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Health-related physical fitness and its relationship to objectively measured physical activity in children McGuire, Karen Ashlee 2007

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HEALTH-RELATED PHYSICAL FITNESS AND ITS RELATIONSHIP TO OBJECTIVELY MEASURED PHYSICAL ACTIVIY IN CHILDREN by KAREN ASHLEE MCGUIRE B.Sc. Kinesiology, University of Alberta A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Human Kinetics) THE UNIVERSITY OF BRITISH COLUMBIA August 2007 © Karen Ashlee McGuire, 2007 11 ABSTRACT During childhood, physical activity (PA) builds the foundation for a healthy body and is an important determinant of chronic disease risk. Recent reports indicate that children in Canada do not participate in sufficient amounts of PA for optimal health and well-being. Furthermore, certain ethnic groups may be at higher risk of developing chronic disease due to extremely low levels of PA and physical fitness. Literature delineating the relationship between PA and health-related physical fitness in children is inconsistent and has been inhibited by PA measurement tools. Objective measures of PA may overcome many of the limitations associated with other PA measurement tools. The purpose of this investigation was to objectively measure habitual PA, examine differences in PA and health-related physical fitness between Asian and Caucasian children, and determine the relationship between PA and health-related physical fitness. One-hundred seventy boys (n = 79) and girls (n = 91) in grades 4 and 5 from five schools in the Greater Vancouver Region participated. Measures of body composition (Body Mass Index and waist circumference), vascular health (blood pressure), resting heart rate, musculoskeletal fitness (grip strength, sit-and-reach, curl-ups and push-ups) cardiorespiratory fitness (Leger shuttle run) and habitual PA (via accelerometry) were obtained over a 1-week period. Results indicated that boys participated in 134 minutes and girls accumulate 114 minutes of moderate-to-vigorous physical activity (MVPA) per day. Only 30 minutes and 15 minutes per day were accumulated in bouts exceeding 5 minutes in duration in boys and girls respectively. During the school day the percentage of time spent in MVPA for recess, lunch hour and Physical Education class was 28%, 35% and 13% in boys and 18%, 27% and 16% in girls. Caucasian girls accumulated more MVPA per day, had significantly higher counts per minute and had higher aerobic fitness than Asian girls (p<0.05). There was no significant difference in musculoskeletal fitness. Caucasian boys had significantly higher counts per minute, higher aerobic fitness, and significantly higher musculoskeletal fitness scores (p<0.05) than Asian boys. Physical activity did not significantly predict cardiorespiratory or musculoskeletal fitness in either boys or girls. This investigation demonstrated that physical activity during the school day was low. Caucasian boys and girls obtained higher PA and fitness levels than Asian boys and girls. These findings suggest that all children may be at higher risk for health complications associated with low levels of PA, especially those of Asian ethnicity. TABLE OF CONTENTS ABSTRACT TABLE OF CONTENTS LIST OF TABLES \ LIST OF FIGURES vi ABBREVIATIONS i: OPERATIONAL DEFINITIONS REFERENCES FOR OPERATIONAL DEFINITIONS x ACKNOWLEDGEMENTS xi CHAPTER I Introduction CHAPTER II Literature Review 2.1 Physical Activity Patterns in Children 2.1.1 General Physical Activity Patterns 5 2.1.2 Guidelines for Physical Activity 7 2.1.3 Summary 10 2.2 Ethnicity, Physical Activity and Physical Fitness 12.2.1 Ethnic Differences in Physical Activity 1 2.2.2 Ethnic Differences in Health-Related Physical Fitness 12 2.2.3 Summary2.3 Physical Activity in Relation to Health-Related Physical Fitness 12 2.3.1 Physical Activity and Weight Status 13 2.3.2 Physical Activity and Vascular Status 6 2.3.3 Physical Activity and Musculoskeletal Fitness 12.3.4 Physical Activity and Cardiorespiratory Fitness 7 2.3.5 Summary. 18 2.4 Motor Performance in Childhood 19 2.4.1 Muscular Strength and Endurance 20 2.4.2 Cardiorespiratory Fitness 22.4.3 Flexibility 21 2.4.4 Summary2.5 Assessing Physical Activity2.5.1 Self-Report 2 2.5.2 Direct Observation 22.5.3 Heart Rate Monitoring 3 2.5.4 Pedometers2.5.5 Indirect Calorimetry2.5.6 Doubly-Labeled Water 24 2.5.7 Accelerometry2.5.8 Summary .25 CHAPTER III Methodology 26 3.1 Participants 23.1.1 General Participant Characteristics3.2 Cardiovascular Disease Risk Assessments 27 3.2.1 Anthropometryiv 3.2.2 Vascular Health 27 3.2.3 Musculoskeletal Fitness3.2.4 Cardiovascular Fitness 8 3.2.5 Physical Activity3.3 Procedure 9 3.3.1 Day 1: Weight Status, Vascular Health and Health-Related Physical Fitness Measures 30 3.3.2 Day 2: Activity Monitor Distribution 31 3.3.3 Day 7: Activity Monitor Pick-Up 2 3.3.4 Physical Activity Data Reduction3.3.5 Statistical Analysis 33 CHAPTER IV Results 6 4.1 General Subject Characteristics 34.2 Physical Activity Patterns 7 4.3 Ethnic Differences in Physical Activity and Health-Related Physical Fitness 38 4.4 Regression Analysis 39 4.5 Intraclass Correlation 40 CHAPTER V Discussion 43 5.1 Physical Activity Patterns in Children5.1.1 General Physical Activity Patterns5.1.2 Gender Differences in Physical Activity and Health-Related Physical Fitness 44 5.1.3 Physical Activity Guidelines 45 5.2 Ethnicity, Physical Activity and Physical Fitness 47 5.2.1 Ethnic Differences in Physical Activity5.2.2 Ethnic Differences in Health-Related Physical Fitness 49 5.3 Physical Activity and Physical Fitness 51 5.3.1 Physical Activity and Musculoskeletal Fitness 55.3.2 Physical Activity and Cardiorespiratory Fitness 2 5.3.3 Physical Activity and Physical Fitness 53 5.3.4 Physical Activity and Weight Status5.3.5 Physical Activity in Relation to Vascular Health 54 5.4 Future Directions 55 5.5 Limitations5.6 Conclusions 6 Footnotes 7 CHAPTER VI References 58 APPENDICES 70 Appendix AAppendix B 83 Appendix C 7 Appendix D 8 Appendix E 9 Appendix F 90 Appendix G 2 Appendix H 4 V Appendix 1 95 Appendix J 6 Appendix K 7 Appendix L .110 VI LIST OF TABLES Table 2.1. Physical activity guidelines for children 8 Table 2.2. Physical activity and weight status 15 Table 2.3. Physical activity and cardiorespiratory fitness 19 Table 3.1. Classification of physical activity intensity 2Table 4.1. Participant characteristics ^ 36 Table 4.2. Correlations 37 Table 4.3. Physical activity outcome variables 8 Table 4.4. Results of hierarchical multiple regression model for cardiorespiratory fitness in Asian and Caucasian girls 41 Table 4.5. Results of hierarchical multiple regression model for musculoskeletal fitness in Asian and Caucasian girlsTable 4.6. Results of hierarchical multiple regression model for cardiorespiratory fitness in Asian and Caucasian boys 42 Table 4.7. Results of hierarchical multiple regression model for musculoskeletal fitness in Asian and Caucasian boysTable D.1 Average 'on' and 'off times 89 Table E.1 Health-related physical fitness and physical activity data in girls 93 Table E.2 Health-related physical fitness and physical activity data in boys 94 Table E.3 Health-related physical fitness data in children without physical activity data95 Table K.1. T-tests performed between genders 98 Table K.2. ANOVA performed between girls with and without valid physical activity data 9Table K.3. ANOVA performed between boys with and without valid physical activity data 9 Table K.4. ANOVA performed between schools to determine intraclass correlation 99 Table K.5. ANCOVA used to examine differences between Caucasian and Asian boys 100 Table K.6. ANCOVA used to examine differences between Caucasian and Asian girls 100 Table K.7. PCA of musculoskeletal fitness components in all children 101 Table K.8. PCA factor loadings of musculoskeletal fitness components in all children 101 Table K.9. PCA of musculoskeletal fitness in girls 10Table K.10. PCA factor loadings of musculoskeletal fitness components in girls 101 Table K.11. PCA of musculoskeletal fitness components in boys 102 Table K.12. PCA factor loadings of musculoskeletal fitness components 102 Table K.13. PCA of fitness components in all children.. 10Table K.14. PCA factor loadings of fitness components in all children 102 Table K.15. PCA of health-related physical fitness components in all children 103 Table K.16. PCA factor loadings of health-related physical fitness components in all children 10Table K.17. Forward stepwise regression of cardiorespiratory fitness in girls 103 Table K.18. Forward stepwise regression of cardiorespiratory fitness in boys 104 Table K.19. Hierarchical regression of health-related physical fitness component in boys 10Table K.20. Hierarchical regression of health-related physical fitness component in girls. 105 Table K.21. Hierarchical regression of push-ups in boys 10Table K.22. Hierarchical regression of push-ups in girls 6 vii Table K.23. Hierarchical regression of curl-ups in boys 106 Table K.24. Hierarchical regression of curl-ups in girls 7 Table K.25. Hierarchical regression of sit-and-reach in boys 10Table K.26. Hierarchical regression of sit-and-reach in girls 8 Table K.27. Hierarchical regression of grip strength in boys 10Table K.28. Hierarchical regression of grip strength in girls 9 Table K.29. Hierarchical regression of systolic blood pressure in boys 10Table K.30. Hierarchical regression of systolic blood pressure in girls 110 viii LIST OF FIGURES Figure 3.1. Schematic of testing procedure 30 Figure 3.2 Day 1: Schematic outlining the testing procedure 31 Figure 3.3. Decision tree for data reduction 3 ix ABBREVIATIONS AS! BC Action Schools! BC BMI body mass index BP blood pressure CSEP Canadian Society for Exercise Physiology CVD cardiovascular disease DBP diastolic blood pressure EE energy expenditure HR heart rate KKD kilocalories per kilogram MVPA moderate-to-vigorous physical activity PA physical activity PE physical education SBP systolic blood pressure SD standard deviation TEE total energy expenditure U.K. United Kingdom WC waist circumference OPERATIONAL DEFINITIONS Accelerometer: A device worn on the body that provides an objective measurement of physical activity. It consists of piezoelectric technology that when accelerated, emits a voltage that is proportional to the acceleration of the body. The resulting information provides health-related information about physical activity such as frequency, intensity and duration (1). Action Schools! BC (AS! BC): A best-practice physical activity model designed to assist elementary schools in creating individualized action plans to promote healthy living. It provides resources and recommendations for the creation of individualized Action Plans that integrate physical activity and healthy eating into the school environment (for more information visit www.actionschoolsbc.ca). Active: Having an average daily energy expenditure (EE) between 3.0 - 5.9 kilocalories per kilogram (KKD) of body weight. Walking for one hour per day would result in an EE of approximately 3.0 KKD (2). Bouted Activity: Any activity that is accumulated in durations of 5 minutes or more and is related specifically to original data from this document. Canadian Society for Exercise Physiology (CSEP): A non-profit organization composed of professionals interested and involved in the scientific study of exercise physiology, exercise biochemistry, fitness and health (for more information visit www.csep.ca). Cardiovascular/Cardiorespiratorv Fitness: The ability to transport and use oxygen during prolonged, strenuous exercise or work. It reflects the combined efficiency of the lungs, heart, vascular system and exercising muscles in the transport and use of oxygen (3). Children: Children refer to boys and girls between the ages of 5 - 12. Compliance: The act of adhering to the physical activity guidelines set forth by a governing body. Epoch: The time period over which accelerometer counts are averaged (4). Ethnicity: A term which represents a shared history, sense of identity, geography and cultural similarities amoung individuals (5). Fractionalization: Method of segmenting the time spent at different intensities of physical activity. Health-Related Physical Fitness: The components of physical fitness that are related to health status, including cardiovascular fitness, musculoskeletal fitness, body composition and metabolism (3). Inactive: Values of average daily EE less than 1.5 KKD. Walking for no more than one quarter hour would result in an EE less than 1.5 KKD (2). Kilocalorie: the quantity of heat required to raise the temperature of 1 kg (1 L) of water 1° C (specifically from 14.5 to 15.5° C) (6). Moderately Active: Having an average daily EE between 1.5 - 2.9 KKD. Walking for one half hour per day would result in an EE of approximately 1.5 KKD (2). Musculoskeletal Fitness: The fitness of the musculoskeletal system, encompassing muscular strength, muscular endurance, muscular power, flexibility, back fitness and bone health (3). Physical Activity: All leisure and non-leisure body movements resulting in an increased energy output from the resting condition (3). Physical Fitness: A physiologic state of well-being that allows one to meet the demands of daily living or that provides the basis for sport performance, or both (3). Race: A term which implies biological traits indicative of meaningful genetic similarities in a group of individuals (5). Sporadic Activity: Any activity data that is accumulated in durations lasting less than 5 minutes and is related specifically to original data from this document. Tracking: The maintenance of relative rank or position within a group overtime (i.e., those participating in the least amount of PA as children will participate in the least amount of PA as adults). As a general guide, correlations <0.30 are considered low; those between 0.30 and 0.60 are moderate; and those >0.60 are high (7). REFERENCES FOR OPERATIONAL DEFINITIONS 1. Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. Journal of Physical Activity and Health 2005;3:366-383. 2. Cameron C, Craig C, Paolin S. Local opportunities for physical activity and sport: trends from 1999 - 2004. In: Physical Activity Benchmarks Program; 2004. 3. Warburton DER, Whitney Nicol C, Bredin SSD. Health benefits of physical activity: the evidence. CMAJ 2006; 174(6):801 -809. 4. Chen KY, Bassett JDR. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc 2005;37(11(Suppl)):S490-S500. 5. Tremblay MS, Perez CE, Ardern CI, Bryan SN, Katzmarzyk PT. Obesity, overweight and ethnicity: Statistics Canada; 2004 June. 6. McArdle W, Katch F, Katch V. Essentials of Exercise Physiology. 2 ed. Baltimore, Maryland: Lippincott Williams & Wilkins; 2000. 7. Malina RM. Physical activity and fitness: pathways from childhood to adulthood. American Journal of Human Biology 2001;13:162-172. Xlll ACKNOWLEDGEMENTS There are numerous individuals who have assisted me in various ways throughout the duration of my Masters and to whom I am very thankful. I appreciate the personal and academic support from each and every one. To the CPR and LEARN lab members, (Ben, Jess, Dom, Leslie, Lindsay, Shirley, Marc, Steph, and Mika) I owe a special thank-you for the considerable amount of moral support and friendship that was offered over the past few years. I would like to sincerely thank my committee members, Dr. Heather McKay and Dr. PJ Naylor for their advice and intellectual contributions to this investigation. I am very thankful to Action Schools! BC for allowing me to be a part of such a fantastic initiative and hope it continues to thrive and be successful. I also appreciate the support from the staff and students at the Bone Health Research Group throughout data collection and data analysis. I am especially grateful for the enthusiastic children who participated in the accelerometry portion of the AS! BC initiative. The project would not have been completed without their willing participation. I am deeply indebted to both Dale Esliger for his wisdom, time and patience, and his programmer, Eric Finlay who (with Dale's help) provided me with an amazing program to optimize my accelerometry data. I would like to thank Dylan, my family and friends for their continued support and encouragement. I must thank my supervisor and co-supervisor, Dr. Shannon Bredin and Dr. Darren Warburton, for welcoming me into their laboratories, providing me with endless opportunities, and offering support and guidance throughout the duration of the investigation. CHAPTER I 1 Introduction Ninety-one percent of Canadian children are not meeting Canada's Physical Activity Guidelines for Children and Youth (8). This is an indication that many of Canadian children are not physically active enough to maintain an optimal health status (9). Physical activity (PA) is required to maintain normal growth and development and health-related physical fitness (3) throughout childhood. Routine PA is an effective primary and secondary preventive strategy against many types of chronic disease (3, 10). During childhood, PA builds the foundation for a healthy body and can help to reduce the onset of risk factors associated with poor health (11-13). Physical activity is also an important determinant of coronary heart disease risk in youth (14) such that risk decreases in a graded fashion as PA level increases (15). Clearly, a physically active lifestyle is an important component of a child's regular routine. In Canada, evidence indicates that certain ethnic groups are accumulating extremely low levels of regular PA (16-18). Studies have reported that both Asian girls and boys participate in less PA than age-matched Caucasian peers (17, 18). In Britain, investigations have also reported that Asian children have lower cardiorespiratory fitness than their Anglo-Saxon counterparts (19). Low levels of PA and low cardiorespiratory fitness are both associated with cardiovascular disease risk (14) and suggest that children of Asian ethnicity may be a group more vulnerable to ill health. It is therefore important that PA and fitness be evaluated in children of different ethnic backgrounds to target preventative measures appropriately. Literature delineating the relationship of PA to components of health-related physical fitness is inconsistent in children. There is an increasing amount of evidence demonstrating that body composition is inversely related to habitual PA (20-22). No relationship has been reported between PA and vascular status (measured as blood pressure) (14, 23, 24). Weak-to-moderate relationships have been detected between PA and musculoskeletal fitness (25, 26) and recent studies have shown significant positive relationships between PA and aerobic fitness (27-29). In adults, relationships between PA and components of health-related physical fitness have been well-established whereby PA is associated with more favourable outcomes (3, 30). 2 To date, limitations associated with PA measurement tools have inhibited the analysis of PA and health in children (15). The tempo of children's activity is one of rapid change and is typically unstructured and sporadic in nature (31). These factors make capturing activity difficult in this population. As well, children have less developed cognitive skills than adults and are less able to effectively use traditional methods of PA measurement such as self-report questionnaires (32). Therefore, PA assessments for children must be improved to advance research in childhood PA (32) and its connection to health benefits. Objective measures of PA are able to overcome many of the limitations associated with various other PA measurement tools. To accurately assess children's activity patterns, the evaluative instrument must be sensitive enough to detect, code; or record sporadic and intermittent activity (32, 33). The accelerometer is a unique and useful piece of technology that is able to capture and store activity patterns in small time intervals over a period of days or weeks. It can also provide a measure of important health-related dimensions of PA (frequency, intensity, duration). These advantages make the accelerometer an ideal tool to use for the assessment of PA in children, especially when examining relationships to health-related physical fitness or cardiovascular health. Accordingly, the purposes of this investigation were to obtain an objective measure of habitual PA in Canadian children, examine the differences in PA and health-related physical fitness between Asian and Caucasian children, and determine the relationship between PA and health-related physical fitness. We hypothesized that: 1) The majority of children in the present investigation will not meet Canada's Physical Activity Guidelines for Children and Youth. This hypothesis is based on reports from Health Canada stating that less than 50% of children achieve optimal amounts of PA (9). 2) Caucasian children will have higher PA levels than Asian children living in the same geographical location and will achieve higher health-related physical fitness scores. This is based on current evidence which indicates that individuals of Asian ethnicity are less active than Caucasian individuals (16, 17, 34) and achieve lower scores on cardiorespiratory fitness tests (19). 3) Children that participate in more moderate-to-vigorous physical activity (MVPA) per day will have higher health-related physical fitness scores. This hypothesis is 3 based on previous literature reporting positive relationships between PA and various components of health-related physical fitness (25, 26, 29). The present investigation was conducted in collaboration with Action Schools! BC (AS! BC). This initiative is a best practices PA model designed to assist elementary schools in creating individualized school action plans to promote healthy living. The vision of AS! BC is to integrate PA into elementary schools to achieve long-term, measurable and sustainable health benefits. This is a comprehensive study that was being conducted in order to assess the health status of children in British Columbia and to determine whether the AS! BC model is an effective means to positively change school environments, health-related behaviours and the health of children when delivered across geographically diverse regions and cultures over a three year period. The participants of the PA (by accelerometry) component of the AS! BC initiative were a sub-sample of 1459 students and included a multi-ethnic population of 579 children in grades 4 to 5 from schools (n = 9) in the Greater Vancouver Region. Measurements were collected throughout the school year from students enrolled in schools participating in the AS! BC initiative. All participants were a part of the full evaluation component of the AS! BC initiative which, in addition to the measures being taken for the proposed investigation, included questionnaires about family history, nutritional intake and knowledge, PA participation, and psycho-social health. One hundred seventy children (79 boys; 91 girls) from five schools were retained for this investigation. Measurements of body composition (body mass index and waist circumference), vascular health (resting blood pressure), resting heart rate, musculoskeletal fitness (grip strength, curl-ups, pushups, and sit-and-reach), cardiovascular fitness (Leger shuttle run), and physical activity (accelerometry) were obtained over a 1-week period. Following the Introduction, a Review of the Literature will be presented in Chapter 2, which is relevant to the specific hypotheses of the present investigation. A detailed Methodology will be presented in Chapters 3, followed by the Results and Discussion in Chapters 4 and 5, respectively. Finally, nine appendices are included; A) ethics forms for the investigation, B) Health History Questionnaire, C) musculoskeletal fitness test protocols, D) average 'on' and 'off times used to classify valid days of accelerometry wear, E) information sheet for parents and/or guardians of participants, F) Activity Log, G) summary of outcome variables in girls and boys, H) summary of outcome variables in children without valid accelerometry data, I) abstract entitled 'Capturing physical activity tempo in elementary-school-aged children, J) abstract entitled 'Physical activity and antecedents of cardiovascular disease risk in children,' K) statistical analyses, and L) raw data. Upon completion of this thesis, this data will be submitted for publication in Medicine & Science in Sport & Exercise as three separate manuscripts. 5 CHAPTER II Literature Review Throughout childhood PA is required to maintain normal growth and development, health-related physical fitness (3), and to establish lifestyle patterns that will reduce the risk factors for health complications later in life (13). Physical activity is also an important determinant of coronary heart disease risk in youth (14) such that risk decreases in a graded fashion as PA level increases (15). A physically active lifestyle is an important component of children's regular routine and plays a critical role in children's health. In the following review, children's general activity patterns, physical activity guidelines for children, ethnic differences in PA, and PA in relation to health-related fitness are discussed. 2.1 Physical Activity Patterns in Children Children display unique PA patterns in which activity is highly transitory and is rarely sustained for periods greater than 10 minutes in duration (35). There are specific times of the day and days of the week in which activity is more likely to be observed in children (35, 36). Age and gender are two factors consistently recognized in the literature as affecting PA levels in children (37, 38). Although there are numerous guidelines currently available in which to assess PA in children; very few of the recommendations are reflective of children's actual behaviour. As such, the purpose of this section is to examine in further detail the general physical activity patterns of children, as well as physical activity guidelines commonly used to assess PA in children. 