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The independent contribution of executive functions to health related quality of life in older women Davis, Jennifer C; Marra, Carlo A; Najafzadeh, Mehdi; Liu-Ambrose, Teresa Apr 1, 2010

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Davis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Open AccessR E S E A R C H  A R T I C L EResearch articleThe independent contribution of executive functions to health related quality of life in older womenJennifer C Davis1,3, Carlo A Marra2,3, Mehdi Najafzadeh3 and Teresa Liu-Ambrose*1AbstractBackground: Cognition is a multidimensional construct and to our knowledge, no previous studies have examined the independent contribution of specific domains of cognition to health related quality of life. To determine whether executive functions are independently associated with health related quality of life assessed using Quality Adjusted Life Years (QALYs) calculated from the EuroQol EQ-5D (EQ-5D) in older women after adjusting for known covariates, including global cognition. Therefore, we conducted a secondary analysis of community-dwelling older women aged 65-75 years who participated in a 12-month randomized controlled trial of resistance training. We assessed global cognition using the Mini-Mental State Examination (MMSE) and executive functions using the: 1) Stroop Test; 2) Trail Making Test (Part B) and 3) Digits Verbal Span Backwards Test. We calculated QALYs from the EQ-5D administered at baseline, 6 months and 12 months.Results: Our multivariate linear regression model demonstrated the specific executive processes of set shifting and working memory, as measured by Trail Making Test (Part B) and Digits Verbal Span Backward Test (p < 0.01) respectively, were independently associated with QALYs after accounting for age, comorbidities, general mobility, and global cognition. The final model explained 50% of the variation in QALYs.Conclusions: Our study highlights the specific executive processes of set shifting and working memory were independently associated with QALYs -- a measure of health related quality of life. Given that executive functions explain variability in QALYs, clinicians may need to consider assessing executive functions when measuring health related quality of life. Further, the EQ-5D may be used to track changes in health status over time and serve as a screening tool for clinicians.Trial Registration: ClinicalTrials.gov Identifier: NCT00426881.BackgroundHealth related quality of life (HRQL) is an important con-struct that describes an individual's overall health status.It is commonly used in economic evaluations [1] as ameasure of health benefit, and may be more responsiveamong populations with conditions associated with highmorbidity [2]. HRQL is defined by several domains [3],with general agreement that emotion, physical and socialare core domains. These concur with WHO's definitionof health - a state of complete physical, mental, and socialwell-being, and not merely the absence of disease or infir-mity [4]. However, the specific contribution of HRQL toquality of life remains unknown [5] given HRQL is "thesubjective assessment of the impact of disease and treat-ment across the physical, psychological, social, andsomatic domains of functioning and well-being [6]."The use of a generic, preference-based instrument isone method commonly used to assess HRQL [7]. TheEQ-5D is one example of such a generic preference basedutility instrument developed by the EuroQol Group [8].The EQ-5D captures 243 unique health states and cap-tures the following domains using a short five-item ques-tionnaire: 1) mobility, 2) self-care, 3) usual activities, 4)* Correspondence: tlambrose@exchange.ubc.ca1 Centre for Hip Health & Mobility, University of British Columbia & Vancouver BioMed Central© 2010 Davis et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.pain and 5) anxiety/depression [8]. Individuals' prefer-Coastal Health Research Institute (VCHRI), 301-2647 Willow Street, Vancouver, British Columbia, V5Z 3P1, CanadaFull list of author information is available at the end of the articleDavis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Page 2 of 8ences for the scoring of the EQ-5D were estimated usingthe time trade off technique on a random sample ofadults taken from the population living in the York (UK)region (N = 3000) [9]. The EQ-5D is the most widely usedgeneric instrument that uses a utility-based scoringapproach, yielding a single summary score on a commonscale to facilitate comparison across different health con-ditions and patient populations [10,11]. The single sum-mary score, defined as a health state utility value (HSUV)is anchored at zero - a health state equivalent to deathand 1.0 - a state of "full health." HSUVs less than zero aredefines health states worse than death.HSUVs are used to calculate Quality Adjusted LifeYears (QALYs) to account