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Developing a comprehensive measure of mobility: mobility over varied environments scale (MOVES) Hirsch, Jana A; Winters, Meghan; Sims-Gould, Joanie; Clarke, Philippa J; Ste-Marie, Nathalie; Ashe, Maureen; McKay, Heather A May 25, 2017

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RESEARCH ARTICLE Open AccessDeveloping a comprehensive measure ofmobility: mobility over varied environmentsscale (MOVES)Jana A. Hirsch1,2, Meghan Winters2,3* , Joanie Sims-Gould2,4, Philippa J. Clarke5,6, Nathalie Ste-Marie7,Maureen Ashe2,4 and Heather A. McKay2,4,8AbstractBackground: While recent work emphasizes the multi-dimensionality of mobility, no current measure incorporatesmultiple domains of mobility. Using existing conceptual frameworks we identified four domains of mobility(physical, cognitive, social, transportation) to create a “Mobility Over Varied Environments Scale” (MOVES). We thenassessed expected patterns of MOVES in the Canadian population.Methods: An expert panel identified survey items within each MOVES domain from the Canadian CommunityHealth Survey- Healthy Aging Cycle (2008–2009) for 28,555 (weighted population n = 12,805,067) adults (≥45 years).We refined MOVES using principal components analysis and Cronbach’s alpha and weighted items so each domainwas 10 points. Expected mobility trends, as assessed by average MOVES, were examined by sociodemographic andhealth factors, and by province, using Analysis of Variance (ANOVA).Results: MOVES ranged from 0 to 40, where 0 represents individuals who are immobile and 40 those who are fullymobile. Mean MOVES was 29.58 (95% confidence interval (CI) 29.49, 29.67) (10th percentile: 24.17 (95% CI 23.96, 24.38), 90th percentile: 34.70 (CI 34.55, 34.85)). MOVES scores were lower for older, female, and non-white Canadianswith worse health and lower socioeconomic status. MOVES was also lower for those who live in less urban areas.Conclusions: MOVES is a holistic measure of mobility for characterizing older adult mobility across populations.Future work should examine individual or neighborhood predictors of MOVES and its relationship to broader healthoutcomes. MOVES holds utility for research, surveillance, evaluation, and interventions around the broad factorsinfluencing mobility in older adults.Keywords: Mobility limitation, Measurement, Methods, Functionally-Impaired elderly, Aged, Elderly, Surveys andquestionnaires, Social interaction, TransportationBackgroundWhile the pace and pattern of population shifts differacross the world, the older population is increasing glo-bally [1]. In North America the proportion of the popu-lation 65 years and older is expected to rise from 12.8%in 2008 to 20.8% in 2040 [1]. This unprecedented shiftdemands that systems and communities meet needs ofthis aging demographic. Mobility restrictions influenceolder adult independence [2], constrict community en-gagement [3, 4], and increase negative health outcomesand premature mortality [5, 6]. Thus it is imperative thatwe devote collective attention to strategies and tools thatsupport maintaining mobility later in life.Mobility is multi-dimensional and includes the import-ance of social and community engagement, use of trans-portation, and cognition [7, 8]. The Canadian Institutefor Health Research (CIHR) acknowledged this broaderdefinition of mobility; in the Mobility in Aging StrategicInitiative (CIHR Institute of Aging) mobility was definedas encompassing participation in society, as well as the* Correspondence: mwinters@sfu.ca2Centre for Hip Health and Mobility, Robert H.N. Ho Research Centre,University of British Columbia, 5th Floor, 2635 Laurel St, Vancouver, BC,Canada3Faculty of Health Sciences, Simon Fraser University, 8888 University Drive,Burnaby, BC, CanadaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. 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.Hirsch et al. BMC Public Health  (2017) 17:513 DOI 10.1186/s12889-017-4450-1ability to drive and access public transportation [9]. Inthe transportation realm, mobility is often measured astrip rate (any mode). In addition, transportation studiesrecognized that mobility should include one of the fol-lowing dimensions: 1) access to places of desire (such asvisiting family or friends), 2) psychological benefits oftravel (either social contact or independence), or 3) ben-efits of physical movement itself and potential travel[10–12]. Urban planning recognized community envi-ronments as important in shaping mobility [13–20].Understandably, advocacy groups focused on the roleneighborhoods play in maintaining independence andmobility for older adults [21, 22].Methods used to assess mobility vary across researchstudies and fields [7]. However, existing metrics oftenfocus on an individual’s capacity for, or enacted physicalfunction. Cognitive ability to engage, social connectionswith an older person’s community, or transportationchoices are most often excluded from these metrics.Current measures of mobility include assessments oftransfer