2.1.1 General Physical Activity Patterns The tempo of children's PA is one of rapid change. Activities at all levels of intensity are highly transitory and have a mean duration of 6 seconds as determined by direct observation in children ages 6-10 years of age (31). Children's PA consists of short bursts of intense activities that are interspersed by brief intervals of low or moderate intensity activity (31). Hoos et al. (35) estimated that children (ages 8-11 years) spend approximately 19% of their total active time on high intensity activities, such as playing or running outside, and over half of their waking time in low intensity activities, such as playing computer games, even though they change activities and the level of intensity at 6 frequent intervals (31, 35). More recently, Baquet et al. (39) observed that children only spent approximately 10% of their time in activities of moderate intensity or greater and a mere 2.4% of that time was spent in vigorous activity (39). Moderate-to-vigorous PA has been most commonly observed during school break times and seems to be less prominent during the after-school hours(40) and during physical education (PE) lessons (40-42). Sleap and Warburton (40) have shown that only 31 % of children performed a sustained 5 minute bout of MVPA and even fewer participated in a bout sustained for 10 minutes during PE lessons (40), a time when it is assumed that children are achieving substantial amounts of quality exercise. The amount of time allocated to PE in British Columbia for grades 4 and 5 students is on average, 40 minutes three times per week. It is estimated that during each PE session, children is only likely to be aerobically active for 6% of the allotted 30-40 minutes of time (42). The recommendations from the British Columbia Ministry of Education are to spend approximately one half of PE time (15-20 minutes) practicing activities that encourage active living and enhance health (43). This evidence suggests that these guidelines are not being met. Despite participating in greater amounts of MVPA during recess than during PE, more than 50% of recess time is spent in activities of light or sedentary intensity (44). Although children participate in more activity during the school day, the total amount of PA during that time period is still unacceptably low. Also somewhat alarming is the finding that PA outside of school hours (40, 45) and on weekends was low (36, 46). Since children spend significantly more time at home than at school this suggests that children will engage in sedentary activities more often unless the stimulus or opportunity to be active is a part of a regimented schedule (40). Existing data indicates that there may be substantial variation in PA levels in adults and children. Physical activity monitored over the course of a week may be precise but may not represent usual activity (47). In adults, anywhere from two to nine PA measures are recommended throughout the year (depending on the measurement tool) to obtain reliable measures of PA (47). Similarly, in children there is substantial instability in PA levels over a one year period as measured by accelerometry even with 6 or 7 days of wear per collection time (48). Kristensen et al. (36) found that there was a significant variation in activity depending on the day of the week and month of the year PA was measured in 8 to 10 year old children . The greatest amount of intra-individual variation occurred on the weekends. During the winter months (36, 48) or 7 during less pleasant months (49) less activity was accumulated. However, in an environment without marked seasonal variability, seasonality plays a limited role in PA levels and a single measure of PA is sufficient to estimate habitual activity levels (50). Significant gender differences are common in PA levels. Boys display higher levels of PA than girls of the same age (11, 38, 51) and boys spend significantly more time in vigorous PA than girls (11, 39, 52). Boys also participate in a greater number of longer bouts of higher intensity activity (39). Rowlands et al. (53) suggested that vigorous intensity PA may explain the differences in total activity between genders. Sleap and Warburton (40) found no difference in PA levels between boys and girls aged 5-11 years, however their findings showed that boys primarily played games such as soccer and girls were more likely to participate in games such as dancing, gymnastics and netball. There is a trend for the total amount of PA to decline in both genders as a function of age whereby girls show a more rapid decrease in levels than boys (37, 38, 46). A decrease in PA of 83% from age 9 or 10 to age 18 or 19 has been reported in girls (54). Part of this decline in PA can be attributed to increased demands being placed on children throughout the education process, specifically with time spent sitting at school and amount of homework given to be completed after school (45). Behavioural research has suggested that girls participate in less PA than boys due to less social support from family members and friends, lower self-efficacy and activity competence scores, and less enjoyment in sporting activities (55-57). 2.1.2 Guidelines for Physical Activity When assessing PA patterns in children it is important to clearly define what 'being active' is (32) and to understand how PA is accumulated in order to determine compliance with a specific PA guideline (1). Conclusions regarding PA status are heavily dependent on the PA guidelines selected to examine the population of interest (58). There are currently numerous variations of guidelines available and there is currently debate as to which set is optimal for children (52) (see Table 2.1 for a description of the guidelines). 8 Table 2.1. Description of physical activity guidelines for children. Source Guideline Description Health Canada & Canadian Society for Exercise Physiology 60 minutes of moderate activity and 30 minutes of vigorous activity each day. Accumulate activity in bouts of 5-10 minutes. Decrease sedentary activity by 90 each day. Examples of moderate activity are brisk walking and bike riding. Examples of vigorous activity are running or playing soccer. Healthy People 2010, goal 22.6 30 minutes of moderate activity on 5 or more days of the week. Accumulate in bouts of at least 1 minute. Activity of an intensity of 3 or more METs. Healthy People 2010, goal 22.7 20 or more minutes of vigorous activity that promotes the development of cardiorespiratory fitness on at least 3 days of the week Activity of an intensity of 6 or more METs. United States 30-60 minutes of accumulated moderate intensity activity per day. Moderate intensity activity on most or all days of the week. International Consensus Conference on Physical Activity Guidelines for Adolescents 20 minutes of moderate-to-vigorous activity at least 3 times per week in addition to minimal amount of activity (i.e. 30 minutes of moderate activity) Daily activity as part of lifestyle activities. American College of Sports Medicine Opinion Statement 20-30 minutes of vigorous activity per day. Recreational and fun aspects of activity should be emphasized. United Kingdom Expert Consensus Group 60 minutes of activity that is at least moderate intensity per day. Activity of an intensity of 3 or more METs on at least 5 days per week. The American College of Sports Medicine (ACSM) developed the first formal guidelines for adolescents and children based on adult guidelines. This organization recommended that children achieve 20 - 30 minutes of vigorous exercise each day (59). It is assumed, however, that the PA requirements for optimal health in children are different than those needed by adults (32) because children are less physically developed and typically do not engage in the same types of activity patterns (35). For example, adults are more likely to participate in structured activities such as a 30 minute jog whereas children are more apt to engage in unplanned games throughout the day (40). Investigations by Sleap and Warburton (40) indicate that only 8-14% of 5 - 11 year-old children regularly participate in aerobic exercise bouts that exceed 10 minutes in duration. In contrast, Armstrong and Welsman (46) determined (using heart rate monitoring) that 89% of boys and 69% of girls beginning school achieved one 10-minute bout of PA over the measurement period of 3 days. Almost all children achieved at least one 5-minute bout. Sustained 20 minute bouts of either moderate or vigorous activity were rare in all age groups (46). According to this evidence, a bout of activity prescribed to children should be no greater than 10 minutes in duration. Health Canada and the Canadian Society for Exercise Physiology (CSEP) created revised PA guidelines for children and youth in 2002 (9) that address some of the issues present in other guidelines. These guidelines state that children should incorporate an additional 60 minutes of moderate PA and 30 minutes of vigorous PA into their current daily routines. Consistent with current research, they recommend that the PA be accumulated in bouts of 5 - 10 minutes throughout the day. These guidelines also recommend that children decrease their current amount of time spent in sedentary activities by 90 minutes per day. This recommendation is under the assumption that children spend more than 90 minutes per day involved in sedentary activities. These guidelines are based on expert opinion and are in accordance with international guidelines which state that between 3.0 - 5.9 KKD need to be expended daily to be considered active (2). The wide variation in guidelines used to determine PA levels makes obtaining the prevalence of compliance in children extremely difficult. Furthermore, uncertainty in the accuracy of these measures is increased due to the numerous limitations associated with data acquisition of PA (58). For example, Pate et al. (58) examined the compliance of students (grades 1-12) to three different PA guidelines: Healthy People 2010, Objective 22.7; Healthy People 2010, Objective 22.6; and United Kingdom Expert Consensus Group (refer back to Table 2.1). The percentage of students meeting the specific criteria of the three guidelines were < 3%, 90% and 69.3%, respectively. Using accelerometry, Riddoch et al. (38) found that virtually all 9 year old children meet the United Kingdom Expert Consensus Group PA recommendations and Janz et al. (60) also found that most children were meeting the PA guidelines put out by the International Consensus Conference on Physical Activity for Adolescents. Conversely, Armstrong et al. (52) determined through heart-rate monitoring that many children have adopted the sedentary lifestyle that is associated with a decrease in cardiovascular health and are therefore not participating in acceptable amounts of PA. This clearly demonstrates the confusion in the current literature associated with determining PA levels in children. 2.1.3 Summary Children's activity is typically intermittent and sporadic in nature between the ages of 5-11 years of age (31). It consists of short bursts of intense activity interspersed with intervals of low activity (31) and is quite variable over a 1-year period (36, 48). Children are the most active during school break times (61) however recent evidence indicates that activity during this time is still quite low (44). Boys are consistently more active than girls (38, 51) and with age, there is a decrease in activity levels (37, 38). There are numerous guidelines available and there is currently debate as to which provide children with the optimal amount of PA for positive health status. It has been recommended that guidelines for children be tailored to their unique PA patterns (32). Determining the prevalence of PA in children is difficult due to the use of various guidelines. Accordingly, the first objective of this investigation was to measure habitual PA and determine the percentage of children meeting the Canadian Physical Activity Guidelines for Children and Youth. These guidelines are currently the only ones available that are tailored specifically to the intermittent activity patterns in children. We hypothesize that the majority of children will not meet these guidelines. 2.2 Ethnicity, Physical Activity and Physical Fitness Various reports indicate differences in unfavourable health behaviours between ethnic groups (16, 34). This illustrates the need to obtain measures of PA and fitness in a group of multicultural children. It is also important to identify vulnerable groups at higher risk of developing chronic health complications for both personal and public benefits. In Vancouver there is a large population of Asian individuals however, the literature describing the difference in PA levels and health-related physical fitness between Asian and Caucasian children is sparse. Available data demonstrate a less-favourable health profile in Asian children compared to the Caucasian children (19, 62). 11 This section will examine the existing literature and compare PA and fitness between Caucasian and Asian children. 2.2.1 Ethnic Differences in Physical Activity Data describing differences in PA between Caucasian and Asian ethnic groups of all ages residing within the same city are limited and reports are conflicting. Information obtained from the Canadian Community Health Survey (2000/01 and 2003) on adults indicated that the prevalence of PA was lowest in South and West Asian groups and highest in Caucasian groups. South Asian groups are include East Indian, Pakistani, and Sri Lankan, while West Asian groups are includef Afghan and Iranian (16). Differences in PA between ethnic groups in youth have been detected but are not consistent across studies. In a Vancouver-based investigation, MacKelvie and colleagues (17) reported that 9 - 10 year old Caucasian females participated in significantly more loaded PA (defined as activity with a higher impact than walking) and considerably more extracurricular sporting activities than age-matched Asian girls. Data from the National Longitudinal Study of Adolescent Health in the United States showed that a substantial number of Asian females (n = 922, grades 7 -12) accumulated less than two 20-minute sessions of MVPA per week (34). Conversely, McKay et al. (18) found no difference in PA between Asian and Caucasian girls living in the same geographical location. Information from the National Longitudinal Study of Adolescent Health indicated that in adolescents boys (n = 6701, grades 7 -12) the difference in PA between ethnic groups was present but small (63). MacKelvie et al. (64) reported no difference in PA between Asian and Caucasian boys. However, in an earlier study conducted in Vancouver, significant differences in PA were found. Asian boys were 15% less active than Caucasian peers (18). Despite discrepancies between studies, there is a trend for Caucasian children to participate in more PA than age-matched Asian children. Since the investigations in children utilized questionnaires requiring activity recall, it is possible that PA is not accurately measured. With a less subjective and more sensitive instrument to measure PA, a more clearly defined trend may emerge in both boys and girls. Furthermore, ethnic differences in PA have been reported to persist into adulthood (65) suggesting that in Canada, the low levels of PA will continue throughout the lifespan unless interventions are implemented. This highlights the importance of further examining the ethnic differences in PA. 2.2.2 Ethnic Differences in Health-Related Physical Fitness Limited research has been conducted on the components of health-related physical fitness (musculoskeletal and cardiorespiratory fitness) and how they differ between Asian and Caucasian children living in the same geographical location. In the United Kingdom, lower levels of physical fitness in Asian as compared to Anglo-Saxon children have been observed (66). Children of Indian (South Asian) background achieved lower scores than children of other ethnicities in the cardiorespiratory, test (power output against load at 85% of the maximum heart rate) utilized in the study (19). In previous investigations from our study group, it was documented that Caucasian children completed more laps in the 20 m Leger shuttle run test, indicating higher aerobic fitness, than Asian children (67). No differences in weight status or vascular health between ethnicities were reported, however, the latter study demonstrated a less favourable cardiovascular health profile, as measured by heart rate variability, in the Asian children (67). The trends were the same for boys and girls in both investigations. 2.2.3 Summary There is a trend for Caucasian children to participate in greater amounts of PA than age-and-sex-matched Asian peers. In Britain and Canada, Asian children are less aerobically fit than Caucasian children. However, for the most part, no studies have looked at the difference in musculoskeletal fitness between Caucasian and Asian children. In a multicultural city such as Vancouver, these observations warrant further investigation. Accordingly, the second objective of this study was to examine ethnic differences in PA levels and health-related physical fitness. We hypothesize that Caucasian children will have higher PA levels and will achieve higher health-related physical fitness scores than Asian children. 2.3 Physical Activity in Relation to Health-Related Physical Fitness Physical activity and health-related physical fitness are independent indicators of health status. In children, PA and components of health-related fitness have only a weak to moderate relationship (7). This is attributed to the difficulty in obtaining 13 measurements in children, the variable nature of children's PA, normal growth and development, and the effect of social, cultural, and environmental factors. There is keen interest in establishing this relationship because research suggests that if PA and fitness are established in childhood, the active children will become active adults and benefit from positive health outcomes (25). The following section reviews the current literature describing the relationships between PA and components of health-related physical fitness. 2.3.1 Physical Activity and Weight Status Body mass index (BMI) and waist circumference (WC) are both well-established predictors of CVD risk factors amoung children. Body mass index is thought to be a good indicator of overall adiposity whereas WC is an indicator of visceral adipose tissue (68, 69). Evidence in adults indicates that WC can predict health risk beyond that predicted by BMI alone (69). In adults, risk for CVD increases in a graded fashion with a move from one BMI category to the next BMI category. Within each BMI category, those with a high WC have a less favourable cardiovascular profile than those with a normal WC (70). Even though preliminary research in children has demonstrated patterns similar to those of adults, the added variance above that predicted by BMI alone or WC alone was minimal and of no clinical significance (69). In this investigation, BMI and WC were first used to predict risk factors for coronary artery disease (blood lipids, glucose, and insulin levels) in children (n = 2597, ages 5-18 years). In the second analysis children were stratified according to weight status and risk factors were compared for groups with low and high WC values (69). Weight status also remains stable over time. Indicators of obesity and adipose tissue distribution (BMI, WC, Sum of 5 Skinfolds) remained relatively constant across a seven-year time span in the Canadian population (14). In a four year study in Texas the strongest predictor of BMI at the end of the study was BMI at the beginning of the study (71). This data implies that the risk factors associated with adiposity will also track from childhood into adulthood and therefore, will predict adult CVD outcomes (72). Thus, it is imperative to target children with preventative strategies and intervention initiatives to reduce the incidence of adult obesity and cardiovascular complications (73). With the use of more objective PA measures, there is an increasing amount of evidence demonstrating that body composition, assessed either by BMI or percentage 14 fat, is inversely correlated with habitual PA. Children in the top fertile of PA have statistically significantly lower BMI scores and lower percentage body fat than children in the lowest tertile of PA (20, 22, 27, 74) (see Table 2.2). In an international comparison of overweight and PA in children, it was observed that the likelihood of being overweight was significantly lower in a dose-response relationship with higher PA levels as measured by self-report (74). Similar evidence was discovered in a cross-sectional investigation between objectively measured PA and obesity measured as fat mass (assessed by dual x-ray absorptiometry) and BMI (75). There is a trend for children who spend the most time engaged in MVPA to have healthier body composition profiles than those children who spend the least amount of time participating in MVPA (22, 76). Dencker and colleagues (77) reported that it was time spent specifically in vigorous activity and not moderate activity that was linked to low obesity status. The relation between body composition and PA levels are generally not gender specific, with both boys and girls demonstrating the same trends. A few studies have detected gender differences in this relationship but it is not consistent between studies (22, 27, 78). Girls typically carry more fat than boys and are also typically less active (27). 15 Table 2.2. Description of investigations examining the relationship of PA and weight status. PA=physical activity, HR=heart rate, BMI=body mass index Author (Year) Subjects Measurement Tools Results Raitakari et al. (1997) N = 2358 Boys = 1114, Girls = 1244 Age: 9-24 years Height, weight, subscapular skinfold, PA questionnaire. Higher PA levels were associated with lower BMI in males and skinfolds in males and females. Rowlands et al. (1999) N = 34 Boys= 17, Girls = 17 Ages: 8.3-10.8 years Height, weight, skinfolds, Tritrac RT3 accelerometer, pedometer (worn 6 days), HR telemetry (worn 1 day). PA measures from the Tritrac and pedometer had significant inverse correlations with fatness. Ekelund et al. (2001) N = 82 Boys = 42, Girls = 40 Age: 14.8 years Height, weight, skinfolds, HR monitoring (3 days) converted to total energy expenditure. No significant relationships between PA variables and body fat. Abbott & Davies (2004) N = 47 Boys = 23, Girls = 24 Age: 5-10.5 years Height, weight, '°0 dilution space (body fat), doubly -labelled water (10 day urine collection), Tritrac R3D (worn for 4 days). PA was significantly inversely correlated with percentage body fat and BMI. Janssen et al. (2005) N =137593 No gender differenentiation Age: 10-16 years Self-report questionnaire to obtain height, weight, PA. Significant inverse relationship between PA and BMI classification in 29/34 countries. Dencker et al. (2006) N = 248 Boys= 126, Girls = 101 Age: 8.6-11.0 years Height, weight, dual energy x-ray absorptiometry, Actigraph accelerometer (worn 4 days). Children with higher percentage body fat were significantly less active. Only vigorous PA was linked to obesity status. 16 2.3.2 Physical Activity and Vascular Status Epidemiological studies have demonstrated that physically inactive adults have higher blood pressure (BP) than their physically active counterparts and have an increased risk of developing hypertension (30). Since hypertension is a primary risk factor for CVD, the diagnosis, treatment, and prevention of high BP is important (79). Wareham et al. (30) suggests that low habitual energy expenditure (PA) is closely related to increasing BP and that it would only take a 30 minute walk most days of the week to achieve a significant drop in systolic blood pressure (SBP) values in the adult population. Over a seven-year span in the Canadian population the best predictor of BP at the end of the seven years was the baseline BP measure (79). In females, PA levels were also a significant predictor of follow-up BP (79). Interventions aimed at increasing PA levels (and consequently decreasing the prevalence of CVD risk in children) have the potential to result in long-term health benefits. In the Cardiovascular Risk in Young Finns Study, there were no differences in systolic or diastolic blood pressure between active and inactive children (14) which would suggest that PA has little effect on vascular health in children or that the detrimental effects of an inactive lifestyle on the vascular system are not advanced enough to be detected by measures of BP in children. Other investigations have also found that daily PA in youth was not related to either SBP or diastolic blood pressure (DBP) (23, 24) or the associations found were weak (80). Some studies have noted a lower BP in the more active children. When body fat was accounted for in these studies, the relationship disappeared (12). Studies examining changes in BP, both SBP and DBP, measures over time are equivocal with some detecting a secular increase and others reporting a decrease in children (81). A difference has been detected between genders with boys having a slightly higher SBP than girls of the same age (82). 