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Mobility and cognition are associated with wellbeing and health related quality of life among older adults:… Davis, Jennifer C; Bryan, Stirling; Li, Linda C; Best, John R; Hsu, Chun L; Gomez, Caitlin; Vertes, Kelly A; Liu-Ambrose, Teresa Jul 5, 2015

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RESEARCH ARTICLE Open AccessMobility and cognition are associated withwellbeing and health related quality of life amongolder adults: a cross-sectional analysis of theVancouver Falls Prevention CohortJennifer C. Davis1,2,4, Stirling Bryan1,2,4, Linda C. Li3,4,5, John R. Best3,4,6,7,8, Chun Liang Hsu3,4,6,7,8, Caitlin Gomez4,6,Kelly A. Vertes4,6 and Teresa Liu-Ambrose3,4,6,7,8*AbstractBackground: Ascertaining individuals’ quality of life and wellbeing is essential in public health and clinical research.The impact of these two pressing geriatric syndromes – impaired mobility and cognitive function – on wellbeingand quality of life is not well examined. Hence, our objective was to identify key clinically relevant outcomemeasures of mobility and cognitive function that explain variation in wellbeing and health related quality of life(HRQoL) among community dwelling older adults.Methods: We conducted a cross-sectional analysis of 229 participants presenting to the Vancouver Falls PreventionClinic from June 2010 through October 2013. The linear regression models included two dependent variables: theICECAP-O assessing wellbeing and the EQ-5D-3L assessing HRQoL. Key independent variables included the ShortPerformance Physical Battery (SPPB) and the Montreal Cognitive Assessment (MoCA). Covariates included FunctionalComorbidity Index (FCI), sex and age. In the two multiple linear regression models, age was statistically controlled.Other covariates (i.e., sex and FCI) were included based on statistical significance (i.e., p < 0.05).Results: The SPPB was significantly associated with HRQoL and with wellbeing after adjusting for known covariates(p < 0.05, Unstandardized ß (Standard Error) 0.023 (0.006) for HRQoL and 0.016 (0.003) for wellbeing). The MoCA wassignificantly associated with wellbeing after adjusting for known covariates (p = 0.006), Unstandardized ß (StandardError) 0.005 (0.002) but not with health related quality of life (p > 0.05).Conclusion: We found that a measure of mobility and balance was associated with HRQoL and wellbeing. However,cognitive function was associated with wellbeing only. This study highlights the potential importance of consideringwellbeing as an outcome measure if interventions are intended to have a broader impact than health alone.Keywords: Mobility, Cognition, Quality of life, WellbeingBackgroundAscertaining individuals’ quality of life is a critically rele-vant activity for public health decision making and clin-ical research [29] and should be considered a priority.The outcomes of health care interventions are likely tohave impact that extend broadly to quality of life out-comes [9]. For example, older adults who are able tomaintain their mobility and overall functional independ-ence are likely to feel more secure and a better generalsense of wellbeing. Such feelings may not be reflectedfully by ascertaining health related quality of lifealone (HRQoL) as compared with quality of life/wellbeing.As such, examining quality of life more broadly may be animportant supplement to accurately value the impact ofvarious interventions aimed at combatting cognitivedecline and mobility impairments among older adults.Wellbeing can be assessed using the ICECAP-O indexof capability, a preference-based outcome measure.* Correspondence: tlambrose@exchange.ubc.ca3Department of Physical Therapy, 2177 Wesbrook Mall, Vancouver, Canada4University of British Columbia, Vancouver, BC V6T 2B5, CanadaFull list of author information is available at the end of the article© 2015 Davis et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Davis et al. BMC Geriatrics  (2015) 15:75 DOI 10.1186/s12877-015-0076-2Preference-based outcome measures are distinct fromother health or wellbeing status instruments becausethey provide insight into individuals within society’s val-uations of specific states of health or wellbeing status.The ICECAP-O was developed to provide a broader as-sessment of gains or losses that extend beyond healthalone – wellbeing (ie., quality of life more broadly)[5, 31]. It is described by its developers as a measure ofwellbeing and