake patterns.  The YEPQ is a 70 item questionnaire used to measure nine scales of eating behaviour: uninhibited, oversnacking, binging, dieting, satiation–full, satiation–nausea, satiation–guilty, attribution of overweight to physical factors and attribution of overweight to emotional factors. This questionnaire was tested for validity and reliability in a sample of college students at the time of development with Cronbach’s alpha coefficients ranging from 0.69 to 0.92.   However, the original published study did not report on response formats or scoring methods.  Items on the two scales which assessed the importance the individual placed on the contribution of physical or emotional factors to being overweight were scored with a 4-point response format (very important, quite important, not very important, not at all important).  All other items were scaled on a 5-point response format (never, seldom, sometimes, often, very often) and thus the validity of the tool may be impacted. A copy of the modified questionnaire with the adapted scoring system is included in Appendix 12. 3.2.7 Statistical analysis All statistical analyses were completed using SPSS version 17.0.  Descriptive statistics are presented as means and standard deviations.  A p-value of <0.05 was considered to reflect a statistically significant difference.  Independent t-tests were used to compare questionnaire subscale scores between groups based on SCI (paraplegic vs. 94  tetraplegic) and gender. Nutrient intakes were calculated and averaged from six days of food records.  The group was divided on the basis of cognitive restraint scores using the group median as the cut-point. Chi-square or Fisher’s exact test for small cell size were used to test for a difference in proportions based on cognitive restraint score (high vs. low).  Pearson’s product moment correlation was used to explore the relationship between the questionnaire subscale scores and anthropometric or dietary intake variables.  Because these analyses were considered exploratory, a p-value of <0.05 was determined to be significant with greater emphasis placed on p-values of <0.01 to avoid Type 1 statistical error for correlational data.    3.3 Results 3.3.1 Participant characteristics A total of 32 athletes met the inclusion criteria, consented to participate and completed all components of the study.  Most subjects were on the wheelchair rugby team (n=20) with the remainder of athletes competing in wheelchair basketball (n=7), para-alpine skiing (n=3) and wheelchair athletics (n=2). When described by level of SCI, the group was made up of 20 participants with tetraplegia (17 male, 3 female) and 12 participants with paraplegia (7 male, 5 female).  There were 20 participants with complete SCI (AIS-A) and 12 with incomplete SCI (AIS-B through D). Background characteristics including age (years), height (m), weight (kg) and body mass index (BMI; kg·m-2) of the group with subgroup analyses based on gender are presented in Table 3.1. There were no differences between men and women pertaining to age but as expected, the men were taller, weighed more and had a greater BMI.  No difference was detected in the sum of skinfold thickness between men and women.  When the population was stratified by level of SCI (Table 3.2), no significant differences in age or anthropometrics were detected between those with paraplegia or tetraplegia.    95  Table 3.1  Participant anthropometrics with subgroup analyses based on gender  Group (N=32) Men (n=24) Women (n=8) p-value Characteristic      Age (year) 30.6 ± 6.2a 30.5 ± 6.7 30.6 ± 4.7 0.974 Anthropometrics      Length (cm) 177.2 ± 8.9 179.2 ± 9.0 171.1 ± 5.8 0.025  Weight (kg) 67.0 ± 13.7 70.9 ± 13.3 55.4 ± 6.0 0.004  BMI (kg·m-2) 21.3 ± 3.4 22.1 ± 3.5 18.9 ±  1.9 0.022  Sum of Skinfolds (mm) 51.1 ± 20.6 50.9 ± 22.8 51.5 ± 13.4 0.949 Abbreviations: BMI, body mass index; cm, centimetre; mm, millimetre; kg, kilogram; m, metre a mean ± standard deviation p-value <0.05 considered significant.  Student’s t-test used to compare means between groups. Sum of skinfolds from four upper body sites: bicep, tricep, subscapular, iliac crest    Table 3.2   Participant anthropometrics with subgroup analyses based on level of spinal cord injury  Group (N=32) Paraplegia (n=12) Tetraplegia (n=20) p-value Characteristic      Age (year) 30.6 ± 6.2a 29.3 ± 5.1 31.4 ± 6.7 0.360 Anthropometrics      Length (cm) 177.2 ± 8.9 174.1 ± 8.6 179.0 ± 8.8 0.128  Weight (kg) 67.0 ± 13.7 64.6 ± 13.0 68.4 ± 14.2 0.455  BMI (kg·m-2) 21.3 ± 3.4 21.3 ± 3.7 21.2 ± 3.4 0.952  Sum of Skinfolds (mm) 51.1 ± 20.6 50.2 ± 18.0 51.6 ± 22.5 0.864 Abbreviations: BMI, body mass index; cm, centimetre; mm, millimetre; kg, kilogram; m, metre a mean ± standard deviation p-value <0.05 considered significant.  Student’s t-test used to compare means between groups. Sum of skinfolds from four upper body sites: bicep, tricep, subscapular, iliac crest    96  3.3.2 Dietary intake Dietary intake was averaged from six days of food records with a reported energy intake of 2115 ± 420 kcal per day for the group and a macronutrient distribution of 54% of energy from carbohydrate, 29% from fat and 18% from protein. No statistically significant differences for any of the nutrients were detected upon subgroup analyses based on gender (Table 3.3) or SCI (Table 3.4).  The dietary intakes of those with paraplegia showed a tendency to be higher in fat but again these differences did not reach statistical significance.    Table 3.3   Average nutrient intakes with subgroup analyses based on gender Average Nutrient Intakesa Group (N=32) Men (n=24) Women (n=8) p-value  Energy  (kcal) 2115 ± 420b 2156 ± 431 1991 ± 383 0.345  Carbohydrate (g) 285.1 ± 56.9 289.9 ± 57.0 270.6 ± 57.7 0.415  Fat (g) 67.4 ± 18.8 69.3 ± 18.8 63.4 ± 19.1 0.444  Protein (g) 92.8 ± 21.6 93.1 ± 21.2 92.1 ±24.2 0.917 Abbreviations: kcal, kilocalorie; g, gram a nutrient intakes averaged from six days of reported dietary intake  b mean ± standard deviation p-value <0.05 considered significant. Student’s t-test used to compare means between groups.   Table 3.4  Average nutrient intakes with subgroup analyses based on level of spinal cord injury Average Nutrient Intakesa Group (N=32) Paraplegia (n=12) Tetraplegia (n=20) p-value  Energy  (kcal) 2115 ± 420b 2239 ± 414 2040 ± 416 0.199  Carbohydrate (g) 285.1 ± 56.9 294.4 ± 64.3 279.5 ± 52.9 0.482  Fat (g) 67.4 ± 18.8 74.7 ± 18.6 63.7 ± 18.1 0.052  Protein (g) 92.8 ± 21.6 102.3 ± 19.2 87.1 ± 21.3 0.112 Abbreviations: kcal, kilocalorie; g, gram a nutrient intakes averaged from six days of reported intake (home and training camp) b mean ± standard deviation p-value <0.05 considered significant.  Student’s t-test used to compare means between groups.  97  3.3.3 Three-Factor Eating Questionnaire   The means and standard deviations for each of the three scales of the Three-Factor Eating Questionnaire (TFEQ) are presented in Table 3.5.  No statistical differences were detected for any of the three scales (cognitive dietary restraint, disinhibition or hunger) when compared within the group based on gender or SCI (paraplegia vs. tetraplegia).  When analyses were done to compare mean scores between subgroups based on completeness of SCI, a difference between the mean scores for disinhibition was detected as those with incomplete SCI reported a higher score compared to those with complete SCI (3.75 ± 1.5 vs. 2.2 ± 1.7, p=0.014) (data not shown).  No other significant differences between mean scores based on level or completeness of SCI were detected.    Table 3.5  Mean scores from the Three-Factor Eating Questionnaire for the group with subgroup analyses based on gender and level of spinal cord injury    n Cognitive Dietary Restraint Disinhibition Hunger Maximum Score  21 16 14 Group 32 10.8 ± 4.7a 2.8  ± 1.8 3.1  ± 2.2 Gender      Men 24 11.1 ± 5.0 2.7  ± 1.8 3.2  ± 2.2  Women 8 9.8 ± 4.0 3.0 ± 1.9 2.9  ± 2.4 Level of SCI      Paraplegic 12 11.5 ± 4.6 3.2 ± 1.5 3.2 ± 2.0  Tetraplegic 20 10.4 ± 4.8 2.6 ± 1.9 3.1 ± 2.3 Abbreviations: SCI, spinal cord injury a scores presented as mean ± standard deviation Comparison between sub-groups (men vs. women; paraplegic vs. tetraplegic) done using student’s t-test for independent samples. p-value < 0.05 considered significant.  No significant differences detected.    98  3.3.4 Yale Eating Patterns Questionnaire The means and standard deviations for each of the nine scales of the Yale Eating Patterns Questionnaire (YEPQ) for the group are presented in Table 3.6.  Student’s t-test was used to compare means for each of the subscales between groups based on gender and level of SCI. No statistically significant differences between mean scores were detected for any of the nine scales for either subgroup analyses.   Table 3.6  Mean scores from the Yale Eating Patterns Questionnaire for the group with subgroup analyses based on gender and level of spinal cord injury YEPQ  Subscale Categories Maximum Score Group (N=32) Men (n=24) Women (n=8) Paraplegia (n=12) Tetraplegia (n=20) Uninhibited 45 21.5 ± 3.8a 21.8 ± 4.0 20.6 ± 3.1 21.1 ± 3.4 21.8 ± 4.1 Oversnacking 60 25.6 ± 4.9 25.6 ± 4.9 25.6 ± 5.4 24.2 ± 4.2 26.1 ± 5.6 Binging 65 29.8 ± 4.7 29.5 ± 4.8 30.9 ± 4.7 28.8 ± 5.2 30.5 ± 4.5 Dieting 25 11.2 ± 2.8 11.1 ± 2.8 11.5 ± 3.0 12.3 ± 2.9 10.6 ± 2.6 Satiation: Full 25 15.2 ± 2.3 15.4 ± 2.3 14.4 ± 2.3 15.5 ± 2.5 15.0 ± 2.3 Satiation: Nausea 40 16.6 ± 4.2 17.1 ± 4.4 14.9 ± 3.1 15.8 ± 3.4 17.1 ± 4.6 Satiation: Guilty 30 12.0 ± 3.1 12.0 ± 3.2 12.0 ± 3.0 11.4 ± 3.1 12.4 ± 3.1 Attribution of overweight to physical factors 40 18.2 ± 4.8 18.2 ± 5.2 18.3 ± 3.8 17.6 ± 2.9 18.6 ± 5.7 Attribution of overweight to emotional factors 8 3.8 ± 1.3 4.0 ± 1.4 3.4 ± 0.9 3.4 ± 0.8 4.1 ± 1.5 Abbreviations: YEPQ, Yale Eating Patterns Questionnaire  a scores presented as mean ± standard deviation  p-value <0.05 considered significant. Comparison between subgroups (men vs. women, paraplegia vs. tetraplegia) done using student’s t-test for independent samples     99  3.3.5 High versus low cognitive dietary restraint Individuals were categorized as high or low restraint using the group median score for the TFEQ restraint scale as the cut-point.  