2.3.3 Physical Activity and Musculoskeletal Fitness High levels of musculoskeletal fitness in adults are associated with positive health status (83) and are also related to independence, and functional performance in elderly individuals (3, 83). In Japanese men poor muscular fitness was associated with an increased risk of mortality (84) and in work by Katzmarzyk and Craig (85), an increased risk of all-cause mortality was found in men and women in the lower quartile for sit-up performance. Handgrip, another indicator of musculoskeletal fitness, is also a 17 significant predictor of mortality in adult men (86). Although current evidence states that musculoskeletal fitness is protective against CVD risks and complications, more research is required to clarify the distinct relationships between the various measures of musculoskeletal fitness and the health-related benefits they provide. Recent data suggests that increasing musculoskeletal fitness may help to prevent unhealthy weight gain in the Canadian population (87). The relationship between musculoskeletal fitness and PA is inconsistent in children and has not been researched extensively. A few studies have demonstrated a significant but weak-to-moderate relationship between PA and various measures of musculoskeletal fitness (25, 26). Longitudinal studies of adolescents demonstrate a positive influence of habitual PA on upper body muscular endurance (88). In children, grades 4 to 6, it was found that tracking for sit-and-reach and pull-ups was high, and for sit-ups was moderate over a three year time period (89). In the Canadian population, there is moderate-to-high stability of sit-ups, grip strength, and sit-and-reach over a seven-year time frame (84). This study suggested that musculoskeletal fitness tracks better than PA levels and that the stability increases in adulthood. There are however, consistent decreases throughout the years in all measures of musculoskeletal fitness indicators beginning at approximately the mid-teens to early 20's (84). 2.3.4 Physical Activity and Cardiorespiratory Fitness Evidence supporting the importance of childhood physical fitness and PA as protective against health-related complications is becoming increasingly prevalent (60). Cardiorespiratory fitness and PA are thought to be important determinants of CVD risk in youth (14). Since both cardiorespiratory fitness and PA track at moderate levels across the lifespan (7) early measurement and prevention is imperative to increase PA and fitness in later years (60). It is already established that low cardiorespiratory fitness in adults is a strong predictor of CVD and all-cause mortality (3). Fortunately, even when CVD risk factors are present in men, high levels of cardiorespiratory fitness offer some degree of protection against premature mortality (7). In cross-sectional and longitudinal studies and in studies of large sample size (n £ 186) it has been demonstrated consistently that more active youth perform better in cardiovascular endurance tasks (7, 27, 90). Recent studies of varying sample sizes 18 have been more consistent in detecting a significant relationship between aerobic fitness and PA in children when objective measurement tools were utilized. This relationship becomes stronger when aerobic fitness is related to time spent in vigorous PA as opposed to all time spent in PA (29, 91), but this also is not consistent across studies (28). The relationship between aerobic fitness and PA levels was found to be similar between genders (27, 29, 60, 92) despite boys attaining higher aerobic fitness scores and PA levels than girls (see Table 2.3). 2.3.5 Summary In children, there is increasing evidence of a positive relationship between weight status and PA (21, 22), and cardiorespiratory fitness and PA (26, 29). To date, there is no evidence of a relationship between vascular health and PA (12) and the relationship between musculoskeletal fitness and PA is weak to moderate at best (25, 26). These trends are generally similar in both genders. Measurement of both PA and fitness components are difficult in children and may partially explain why stronger relationships have not been detected. As such, the third objective of this investigation is to determine the relationship between PA and health-related physical fitness using an objective PA measurement tool. We hypothesize that children that participate in more MVPA per day will have higher health-related physical fitness scores. 19 Table 2.3. Description of investigations examining the relationship between PA and cardiorespiratory fitness. EE=energy expenditure, TEE=total daily energy expenditure «nu=aC^£ eJlergy exPenditure- PWC150=physical working capacity at a heart rate of 150bpm, HR=heart rate. Author (Year) Subjects Measurement Tools Results Dencker et al. (2006) N = 477 Boys= 140, Girls = 108 Age: 8-11 years BMI, self-evaluated Tanner Stage, MTI model 7164 accelerometer (worn 4 days), indirect measurement V02peak on cycle ergometer. A weak but significant relationship between V02peakand mean daily PA. The correlation between V02peak and vigorous PA was stronger. Rowlands et al. (1999) N = 34 Boys = 17, Girls = 17 Age: 8.3-10.8 years Height, weight, skinfolds, Trirac Rt3 accelerometer, pedometer (worn 6 days), HR telemetry (worn 1 day), endurance time on Bruce Maximal Protocol Test. Output measures from Tritrac and pedometer were significantly and positively correlated with aerobic fitness. HR had a weaker correlation to fitness. Ekelund et al. (2001) N = 82 Boys = 42, Girls = 40 Age: 14.8 years Height, weight, skinfolds, HR monitoring (3 days) converted to TEE, indirect measures of V02peak on treatmill, maturity. V02peak was significantly and positively related to AEE. Pate et al. (1990) N = 1558 Boys = 776, Girls = 782 Grades: 3-4 2 questionnaires, 1 completed by teacher and 1 by parent (PA), skinfolds, 1.6km run/walk time test. PA and physical fitness were significantly and positively associated. 2.4 Motor Performance in Childhood Throughout middle childhood and adolescence, differences exist between genders and age groups in motor performance (93), which affects the ability of the children to participate in PA and perform the health-related fitness tasks. Thus, the relationship between habitual PA and fitness may be confounded by changes associated with normal growth and maturation (25). Cross-sectional data has indicated that more active boys have better levels of motor performance (94). Performance steadily improves in 20 boys until 17 or 18 years of age and in girls, performance plateaus at 14 years of age (95). Until age 14, girls' performance is on average within one standard deviation of boys' performance. Excess body fat negatively affects motor performance, especially when movement of the entire body is required. However, the bigger child is typically the stronger child (93). This section examines the development of the components of health-related physical fitness throughout childhood and adolescence. 2.4.1 Muscular Strength and Endurance Strength increases linearly with chronological age and follows a growth pattern that is similar to the growth spurt pattern that occurs in adolescence (96, 97). There are sex differences in the development of strength (95, 97), with boys demonstrating greater strength than girls (93) and having greater gains in performance (98). For example, grip strength in males increases by over 300% from ages 7 to 17 years (99) whereas grip strength in females increases by approximately 260% (100). This difference in strength is not substantial until after approximately 13 years of age (97). Sex differences may be due in part to the greater size and fat-free mass advantage of boys. In addition, boys tend to demonstrate greater strength per unit of muscle area than girls (93). Increases in muscular endurance are similar to those in strength and occur until around age 17 in boys and age 13-15 in girls as measured by the flexed arm hang (98) and sit-up test (101). Performance in boys exceeded that of girls in both tests (98). In the flexed arm hang boys improved by 143% from ages 5 to 10 whereas girls improved only 97% during the same time span (98). 2.4.2 Cardiorespiratory Fitness Maximal oxygen consumption, a measure of aerobic endurance, improves until the late teens (102), however when reported relative to body weight values remain stable in boys from 8 to 18 years at around 48 - 50 ml-kg"1 min"1 but decline in girls from 45 - 35 ml-kg"1-min"1 (102). Between the ages of 10 - 12 years, boys' values are approximately 12% higher than girls. More specific to performance tests of aerobic endurance, running speed increases linearly from 5 years until 17 years in boys and 11 or 12 years in girls with only a slight change thereafter (93). After 9 years of age males have better running scores than females. In tests of maximal oxygen consumption and running 21 speed, the performance difference between males and females becomes magnified during adolescence (93). 2.4.3 Flexibility In males, flexibility as measured by the sit-and-reach test, declines after age 8 until approximately age 12 after which it increases until age 18. In females, flexibility increases to age 14 years (93). The differential increase in flexibility in boys and girls at ages 13 and 11 respectively, parallels their respective growth spurts in trunk length (98). Overall, males experience a net loss in flexibility whereas females experience a slight increase to age 14 or 15 (98). Females are more flexible than males at all ages (93, 98). 2.4.4 Summary Motor performance may affect the ability of children to perform PA and components of the health-related physical fitness tests, thereby confounding the relationship between PA and fitness. Boys outperform girls on most tasks, excluding flexibility, and this difference becomes magnified throughout adolescence. Performance in girls typically plateaus around ages 12 to 14 whereas boys continue to improve until age 17 or 18. More research is needed to provide a clear and definitive relationship between PA levels in children and measures of health-related physical fitness. As well, research specifically aimed at examining the difference in PA patterns and health-related fitness variables within ethnic groups are in need because there is the potential for genetic susceptibility to cardiovascular risk factors in some populations. Motor performance affects the ability of children to participate in and perform various physical tasks and therefore is an important consideration when examining these relationships in children. 2.5 Assessing Physical Activity Objective measures of PA need to be obtained from children in order to correctly assess their current level of PA and its relationship to measures of health-related physical fitness. Assessing PA in children is more challenging than in adults due to the unique developmental and behavioural aspects of children. A study by Bailey et al. (31) found that while observing children 95% of the vigorous activities lasted less than 15s 22 and 95% of the rest periods were less than 15s. From this data the author deduced that short, intermittent bouts of vigorous activity with frequent rest periods of longer duration are typical of children. Similarly, Baquet et al. (39) determined that 80% of the MPA bouts and over 93% of the VPA bouts in children lasted less than 10s. Although there are many instruments available for PA measurement, few are able to capture children's activity accurately. The following section describes methods available for monitoring PA and reviews the strengths and limitations of each. 2.5.1 Self-Report Questionnaires and surveys have traditionally been used to measure PA levels and although these methods appear to be acceptable to use in adult studies, their accuracy with respect to children is highly questionable (103). It has been documented that children less than 12 years of age are not able to recall activities accurately or quantify the time-frame of activity (104). This is because children have less developed cognitive skills and therefore are less able to effectively use self-report questionnaires. Vigorous PA is generally overestimated using self-report methods due to a child's exaggerated perception of time and/or effort and the difficulty in correctly capturing sporadic bouts of activity (32, 105). However, PA of moderate intensity can be achieved through many daily activities which are not typically thought to contribute to PA, are non-planned, less memorable and quantifiable, and therefore more likely to be underestimated by self-report methods utilized with children (15, 38). 2.5.2 Direct Observation Direct observation of PA has distinct advantages over other methods of assessing PA with the most significant one being the high resolution at which PA is recorded. Trained observers have the ability to measure the duration, intensity, and frequency of specific activities in a variety of environments and are also able to capture sporadic or short bouts of activity (31). This method allows for comprehensive information to be gathered about the subject's PA patterns. The limitations of this technique are that it requires a substantial amount of time for adequate staff training and data collection (31) and is therefore costly (46). In addition, subject reactivity may be problematic due to the presence of a trained observer (46). The observation technique is not feasible in large investigations due to time constrictions. Also, studies utilizing direct observation as the 23 method of assessing PA are less than 12 hours in duration (21). This eliminates the possibility of looking at habitual PA patterns and temporal or seasonal variations. 2.5.3 Heart Rate Monitoring Heart rate (HR) monitoring has been accepted as a valid and reliable measure of PA but it is an indirect measure that indicates the relative stress placed on the cardiovascular system (106). It is based on the assumption of a linear relationship between HR and oxygen consumption. Heart rate data is strongly related to energy expenditure, can be utilized in large studies and can collect data in small time intervals over a relatively long time period (32, 51). This linear relationship between HR and oxygen consumption allows intensity of activity to be determined. Unfortunately this relationship is no longer linear at high intensities of activity and is therefore less accurate during vigorous activity. Another downfall of this method is that HR is sensitive to emotional stress, environmental stress, and body position. As well, since HR response tends to lag behind changes in physical movement, the rapid transitions between intensity of movement in children may be masked (51, 105). 2.5.4 Pedometers Pedometers provide an objective measure of total step counts over a given period but most do not have the ability to look at frequency or intensity of PA, time stamp the step counts (32) or store data for extended periods (107). Newer models are time-stamped, thus overcoming some of the previous limitations. They are not able to record counts during cycling or increases in energy expenditure due to increased load or movement up an incline (46). They are also known to underestimate vigorous intensity PA (53) and are relatively easy to tamper with. They are however, inexpensive, re usable and non-reactive tools suitable for use in large-scale investigations (108). 2.5.5 Indirect Calorimetry Under controlled laboratory conditions indirect calorimetry is used to determine energy expenditure associated with resting metabolic rate, the thermic effect of food, and the thermic effect of exercise (32). More recently, portable, lightweight metabolic systems have been introduced. They can be used under free-living conditions but this is still too cumbersome to be undertaken with children (46) and is not a feasible option in a 24 large scale study. A major limitation of this technique is its inability to examine the specifics of PA patterns (32). 2.5.6 Doubly-Labeled Water Doubly-labeled water is considered to be the gold standard measurement of energy expenditure or PA in free-living situations (46). Doubly-labeled water gives a direct measure of carbon dioxide production and disappearance rates of the isotopes in the urine, blood, or saliva (4). This yields estimates of energy expenditure, taking the thermic effect of food into consideration. It is based on the difference in rates of turnover of hydrogen and oxygen in body water (4). It is non-invasive and can measure activity over a period of 1 - 2 weeks (32) and has low reactivity (46) but is also expensive (32). An important consideration is that energy expenditure is a physiological consequence of PA and the two are distinct constructs so it cannot directly measure PA (46). Using this method it is impossible to determine specific PA patterns (32, 46). 2.5.7 Accelerometry To accurately assess children's activity patterns, the instrument must be sensitive enough to detect, code, or record sporadic and intermittent activity (32, 109). The accelerometer is a unique and useful piece of technology that is able to capture and store activity patterns in small time intervals over an extended period of time. This device is also small and unobtrusive, (110) permitting participant freedom of movement. The accelerometer is able to measure the intensity of body or body segment accelerations through some form of piezoelectric or piezoresistive acceleration sensor technology (1). The sensor consists of a piezoelectric element and a seismic mass. When the sensor undergoes acceleration the seismic mass causes the piezoelectric element to bend and a voltage signal that is proportional to the applied acceleration is emitted (4). Recorded accelerations are converted to a quantifiable digital signal referred to as a 'count' (46). Frequency-filtering techniques are incorporated into the units to exclude accelerations unlikely to be generated by human movement (1). The accelerometer is able to measure important health-related dimensions of PA such as frequency, duration, and intensity of movement, and provide a chronological recording of these components (103). A downfall of this device is that not all activity is reflected in acceleration and deceleration such as upper body movements, movement 25 up an incline, and cycling (32, 110). Despite these limitations the accelerometer has been found to be a valid tool to use when measuring PA in children (103, 111) because the most common activities participated in by children are locomotor in nature (such as soccer, brisk walking, and general play or chasing games) (40). 2.5.8 Summary Numerous tools are available to assess PA, each associated with specific limitations and strengths. The accelerometer appears to be an ideal measurement tool to use for assessment of children in field studies due to its ability to capture sporadic activity in short time intervals over extended time periods. A robust and accurate PA measurement tool, such as the accelerometer, is required to obtain a thorough understanding of the unique PA patterns observed between ethnic groups and to examine the relationship between PA and various components of health-related physical fitness in children. CHAPTER III Methodology 3.1 Participants Participants were a sub-sample of the 1459 involved in the AS! BC initiative and included a multi-cultural group of 579 boys (n = 284) and girls (n = 295) in grades four to five (ages 8 to 11) from schools (n = 9) in the Greater Vancouver Region. Of these children, 36 students were absent on the day the activity monitors were distributed, 11 technical errors occurred with the devices, seven children moved between the time of consent and data collection, and one child refused to wear the device. From this group, 106 of the children wore the monitor during a week in which a Professional Development Day occurred. Initial examination of the data suggested that the Professional Development Day altered the PA profile and therefore these children were removed from the analysis. One school (n = 44) wore the monitors over a different time period (Friday to Tuesday) due to technical difficulties. As a result the children had only one full weekday of wear and thus, were also removed from the analysis. Children (n = 12) with less than 3.5 days wore the monitor for an average of two hours less per day and were also removed. In the remaining group of children, only those that wore the monitor during the average 'on' and 'off' times were retained for analysis. One of these children was removed due to physiologically unlikely BP data (143/128), two participants had BP readings that exceeded 120/80, three children had no data for the health-related physical fitness measures, and 17 children were of ethnic groups other than Caucasian or Asian. One-hundred seventy participants (n = 79 boys and n = 91 girls) from five schools were retained for the final analysis. Written informed consent was obtained from the parents and/or local guardians of the children. The investigation was carried out according to the ethical guidelines set by the University Clinical Research Ethics Board for research involving human participants (see Appendix A for ethics forms). 3.1.1 General Participant Characteristics Birth-date, gender and ethnicity were provided by the parents and/or guardians on the Health History Questionnaire (see Appendix B). Children's ethnicity was determined, for example, as being North American and/or European if both parents or 27 all four grandparents were born in North America or Europe. If the ethnicity data provided on the questionnaire was not clear or was incomplete the child was asked to classify their parents and grandparents birthplace to determine ethnicity. If the information was still incomplete, any child that had, for example, both parents or all four grandparents born in Canada (and/or Europe) the child was considered 'North American/European'. For the purposes of this investigation, children were classified as: 1) Caucasian (European decent), 2) Asian (South, East and Southeast) or 3) other. Only children in the first two classifications were used in the analysis because the sample lacked sufficient participants from other ethnic groups. 3.2 Cardiovascular Disease Risk Assessments 3.2.1 Anthropometry Height (cm) was measured to the nearest 1 mm with a portable stadiometer. Weight (kg) was recorded to the nearest 0.1 kg on an electronic scale (SECA Germany). Shoes were removed for both of these measures. Two measurements of height and weight were averaged for analysis. BMI (kg/m2) was calculated from these measures. Classifications of our participant's weight to height status was defined by internationally established values (112). Waist circumference was taken midway between the iliac crest and the bottom of the ribcage with an anthropometric tape (113). This measure was taken over top of a light shirt. Two measurements were taken and averaged for analysis. 3.2.2 Vascular Health Participants had their BP taken using an automatic sphygmomanometer (left arm). Resting heart rate was obtained simultaneously. Systolic and diastolic BP (mmHg) and resting HR (bpm) measurements were used in these analyses. Children with resting BP above the 95th percentile (120/80) were excluded from further testing. 3.2.3 Musculoskeletal Fitness The musculoskeletal fitness component of this investigation was comprised of grip strength, push-ups, curl-ups and sit-and-reach. The assessment protocols are modified versions of those developed by CSEP (114). For a detailed description of testing 28 instructions refer to Appendix C. Children were asked to complete as many push-ups and curl-ups as possible. Maximum grip strength was determined by summing the maximum score from the greater of two trials of the right and left hand. Sit-and-reach scores were determined by the maximum distance reached over two trials. Maximum grip strength (kg) and sit-and-reach (cm) scores were used in these analyses. 3.2.4 Cardiovascular Fitness Cardiovascular fitness was measured via the shuttle run which has been found to be reliable in children (115). For the Leger shuttle run, the children were required to run back and forth between a 20 m distance and touch the 20 m line simultaneously with a sound signal that emitted from a prerecorded tape. The starting speed is 8.5 km/hr and is increased by 0.5 km/hr every minute. The test was completed when the child was not able to maintain the set pace. The total number of laps performed by each child was recorded and further used in the analysis. The shuttle run permitted as many as 15 - 20 children to run simultaneously. 