capability, conceptually linked to Sen'scapability approach which defines wellbeing in terms ofwhat individuals are able to do, not what individualsactually do [5, 6, 32, 33]. Specifically, this approach isbased on assessing an individuals capability to achievevalued functionings [23]. Capabilities reflect an individ-ual’s ability to perform specific tasks. Sen emphasizesthat an individual’s capabilities are most useful in asses-sing impact [33].HRQoL is frequently ascertained using the EQ-5D[24]. The EQ-5D three level (3L) (EQ-5D-3L) versioncaptures 243 health states [24] and assesses an individ-ual’s HRQoL according to the following attributes: mo-bility, self-care, usual activities, pain and, anxiety ordepression. The EQ-5D is the most widely used genericinstrument that uses a utility-based scoring approach,yielding a single summary score (i.e., Health State UtilityValue (HSUV)) on a common scale to facilitate compari-son across different health conditions and patient popula-tions [24]. The HSUV is anchored at zero – a health stateequivalent to death and 1.0 – a state defined as “fullhealth”. HSUVs less than zero define health states worsethan death. The EQ-5D is one example of a tool that isused to attach a metric to measure ‘health’. HSUVs are ahighly relevant and important outcome in both clinicalresearch and clinical practice. Yet, they may not fully cap-ture quality of life outcomes more broadly. As such, it ispossible that HSUVs may underestimate potential benefitsof health care or public health interventions.Impaired cognitive and mobility critically impact olderadult's HRQoL and wellbeing [2, 26]. Impaired mobilityis a major concern for older adults and is associated withgreater risk for disability, institutionalization, and death[30]. Cognitive impairments and mobility issues oftenco-exist and their temporal relationship appears to bebi-directional. Impaired mobility is becoming recognizedas a neurological biomarker of dementia during preclinicalstages [4]. Current evidence also suggests cognitive declineand mobility share common underlying pathophysiology(i.e., vascular pathology and inflammation) [7, 13]. Specif-ically, the Health, Aging and Body Composition Study [1]demonstrated that baseline lower executive functions pre-dicted subsequent decline in gait speed. Recently, rates ofdecrease in gait speed were shown to be significantly dif-ferent between older adults who developed MCI and thosewho did not [3]. Given that both impaired cognitivefunction and impaired mobility contribute to loss of func-tional independence which is associated with reducedquality of life, greater risk for institutionalization, and in-creased mortality – there is a critical need to further in-vestigate the specific contribution of cognitive functioningand mobility to HRQoL and wellbeing. Understanding keydeterminants of HRQoL and wellbeing will help informfuture intervention strategies aimed at combatting cogni-tive and functional decline and thus striving to maintainor improve individual’s HRQoL and wellbeing.Hence, the objective of our study was to determineand compare key factors relating to mobility and cogni-tive function that explain significant variation in HRQoLand capability/wellbeing among community dwellingolder adults.MethodsStudy designWe conducted a cross-sectional analysis of a cohort of229 participants (complete case analysis) who presentedto the Vancouver Falls Prevention Clinic from June 2010through October 2013 for a baseline assessment.Ethical approval was obtained from the VancouverCoastal Health Research Institute and the University ofBritish Columbia’s Clinical Research Ethics Board (H09-02370). All participants provided written informedconsent.ParticipantsThe sample consisted of women and men referred bytheir general practitioner or emergency department phys-ician to the Vancouver Falls Prevention Clinic. Commu-nity dwelling women and men who lived in the lowermainland region of British Columbia were eligible forstudy entry if they: were adults ≥ 70 years of age referred by a medicalprofessional to the Falls Prevention Clinic as a resultof seeking medical attention for a non-syncopal fallin the previous 12 months; understood, spoke, and read English proficiently; had a Physiological Profile Assessment (PPA) [21]score of at least 1.0 SD above age-normative valueor Timed Up and Go Test (TUG) [36] performanceof greater than 15 seconds or one additionalnon-syncopal fall in the previous 