skills, gait, or wheelchair mobility [23–25]. Ac-tivities of daily living (ADL) and instrumental activitiesof daily living (IADL) are also used to assess mobilityclinically [26, 27]. These methods were criticized as fail-ing to capture what people actually do in their daily lives[28] or how an individual is involved in social situations[29]. Life-space measures attempt to capture broadermobility, by including mobility inside the home, outsidethe home, within the neighborhood, and beyond [28].Yet the life-space measure does not capture transporta-tion patterns or community engagement of older adultsdirectly. Given the expanding definition of mobility, andthe importance of mobility for older adults, there is aneed for measures of mobility that encompass these do-mains. Therefore, we respond to both the opportunityand need for a holistic measure of older adult mobilitythat includes physical, cognitive, social, and transporta-tion domains.Thus the objectives of our study were twofold: 1) tocreate a Mobility Over Varied Environments Scale(MOVES) using a large, population based study ofCanadian older adults, and 2) to apply MOVES to exam-ine the distribution of mobility across sociodemographicand health characteristics of the Canadian population.This second objective allows us to examine the perform-ance of MOVES. For this, we hypothesize that MOVESwill follow known patterns of mobility, including lowermobility for Canadians in worse health, at older ages, orwith lower socioeconomic status.Conceptual frameworksMOVES draws on the comprehensive mobility frame-work outlined by Webber et al. [7] and the WorldHealth Organization’s International Classification ofFunctioning, Disability, and Health (ICF) [8]. Webber etal. defined mobility broadly as “the ability to move one-self (e.g., by walking, by using assistive devices, or byusing transportation) within community environmentsthat expand from one’s home, to the neighborhood, andto regions beyond.” This framework acknowledges thatmobility takes many forms, including walking, using awheelchair, driving, and using alternate forms of trans-portation. The Webber framework identifies five key do-mains that determine older adult mobility: physical,cognitive, psychosocial, environmental, and financial [7].These domains are interrelated. For example, an individ-ual’s physical impairments (physical) with or without ac-companying psychological factors (e.g. depression) cancontribute to the development of fear of falling (cogni-tive), leading to activity restriction and reduced socialengagement (psychosocial). Similarly, the ICF has abroad description of mobility that captures both indoorand outdoor movement as well as the use of assistive de-vices and transportation. Further, the description in-cludes participation in activities and environmentalfactors that play a role in mobility.MethodsMOVES creationWe created MOVES based on the two conceptualframeworks outlined above. Its design was executed inan iterative process involving qualitative and quantitativeresearchers across multiple fields (Figure 1). The processhad two broad steps: 1) concept-based creation ofMOVES; and 2) statistical refinement, scoring and finalcompilation.Concept-based MOVES creationAn expert panel of researchers and staff (n = 10) fromgerontology, epidemiology, family medicine, transporta-tion, and health behavior played a critical role in itemselection. First, they helped synthesize existing mobilityframeworks. Second, after two researchers separatelyidentified items from the Canadian Community HealthSurvey- Healthy Aging (CCHS-HA) that related to themobility frameworks, the expert panel determined whichitems to include.Statistical refinementOn the selected items, we ran Cronbach’s alpha and aconfirmatory Principal Component Analysis (PCA) todetermine whether: 1) items were contributing to theirrespective domains and the overall score, 2) the itemsgrouped together as anticipated, and 3) what proportionof variance was explained by these items. Items were thecombined into a final MOVES.Hirsch et al. BMC Public Health  (2017) 17:513 Page 2 of 14Understanding canadian mobility using MOVESWe applied MOVES to the CCHS-HA to better under-stand the distribution of mobility in the Canadian popula-tion. The CCHS-HA is a cross-sectional survey(n = 30,865) of the Canadian population living in the 10provinces across Canada (Canadian territories were ex-cluded). Details can be found elsewhere [30]. Briefly theHealthy Aging component was completed December 2008through November 2009 and surveyed people (≥ 45 years)using computer assisted personal interviewing (94% of in-terviews conducted in person) achieving an overall re-sponse rate of 74.4%. For the creation of MOVES, weincluded CCHS-HA participants who had all componentitems that comprised MOVES (final n = 28,555).Weighted frequencies were used to describe thesociodemographic characteristics of the CCHS-HAsample. MOVES mean score was examined acrosseach sociodemographic characteristic. We obtained p-values for comparisons