3.2.5 Physical Activity Physical activity was measured objectively for 5 days using the GT1M Activity Monitor. It is designed to ascertain normal human movement without impeding activity (103) and has been shown to provide valid and reliable estimates of childhood PA (71). The GT1M is small and compact weighing 27 g and has dimensions of 3.8 x 3.7 x 1.8 cm. It is equipped with 1 Megabyte of non-volatile flash memory and a rechargeable 3.7 V Lithium Ion battery. It is designed to detect acceleration signals ranging in magnitude from 0.05 to 2.00 g with a frequency response of 0.25 - 2.50 Hz. This frequency is able to detect normal human motion and reject motion from other sources such as riding in a vehicle. Each sample is summed over a user-specified epoch (116). For this investigation the epoch was set at 15 seconds and provided both acceleration and step-counts. A short epoch was chosen in order to capture the short bouts of higher intensity activity performed by children. The activity monitor was attached to an elastic belt and worn at the waist above the iliac crest. Participants were asked to wear the monitors for 5 consecutive days (3 weekdays and 2 weekend days) for 12 consecutive hours each day (8 am-8 pm was the suggested time interval) as this is within the four to seven day recommended time frame for obtaining a reliable estimate of habitual PA (r = 0.80) (117). The children were instructed to remove the monitors at night and while swimming, bathing, or showering. The GT1M Activity Monitor measured the duration, frequency, and intensity of PA which was assessed throughout the weekday and weekend days. Specifically for this project, the outcome variables used were: 1) average counts per day, 2) average counts per minute, 3) average MVPA accumulated per day, 4) average MVPA accumulated per weekday, 5) average MVPA accumulated per weekend day, 6) average sporadic MVPA accumulated per day, 7) average minutes of MVPA accumulated in bouts of 5 minutes or more, and 8) MVPA accumulated throughout the school day. Age-specific cut points were obtained from Trost et al., (118) who performed a rigorous calibration study on children of similar age with indirect calorimetry as the criterion measure. Since these cut points were established using 1 minute epochs, the values were divided by 4 for use with the shorter epoch length utilized in this investigation (see Table 3.1 for cut-points and classification of PA). Table 3.1. Age-specific classification of physical activity intensity by METs and counts (per 15 seconds). Cut-points obtained from Trost et al. (118). Intensity METs Counts (8yrs) Counts (9 yrs) Counts (10 yrs) Counts (11 yrs) Sedentary <1.5 0-49 0-56 0-62 0-70 Light 1.5-2.99 50-200 57-226 63-254 71-283 Moderate 3-5.99 201-827 227-874 255-293 284-976 MVPA 3+ 201-32767 227-32767 255-32767 284-32767 Vigorous £6 828-32767 875-32767 924-32767 977-32767 3.3 Procedure Participants were evaluated over a one week period. A trained research team of approximately three to four AS! BC investigators conducted anthropometry, vascular health, musculoskeletal fitness and cardiovascular fitness measures. For each day of measurements, children were temporarily excused from their classrooms in groups of 10 to 15 at a time. Testing took place in the gymnasium of the school. Day 1 consisted of measurements of anthropometry, vascular health, musculoskeletal fitness, and cardiovascular fitness (see Figure 3.1). Day 2 involved the distribution of GT1M Activity 30 Monitors to all of the participating children in the school. Day 7 consisted of monitor pickup and gift distribution to the children who returned the monitors Figure 3.1. Schematic of testing procedure. Day One Measurement of health-related fitness variables Day Two Distribution of GT1M Activity Monitors Day Three GT1M Activity Monitor pick up and gift distribution 3.3.1 Day 1: Weight Status, Vascular Health and Health-Related Physical Fitness Measures The first day of assessments comprised of anthropometry, vascular health, musculoskeletal fitness and cardiorespiratory fitness measurements (see Figure 3.2). Measurements of weight and WC are potentially sensitive issues with some children. To help alleviate any emotional anxiety children may experience during these measurements, each child had his or her weight status taken individually to ensure privacy. Vascular health measurements were taken prior to physical testing. Musculoskeletal testing (i.e., grip strength, push-ups, curl-ups and sit-and-reach) was conducted next, followed by the shuttle run. It is important that the shuttle run be completed last because it requires maximal physical exertion and the fatigue the children experience after it is completed may negatively affect the musculoskeletal tests. These tests took approximately one hour. Figure 3.2 Day 1: Schematic outlining the testing procedure. 31 Vascular Health Weight Status Resting Blood Pressure Height i Weight Waist Circumference Musculoskeletal Fitness Cardiorespiratory Fitness Grip Strength 1 Leger Shuttle Run Sit-and-Reach Push-ups Partial Curl-up 3.3.2 Day 2: Activity Monitor Distribution Day Two always occurred on a Wednesday. On this day schools were entered in the morning and up to 75 students were fitted with monitors. Each classroom of students that participated in the study was given a detailed talk instructing them as to how and when the monitors needed to be worn. Then each child was individually fitted with a monitor and given an information package for their parent(s)/guardian(s). This took approximately 30 minutes per classroom. Parents/guardians were provided with an information letter and contacted by phone on the evening that their child was fitted with a monitor to provide an opportunity for clarifications to be made about the purpose and desired outcome of the study and for general questions (see Appendix E for information letter). The parents were also asked to complete a log which indicated the time the monitor was placed on the child in the morning and the time it was removed in the evening, the times (if any) that the monitor was removed during the day, as well as any unusual circumstances in which the child's regular routine was significantly affected (i.e., illness, weather) (see Appendix F for log). 32 3.3.3 Day 7: Activity Monitor Pick-Up Monitors were returned to researchers the following Monday morning at the school. In exchange for returning the monitor the students were given a small gift and had their name entered into a draw for a larger prize. 3.3.4 Physical Activity Data Reduction After each week of data collection, the data were immediately downloaded to a laboratory computer. Data were then scanned for spurious measures, malfunctioning units, and compliance with wear guidelines. Monitor on (time the child put the monitor on in the morning) and off (time the child removed the monitor before bed) times were determined using both the log sheets and a visual inspection of the file. In the case where the information on the log sheet did not match the data in the file or the log sheet was not returned, the objective information in the file was used. Only 13% of the participants (54.5% girls and 45.4% boys) with usable PA data did not return the log sheet. The participants were required to wear the monitor for at least 3 days of wear to be valid for analysis (see Figure 3.1) and initially it was decided that 8 hours would constitute a full day of wear (45). However, due to the large variability in the number of hours of wear (the range was 8-17 hrs) and hours of the day the monitor was worn, a day was considered valid if it fell within ± 2 standard deviations (SD) of the average 'on' and 'off time for that day. The average on and off times for each day were calculated to determine the new hours of acceptable wear (see Appendix D for average wear hours per day). If a child wore the monitor too long (i.e., the monitor was put on before the average on time or removed after the average off time) the extra wear time was excluded from the analysis so that all children were wearing the monitor during the same time frame If files met the criteria for analysis, the data were subjected to custom software designed to optimize and standardize the production of PA outcome variables. This software allows the user to specify cut-points, time periods of interest to be examined, and fractionalizations of PA for further statistical analyses. Figure 3.3. Decision tree for data reductiorj. 33 3.3.5 Statistical Analysis Means and standard deviations (SDs) were calculated for all outcome variables. All variables were tested for normal distribution (i.e., skewness or kurtosis) and were transformed when necessary. Bivariate correlations were used to determine the relationships between all variables. Analysis of Variance (ANOVA) was chosen to determine if there were any differences in weight status, vascular or health-related physical fitness between the children who had acceptable PA data and those that did not. T-tests were used to determine if gender differences existed between variables. Analysis of Covariance (ANCOVA) was used to investigate the association between ethnic groups (Caucasian and Asian) and average MVPA per day, counts per minute, and health-related physical fitness scores. The PA outcome variables were chosen to compare an index of the amount and intensity of activity. Previous literature has demonstrated that boys and girls are not a homogenous group (11, 27, 38, 52); therefore, ANCOVAs were performed separately for boys and girls. The covariates were chosen based on their known or observed relationship to PA in children. Factors that may affect the relationship of PA to health-related physical fitness and cardiovascular health in children are: 1) weight (an increase in weight has been related to decreased PA (22, 27, 74)), 2) height (52), and 3) age (as age increases PA decreases (37, 38, 52)). A musculoskeletal fitness composite score was created using Principal Component Analysis. Variables were transformed into z-scores before being subjected to principal component analysis to standardize units. The first principal component of the musculoskeletal fitness scores (sit-and-reach, curl-ups, push-ups, grip strength) was retained for further analysis. The first principal component explained 39% of the variance in the original variables. The correlations between the original variables and the first principal component are reported as factor loadings representing their percentage contribution to the overall score. The factor loadings were 0.380 (sit-and-reach), 0.762 (curl-ups), 0.678 (push-ups), and 0.612 (grip strength). Push-ups, curl-ups and grip strength contributed significantly to the overall score with sit-and-reach contributing very little. From the factor loadings, a total musculoskeletal fitness score is derived using the following formula (sit-and-reach * 0.380) + (curl-ups * 0.762) + (pushups * 0.678) + (grip strength * 0.612). This score is used to compare musculoskeletal fitness between the ethnic groups and to determine the relationship between PA and musculoskeletal fitness. Initially, analyses were carried out separately for boys and girls, however, since the factor loading was within 0.1 for all components, the components contributed similarly to the overall score and there was approximately only a 1% difference in explained variance between boys and girls, the groups were collapsed into one to create the composite score. Hierarchical regression was used to estimate the contribution of ethnicity and PA to health-related physical fitness components. Variables were entered in the following order: 1) age, height, weight (control variables), 2) ethnicity (independent variable), 3) counts per minute (independent variable), 4) MVPA per day (independent variable). The order of variable input into the regression analysis was determined through established relationships between age, weight, height (maturity) and the health-related physical fitness components. Ethnicity was entered as the second step in the model and the PA outcome variables was entered last to examine the unique relationship between PA and fitness without the influence of the previous variables. Intraclass correlation (ICC) was calculated to examine the magnitude of variation between schools. A 1-way ANOVA was run to obtain the sum of squares for between school differences and within school differences for total laps run in the Leger shuttle run, the musculoskeletal fitness score, average counts per minute and average MVPA per day. The calculation of ICC for each variable was as follows: = Sum of squares for between school differences (Sum of squares for between school + within school differences) Data were analyzed using SPSS statistical software, Windows Version 14.0. Significance was set at p< 0.05 for all statistical analyses. CHAPTER IV Results 36 4.1 General Subject Characteristics Descriptive variables for boys and girls that remained in the final analysis are summarized in Table 4.1. Of the 170 children used in the analyses, 17.6% were classified as overweight (56.7% boys and 66.7% Asian) and 3.5% were classified as obese (50% boys and 66.7% Asian) using age and sex-specific cut-off values (119). Table 4.1. Means and SDs of participant characteristics. Body Mass lndex=BMI, SBP=systolic blood pressure, DBP=diastolic blood pressure, MSK Score=musculoskeletal fitness score. N Boys N Girls Age (years) 79 10.0 ±0.6 91 10.0 ±0.6 Height (cm) 79 141.3 ±7.6 91 141.2 ±7.5 Weight (kg) 79 36.6 ± 8.0 91 35.3 ±7.7 BMI (kg/mz) 79 18.2 ±2.8 91 17.6 ±2.6 SBP (mmHg) 77 98.2 ± 7.0 91 96.9 ±9.1 DBP (mmHg) 77 63.1 ±7.1 91 62.0 ± 8.9 Heart Rate (bpm) 76 87.2 ± 10.9 89 87.7 ± 12.7 Total Laps 79 25.1 ± 14.8 91 21.3± 11.6 MSK Score 79 -.0434 ± 1.4677 91 -.0336 ± 1.8757 There were significant correlations between weight status, the musculoskeletal fitness score, total laps run (Leger shuttle run test) and PA outcome variables (see Table 4.2). There were no significant correlations between vascular health and PA or weight status and PA. Data were examined for normal distribution and tests to detect skewness or kurtosis were completed. Minutes per day of bouted activity, total laps and BMI were negatively skewed and therefore log transformed. The curl-up and push-up data demonstrated a one-sided distribution and underwent reciprocal transformation. 37 Table 4.2. Significant correlations between anthropometric, fitness and physical activity variables. (**) denotes p < 0.001, (*) denotes p < 0.05 MSK Score=musculoskeletal composite score, CPM=counts per minute, MVPA=moderate-to-vigorous physical activity Height Weight Age MSK Score Total Laps CPM MVPA Height 1 .752 .461** NS NS NS NS Weight .752** 1 .270** NS -.192* NS NS Age .461** .270** 1 NS NS -.177* -.306** MSK Score NS NS NS 1 .507** .171* .152* Total Laps NS -.192* NS .507** 1 .221** .189* CPM NS NS -.177* .171* .221** 1 .925** MVPA NS NS -.306** .152* .189* .925** 1 There were no differences observed in weight status, vascular or health-related physical fitness variables between the groups who met the inclusion criteria for PA data and those who did not (in both the girls and the boys). There were 52 boys and 47 girls who did not have valid PA data of which 38% and 57%, respectively, were Caucasian. There was no significant difference in weight status, vascular or health-related physical fitness between boys and girls. 4.2 Physical Activity Patterns On average, the monitors were worn for 13.5 ± 1.1 hours per day with boys accumulating 408499.7 ± 106831.0 and girls accumulating 343807.8 ± 106912.8 counts per day (see Table 4.3 for PA outcome variables in boys and girls). Boys obtained significantly more counts per minute on average than girls (p < 0.001), average MVPA per day (p < 0.001), average MVPA per weekend (p < 0.03) and weekday (p < 0.001), and accumulated more bouted minutes of MVPA than girls (p < 0.001). There was no significant difference in minutes of sporadic MVPA between the genders. In both genders, significantly more MVPA occurred on the weekdays than on weekend days (p < 0.001). One child (Asian male) met Canada's Physical Activity Guidelines for Children and Youth. 38 Table 4.3. Means and SDs of all PA outcome variables. Avg=average, MVPA=moderate-to-vigorous physical activity, PE=physical education. Boys (N = 79) Girls (N = 91) Avg Hours of Wear Per Day 13.5 ± 1.12 13.4 ±1.1 Avg Counts Per Day 408499.7 ± 106831.0 343807.8 ± 106912.8 Avg Counts Per Minute 503.6 ± 122.7 429.8 ± 140.7 Avg Minutes of MVPA Per Day 133.9 ±33.9 113.5 ±35.9 Avg Minutes of MVPA Per Weekday 149.0 ±37.4 124.6 ±40.4 Avg Minutes of MVPA Per Weekend Day 112.7 ±45.0 98.4 ± 43.9 Avg Minutes of Sporadic MVPA Per Day 104.7 ±24.0 98.0 ± 27.8 Avg Minutes of Bouted MVPA Per Day 29.2 ± 20.9 15.5 ± 12.5 Total Minutes of School Day MVPA 65.1 ±21.0 52.4 ± 17.5 Total Minutes of Recess MVPA 5.1 ±4.3 3.4 ±3.1 Total Minutes of Lunchtime MVPA 16.5 ±7.4 12.6 ±5.2 Total Minutes of PE MVPA 5.3 ±6.2 6.5 ±7.1 In boys, 26.7% of morning recess (5.1 ± 4.3 minutes) was spent in MVPA, 35.4% of lunch hour (16.5 ± 7.4 minutes), and only 13.1% of PE (5.3 ± 6.2 minutes). Girls accumulated MVPA for 18.3% of recess time (3.4 ± 3.1 minutes), 27.1% of lunch hour (12.6 ± 5.2 minutes) and 16.0% of PE (6.5 ± 7.1 minutes) in MVPA. Total minutes of MVPA during the school day accounted for 24.6% (65.1 ± 21.0 minutes) and 20.8% (52.4 ± 17.5 minutes) of the total school day in boys and girls, respectively. 4.3 Ethnic Differences in Physical Activity and Health-Related Physical Fitness 39 Caucasian girls ran significantly more laps than Asian girls in the Leger shuttle run (p < 0.01), had significantly higher counts per minute (p < 0.001), and average minutes of MVPA per day (p < 0.001). There was no significant ethnic difference in the musculoskeletal fitness score for girls. Caucasian boys ran significantly more laps than Asian boys in the Leger shuttle run (p < 0.01), had significantly higher counts per minute (p < 0.03) and achieved significantly higher scores on the musculoskeletal fitness score (p < 0.01). There were no significant ethnic differences in the average minutes of MVPA per day in boys. Initially, the difference in each individual component of musculoskeletal fitness was examined between ethnic groups. Due to the large variation in scores between both male and female ethnic groups, the homogeneity of variance tests were not met for push-ups and sit-and-reach in girls and curl-ups and grip strength in boys (see Appendix J). 4.4 Regression Analysis The variance in health-related physical fitness (total laps run and the musculoskeletal fitness score were examined separately) explained by PA (average counts per minute and MVPA per day) was examined by entering the following independent variables sequentially into a hierarchical regression: 1) age, height, weight, 2) ethnicity, 3) counts per minute and, 4) MVPA per day. Results are presented in Tables 4.4 - 4.5 (girls) and Tables 4.6 - 4.7 (boys). In girls, age, height, and weight accounted for 11.2% of the variance in total laps run in the Leger shuttle run (p < 0.01), with ethnicity contributing another 8.3% (p < 0.03). Neither counts per minute or MVPA per day contributed significantly to the model. None of the independent variables significantly predicted the musculoskeletal composite score in girls. In boys, age, height, and weight accounted for 19.7% of the variance in total laps run (p < 0.001) and ethnicity accounted for 7.8% more (p < 0.01). Neither counts per minute or MVPA per day significantly contributed to the model. Ethnicity was the only independent variable that significantly contributed to the prediction (8.4%) of the musculoskeletal composite score (p < 0.01). Two other composite scores were created to: 1) examine the relationship between the combination of musculoskeletal fitness components and total laps run to PA and 2) examine the relationship of a composite score of all health-related physical fitness 40 components and PA (see Appendix J). The correlation between both of these fitness scores and PA was lower than the individual components of health-related physical fitness and PA and the correlation between PA and the musculoskeletal composite score reported. The factor loadings of the individual components within the final score can explain this. For example, in the composite score that included all health-related physical fitness components, BMI and WC had the highest factor loadings. Body mass index and WC had very weak and non-significant correlations to PA. When these more heavily-weighted components were added to the rest of the scores, the components (such as total laps) that were more highly correlated to PA became diluted. Forward stepwise regression was also used to examine the relationship between PA and health-related physical fitness. The same independent variables that were entered into the hierarchical regression (height, weight, age, ethnicity, MVPA per day and counts per minute) were entered simultaneously into the stepwise equation. In girls, weight and ethnicity significantly predicted fitness and in boys, height, weight, and ethnicity predicted fitness (see Appendix J). 4.5 Intraclass Correlation Intraclass correlations were as follows: 0.27, 0.07, 0.07, and 0.07 for total laps run, musculoskeletal composite score, average counts per minute and average MVPA per day, respectively. 41 Table 4.4. Results of hierarchical multiple regression model for cardiorespiratory fitness (total laps run in the Leger Shuttle Run test) in Asian and Caucasian girls. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity. Fitness Variable in Model Standardized Beta Unstandardized Beta Adjusted R2 R2Change Total Laps Run Height (p < 0.02) .349 .025 0.112 0.141 Weight (p < 0.002) -.461 -.033 0.112 0.141 Age (p < 0.03) -.258 -.234 0.112 0.141 Ethnicity (p < 0.03) -.250 -.275 0.189 0.083 CPM (p < 0.28) .369 .001 0.179 0.000 MVPA (p < 0.28) -.358 -.005 0.181 0.011 Table 4.5. Results of hierarchical multiple regression model for musculoskeletal fitness in Asian and Caucasian boys. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity. Fitness Variable in Model Standardized Beta Unstandardized Beta Adjusted R2 R2 Change Musculoskeletal Score Height (p < 0.26) .189 .044 -0.008 0.025 Weight (p < 0.85) -.030 -.007 -0.008 0.025 Age (p < 0.99) .001 .003 -0.008 0.025 Ethnicity (p < 0.37) -.105 -.368 0.014 0.033 CPM (p < 0.96) -.016 .000 0.045 0.040 MVPA (p < 0.46) .259 .013 0.040 0.006 42 Table 4.6. Results of hierarchical multiple regression model for cardiorespiratory fitness (total laps run in the Leger Shuttle Run test) in Asian and Caucasian boys. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity. Fitness Variable in Model Standardized Beta Unstandardized Beta Adjusted R2 R2 Change Total Laps Run Height (p<0.01) .459 .036 0.164 0.197 Weight (p< 0.001) -.614 -.045 0.164 0.197 Age (p < 0.64) -.055 -.053 0.164 0.197 Ethnicity (p<0.01) -.274 -.322 0.235 0.078 CPM (p < 0.78) -.069 -.001 0.233 0.008 MVPA (p < 0.45) .187 .003 0.229 0.006 Table 4.7. Results of hierarchical multiple regression model for musculoskeletal fitness (composite score) in Asian and Caucasian boys. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity. Fitness Variable in Model Standardized Beta Unstandardized Beta Adjusted R2 R2 Change Musculoskeletal Score Height (p < 0.06) .362 .071 0.048 0.084 Weight (p<0.16) -.248 -.046 0.048 0.084 Age (p < 0.23) -.151 -.370 0.048 0.084 Ethnicity (p < 0.004) -.340 -1.008 0.123 0.084 CPM (p < 0.40) -.225 -.003 0.129 0.017 MVPA (p < 0.74) .089 .004 0.118 0.001 43 CHAPTER V Discussion 5.1 Physical Activity Patterns in Children 5.1.1 General Physical Activity Patterns The general trends in our data are consistent with published literature (40, 120). In boys, almost 80% of average MVPA per day is spent in sporadic activity and in girls almost 90%, illustrating the highly transitory nature of children's PA. Similar to our results, Sleap and Warburton (40) and Epstein and colleagues (51) reported that children accumulated approximately two hours per day of MVPA, however very little of this time was spent in sustained bouts of activity. Significantly more activity occurred throughout the weekdays as compared to the weekend days. In boys, approximately 150 minutes of MVPA per day were achieved on the weekday compared to 115 minutes on the weekend day. The average accumulated amount of MVPA per day on the weekdays was 125 minutes in girls compared to 100 minutes on the weekend. Recommendations from British Columbia and the United States mandate that 50% of PE time should be dedicated to active time (43, 121). Stratton and Mullen (122) extended these recommendations with the suggestion that recess time should also consist of 50% active time. We found that total minutes of MVPA during the school day accounted for 24.6% and 20.8% of the total school day in boys and girls, respectively. In boys, 26.7% of morning recess was spent in MVPA and in girls, 18.3%. During lunch hour (which includes both a lunch and recess time) boys spent 34.5% of the time in MVPA and girls spent 27.1%. During PE girls accrued MVPA for 13.1% of class and boys 16.0%. The boys are recording a similar number of minutes of MVPA during recess and PE despite PE being twice the length of recess. This indicates that boys participate in greater amounts of PA during free play times as opposed to structured class time dedicated to PA. Our data shows that children in Vancouver are not meeting the recommendations for activity participation during either PE or recess times. In the U.K., percentage time observed in MVPA at recess was 32.9% in boys and 25.3% in girls (44). These percentages are higher than our observations but still far from achieving the guidelines. These results become even more alarming considering that it 44 is during these school break times when MVPA has been most commonly seen in children (40). Schools in California report that 40% of a 30 minute PE class is dedicated to MVPA (123), and although this is still below recommended levels, it is much higher than that reported in British Columbia. We found that 13% (boys) and 16% (girls) of the allotted PE time was spent in MVPA whereas Parcel et al. (42) estimated that children were aerobically active for 6% of PE time. Using pedometers to assess PA, it was found that the steps accumulated during PE accounted for 8 and 11% of total steps per day in boys and girls, respectively (124). It is clear that PE class is not providing sufficient opportunity for PA and insufficient PA is occurring during recess. Recent research has shown that children who were least active during the school day were also the least active after school hours and on the weekend days (61). This information highlights the necessity of school-based interventions to supplement regular school day activity and to provide children with the skills to be more active throughout the whole day. 5.1.2 Gender Differences in Physical Activity and Health-Related Physical Fitness We found that boys participated in significantly more MVPA per day and on average, had higher counts per minute (indicating more time spent in PA of higher intensity) than girls. Gender differences in PA have been fairly well-established with boys displaying higher levels of PA than girls of the same age (11, 34, 36, 38, 51) and boys spending significantly more time in vigorous PA than girls (11, 39, 52). Evidence has also shown that boys participate in a greater number of longer bouts of higher intensity activity than girls (39). Specifically in this investigation, boys accumulated double the amount of bouted MVPA minutes than did girls. Rowlands et al. (53) suggested that vigorous intensity PA may explain the differences in activity that occurs between genders. In addition, our results also imply that time spent in bouted MVPA also accounts for the disparity between boys and girls since we found no significant difference between average minutes of sporadic MVPA per day. This information suggests that boys may be more likely than girls to participate in structured, planned PA at high intensities. Boys generally perform better in cardiorespiratory fitness tests (26, 27, 60, 125) and in musculoskeletal fitness tests (26, 98) than age-matched girls. Although our observations were not found to be significant, there was a trend for boys to complete 45 more laps in the shuttle run test. Our group previously reported no significant differences in cardiovascular fitness between boys and girls in a similar group of participants (67). The results of other studies may not be directly comparable to the present study due to differences in cardiorespiratory and musculoskeletal tests and participant characteristics. 