12 months; were expected to live greater than 12 months (basedon the geriatricians’ expert opinion); were able to walk 3 m with or without an assistivedevice; and were able to provide written informed consent.We excluded those with a neurodegenerative disease(e.g., Parkinson’s disease) or dementia, patients who hasDavis et al. BMC Geriatrics  (2015) 15:75 Page 2 of 7a stroke in the past 12 months, those with clinically sig-nificant peripheral neuropathy or severe musculoskeletalor joint diseases, and anyone with a history indicative ofcarotid sinus sensitivity (i.e., syncopal falls).Vancouver falls prevention clinic measuresA comprehensive set of measurements relating to mobil-ity and cognitive function that were collected are de-scribed below.Outcome measuresThe primary outcomes of interest were wellbeing andHRQoL.WellbeingWe assessed wellbeing using the ICECAP-O [5, 6, 17].The ICECAP-O is a five item multiple choice question-naire that measures an individual’s wellbeing and qualityof more broadly according to five attributes: attachment(love and friendship), security (thinking about the futurewithout concern), role (doing things that make you feelvalued), enjoyment (enjoyment and pleasure) and con-trol (independence). Each domain has four possible re-sponse options. The ICECAP-O can be used to calculatea global capability index score on a zero to one scalewhere zero represents no capability and one representsfull capability.Health related quality of lifeWe assessed HRQoL using the EQ-5D three level version(3 L). The EQ-5D-3L is a short five item multiple choicequestionnaire that measures an individual’s HRQoL andhealth status according to the following five domains:mobility, self-care, usual activities, pain and anxiety/de-pression [11]. Each domain has three possible responseoptions indicating no problems, some problems or severeproblems. The EQ-5D-3L health state utility values(HSUVs) at each time point are bounded from −0.54 to1.00 where a score of less than zero is indicative of ahealth state worse than death. The HSUVs representvalues that individuals within society assign – values forspecific health states such as having rheumatoid arthritisrelative to perfect health – these are UK societal values forgiven health states.Predictor variablesThe Short Physical Performance Battery (SPPB) [15] wasused to assess mobility and balance. For the Short Phys-ical Performance Battery, participants were assessed onperformances of standing balance, walking, and sit-to-stand. Each component is rated out of four points, for amaximum of 12 points; a score < 9/12 predicts subse-quent disability [16].Executive functionsThere is no unitary executive function – rather, thereare distinct processes. Three key executive processesthat are distinct processes include: 1) selective attentionand conflict resolution (or response inhibition) 31; 2) setshifting; and 3) updating (or working memory). Executivefunctions will be assessed using the Montreal CognitiveAssessment (MoCA). The MoCA is a brief screening toolfor MCI [27] with high sensitivity and specificity, was usedto categorise participants as with, or without, possibleMCI. It is a 30-point test covering eight cognitive do-mains: 1) attention and concentration; 2) executive func-tions; 3) memory; 4) language; 5) visuo-constructionalskills; 6) conceptual thinking; 7) calculations; and 8) orien-tation. Scores below 26 are considered to be indicative ofpossible MCI. A bonus point is given to individual’s withless than 12 years of education. Information processingspeed will be indexed using the Digit Symbol SubstitutionTest (DSST) 35. For this task, participants first presentwith a series of numbers (1 to 9) and their correspondingsymbols. They are asked to draw the correct symbol forany digit - placed randomly in pre-defined series - in 60 s.A higher number of correct answers in this time period in-dicated a better executive functions and processing speed.Descriptive variablesPhysiological falls risk was assessed using the short formof the Physiological Profile Assessment (PPA). The PPAis a valid [58, 59] and reliable [60] measure of falls risk.Based on a participant’s performance in five physiologicaldomains – postural sway, reaction time, strength, proprio-ception, and vision – the PPA computes a falls risk score(standardized score) that has a 75 % predictive accuracyfor falls in older people [20, 22]. A PPA Z-score of ≥ 0.60indicates high physiological falls risk [10].We assessed global cognition using the Mini MentalState Examination (MMSE). The MMSE is a widely usedand well-known questionnaire used to screen for cognitiveimpairment (i.e., MMSE <24) [12]. It is scored on a 30-point scale with a median score of 28 for healthy commu-nity dwelling octogenarians with more than 12 years ofeducation [12]. The MMSE may underestimate cognitiveimpairment for frontal system disorders because it has noitems specifically addressing executive function [12].Functional comorbidity index (FCI) was calculated toestimate the degree of comorbidity associated with phys-ical functioning [14]. This scale’s score is the total numberof comorbidities. We also collected information relatingto living status (i.e., alone, with others or assisted living)and level of education.Statistical analysisWe analyzed all data using STATA version 10.1. We re-port descriptive data for all variables of interest for thisDavis et al. BMC Geriatrics  (2015) 15:75 Page 3 of 7cross-sectional analysis. For data that are normally dis-tributed we report mean and standard deviation and fre-quencies (%) depending on the measure. The nature ofthe relationship between the continuous independent(SPPB, PPA, MoCA and MMSE) and dependent variables(ICECAP-O and EQ-5D-3L) of interest were examinedusing two-way scatter plots. Bivariate relationships be-tween the independent variables and the two dependentvariables of interest were ascertained using Pearson corre-lations. Linear regression models were constructed withthe following two dependent variables: wellbeing (assessedusing the ICECAP-O) and HRQoL (assessed using theEQ-5D-3L). Independent variables included the SPPB,PPA, MoCA and MMSE. Covariates investigated in thebivariate analysis included FCI, sex and age. In our twomultiple linear regression models (i.e., using the twodependent variables: wellbeing and HRQoL), age was sta-tistically controlled by forcing this variable into both re-gression models. Other covariates (i.e., sex and FCI) werekept in based on their statistical significance. Co-linearityof all variables was ascertained and for variables that werehighly co-linear, the variable with the strongest bivariateassociation was included in the final regression model. Weassessed the assumptions of normality of the residualsand heteroscedasticity. Lastly, we conducted exploratorydomain specific comparisons of the ICECAP-O and theEQ-5D-3L with the SPPB. We used Spearman correl-ation coefficients for the specific domains of the EQ-5D(mobility, self-care, usual activities, pain and depres-sion) & ICECAP-O (attachment, security, role, enjoy-ment and control) with the SPPB.ResultsTwo-hundred and twenty-nine participants are includedin our analysis.ParticipantsTable 1 reports descriptive statistics for our variables ofinterest for this cohort. This cohort of community-dwelling older adults had a mean (SD) EQ-5D-3L HSUVof 0.78 (0.22) and a mean (SD) ICECAP-O of 0.82 (0.12).Participants were classified as having high falls risk with amean PPA score of 1.6 ± 1.0. Further, the mean MoCAscore was 22 ± 4.Correlation coefficientsTable 2 reports the correlation coefficients between in-dependent variables of interest and both health relatedquality of life (EQ-5D-3L) and wellbeing (ICECAP-O).The FCI (p < 0.01) and sex (p < 0.05) were significantlyassociated with health related quality of life. The strengthof the correlation for sex was negligible and the strengthof the correlation for FCI was weak. The SPPB (p < 0.01),PPA (p < 0.05), MoCA (p < 0.05) and DSST (p < 0.05) weresignificantly associated with wellbeing. The strength of thecorrelation was moderate for the SPPB, negligible for thePPA, weak for the MoCA and negligible for the DSST.The SPPB, was significantly associated with both healthrelated quality of life and wellbeing (p < 0.01). Measures ofexecutive functions (i.e., MoCA and DSST) were not sig-nificantly associated with health related quality of life. Incontract, measures of executive functions were signifi-cantly associated with wellbeing.Multivariate linear regressionThe SPPB was significantly associated with HRQoL andwellbeing after adjusting for (age, FCI and sex for HRQoLand age, sex and MoCA for wellbeing) (p < 0.05). The totalvariance accounted for by the final model for health re-lated quality of life was 13 % and for wellbeing was 15 %(Table 3). The SPPB accounted for an additional 7 % ofthe total variance in the final model for health relatedquality