The median score was 11.5 and those with scores of 11 or below were classified as low restraint and those with scores of 12 or above were classified as high restraint.  On the basis of gender, completeness of SCI and level of SCI, the percentage of individuals with low and high restraint was equally divided as shown in Table 3.7.  Based on gender, 50% of men and 50% of women were classified as high restraint.  A slightly greater proportion of athletes with paraplegia were classified as high restraint as compared to athletes with tetraplegia but this was not a significant difference when tested with Fisher’s exact test.     Table 3.7  Percentage of participants categorized as either low or high restraint on the basis of cognitive dietary restraint score with subgroup analyses based on gender, spinal cord injury level and function    n Low Restrainta  (≤ 11) High Restraint  (≥12) p-valueb Gender      Male  24 12 (50%) 12 (50%) 0.657  Female  8 4 (50%) 4 (50%) Spinal Cord Injury - Level      Paraplegia  12 5 (42%) 7 (58%) 0.358  Tetraplegia  20 11 (55%) 9 (45%) Spinal Cord Injury - Function      Complete  20 10 (50%) 10 (50%) 0.642  Incomplete  12 6 (50%) 6 (50%) a Individuals were classified as low or high restraint based on the median score (11.5) for the Three-Factor Eating Questionnaire cognitive dietary restraint scale. b Fisher’s Exact test was used to test for differences in the percentages of low and high restraint between groups.  p-value < 0.05 was considered significant. (2x2 cross table)      100  Results of anthropometric and dietary intakes as well as the scores for each of the scales from the TFEQ and YEPQ were compared with the group stratified based on cognitive dietary restraint scores, as shown in Table 3.8.  No differences in BMI scores or the sum of four upper skinfold measurements between low or high restraint groups were detected, although those with high restraint tended to have a slightly greater sum of skinfolds.  No differences in any of the absolute parameters of dietary intake reported based on restraint were detected although the higher restraint group had protein intakes account for a greater proportion of total energy.  No difference was detected in the percentages of calories from carbohydrate or fat intake.  By design, the high restraint group had a significantly higher cognitive dietary restraint score compared to the low restraint group and those with higher restraint scores also had a relatively higher disinhibition score.  No differences based on the hunger scale were detected between the groups.  A comparison of the YEPQ scale scores between low and high restraint groups revealed differences for the dieting and satiation – guilty subscales as those in the high restraint group scored higher for both of those traits.    101  Table 3.8   Comparison of anthropometrics, dietary intake and eating patterns scores between those with low and high cognitive dietary restraint scores      Low Restraint (n=16) High Restraint (n=16) p-value Anthropometrics     BMI (kg·m-2) 20.9 ± 3.3a 21.7 ± 3.6 0.511  Sum of Skinfolds (mm) 45.0 ± 19.1 57.2 ± 20.9 0.095 Dietary Intake     Energy  (kcal) 2210 ± 375 2020 ± 452 0.207  Carbohydrate (g) 296.2 ± 49.6 274.0 ± 62.9 0.276  Fat (g) 72.9 ± 18.5 62.8 ± 18.2 0.130  Protein (g) 92.8 ± 17.5 92.8 ± 25.6 0.997  Fibre (g) 19.3 ± 3.7 20.4 ± 5.1 0.462  % Energy from carbohydrate 53.9 ± 3.9 54.6 ± 5.5 0.697  % Energy from fat 29.6 ± 3.8 27.8 ± 4.6 0.248  % Energy from protein 16.9 ± 2.0 18.4 ± 2.1 0.042  Calcium (mg) 793 ± 173 914 ± 385 0.267  Vitamin D (IU) 166.1 ± 44.9 162.4 ± 41.8 0.814 Three-Factor Eating Questionnaire      Cognitive Dietary Restraint 6.8 ± 3.0 14.7 ± 2.1 <0.001  Disinhibition 2.1 ± 1.7 3.5 ± 1.6 0.019  Hunger 2.8 ± 1.9 3.4 ± 2.4 0.430 Yale Eating Patterns Questionnaire     Uninhibited 22.6 ± 3.6 20.4 ± 3.7 0.092  Oversnacking 26.1 ± 6.2 25.1 ± 3.3 0.553  Binging 29.3 ± 5.4 30.4 ± 4.0 0.486  Dieting 9.9 ± 2.3 12.5 ± 2.7 0.007  Satiation – Full 14.9 ± 2.3 15.4 ± 2.4 0.606  Satiation – Nausea 17.1 ± 4.3 16.0 ± 4.1 0.456  Satiation – Guilty 10.4 ± 2.5 13.6 ± 2.8 0.002  Attributes – Physical 18.9 ± 6.4 17.5 ± 2.4 0.425  Attributes – Emotional 3.7 ± 1.6 4.0 ± 1.0 0.505 Abbreviations:  BMI, body mass index;  kcal, kilocalorie; IU, International Units a mean ± standard deviation Low and High restraint group divided on the basis of group median score of 11.5. Comparison between sub-groups done using student’s t-test for independent samples. p-value <0.05 considered significant and indicated in bold font. Sum of skinfolds from four upper body sites: bicep, tricep, subscapular, iliac crest   102  3.3.6 Correlation between TFEQ and YEPQ subscales with anthropometrics and selected nutrients The potential association between each of the three subscales for TFEQ was tested against anthropometric and dietary intake variables.   As presented in Table 3.9, no significant correlations were detected between cognitive dietary restraint and any of the anthropometric or selected nutrient intake variables.  A moderate positive association was detected between disinhibition and the sum of skinfolds. The hunger scores were moderately associated with a number of the dietary intake variables including energy, carbohydrate and protein.    Table 3.9   Association of cognitive restraint, disinhibition and hunger with anthropometric and dietary intake    (Group N=32) Cognitive Dietary Restraint Disinhibition Hunger  r p-value r p-value r p-value Anthropometrics        BMI (kg·m-2) 0.281 0.120 0.268 0.138 0.297 0.099  Sum of Skinfolds (mm) 0.349 0.050 0.513 0.003 0.248 0.171 Dietary Intake        Energy  (kcal) -0.248 0.171 0.044 0.810 0.354 0.047  Carbohydrate (g) -0.235 0.196 -0.011 0.954 0.361 0.042  Fat (g) -0.246 0.175 0.077 0.674 0.240 0.185  Protein (g) -0.010 0.955 0.158 0.388 0.456 0.009 Abbreviations: BMI, Body mass index;  kcal, kilocalorie;  IU, International Units; r, Pearson’s product moment correlation coefficient correlation with p-value <0.05 considered significant and in bold font Sum of skinfolds are from four upper body sites: bicep, tricep, subscapular, iliac crest.     103  Pearson’s product moment correlations between each of the nine subscales of the YEPQ and anthropometrics or selected were examined. Results presented in Table 3.10 are limited to the two subscales of the YEPQ in which a statistically significant association with at least one of the anthropometric or dietary variables was detected.  Correlation data for subscales with no association to anthropometric or dietary intake variables are not shown. A moderate positive association between the sum of skinfolds and the satiation-guilty subscale was detected but no correlation with BMI was detected for any of the subscales. The dieting subscale was detected to have a moderate positive association with protein intake.  Table 3.10 Correlation of selected scales from Yale Eating Patterns Questionnaire  with anthropometrics and dietary intake      Dieting Satiation - Guilty  r p-value r p-value Anthropometrics      BMI (kg·m-2) 0.053 0.772 0.242 0.183  Sum of Skinfolds (mm) 0.178 0.329 0.382 0.031 Dietary Intakes      Energy  (kcal) 0.264 0.145 -0.355 0.046  Carbohydrate (g) 0.145 0.427 -0.376 0.034  Fat (g) 0.336 0.060 -0.234 0.198  Protein (g) 0.418 0.017 -0.226 0.213 Abbreviations: BMI, Body mass index;  mm; millimetre; kcal, kilocalorie; r, Pearson’s product moment correlation coefficient; g, gram; k, kilogram p-value < 0.05 considered significant and in bold font Data from scales of the Yale Eating Patterns Questionnaire without any significant correlation not presented. Sum of skinfolds are from four upper body sites: bicep, tricep, subscapular, iliac crest.   104 3.4 Discussion  The assessment of the psychological constructs of cognitive dietary restraint, disinhibition and hunger in athletes with SCI contributes novel information to this body of literature.  While significant differences were not detected based on gender or level of SCI for any of the three scales within the TFEQ, the cognitive dietary restraint scores for the men in the group were considerably higher than expected with a mean score of 11.1 ± 5.0.  In comparison, a sample of 60 adult males reported a mean restraint score 6.7 ± 3.7 (32). Another sample of obese men in a weight reduction study reported baseline restraint scores of 5.0 ± 3.7, which increased to 10.0 ± 4.1 during the energy restriction phase and then returned to 7.0 ± 4.3 at the end of the maintenance phase of the intervention (56).  The mean restraint scores for the female athletes with SCI in this study were similar to scores of many other studies of college aged women, which ranged from 6.4 ± 4.8 for vegetarian women (24) to 11.0 ± 5.4 for female runners with stress fractures (22) with many studies reporting a mean or median score between 8 and 10 (23, 25, 26).    The disinhibition scores in this group of athletes were slightly lower than anticipated, with a mean value of 2.8 ± 1.8, compared to typical scores for both men and women between 5 and 7 out of a possible 16 (20, 22, 23, 29, 32, 56, 57).  Hunger scores are also typically higher in able-bodied populations in the range of 5 to 7 out of a possible 14 (22, 23, 32, 56, 57) compared to the mean hunger score of 3.1 ± 2.2 in this group of athletes with SCI.  The stronger expression of cognitive dietary restraint with weaker expression of disinhibition and hunger traits identifies a unique pattern of eating behaviours and attitudes in these athletes, especially the men.  The profile of eating behaviours assessed by the TFEQ can be described as the purposeful monitoring and perhaps intentional limiting of food intake to maintain body composition without an associated loss of control with intake of certain foods and low susceptibility to hunger cues. Even among SCI athletes with high restraint, scores for disinhibition were still lower than population norms. As a group, these athletes appear to be monitoring and potentially restricting their dietary intake but with an attenuated sense of disinhibition and weaker hunger and satiety cues.   105 When the athletes were categorized based on a median split of cognitive dietary restraint scores, no differences in the energy, macronutrient or micronutrient intakes were detected.  