5.1.3 Physical Activity Guidelines Our data from Vancouver is even more alarming than that reported in the 2007 Report Card on Physical Activity for Children & Youth. They found that only 9% of Canadian children met the guidelines while in our study, only one child met the guidelines of 90 minutes of MVPA per day. Despite boys participating in approximately 134 minutes and girls accumulating 114 minutes of MVPA per day, only 30 and 15 minutes of average MVPA per day in boys and girls, respectively was accumulated in activities lasting 5 or more minutes in duration. In other words, boys on average are only accumulating one third of the amount recommended and girls, one sixth. In a study of similarly-aged children, Andersen et al. (15) reported more positive data, indicating that the top three most active quintiles of children in the U.K. were achieving over 90 minutes of activity per day in durations of 5 to 10 minutes. Children in the lowest quintile of PA were still accumulating more activity in sustained periods than the children in the present study. Results are not directly comparable due to the difference in cut-points and epoch lengths used between studies. Andersen et al. (15) used a threshold of 2000 cpm from a 1-minute epoch to define MVPA whereas we used age-specific cut-points developed by Trost and colleagues (118). An investigation in Sweden demonstrated the effects on accumulated activity associated with using different epoch lengths. Epoch lengths of 5, 10, 20, 40 and 60 seconds were used and minutes spent in 10-minute bouts were compared. No 10-minute periods of continuous activity were recorded in the 5 and 10-second bouts however, almost 3 bouts were recorded using the 1-minute epoch (126). This difference is quite dramatic and suggests that with the short epoch that was chosen for this study, bouted activity is not as likely to be observed. Furthermore, the magnitude of the epoch effect is amplified in the most highly active children (126) since the 1-minute epoch is known to dilute the children's vigorous intensity (38). 46 In addition to varying epoch lengths and cut-points, there is no consistency in the interpretation of results from studies determining children's compliance with PA guidelines depending on whether assessments are based on intermittent, accumulated or sustained durations of activity. Methods of PA data acquisition and reduction are highly variable (and the latter is rarely reported) and can have significant effects on the data. In many studies, actual minutes of PA and criteria for qualifying data as acceptable are not published. Finally, guidelines used to assess children's activity level differ between studies (refer back to Table 2.1). Combined, these limitations in make comparisons between studies reporting PA difficult. Since sporadic activity is typical in children of this age (31) and constitutes such a large percentage of total MVPA time, it is extremely valuable information. Short epochs were used for the collection of the PA data to ensure that the intermittent and short bouts of activity were captured (see Appendix I). In addition, we utilized accelerometry to measure PA so the data obtained is objective and is both date and time-stamped. We are confident that our data provides an accurate representation of physical activity patterns in the population examined. To determine activity intensity, we used age-specific cut-points developed by a group of influential researchers in the field of accelerometry (118). An investigation comparing the utility of three commonly used cut-points in children documented that these specific cut-points are able to accurately categorize activity into the correct intensity categories (127). In addition, our group analyzed the PA data with custom designed software which allowed us to examine the PA patterns in great detail. For example, we were able to determine the number of MVPA minutes that occurred during each segment of the school day and we were able to fractionalize the MVPA minutes to determine the amount that occurred in bouts. Furthermore, decisions regarding inclusion and exclusion criteria were based on the average day of a child in our study. The average 'on' and 'off times were calculated for each day and only those children who wore the monitor within those hours were retained in the analysis. Although this decreased our sample size, it also eliminated bias that might occur with variable wear hours. The 'on' and 'off times that were used to determine valid days are normal wear hours for the children and were not arbitrarily chosen. Thus, the data presented is representing what occurs in an average day for the children. Finally, we examined PA compliance using the only guidelines available that 47 consider the specific characteristics of PA in children. These guidelines are based on expert opinion. The available data suggesting that children are not meeting the physical activity guidelines support our first hypothesis. Additional reports showing that children are not meeting activity recommendations within the school day provides clear evidence that children are not acquiring enough PA. 5.2 Ethnicity, Physical Activity and Physical Fitness 5.2.1 Ethnic Differences in Physical Activity We found that Caucasian girls accumulate greater amounts of higher intensity activity (as indicated by the average counts per minute) than Asian girls and participated in significantly more MVPA per day. In a study of 10 year-old Asian and Caucasian girls in Vancouver, Caucasian girls engaged in almost double the number of extracurricular or sport activities and double the amount of loaded activity (defined as activity that has a greater impact than walking) than the Asian girls. The difference in general activity, assessed by the Physical Activity Questionnaire for Children, was only slight (17). Investigations based in the United States also showed Asians participating in less vigorous intensity exercise than Anglo-Saxon individuals (128) and achieving lower scores on PA questionnaires (62). Gordon-Larsen et al. (34) discovered that almost 50% of the Asian girls in their study engaged in 2 or less bouts of MVPA per week. Of the 3 ethnic groups examined, the non-Hispanic white females were most likely to participate in MVPA and the Asian girls were least likely to participate in MVPA. Only one study from the Vancouver region did not exhibit a significant difference in PA between the Asian and Caucasian girls (18). Results from this investigation indicate that Asian boys participate in less high intensity activity than Caucasian boys. The data indicating that there is no difference in total amounts of accumulated MVPA per day between Asian and Caucasian boys is consistent with data presented by MacKelvie et al. (64) who found, using the Physical Activity Questionnaire for Children, that there was no significant difference in PA between 10 year - old Asian and Caucasian boys in Vancouver. In addition, information from the National Longitudinal Study of Adolescent Health in the United States reported that in adolescent boys, there was minimal difference in PA (as measured by self-48 report) between Asian boys and boys of other ethnicities (34). Evidence from McKay et al. (18) Vancouver-based study demonstrated that Caucasian boys were significantly more active than their Asian peers and approximately five times as many Caucasian boys participated in extracurricular sport activities (18). Reasons for these disparities could stem from the methods utilized to obtain estimates of PA. In our investigation, PA data was measured objectively using accelerometry whereas self-report questionnaires were used in the other studies. The benefits of accelerometry are demonstrated in our results. In boys we found a difference in the intensity of activity between ethnic groups in the absence of a difference in MVPA per day. The accelerometer detects acceleration of the body and provides a direct measure of PA intensity in counts. It is therefore better able to capture high intensity activity in comparison to measures of self-report which were used in the other investigations. This eliminates some of the limitations and bias associated with self-report and may be the reason a difference was detected. It is possible that the activity levels of the Asian children in our study were underestimated based on MacKelvie et al. (17) who reported that in general, Asian girls living in Vancouver more commonly participate in swimming lessons than other sports. The accelerometer is not waterproof and therefore is not able to be worn for water-based activities. Despite this, the difference in PA was very highly significant (p < 0.001) indicating that very high amounts of swimming would need to be accumulated to account for the difference in PA. Other factors that may contribute to why Asian children are less active than Caucasian children have been considered within the literature. Psychology research suggests that Asian children (both male and female) perceive a lack of control over engaging in PA and therefore are less likely to participate in PA (129). This lack of control may be due to factors such as being required to allot a greater amount of time to academic endeavors (129). For example, Asian children are twice as likely as Caucasian children to spend time in academic lessons (i.e., music lessons, mathematics, etc.) after school as opposed to sporting activities (18). Specifically in girls, culture may consider strenuous activity to be unacceptable and activity is therefore not encouraged (62). Social factors, such as parental support or level of activity of the parent(s) may also influence PA behaviours in children (130). Positive associations between parental encouragement of PA and children's immediate activity have 49 consistently been shown (131, 132). Statistics Canada reported that Asian men and women in Canada also accumulate lower levels of PA than Caucasian men and women (16), thus there is the potential for this to influence the level of MVPA in Asian children. 5.2.2 Ethnic Differences in Health-Related Physical Fitness Limited research has been conducted on the components of health-related physical fitness and how those differ between Asian and Caucasian children living in the same geographical location. In the U.K., lower levels of physical fitness in Asian as compared to Anglo-Saxon children have been reported (66). More specifically, in Britain, one third of children of Indian (South Asian) background were unable to complete the cardiorespiratory test (power output against load at 85% of the maximum heart rate) utilized in the study. Moreover, those that did complete the test achieved lower scores than children of other ethnicities (19). This trend was the same for both genders. Although we found that Asian girls completed significantly fewer laps in the Leger shuttle run test than Caucasian girls we found no difference in the musculoskeletal fitness score between ethnicities in girls. In boys, there was a significant difference with the Caucasian boys completing more laps and achieving higher musculoskeletal fitness scores than Asian boys. Our data supports previous research from our group demonstrating lower cardiorespiratory fitness in Asian boys and girls (67). Significant correlations between PA, (average counts per minute), and total laps run in the Leger shuttle run (r = 0.230, p < 0.001) suggest that those children who participate in higher intensity activity are more likely to obtain a higher score in the Leger shuttle run. Activity at high intensity is thought to contribute more to healthy levels of cardiorespiratory fitness than lower levels of activity (133). In both the boys and girls, those of Caucasian ethnicity had significantly greater counts per minute than children of Asian ethnicity. In prepubertal children, differences in fitness levels can be partially explained by differences in PA (92). Consequently, this may be one underlying reason why Caucasian children performed better in the shuttle run test. Other possible reasons for the difference in cardiorespiratory fitness between ethnic groups are the same as some of those outlined previously as being reasons for the difference in PA. Asian children are more likely to spend free time engaging in activities with a more academic focus than Caucasian children (18) and cultural norms may discourage participation in vigorous activities which promote physical fitness (62). Furthermore, if 50 Caucasian children are participating in more organized sport (18), they may be enhancing cardiorespiratory fitness to a greater extent than if they were just being regularly active on their own. The musculoskeletal fitness score was significantly higher in Caucasian boys than Asian boys. The same reasons that Caucasian boys completed more laps in the shuttle run than Asian boys could explain why Caucasian boys also have a higher musculoskeletal fitness score. The Caucasian girls, despite participating in greater amounts of high intensity activity and total PA, and completing more laps in the Leger shuttle run, did not have a significantly greater musculoskeletal fitness score. Due to a difference in variance between the Caucasian and Asian children (in both boys and girls) some of the individual components of musculoskeletal fitness did not meet the assumption of homogeneity of variance, (see Appendix J for statistics). Therefore, the aggregate score to compare musculoskeletal fitness between ethnic groups was used. The technique that was used weights each of the components based on their relationship to each other and this weighting is used to calculate the final value of the score (refer back to section 3.3.5). It is possible that the use of this composite scoring system masked the individual differences of the various musculoskeletal fitness components between ethnicities in girls. For example, in the formula used, pushups and grip strength were similarly weighted. If the Caucasian girls completed more pushups on average than the Asian girls but there was no difference in average grip strength scores, the difference in pushup scores between ethnicities would be diluted. The results revealing differences in PA and fitness between Asian and Caucasian children support our second hypothesis. We are confident that the significant differences observed between Asian and Caucasian children were not due to body size or age since differences remained after weight, height, and age were entered as covariates into the ANCOVA. The covariates were chosen based on previous literature or observed relationships in the present dataset. There is a clearly defined relationship between age and PA whereby PA decreases with increasing age (37, 38, 46) and this relationship was detected in the correlations between age and PA variables. Most were significant and negative (r = -0.160 - (-0.306)). Height was used as an index of maturity. As individuals mature, PA decreases (52). Finally, a substantial amount of literature has demonstrated that weight is significantly and inversely related to PA (22, 51 74). Although we found no relationship between weight status and PA, it was still entered based on findings in the literature. To our knowledge, this is the first study in Canada using accelerometry to compare PA levels between Asian and Caucasian children. It is also the first to examine differences in musculoskeletal fitness between ethnic groups living in the same geographic region. It therefore provides extremely important information regarding fitness and activity in these two diverse groups and will contribute significantly to the limited literature regarding ethnic differences in PA and health-related physical fitness and PA patterning in children. 5.3 Physical Activity and Physical Fitness 5.3.1 Physical Activity and Musculoskeletal Fitness Investigations relating PA to indicators of musculoskeletal fitness in children report equivocal results (88). Sallis et al. (26) reported that the combination of a physical activity index (multiple measures of PA were taken and combined to create one variable) and gender accounted for 9.1%, 6.7%, and 5.1% of the variance in pull-ups, sit-ups, sit-and-reach scores, respectively in fourth-grade children. In comparison, PA did not significantly predict our aggregate score of musculoskeletal fitness components in either girls or boys. There was little difference in the correlations between the musculoskeletal fitness score and the individual components of musculoskeletal fitness that were significantly related to PA (push-ups was the largest and only musculoskeletal fitness component significantly related to PA) so it is unlikely that the use of the individual components would result in substantial differences to the findings reported, (see Appendix J for statistics). Correlations between the musculoskeletal fitness score and PA in the present study were weak, but significantly related (r = 0.152 - 0.172, p < 0.05). These relationships are similar to those reported by Katzmarzyk et al. (25) between MVPA (measured by self-report) and individual components of musculoskeletal fitness. One study has also reported no significant relationship between PA and musculoskeletal fitness (assessed as maximal muscle strength of the legs) in children (125). The fitness variables differ between studies and may represent slightly different domains of musculoskeletal fitness, as did the participants, making direct comparisons 52 difficult and possibly affecting the results. Although measurement of PA was more comprehensive in the investigation by Sallis and colleagues (26) and provided a more holistic picture of the children's PA patterns, the correlations between individual PA components were quite low. The participants in the present investigation were not an ethnically homogenous group and we had less power which could also contribute to the difference. 5.3.2 Physical Activity and Cardiorespiratory Fitness In contrast to our findings that PA (counts per minute and MVPA per day) did not contribute to the prediction of cardiorespiratory fitness in children, previous research has demonstrated significant findings whereby PA accounts for 10 - 21% of the variance in cardiorespiratory fitness. Dencker and colleagues (29) concluded that the combination of mean daily PA and vigorous PA explained 10% (1% and 9%, respectively) of the variance in V02Peak in a group of 8 to 11 year-old children. Sallis et al. (26) reported that the combination of a physical activity index (multiple measures of PA were taken and combined to create one variable) and gender accounted for 11% of the variance in the mile run test. Pate et al. (134) also used numerous measures of PA combined with age and gender as the independent variables to account for 21% of the variance in the 1.6-km run/walk test. There are a variety of reasons to explain this difference. None of the studies mentioned entered weight into the regression model. Weight had a weak but significant correlation to total laps run (r = 0.192, p < 0.05) in the present investigation, significantly contributed to our model (p < 0.001 in boys and p < 0.002 in girls) and has an established relationship to fitness outcomes in children (125). This information suggests that it may confound the relationship between PA and physical fitness and should be accounted for when explaining the variance between these factors. Participant characteristics also differed; participants of the present study were of two different ethnicities whereas participants of the other studies were a more homogenous group. In the latter study a small percentage of the participants were of different ethnicity than the majority group however, this was not accounted for in the statistical analyses. Ethnicity contributed significantly in the present investigation, accounting for 7.8 - 8.3% of the variance in cardiorespiratory fitness independent of height, weight and age. Different tests of cardiorespiratory fitness were used in the studies however the correlations 53 between MVPA per day and the aerobic fitness tests used were similar between studies. This suggests that the inconsistent results should not be attributed to the measurement tools. 5.3.3 Physical Activity and Physical Fitness The correlation between the musculoskeletal fitness score and total laps run (r = 0.507, p < 0.001) is suggestive that the musculoskeletal fitness score may be a better predictor of cardiorespiratory fitness (or vise versa) than measures of PA. (See Appendix J, for statistics). Physical activity is a behaviour (7) and is therefore prone to variation (36) and influence from numerous environmental, cultural (7) and social situations. Levels of PA may be transient and hence, more difficult to relate to a variable or condition at one time period. Physical fitness is a physiological state (7) or attribute (125), making it a more stable entity and less prone to variation and influence from external factors. Physical fitness is thought to develop as a result of prolonged PA. Although fitness does change, measurement at one time point may be more likely to accurately represent the physiological state of an individual. This is indicated by tracking studies which show that physical fitness has higher inter-age correlations and more stability over time than indications of PA (7). Moreover, a recent tracking study in children reported that when sources of variation are controlled, results showed moderate stability of PA (120), thereby demonstrating its more variable nature. Factors such as biological and behavioural domains of change associated with normal growth and maturation, environmental or cultural settings in which subjects were raised (25), genetics (135), diet, and motivational (26) or psychosocial aspects have also been suggested as contributors to the variance in physical fitness. 5.3.4 Physical Activity and Weight Status There is an increasing amount of evidence in the literature demonstrating that body composition, assessed either by BMI or percentage fat, is inversely correlated with habitual PA (20, 22, 27, 74). We found no significant relationship between any PA variables and indices of weight status (BMI and WC) and no trends were evident in the data. Prevalence of overweight in this dataset was 6% lower in girls and 12% lower in boys than previously published reports during the pilot phase of this project (136). Thus, there may not have been sufficient numbers of overweight children to detect a 54 difference in PA, especially since the PA data was quite variable. Alternatively, the overweight children in the sample may be as active as normal weight children. In support of our data, one of the major findings in a study by Grund et al. (125) was that there are no differences in PA between normal weight, overweight and obese children. The measurement tools used should not have contributed to the difference in results between our study and those that did report a significant difference. Indices of weight status used in this investigation (BMI) have been found to be inversely related to PA levels in other cohorts using a variety of tools for PA assessment. It is possible that in this cohort of children, dietary factors may contribute more significantly to the development of overweight in children, or overweight children may be engaging in more PA as a form of weight control (26). Alternatively (or additionally), in overweight or obese individuals, the accelerometer is further from the body's centre of gravity than on a normal weight individual. This results in the accelerometer experiencing greater acceleration for any given movement. It is possible that in the obese children, the accelerometer was recording excessive movement which may contribute to the higher PA. 5.3.5 Physical Activity in Relation to Vascular Health In normal weight children, PA may have little effect on vascular health as indicated by previous investigations that reported no significant findings (14, 20). When body weight is accounted for, studies that did report lower BP values in more active children found that the relationship disappeared (12). Children with lower PA had greater body fat which was responsible for the high BP values (12). Alternatively, the detrimental effects of an inactive lifestyle on the vascular system may not be advanced enough to be detected by a BP machine. We recently revealed that PA accounts for 6% of the variance in small artery compliance as measured by arterial tonometry (see Appendix G) suggesting that the use of a device more sensitive to blood vessel change may be better able to establish this relationship. Although the relationships between PA and the components of health-related physical fitness did not support our third hypothesis, our results contribute important information to the existing literature. Various statistical tests were completed to thoroughly examine the data. Within our regression analyses we controlled for factors (height, weight, age and ethnicity) known to contribute to the relationship between PA and physical fitness and we are therefore confident in the data we are reporting. The fitness measures utilized in the present investigation are rigorous, are commonly used in children (115), and provide pertinent information regarding fitness, especially since we utilized a variety of health-related physical fitness tests to obtain a comprehensive profile of the children. As has previously been mentioned in the document, the accelerometer is a sensitive and objective tool that is ideal for PA measurement in children. We did not find PA to be a significant predictor of fitness in children however our results suggest that musculoskeletal fitness may be a stronger predictor of cardiorespiratory fitness than PA. 5.4 Future Directions This study provides important baseline information for AS! BC to which the effects of the intervention can be compared. Follow-up investigations may provide important evidence regarding the contribution of bouted activity to health outcomes, the implications associated with PA patterning, and between ethnic groups, detect where the difference in activity is occurring. Further exploration of the relationship between musculoskeletal and cardiorespiratory fitness is also warranted. 