for the quality of life of apatient (measured using health utilities from a genericpreference based utility instrument such as the EQ-5D) ina given health state and the time spent in that healthstate. Briefly, the QALY is a useful measure of health ben-efit because it simultaneously captures both quantity andquality gains or losses [12]. A key benefit of the QALY isthat it enables direct comparison of patient outcomesacross diseases and diverse health interventions [12].Also, it accounts for changes in both morbidity and mor-tality under a common metric. QALYs are defined as ameasure of health benefit in terms of time spent in aseries of quality-weighted health states, in which thequality weights reflect the desirability of living in thestate, typically anchored at "perfect" health (weighted 1.0)to dead (weighted 0.0)" [13]. The quality weights spent ineach state are multiplied by the time spent in each state.The sum of all these products is the total number ofQALYs. QALYs are one measure used to assess HRQL.Clinical measures that are associated with HRQLinclude cognition, physical disability and chronic condi-tions such as rheumatoid arthritis, sex, social functioningand physical activity [14]. Specifically, two studies dem-onstrated that adults with a physical disability, asthma orwho are female had significantly increased odd ratios forpoor HRQL [15,16]. Among individuals with Alzheimer'sdisease, global cognition as measured by the Mini MentalState Examination (MMSE), was associated with HSUVsmeasured by the EQ-5D [17].Cognition is a multidimensional construct and to ourknowledge, no previous studies have examined the inde-pendent contribution of specific domains of cognition toHRQL. We hypothesize that executive functions may beof particular importance to HRQL. Executive functionsare higher-order cognitive processes that control plan-ning, initiation, sequencing and monitoring of complexgoal directed behavior [18,19]. These cognitive processesare essential to the person's ability to carry out health-Hence, in this study, we examined whether executivefunctions are independently associated with HRQL incommunity-dwelling older women, calculated using theEQ-5D HSUVs at three time points, after accounting forglobal cognitive function and known covariates.MethodsStudy Design and ParticipantsThe total sample for this analysis consisted of 135 womenwho consented and completed a randomized controlledtrial of exercise (NCT00426881; Brain Power study) thataimed to examine the effect of once-weekly and twice-weekly resistance training on cognitive performance ofexecutive functions. The design and the primary resultsof the Brain Power study have been reported elsewhere[21]. Briefly, participants enrolled in Brain Power were:aged 65 to 75 years, community-dwelling, and had aMMSE ≥ 24.Functional Comorbidity IndexFunctional Comorbidity Index was calculated to estimatethe degree of comorbidity associated with physical func-tioning [22]. This scale's score is the total number ofcomorbidities.Global Cognition Measures - Mini Mental State ExaminationThe MMSE is a widely used and well-known question-naire used to screen for cognitive impairment (i.e.,MMSE <24) [23]. It is scored on a 30-point scale with amedian score of 28 for more normal octogenarians withmore than 12 years of education [23]. The MMSE mayunderestimate cognitive impairment for frontal systemdisorders [24] because it has no items specificallyaddressing cognitive function [23].Central Executive Functions--Set Shifting, Updating and Response InhibitionOur assessment of executive functions were composed ofthree tests that measure different aspects of executivefunctions: [18] 1) Trail Making Test Part B, 2) Verbal Dig-its Backward Test and 3) Stroop Colour-Word Test.Trail Making Part BWe used the Trail Making Part B test to assess set shift-ing. Set shifting refers to an individuals ability to go backand forth between multiple tasks or mental sets [25]. Thetest consists of one page with circled letters (A-L) andnumbers (1-13). We instructed participants to draw a sin-gle line as quickly and accurately as possible from 1 to A,A to 2, 2 to B and so forth until the task was completed.We recorded the number of errors and the length of timethe task took. To index set shifting, we calculated the dif-promoting behaviours [20], such as medication manage-ment, dietary and lifestyle changes, self-monitoring ofresponses, and follow-up with health care professionals.ference between Part B and Part A completion time.Smaller difference scores indicate more cognitive flexibil-Davis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Page 3 of 8ity. Reliability scores for the Trail Making Part B variedfrom moderate to excellent [26].Verbal Digits BackwardWe used the Verbal Digits Backward test to assess work-ing memory [27]. Working memory (updating) refers toan individuals ability filter incoming information for rele-vance to the current