across categories from t-testsand analysis of variance (ANOVA). MOVES meanscore was also compared across age and genderwithin each province. We weighted all results usingthe Statistics Canada proportional sampling schemeand applied Balanced Repeated Replication (BRR) with500 bootstrap weight variables to obtain the correctstandard errors for ANOVA. All analyses were con-ducted using SAS, version 9.4 (SAS Institute Incorpo-rated: Cary, NC).Fig. 1 Iterative process to create the Mobility Over Varied Environment Scale (MOVES). Dotted lines indicate the involvement of an expert panelof qualitative and quantitative researchers who played three key roles: 1) helping to synthesize the mobility frameworks 2) selecting specific itemsbased on questions identified in CCHS-HA and 3) establishing guiding principles for the creation of MOVES that were used to select specific itemsin CCHS-HA. Note that the creation of MOVES was primarily based in conceptual frameworks and then underwent statistical refinement to bothconfirm frameworks and tailor the MOVES measure. A sensitivity analysis was run including all items based on frameworks (had barriersand limitations within each domain as well as an additional financial domain)Hirsch et al. BMC Public Health  (2017) 17:513 Page 3 of 14Results- moves creationItem selectionTo select items, the expert panel established four guid-ing principles: 1) MOVES should focus on actualized orrealized mobility of an individual, rather than potentialfor mobility (e.g. how often one engages in communityactivities versus whether community activities exist), 2)if there were existing metrics within a domain, thesemetrics should remain intact, rather than being split intotheir component parts, 3) where possible, MOVESshould be an absolute rather than a relative metric, to beapplicable beyond the Canadian population, and 4) itemsshould represent components, rather than outcomes, ofmobility (e.g. loneliness was excluded as it may resultfrom low social engagement).MOVES domainsIn practice, the measurement of Webber’s psychosocialdomain and cognitive domain overlap. Therefore, to de-velop MOVES we modified the psychosocial domain tobe primarily social, based on the complementary domainfrom the ICF, “activities and participation.” This domainincludes interpersonal interactions and relationships, aswell as community social and civic life. Similarly, manyof the environmental determinants in both Webber andICF models are related to service systems and policiesthat influence transportation mode. Therefore, this do-main was conceptualized more narrowly in our work as“transportation.”PhysicalOur expert panel identified eight items (five of whichwere barriers or limitations) to include in the physicaldomain (Table 1). We used activities of daily living(ADL), ambulation, and physical activity items to cap-ture physical function and activity. ADL items exclud-ing meal preparation come from the Older AmericansResources and Services (OARS) MultidimensionalFunctional Assessment Questionnaire© (OMFAQ)[31]. Ambulation items were from the adapted versionof the Health Utilities Index (HUI) mark 3 [32], a val-idated instrument which provides a description of anindividual’s overall functional health. Because seden-tary behavior and physical activity independently pre-dict successful aging [33], physical activity wasmeasured using the Physical Activity Scale for theElderly (PASE), a validated and copyrighted instru-ment (1991) developed by the New England ResearchInstitutes (NERI) to provide an overall assessment ofself-reported occupational, household and leisure ac-tivities over the past seven days in older persons [34].Barriers and limitations included reporting a healthcondition limiting participation in activities, publictransportation use, or health improvements.CognitiveIn the psychological and cognitive domain, we used twoitems, one for cognition and one that measured fear offalling. Cognition was captured with the HUI cognitivehealth status [32]. This measures whether a respondentcan remember most things, think clearly, and solve day-to-day problems. We used fear of falling to tap into self-efficacy around mobility. A survey item related to fear offalling was administered to all those 65 years or older(response categories: not worried or concerned, worriedor concerned but haven’t stopped activities, and worriedor concerned and have stopped activities).TransportationTransportation was measured using four items, one rep-resented travel mode of the respondent and three re-ported transportation-related barriers and limitations.For travel mode, participants answered the question, “inthe past month, which of the following (other) forms oftransportation have you used?” Respondents were giventhe options: passenger in a motor vehicle; taxi; publictransportation such as bus, rapid transit, subway ortrain, accessible transit, cycling, walking, wheelchair ormotorized cart, or none. Barriers and limitation includedreporting transportation problems that limited their par-ticipation