of life. The SPPB accounted for an additional 10 %of the total variance in the final model for wellbeing. TheTable 1 Characteristics of the Vancouver Falls Prevention cohort(n = 229)Variables Mean (SD) or Number (%)EQ-5D-3L 0.785 (0.218)ICECAP-0 0.819 (0.122)Age (years) 82.4 (6.7)Living status (n = 186)Lives alone 68 (36.6 %)Lives with others 94 (50.5 %)Assisted living 24 (12.9 %)Education (n = 220) < Grade 9 18 (8.2 %)Grades 9–13, no diploma 44 (20 %)High school with diploma 44 (20 %)Trades school 17 (7.8 %)Some university 29 (13.2 %)University 68 (30.0 %)Sex (Male/Female) 79/150 (34.5 %/65.5 %)FCI 2.5 (1.9)SPPBa 7.2 (2.5)PPAb 1.6 (1.0)MMSE (max 30 pts) 26.7 (2.6)MoCA (max 30 pts) 22.1 (4.5)DSST 19.8 (7.6)FCI: Functional Comorbidity IndexSPPB: Short Performance Physical BatteryPPA: Physiological Profile AssessmentMMSE: Mini-Mental State ExaminationMoCA: Montreal Cognitive AssessmentDSST: Digit Symbol Substitution TestaA SPPB score of < 9/12 predicts subsequent disabilitybA PPA Z-score of ≥ 0.60 indicates high physiological falls riskDavis et al. BMC Geriatrics  (2015) 15:75 Page 4 of 7MoCA accounted for an additional 3 % of the total vari-ance in the final model for wellbeing.Domain specific comparisons of the EQ-5D-3L and theICECAP-O with the SPPBFour of the five EQ-5D domains (mobility, self-care,usual activities and pain) were significantly associatedwith the SPPB (Table 4). Four of the five ICECAP-O do-mains (i.e., attention, role, enjoyment and control) weresignificantly associated with the SPPB.DiscussionThis study demonstrated that the MoCA, a measure ofcognitive function and executive function, was signifi-cantly associated with wellbeing after accounting forknown covariates and the SPPB. Of note, cognitive func-tion was not significantly associated with HRQoL. Execu-tive functions often decline substantially with aging [19].Intact executive functioning is essential to the ability tocarry out health-promoting behaviours [34], such as medi-cation management, dietary and lifestyle changes, self-monitoring of responses, and follow-up with health careprofessionals. Wellbeing, assessed using the ICECAP-O,taps into an individual’s capability to achieve desired func-tionings (i.e., this can be thought of as an individual’s cap-acity to follow through with what they want to achieve). Itis conceivable that an individual with higher executivefunctioning may be more competent in achieving theirtargets which may explain the significant association withwellbeing and not HRQoL.The differential findings between the instrumentsassessing wellbeing and HRQoL highlight two importantimplications for future research. Given that both theEQ-5D and the ICECAP-O were largely developed foruse in economic evaluations (i.e., a simultaneous evalu-ation of costs and effectiveness of intervention strategies),it is important to consider the consequences of our find-ings in this context. First, interventions aimed at combat-ting cognitive decline may often result in broader healthbenefits that may not be captured by assessing HRQoLalone [35]. Resultant economic evaluations of interven-tions may underestimate gains or losses in health status.Hence, it may be pertinent to consider measuring QoLmore broadly. Second, cognition is not measured bydirectly by the EQ-5D or the ICECAP-O. The lack ofTable 2 Correlation coefficient matrix (n = 229)Variables EQ-5D-3L ICECAP-OAge (years) 0.0933 −0.0904Sex (Male/Female/Both) −0.126* −0.0473FCI −0.212** −0.0805SPPB 0.256** 0.353**PPA 0.0792 −0.157*MMSE (max 30 pts) −0.124 0.0748MoCA (max 30 pts) −0.0841 0.236*DSST −0.0077 0.163**p < 0.05**p < 0.001FCI: Functional Comorbidity IndexSPPB: Short Performance Physical BatteryMoCA: Montreal Cognitive AssessmentDSST: Digit Symbol Substitution TestTable 3 Multiple Linear Regression Summary examining thecontribution of mobility and/or cognition function on healthrelated quality of life and wellbeing (n = 229)EQ-5D-3 LIndependent Variables R2 Unstandardized ß P-value(Standard Error)Modela 0.134Age 0.005 (0.002) 0.024*FCI −0.018 (0.007) 0.013*Sex (Male/Female) −0.06 (0.03) 0.045*SPPB 0.023 (0.006) 0.000**ICECAP-OModelb 0.154Age 0.0006 (0.0012) 0.636MoCA 0.005 (0.002) 0.006**SPPB 0.016 (0.003) 0.000***p < 0.05**p < 0.001aModel 1: Additional variation explained by the SPPB = 8.4 %bModel 2: Additional variation explained by the MoCA = 5.1 % and Model 2:Additional variation explained by the SPPB = 9.6 %FCI: Functional Comorbidity IndexSPPB: Short