5.5 Limitations The method of PA measurement and protocol of obtaining habitual PA data in this investigation is based on the assumption that children's PA habits are relatively constant and that we are able to accurately capture this constant level of PA in only a few days. Due to the immense interest in PA patterning in children, there has recently been substantial research in the numerous sources of natural variation in this behaviour. Kristensen et al. (36, 120) determined several variations that could significantly affect PA levels in 8 to 10 year-olds and Mattocks et al. (48) concluded that intra-individual variation and seasonal variation were substantial. Since we are estimating habitual PA from one time-point, there is the potential for variation error within our data. For example, data collection occurred over the months of early November to early February. In the month of January the city of Vancouver experienced excessive amounts of rain. Previous literature has demonstrated that children are more active during the more pleasant months of the year (36) so during this time period, children may have had lower PA levels than normal. This variation may 56 partially explain why we found no relationship between PA and health-related physical fitness. The ICC was used to estimate the effects of school on the PA and health outcome measures used in the analysis. For total laps run the ICC was 0.27 suggesting that there was substantial effect of the school. The variation may be due to the school environment or it may indirectly be due to the community the school is in (61). The ICC was low (0.07) for the other components indicating that there was more variation between the participants than between the schools in the data. 5.6 Conclusions Only one child in the present investigation met the recommendations of Canada's Physical Activity Guidelines for Children. Moreover, children are not meeting activity level recommendations during the school day. Low levels of MVPA suggest that many children in Vancouver may be at risk for poor health due to insufficient PA. Our results demonstrated that Caucasian children had higher levels of PA and physical fitness than Asian children. Low levels of PA and low fitness levels are important modifiable risk factors for cardiovascular disease risk and are associated with various health complications. The lower levels of PA and fitness in Asian children indicate that this ethnic group may be a vulnerable group at a higher risk for associated cardiovascular and health complications with increasing age. Physical activity was not a significant predictor of fitness in this cohort of children. Our results suggest that musculoskeletal fitness may be a more powerful predictor of cardiorespiratory fitness (and vise versa). Combined, these findings suggest that implementation of interventions are warranted to encourage PA participation in children and assist in the prevention of chronic health complications. 57 Footnotes 1. Raw data is attached in Appendix 58 CHAPTER VI References 1. Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. Journal of Physical Activity and Health 2005;3:366-383. 2. Cameron C, Craig C, Paolin S. 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Eisenmann JC. Secular trends in variables associated with the metabolic syndrome of north american children and adolescents: a review and synthesis. Am J Hum Biol 2003;15:786-794. 82. Baranowski T, Tsong Y, Henske J, Dunn K, Hooks P. Ethnic variation in blood pressure among preadolescent children. Pediatr Res 1988;23:270-274. 83. Warburton DER, Gledhill N, Quinney A. Musculoskeletal fitness and health. CJAP 2001 ;26(2):217-237. 84. Fortier MD, Katzmarzyk PT, Malina RM, Bouchard C. Seven-year stability of physical activity and musculoskeletal fitness in the Canadian population. Med Sci Sports Exerc 2001 ;33(11): 1905-1911. 85. Katzmarzyk PT, Craig CL. Musculoskeletal fitness and risk of mortality. Med Sci Sports Exerc 2002;34(5):740-744. 86. Rantanen T, Harris T, Leveille S, Visser M, Foley D, Masaki K, Guranlnik J. Muscle strength and body mass index as long-term predictors of mortality in initially healthy men. J. Gerontol A Biol Sci Med Sci 2000;55:M168-M173. 87. Mason C, Brien S, Craig C, Gauvin L, Katzmarzyk P. Musculoskeletal fitness and weight gain in Canada. Med Sci Sports Exerc 2007;39(1):38-43. 88. Strong WB, Malina RM, Blimkie JR, Daniels SR, Dishman RK, Gutin B, Hergenroeder AC, Must A, Nixon PA, Pivarnik JM, Rowland T, Trost S, Trudeau F. Evidence based physical activity for school-age youth. J Pediatr 2005;146:732-737. 89. Marshall SJ, Sarkin JA, Sallis JF, McKenzie TL. Tracking of health-related fitness components in youth ages 9 to 12. Med Sci Sports Exerc 1998;30(6):910-916. 90. Pate R, Dowda M, Ross JG. Associations between physical activity and physical fitness in American children. AJDC 1990;144:1123-1129. 91. Ruiz J, Rizzo N, Hurtig-Wennlof A, Ortega F, Warnberg J, Sjostrom M. Relations of total physical activity and intensity to fitness and fatness in children: the European Youth Heart Study. Am J Clin Nutr 2006;84:299-303. 92. Eiberg S, Hasselstrom H, Gronfeldt V, Froberg K, Svensson J, Anderson LB. 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New York: Macmillan; 1985. 100. Methony E. The present status of strength testing for children of elementary school and preschool age. Res Q 1941;12:115-130. 101. AAHPERD. Health related physical fitness test manual. Reston, Va: AAHPERD Publications; 1980. 102. Armstrong N, Welsman J. Development of aerobic fitness during childhood and adolescence. Ped Exerc Sci 2000;12:128-149. 103. Janz KF. Validation of the CSA accelerometer for assessing children's physical activity. Med Sci Sports Exerc 1994;26(3):369-375. 104. Pate R. Physical activity assessment in children and adolescents. Crit Rev Food Sci Nutr 1993;33:321-6. 105. Bjornson KF. Physical activity monitoring in children and youths. Pediatr Phys Ther 2005;17:37-45. 106. Armstrong N. Young people's physical activity patterns as assessed by heart rate monitoring. J Sports Sci 1998;16:S9-16. 107. Bassett JDR. Validity and reliability issues in objective monitoring of physical activity. Res Q Exerc Sport 2000;71(2):30. 108. Rowland T, Eston RG, Ingledew DK. Measurement of physical activity in children with particular reference to the use of heart rate and pedometry. Sports Med 1997;24:258-72. 109. Freedson P, Miller. Research Quarterly for Exercise and Sport 2000;71(2):21. 110. Freedson PS, Miller K. Objective monitoring of physical activity using motion sensors and heart rate. Res Q Exerc Sport 2000;71(2 Suppl):S21-9. 111. Eisenmann JC, Strath SJ, Shadrick D, Rigsby P, Hirsch N, Jacobson L. Validity of uniaxial accelerometry during activities of daily living in children. Eur J Appl Physiol 2004;91(2-3):259-63. 112. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. British Medical Journal 2000;320(7244): 1240-1243. 113. CSEP. The Canadian Physical Activity, Fitness & Lifestyle Approach. 3rd ed. Ottawa: Canadian Society for Exercise Physiology; 2003. 114. CSEP CSfEP. Canadian Physical Activity, Fitness and Lifestyle Appraisal Manual. Ottawa: Canadian Society for Exercise Physiology; 1996. 115. Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci 1988(6):93-101. 116. Actigraph. GT1M Technical Information. In: http://www.theactigraph.com/PDFs/GT1MTechnicalDocumentation.pdf; 2007. 117. Trost SG, Pate RR, Freedson PS, Sallis JF, Taylor WC. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc 2000;32(2):426-431. 118. Trost SG, Pate RR, Sallis JF, Freedson PS, Taylor WC, Dowda M, Sirard J. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc 2002;34(2):350-355. 119. Cole TJ, Bellizza MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international study. Br Med J 2000(320):1-6. 120. 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The FATS: an observational system for assessing physical activity in children and associated parent behavior. Behav Assess 1984;6:333-345. 132. Klesges R, Mallott J, Boschee P, Weber J. The effects of parental influences on children's food intake, physical activity, and relative weight. Int J Eating Disord 1986;5:335-346. 133. Massin M, Bourguignont A, Lepage P, Gerard P. Patterns of physical activity defined by continuous heart rate monitoring among children from Liege. Acta Clinica Belgica 2004;59(6):340-5. 134. Pate RR, Dowda M, Ross JG. Associations between physical activity and physical fitness in American children. AJDC 1990;144:1123-1129. 135. Bouchard C, Shephard R, Stephens T, Sutton J, McPherson B. Exercise, fitness, and health: the consensus statement. In: Bouchard C, Shephard R, Stephens T, Sutton R, McPherson B, editors. Exercise, Fitness, and Health: A Consensus of Current Knowledge. Champaign, III: Human Kinetics; 1990. p. 3-28. 136. McKay H. Action Schools! BC Phase 1 (Pilot) Evaluation Report and Recommendations. Vancouver: University of British Columbia; 2004. 137. CSEP. Canadian Physical Activity, Fitness & Lifestlye Appraisal Manual. 1st ed. Ottawa: Canadian Society for Exercise Physiology; 1996. 72 Action Schools! BC Consent Form for Families Please read the following with your child, and if you and your child would like to participate please sign the attached form and return the signed form in the stamped, addressed envelope provided. You may keep the other pages for your records. Procedures. Your child's participation in the Action Schools! BC (AS! BC) Research Study will involve two in-school testing sessions in the Fall and Spring of the next two school years. All children will participate in the Anthropometry and Questionnaire components and a smaller random sample of students will participate in the Cardiovascular Health and Musculoskeletal Fitness component. 1. Anthropometry: Measures of height, weight and calf and waist circumference will be taken. Total Time - 10 minutes: Fall and Spring. 2. Questionnaires: Your child will be assisted in the completion of questionnaires that will assess their physical activity, nutrition, self-esteem and attitudes and perceptions about physical activity. A trained research assistant will discuss the importance of these assessments with the children. Total Time - 1 hour: Fall, Winter and Spring. 3. Cardiovascular Health and Musculoskeletal Fitness: We will evaluate aerobic fitness using a shuttle run in which students repeatedly run 20 meter laps in time with a clearly audible "beep" until they become tired and choose to stop. Musculoskeletal fitness (i.e. muscle strength and power) will be assessed using a hand held dynamometer. A research assistant will provide clear instructions for each procedure to the students. Resting blood pressure and heart rate will be recorded before all fitness procedures. A smaller group of students (25%) will be recruited for this portion of the study. Total Time - 45 minutes: Fall and Spring. Health History Questionnaire: If you and your child agree to participate in the AS! BC Research Study, you will be asked to complete the attached Health History Questionnaire to determine if there are any health reasons to exclude your child from the research study and to identify any conditions or medications that may affect study outcomes. Possible Harms: None. 74 Consent Form - September 2006 Please Jill out both sides of this form and return it in the stamped, addressed envelope provided. Please keep the other pages for your records. Parent's Consent Statement: I/We ' the (Please print the name of one or both parents/guardians) parents/guardians of have received and read all (Please print child's first and last name) 6 pages of the information letter and consent form and understand the purpose and procedures of the Action Schools! BC Research Study as described. Please check (S) one. I agree to have my child participate in the 3-year Action Schools! BC Research Study (anthropometry, questionnaires) with the understanding that my child may or may not be randomly selected to participate in the cardiovascular health and musculoskeletal fitness portion of the study. I do not agree to have my child participate in Action Schools! BC Research Study. I understand that at any time during the 3-year Action Schools! BC Research Study we will be free to withdraw without jeopardizing any medical management, employment or educational opportunities. I understand the contents of all six pages of this form and the proposed procedures. I have had the opportunity to ask questions and have received satisfactory answers to all inquiries regarding this program. Signature of Parent or Guardian Date Printed name of the Parent or Guardian signing above (Continued on other side) 75 Child's Statement: I have talked with my parents/guardians about the Action Schools! BC Program and Research Study and I understand what I will be asked to do. I understand that if I want to I can stop being in the research study at any time and I will still be able to participate in activities at my school. I have had the chance to ask questions and have received satisfactory answers to all of my questions. Signature of Child Date Printed name of child School Name Grade and Division 78 Action Schools! BC Consent Form for Families Please read the following with your child, and if you and your child would like to participate please sign the attached form and return the signed form in the stamped, addressed envelope provided. You may keep the other pages for your records. Procedures. Your child's participation in the Action Schools! BC (AS! BC) Research Study will involve two in-school testing sessions in the Fall and Spring of the next two school years. All children will participate in the Anthropometry and Questionnaire components and a smaller random sample of students will participate in the Cardiovascular Health and Musculoskeletal Fitness and Accelerometer components. 4. Anthropometry: Measures of height, weight and calf and waist circumference will be taken. Total Time - 10 minutes: Fall and Spring. 5. Questionnaires: Your child will be assisted in the completion of questionnaires that will assess their physical activity, nutrition, self-esteem and attitudes and perceptions about physical activity. A trained research assistant will discuss the importance of these assessments with the children. Total Time - 1 hour: Fall and Spring. 6. Cardiovascular Health and Musculoskeletal Fitness: We will evaluate aerobic fitness using a shuttle run in which students repeatedly run 20 meter laps in time with a clearly audible "beep" until they become tired and choose to stop. Musculoskeletal fitness (i.e. muscle strength and power) will be assessed using a hand held dynamometer. A research assistant will provide clear instructions for each procedure to the students. Resting blood pressure and heart rate will be recorded before all fitness procedures. A smaller group of students (25%) will be recruited for this portion of the study. Total Time - 45 minutes: Fall and Spring. 7. Accelerometers: We will monitor children's physical activity with accelerometers. Children will wear the accelerometer (on a belt around their waist) from the time they get up until the time they go to bed (approximately 12 hours) for 5 consecutive days. A research assistant will provide clear instructions for how to wear the accelerometer. A small group of students (25%) who participate in the cardiovascular component (item 3 above) will be recruited for this portion of the study. Total time - 45 minutes in the Fall for a session on accelerometer instructions. Accelerometers will be worn for 5 days in the Fall and Spring. 80 Consent Form - September 2006 Please fill out both sides of this form and return it in the stamped, addressed envelope provided. Please keep the other pages for your records. Parent's Consent Statement: I/We the {Please print the name of one or both parents/guardians) parents/guardians of have received and read all {Please print child's first and last name) 6 pages of the information letter and consent form and understand the purpose and procedures of the Action Schools! BC Research Study as described. Please check (S) one. I agree to have my child participate in the 3-year Action Schools! BC Research Study (anthropometry, questionnaires) with the understanding that my child may or may not be randomly selected to participate in the cardiovascular health and musculoskeletal fitness and accelerometer portions of the study. I do not agree to have my child participate in Action Schools! BC Research Study. I understand that at any time during the 3-year Action Schools! BC Research Study we will be free to withdraw without jeopardizing any medical management, employment or educational opportunities. I understand the contents of all six pages of this form and the proposed procedures. I have had the opportunity to ask questions and have received satisfactory answers to all inquiries regarding this program. Signature of Parent or Guardian Date Printed name of the Parent or Guardian signing above (Continued on other side) 81 Child's Statement: I have talked with my parents/guardians about the Action Schools! BC Program and Research Study and I understand what I will be asked to do. I understand that if I want to I can stop being in the research study at any time and I will still be able to participate in activities at my school. I have had the chance to ask questions and have received satisfactory answers to all of my questions. Signature of Child Date Printed name of child School Name Grade and Division 82 The University of British Columbia Office of Research Services Clinical Research Ethics Board-Room 210, 828 West 10th Avenue, Vancouver, BC V5Z 1L8 ETHICS CERTIFICATE OF EXPEDITED APPROVAL: RENEWAL PRINCIPAL INVESTIGATOR: Heather A. McKay DEPARTMENT: UBC CREB NUMBER: H02-70537 INSTITUTION(S) WHERE RESEA N/A Other locations where the research will be < N/A RCH WILL BE CARRIED OUT: :onducted: CO-INVESTIGATOR(S): Kate Reed Darren Warburton Parti-Jean Naylor Karim Miran-Khan Ryan Rhodes Heather Macdonald SPONSORING AGENCIES: Provincial Health Services Authority - "Action Schools! BC: Hormones Children" - "Action Schools! BC" UBC Start-up Funds - "Action Schools! BC" & Lipids in Action Schools! BC PROJECT TITLE: Action Schools! BC EXPIRY DATE OF THIS APPROVAL: December 4,2007APPROVAL DATE: December 4, 2006 CERTIFICATION: In respect of clinical trials: 1. The membership of this Research Ethics Board complies with the membership requirements for Research Ethics Boards defined in Division 5 of the Food and Drug Regulations. 2. The Research Ethics Board carries out its functions in a manner consistent with Good Clinical Practices. 3. This Research Ethics Board has reviewed and approved the clinical trial protocol and informed consent form for the trial which is to be conducted by the qualified investigator named above at the specified clinical trial site. This approval and the views of this Research Ethics Board have been documented in writing. The Chair of the UBC Clinical Research Ethics Board has reviewed the documentation for the above named project. The research study, as presented in the documentation, was found to be acceptable on ethical grounds for research involving human subjects and was approved for renewal by the UBC Clinical Research Ethics Board. Approval of the Clinical Research Ethics Board by one of: Dr. Bonita Sawatzky, Associate Chair 1.2 1.3 1.4 How long have you lived in North America? Years: Months: Where did your family live before moving to North America? How would you classify your family ethnically? (i.e., Caucasian-Canadian, Japanese-Canadian, etc.) 84 ABOUT YOUR CHILD: Child's birth weight Circle one: Grams or Lbs/Ozs 2.0 Nutrition History: 2.1 Who prepares your child's meals (i.e. mother, father, grandmother, nanny)? 2.2 Does your child drink milk every day? YES: if yes: How many cups per day? Has your child always drank milk every day (after being weaned from breast or bottle)? yes no if no, at what age did she/he start drinking milk every day? years old. NO: if no: Has your child ever drank one or more cups of milk per day (after being weaned from breast or bottle)? yes: at what age did she/he stop drinking milk every day? years old. How many cups did he/she drink until that age? cups per day no: (never drank milk on a daily basis after being weaned) 2.3 Is your child on a special diet? Yes No If yes: vegetarian low sodium low cholesterol other Please specify: 85 3.0 Medical History and Status: 3.1 Has your child ever been treated for any of the following conditions? Yes No food allergies 0 0 hypothyroidism 0 0 other allergies 0 0 hyperthyroidism 0 0 asthma 0 0 other conditions (please list) 3.2 Is your child currently taking any medications? Yes No If yes, what medication(s) is your child taking? What are these medication(s) for? 3.3 Has your family doctor ever said that your child has a heart condition and that he/she should only do physical activity recommended by a doctor? Yes No 3.4 Does your child complain of chest pain when they are doing physical activity? Yes No 3.5 In the past month, has your child complained of chest pain when they were not doing any physical activity? Yes No 3.6 Does your child have a bone or joint problem that could be made worse by a change in their physical activity? Yes No 3.7 Does your child lose their balance because of dizziness or do they ever lose consciousness? Yes No 3.8 Do you know of any other reason why your child should not participate in physical activity? Yes No 86 4.0 Bone History: 4.1 Has your child ever been hospitalized, confined to bed or had a limb immobilized (i.e., arm in a cast)? Yes No If yes: list condition, approximate date and time involved (Example: wrist fracture summer, 1990 10 weeks) Reason Date Time Involved 4.2 Is there a history of wrist, hip, or spine fractures in your family? Yes No If yes: indicate who was affected mother father maternal grandmother paternal grandmother maternal grandfather paternal grandfather 4.3 Is there a history of osteoporosis in your family? Yes No If yes: indicate who was affected mother father maternal grandmother paternal grandmother maternal grandfather paternal grandfather 4.4 Is there a history of any other bone disease in your family? Yes No If yes: please indicate the family member(s) affected 1. 2. What is the name of the condition(s) affecting this family member? 1. 2. 5.0 Physical Activity: 5.1 How would you rate the physical activity level of your child? Physical activity is defined as vigorous activity that makes them sweat and/or breathe hard. Inactive Sometimes active Moderately active Often active Very active THANK YOU FOR YOUR PARTICIPATION 87 Appendix C Sit-and-Reach The participant will begin by performing two 15 second stretches per leg before proceeding to the sit and reach measurement. The participant will remove their shoes and sit with their feet flat against the sit and reach block. Their feet will be placed just wider than the width of the sliding mechanism. The participant will place one hand on top of the other and situate their fingertips at the edge of the sliding mechanism. As they breathe out, the participant will reach forward as far as possible keeping their legs straight. This measurement will be repeated and the highest score (cm) will be recorded (137). Grip Strength The participant will stand holding the dynamometer in their hand with the arm holding the dynamometer abducted 45° from their body. While breathing normally, they will squeeze the dynamometer as hard as possible. Two measurements will be taken for each hand and the highest score on either hand will be recorded (137). Push-ups The participant will lie in a prone position and place their hands on the floor just wider than their shoulders (finger tips pointing forward). Their feet will be placed together and their legs and body will be held in a straight line. The participant will begin with their body lifted off of the floor with only their hands and toes in contact with the ground. Using their toes as a fulcrum the participant will bend their arms to lower their body towards the floor. They will lower their body until their arms reach a 90° angle at the elbow joint after which, they will straighten their arms to return to the starting position. The participant will complete as many consecutive push-ups as possible in a rhythmical fashion. The push-up assessment will be terminated for the following reasons: volitional fatigue, incorrect technique for more than two consecutive push-ups, inability to maintain a rhythmical pace (137). Curl-ups The participant will lie supine with their arms at their sides, knees bent to 90°, feet together and flat on the floor. They will curl their body upwards while sliding their fingers along the ground towards their feet. The participant will curl-up until their fingers have travelled 10cm from their starting position. Curl-ups will be performed keeping pace with the rhythm of a metronome. The metronome pace will be set at 40 bpm. The participants will perform as many curl-ups as possible. The curl-up assessment will be terminated for the following reasons: volitional fatigue or inability to curl-up the required 10 cm (137). 