or ability to improve their health.SocialSocial aspects of mobility were measured using threeitems: a sense of belonging to the local community; fre-quency of participation in community activities; and tan-gible social support. Sense of belonging was measuredby asking respondents “How would you describe yoursense of belonging to your local community? Frequencyof community-related activity participation was assessedby participation in any type of community-related activ-ity during the previous 12 months and then categorizedas participation once a year, once a month, once a week,or once a day. Tangible social support was taken fromthe Medical Outcomes Study (MOS) Social Support Sur-vey [35]. This scale ranges from 0 to 16 and was notasked during proxy interviews; therefore proxy respon-dents do not have a MOVES score.FinancialThe expert panel identified that an individual’s financialstanding influences and interacts with the other do-mains. However, since income or wealth are not actual-ized mobility, we only included financial markers ofwhether an individual felt cost prohibited them from be-ing mobile or engaging with their community (barriersand limitations). Ultimately, this domain was not in-cluded in MOVES due to findings during the statisticalrefinement process described below.Hirsch et al. BMC Public Health  (2017) 17:513 Page 4 of 14Table 1 Full set of component items for each domain of the MOVES included in both the final MOVES score and sensitivity analysisItem Points toward MOVESa Weighted Percent ofresponses (95% CI)Physical DomainInstrumental & Basic Activities of Daily Living ClassificationNo Functional Impairment 10 89.1 (88.5, 89.6)Mild Impairment 7.5 7.9 (7.4, 8.4)Moderate Impairment 5 2.1 (1.8, 2.3)Severe Impairment 2.5 0.5 (0.4, 0.6)Total Impairment 0 0.5 (0.4, 0.6)Ambulation (Mobility)Able to walk around the neighbourhood without difficulty,and without walking equipment10 93.4 (93.0, 93.8)Able to walk around the neighbourhood with difficulty;but does not require walking equipment or the help of another person8 1.2 (1.0, 1.4)Able to walk around the neighbourhood with walking equipment,but without the help of another person6 3.7 (3.4, 4.0)Able to walk only short distances with walking equipment,and requires a wheelchair to get around the neighbourhood4 0.3 (0.2, 0.3)Unable to walk alone, even with walking equipment. Able to walkshort distances with the help of another person, and requires awheelchair to get around the neighbourhood2 1.1 (0.9, 1.2)Cannot walk at all 0 0.3 (0.3, 0.4)Physical Activity Scale for the Elderly (PASE) ScoreQuartile 1 10 25.1 (24.0, 26.1)Quartile 2 6.67 25.1 (24.1, 26.1)Quartile 3 3.33 25.1 (24.1, 26.0)Quartile 4 0 24.8 (23.9, 25.7)Reported that health condition limited participation in (more) activitiesb −1 5.8 (5.4, 6.2)Reported that health condition limited use of public transportationb −1 2.0 (1.8, 2.2)Reported that health condition limited use of accessible transportationb −1 0.5 (0.4, 0.5)Reported that physical condition is a barrier to improve healthb −1 2.8 (2.5, 3.1)Reported that disability or health problem is a barrier to improveb −1 3.6 (3.3, 4.0)Cognitive DomainCognitionAble to remember most things, think clearly and solve day to day problems 10 73.9 (73.0, 74.9)Able to remember most things, but have a little difficulty when trying to thinkand solve day to day problems8 2.2 (1.9, 2.5)Somewhat forgetful, but able to think clearly and solve day to day problems 6 17.2 (16.3, 18.0)Somewhat forgetful, and have a little difficulty when trying to think or solveday to day problems4 5.0 (4.6, 5.5)Very forgetful, and have great difficulty when trying to think or solve day today problems2 1.4 (1.2, 1.6)Unable to remember anything at all, and unable to think or solveday to day problems0 0.2 (0.1, 0.3)Fear of fallingNot applicable (<65 years old) 10 68.1 (67.2, 68.9)Not worried or concerned about future falls 10 21.2 (20.5, 21.9)Worried or concerned about future falls, have not stopped activities 5 6.0 (5.6, 6.3)Worried or concerned about future falls, have stopped some activities 0 4.8 (4.5, 5.0)Hirsch et al. BMC Public Health  (2017) 17:513 Page 5 of 14Table 1 Full set of component items for each domain of the MOVES included in both the final MOVES score and sensitivity analysis(Continued)Transport DomainNumber of modes (comprised of the modes below)cNo Modes 0 0.3 (0.2, 0.4)1 Mode 2.5 30.2 (29.2, 31.2)2 Modes 5 36.7 (35.6, 37.8)3 Modes 7.5 25.4 (24.4, 26.3)4 Modes 10 7.5 (6.8, 8.1)Modes of transport used in past monthcDrive (at least once in past week) 2.5 83.0 (82.3, 83.7)Passenger (passenger/taxi) 2.5 64.5 (63.4, 65.6)Transit (public transit/accessible transit) 2.5 21.9 (21.0, 22.9)Active travel (cycling/walking) 2.5 40.2 (39.1, 41.3)Reported that transportation problems limited participationin (more) activitiesb−1 1.3 (1.1, 1.5)Reported that transportation problems is a barrierto improve healthb−1 0.2 (0.1, 0.3)Reported that transportation problems are the reason theydid not see the dentistb−1 0.0 (0.0, 0.1)Social