task and subsequently update infor-mational content replacing old non-relevant informationwith new relevant incoming information [25]. Seven pairsof random number sequences were read aloud by anassessor at one number per second. The first sequence ofnumbers is three and the sequence was increased by onenumber up to a length of nine digits. Participantsrepeated each sequence in exactly the reverse order untilthey failed two attempts of the same sequence length. Itwas scored on a 14-point scale with higher scores indicat-ing a better performance. For the verbal digits forwardtest, the participant's task is to repeat each sequenceexactly as it is given. The difference between the verbaldigits forward test score and the verbal digits backwardtest score was used as an index of the central executivecomponent of working memory. Smaller differencescores indicate better working memory.Stroop TestThe Stroop Test, assessed response inhibition [28]including deliberate inhibition of automatic, dominant orroutine responses [26]. For the primary test condition,participants were presented colour-words printed inincongruent coloured inks (e.g., the word "BLUE" printedin red ink) and were required name the ink colour thatthe words were printed while ignoring the word itself. Werecorded the time participants took to read each condi-tion. The ability to selectively attend and control responseoutput was calculated as the time difference between thetest condition and the priming condition (e.g., colouredX's). Smaller time differences indicate better selectiveattention and conflict resolution.Preference Based Measures - HSUV InstrumentThe HSUV instrument we used was the EQ-5D. Majordifferences between the EQ-5D and other preferencebased measures were outlined previously [8]. The EQ-5Ddoes not directly measure cognition; another genericpreference based instrument, the Health Utilities IndexMark 3 (HUI3) does [29]. To our knowledge, no previousstudies have examined the association between executivefunctions and HRQL using the HUI3. Therefore, wechose the EQ-5D given that is it the most widely usedgeneric preference based utility instrument that has beenused among individuals with cognitive decline.The EQ-5D is a short five item generic HSUV instru-a weakness in terms of its responsiveness and sensitivity[32]. The EQ-5D is used for cross-national comparisonsof health status [33] and captures 243 unique health states[8]. We used the EQ-5D to calculate QALYs as an assess-ment of an individual's HRQL according to the followingfive EQ-5D domains: mobility, self-care, usual activities,pain, and anxiety/depression. Each domain has three pos-sible options that either indicates no problems, someproblems or severe problems. The EQ-5D HSUVs at eachtime point are bounded from -0.54 to 1.00 where a scoreof less than zero is indicative of a health state worse thandeath. We used three HSUVs for each individual from theEQ-5D at baseline, 6 months and 12 months to calculateQALYs for each individual. Specific to this study only,QALYs are a measure of HRQL because zero participantsdied and all participants were followed for the same timeperiod, thus any changes in QALYs are due to quality oflife, rather than quantity of time spent in a given healthstate.Timed Up and GoWe used the Timed Up and Go Test (TUG) to assess gen-eral mobility [34]. Participants were instructed to risefrom a chair with their arms crossed (seat height 45 cm),walk a distance of three meters, turn around, walk back tothe chair, and sit down with their arms crossed aroundtheir chest. We timed each trial and took the mean of twotrials for our statistical analysis.Data AnalysisWe analysed all data using STATA version 10.0. Our basecase analysis included 135 women based on recommen-dations for multiple imputation of missing cost andHSUV data [35]. For all discrete time points, we used acombination of multiple imputation and bootstrapping toestimate uncertainty caused by missing values and wereport both the imputed data set analysis and a completecase analysis. Our complete case analysis consisted of 89participants for the EQ-5D who had all three HSUVs atbaseline, 6-months and 12-months.We report descriptive data for all variables of interest.For data that are normally distributed we report meanand standard deviation and frequencies depending on themeasure. For data that were significantly skewed, wereport median and interquartile range. We used the Pear-son product moment correlation coefficient to determinethe level of association between QALYs and age, group,education, average waist girth, functional comorbidityindex, general mobility, global cognition and executivefunctions.In our multiple linear regression model, age, group,ment designed to assess HRQL [30]. The EQ-5D shortstructure was considered a strength in terms of highresponse rates, participant burden and feasibility [31] andeducation, average waist girth, functional