Many studies have compared dietary intakes within groups on the basis of restraint scores (20, 34, 36, 39, 42, 43, 58) with a tendency for those with higher levels of dietary restraint to report lower energy intakes (20, 34-36, 38-40).  The percentage of energy from protein tends to be higher (20, 39, 42, 58) while the proportion of energy from fat tends to be less (20, 34, 42).  While significant differences in the absolute macronutrient intakes were not detected between those with high and low restraint, a significant difference was detected in the percentage of energy from protein.  This may reflect an athlete’s desire or attempt to maintain an adequate protein intake within a limited energy intake.   Significant correlations were not detected between the scores for cognitive dietary restraint and any of the anthropometric or dietary intake data.  A moderate positive correlation was detected between the scores for disinhibition and the sum of skinfolds, although BMI was not associated with disinhibition.  The literature confirms that disinhibition scores are more likely to be associated with measures of body composition and weight than are scores for cognitive dietary restraint (28), although the relationship is influenced by the strength and presence of other eating behaviours and attitudes (29, 59, 60).  These results illustrate that although dietary intake and body composition were similar between those with low and high dietary restraint, the athletes had very different ways of thinking about the relationship between food intake and body size.   The results from each of the nine subscales from the YEPQ were presented for this group of athletes with SCI.  Unfortunately, a very limited number of studies exist for comparative purposes. In comparison to the original population of college students this instrument was developed with, the athletes in this study scored higher on the dieting scale (11.2 ± 2.8 vs. 7.6 ± 1.6 ), scored similar on the scales for satiation-full (15.2 ± 2.3 vs. 16.5 ± 3.9 ), satiation-guilty (12.0 ± 3.1 vs. 14.8 ± 5.2) and scored lower on the remaining scales of uninhibited (21.5 ± 3.8 vs. 25.2 ± 4.9),  oversnacking (25.6 ± 4.9 vs. 34.2 ± 6.3), binging (29.8 ± 4.7 vs. 40.0 ± 7.3), satiation-nausea (16.6 ± 4.2 vs. 23.6 ± 10.5), attribution of overweight   106 to physical factors (18.2 ± 4.8 vs. 27.8 ± 7.3) and attribution of overweight to emotional factors (3.8 ± 1.3 vs. 6.8 ± 2.2). A subjective profile of the eating behaviours and attitudes of the athletes as assessed by the YEPQ is subjectively similar to the profile observed on the TFEQ.  Similar characteristics include an emphasis on the importance of dieting with some tendency towards the uninhibited eating of foods for enjoyment and eating to satiety with some guilt associated with when to stop eating.  Tendencies towards oversnacking and binging were low and any attribution of physical or emotional causes of weight gains were also low.  While the YEPQ offers great potential as a psychometric instrument to measure food and eating related behaviours and attitudes in the general population, this tool has yet to withstand validation in a variety of populations.  Studies that have used this tool are quite limited and often only the binging and oversnacking subscales have been used in obesity or bulimia related studies.   3.4.1 Strengths and limitations The strengths of this study include the choice of psychometric instrument used to assess eating behaviours and the strategies used to improve the accuracy of the self-reported food diaries. The TFEQ is widely recognized as a robust instrument used to assess the construct of cognitive dietary restraint with extensive studies using this tool as a primary method of assessment.  The validity of the restraint scale of the TFEQ has good internal consistency (Cronbach’s alpha 0.80) (55) with good test-retest reliability (18).  The psychometric properties of the restraint scale of the TFEQ were tested in a population of college aged women of differing ethnic background and found good temporal stability over a 5 month period (61).  Bond et al. found strong intercorrelations among the original TFEQ scales and the subscales tested in the study (correlation of 0.69 – 0.87) (62).  As with most psychometric instruments, there is some debate over the validity of the constructs that the tool is intended to measure and the clinical relevance or application of the findings (55, 61-63). Over time, all tools tend to evolve and change but at the present time the TFEQ is considered to be a robust tool to assess cognitive dietary restraint in a variety of populations.   107 A second strength of this study was the repeated measurement of the self-reported three-day food records to capture usual intakes while at home as well as when athletes were with the team.  The accuracy of the dietary reporting was enhanced by a number of methods including a detailed tutorial provided to athletes by a Registered Dietitian, having that same individual present during meals while at training camp to observe intakes and to answer any questions or provide clarity on how to record intake.   This study was exploratory in nature with a key objective to assess the eating behaviours and attitudes in a population of elite Canadian athletes with SCI.  As a result of the study design, there are a number of limitations that the reader should be aware of, and accordingly, the results should be interpreted with caution.  The primary objective of this study was to measure and evaluate the dietary adequacy of athlete with SCI (presented in detail in Chapter 2).  While significant differences were not detected between groups with differing cognitive dietary restraint scores, this study was not powered adequately to detect differences on the basis of restraint scores.  While the number of women in this study was not adequate for independent analyses, the most interesting finding of higher than expected dietary restraint scores was detected among the men which had a reasonable group size.   3.4.2 Future directions The presence of a strong tendency towards dietary restraint in athletes with SCI has not been reported in the literature, and thus, hypotheses regarding why restraint may be elevated or the potential physiological impacts of chronically high dietary restraint behaviours have not been established or tested.  It has been previously well documented that high dietary restraint is associated with elevated urinary and salivary cortisol concentrations (20-22) and potential effects on bone mineral density (23, 25, 27).  The stimulation of the hypothalamic-pituitary-adrenal axis may further affect an already tenuous bone density situation in those with SCI (64).  If the same relationship holds true between cognitive dietary restraint and elevated cortisol concentration, the impact on bone health may be significant as this population is already at great risk for reduced bone mineral content (65-67).     108 It is purely speculative at this point as to why a relatively high cognitive dietary restraint score was observed in this group of athletes.  One may hypothesize that appetite and dietary intake may be influenced as a result of the SCI secondary to altered autonomic control, changes to hormonal regulation or simply that energy requirements are significantly less following SCI.  Perhaps the cognitive control of dietary intake is a necessity for these athletes in order to avoid weight gain and obesity as a consequence of the reduced energy requirements following SCI.  These athletes may be actively regulating their dietary intakes to maintain or achieve a desired body composition specific to the needs of the sport to contribute to their athletic successes.  The observation of elevated cognitive dietary restraint in athletes with SCI offers many potential hypotheses to be developed and tested in order to better understand this phenomenon. 3.4.3 Conclusions It has been previously established that energy requirements for adults with SCI are less than predicted and energy intakes are often considerably less than expected based on body mass and physical activity level (12, 68-70).  The most common rationale for this reduced energy requirement has been related to the reduction in lean tissue following SCI (8-11).  In athletes with SCI, the balance between energy intake, energy requirements and energy expenditure for physical training creates an interesting paradigm of energy balance.  Added to this picture, is now the additional influence of eating behaviours and attitudes.  These athletes may be purposefully monitoring or limiting dietary intake to avoid the high prevalence of obesity associated with SCI (7, 8, 10) or perhaps to maintain an ideal body composition for their sport performance.  It would be interesting to further investigate this observation and test if the presence of a stronger cognitive dietary restraint trait is repeated in other groups with SCI.  Future studies investigating the hormonal influence and regulation of appetite and energy intake in those with SCI would benefit from including an assessment of eating behaviours and attitudes.  Many questions remain unanswered but it would be prudent for coaches, sport scientists and Registered Dietitians working with these athletes to explore eating behaviours and attitudes, especially in those athletes who appear to have a very limited dietary intake.     109 3.5 References   1. Bauman WA, Adkins RH, Spungen AM, Waters RL. The effect of residual neurological deficit on oral glucose tolerance in persons with chronic spinal cord injury. Spinal Cord 1999;37:765-71.  