DomainSense of belonging to local communityVery strong 10 22.1 (21.2, 23.0)Somewhat strong 6.67 44.0 (42.8, 45.1)Somewhat weak 3.33 23.4 (22.5, 24.4)Very weak 0 10.1 (9.5, 10.8)Frequency of participation in a community-related activityDid not participate in a community-related activity 0 1.9 (1.7, 2.1)Participated at least once a year 2.5 4.5 (4.0, 4.9)Participated at least once a month 5 20.8 (19.8, 21.7)Participated at least once a week 7.5 60.9 (59.8, 62.0)Participated at least once a day 10 11.9 (11.2, 12.7)Tangible social support (higher values indicate higher social support)Index score from 0 to 16 0 0.8 (0.6, 1.0)1 0.625 0.4 (0.3, 0.6)2 1.25 0.6 (0.4, 0.7)3 1.875 0.7 (0.6, 0.9)4 2.5 1.2 (0.9, 1.4)5 3.125 1.0 (0.8, 1.1)6 3.75 1.4 (1.1, 1.6)7 4.375 1.7 (1.4, 1.9)8 5 3.1 (2.7, 3.4)9 5.625 2.6 (2.3, 2.9)10 6.25 3.8 (3.4, 4.2)11 6.875 4.3 (3.9, 4.7)12 7.5 10.9 (10.1, 11.7)13 8.125 6.4 (5.9, 7.0)Hirsch et al. BMC Public Health  (2017) 17:513 Page 6 of 14Statistical refinement, scoring and compilationWe ran Cronbach’s alpha to determine internalconsistency for all items (0.61) and within each domain(range from 0.11–0.64). By examining Cronbach’s alphaif each item were deleted, we identified that MOVESperformed equally well without barrier and limitationitems (including all those in the financial domain). Afterremoving these items, the final MOVES standardizedCronbach’s alpha was 0.58. The cognitive domain hadthe lowest internal consistency, likely because cognitivefunction and fear of falling tap into different, yet related,elements of mobility-related cognition or psychology.We ran PCA both on all items identified by the expertpanel and just on items remaining after Cronbach’s alphaanalysis. We ran these PCA with no restrictions placedon number of factors as well as with factor constraintsequal to the number of domains. In general, itemsgrouped within the anticipated domains. However, fearof falling loaded onto both the cognitive and physicaldomain and transportation mode loaded onto a numberof factors. This cross-over between factors was expected,as theoretical frameworks include interconnectednessbetween domains. PCA also confirmed we should re-strict to only four domains; in the solution including allitems, the first five factors accounted for only 39.1% ofvariance (all five had eigenvalues greater than 1). In thesolution using only the subset of items indicated byCronbach’s alpha, the first four factors accounted for62.3% of the variance (only the first three had eigen-values greater than 1). Thus, statistical refinement usingPCA confirmed that removing barriers and limitations(including the entire financial domain) from MOVEScreated an equally sound score.We provide final items and scoring for items inMOVES in Table 1. All items (except PASE) werecategorical and were left in their original metricsbased on the guiding principle for absolute versusrelative items. Scores were recoded so higher valuesindicate greater mobility and then were scaled to 10points. As recommended by Statistics Canada [30],PASE data were used as quartiles. Since respondentsaged under 65 were not asked about their fear of fall-ing, we allocated them 10 points. We chose to allo-cate points based on the number of transportationmodes each respondent reported. We did notprioritize active mode, aligned with the conceptualframeworks that considered all forms of transporta-tion as important to mobility. We grouped transpor-tation modes as: driving oneself (having a driver’slicense and driving at least once in the previousmonth), being driven (being a passenger or taking ataxi), taking public or accessible transit (where access-ible transit included service designed for persons withdisabilities or mobility issues), and active transit(walking or cycling for transportation). Items withineach domain were averaged, so each domain receivedan equal weight of 10 points. The final MOVES wascreated by summing across four domains for a pos-sible score of 0 to 40.ResultsCanadian mobilityCCHS sampleIn the weighted sample, 49% were female, most werebetween ages 45 and 64 (Table 2). A majority weremarried, white, Canadian, still working, have post-secondary education, own their homes, live in singledetached houses, and live with their family. Most donot receive home care, were satisfied or extremelysatisfied with life, have at least one chronic condition,drink regularly, do not have arthritis, have experi-enced no falls, and did not feel depressed or loseinterest in things (Table 3).Table 1 Full set of component items for each domain of the MOVES included in both the final MOVES score and sensitivity analysis(Continued)14 8.75 8.3 (7.7, 9.0)15 9.375 9.9 (9.2, 10.6)16 10 43.0 (41.8, 44.1)Financial DomaindReported that cost limited participation in (more) activitiesb −1 3.2 (2.8, 3.6)Reported that cost limited use of public transportationb −1 0.6 (0.4, 0.8)Reported that cost limited use of accessible transportationb −1 0.3 (0.2, 0.5)Reported that cost is a barrier to improve healthb −1 1.5 (1.3, 1.7)aAll component