comorbidityindex, general mobility and global cognition were statisti-cally controlled by forcing these six variables into theDavis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Page 4 of 8regression model first (Model 1). These independentvariables were determined based on the results of thePearson product moment coefficient analyses (i.e., alphalevel ≤ 0.05) and assumed biological relevance, such asMMSE and waist girth were entered into the modelregardless of the results of the correlation analyses. Eachof the executive functions (i.e., Trail Making Part B, Dig-its Backwards, Stroop Colour Word) was then enteredsequentially into the model. Those that significantlyadded to the model (i.e., significant change in R2) werekept in the model. Digits Backward was entered last intothe model. We assessed the assumptions of normality ofthe residuals and heteroscedasticity.ResultsWe report the results of both the imputed case analysisand the complete case analysis. For the complete caseanalysis, we calculated QALYs from the EQ-5D for 89 ofthe 135 participants.SampleTable 1 reports descriptive statistics for descriptive vari-ables (age, baseline EQ-5D HSUV, group, education,average waist girth, functional comorbidity index, trailmaking part A, trail making part B, Digits Forward,MMSE and TUG) and our outcome of interest (QALYs).Participants included in our imputed and case analysiswere similar on demographic characteristics. Overall, thiscohort of community-dwelling senior women were highfunctioning individuals as indicated by their baseline EQ-5D HSUVs of 0.82 (SD: 0.19) and 0.85 (SD: 0.18) for theimputed and complete case sets, respectively. Further, themean MMSE was greater than 28 (max 30 points).Correlation CoefficientsTable 2 reports the correlation coefficients between vari-ables of interest and QALYs. Age, education, baseline EQ-5D HSUV, average waist girth, functional comorbidityindex, TUG, set shifting (assessed by the difference scorefor Trail Making Part B and A) and working memory(assessed by the difference score for Digits Forward andBackward) were significantly associated with QALYs cal-culated from the EQ-5D (p < 0.05). Group and responseinhibition (assessed by using the Stroop Colour-Wordtest) were not significantly associated with QALYs calcu-lated from the EQ-5D (p > 0.05).Multivariate Linear Regression Results for QALYs calculated from the EQ-5DThe Trail Making Part B was a significant and indepen-dent predictor for HRQL as assessed by the EQ-5D (p <0.01). Digits Backward was also a significant predictor forHRQL based on the EQ-5D (p < 0.01) based on theresults of the imputed data set (for complete case set, p =0.09). Adding the Trail Making Part B and the DigitsBackward resulted in an R2 change of 4% (p < 0.01). Thetotal variance accounted for by our final model was 50%(Table 3). The R2 and R2 change from both imputed andcomplete case analysis were identical to 1/100 decimal.The Stroop Colour-Word task did not significantlyimproved the model after accounting for age, group, edu-Table 1: Characteristics of the Brain Power cohort at baseline (N = 89)Variable at Baseline Imputed Data Set Complete Case SetMean Standard Deviation Mean Standard DeviationQALY (EQ-5D) 0.83 0.17 0.83 0.17Age (years) 69.6 3.0 69.7 3.0Baseline EQ-5D HSUV 0.82 0.19 0.85 0.18Average waist girth (cm) 86.3 13.0 87.3 12.4Function Comorbidity Index 2.1 1.7 2.0 1.6Trail A (sec) 55.3 18.3 54.4 17.7Trail B (sec) 101.2 41.7 97.0 36.7Trail B - Trail A 42.5 29.9 46.2 34.8Digits Forward (max 14 pts) 7.9 2.3 7.9 2.3Digits Backward (max 14 pts) 4.5 2.4 4.3 2.4Digits Forward - Digits Backward 3.7 2.3 3.4 2.3MMSE (max 30 pts) 28.6 1.3 28.7 1.4Timed Up and Go Test (sec) 6.6 1.4 6.7 1.5Davis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Page 5 of 8cation waist girth, functional comorbidity index, generalmobility and global cognition.DiscussionRelationship between executive functions and QALYs - HRQLPersons who experience cognitive decline have a reducedquality of life [36]. To our knowledge, our study is the firstto demonstrate the independent association between keyexecutive processes as measured by standard neuropsy-chological tests, and QALYs measured prospectively overone year among high functioning community-dwellingsenior women. Of particular importance, this indepen-dent association was found in this cohort of seniorwomen after accounting for age, waist girth, functionalcomorbidity index, general mobility and global cognition.Also, our final model explained 50% of the variation inQALYs; regression models in clinical research often donot account for such a large amount of variance [37].We specifically found that, both set shifting and work-ing memory, were independently associated HRQL, mea-sured by QALYs calculated from the EQ-5D HSUVs. Ournovel result extends previous findings that set shifting, asthe Trail Making B Test, is associated with factors thatmay influence QALYs: 1) mobility [38,39]; 2); medicationadherence [40]; 3) driving performance [41]; 4) anxietyand emotional regulation [42]. We highlight that mobilityand anxiety/depression are domains in the EQ-5D. Addi-tionally, our current finding also extends previous find-ings that working memory is associated with painseverity; pain is one of the five domains of the EQ-5D;therefore, we would expect an association with QALYs[43].We acknowledge that executive functions are only oneaspect of cognition and were the sole cognitive processesexplored in the Brain POWER study [21]. The BrainPOWER study focused specifically on executive func-tions because these cognitive processes: 1) decline sub-stantially with aging [44]; 2) are associated with the abilityto carry out health-promoting behaviours [45]; and 3) aremost responsive to exercise training [46]. Because execu-Table 2: Correlation coefficient matrix‡Variable at Baseline Imputed Data SetQALYs (EQ-5D)Age -0.298**Group 0.0913Education 0.3106**Average waist girth -0.232*Function Comorbidity Index -0.488**Trail B - Trail A -0.1084*Digits Forward - Digits Backward -0.1710**MMSE 0.051Timed Up and Go -0.598**Stroop -0.113‡ Results from both imputed and complete case analysis were identical to 0.000 decimal.* p < 0.05** p < 0.01Table 3: Bivariate and Multiple Linear Regression Summary for QALYs in Older Women Calculated from EQ-5D HSUVs‡Imputed Data Set Complete Case SetIndependent Variables Unstandardized ß (Standard Error)P-value Unstandardized ß (Standard Error)P-valueModel R2 0.536 R2 0.536Trail B - Trail A 0.0012 (0.0002) 0.00** 0.0012 (0.0005) 0.031*Digits Forward -Digit Backward -0.011 (0.003) 0.00** -0.011 (0.007) 0.084Age 0.0008 (0.0022) 0.717 0.0008 (0.0052) 0.88Group -0.008 (0.008) 0.296 -0.008 (0.018) 0.66Education 0.025 (0.005) 0.00** 0.02 (0.01) 0.024*Average waist girth -0.0006 (0.0005) 0.240 -0.0006 (0.0012) 0.62Functional Comorbidity Index -0.036 (0.004) 0.00** -0.036 (0.009) 0.00**Timed Up and Go -0.062 (0.005) 0.00** -0.06 (0.01) 0.00**MMSE -0.017 (0.005) 0.001 -0.02 (0.01) 0.15‡ R2 and R2 change were the same for both imputed and complete case analysis* p < 0.05** p < 0.01Davis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Page 6 of 8tive functions are associated with the ability to carry outhealth-promoting behaviours [45], we hypothesized thatreduced executive functioning may directly impact theoverall health status of older adults. However, weacknowledge that other cognitive domains may also influ-ence health status. Hence, future studies are needed toexplore the contribution of other cognitive domains tohealth status in older adults.Establishing a relationship between working memory and health related quality of lifeOur finding of both set shifting and working memorycontributing to health related quality of life concur andextend previous studies examining the association of cog-nitive function and instrumental activities of daily living.Instrumental activities of daily living include the ability toprepare a balanced meal, remember appointments, keepfinancial records and take medications as prescribed [47].Health related quality of life is related to one's ability toperform instrumental activities of daily living [48] andone's overall mobility [49] Previous studies have demon-strated that executive functions are associated withinstrumental activities of daily living and functional sta-tus among older adults [50,51]. Specifically, the TrailMaking B Test is an independent predictor of the instru-mental activities of daily living [50,51].Response inhibition and health related quality of life - comparison with another studyOur findings for the Stroop test and health related qualityof life differ from those of previous research [52]. Specifi-cally, one study conducted among 72 older adults withstable cardiovascular disease found a significant associa-tion between response inhibition and instrumental activi-ties of daily living [52]. Differences in the populationstudied may be a potential reason for our conflicting find-ing. Participants of the Brain POWER [21] cohort werehigh functioning individuals. Hence, a ceiling effect forboth instrumental activities of daily living and healthrelated quality of life as assessed by the EQ-5D may haveexisted. Further research is needed to better understandthe contribution of response inhibition to health relatedquality of life.Contrasting the imputed and complete case analysesThe lack of a significant association between global cog-nition and HRQL for our complete case analysis was con-trary to the results of our imputed data set analysis. Thisdifference likely was due to the smaller sample size of thecomplete case analysis. A previous study found a linearrelationship between HRQL, assessed using the Assess-ment of Quality of Life instrument, and MMSE in indi-Bland and Altman highlighted study findings that are sta-tistically nonsignificant is not an indication that thesefindings are indeed