88 Appendix D Table D.1 Average 'on' and 'off times for each morning and evening during the measurement of habitual physical activity. Day of the Week Morning (on time) Standard Deviation Range Evening (off time) Standard Deviation Range Wednesday N/A N/A N/A 21:44:00 0:58:00 20:45:45-22:42:00 Thursday 7:38:00 0:44:55 6:53:15-8:23:00 21:20:00 1:11:00 20:09:00-22:31:00 Friday 8:06:00 1:06:27 6:59:30-9:12:30 21:58:30 1:26:45 20:31:00-23:39:00 Saturday 9:04:30 1:25:45 7:38:45-10:30:15 22:01:15 1:37:45 20:23:30-23:39:00 Sunday 9:15:07 1:24:36 7:50:30-10:39:45 21:14:44 1:15:15 19:59:30-22:30:00 90 Appendix F Acfi<!n-Schools/Be ACTION SCHOOLS! BC 5-DAY ACTIVITY LOG - Spring 2006 Name: School: Grade: Division: Directions: 1) Please have your child wear the motion sensor under their clothing. 2) The motion sensor should be fitted snugly on the waist with the sensor positioned in the front above the hip. The belt should feel comfortable but not floppy. 3) The motion sensor should be worn for 12 hours (8 AM - 8 PM) and should only be removed during that period if the child is going swimming, having a bath or a shower. It is not waterproof. 4) Please note the time when the motion sensor is first put on the child and when it is taken off daily on the log on the reverse side of this form as well as anything that affected your child's movement patterns on any given day. 5) The motion sensor is like a smart 'pedometer' but it is very valuable. Please have your child put on the motion sensor on Monday morning to take it into school and an AS! BC researcher will collect them from the classroom. Thank you very much for you help! 91 Monitor: Wednesday Thursday Friday Saturday Sunday Dates On Time AM Off Time PM Did weather change your routine? No No No No No Yes Yes Yes Yes Yes Did illness change your routine? No No No No No Yes Yes Yes Yes Yes Was motion sensor removed during wear time? No No No No No Yes Yes Yes Yes Yes If yes, what time? : to : to : to : to : to Why was the monitor removed? Any problems? Please explain. 92 Appendix G Table E.1 Description of health-related physical fitness and physical activity data in Caucasian and Asian girls. BMI=body mass index, MVPA=moderate-to-vigorous physical activity. Variable N Caucasian Girls N Asian Girls Age (years) 38 10.0 ±0.6 53 10.0 ±0.6 Height (cm) 38 1.41.5± 7.6 53 140.9 ±7.5 Weight (kg) 38 35.4 ± 6.4 53 35.2 ± 8.6 BMI (kg/mO 38 17.6 ±2.0 53 17.5 ±3.0 Waist Circumference (cm) 38 62.6 ± 5.7 53 62.9 ± 7.9 Systolic Blood Pressure (mmHg) 38 96.7 ± 9.3 53 97.0 ±9.0 Diastolic Blood Pressure (mmHg) 38 62.0 ± 9.7 53 62.0 ± 8.4 Pulse Rate (bpm) 36 86.8 ± 14.0 53 88.4 ± 11.8 Total Laps Run 38 26.0 ± 13.0 53 18.0 ± 10.0 Sit-and-Reach (cm) 38 26.0 ± 10.0 53 29.0 ±7.0 Curl-ups 38 14.0 ± 18.0 53 9.0 ±9.0 Push-ups 38 6.0 ±9.0 53 2.0 ±5.0 Grip Strength (kg) 38 33.0 ± 8.0 53 32.0 ±7.0 Musculoskeletal Fitness Score 38 0.3852 ±2.3157 53 -0.2762 ± 1.0910 Average Counts Per Minute 38 499.1 ± 158.3 53 380.1 ± 102.0 Average MVPA Per Day 38 127.0 ±40.8 53 103.9 ±28.6 93 Table E.2 Description of health-related physical fitness and physical activity data in Asian and Caucasian boys. BMI=body mass index, MVPA=moderate-to-vigorous physical activity. Variable N Caucasian Boys N Asian Boys Age (years) 35 10.0 ±0.6 44 10.0 ±0.6 Height (cm) 35 142.4 ±6.5 44 140.4 ±8.3 Weight (kg) 35 36.2 ± 7.4 44 36.9 ±8.5 BMI (kg/mz) 35 17.7 ±2.3 44 18.6 ±3.0 Waist Circumference (cm) 35 63.4 ± 6.6 44 65.7 ± 8.0 Systolic Blood Pressure (mmHg) 34 97.3 ±7.0 43 98.9 ± 7.0 Diastolic Blood Pressure (mmHg) 34 62.1 ±6.0 43 63.9 ± 7.9 Pulse Rate (bpm) 33 85.3 ± 10.5 43 88.7 ± 11.1 Total Laps Run 35 31.0 ± 15.0 44 21.0± 12.0 Sit-and-Reach (cm) 35 24.0 ±6.0 44 25.0 ±7.0 Curl-ups 35 21.0 ±22.0 44 8.0 ±7.0 Push-ups 35 7.0 ±8.0 44 4.0 ±5.0 Grip Strength (kg) 35 36.0 ± 6.0 44 34.0 ± 7.0 Musculoskeletal Fitness Score 35 0.5858 ± 1.6407 44 -0.4660 ± 1.1581 Average Counts Per Minute 35 540.2 ± 120.6 44 474.5 ± 117.8 Average MVPA Per Day 35 140.1 ±32.5 44 129.0 ±34.6 94 Appendix H Table E.3 Description of health-related physical fitness data in children without physical activity data. BMI=body mass index. Variable N Boys N Girls Age (years) 52 10.0 ±0.5 46 9.8 ±0.8 Height (cm) 50 141.4 ±6.9 47 141.0 ±7.3 Weight (kg) 50 37.3 ± 9.7 46 35.6 ±6.8 BMI (kg/m') 50 18.5 ±3.4 46 17.8 ±2.8 Waist Circumference (cm) 50 65.6 ± 9.4 47 62.2 ± 6.6 Systolic Blood Pressure (mmHg) 47 96.5 ±9.5 41 96.2 ± 10.8 Diastolic Blood Pressure (mmHg) 47 63.3 ±8.6 41 63.5 ± 11.1 Pulse Rate (bpm) 46 89.9 ± 12.5 41 89.6 ± 12.8 Total Laps Run 48 26.6 ± 13.3 42 23.3 ± 12.1 Sit-and-Reach (cm) 49 24.0 ±7.0 42 28.0 ± 7.0 Curl-ups 49 13.0 ± 15.0 42 11.0±8.0 Push-ups 49 7.0 ±8.0 42 5.0 ±5.0 Grip Strength (kg) 48 35.0 ± 7.0 42 32.0 ± 7.0 Musculoskeletal Fitness Score 48 0.1016 ± 1.6751 42 0.0729 ± 1.1230 95 Appendix I Capturing Physical Activity Tempo in Elementary School-Aged Children K. Ashlee McGuire1, Lindsay A. Nettlefold1, Shannon S.D. Bredin1, Heather A. McKay1, Patti-Jean Naylor2, Darren D.E. Warburton1. 1University of British Columbia, Vancouver, BC; 2University of Victoria, Victoria, BC The tempo of children's activity has been documented to be sporadic and rapidly changing. These characteristics make data acquisition in this age group challenging. Accelerometers are popular in the assessment of physical activity in youth however data is commonly captured in 1 minute epochs, consequently masking sporadic activity. Therefore, the purpose of this investigation was to determine the tempo of children's physical activity using a 15 second epoch with a specific emphasis on the time spent in moderate-to-vigorous intensity physical activity (MVPA; £3 METs). To assess habitual physical activity, children (8-11 yrs) wore a GT1M activity monitor at the hip during waking hours over a 5 day period. All children were part of a larger investigation (Action Schools! BC). To be included in the analysis, children were required to wear the monitor for at least 8 hours per day on at least 4 days. One hundred fifty-seven children met the criteria. Age-specific cut-points developed by Trost and colleagues were revised for use with 15 second epochs and data was analyzed using customized software. Our results indicate that children spend 15% of their monitored time in MVPA. Eighty-five percent of the total time spent in MVPA was accumulated in bouts of activity less than 5 minutes in duration and on average, these bouts lasted only 31 seconds. Eight percent of the activity bouts lasted between 5 and 10 minutes; 5% in 10 to 20 minute bouts; and 2% in bouts lasting greater than 20 minutes. Only 29% of the children registered at least 1 bout lasting greater than 20 minutes, while 68% registered at least 1 bout of activity lasting 10 to 20 minutes and 97% accumulated activity in bouts of 5 to 10 minutes in duration. The results of this investigation suggest that a 15 second epoch has sufficient resolution to detect the sporadic activity that is typical of children. 96 Appendix J Physical Activity and Antecedents of Cardiovascular Disease in Children A. McGuire1, S.S.D. Bredin1, H.A. McKay1, P.J. Naylor2, L.T.L. Horita1, D.E.R. Warburton1. 1 University of British Columbia, Vancouver, British Columbia 2 University of Victoria, Victoria, British Columbia Background: Reduced arterial compliance is an important predictor of cardiovascular disease. It precedes the development of traditional cardiovascular disease risk factors. In adults, increased physical activity is associated with improved arterial compliance. However, it is unknown whether regular daily physical activity in children exerts a similar positive influence on arterial compliance. Purpose: The primary purpose of this investigation was to examine the relationship between moderate-to-vigorous physical activity (MVPA) and arterial compliance in children. Methods: To assess habitual physical activity, children (n=115, 8-11 yrs) wore a GT1M Activity Monitor for 13 hrs (on average) daily over a 5 day period. We also obtained concurrent measures of blood pressure (mmHg), arterial compliance (small and large artery, ml/mmHg) and weight status (Body Mass Index, kg/m2). Data were analyzed using Pearson Partial Correlation. Results: 16.5% were classified as overweight and 5.2% were hypertensive. MVPA (counts per minute corresponding to £3 METS) accounted for approximately 6% of the variance in small artery compliance independent of Body Mass Index, systolic blood pressure, age and gender. Conclusion: There was a positive relationship between MVPA and vascular health in this group of generally healthy children. This extends our previous findings by showing that objectively measured physical activity is also predictive of vascular health. An intervention designed to test the effect of MVPA on arterial compliance would further delineate this relationship. 97 Appendix K Table K.1. T-tests performed between genders. (*) denotes significance. MVPA=moderate-to-vigorous physical activity Variable Boys (Mean) Girls (Mean) Significance Level Musculoskeletal Fitness Score 0.1501 -.1303 p < 0.26 Total Laps 25.0 21.0 p < 0.06 Sit-and-Reach (cm) 25.0 28.0 p < 0.06 Grip Strength (kg) 35.0 32.0 p < 0.04 Height (cm) 141.3 141.2 p < 0.89 Weight (kg) 36.6 35.3 p < 0.25 Body Mass Index (kg/m*1) 18.2 17.6 p<0.12 Systolic Blood Pressure (mmHg) 98.0 97.0 p < 0.28 Diastolic Blood Pressure (mmHg) 63.0 62.0 p < 0.23 Heart Rate (bpm) 87.0 88.0 p < 0.86 Counts Per Minute 503.6* 429.8 p < 0.001 Minutes of MVPA Per Day 133.9* 113.5 p< 0.001 Minutes of MVPA Per Weekday 149.0* 124.6 p < 0.001 Minutes of MVPA Per Weekend Day 112.7* 98.4 p < 0.04 Minutes of Sporadic MVPA Per Day 104.7 98.0 p < 0.09 Minutes of Bouted MVPA Per Day 29.2* 15.5 p < 0.001 Table K.2. ANOVA performed between girls with and without valid physical activity data. Variable Girls Without PA Data (Mean) Girls With PA Data (Mean) Significance Level Age (years) 9.8 10.0 p<0.09 Height (cm) 141.0 141.2 p<0.71 Weight (kg) 35.6 35.2 p < 0.58 Total Laps 23.0 21.0 p < 0.68 Musculoskeletal Fitness Score 0.0729 -0.0336 p<0.16 98 Table K.3. ANOVA performed between boys with and without valid physical activity data. Variable Boys Without PA Data (Mean) Boys With PA Data (Mean) Significance Level Age (years) 10.0 10.0 p < 0.82 Height (cm) 141.4 141.3 p < 0.59 Weight (kg) 37.3 36.6 p < 0.42 Total Laps 27.0 25.0 p < 0.70 Musculoskeletal Fitness Score 0.1016 -0.0434 p < 0.43 Table K.4. ANOVA performed between schools to determine intraclass correlation. MVPA=moderate-to-vigorous physical activity Variable Between Schools Variance Within Schools Variance Total Variance Intraclass Correlation Counts Per Minute 1.073 13.901 14.974 0.07 Musculoskeletal Fitness Score 17.030 226.045 243.075 0.07 Body Mass Index (kg/m2) 0.188 3.363 3.551 0.07 Total Laps 14.700 39.933 54.633 0.27 Minutes of MVPA Per Day 1.074 14.631 15.705 0.07 99 Table K.5. ANCOVA used to examine differences between Caucasian and Asian boys. Age, weight and height were entered as covariates. MVPA=moderate-to-vigorous physical activity Variable Significance Level Levene's Test (Significance) Musculoskeletal Fitness Score p < 0.007 p < 0.09 Total Laps p < 0.03 p < 0.84 Curl-ups p < 0.04 p < 0.001 Push-ups p < 0.20 p < 0.62 Sit-and-Reach (cm) p < 0.28 p < 0.66 Grip Strength (kg) p<0.31 p<0.01 Systolic Blood Pressure (mmHg) p < 0.36 p<0.19 Diastolic Blood Pressure (mmHg) p < 0.69 p < 0.23 Heart Rate (bpm) p<0.19 p < 0.92 Body Mass Index (kg/rr^) p < 0.22 p < 0.08 Waist Circumference (cm) p < 0.24 p < 0.09 Counts Per Minute p < 0.03 p < 0.68 Minutes MVPA Per Day p<0.12 p<0.41 Table K.6. ANCOVA used to examine differences between Caucasian and Asian girls. Age, weight and height were entered as covariates. MVPA=moderate-to-vigorous physical activity Variable Significance Level Levene's Test (Significance) Musculoskeletal Fitness Score p < 0.09 p<0.01 Total Laps p<0.01 p < 0.06 Curl-ups p < 0.32 p < 0.004 Push-ups p < 0.009 p < 0.23 Sit-and-Reach (cm) p < 0.78 p < 0.04 Grip Strength (kg) p < 0.58 p < 0.26 Systolic Blood Pressure (mmHg) p < 0.79 p < 0.95 Diastolic Blood Pressure (mmHg) p < 0.97 p < 0.20 Heart Rate (bpm) p < 0.59 p < 0.29 Body Mass Index (kg/m^) p < 0.76 p < 0.004 Waist Circumference (cm) p < 0.68 p<0.17 Counts Per Minute p< 0.001 p< 0.001 Minutes MVPA Per Day p< 0.001 p< 0.001 100 Table K.7. Principal Component Analysis in the whole group. One component extracted from the musculoskeletal fitness components. Component Eigenvalue Percentage Variance 1 1.560 38.994 Table K.8. Factor loadings based on one component for the musculoskeletal fitness score in the whole group. Factor Factor Loading Sit-and-Reach 0.380 Curl-ups 0.762 Push-ups 0.678 Grip Strength 0.612 Table K.9. Principal Component Analysis in girls only. One component extracted from the musculoskeletal fitness components. Component Eigenvalue Percentage Variance 1 1.566 39.158 Table K.10. Factor loadings based on one component for the musculoskeletal fitness score in girls. Factor Factor Loading Sit-and-Reach 0.495 Curl-ups 0.758 Push-ups 0.680 Grip Strength 0.534 Table K.11. Principal Component Analysis in boys only. One component extracted from the musculoskeletal fitness components. Component Eigenvalue Percentage Variance 1 1.585 39.616 Table K.12. Factor loadings based on one component in the musculoskeletal fitness score in boys. Factor Factor Loading Sit-and-Reach 0.435 Curl-ups 0.744 Push-ups 0.654 Grip Strength 0.643 Table K.13. Principal Component Analysis in the whole group. One component extracted from the musculoskeletal and cardiorespiratory fitness components. Component Eigenvalue Percentage Variance 1 2.098 41:952 Table K.14. Factor loadings based on one component in the musculoskeletal and cardiorespiratory fitness score in the whole group. Factor Factor Loading Sit-and-Reach 0.351 Curl-ups 0.686 Push-ups 0.828 Grip Strength 0.467 Total Laps 0.774 102 Table K.15. Principal Component Analysis in the whole group. Two components extracted from all health-related physical fitness components. Component Eigenvalue Percentage Variance 1 2.479 35.416 2 1.817 25.959 Table K.16. Factor loadings based on two components for the total health-related physical fitness score. Factor Factor Loading (Component 1) Factor Loading (Component 2) Sit-and-Reach 0.356 0.118 Curl-ups 0.504 0.463 Push-ups 0.664 0.486 Grip Strength -0.037 0.767 Total Laps 0.666 0.415 Body Mass Index -0.770 0.533 Waist Circumference -0.787 0.535 Table K.17. Forward stepwise regression of cardiorespiratory fitness in girls. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R* Change Total Laps MSK Score (p< 0.001) .460 .618 .184 .193 Weight (p < 0.006) -.257 .139 .242 .066 103 Table K.18. Forward stepwise regression of cardiorespiratory fitness in boys. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity, MSK=musculoskeletal Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R1 R* Change Total Laps Weight (p<0.01) -.375 -.697 .400 .446 Height (p<0.17) .210 .412 .400 .446 CPM (p < 0.70) .083 .010 .400 .446 MVPA Per Day (p < 0.49) .148 .065 .400 .446 MSK Score (p< 0.001) .528 5.284 .400 .446 Ethnicity (p < 0.63) -.049 -1.447 .400 .446 Table K.19. Hierarchical regression of health-related physical fitness component in boys. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity, HRPF=health-related physical fitness Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R* Change Total HRPF Score Weight (p< 0.001) -.579 -.216 .157 .189 Height (p < 0.01) .439 .173 .157 .189 Age (p<0.91) -.013 -.067 .157 .189 Ethnicity (p < 0.003) -.330 -1.967 .243 .093 CPM (p < 0.46) -.191 -.005 .233 .000 MVPA Per Day (p < 0.45) .198 .017 .229 .006 104 Table K.20. Hierarchical regression of health-related physical fitness component in girls. CPM=counts per minute, MVPA=moderate-to-vigorous physical activity, HRPF=health-related physical fitness Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R' Change Total HRPF Score Weight (p< 0.001) -.352 -.143 .043 .075 Height (p<0.01) .321 .133 .043 .075 Age (p<0.91) -.108 -.557 .043 .075 Ethnicity (p < 0.003) -.202 -1.269 .111 .076 CPM (p < 0.46) .122 .003 .130 .028 MVPA Per Day (p < 0.45) .067 .006 .120 .000 Table K.21. Hierarchical regression of push-ups in boys. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R' Change Push-ups Weight (p < 0.008) .461 .895 .147 .191 Height (p < 0.32) -.182 -1.395 .147 .191 Age (p < 0.94) -.010 -.066 .147 .191 Ethnicity (p<0.17) .169 -1.269 .156 .020 CPM (p < 0.75) .037 .003 .146 .001 105 Table K.22. Hierarchical regression of push-ups in girls. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R' Change Push-ups Weight (p < 0.53) -.098 -.192 .095 .136 Height (p < 0.48) .118 .901 .095 .136 Age (p < 0.23) .136 .901 .095 .136 Ethnicity (p < 0.04) .259 .212 .153 .064 CPM (P < 0.27) -.122 -.162 .155 .012 Table K.23. Hierarchical regression of curl-ups in boys. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted Rz RzChange Curl-ups Weight (p< 0.001) .250 .410 .062 .110 Height (p<0.01) -.159 -1.034 .062 .110 Age (p<0.91) -.074 -.425 .062 .110 Ethnicity (p < 0.003) .222 .153 .086 .035 CPM (p < 0.46) .042 .059 .075 .001 106 Table K.24. Hierarchical regression of curl-ups in girls. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R2 R^ Change Curl-ups Weight (p< 0.001) .128 .228 .053 .096 Height (p<0.01) -.243 -1.691 .053 .096 Age (p < 0.91) .210 1.267 .053 .096 Ethnicity (p < 0.003) .039 .029 .049 .006 CPM (p < 0.46) -.147 -.177 .055 .017 Table K.25. Hierarchical regression of sit-and-reach in boys. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted Rz Rz Change Sit-and-Reach Weight (p < 0.53) .108 .154 .120 .165 Height (p < 0.33) -.178 -1.007 .120 .165 Age (p < 0.03) -.261 -1.304 .120 .165 Ethnicity (p < 0.39) -.107 -.065 .111 .003 CPM (p<0.14) -.177 -.214 .126 .025 107 Table K.26. Hierarchical regression of sit-and-reach in girls. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R' Change Sit-and-Reach Weight (p < 0.34) .154 -.259 .107 .148 Height (p < 0.92) -.016 -.107 .107 .148 Age (P < 0.32) -.116 -.665 .107 .148 Ethnicity (p < 0.23) .155 .110 .104 .007 CPM (p < 0.23) .138 .159 .109 .015 Table K.27. Hierarchical regression of grip strength in boys. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R* Change Grip Strength Weight (p<0.71) .059 .060 .247 .286 Height (p < 0.003) .524 2.095 .247 .286 Age (p < 0.52) -.073 -.257 .247 .286 Ethnicity (p < 0.30) -.121 -.051 .242 .005 CPM (p < 0.34) -.105 -.090 .243 .011 108 Table K.28. Hierarchical regression of grip strength in girls. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R' Change Grip Strength Weight (p < 0.02) .306 .352 .397 .425 Height (p < 0.003) .416 1.877 .397 .425 Age (p < 0.56) -.055 -.215 .397 .425 Ethnicity (p < 0.92) -.010 -.005 .394 .004 CPM (p < 0.06) .174 .136 .412 .023 Table K.29. Hierarchical regression of systolic blood pressure in boys. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R' Change Systolic Blood Pressure Weight (p < 0.006) .458 .154 .230 .270 Height (p < 0.86) .030 .040 .230 .270 Age (p < 0.64) .055 .066 .230 .270 Ethnicity (p < 0.57) .068 .010 .223 .004 CPM (p < 0.98) .002 .001 .212 .000 109 Table K.30. Hierarchical regression of systolic blood pressure in girls. CPM=counts per minute. Fitness Variables in Model Standardized Beta Unstandardized Beta Adjusted R* R' Change Systolic Blood Pressure Weight (p < 0.20) .211 .096 .036 .270 Height (p < 0.56) .104 .184 .036 .270 Age (p < 0.33) -.118 -.181 .036 .270 Ethnicity (p < 0.99) -.002 .000 .029 .004 CPM (p<0.10) -.196 -.061 .049 .000 110 Appendix L Raw Data C2E2ID My ID T1 Gender sthtc weightC BMI waistC BPSY BPDIA 1101 mpama4maf1 M 154.25 43.45 18.26163 70.5 105 64 1168 fqupa5bc1 F 141.5 40.7 20.32739 66.5 99 61 1169 fhusy4maf1 F 141.1 32.8 16.47479 67.2 111 68 1207 mpawi4maf1 M 127.8 24.4 14.93923 55.9 92 61 1233 fscha4wf1 F 138.6 31.6 16.4498 55.15 99 58 1283 fbaha5wf1 F 138.25 31.9 16.69016 60.55 82 55 1325 mkhsa5moc1 M 148.15 33.4 15.21749 62.15 90 51 1349 mdudo5moc1 M 132.7 30.5 17.3204 65.55 96 66 1397 fmoro5bc1 F 140.35 30.85 15.66139 59.05 107 72 1457 fdrma4maf1 F 136.45 27.3 14.66275 58.3 91 52 1491 ffial5bc1 M 154.7 44.95 18.78231 66.35 101 69 1508 mmijo4bc1 M 135.25 31.7 17.32945 57.8 98 66 1844 mfrju5wf1 M 139.8 29.4 15.04295 57.95 99 56 2080 mchma4nc1 M 142.8 49.75 24.39701 79.65 104 65 2124 marki4wf1 M 144.9 39.7 18.90835 65.9 92 66 2147 ferol4maf1 F 133.45 26.9 15.10481 59.35 109 61 2314 mmash4maf1 M 136.85 33 17.62075 63.35 98 66 2345 mdema4moc1 M 144.15 44.9 21.60812 79.45 107 75 2383 fdhsu5moc1 F 145.8 45.05 21.19238 71.9 99 53 2432 myada4maf1 M 141.15 32.1 16.11177 61 100 61 2488 mrami5bc1 M 147.55 40.25 18.48789 64.3 102 69 2521 mgrka5moc1 M 158.15 42.4 16.95225 61.35 90 69 2556 fmcsa5wf1 F 139.5 30 15.41604 58 92 48 2650 fgugo5moc1 F 144.25 33 15.85924 61.9 91 59 2668 falsa5bc1 F 134.6 27 14.903 54.5 92 56 2693 fmoch5nc1 F 146.7 32.6 15.14807 62.6 99 60 2713 mromi5maf1 M 150.2 38.8 17.19855 63.35 102 61 2887 mgolu4maf1 M 145.2 41.45 19.66035 69.9 94 59 2892 fpjal5maf1 F 151.05 34.8 15.25239 58.85 103 68 2921 fmica5maf1 F 140.35 34.1 17.31129 61.4 106 72 3038 fnggl4moc1 F 128.4 26.2 15.89173 60.3 92 57 3161 fchgi5maf1 F 146.45 46.1 21.49424 69.85 108 76 3180 mfejo5wf1 M 147.25 37.3 17.20276 65 99 55 3284 mrojo4moc1 M 132.15 40.4 23.1338 79.05 92 57 3286 mgeda5maf1 M 139.15 28.6 14.77065 57.75 99 73 3292 mtajo4nc1 M 130.05 29.8 17.61958 60.85 102 73 3319 mslmi5maf1 M 147.4 44.8 20.61973 68.6 89 68 3349 mcabr4maf1 M 148.75 72.6 32.81124 94.85 119 . 