items coded so that higher points indicate more positive mobility and then scaled to be between 0 and 10 points. Barriers each coded aspenalties of 1 pointbStatistical refinement using Cronbach’s alpha identified that barrier and limitation items (including all of those in the financial domain) were not adding to theoverall MOVES or the domains. These items only used in sensitivity analysescNumber of modes was used for the MOVES, but the breakdown of each transport mode is also presented for descriptive purposesdThe Financial Domain was not included in the final MOVES due to results of the statistical refinementHirsch et al. BMC Public Health  (2017) 17:513 Page 7 of 14Table 2 Distribution of MOVES across sociodemographic and economic characteristicsSociodemographic or Economic Characteristics Weighted Percentage (95% CI) Mean MOVES (95% CI) p-value for MOVESaSex <.0001Male 44.9 (43.7, 46.0) 29.9 (29.8, 30.0)Female 49.0 (47.9, 50.2) 29.3 (29.1, 29.4)Age <.000145–54 36.7 (35.4, 37.9) 30.8 (30.6, 30.9)55–64 28.0 (27.1, 28.9) 30.4 (30.2, 30.5)65–74 16.6 (16.0, 17.2) 28.5 (28.3, 28.6)75–84 9.7 (9.3, 10.1) 26.5 (26.3, 26.7)85+ 3.0 (2.8, 3.2) 24.0 (23.7, 24.3)Worked at job or business <.0001Yes 57.3 (56.2, 58.3) 31.0 (30.8, 31.1)No 38.8 (37.8, 39.8) 27.6 (27.4, 27.7)Retirement status <.0001Completely retired 33.0 (32.0, 33.9) 27.9 (27.8, 28.0)Partially retired or not retired 61.7 (60.7, 62.7) 30.7 (30.6, 30.8)Highest level of education <.0001Less than or secondary school graduation 24.6 (23.7, 25.5) 27.9 (27.8, 28.1)Some post-secondary 4.5 (4.1, 5.0) 29.7 (29.3, 30.1)Post-secondary graduation 64.9 (63.9, 65.9) 30.2 (30.1, 30.3)Total household income from all sources <.0001Less than $19,999 9.1 (8.6, 9.6) 26.3 (26.0, 26.5)$20,000 TO $49,999 28.3 (27.3, 29.3) 28.5 (28.4, 28.6)$50,000 TO $99,999 33.8 (32.6, 35.0) 30.4 (30.2, 30.5)$100,000 and over 24.1 (22.9, 25.2) 31.5 (31.3, 31.7)Region or Province of residence <.0001Atlantic 7.2 (6.9, 7.5) 29.1 (28.9, 29.2)Quebec 23.2 (22.3, 24.2) 28.9 (28.7, 29.0)Ontario 36.4 (35.2, 37.6) 29.6 (29.4, 29.7)Prairies 14.6 (14.0, 15.2) 30.2 (30.1, 30.4)British Columbia 12.5 (11.8, 13.2) 30.5 (30.2, 30.7)Population size group 0.0033Rural area 15.1 (14.2, 16.0) 29.2 (29.0, 29.4)Urban area < 100,000 13.4 (12.7, 14.2) 29.4 (29.2, 29.6)≥ 100,000 to <500,000 21.2 (20.3, 22.0) 29.5 (29.4, 29.7)≥ 500,000 44.2 (43.1, 45.3) 29.8 (29.6, 29.9)Flag for tenure of dwelling <.0001Not owned by the respondent 16.9 (16.1, 17.7) 27.8 (27.6, 28.0)Owned by the respondent 77.5 (76.6, 78.4) 30.0 (29.9, 30.1)Type of dwelling <.0001Single detached house 67.4 (66.4, 68.4) 30.0 (29.9, 30.1)Double, row or terrace, duplex house 10.6 (10.0, 11.3) 29.5 (29.2, 29.8)Low-rise or high rise apartment 14.2 (13.6, 14.9) 27.9 (27.7, 28.1)Mobile home or other 1.6 (1.4, 1.8) 28.6 (28.2, 29.1)Hirsch et al. BMC Public Health  (2017) 17:513 Page 8 of 14MOVES descriptive statisticsWithin the 28,555 adults with complete data to createMOVES, the 10th percentile of MOVES was 24.2(95% confidence interval (CI) 24.0, 24.4) and the 90thpercentile was 34.7 (CI 34.6, 34.9), with a mean of29.6 (CI 29.5, 29.7). Scores were generally high withineach MOVES domain, although differences existed ineach domain by age (Figure 2). Out of 10, Canadiansscored a mean physical mobility of 8.1 (95% CL 8.1,8.1), mean cognitive mobility of 9.0 (95% CL 9.0, 9.1),and mean social mobility of 7.1 (95% CL 7.0, 7.1).Over 90% used between one and three transportationmodes, giving a mean transportation mobility score of5.2 (95% CL 5.2, 5.3).MOVES was higher for those who were younger, male,white, better educated, employed, higher income, mar-ried, home owners, born in Canada, and living in largerurban areas (Table 2). Higher MOVES was also associ-ated with healthier behaviors and better health outcomes(Table 3). Those with excellent self-perceived health hadan average MOVES of 31.2 (CI 31.0, 31.4), comparedwith those with poor self-perceived health, who had anaverage MOVES of 24.0 (CI 23.5, 24.4). Lower values forMOVES by age were statistically significantly differentfor males and females (p < 0.001) with females having asteeper decline in mobility across age groups (Figure 3).MOVES varied across the provinces (p < 0.0001) anddeclines across age groups also varied by province/re-gion (p = 0.065, Additional file 1: Fig. S1). Similarly,gender differences in mobility decline differed by prov-ince/region (p = 0.070, Additional file 2: Fig. S2a andAdditional file 3: Fig. S2b).DiscussionWe used data from a large, population-based study tocreate a comprehensive measure of mobility, MOVES,that encompasses multiple domains of actualized mobil-ity for mid- to late-life adults living in the community.Grounded in evidence and conceptual frameworks, andrefined using input from experts and statistical analysis,MOVES captures the complexity inherent in mobility,including physical, cognitive, social, and transportationdomains. Across the representative sample of Canadianolder adults, MOVES aligns with expected mobility pat-terns (higher