2. Bauman WA, Kahn NN, Grimm DR, Spungen AM. Risk factors for atherogenesis and cardiovascular autonomic function in persons with spinal cord injury. Spinal Cord 1999;37:601-16.  3. de Groot S, Dallmeijer AJ, Post MW, Angenot EL, van den Berg-Emons RJ, van der Woude LH. Prospective analysis of lipid profiles in persons with a spinal cord injury during and 1 year after inpatient rehabilitation. Arch Phys Med Rehabil 2008;89:531-7.  4. Bauman WA, Spungen AM. Disorders of carbohydrate and lipid metabolism in veterans with paraplegia or quadriplegia: a model of premature aging. Metabolism 1994;43:749-56.  5. Gibson AE, Buchholz AC, Martin Ginis KA, et al.  C-reactive protein in adults with chronic spinal cord injury: increased chronic inflammation in tetraplegia vs paraplegia. Spinal Cord 2008;46:616-21.  6. Huang CC, Liu CW, Weng MC, Chen TW, Huang MH. Association of C-reactive protein and insulin resistance in patients with chronic spinal cord injury. J Rehabil Med 2008;40:819-22.  7. Nuhlicek DN, Spurr GB, Barboriak JJ, Rooney CB, el Ghatit AZ, Bongard RD. Body composition of patients with spinal cord injury. Eur J Clin Nutr 1988;42:765-73.  8. Spungen AM, Adkins RH, Stewart CA, et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol 2003;95:2398-407.  9. Jones LM, Goulding A, Gerrard DF. DEXA: a practical and accurate tool to demonstrate total and regional bone loss, lean tissue loss and fat mass gain in paraplegia. Spinal Cord 1998;36:637-40.  10. Spungen AM, Wang J, Pierson RN Jr, Bauman WA. Soft tissue body composition differences in monozygotic twins discordant for spinal cord injury. J Appl Physiol 2000;88:1310-5.  11. Maggioni M, Bertoli S, Margonato V, Merati G, Veicsteinas A, Testolin G. Body composition assessment in spinal cord injury subjects. Acta Diabetol 2003;40:S183-6.  12. Buchholz AC, McGillivray CF, Pencharz PB. Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition. Am J Clin Nutr 2003;77:371-8.    110 13. Mojtahedi MC, Evans EM. Predication equations overestimate resting metabolic rate of spinal cord injured athletes. Med Sci Sports Exerc 2005;37:S437(Abstract).  14. Potvin A, Nadon R, Royer D, Farrar D. The diet of the disabled athlete. Sci Sports 1996;11:152-6.  15. Lally DA, Wang JH, Goebert DA, Quigley RD, Hartung GH. Performance training and dietary characteristics of American and Japanese wheelchair marathoners. Med Sci Sports Exerc 1991;23:S101(Abstract).  16. Ribeiro SM, Da Silva RC, De Castro IA, Tirapegui J. Assessment of nutritional status of active handicapped individuals. Nutr Res 2005;25:239-49.  17. Byrne S, McLean N. Elite athletes: effects of the pressure to be thin. J Sci Med Sport 2002;5:80-94.  18. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res 1985;29:71-83.  19. Bryant EJ, King NA, Blundell JE. Disinhibition: its effects on appetite and weight regulation. Obes Rev 2008;9:409-19.  20. McLean JA, Barr SI, Prior JC. Cognitive dietary restraint is associated with higher urinary cortisol excretion in healthy premenopausal women. Am J Clin Nutr 2001;73:7-12.  21. Anderson DA, Shapiro JR, Lundgren JD, Spataro LE, Frye CA. Self-reported dietary restraint is associated with elevated levels of salivary cortisol. Appetite 2002;38:13-7.  22. Guest NS, Barr SI. Cognitive dietary restraint is associated with stress fractures in women runners. Int J Sport Nutr Exerc Metab 2005;15:147-59.  23. Van Loan MD, Keim NL. Influence of cognitive eating restraint on total-body measurements of bone mineral density and bone mineral content in premenopausal women aged 18-45 y: a cross-sectional study. Am J Clin Nutr 2000;72:837-43.  24. Barr SI, Janelle KC, Prior JC. Vegetarian vs nonvegetarian diets, dietary restraint, and subclinical ovulatory disturbances: prospective 6-mo study. Am J Clin Nutr 1994;60:887-94.  25. Barr SI, Prior JC, Vigna YM. Restrained eating and ovulatory disturbances: possible implications for bone health. Am J Clin Nutr 1994;59:92-7.  26. McLean JA, Barr SI. Cognitive dietary restraint is associated with eating behaviors, lifestyle practices, personality characteristics and menstrual irregularity in college women. Appetite 2003;40:185-92.    111 27. McLean JA, Barr SI, Prior JC. Dietary restraint, exercise, and bone density in young women: are they related? Med Sci Sports Exerc 2001;33:1292-6.  28. Provencher V, Drapeau V, Tremblay A, Despres JP, Lemieux S. Eating behaviors and indexes of body composition in men and women from the Quebec family study. Obes Res 2003;11:783-92.  29. Drapeau V, Blundell J, Therrien F, Lawton C, Richard D, Tremblay A. Appetite sensations as a marker of overall intake. Br J Nutr 2005;93:273-80.  30. de Lauzon-Guillain B, Romon M, Musher-Eizenman D, et al. Cognitive restraint, uncontrolled eating and emotional eating: correlations between parent and adolescent. Matern Child Nutr 2009;5:171-8.  31. de Lauzon-Guillain B, Romon M, Deschamps V, et al. The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population. J Nutr 2004;134:2372-80.  32. Harden CJ, Corfe BM, Richardson JC, Dettmar PW, Paxman JR. Body mass index and age affect Three-Factor Eating Questionnaire scores in male subjects. Nutr Res 2009;29:379-82.  33. Provencher V, Drapeau V, Tremblay A, et al. Eating behaviours, dietary profile and body composition according to dieting history in men and women of the Quebec Family Study. Br J Nutr 2004;91:997-1004.  34. Mulvihill CB, Davies GJ, Rogers PJ. Dietary restraint in relation to nutrient intake, physical activity and iron status in adolescent females. J Hum Nutr Diet 2002;15:19-31.  35. Lluch A, Herbeth B, Méjean L, Siest G. Dietary intakes, eating style and overweight in the Stanislas Family Study. Int J Obes Relat Metab Disord 2000;24:1493-9.  36. Klesges RC, Isbell TR, Klesges LM. Relationship between dietary restraint, energy intake, physical activity, and body weight: A prospective analysis. J Abnorm Psychol 1992;101:668-74.  37. Klesges RC, Klem ML, Bene CR. Effects of dietary restraint, obesity, and gender on holiday eating behavior and weight gain. J Abnorm Psychol 1989;98:499-503.  38. Tuschl RJ, Platte P, Laessle RG, Stichler W, Pirke KM. Energy expenditure and everyday eating behavior in healthy young women. Am J Clin Nutr 1990;52:81-6.  39. French SA, Jeffery RW, Wing RR. Food intake and physical activity: a comparison of three measures of dieting. Addict Behav 1994;19:401-9.    112 40. Laessle RG, Tuschl RJ, Kotthaus BC, Pirke KM. Behavioral and biological correlates of dietary restraint in normal life. Appetite 1989;12:83-94.  41. de Castro JM. The relationship of cognitive restraint to the spontaneous food and fluid intake of free-living humans. Physiol Behav 1995;57:287-95.  42. Rideout CA, McLean JA, Barr SI. Women with high scores for cognitive dietary restraint choose foods lower in fat and energy. J Am Diet Assoc 2004;104:1154-7.  43. Goulet J, Provencher V, Piche ME, et al. Relationship between eating behaviours and food and drink consumption in healthy postmenopausal women in a real-life context. Br J Nutr 2008;100:910-7.  44. Borg G. Borg's perceived exertion and pain scales. 1st ed. Champaign Illinois: Human Kinetics, 1998.  45. Kristeller JL, Rodin J. Yale Eating Patterns Questionnaire. Addict Behav 1989;14:631-42.  46. Marino RJ, Barros T, Biering-Sorensen F, et al. International standards for neurological classification of spinal cord injury. J Spinal Cord Med 2003;26:S50-6.  47. Maynard FM Jr, Bracken MB, Creasey G, et al. International Standards for Neurological and Functional Classification of Spinal Cord Injury. American Spinal Injury Association. Spinal Cord 1997;35:266-74.  48. Curt A, Schwab ME, Dietz V. Providing the clinical basis for new interventional therapies: refined diagnosis and assessment of recovery after spinal cord injury. Spinal Cord 2004;42:1-6.  49. Graves DE, Frankiewicz RG, Donovan WH. Construct validity and dimensional structure of the ASIA motor scale. J Spinal Cord Med 2006;29:39-45.  50. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Abridged edition. Champaign, Ill.; United States: Human Kinetics Publishers, 1991.  51. Magkos F. Methodology of dietary assessment in athletes: concepts and pitfalls. Curr Opin Clin Nutr Metab Care 2003;6:539-49.  52. Livingstone MB, Black AE. Markers of the validity of reported energy intake. J Nutr 2003;133:S895-920.  53. Nutrition Research Division and Biostatistics and Computer Application Division, Health Protection Branch. Canadian Nutrient File, Compilation of Canadian Food Composition Data.   113 Health Canada 2007. Internet: http://www.hc-sc.gc.ca/fn-an/nutrition/fiche-nutri-data/cnf_aboutus-aproposdenous_fcen-eng.php (accessed 03/14/2010). 54. United States Department of Agriculture, Agricultural Research Service. USDA National Nutrient Database for Standard Reference. Version Release 2007. Internet: http://www.nal.usda.gov/fnic/foodcomp (accessed 03/14/2010).  55. Laessle RG, Tuschl RJ, Kotthaus BC, Pirke KM. A comparison of the validity of three scales for the assessment of dietary restraint. J Abnorm Psychol 1989;98:504-7.  56. Lejeune MP, Van Aggel-Leijssen DP, Van Baak MA, Westerterp-Plantenga MS. Effects of dietary restraint vs exercise during weight maintenance in obese men. Eur J Clin Nutr 2003;57:1338-44.  57. Paradis AM, Godin G, Lemieux S, Perusse L, Vohl MC. Eating behaviours of non-obese individuals with and without familial history of obesity. Br J Nutr 2009;101:1103-9.  58. Rideout CA, Linden W, Barr SI. High cognitive dietary restraint is associated with increased cortisol excretion in postmenopausal women. J Gerontol A Biol Sci Med Sci 2006;61:628-33.  59. Hays NP, Bathalon GP, McCrory MA, Roubenoff R, Lipman R, Roberts SB. Eating behavior correlates of adult weight gain and obesity in healthy women aged 55-65 y. Am J Clin Nutr 2002;75:476-83.  60. Savage JS, Hoffman L, Birch LL. Dieting, restraint, and disinhibition predict women's weight change over 6 y. Am J Clin Nutr 2009;90:33-40.  61. Bardone-Cone A, Boyd CA. Psychometric properties of eating disorder instruments in Black and White young women: internal consistency, temporal stability, and validity. Psychol Assess 2007;19:356-62.  