79 3411 myoke5maf1 M 156.35 64.2 26.26269 85.35 107 61 3423 fdhpo5moc1 F 143.5 33.55 16.29254 63.2 86 54 3455 mjapr5moc1 M 142.95 37.95 18.57135 68 102 64 3558 fmiam4moc1 F 130.35 27.05 15.92008 54.9 99 50 3594 fsish4bc1 F 130.4 28.7 16.87822 61.45 84 44 3627 fchan4maf1 F 132.6 35.1 19.96274 69.25 93 61 3678 fchgr5maf1 F 135.2 33.8 18.49112 63.75 83 67 3693 fdhma4moc1 F 144.7 32.7 15.61747 55.3 94 60 3758 mvejo4wf1 M 141.05 42.3 21.26151 70.45 102 66 3767 fdhsh4moc1 F 159.3 53 20.88547 72.85 93 60 3810 fshlu5maf1 F 149.55 38.3 17.12482 64.5 110 78 Ill Raw Data 3811 fjale5nc1 F 148.5 46.55 21.10896 72.6 102 71 3820 mmibr4bc1 M 134.85 28.75 15.81015 57.2 92 64 3826 fwaer5maf1 F 128.25 23.2 14.105 52.9 93 73 3853 mhake5maf1 M 140.55 31.9 16.14838 58.6 87 63 3941 mdupa4moc1 M 137.4 26.7 14.14288 56.75 89 51 3974 frova4nc1 F 136.7 24.9 13.32485 51.7 85 66 4023 fswra5bd F 164.2 48.6 18.02561 64.35 104 60 4106 msaka5moc1 M 143 31.1 15.20857 59.05 92 56 4221 msudi4moc1 M 133.05 31.85 17.99201 65.7 96 64 4229 mpabr5maf1 M 143.8 32.95 15.93447 61.25 108 61 4303 fpeha5bd F 149.45 40 17.90887 62.6 104 74 4319 mkike5bc1 M 130.25 30.3 17.86023 58.55 91 56 4338 fmema4maf1 F 150.95 37.3 16.36977 65.6 92 46 4427 fjema5wf1 F 135.15 37.6 20.58523 68.35 90 61 4470 fkras5bc1 F 138.05 30.6 16.05642 54.15 91 56 4675 fatpr5moc1 F 154.5 38.9 16.29644 58.55 99 52 4678 fhilu5moc1 F 149.1 43.8 19.70239 62.9 97 68 4866 mleso5wf1 M 149.25 35.2 15.80207 57.1 94 59 4896 flaja4maf1 F 138.55 35.7 18.59753 64.95 95 55 4922 mluli5moc1 M 151.65 54.7 23.78496 78.5 116 67 5014 fdama5moc1 F 150.5 47.75 21.08145 71.8 109 87 5053 fgrti5wf1 F 144.6 38.4 18.36516 68 91 68 5116 fprga5maf1 F 138.05 27.9 14.63967 55.1 95 58 5119 fanel4wf1 F 138.05 36.75 19.28344 65.9 112 76 5125 fhoma5bc1 F 147.75 40.8 18.68982 63.85 93 51 5208 fdech5bc1 F 145.7 39.4 18.55996 62.45 99 68 5216 fgisa5bc1 F 150.45 44.9 19.83636 67.5 99 63 5302 mpoma5bc1 M 140 30.1 15.35714 55.5 101 66 5313 fsujo4maf1 F 149.35 42.3 18.964 68.75 109 68 5323 fhona5maf1 F 133 26.1 14.75493 57.5 100 60 5380 mlipa4bc1 M 126.15 25.2 15.83529 56.5 93 60 5430 flija5moc1 F 145.6 29.45 13.89193 53.45 95 61 5521 marno5moc1 M 152 39.8 17.22645 69.4 94 52 5544 fcsju4wf1 F 142.75 34.9 17.12668 60.45 103 71 5573 mgral4wf1 M 142 36.35 18.02718 64 84 51 5583 fsuan4maf1 F 126.85 27.2 16.90394 56.9 93 53 5689 mgudo4wf1 M 134.6 37.2 20.53302 71.05 100 67 5733 malst5wf1 M 146.65 36.5 16.97183 64.1 99 58 5735 mzhaa5maf1 M 143.3 41.7 20.3069 65.25 107 65 5800 monal4moc1 M 139.15 33 17.04306 59.5 96 60 5805 mfljo5maf1 M 143.55 37.7 18.29511 67.6 96 57 5831 fxizh5maf1 F 146.5 32.3 15.04968 62.6 93 54 5868 mmran5maf1 M 147.25 41.5 19.1398 65.05 102 64 5945 mbada4nc1 M 144.65 47.65 22.7733 72.2 110 71 5949 mmacy5bc1 M 137.35 36.7 19.45399 66.2 89 55 6027 fraa!4bc1 F 138.35 34.9 18.23338 ' 62.05 77 47 6039 mliju5bc1 M 130.05 26 15.37279 51.7 98 70 6081 mkaap5moc1 M 141.75 47.4 23.59023 76.2 102 84 6108 fpeje4bc1 F 141.25 33.6 16.84079 64.85 93 57 6195 fwaas4wf1 F 126.85 29.5 18.33332 59 91 68 6247 fphsa4maf1 F 132.6 31.8 18.0859 63.35 113 74 112 Raw Data 6333 fkelo5wfl F 134.65 33.7 18.58734 65.25 87 51 6367 mlija5nc1 M 141.9 34.3 17.0345 63.3 98 57 6543 mmita4bc1 M 135.1 28.7 15.7243 57.6 87 57 6565 femmi4bc1 F 131.65 29.05 16.76117 57.55 87 64 6572 fsuan5maf1 F 140.45 31.2 15.81653 64.1 111 71 6655 mshra4moc1 M 134.5 25.4 14.04071 52.1 97 58 6673 fchje4bc1 F 129.25 25.2 15.0848 51.9 116 97 6721 fduar5nc1 F 149.05 58 26.10742 82.95 84 66 6758 fhoch5maf1 F 134.05 26.5 14.74729 56.25 107 68 6760 fgoch5bc1 F 134.2 29.05 16.13025 62.1 94 69 6788 myary4wf1 M 135.35 30.9 16.86716 56.4 103 69 6834 fhehe5bc1 F 146.4 40.3 18.80282 69.05 86 56 6845 mhyra5moc1 M 157 53.5 21.70473 74.1 107 54 6998 mwaba5maf1 M 144.75 33.3 15.89304 58.05 103 57 7034 fkwkr5bc1 F 140.6 30.4 15.37811 57.85 85 56 7058 msoje5maf1 M 159.35 51 20.08473 72.5 110 60 7102 mneia4maf1 M 148.85 44 19.85889 75.9 99 58 7225 mclad4bc1 M 131.4 27.35 15.84042 58.35 84 54 7247 merim4bc1 M 137.4 29.9 15.8379 59.4 91 60 7376 fchro5moc1 F 139.1 38 19.63945 68.8 94 56 7386 mchch4maf1 M 151.45 48.95 21.34097 75.9 108 62 7533 mmore4maf1 M 147.4 37.5 17.25982 62.7 96 56 7558 mlavi5maf1 M 144.15 47 22.61875 74.55 96 65 7658 mheth4wf1 M 147.45 40.5 18.62797 65.95 94 63 7662 mgrde5maf1 M 135.65 28.6 15.5427 59.2 103 63 7664 fmaha4bc1 F 137.7 27.6 14.55597 57.25 80 52 7673 mchth4maf1 M 140.2 41.4 21.06223 71.1 102 59 7717 fnoem5wf1 F 147.15 52.6 24.29211 83.6 107 53 7864 mkobr5maf1 M 142.45 37.3 18.38163 67.25 108 81 7932 flaja5maf1 F 139.45 39.55 20.33806 72.25 105 73 7938 fluda5moc1 F 147.2 51.75 23.88332 84.85 107 69 7977 mwoke4maf1 M 139.4 33.8 17.39367 62.1 103 75 8021 fkwwi4moc1 F 151.1 30.5 13.35891 60.55 94 53 8035 fstmi4maf1 F 136.6 35.3 18.91792 66.9 112 65 8073 fgrsu5moc1 F 140.45 38.05 19.28906 64.05 99 67 8313 fluca5maf1 F 139.35 30.8 15.86123 60.05 86 60 8436 mloet5wf1 M 137.45 30.4 16.09104 59.05 88 53 8480 fmobr4maf1 F 131.05 27 15.72135 55 80 60 8508 ftase4maf1 F 135.85 31.25 16.93288 58.95 88 52 8538 fleyo5maf1 F 147.55 54.8 25.1711 76.95 109 57 8579 fhech4maf1 F 140.45 32.6 16.52624 58.65 86 67 8591 fbash5nc1 F 148.65 48.9 22.12988 76.2 103 61 8664 marju4nc1 M 133.1 28.1 15.86172 61.7 88 73 8703 mbhka4moc1 M 139.6 26.55 13.62366 54 102 62 8714 floem5wf1 F 155.25 43.45 18.02713 63.95 116 77 8728 fraha5moc1 F 139.85 30.65 15.67132 54.7 94 58 8867 mzike5wf1 M 139.05 38.05 19.67944 64.95 101 75 8910 mhush5nc1 M 138.5 47.1 24.55395 81.45 107 66 8915 mchhu4maf1 M 124.8 25.1 16.11553 55.85 95 60 8943 fphda4nc1 F 140.5 37.05 18.76876 63.55 95 59 8947 fwosa5bc1 F 138.05 25.9 13.59023 52.95 90 61 113 8981 fdhpa5moc1 F 9000 fbaly5maf1 F 9032 fchch4maf1 F 9038 mtrda5nc1 M 9147 fyaka4maf1 F 9170 mchda4maf1 M 9546 fboda4wf1 F 9584 fzava5maf1 F 9585 mther5bc1 M 9605 fmema5moc1 F 9607 mmako4maf1 M 9676 fjije4moc1 F 9693 frojh4maf1 F 9710 mvelo4wf1 F 9733 mjuma5maf1 M 9808 fsoan5maf1 F 9869 mwomo4bc1 M 9916 flash4moc1 F 1017 mveda4maf1 M 1176 mchjo4maf1 M 1275 fhama4bbf1 F 1464 mriia5bbf1 M 1700 fbrma4wf1 F 1864 filan5maf1 F 2053 mpapa4mf1 M 2066 finke5bbf1 F 2265 ffrka4mf1 F 2273 mzhje5maf1 M 2415 mswan5mf1 M 2649 fsaje4nc1 F 2701 fwage5wf1 F 2709 fcala5bf1 F 2714 fhash4mf1 F 2879 mwaco4bc1 M 2995 melli4maf1 M 3050 mhora5maf1 M 3054 floca4bbf1 M 3437 mchto5ma1 M 3651 mroro4nc1 M 3682 mkijo5maf1 M 3685 mbaru4maf1 M 3692 fhaki4bbf1 F 3770 faral5wf1 F 3856 mfavi5nc1 M 3991 mdebh4moc1 M 4011 manbr5nc1 M 4066 fmcme5wf1 F 4233 fzhli4maf1 F 4247 mlaro5bf1 M 4381 flesa5bf1 F 4593 mahav4moc1 M Raw Data 145.1 39.5 18.76127 72.5 91 55 154.55 49.4 20.68183 66.4 102 57 140.2 36.1 18.36586 63.1 110 68 149.6 46.5 20.77733 70.7 107 75 145.55 40 18.88146 66.75 95 60 133.65 28.2 15.78742 59 86 53 143.05 33.6 16.41964 60.75 100 59 147.6 41.5 19.04914 66.7 95 56 141.35 34.4 17.21737 62.95 104 70 138.75 28.85 14.9858 52.25 84 55 138.75 31.8 16.51814 60.25 109 60 134.45 29.7 16.4299 55.9 93 59 130.15 25.95 15.31966 57.95 107 61 138.7 32.9 17.10185 58.65 105 66 136.15 36.5 19.69054 68.05 95 72 132.45 29.9 17.04383 66.2 99 64 135.8 40.45 21.93406 72.3 95 71 134.65 27 14.89193 50.85 94 67 145.15 43 20.40959 69.75 105 80 141.45 36 17.99271 61.1 #NULL! #NULL! 128.1 24.3 14.8084 55.35 86 66 150 60.3 26.8 95.05 104 69 131.25 31.7 18.40181 62.45 86 50 143.9 38.8 18.73743 55.85 97 61 143.45 32.8 15.93943 60.85 100 71 146 39.95 18.74179 71.4 93 59 138.6 29.55 15.38265 58.95 #NULL! #NULL! 149.05 49.1 22.10129 75.35 113 70 147.25 33.25 15.3349 60.05 100 61 137.1 37.2 19.79101 70.05 96 65 134.15 26.9 14.94758 56.25 99 56 135.35 24.3 13.26447 57.05 89 68 134.7 30.1 16.58942 59 102 68 144.1 34.2 16.47017 62.5 84 50 138.15 41.85 21.92773 74.95 #NULL! #NULL! 136.75 30.5 16.30967 62.8 #NULL! #NULL! 142.5 38.1 18.7627 64.45 91 62 143.7 37.2 18.0148 63.45 93 58 136.35 42.55 22.88702 70.45 100 72 143.9 32.6 15.74331 58.1 105 68 146.45 34.5 16.08571 62.45 95 56 134.6 35.1 19.3739 59.85 87 60 145.05 42.7 20.29516 71.6 80 60 138.85 49.9 25.88265 89.5 97 66 143.1 31.45 15.35824 56.8 95 80 144.1 31.3 15.07358 62.7 87 49 140.35 35.9 18.22509 63.9 94 75 142.45 30.2 14.88271 56.35 99 64 134.5 32.15 17.772 61.5 92 55 141.65 35.7 17.79242 61.75 95 69 133.65 30.4 17.01906 62.8 88 69 114 Raw Data 4625 mnipe4maf1 M 145.75 33.6 15.81693 64.45 96 63 4908 mstha5mf1 M 139.5 30.1 15.46743 57 93 55 5011 mdisu4moc1 M 136.9 26.55 14.16634 53.6 86 59 5160 mlira5maf1 M 144.95 40.7 19.37126 67.2 101 62 5180 mlhlo4wf1 F 130.7 24.85 14.54706 56.35 91 61 5187 fsije4maf1 F 144.85 34 16.20473 56.75 97 54 5264 myeto5maf1 M 163.55 74.9 28.00146 90.45 122 84 5400 fjokrtlfl F 146.35 35.05 16.36449 61.85 85 59 5609 fhuda4bbf1 F 144.45 51.1 24.48983 80.85 103 67 5765 mzhal5maf1 M 145.65 42.6 20.08115 68.55 98 60 5932 fbash4bc1 F 135.75 45.05 24.44641 80.1 105 63 5961 morba4bf1 M 138.1 33.8 17.72268 60.8 70 46 5996 mshaa4bbf1 M 134.5 29.8 16.47296 60.55 93 59 6058 fbrsh5maf1 F 154.85 40.6 16.93182 61.95 #NULL! #NULL! 6060 flesh5maf1 F 136.3 29.9 16.09457 55.6 103 67 6062 mmach4bbf1 M 133.35 25.8 14.50887 54.95 105 51 6109 fwija5moc1 F 140.45 42 21.29148 64.05 #NULL! #NULL! 6152 fmcky5mf1 F 151.4 47.2 20.59161 72.15 106 64 6177 mhabo4maf1 M 132.45 32.5 18.52591 65.95 80 52 6208 fkran4wf1 F 132.2 27.1 15.50624 56.75 97 59 6251 fkoma5maf1 F 161.15 35.8 13.7855 62.25 135 120 6279 fnesh5mf1 F 148.45 37.2 16.88039 63.4 104 54 6301 fnosa4bc1 F 137.6 28.9 15.26374 56.6 98 58 6344 mmobr5maf1 M 143.15 36.9 18.0071 64.45 90 75 6529 mpajimafl M 150.85 41.2 18.10534 62.5 97 63 6631 mmemi5bbf1 M #NULL! #NULL! #NULL! #NULL! 106 73 6723 mczig5mf1 M 145.7 32.5 15.30961 57.1 101 68 6814 fruvi4bc1 F 141.35 #NULL! #NULL! 65.95 84 48 6936 fgrme4bbf1 F 139.8 30.5 15.60578 55.8 92 58 7077 mquph5lf1 M 147.55 40 18.37306 65.45 93 56 7175 fdeta5maf1 F 140.75 33.1 16.70826 60.05 93 61 7204 mgash5lf1 M 140.5 28.7 14.53882 56.45 #NULL! #NULL! 7264 fzhci4maf1 M #NULL! #NULL! #NULL! #NULL! 102 72 7293 flodi5maf1 F 151.55 42.7 18.59157 61.9 101 63 7465 floma4bc1 M 145.65 53.4 25.17215 80.55 93 54 7563 fraem4maf1 F 136.35 29.9 16.08277 54.6 #NULL! #NULL! 7577 mwede4bc1 M 146.1 39.8 18.64587 63.9 92 56 7631 fkyme4maf1 F 134.4 28.7 15.88852 56.45 #NULL! #NULL! 7880 fviha5nc1 F 149.9 39.7 17.66799 60.7 110 65 7911 mstda4bbf1 M 137.45 38.5 20.37845 73.9 96 60 7941 mfast5nc1 M 133.9 32.6 18.18262 63.8 96 59 8043 mgabe4bbf1 M 132.85 33.2 18.81113 59.45 91 61 8171 mtuda4mf1 M 148.25 37.2 16.92597 62.05 98 61 8308 mbrma4bbf1 M 144.2 46.1 22.17024 79.85 109 73 8409 fchsy4moc1 F 127.85 29.9 18.29236 58.9 101 65 8411 fwaan5bbf1 F 140.6 32.9 16.64276 61.7 85 54 8466 fholi4bbf1 F 142.1 42 20.79989 68.85 73 57 8520 mvake4bc1 M 122.25 21.4 14.31911 48.8 #NULL! #NULL! 8524 figan5maf1 F 150.7 32.6 14.3546 55.8 88 57 8573 frani5maf1 F 140.15 44.1 22.45186 68.55 109 70 8583 mmefe4mf1 #NULL! #NULL! #NULL! #NULL! 97 63 115 Raw Data 8599 fhije4wf1 F 143.45 31.3 15.21049 55.7 96 60 8629 fbrar5maf1 F 152.55 51.4 22.0871 73.1 99 61 8894 fcaar4maf1 F 136.6 42.4 22.72294 73.4 #NULL! #NULL! 8926 mlaja4bbf1 M 136.95 34.7 18.50143 58.85 #NULL! #NULL! 8982 mlowe4bf1 M 133.45 28.7 16.11554 61.35 110 67 9099 mzech5bbf1 M 143.25 41.3 20.12615 68.6 93 55 9102 ftoda5maf1 F 147.25 43.3 19.96996 64.5 108 71 9139 mstky5wf1 M 139.45 29.9 15.37567 58.05 88 64 9388 mbayo5lf1 M 136.45 30.3 16.27405 59.85 100 75 9433 mfoma4bbf1 M 132.3 27.9 15.93986 62.85 75 59 9496 frecl4maf1 F 134.75 30.05 16.54958 57.6 95 59 9553 fguca4maf1 F 136.45 38 20.4097 63.05 113 82 9651 fobfr4bbf1 F 148.65 41.6 18.82624 61.45 83 60 9669 fsaev4maf1 F 135.8 31.4 17.02669 58.65 88 64 9934 mulma5mf1 M 139.45 41.3 21.23797 73.6 106 67 9996 mguan4mf1 M 154.6 59.3 24.81051 78.1 108 66 116 Raw Data PR SRMAX Curlups Pushups GripTot Lapsrun DOB Start Date Age 98 23 37 3 37 45 3/29/1996 12/7/2005 9.69 87 23 4 5 22 28 8/22/1995 11/16/2005 10.24 99 17 9 14 32 51 1/24/1996 11/30/2005 9.85 84 28 9 3 27 41 10/25/1996 12/7/2005 9.12 103 15 23 2 25 24 3/1/1996 11/2/2005 9.67 74 36 16 8 33 44 10/4/1995 11/2/2005 10.08 90 27 8 0 42 14 7/6/1995 2/1/2006 10.58 104 25 22 2 19 14 5/20/1995 2/1/2006 10.70 99 25 12 2 30 19 11/28/1995 11/16/2005 9.97 83 34 23 21 29 31 8/2/1996 11/30/2005 9.33 84 31 28 2 43 43 8/26/1995 11/16/2005 10.23 86 38 8 3 33 30 4/20/1996 11/16/2005 9.57 86 25 20 28 30 61 6/25/1995 11/2/2005 10.36 93 29 0 0 25 4 7/12/1996 1/11/2006 9.50 92 28 32 10 44 36 10/12/1996 11/2/2005 9.06 #NULL! 27 7 5 29 26 12/2/1996 12/7/2005 9.01 91 32 9 6 34 35 4/29/1996 11/30/2005 9.59 86 19 8 15 38 22 10/6/1996 2/1/2006 9.32 94 24 0 1 41 10 7/25/1995 2/1/2006 10.52 93 26 14 12 40 54 4/22/1996 12/7/2005 9.63 89 28 27 2 50 18 3/19/1995 11/16/2005 10.66 94 21 0 0 41 13 10/3/1995 2/8/2006 10.35 63 21 8 7 39 44 5/6/1995 11/2/2005 10.49 85 14 0 0 30 16 7/14/1995 2/8/2006 10.57 76 31 5 10 31 44 11/9/1995 11/16/2005 10.02 103 16 11 0 30 20 9/10/1995 1/11/2006 10.34 83 21 8 1 38 18 2/16/1995 11/30/2005 10.79 67 24 21 10 33 31 5/2/1996 12/7/2005 9.60 91 16 0 0 37 28 4/13/1995 11/30/2005 10.63 106 29 2 0 24 17 4/6/1995 12/7/2005 10.67 112 34 0 0 19 9 8/12/1996 2/1/2006 9.47 82 22 15 8 43 17 1/24/1995 11/30/2005 10.85 73 6 15 0 33 19 7/17/1995 11/2/2005 10.30 85 33 4 0 42 8 5/5/1996 2/1/2006 9.74 95 25 11 6 26 21 5/31/1995 12/7/2005 10.52 94 37 9 10 37 10 7/4/1996 1/11/2006 9.52 79 28 14 0 42 31 10/19/1995 12/7/2005 10.14 84 28 0 0 36 10 10/20/1995 12/7/2005 10.13 86 25 7 0 40 28 2/10/1995 11/30/2005 10.80 97 28 4 0 31 15 7/8/1995 2/1/2006 10.57 85 15 22 2 46 17 11/29/1995 2/8/2006 10.20 80 22 3 0 30 12 10/28/1996 2/8/2006 9.28 67 20 16 5 25 34 4/28/1996 11/16/2005 9.55 86 25 0 3 26 14 7/1/1996 12/7/2005 9.43 77 27 0 1 32 26 11/15/1995 12/7/2005 10.06 116 30 20 0 46 26 4/21/1996 2/8/2006 9.80 96 16 10 4 41 25 2/27/1996 11/2/2005 9.68 92 28 18 0 41 16 2/18/1996 2/1/2006 9.95 84 39 9 13 45 28 6/4/1995 11/30/2005 10.49 101 32 5 0 42 8 12/18/1995 1/11/2006 10.07 98 29 94 14 27 30 7/1/1996 11/16/2005 9.38 97 29 0 0 22 28 12/12/1995 11/30/2005 9.97 11 Raw Data 78 20 7 7 30 26 1/6/1995 11/30/2005 10.90 109 31 4 0 33 17 10/18/1996 2/8/2006 9.31 92 35 0 0 25 22 8/23/1996 1/11/2006 9.39 72 26 15 1 53 36 9/19/1996 11/16/2005 9.16 95 13 4 15 36 26 5/26/1995 2/8/2006 10.71 108 22 0 0 29 11 1/11/1996 2/1/2006 10.06 92 16 29 2 37 27 2/15/1995 12/7/2005 10.81 91 28 5 3 37 41 1/22/1995 11/16/2005 10.82 71 22 5 1 31 43 4/28/1995 11/16/2005 10.55 74 26 10 7 39 49 2/18/1996 11/30/2005 9.78 81 14 4 0 27 7 8/18/1995 11/2/2005 10.21 67 48 94 42 42 38 8/4/1995 11/16/2005 10.29 66 15 14 0 40 12 2/22/1995 2/8/2006 10.96 101 23 0 0 47 10 7/17/1995 2/8/2006 10.57 66 18 11 12 44 43 2/6/1995 11/2/2005 10.74 91 26 23 9 42 29 11/10/1997 12/7/2005 8.07 111 29 1 0 42 7 8/23/1995 2/1/2006 10.44 81 8 0 0 35 8 2/6/1995 2/8/2006 11.01 86 18 8 5 30 8 10/31/1995 11/2/2005 10.01 92 38 29 0 25 22 11/19/1995 11/30/2005 10.03 77 31 12 3 25 15 10/27/1996 11/2/2005 9.02 65 49 10 2 51 14 9/29/1995 11/16/2005 10.13 80 33 15 8 42 44 2/18/1995 11/16/2005 10.74 88 35 0 2 44 28 4/25/1995 11/16/2005 10.56 69 21 3 1 34 17 7/18/1995 11/16/2005 10.33 88 33 18 0 31 12 1/23/1996 12/7/2005 9.87 87 23 2 0 23 23 9/15/1995 11/30/2005 10.21 83 31 20 0 25 23 11/21/1996 11/16/2005 8.99 87 33 0 0 25 8 9/18/1995 2/8/2006 10.39 75 34 11 2 44 20 1/4/1995 2/8/2006 11.10 #NULL! 29 3 2 31 14 8/29/1996 11/2/2005 9.18 86 27 15 15 34 28 5/26/1996 11/2/2005 9.44 74 28 13 10 25 24 12/28/1996 11/30/2005 8.92 78 28 10 12 36 20 6/21/1996 11/2/2005 9.37 62 21 25 30 42 60 4/20/1995 11/2/2005 10.54 87 26 19 0 36 22 7/30/1995 12/7/2005 10.36 99 23 1 2 25 20 4/6/1996 2/8/2006 9.84 92 30 21 6 40 17 2/13/1995 11/30/2005 10.80 71 35 4 2 30 26 3/6/1995 12/7/2005 10.76 101 25 20 9 37 47 11/28/1995 11/30/2005 10.01 86 27 12 0 40 12 6/25/1996 1/11/2006 9.55 91 31 2 0 33 25 12/28/1995 11/16/2005 9.89 89 41 27 12 40 12 4/9/1996 11/16/2005 9.60 75 24 18 10 25 16 4/28/1995 11/16/2005 10.55 100 23 0 0 40 10 1/25/1995 2/1/2006 11.02 92 20 10 0 33 10 5/31/1996 11/16/2005 9.46 90 25 12 4 23 13 5/19/1996 11/2/2005 9.46 102 32 1 0 26 19 10/11/1996 12/7/2005 9.16 86 37 12 9 34 25 9/14/1995 11/2/2005 10.14 69 20 11 3 35 21 1/17/1995 1/11/2006 10.98 98 33 95 1 31 23 7/1/1996 11/16/2005 9.38 98 25 12 0 21 17 1/30/1996 11/16/2005 9.80 79 29 3 0 32 16 4/23/1995 12/7/2005 10.63 118 Raw Data 86 21 8 1 36 13 1/5/1996 2/1/2006 10.08 93 34 7 1 26 11 11/18/1996 11/16/2005 8.99 72 18 0 0 31 8 10/26/1995 1/11/2006 10.21 94 21 0 10 25 17 9/15/1995 11/30/2005 10.21 128 21 0 0 17 12 1/5/1995 11/16/2005 10.86 97 34 20 12 28 35 9/13/1996 11/2/2005 9.14 69 31 14 0 37 11 8/25/1995 11/16/2005 10.23 78 19 0 0 39 22 12/1/1995 2/8/2006 10.19 86 23 51 20 44 63 10/21/1995 12/7/2005 10.13 70 26 9 0 21 8 8/28/1995 11/16/2005 10.22 95 24 20 0 25 21 3/25/1995 11/30/2005 10.69 82 19 0 4 40 26 5/17/1996 12/7/2005 9.56 94 29 4 0 31 21 10/14/1996 11/16/2005 9.09 96 35 0 9 32 26 2/3/1996 11/16/2005 9.79 80 20 5 0 32 11 7/16/1995 2/8/2006 10.57 88 18 0 0 28 12 1/7/1996 12/7/2005 9.92 69 28 17 16 48 81 4/17/1996 12/7/2005 9.64 89 25 8 9 42 13 11/17/1995 11/30/2005 10.04 98 16 17 2 34 11 5/26/1996 11/2/2005 9.44 78 24 7 16 35 40 8/8/1995 12/7/2005 10.33 82 30 2 0 21 17 8/5/1996 11/16/2005 9.28 72 18 6 0 41 12 11/25/1996 12/7/2005 9.03 98 5 0 0 27 5 11/11/1995 11/2/2005 9.98 89 35 17 0 36 19 12/5/1995 12/7/2005 10.01 93 31 10 1 29 28 12/22/1995 12/7/2005 9.96 70 29 20 0 48 12 4/19/1995 2/1/2006 10.79 94 19 0 0 39 12 11/6/1996 12/7/2005 9.08 89 22 18 0 23 26 4/12/1996 2/8/2006 9.83 90 27 6 8 35 36 6/26/1996 12/7/2005 9.45 82 30 32 2 39 14 3/5/1995 2/1/2006 10.91 90 37 3 1 27 14 9/23/1995 11/30/2005 10.19 80 11 3 0 26 16 7/21/1995 11/2/2005 10.29 93 38 4 6 35 33 9/9/1996 11/30/2005 9.22 103 23 19 5 32 24 3/2/1996 11/30/2005 9.75 79 36 9 2 32 19 11/23/1995 12/7/2005 10.04 93 23 11 6 40 33 2/8/1996 11/30/2005 9.81 94 31 0 0 30 10 8/10/1995 1/11/2006 10.42 87 21 0 2 25 22 6/21/1996 1/11/2006 9.56 112 23 7 4 27 26 8/30/1996 2/8/2006 9.44 114 16 25 5 38 20 1/24/1995 11/2/2005 10.77 119 16 0 0 26 8 1/8/1995 2/8/2006 11.09 97 23 36 3 39 38 4/29/1995 11/2/2005 10.51 85 16 2 0 24 6 10/29/1995 1/11/2006 10.20 91 27 0 11 21 48 7/9/1996 11/30/2005 9.39 91 36 2 0 37 16 10/10/1996 1/11/2006 9.25 84 40 7 0 30 15 3/16/1995 11/16/2005 10.67 73 30 9 0 37 12 3/5/1995 2/8/2006 10.93 90 31 5 2 41 20 3/8/1995 11/30/2005 10.73 92 26 11 4 35 26 8/11/1996 11/30/2005 9.30 80 21 12 0 39 10 1/19/1995 1/11/2006 10.98 92 27 12 1 37 15 4/5/1996 12/7/2005 9.67 79 33 12 14 33 32 8/22/1996 12/7/2005 9.29 80 18 19 8 31 40 2/7/1995 11/2/2005 10.74 119 Raw Data 81 29 75 10 47 27 6/1/1995 12/7/2005 10.52 78 23 5 0 30 15 12/6/1995 11/16/2005 9.95 92 39 9 16 28 12 7/5/1995 2/8/2006 10.60 #NULL! 23 11 13 40 14 7/18/1996 11/30/2005 9.37 109 37 30 0 25 8 11/17/1996 2/8/2006 9.23 79 29 26 19 30 53 7/3/1996 12/7/2005 9.43 74 27 25 37 41 46 11/21/1995 11/2/2005 9.95 67 41 11 14 36 42 4/14/1995 12/7/2005 10.65 83 42 18 0 29 17 8/16/1995 12/7/2005 10.31 88 20 0 0 20 11 9/2/1996 11/16/2005 9.20 92 32 0 0 25 16 3/22/1996 2/1/2006 9.86 88 25 19 0 38 21 1/16/1996 11/30/2005 9.87 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 6/17/1996 11/30/2005 9.45 79 22 0 2 23 20 11/9/1996 1/20/2006 9.20 93 12 0 10 45 21 12/9/1995 1/20/2006 10.12 66 30 21 14 23 19 3/1/1996 11/2/2005 9.67 94 31 18 4 32 17 11/30/2005 ##### 105 29 20 18 30 28 9/24/1996 11/23/2005 9.16 79 17 0 13 34 44 1/22/1995 1/20/2006 11.00 #NULL! 17 5 4 27 45 11/23/2005 6.62 92 25 17 0 32 24 1/3/1995 11/30/2005 10.91 79 19 13 14 40 41 2/22/1995 11/23/2005 10.75 95 28 0 0 26 7 3/22/1996 1/11/2006 9.81 62 22 4 13 26 12 6/21/1995 11/2/2005 10.37 84 29 28 5 23 12 10/25/1995 11/9/2005 10.04 85 21 11 8 22 12 11/7/1996 11/23/2005 9.04 80 22 4 1 37 10 7/27/1996 11/16/2005 9.31 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 8/6/1996 11/30/2005 9.32 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 4/12/1995 12/7/2005 10.66 83 20 0 0 34 29 11/5/1996 1/20/2006 9.21 85 32 11 0 35 13 4/15/1995 11/30/2005 10.63 89 25 0 2 36 9 2/16/1996 1/11/2006 9.90 86 34 .7 0 31 21 8/8/1995 11/30/2005 10.31 85 24 42 24 37 53 1/30/1996 12/7/2005 9.85 99 19 1 0 35 18 4/24/1996 1/20/2006 9.74 66 28 10 0 31 20 9/17/1995 11/2/2005 10.13 104 21 1 2 30 10 8/28/1995 1/11/2006 10.37 98 30 0 0 33 27 6/20/1996 2/1/2006 9.62 108 15 22 5 34 18 5/19/1995 1/11/2006 10.65 77 26 14 15 30 36 1/6/1995 11/2/2005 10.82 96 41 11 1 33 22 3/27/1997 12/7/2005 8.70 75 35 17 26 33 51 10/28/1995 11/9/2005 10.03 84 37 13 14 35 38? 11/9/2005 10.72 93 30 0 0 31 20 10/9/1996 2/1/2006 9.31 84 23 11 3 31 18 6/26/1996 12/7/2005 9.45 81 26 38 31 40 40 9/8/1995 11/23/2005 10.21 97 27 15 0 22 13 5/9/1996 2/8/2006 9.75 88 34 8 8 33 20 9/29/1995 11/30/2005 10.17 94 33 10 6 34 34 10/30/1996 11/2/2005 9.01 108 27 20 2 32 15 10/6/1996 12/7/2005 9.17 #NULL! 20 0 0 #NULL! NULL 6/4/1995 11/30/2005 10.49 87 27 11 9 34 15 5/8/1996 11/9/2005 9.51 109 25 4 0 40 11 8/19/1996 1/20/2006 9.42 120 Raw Data 75 26 16 0 36 16 12/13/1995 11/30/2005 9.97 83 32 0 0 24 16 6/3/1996 11/16/2005 9.45 96 31 10 9 40 32 2/16/1995 11/9/2005 10.73 80 17 7 4 35 33 5/30/1996 1/20/2006 9.64 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 5/4/1995 12/7/2005 10.60 84 38 12 3 24 26 8/4/1995 12/7/2005 10.34 103 33 17 12 34 34 7/13/1995 1/20/2006 10.52 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 9/8/1995 2/8/2006 10.42 94 17 22 17 39 20 4/21/1995 11/23/2005 10.59 84 29 1 2 17 19 11/15/1996 12/7/2005 9.06 110 21 4 2 26 34 5/30/1996 11/2/2005 9.43 79 31 17 10 41 22 11/17/1995 . 11/30/2005 10.06 78 9 23 10 35 20 2/27/1995 11/23/2005 10.74 76 36 2 0 24 11 12/11/1996 11/16/2005 8.93 92 32 7 6 43 30 1/31/1995 11/30/2005 10.83 88 33 10 4 47 37 4/20/1995 11/30/2005 10.61 91 23 23 14 55 43 3/16/1995 1/20/2006 10.85 110 20 17 13 37 50 5/9/1995 11/23/2005 10.54 79 31 0 0 27 7 3/3/1996 11/16/2005 9.71 101 36 22 0 42 56 3/14/1995 1/20/2006 10.86 81 26 20 8 37 25 4/8/1995 11/9/2005 10.59 107 25 16 12 35 35 ? 11/30/2005 10.92 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 6/11/1995 11/9/2005 10.41 86 41 21 1 31 31 3/29/1996 12/7/2005 9.69 91 29 9 4 40 40 5/4/1995 11/30/2005 10.58 60 17 3 0 41 13 6/17/1996 11/16/2005 9.42 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 12/20/1996 12/7/2005 8.96 115 12 42 1 40 24 5/10/1996 11/16/2005 9.52 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 8/27/1996 12/7/2005 9.28 79 24 13 8 41 15 9/14/1995 1/11/2006 10.33 90 22 10 5 37 16 4/15/1996 1/20/2006 9.77 83 13 6 0 26 10 8/28/1995 1/11/2006 10.37 91 25 0 20 39 33 7/2/1996 1/20/2006 9.55 102 21 83 25 42 47? 11/23/2005 ##### 71 16 0 0 43 20 5/30/1996 1/20/2006 9.64 115 23 11 0 23 11 4/11/1996 2/8/2006 9.83 81 35 6 12 31 38 10/5/1995 1/20/2006 10.29 91 30 4 0 46 31 7/24/1996 1/20/2006 9.49 #NULL! 13 0 7 22 5 7/24/1996 11/16/2005 9.31 83 34 25 4 31 18 1/26/1995 11/30/2005 10.84 107 44 2 4 32 20 11/2/1995 11/30/2005 10.08 127 35 3 7 29 21 3/28/1996 11/23/2005 9.66 89 26 19 8 28 14 7/25/1996 11/2/2005 9.27 102 36 4 1 37 24 6/17/1995 12/7/2005 10.48 #NULL! #NULL! #NULL! #NULL! #NULL! #NULL! 11/8/1996 12/7/2005 9.08 #NULL! 28 18 8 39 31 1/21/1996 1/20/2006 10.00 75 16 0 0 21 16 9/14/1996 11/9/2005 9.15 97 24 8 11 36 22 4/25/1995 1/20/2006 10.74 88 31 16 10 43 17 9/19/1995 11/30/2005 10.20 82 23 17 20 38 61 8/2/1995 11/2/2005 10.25 83 19 5 6 30 17 10/18/1995 11/9/2005 10.06 75 32 26 9 36 54 7/10/1996 1/20/2006 9.53 106 21 10 0 28 9 5/31/1996 11/30/2005 9.50 121 Raw Data 97 40 6 5 28 18 1/12/1996 11/30/2005 9.88 91 31 30 0 46 35 1/26/1996 1/20/2006 9.98 100 25 8 7 25 43 12/19/1996 12/7/2005 8.97 107 19 9 4 28 22 6/19/1995 11/23/2005 10.43 81 18 0 1 37 27 4/4/1995 11/23/2005 10.64 122 Raw Data Teacher School Ethnicity Code Wear Weather Avg Wear Total ID ID Code Days Code Hours Counts 235 13413 1 2 0.2 13.03038162 1844871 740 13441 1 1 0.4 14.20742638 2061594 653 13413 2 2 0.8 12.615788 1401196 235 13413 2 0 0.2 12.38531455 1418570 856 13755 1 0 1 14.19519746 1809042 521 13755 1 0 1 11.46798538 1144636 220 13394 2 0 0.8 14.87826159 1414068 220 13394 2 0 0.8 13.34169478 1560389 650 13441 1 0 0.4 13.82560032 1809262 653 13413 1 2 0.8 12.99116054 842437 740 13441 1 0 0.4 14.54383806 2673140 350 13441 2 0 0.4 14.52882417 1911797 546 13755 1 0 0.8 14.02849772 1970807 146 13392 2 2 0.8 14.67736167 2331735 856 13755 1 1 1 11.41135496 1475926 748 13413 1 0 0.2 12.14050407 1481188 653 13413 2 2 0.8 13.46826855 1475721 330 13394 2 0 0.8 13.19309387 1891861 730 13394 2 0 0.2 13.53792419 1017709 435 13413 2 0 0.8 11.5962859 1366065 650 13441 1 2 0.4 12.9605748 1814363 730 13394 2 2 0.8 11.38985161 1149876 521 13755 1 0 1 13.15086502 1150977 730 13394 2 2 0.2 12.58724001 749521 650 13441 1 0 0.4 11.5798037 1882124 140 13392 1 0 0.8 12.33339121 1756141 152 13413 1 2 0.8 12.75257199 1511791 235 13413 1 0 0.2 13.95453999 2102450 886 13413 1 2 0.8 12.40630538 1323842 426 13413 1 0 0.2 13.85650976 1117358 126 13394 2 0 0.6 15.29065291 1303903 168 13413 2 2 0.8 16.04234652 662495 521 13755 1 0 1 13.99456087 3054020 330 13394 2 0 0.8 15.50810676 1806882 426 13413 1 0 0.2 14.81843208 1386605 146 13392 2 0 0.8 14.82103592 1414668 426 13413 1 0 0.2 14.16380892 1831794 435 13413 0 2 0.2 11.49250565 1199520 152 13413 1 2 0.8 14.47436456 1927022 220 13394 2 0 0.8 14.50402596 1639927 710 13394 2 0 0.2 12.03054239 1388741 960 13394 2 2 0.2 13.05224088 1747523 350 13441 1 2 0.4 11.65384086 1112212 235 13413 2 2 0.2 13.06194194 1003620 435 13413 2 0 0.2 14.21710657 1496331 960 13394 2 0 0.2 13.31597539 1124231 856 13755 1 2 1 13.35473759 1754449 126 13394 2 0 0.8 14.24185304 1351248 658 13413 2 2 0.8 12.69418703 1116219 123 Raw Data 140 13392 2 0 0.8 14.26282993 1811441 350 13441 1 0 0.4 11.08035991 845824 886 13413 2 0 0.2 11.98981643 724647 886 13413 1 2 0.8 11.99502373 2041619 960 13394 2 0 0.2 14.63494352 2473362 451 13392 2 0 0.8 14.00944632 1170062 740 13441 1 0 0.4 13.70176622 1292298 710 13394 2 2 0.8 12.52115875 1349643 450 13394 2 0 0.2 12.4347326 1562084 426 13413 1 1 0.2 12.10382813 1277648 740 13441 1 0 0.4 13.36931931 2215439 740 13441 2 0 0.4 14.73149434 1408900 653 13413 1 2 0.8 12.33420855 1202207 521 13755 1 1 1 12.44261053 1409363 740 13441 1 0 0.4 13.55507045 2778271 710 13394 2 0 0.2 14.26568764 1042470 710 13394 2 0 0.2 10.78489554 611894 521 13755 2 0 1 13.72558047 1628605 748 13413 2 0 0.2 10.69961172 1807906 220 13394 2 0 0.8 14.68529459 1854896 710 13394 1 0 0.2 14.10089303 1724921 147 13755 1 0 1 13.18547331 3143363 658 13413 2 2 0.8 12.42113165 1379103 621 13755 1 0 1 12.69394721 1354067 650 13441 1 0 0.4 13.28454058 2127978 740 13441 1 0 0.4 13.5788847 2185348 740 13441 1 0 0.4 13.22929153 1558457 740 13441 1 0 0.4 11.85187537 1487769 435 13413 2 0 0.2 14.49245291 948589 658 13413 2 2 0.8 14.86404834 1063301 350 13441 2 2 0.4 13.90958203 1907709 710 13394 2 0 0.2 14.49772018 1681445 710 13394 2 0 0.8 14.29998015 1772971 856 13755 1 0 1 13.45981346 2251708 856 13755 1 2 1 12.83602138 1342373 653 13413 2 2 0.2 12.81245577 1324072 621 13755 1 2 1 13.2431574 1164362 521 13755 1 0 1 14.02849772 2476019 435 13413 2 0 0.8 14.21520143 1847328 730 13394 2 0 0.8 12.55010915 1816708 168 13413 2 0 0.8 13.23881723 789370 435 13413 2 0 0.2 14.27902362 1034613 168 13413 1 0 0.8 12.2113691 2181715 451 13392 2 0 0.8 13.15388311 2454646 740 13441 1 0 0.4 13.30454455 2304014 350 13441 1 2 0.4 14.03607601 2286340 740 13441 1 2 0.4 12.86064205 1552021 220 13394 2 2 0.6 13.48854211 1017313 130 13441 1 0 0.4 11.9352518 1168040 147 13755 1 0 1 13.95324469 1065839 748 13413 2 2 0.2 12.40154512 1106598 124 Raw Data 546 13755 1 0 1 11.19611808 1791455 657 13392 2 0 0.8 15.48108644 1986185 350 13441 1 1 0.4 11.61153798 1363879 350 13441 1 0 0.4 13.21500298 1812944 435 13413 2 0 0.2 .13.88465965 1457031 126 13394 2 0 0.2 14.05231197 1174641 350 13441 2 0 0.4 14.05993253 1566947 140 13392 2 0 0.8 14.57547512 1478454 168 13413 2 2 0.8 14.59967963 965758 740 13441 1 0 0.4 14.32648164 671960 147 13755 2 0 1 15.14362405 1580825 650 13441 2 2 0.4 12.85270745 954170 710 13394 2 0 0.2 14.43048224 2673618 426 13413 1 0 0.2 14.12955854 2260111 650 13441 2 0 0.4 13.58460012 977491 658 13413 2 2 0.2 12.39833792 1133996 163 13413 1 0 0.2 11.27438371 1382195 350 13441 1 0 0.4 14.32818579 1418867 350 13441 1 0 0.4 14.31331613 1748974 710 13394 2 0 0.2 13.09783687 1006337 748 13413 2 0 0.2 13.43789353 1071120 163 13413 1 0 0.2 13.66556857 2167628 886 13413 2 2 0.8 14.08062001 1748628 147 13755 1 0 1 15.08455076 1656405 426 13413 1 0 0.2 13.54744989 1836389 350 13441 2 0 0.4 13.88084937 2069972 435 13413 2 0 0.2 13.04918898 1229318 546 13755 1 1 1 12.49693173 1269056 435 13413 2 0 0.2 14.24092082 1443942 426 13413 1 0 0.2 13.01591586 1312939 390 13394 2 0 0.8 14.69358211 1004801 235 13413 2 0 0.2 13.18452074 1706730 960 13394 2 0 0.2 14.55336376 987357 163 13413 2 2 0.8 14.86404834 1063301 220 13394 2 0 0.8 12.77491566 1346007 152 13413 2 2 0.8 13.5687453 1410105 521 13755 1 0 1 11.18949843 1014057 653 13413 1 2 0.8 12.271539 2187334 886 13413 2 2 0.8 13.82628399 1375395 426 13413 2 0 0.2 15.03562259 1250647 653 13413 1 2 0.8 11.73403161 1212354 140 13392 2 1 0.8 13.40339601 1389736 451 13392 2 0 0.8 14.68481842 2183768 960 13394 2 0 0.2 15.88206388 2358029 546 13755 1 0 1 12.08049216 1099830 710 13394 2 0 0.2 14.14566382 1261715 147 13755 1 2 1 13.34465195 1567407 140 13392 2 2 0.8 13.45259925 1806361 653 13413 2 2 0.8 13.02157665 1101429 146 13392 2 0 0.8 16.26608454 1569522 740 13441 2 0 0.4 14.74442045 2351298 125 Raw Data 730 13394 2 0 0.2 13.21405041 1304790 658 13413 2 2 0.8 13.74841316 1046612 886 13413 2 2 0.8 12.168028 1120395 657 13392 2 0 0.8 14.7638968 1215299 235 13413 2 0 0.2 13.87227625 1684341 748 13413 2 0 0.2 13.83417345 1496783 621 13755 1 2 1 12.81388013 1557653 426 13413 1 0 0.2 14.61692623 1334702 650 13441 1 0 0.4 12.24044292 1372835 710 13394 2 0 0.2 14.0978483 1192691 886 13413 1 2 0.8 13.6625753 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