nonsignificant or not of clinicalimportant. Rather, because studies lack the necessarypower to detect real, and clinically worthwhile, differ-ences in treatment, that we should not interpret or con-clude that this is necessarily evidence of no effect.Therefore, because our findings are consistent with oneprevious study [53], we interpret the discrepant results asa lack of statically power to detect a difference given thesmaller sample size in our complete case analysis.Timed Up and Go was a key explanatory variable in our modelWe found that the TUG [34] was most strongly associ-ated with HRQL in our bivariate analyses, accounting for27% of the variation in QALYs. One previous study foundthat functional ability/pain explained most of the varia-tion in global utility score; however, this assessment wasnot based on a specific measure of mobility such at theTUG [55]. Three previous studies investigated the associ-ation between the TUG and the Physical Functiondomain of the SF-36 [56-58] and two indicated the TUGexplained approximately 20% of the variation [57,58].Therefore, our findings extend previous work with a dif-ference preference based generic utility instrument dem-onstrating that TUG is strongly associated with EQ-5DHSUVs.ConclusionsWe note that our small study sample consisted only ofolder community dwelling women who were cognitivelyintact; therefore, we cannot say with certainty that thesefindings are generalizable to older women with mild cog-nitive impairment or dementia, older men, other agegroups and adults who are not community-dwelling.Thus, our study highlights the need for future prospec-tive studies to ascertain whether our present findingapply to other clinical populations and whether changesin executive functions, specifically the cognitive pro-cesses of set shifting and working memory are causallylinked to changes in HRQL assessed using generic prefer-ence based HSUV instruments, such as the EQ-5D. Ourfindings indicate that EQ-5D HSUVs over time can belargely explained by baseline measures of age, waist girth,functional comorbidity index, general mobility, globalcognition and the cognitive processes of set shifting andworking memory. Given that set shifting and workingmemory explain a statistically significant amount of vari-ability in QALYs, clinicians may need to consider assess-ing these cognitive processes in response to patientsviduals with Alzheimer's disease -- a finding similar tothat of our imputed data set analysis [53]. Further, the"absence of evidence is not evidence of absence [54]."perceived health status (i.e., health related quality of life).Competing interestsThe authors declare that they have no competing interests.Davis et al. BMC Geriatrics 2010, 10:16http://www.biomedcentral.com/1471-2318/10/16Page 7 of 8Authors' contributionsJCD was principal investigator for the evaluation of HRQL and healthcareresource use and, was responsible for design, data analysis, data interpretation,writing of manuscript. TLA was principal investigator for the Brain Power studyand was responsible for study concept and design, acquisition of data, dataanalysis and data interpretation, and reviewing of the manuscript. CAM andMN were responsible for design, data interpretation and critical review of man-uscript.AcknowledgementsThe Vancouver Foundation (BCMSF, Operating Grant to TLA) and the Michael Smith Foundation for Health Research (MSFHR, Establishment Grant to TLA) provided funding for this study. CAM is funded by a Canada Research Chair in Pharmaceutical Outcomes and a Michael Smith Foundation for Health Research Scholar Award. TLA is funded by a Michael Smith Foundation for Health Research Scholar Award. JCD is funded by a Michael Smith Foundation for Health Research Senior Graduate Studentship and a Canadian Institute for Health Research Canada Graduate Scholarship. These funding agencies did not play a role in study design. We obtained approval for the Brain Power study from UBC Clinical Ethics Review Board.Declaration of Sources of Funding: This work was supported by the Vancou-ver Foundation (BCM06-0035), the Michael Smith Foundation for Health Research Establishment Grant (CI-SCH-063(05-1)CLIN) to TLA, a Michael Smith Foundation for Health Research Senior Graduate Studentship to JCD and a Canadian Institute for Health Research PhD Canada Graduate Scholarship to JCD.Sponsor's Role: None.Conflict of Interest: All authors have nothing to declare.Author Details1Centre for Hip Health & Mobility, University of British Columbia & Vancouver Coastal Health Research Institute (VCHRI), 301-2647 Willow Street, Vancouver, British Columbia, V5Z 3P1, Canada, 2Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada and 3Collaboration for Outcomes Research and Evaluation, St Paul's Hospital, 620B 1081 Burrard Street, University of British Columbia, Vancouver, British Columbia, V6Z 1Y6, CanadaReferences1. 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