Performance Physical BatteryMoCA: Montreal Cognitive AssessmentTable 4 Spearman Correlation Coefficient Matrix Summary for aMeasure of Balance and Mobility with Health Related Quality ofLife and Wellbeing DomainsInstrument Short Physical Performance BatteryEQ-5D Individual DomainsMobility −0.2577*Self-Care −0.1295*Usual activities −0.1679*Pain −0.1400*Depression −0.1230ICECAP-O Individual DomainsAttachment −0.1382*Security −0.1099Role −0.2048*Enjoyment −0.1749*Control −0.3615**p < 0.05Davis et al. BMC Geriatrics  (2015) 15:75 Page 5 of 7association between the HRQoL and the MoCA may bethe result of the EQ-5D not containing a domain that re-lated to cognition – an issue previously debated in theliterature [8]. The ICECAP-O also does not include a cog-nitive domain. However, by design the constructs and cap-abilities to achieve the desired functionings that comprisethe ICECAP-O may better tap into aspects of cognitivefunction compared with the constructs of the EQ-5D. Assuch it is important to carefully consider the domains andconstructs assessed when choosing an outcomes instru-ment to assess wellbeing.We found that the SPPB, a valid and reliable measureof mobility and balance, explained significant variationin both HRQoL and wellbeing (Table 4). This observa-tion may seem intuitive for the EQ-5D since one of thedomains of the EQ-5D is mobility. One recent studydemonstrated a correlation between lower EQ-5D scoresand poor SPPB performance [18]. The ICECAP-O doesnot directly measure mobility. However, we found thatthe SPPB explained a larger amount of variation in theICECAP-O score than the EQ-5D-3L score. Given thatthe ICECAP-O is a capability index – it is designed to as-certain an individual’s capability to achieve valued func-tionings [23]. Hence, it is highly conceivable thatperformance on the SPPB may be related to the domainsof security (thinking about the future without concern),role (doing things that make you feel valued), enjoyment(things that make you feel valued). For example, it may bethat having mobility allows you to do the things that youwant to do and to do the things that makes you feel valued– the ICECAP-O is able to tap into individuals’ capabilities(i.e., their capability to achieve desired functionings).We observed a significant association between sex andHRQoL. A significant sex effect was not detected forwellbeing. Previously, women previously reported notbeing content with their HRQoL even with normal phys-ical function [28]. Further, one study demonstrated thatwomen have poor mobility compared with men and re-port being most affected by their musculoskeletal statusand depressive symptoms [25]. These are two symptomsthat would be likely captured more directly by the EQ-5D domains of mobility, usual activities, pain andanxiety/depression.We also noted that age explained a significant amountof variation in wellbeing but not in HRQOL. One ex-planation for this observation is that the ICECAP-O wasdesigned specifically for older adults and may be moresensitive to detecting age related changes. The EQ-5Dwas designed for a general population of adults and thusmay be less responsive among targeted populations suchas older adults.Participants included in this study were referred byhealth care providers to the study based on perceived fallrisk and specifically sustaining a fall in the past 12 months.As such, the results of this study may not be generalizableto other low risk populations. On the other hand, this isan at-risk population for which findings are highly rele-vant for future targeted intervention. This cross-sectionalanalysis does not allow us to ascertain the temporalrelationship between mobility and cognition in relation toHRQoL and wellbeing. This analysis was based on acomplete case analysis. We chose not to report the im-puted dataset here because the findings of the imputeddata set concurred with the complete case analysis. Fur-ther, this study did not explore any type of mediation ana-lyses. It is possible that risk of falls or falls self-efficacycould mediate the relationship between mobility or cogni-tion and HRQoL or wellbeing. The next logical step is toconduct a longitudinal analysis ascertaining the key pre-dictors and mediators of change in wellbeing and changein HRQoL over time. This will help us tailor and target fu-ture intervention strategies most effectively.ConclusionsThis study highlights that both mobility and cognitivefunction are associated with HRQoL and