62. Bond MJ, McDowell AJ, Wilkinson JY. The measurement of dietary restraint, disinhibition and hunger: an examination of the factor structure of the Three Factor Eating Questionnaire (TFEQ). Int J Obes Relat Metab Disord 2001;25:900-6.  63. Mazzeo SE, Aggen SH, Anderson C, Tozzi F, Bulik CM. Investigating the structure of the Eating Inventory (Three-Factor Eating Questionnaire): a confirmatory approach. Int J Eat Disord 2003;34:255-64.  64. Rideout CA. Cognitive dietary restraint and factors related to bone mineral density and body weight in postmenopausal women. (Doctoral Dissertation) University of British Columbia 2006.   114 65. de Bruin ED, Dietz V, Dambacher MA, Stussi E. Longitudinal changes in bone in men with spinal cord injury. Clin Rehabil 2000;14:145-52.  66. Kiratli BJ, Smith AE, Nauenberg T, Kallfelz CF, Perkash I. Bone mineral and geometric changes through the femur with immobilization due to spinal cord injury. J Rehabil Res Dev 2000;37:225-33.  67. Garland DE, Stewart CA, Adkins RH, et al. Osteoporosis after spinal cord injury. J Orthop Res 1992;10:371-8.  68. Buchholz AC, Pencharz PB. Energy expenditure in chronic spinal cord injury. Curr Opin Clin Nutr Metab Care 2004;7:635-9.  69. Buchholz AC, McGillivray CF, Pencharz PB. Physical activity levels are low in free-living adults with chronic paraplegia. Obes Res 2003;11:563-70.  70. Bauman WA, Spungen AM, Wang J, Pierson RN Jr. The relationship between energy expenditure and lean tissue in monozygotic twins discordant for spinal cord injury. J Rehabil Res Dev 2004;41:1-8.      115 Chapter 4: Conclusion     116 4.1 General discussion Athletes with spinal cord injury (SCI) are training with increased frequency and intensity, looking for a competitive edge in order to achieve their athletic goals.  Often, performance nutrition is included in the arsenal of weapons available to optimize training, recovery and ultimately performance.  While there is a wealth of information to assist able-bodied athletes in adjusting their dietary intake to support training, there is very limited information on what athletes with SCI are consuming, and virtually no evidence to support altering intakes.  Thus, the primary goal of this study was to better understand the usual dietary intakes of elite Canadian athletes with SCI as a step towards enhancing the current state of knowledge.    This chapter summarizes and synthesizes the information related to, and obtained from the study.  It begins with an overview of previous literature on the metabolic and physiological changes associated with SCI including a brief summary of the limited number of published studies on the dietary intakes of this population.  Next, the general conclusions of the study are presented with reference to the research objectives. An evaluation of the strengths and limitations of the study is then presented along with some suggested avenues for further research on this topic.  This chapter then finishes with some practical applications of the findings from the study.  4.2 Summary of the current state of knowledge SCI is associated with many significant metabolic and physiological changes, both of which may influence dietary intakes.  Those with SCI are at greater risk of glucose intolerance (1-4) with resistance to the action of insulin in mediating glucose uptake by peripheral tissues (4-6).  Lipid metabolism is also altered with lower concentrations of high density lipoprotein (HDL) cholesterol (7) and elevated low density lipoprotein (LDL) cholesterol concentrations in approximately 25% of individuals (8, 9).  Increased physical activity and improved cardiovascular fitness improves the HDL cholesterol concentrations (9-13).  The level of neurological impairment is associated with the degree to which glucose   117 and lipid metabolism are affected, as those with complete tetraplegia are most at risk for hyperinsulinemia (14) and depressed HDL cholesterol concentrations (2, 15).   Many changes to body composition are also observed following SCI.  Spungen et al. (16) found that those with SCI have a much higher total fat mass and percent body fat with increased prevalence of abdominal obesity (17-20).  There is a decrease in total body lean tissue mass (16, 21, 22) while the amount of lean tissue in the regions above the level of injury are similar compared to matched controls (23).  Body composition in athletes with SCI has been assessed in a relatively limited number of studies and the effect of physical training on body composition remains somewhat inconclusive.  While training improves measures of fitness (24), the effect on body composition is not as apparent with a tendency towards a reduced percent body fat above the level of neurological impairment but not a systemic reduction in percentage of body fat (24-28).    Resting energy expenditure in adults with SCI is considerably less than in matched able-bodied controls (29-32).  In most cases, this reduced resting energy expenditure can be explained by a decrease in lean tissue (29-37) which is the best predictor of resting metabolic rate in those with paraplegia (30).  The studies have typically measured resting energy expenditures in sedentary adults with SCI and the impact of high intensity, high frequency physical activity on the total energy expenditure in athletes with SCI has not yet been measured.   The dietary intakes of community living adults with SCI have been studied with a sufficient level of detail in four studies to date (19, 38-40).   As resting energy expenditure is reduced following SCI, it is not surprising that the reported energy intakes are less than predicted (19, 38-40).  Within this reduced energy intake, the distribution of macronutrients generally falls within the recommended ranges with the exception of consistently low fibre intakes.  The intakes of several micronutrients are cause for concern with mean intakes of folate, vitamin C and magnesium identified as being suboptimal.  Mean intakes of calcium and vitamin D are consistently below the recommended intake level for healthy adults.  The dietary intakes of athletes with SCI have been studied by three groups (41-43). Energy intakes were reduced with an appropriate macronutrient distribution and some indication   118 of micronutrient inadequacies.  Potvin et al. (41) reported mean micronutrient intakes compared to a reference standard.  In this small group of marathoners, micronutrient intakes were above the cut-point with the exception of vitamin E and zinc.  Interestingly, this group reported mean calcium and vitamin D intakes which were above the recommended amount.  The studies by Lally et al. (42) and Ribeiro et al. (43) did not systematically assess or report mean intakes of micronutrients.   Many gaps in the literature are apparent. There is sufficient evidence that energy intakes are below those of able-bodied individuals yet there is limited information on the overall quality or adequacy of the diets of those individuals with SCI within this reduced energy. A considerable prevalence of suboptimal nutrient intakes has been identified in community living adults with SCI (19, 40). A very limited number of reports suggest that athletes with SCI consume similar amounts of energy as compared to non-athletes with SCI but suggest there may be differences in the micronutrient intakes and prevalence of inadequacy.   4.3 General conclusions This study contributes to the current body of knowledge as it relates to the dietary intakes of athletes with SCI by assessing the current dietary intakes of a varied population of elite Canadian athletes with SCI.  Dietary intakes representative of the individuals’ usual or home diets in addition to dietary intakes while the athletes were at a national team event were assessed.  This study assessed several of the factors which influence dietary intakes such as nutrition knowledge, supplement usage, activity level and behaviours or attitudes associated with eating.  The specific objectives and key findings from this study are summarized in Table 4.1.   The assessment of dietary adequacy was comparable to the results in a sample of community living adults with SCI as reported by Walters et al. (40).  There was a relatively low energy intake, macronutrient intakes generally fell within the Acceptable Macronutrient Distribution Range (AMDR) and there was a high prevalence of mean micronutrient intakes below the Estimated Average Requirement (EAR) or Adequate Intake (AI) (44).  A   119 comparison of the results between the Walters et al. (40) report and the findings of this study suggest that the athletes in this study have not made significant alterations to the nutrient density of their dietary choices to enhance intakes.   While at the national team event, the energy intakes of the athletes differed slightly with a tendency to consume more calories.  The slightly increased energy intake may be related to an increased training volume (492 ± 266 minutes vs. 366 ± 172 minutes, p<0.05) while at training camp.  More likely, the tendency towards increased energy intake is secondary to the food choices available and the reliance on restaurant type meals.  If the food available included nutrient dense, lower calorie foods one would assume that the prevalence of nutrient inadequacy would be less with the increased volume of food consumed.   Despite the increased energy intake, a greater proportion of athletes had nutrient intakes below the EAR while at training camp.  The women in this study were able to maintain consistency in their diets between home and training environments without significant differences in mean intakes or the proportion of women with intakes below the EAR.  A subjective comparison of the intakes of the athletes and the dietary intakes of Canadians (45) showed that the nutrient intakes between the two groups were similar.  Canadian men consumed approximately 600 kcal greater per day compared to the male athletes with SCI, while the energy intakes of the women were similar.  