for those who were younger, higher socio-economic status, and in better health).The creation of a holistic mobility score bridges gapsbetween other classification systems, as it better captureswhere people go, what they do in their daily lives, andtheir social connections to others. In contrast to typicalclinical measures that focus on physical capacity [6],MOVES provides researchers, practitioners, and policy-makers the opportunity to evaluate actualized mobilitymore broadly. Particularly noteworthy is our inclusion oftransport modes. Older adults out-of-home activitylevels decrease with driving cessation [3], and cessationof driving was associated with worse health outcomes[36], although directionality of these associations isTable 2 Distribution of MOVES across sociodemographic and economic characteristics (Continued)Household size <.0001Alone 17.5 (16.9, 18.2) 27.8 (27.7, 28.0)2 people 45.3 (44.2, 46.4) 29.6 (29.5, 29.7)3 or more 31.1 (29.9, 32.3) 30.5 (30.4, 30.7)Marital status <.0001Married or common-law 69.8 (68.9, 70.7) 30.1 (30.0, 30.2)Widowed, separated, or divorced 18.3 (17.7, 19.0) 27.7 (27.5, 27.8)Single, or never married 5.8 (5.3, 6.2) 28.8 (28.5, 29.2)Cultural/Racial Background 0.0251Not White 11.4 (10.6, 12.2) 29.3 (29.0, 29.5)White 83.0 (82.1, 83.9) 29.6 (29.5, 29.7)Immigrant 0.0042Yes 22.8 (21.9, 23.8) 29.3 (29.1, 29.5)No 71.6 (70.6, 72.6) 29.7 (29.6, 29.8)County of Birth <.0001Canada 70.9 (69.9, 72.0) 29.7 (29.6, 29.8)Other North America 1.1 (0.9, 1.3) 29.8 (29.2, 30.4)Others 22.3 (21.4, 23.3) 29.3 (29.1, 29.5)aP-values from t-test or ANOVA testing for differences in mean MOVE. All results weighted using the Statistics Canada proportional sampling scheme and appliedBalanced Repeated Replication (BRR) with 500 bootstrap weight variables to obtain the correct standard errors for ANOVAHirsch et al. BMC Public Health  (2017) 17:513 Page 9 of 14Table 3 Distribution of MOVES across healthcare utilization, health behaviours, or health outcomesHealth Characteristics Weighted Percentage (95% CI) Mean MOVES (95% CI) p-value for MOVESaReceipt of Home Care <.0001Did not receive home care 81.9 (81.2, 82.7) 30.1 (30.0, 30.2)Informal home care only 1.8 (1.6, 2.0) 26.9 (26.6, 27.2)Formal home care only 7.6 (7.1, 8.0) 25.8 (25.2, 26.4)Both formal and informal home care 2.7 (2.4, 2.9) 23.5 (23.1, 24.0)Self-perceived health <.0001Excellent 19.4 (18.4, 20.4) 31.2 (31.0, 31.4)Very good 32.0 (30.9, 33.0) 30.6 (30.4, 30.7)Good 28.3 (27.4, 29.3) 29.1 (29.0, 29.3)Fair 10.8 (10.2, 11.4) 26.7 (26.5, 27.0)Poor 3.5 (3.1, 3.8) 24.0 (23.5, 24.4)Satisfaction with Life Scale <.0001Extremely dissatisfied 1.2 (1.0, 1.4) 25.6 (24.8, 26.4)Dissatisfied 3.1 (2.7, 3.5) 26.8 (26.2, 27.4)Slightly dissatisfied 6.2 (5.6, 6.7) 27.4 (27.0, 27.8)Neutral 1.7 (1.4, 1.9) 27.6 (26.9, 28.3)Slightly satisfied 13.2 (12.5, 14.0) 28.6 (28.4, 28.8)Satisfied 47.3 (46.2, 48.5) 29.9 (29.8, 30.0)Extremely satisfied 23.6 (22.7, 24.6) 30.9 (30.7, 31.0)Reported having a chronic condition <.0001Has at least one chronic condition 73.1 (72.0, 74.2) 29.2 (29.1, 29.3)Has no chronic conditions 21.0 (19.9, 22.0) 30.9 (30.8, 31.1)Smoking Status <.0001Smoker 17.4 (16.5, 18.3) 29.2 (29.0, 29.4)Former smoker 45.8 (44.7, 46.9) 29.7 (29.6, 29.9)Never smoked 30.7 (29.7, 31.8) 29.5 (29.4, 29.7)Drinking Status <.0001Regular drinker 58.0 (56.9, 59.1) 30.3 (30.2, 30.4)Occasional drinker 16.3 (15.5, 17.2) 29.2 (28.9, 29.4)Did not drink in the last 12 months 19.6 (18.8, 20.4) 27.7 (27.5, 27.9)Reported having arthritis <.0001Yes 26.0 (25.1, 26.9) 28.1 (27.9, 28.2)No 67.9 (66.9, 68.9) 30.2 (30.1, 30.3)Self-reported BMI <.0001Underweight (<18.50) 1.4 (1.2, 1.5) 27.1 (26.4, 27.7)Normal weight (18.50–24.99) 37.4 (36.3, 38.5) 29.6 (29.5, 29.8)Overweight (25.00–29.99) 36.2 (35.1, 37.3) 29.8 (29.7, 30.0)Obese-class I, class 2, class 3 (≥30.00) 21.2 (20.3, 22.2) 29.4 (29.2, 29.6)Number of falls (only 65+) <.0001No 73.7 (72.7, 74.7) 27.8 (27.7, 27.9)One fall 11.5 (10.7, 12.2) 26.4 (26.0, 26.7)Two or more falls 6.3 (5.8, 6.8) 24.2 (23.7, 24.7)Hirsch et al. BMC Public Health  (2017) 17:513 Page 10 of 14unclear. Thus, including both automobile use and trans-portation alternatives was critical to characterizing olderadult mobility. Another novel component of MOVES isits ability to capture social engagement and mobilitythrough tangible social support, sense of belonging, andfrequency of participation in community events. Linksbetween social support, health, and overall mortalityhave been well documented [37, 38], giving further cre-dence to the importance of including social connectionsand community participation in a mobility score.The sociodemographic and economic patterns we ob-served in MOVES align with previous literature on olderadult activity [39]. As expected, mobility declines withage. MOVES is higher for men, and declines over agewere steeper for women than for men. This differentialdecline is consistent with reports of ADL in olderwomen [40], and may be due to smaller support net-works due to employment patterns, or potential differ-ences