wellbeing.Specifically, this study provides preliminary evidencethat the ICECAP-O taps into important aspects of cog-nition – executive functions and the EQ-5D does not.As such, this study provides a platform for future longi-tudinal studies and intervention studies to 1) examinetemporal relationships and mediating factors of mobilityand cognition with HRQoL and wellbeing, 2) explore theuse of appropriate instruments based on the intended im-pact of the intervention and 3) target mobility and cogni-tion to improve wellbeing and slow age related declines.Competing interestThe authors declare that they have no competing interests.Authors’ contributionsTLA and JCD were responsible for study concept and design, acquisition ofdata, data analysis and interpretation, writing and reviewing of themanuscript. JCD, TLA, SB, CLH, LL, JRB, CG, and KAV drafted and revised themanuscript. All authors read and approved the final manuscript.AcknowledgementThe Canadian Institute for Health Research Emerging Team Grant (CIHR, MOB-93373 to Karim Khan, TLA, LL) provided funding for this study. TLA and LL arefunded by a MSFHR Scholar Award and CIHR New Investigator. JCD and JRB arefunded by a CIHR and MSFHR Postdoctoral Fellowship. These funding agenciesdid not play a role in study design. We thank the Vancouver Falls PreventionCohort study participants. TLA and LL are Canada Research Chairs. CLH is aAlzheimer Society Research Program Doctoral Trainee.Author details1Centre for Clinical Epidemiology and Evaluation, 828 West 10th Avenue,Vancouver, Canada. 2Vancouver Coastal Health Research Institute (VCHRI),Vancouver, BC V6T 2B5, Canada. 3Department of Physical Therapy, 2177Wesbrook Mall, Vancouver, Canada. 4University of British Columbia, Vancouver,BC V6T 2B5, Canada. 5Arthritis Research Centre of Canada, 5591 No. 3 Road,Richmond, BC V6X 2C7, Canada. 6Aging, Mobility, and Cognitive NeuroscienceLab, 2211 Wesbrook Mall, Vancouver, Canada. 7Djavad Mowafaghian Centrefor Brain Health, University of British Columbia & VCHRI, 2215 Wesbrook Mall,Davis et al. BMC Geriatrics  (2015) 15:75 Page 6 of 7Vancouver, British Columbia V6T 1Z3, Canada. 8Center for Hip Health andMobility, 311-2647 Willow Street, Vancouver, BC V5Z 1 M9, Canada.Received: 23 September 2014 Accepted: 23 June 2015References1. Atkinson HH, Rosano C, Simonsick EM, Williamson JD, Davis C, Ambrosius WT,et al. Cognitive function, gait speed decline, and comorbidities: the health,aging and body composition study. J Gerontol A Biol Sci Med Sci.2007;62(8):844–50.2. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi H. Forecasting theglobal burden of Alzheimer's disease. Alzheimers Dement. 2007;3:186–91.3. Buracchio T, Dodge HH, Howieson D, Wasserman D, Kaye J: The trajectoryof gait speed preceding mild cognitive impairment. Arch Neurol.2010;67:980-86.4. Burgess PW, Alderman N, Evans J, Emslie H, Wilson BA. The ecological validityof tests of executive function. J Int Neuropsychol Soc. 1998;4(6):547–58.5. Coast J, Flynn TN, Natarajan L, Sproston K, Lewis J, Louviere JJ, et al. Valuingthe ICECAP capability index for older people. Soc Sci Med. 2008;67(5):874–82.6. Coast J, Peters TJ, Natarajan L, Sproston K, Flynn T. An assessment of theconstruct validity of the descriptive system for the ICECAP capabilitymeasure for older people. Qual life res int j qual life asp treat care rehab.2008;17(7):967–76.7. Cotman CW, Berchtold NC, Christie LA. Exercise builds brain health: keyroles of growth factor cascades and inflammation. Trends Neurosci.2007;30(9):464–72.8. Couzner L, Crotty M, Norman R, Ratcliffe J. A comparison of the EQ-5D-3Land ICECAP-O in an older post-acute patient population relative to thegeneral population. Appl Health Econ Health Policy. 2013;11(4):415–25.9. Couzner L, Ratcliffe J, Lester L, Flynn T, Crotty M. Measuring and valuingquality of life for public health research: application of the ICECAP-Ocapability index in the Australian general population. Int J Public Health.2013;58(3):367–76.10. Delbaere K, Close JC, Brodaty H, Sachdev P, Lord SR: Determinants ofdisparities between perceived and physiological risk of falling amongelderly people: cohort study. BMJ. 2010;341:c4165.11. Dolan P. Modeling valuations for EuroQol health states. Med Care.1997;35(11):1095–108.12. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practicalmethod for grading the cognitive state of patients for the clinician.J Psychiatr Res. 1975;12(3):189–98.13. Giovannini S, Onder G, Liperoti R, Russo A, Carter C, Capoluongo E, Pahor M,Bernabei R, Landi F: Interleukin-6, C-reactive protein, and tumor necrosisfactor-alpha as predictors of mortality in frail, community-living elderlyindividuals. J Am Geriatr Soc, 2011;59(9):1679–1685.14. Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidityindex with physical function as the outcome. J Clin Epidemiol.2005;58(6):595–602.15. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor ofsubsequent disability. N Engl J Med. 1995;332(9):556–61.16. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-ExtremityFunction in Persons over the Age of 70 Years as a Predictor of SubsequentDisability 10.1056/NEJM199503023320902. N Engl J Med. 1995;332(9):556–62.17. ICECAP capability measures. http://www.birmingham.ac.uk/research/activity/mds/projects/HaPS/HE/ICECAP/index.aspx18. Krabbe PF, Stouthard ME, Essink-Bot ML, Bonsel GJ. The effect of adding acognitive dimension to the EuroQol multiattribute health-status classificationsystem. J Clin Epidemiol. 1999;52(4):293–301.19. Kuo H-K, Lipsitz LA. Cerebral White Matter Changes and Geriatric Syndromes: IsThere a Link? J Gerontol A Biol Sci Med Sci. 2004;59(8):M818–26.20. Lord S, Clark R, Webster I. Physiological factors associated with falls in anelderly population. J Am Geriatr Soc. 1991;39:1194–200.21. Lord S, Sherrington C, Menz H. A physiological profile approach for fallsprevention. In: Falls in older people Risk factors and strategies forprevention. Cambridge: Cambridge University Press; 2001. p. 221–38.22. Lord S, Ward J, Williams P, Anstey K. Physiological factors associated withfalls in older community-dwelling women. J Am Geriatr Soc. 1994;42:1110–7.23. Lorgelly PK, Lawson KD, Fenwick EA, Briggs AH. Outcome measurement ineconomic evaluations of public health interventions: a role for the capabilityapproach? Int J Environ Res Public Health. 2010;7(5):2274–89.24. Marra CA, Esdaile JM, Guh D, Kopec JA, Brazier JE, Koehler BE, et al. Acomparison of four indirect methods of assessing utility values inrheumatoid arthritis. Med Care. 2004;42(11):1125–31.25. Morita M, Takamura N, Kusano Y, Abe Y, Moji K, Takemoto T, et al.Relationship between falls and physical performance measures amongcommunity-dwelling elderly women in Japan. Aging Clin Exp Res.2005;17(3):211–6.26. Murray C, Lopez A: Global and regional descriptive epidemiology ofdisability: incidence, prevalence, health expectancies, and years lived withdisability. In: The Global Burden of Disease and Risk Factors. Edited byLopez AD, Mathers CD Ezzati M, Jamison DT, Murray CJL. Boston:The Harvard School of Public Health. 1996:201–246.27. Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, Collin I,et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool formild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9.28. Oh B, Cho B, Choi HC, Son KY, Park SM, Chun S, et al. The influence oflower-extremity function in elderly individuals' quality of life (QOL): ananalysis of the correlation between SPPB and EQ-5D. Arch Gerontol Geriatr.2014;58(2):278–82.29. Ravens-Sieberer U. Measuring and monitoring quality-of-life in populationsurveys: still a challenge for public health research. Soz Praventivmed.2002;47(4):203–4.30. Rosano C, Newman AB, Katz R, Hirsch CH, Kuller LH. Association BetweenLower Digit Symbol Substitution Test Score and Slower Gait and GreaterRisk of Mortality and of Developing Incident Disability in Well-FunctioningOlder Adults. J Am Geriatr Soc. 2008;56(9):1618–25.31. Rowen D, Brazier J, Tsuchiya A, Alava MH: Valuing states from multiplemeasures on the same visual analogue sale: a feasibility study. Health Econ.2012;21(6):715–29. doi:10.1002/hec.1740. Epub 2011 May 27.32. Sen A: Commodities and capbilities. New Delhi; Commodities andCapabilities. 1987:1–46.33. Sen A, Nussbaum M. Capability and well-being. Oxford: Oxford UniversityPress; 1993.34. Stuss DT, Alexander MP. Executive functions and the frontal lobes: aconceptual view. Psychol Res. 2000;63(3–4):289–98.35. West RL. An application of prefrontal cortex function theory to cognitiveaging. Psychol Bull. 1996;120(2):272–92.36. Whitney JC, Lord SR, Close JC. Streamlining assessment and intervention ina falls clinic using the Timed Up and Go Test and Physiological ProfileAssessments. Age Ageing. 2005;34(6):567–71.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitDavis et al. BMC Geriatrics  (2015) 15:75 Page 7 of 7


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