Overall, the patterns of dietary inadequacies of micronutrients were similar between the athletes with SCI and Canadians across the country.   The assessment of the psychological constructs of cognitive dietary restraint, disinhibition and hunger in athletes with SCI contributes novel information to this body of literature.  The cognitive dietary restraint scores for the men in the group were higher than expected while the disinhibition and hunger scores were lower than the norms presented in the literature.  The pattern of a relatively high cognitive dietary restraint with low disinhibition and hunger was observed in both men and women.  The stronger expression of cognitive dietary restraint with weaker expression of disinhibition and hunger traits identifies a unique pattern of eating behaviours and attitudes in these athletes, especially the men.  The profile of eating behaviours assessed by the Three-Factor Eating   120 Questionnaire (46) can be described as the purposeful monitoring and perhaps intentional limiting of food intake to maintain body composition without an associated loss of control with intake of certain foods and low susceptibility to hunger cues. Similar to the findings with the TFEQ, the results of the Yale Eating Pattern Questionnaire (47) describe the eating attitudes of the athletes to be concerned with dieting with some tendency towards the uninhibited eating of foods for enjoyment and eating to satiety with some guilt associated with when to stop eating.  There was a low tendency towards oversnacking and binging, similar to the disinhibition scale of the TFEQ.    121  Table 4.1  Summary of key results with regards to specific objectives Chapter 2  Chapter 2 Specific objective Summary of key results 1. Evaluate the dietary adequacy of macronutrient and micronutrient intakes of elite athletes with a spinal cord injury using the Dietary Reference Intakes for macronutrients (48), vitamins and elements (49-53) as comparative tools.  The prevalence of nutrient inadequacy was significant for men while at training camp with greater than 25% of men reporting mean intakes below the EAR for riboflavin, folate, vitamin B12, magnesium and zinc. At home, the prevalence of nutrient inadequacy for men decreased as greater than 25% of men reported mean intakes below the EAR for only folate, magnesium and zinc.  For men, mean intakes of calcium (775 ± 206 mg) and vitamin D (86.9 ± 65.5 IU) were poor. Women had mean intakes above the AI for calcium (1089 ± 419 mg) but not vitamin D (165.6 ± 130.1 IU).    Only 17% of men and 13% of women reported intakes above the AI for vitamin D, whereas 21% of men and 50% of women had mean calcium intakes above the AI.   The macronutrient intakes were within the AMDRs for men (55.6% carbohydrate, 28.1% fat, 17.9% protein) and women (53.3% carbohydrate, 28.9% fat, 17.9% protein), and most individuals had intakes within the AMDRs.  The predictive equations for EER developed by the IOM did not accurately reflect reported energy intake, even when the coefficients for sedentary or low activity levels were used.  2. Assess the usage of supplemental vitamins and minerals and evaluate the impact on dietary adequacy.  At home, 44% of athletes consumed a nutritional supplement.  At training camp, this decreased to 34% of athletes.  For men, the additional nutrients improved the proportion with mean intakes above the AI for vitamin D (29%) and increased the mean intake of vitamin D to 176.8 ± 200.1 IU.  For almost all other nutrients, the mean intake was significantly increased but the proportion of men with intakes below the EAR did not improve.  For women, the additional nutrients did not significantly increase the mean intakes or change the proportion of women with intakes above the AI or below the EAR.       122  Chapter 2  Chapter 2 Specific objective Summary of key results 3. Compare and contrast the energy and nutrient intakes of athletes while training at home and at a national team event.    Athletes consumed a slightly greater energy intake while at training camp.  This was statistically significant for men (2285 ± 540 kcal vs. 2028 ± 528 kcal, p<0.05), rugby athletes (2213 ± 556 kcal vs. 1899 ± 566 kcal) and those with incomplete SCI (2279 ± 481 kcal vs. 1910 ± 494 kcal, p<0.05).  While at home, men had a greater intake of riboflavin (1.8 ± 0.6 mg vs. 1.4 ± 0.5 mg, p<0.05), thiamin (1.7 ± 0.6 mg vs. 1.4 ± 0.4 mg, p<0.05), calcium (856 ± 330 mg vs. 693 ± 204 mg, p<0.05) and vitamin D (160.1 ± 133.4 IU vs. 38.5 ± 78.3 IU, p<0.05), despite their lower energy intakes.  No differences were detected between home and training camp intakes for women.  4. Assess athletes’ understanding of nutrition knowledge and recommendations using a nutrition knowledge questionnaire (54).  Women achieved a total score of 76.1 ± 7.9 (out of a possible 110) which was higher than the score 66.7 ± 10.8 achieved by men (p<0.05).  The women scored higher on determining the food sources of nutrients (p<0.05).  No differences were detected between groups on the basis of sport or SCI.   The total score on the nutrition knowledge questionnaire was similar to the results from a Canadian population with an average score of 71 (55).  5. Compare and contrast the energy and nutrient intakes of elite athletes with a spinal cord injury to the Canadian population (45).  Energy intakes of male athletes with SCI were considerably less than Canadian men (2156 kcal vs. 2737 kcal).  Energy intakes of female athletes were comparable to Canadian women (1991 kcal vs. 1902 kcal).  Athletes with SCI and adult Canadians had similar proportions of individuals with intakes below the EAR for riboflavin, thiamin, vitamin B12, vitamin C, phosphorus and magnesium above the AI for calcium and vitamin D.    Similar to Canadians, mean sodium intakes for men (4142 ± 1159 mg) and women (3368 ± 11085 mg) were well above the AI.  Athletes with SCI had a greater proportion of individuals with intakes below the EAR for folate (men only), niacin and zinc as compared to Canadians.  Overall, the patterns of dietary inadequacies of micronutrients were similar between the athletes with SCI and Canadians across the country.   123   Chapter 3 Chapter 3 Specific objective Summary of key results 1. Describe food related attitudes and behaviours as measured by the Three-Factor Eating (46) and Yale Eating Patterns Questionnaires (47).  The scores for the cognitive dietary restraint scale from the TFEQ were higher than expected for men (11.1 ± 5.0) while the scores for women (9.8 ± 4.0) were comparable to able-bodied groups.   The group score for disinhibition (2.8 ± 1.8) and hunger (3.1 ± 2.2) were lower than able-bodied populations.  The scores of the YEPQ subscales showed athletes with SCI scored higher on the dieting scale, scored similar on the scales for uninhibited, satiation-full, satiation-guilty and scored lower on the remaining scales of oversnacking, binging, satiation-nausea, attribute-physical and attribute-emotion as compared to an able-bodied population.   2. Explore cognitive dietary restraint scores and associations with dietary intake, anthropometrics and SCI characteristics.  Athletes with higher cognitive dietary restraint scores had a greater percentage of energy from protein (18.4 ± 2.1% vs. 16.9 ± 2.0%) but no other differences in dietary intake or anthropometrics were detected.   Athletes with higher cognitive dietary restraint scores also scored higher on dieting (12.5 ± 2.7 vs. 9.9 ± 2.3, p<0.01) and satiation-guilty (13.6 ± 2.8 vs. 10.4 ± 2.5, p<0.01) subscales of the YEPQ.  Cognitive dietary restraint scores were not significantly correlated with dietary intakes or anthropometric measures.  Disinhibition scores showed a moderate positive correlation to the sum of skinfolds (r=0.513, p=0.003).  Abbreviations:  AI, Adequate Intake; AMDR, Acceptable Macronutrient Distribution Range; EAR, Estimated Average Requirement; EER, estimated energy requirement; kcal, kilocalorie; IOM, Institute of Medicine; mg, milligram;  IU, International Units; SCI, spinal cord injury; TFEQ, Three-Factor Eating Questionnaire; UL, Tolerable Upper Limit; YEPQ, Yale Eating Patterns Questionnaire     124 4.4 Strengths and limitations This cross-sectional study was designed to enhance the understanding of the dietary intakes of elite athletes with SCI and also to explore some of the factors which might influence those dietary choices.  A strength of this study is the athletes who agreed to participate.  Of the athletes approached in person to participate, 83% consented to participate and 78% completed all aspects of the study including a second food and activity diary which was completed once the athlete returned home.  The athletes who completed all aspects of the study provided detailed and accurate information as all questions were answered with no missing data. Many influencing factors were assessed including physical activity for the same days food intake was reported with detailed information on the time participating in an activity as well as a rating of perceived exertion.  Nutrition knowledge, eating attitudes and behaviours were also assessed as these are key factors which may influence dietary intake.  Nutrient adequacy was determined using the framework established by the Institute of Medicine (44) which allowed for the assessment of the prevalence of nutrient inadequacy in a group.   This study did have some limitations. Of the participants who completed the study, most were male (75%) and had a SCI resulting in tetraplegia (63%).  This sample was not representative of elite athletes competing in Canada as the number of athletes in wheelchair athletics and female athletes were under represented.  The coaches were generally interested in supporting this research study. However, the athletes’ training and competition schedules were such that it was not possible to identify a team event at which data collection could take place.  The distribution of athletes based on sport and gender made statistical comparisons between groups difficult and subtle differences based on sport may not have been detected. A second limitation with this study was the reliance on self reporting of dietary intake and activity and the risk of inaccurate reporting.  The inherent risks in collecting data in this manner were minimized using a number of methods including providing all participants with a detailed explanation of how to accurately record food intake, having a   125 researcher present for the first three days, observation of food intake and clarification of recorded items as needed. It is the author’s impression that the athletes were motivated to provide accurate data based on the level of detail in the food records for most diaries and the fact that 94% of participants completed the second set of food records.  While this study did have some limitations, the primary objective was to collect preliminary data on dietary intakes and assess overall adequacy which was achieved. 4.5 Future directions While the exploratory nature of this study meant that definitive answers were not obtained, several exciting directions for future research were identified.  Reported energy intakes were typically quite low given the athletes’ level of activity. It would be valuable to validate the estimated energy requirements of these athletes using the doubly labeled water method.  This would be a more accurate measure of energy expenditure with the potential to develop a predictive equation to estimate energy requirements specific to this population.  The development of a valid and reliable prediction equation would be an important tool to assist with individual nutrition assessment, meal planning and nutrition education of the athletes.   As these are high performance athletes, the goal is to train and use the most energy efficient metabolic systems for athletic performance.  Preliminary work has shown alterations in substrate use during exercise in those with SCI with a greater reliance on carbohydrate as the primary substrate (56).  Spendiff and Campbell studied the effect of carbohydrate ingestion during exercise and found improved performance with the use of an 8% glucose drink (57-59). However, impact of this additional carbohydrate load in the context of the athlete’s total diet has yet to be evaluated.  The consumption of 500 ml of a typical glucose drink (6% carbohydrate) at 10 training sessions per week could contribute an additional 1200 kcal per week.  This could lead to increased weight gains or displace more nutrient dense foods, thereby contributing to nutrient inadequacies.  If athletes with SCI are using metabolic systems differently than able-bodied athletes, perhaps macronutrient intakes in differing amounts would positively affect performance.    126 The prevalence of nutrient inadequacy was determined using the EAR and AI as the reference values.  These requirements were established with data typically from healthy able-bodied adults.  The requirement distribution for micronutrients is unknown for adults with SCI.  Perhaps the established AI and EAR reference values do not accurately reflect the true needs of adults with SCI and the proportion of individuals with inadequate intakes is over estimated. Perhaps with some nutrients, the requirements are increased with SCI and perhaps the percentage of individuals below the requirement amount is actually less than estimated with the current reference values.    The presence of a relatively strong tendency towards cognitive dietary restraint is intriguing and leads to many questions.  It would be interesting to assess the association between dietary restraint and cortisol excretion as cortisol is one parameter within the hypothalamic-pituitary-adrenal axis and may potentially impact bone density.  Appetite and dietary intake may be influenced by the reduction in total muscle mass or they may be affected by altered autonomic control, changes to hormonal regulation or intentional attempts to limit intake to maintain a desirable body weight to contribute to athletic success.  Regulation and control of appetite and dietary intake involve incredibly complex processes.  Injury to the spinal cord, high performance athletics, reduced energy intake and now cognitive restraint add many layers of complexity to this puzzle.   4.6 Applications  This research identified that while athletes with SCI are consuming a diet with macronutrient intakes that fall within the AMDR, the energy intakes are lower than anticipated given the frequency of physical training.  Within this energy reduced diet, a higher than anticipated prevalence of micronutrient inadequacies was observed. When the athletes were at training camp, the occurrence of suboptimal micronutrient intakes increased, particularly among the male athletes.  Coaches, administrators and Registered Dietitians working with these teams and athletes can apply this knowledge to work to improve the dietary intakes of athletes.       127 The provision of food services while athletes are away with their respective national team ranges dramatically from athletes fully responsible for the procurement of all food and meals to fully customized and catered meals and snacks.  The food choices are often dictated by factors outside the control of athletes such as access to grocery stores, access to refrigerators and microwaves, types of restaurants nearby, cost and training schedules which may not allow sufficient time for meals. The provision of meals and snacks that are lower in fat and energy but nutrient dense would likely improve the nutrient quality of food choices, the overall nutrient density and micronutrient intakes.  Alternately, if it is not possible to provide meals and snacks directly to the athletes, ongoing nutrition education may help athletes acquire the skills and knowledge to improve their dietary choices.  Athletes could benefit from practical skills and information related to making better meal choices when eating out, grocery store options, travel safe foods and portable meal options to name a few. Nutrition knowledge scores in this group of athletes were comparable to other Canadians, and suggest athletes had a reasonable understanding of general diet recommendations.  However, knowledge related to food sources of nutrients and strategies to choose everyday foods were two topics which were identified as potential areas to improve knowledge.  If athletes, in particular men, were more aware of foods that contain the micronutrients they may be lacking in their diet the nutrient density may improve.  The basic nutrition information should also be tailored to meet the demanding lifestyles of elite athletes with the added challenges of frequent travel, time constraints, food preparation abilities and potential financial constraints.  In this study, vitamin and mineral supplements were consumed by slightly less than half of all athletes.  A variety of supplements were consumed with multivitamins consumed most often.  Female athletes may benefit from a more tailored approach to micronutrient supplementation targeting vitamins and minerals that are marginal as the overall dietary adequacy was reasonable.  While for the male athletes, considerable improvements to the overall quality of their diets should be the initial focus and then after careful reassessment of dietary adequacy micronutrient supplementation may be warranted.     128 The assessment of eating behaviours and attitudes provides us with an additional consideration when evaluating the dietary intakes of athletes with SCI.  Many questions remain unanswered but it would be prudent for coaches, sport scientists and Registered Dietitians working with these athletes to explore eating behaviours and attitudes, especially in those athletes who appear to have a very limited dietary intake. This exploratory study of the nutrient intakes of elite Canadian athletes with SCI established baseline information on the intakes and dietary adequacy in this novel population.  Many aspects of dietary intake, activity, supplement use, details of the SCI and the attitudes associated with eating were explored.  The key findings of this study suggest that there is significant potential for athletes with a spinal cord injury to improve the micronutrient density of their diets.   129  4.7 References  1. Bauman WA, Spungen AM. Disorders of carbohydrate and lipid metabolism in veterans with paraplegia or quadriplegia: a model of premature aging. Metabolism 1994;43:749-56.  2. Bauman WA, Adkins RH, Spungen AM, Kemp BJ, Waters RL. The effect of residual neurological deficit on serum lipoproteins in individuals with chronic spinal cord injury. Spinal Cord 1998;36:13-7.  3. Bauman WA, Adkins RH, Spungen AM, Maloney P, Gambino R, Waters RL. Ethnicity effect on the serum lipid profile in persons with spinal cord injury. Arch Phys Med Rehabil 1998;79:176-80.  4. Duckworth WC, Solomon SS, Jallepalli P, Heckemeyer C, Finnern J, Powers A. Glucose intolerance due to insulin resistance in patients with spinal cord injuries. Diabetes 1980;29:906-10.  5. Bauman WA, Spungen AM. Carbohydrate and lipid metabolism in chronic spinal cord injury. J Spinal Cord Med 2001;24:266-77.  6. Bauman WA, Spungen AM. Metabolic changes in persons after spinal cord injury. Phys Med Rehabil Clin N Am 2000;11:109-40.  7. Bauman WA, Spungen AM, Zhong YG, Rothstein JL, Petry C, Gordon SK. Depressed serum high density lipoprotein cholesterol levels in veterans with spinal cord injury. Paraplegia 1992;30:697-703.  8. Jones LM, Legge M, Goulding A. Factor analysis of the metabolic syndrome in spinal cord-injured men. Metab Clin Exper 2004;53:1372-7.  9. El-Sayed MS, Younesian A. Lipid profiles are influenced by arm cranking exercise and training in individuals with spinal cord injury. Spinal Cord 2005;43:299-305.  10. Brenes G, Dearwater S, Shapera R, LaPorte RE, Collins E. High density lipoprotein cholesterol concentrations in physically active and sedentary spinal cord injured patients. Arch Phys Med Rehabil 1986;67:445-50.  11. de Groot S, Dallmeijer AJ, Post MW, Angenot EL, van der Woude LH. The longitudinal relationship between lipid profile and physical capacity in persons with a recent spinal cord injury. Spinal Cord 2008;46:344-51.  12. Manns PJ, McCubbin JA, Williams DP. Fitness, inflammation, and the metabolic syndrome in men with paraplegia. Arch Phys Med Rehabil 2005;86:1176-81.   130  13. Bostom AG, Toner MM, McArdle WD, Montelione T, Brown CD, Stein RA. Lipid and lipoprotein profiles relate to peak aerobic power in spinal cord injured men. Med Sci Sports Exerc 1991;23:409-14.  14. Bauman WA, Adkins RH, Spungen AM, Waters RL. The effect of residual neurological deficit on oral glucose tolerance in persons with chronic spinal cord injury. Spinal Cord 1999;37:765-71.  15. Schmid A, Knoebber J, Vogt S, et al. Lipid profiles of persons with paraplegia and tetraplegia: sex differences. J Spinal Cord Med 2008;31:285-9.  16. Spungen AM, Adkins RH, Stewart CA, et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol 2003;95:2398-407.  17. Jones LM, Legge M, Goulding A. Healthy body mass index values often underestimate body fat in men with spinal cord injury. Arch Phys Med Rehabil 2003;84:1068-71.  18. Chen Y, Henson S, Jackson AB, Richards JS. Obesity intervention in persons with spinal cord injury. Spinal Cord 2006;44:82-91.  19. Tomey KM, Chen DM, Wang X, Braunschweig CL. Dietary intake and nutritional status of urban community-dwelling men with paraplegia. Arch Phys Med Rehabil 2005;86:664-71.  20. Jeon JY, Steadward RD, Wheeler GD, Bell G, McCargar L, Harber V. Intact sympathetic nervous system is required for leptin effects on resting metabolic rate in people with spinal cord injury. J Clin Endocrinol Metab 2003;88:402-7.  21. Jones LM, Goulding A, Gerrard DF. DEXA: a practical and accurate tool to demonstrate total and regional bone loss, lean tissue loss and fat mass gain in paraplegia. Spinal Cord 1998;36:637-40.  22. Maggioni M, Bertoli S, Margonato V, Merati G, Veicsteinas A, Testolin G. Body composition assessment in spinal cord injury subjects. Acta Diabetol 2003;40:S183-6.  23. Spungen AM, Wang J, Pierson Jr RN, Bauman WA. Soft tissue body composition differences in monozygotic twins discordant for spinal cord injury. J Appl Physiol 2000;88:1310-5.  24. Goosey-Tolfrey VL. Physiological profiles of elite wheelchair basketball players in preparation for the 2000 Paralympic Games. Adapt Phys Activity Q 2005;22:57-66.  25. Ide M, Ogata H, Kobayashi M, Tajima F, Hatada K. Anthropometric features of wheelchair marathon race competitors with spinal cord injuries. Paraplegia 1994;32:174-9.   131  26. Bulbulian R, Johnson RE, Gruber JJ, Darabos B. Body composition in paraplegic male athletes. Med Sci Sports Exerc 1987;19:195-201.  27. Olle MM, Pivarnik JM, Klish WJ, Morrow JR Jr. Body composition of sedentary and physically active spinal cord injured individuals estimated from total body electrical conductivity. Arch Phys Med Rehabil 1993;74:706-10.  28. Mojtahedi MC, Valentine RJ, Arngrimsson SA, Wilund KR, Evans EM. The association between regional body composition and metabolic outcomes in athletes with spinal cord injury. Spinal Cord 2008;46:192-7.  29. Monroe MB, Tataranni PA, Pratley R, Manore MM, Skinner JS, Ravussin E. Lower daily energy expenditure as measured by a respiratory chamber in subjects with spinal cord injury compared with control subjects. Am J Clin Nutr 1998;68:1223-7.  30. Buchholz AC, McGillivray CF, Pencharz PB. Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition. Am J Clin Nutr 2003;77:371-8.  31. Buchholz AC, McGillivray CF, Pencharz PB. Physical activity levels are low in free-living adults with chronic paraplegia. Obes Res 2003;11:563-70.  32. Bauman WA, Spungen AM, Wang J, Pierson RN Jr. The relationship between energy expenditure and lean tissue in monozygotic twins discordant for spinal cord injury. J Rehabil Res Dev 2004;41:1-8.  33. Buchholz AC, McGillivray CF, Pencharz PB. The use of bioelectric impedance analysis to measure fluid compartments in subjects with chronic paraplegia. Arch Phys Med Rehabil 2003;84:854-61.  34. Buchholz AC, Pencharz PB. Energy expenditure in chronic spinal cord injury. Curr Opin Clin Nutr Metab Care 2004;7:635-9.  35. Buchholz AC, Bartok C, Schoeller DA. The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract 2004;19:433-46.  36. Buchholz AC, Rafii M, Pencharz PB. Is resting metabolic rate different between men and women? Br J Nutr 2001;86:641-6.  37. Spungen AM, Bauman WA, Wang J, Pierson RN Jr. The relationship between total body potassium and resting energy expenditure in individuals with paraplegia. Arch Phys Med Rehabil 1993;74:965-8.   132  38. Levine AM, Nash MS, Green BA, Shea JD, Aronica MJ. An examination of dietary intakes and nutritional status of chronic healthy spinal cord injured individuals. Paraplegia 1992;30:880-9.  39. Groah SL, Nash MS, Ljungberg IH, et al. Nutrient intake and body habitus after spinal cord injury: an analysis by sex and level of injury. J Spinal Cord Med 2009;32:25-33.  40. Walters JL, Buchholz AC, Martin Ginis KA. Evidence of dietary inadequacy in adults with chronic spinal cord injury. Spinal Cord 2009;47:318-22.  41. Potvin A, Nadon R, Royer D, Farrar D. The diet of the disabled athlete. Sci Sports 1996;11:152-6.  42. Lally DA, Wang JH, Goebert DA, Quigley RD, Hartung GH. Performance training and dietary characteristics of American and Japanese wheelchair marathoners. Med Sci Sports Exerc 1991;23:S101(Abstract).  43. Ribeiro SML, Da Silva RC, De Castro IA, Tirapegui J. Assessment of nutritional status of active handicapped individuals. Nutr Res 2005;25:239-49.  44. Institute of Medicine. Dietary Reference Intakes. Applications in Dietary Assessment. 1st ed. Washington DC: The National Academies Press, 2000.  45. Health Canada, Statistics Canada. Canadian Community Health Survey, Cycle 2.2, Nutrient Intakes from Food Provincial, Regional and National Summary Data Tables, Volume 1,2 and 3. 1st ed. Ottawa, ON: Her Majesty the Queen in Right of Canada, 2004.  46. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res 1985;29:71-83.  47. Kristeller JL, Rodin J. Yale Eating Patterns Questionnaire. Addict Behav 1989;14:631-42.  48. Institute of Medicine. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein and Amino Acids. 1st ed. Washington DC: The National Academies Press, 2005.  49. Institute of Medicine. Dietary Reference Intakes: Water, Potassium, Sodium, Chloride and Sulfate. 1st ed. Washington DC: The National Academies Press, 2005.  50. Institute of Medicine. Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium, and Zinc. 1st ed. Washington DC: The National Academies Press, 2001.   133  51. Institute of Medicine. Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids. 1st ed. Washington DC: The National Academies Press, 2000.  52. Institute of Medicine. Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin, and Choline. 1st ed. Washington DC: The National Academies Press, 1998.  53. Institute of Medicine. Dietary Reference Intakes for Calcium, Phosphorous, Magnesium, Vitamin D and Fluoride. 1st ed. Washington DC: The National Academies Press, 1997.  54. Parmenter K, Wardle J. Development of a general nutrition knowledge questionnaire for adults. Eur J Clin Nutr 1999;53:298-308.  55. Gottschall-Pass K, Reyno L, Maclellan D, Spidel M. What do adults in Prince Edward Island know about nutrition? Can J Diet Pract Res 2007;68:123-30.  56. Astorino TA, Harness ET. Substrate metabolism during exercise in the spinal cord injured. Eur J Appl Physiol 2009;106:187-93.  57. Spendiff O, Campbell IG. Influence of glucose ingestion prior to prolonged exercise on selected responses of wheelchair athletes. Adapt Phys Activity Q 2003;20:80-90.  58. Spendiff O, Campbell IG. Influence of timing of glucose drink ingestion on selected responses of wheelchair athletes. Adapt Phys Activity Q 2004;21:50-62.  59. Spendiff O, Campbell IG. Influence of pre-exercise glucose ingestion of two concentrations on paraplegic athletes. J Sports Sci 2005;23:21-30.     134  Appendix 1:   University of British Columbia Behavioural Research Board ethics approval certificates              135    136    137      138  Appendix 2:  Consent form        139     140      141       142     143       144   145  Appendix 3:  Letter of invitation        146        147      148   Appendix 4: Details of recruitment  149    150   151  Appendix 5: Participant questionnaire             152    153    154      155  Appendix 6:   Physical activity log with rate of perceived exertion scale   Note: Instruction sheet and sample of one day of activity log included in appendix.  Athletes provided with 3 days of activity logs to record activity.            156    157     158  Appendix 7:  Physical Activity Scale for Individuals with a Physical Disability             159    160    161    162     163  Appendix 8:   Food diary with instructions  Note: Instruction sheet with portion estimation guide and of one day of food diary is included in this appendix.  Athletes provided with 3 days of food diaries to record intake.                  164    165    166     167   Appendix 9:   Nutrition Knowledge Questionnaire                    168    169    170    171    172    173    174   Appendix 10:  Canadian Community Health Survey – summary tables              175    176    177   178  Appendix 11:  Three-Factor Eating Questionnaire       179    180    181   Appendix 12:  Yale Eating Patterns Questionnaire            182    183     184  Appendix 13:  Summary of results for participants      185    186   

Cite

Citation Scheme:

    

Usage Statistics

Country Views Downloads
United States 30 3
Canada 9 0
France 4 0
China 3 1
United Kingdom 2 0
Taiwan 1 1
Botswana 1 0
Russia 1 0
City Views Downloads
Ashburn 10 1
Unknown 9 1
Washington 4 0
London 4 0
Dublin 3 0
Dallas 2 1
Richmond 2 0
Vancouver 2 0
Atlanta 1 0
Phoenix 1 0
Gaborone 1 0
Putian 1 0
Montreal 1 0

{[{ mDataHeader[type] }]} {[{ month[type] }]} {[{ tData[type] }]}
Download Stats

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0071064/manifest

Comment

Related Items