in driving. However, gender differences could alsoresult from survivor bias as studies of functional declineshow men as less likely to survive [40], possibly resultingin a select group of stronger, more mobile males at olderages. Lower MOVES for those with lower income, edu-cation, employment, and home ownership, are consistentwith evidence on the role of socioeconomic status infunctional status [41], chronic disease [42], and mortality[43]. However, there remains controversy about themechanisms linking socioeconomic status to mobility[44]. Income and wealth may factor into neighborhoodchoices, providing fewer options for lower socioeco-nomic adults. Similarly, educational or occupational dif-ferences may afford disparate out of home engagementopportunities or access tools to cope with declines inphysical functioning.Interestingly, we observed higher levels of mobilityfor Canadians living in larger urban areas. This high-lights the need for continued research to differentiatebetween needs of older adults in rural versus urbancentres, and the need to address rural seniors’ healthneeds [45, 46]. Alternatively, larger-scale geographicpatterns by region may be more useful as descriptivedistributions of mobility for resource allocation andhealth care service provision (which is under provin-cial jurisdiction in Canada). Not surprising, we foundthose with higher MOVES had better health out-comes, including self-perceived health, life satisfaction,and fewer chronic conditions, normal body weight,fewer depressive symptoms, and fewer falls. These de-scriptive results are consistent with research findingsthat life space is associated with health and mortality[5, 47]. Our paper investigated whether trends in ournew mobility measure, MOVES, tracked with prevail-ing literature on mobility patterns. More in-depthanalyses should explore the associations betweensociodemographic and economic factors, MOVES andhealth outcomes.Table 3 Distribution of MOVES across healthcare utilization, health behaviours, or health outcomes (Continued)Depressive Symptoms <.0001Did not feel depressed or did not lose interest in things 88.9 (88.2, 89.5) 29.7 (29.6, 29.8)Felt depressed or lost interest in things 7.7 (7.1, 8.3) 28.1 (27.8, 28.5)aP-values from t-test or ANOVA testing for differences in mean MOVE. All results weighted using the Statistics Canada proportional sampling scheme and appliedBalanced Repeated Replication (BRR) with 500 bootstrap weight variables to obtain the correct standard errors for ANOVAFig. 2 Differences in MOVES Domains by AgeHirsch et al. BMC Public Health  (2017) 17:513 Page 11 of 14We acknowledge that MOVES has a number of limita-tions. MOVES was available for only those who an-swered all included items, assumed that those under 65have no fear of falling, and items were restricted to thosepreviously measured in CCHS-HA. Other practitionersmay benefit from adding in questions on size of socialnetwork, cognitive ability to read and understand sign-age, or other measures related to the conceptual frame-works and domains. We also do not know how MOVESwould perform for an institutionalized population. Ourexample using MOVES to examine mobility patternsalso has limitations. First, a Canadian sample may notgeneralize to other populations. Second, our analyses arenot analytic and therefore only show descriptive bivari-ate patterns between MOVES and the sociodemographicand health variables. Further work would be needed,including age- and other adjustments to examine associ-ations causally. Finally, the sample used was apopulation-based sample of community dwellingmiddle-aged and older adults. It does not include peopleliving in institutions, who may have lower mobility. Wedo not know how well the scale could be used to suc-cessfully differentiate between individuals or subgroupswith very low levels of mobility. However, MOVES hasnumerous strengths across potential applications, andfills gaps created by limitations of other classification ap-proaches. MOVES holds utility for researchers workingin other population-based survey samples; since MOVESrelies on common, pre-existing survey items, others withpopulation surveys can derive a score to study holisticmobility. As such, this score is useful for benchmarkingand tracking mobility across large geographic scales.Some MOVES items might not be common to othersurveys. Future studies might test whether substitutingsimilar items can be made without compromising theperformance of MOVES. Similar to the descriptiveanalyses we provide, MOVES can be used to ascertaindifferences across gender, socioeconomic status, geog-raphies and other characteristics. MOVES may also beused in natural experiments to examine changes in mo-bility with policy shifts or infrastructure investments, al-though we were unable to test how sensitive MOVES isto change using this cross-sectional sample. Similarly,researchers can use MOVES to understand the associ-ation between broad mobility and health outcomes, in-cluding self-rated health and overall mortality.Alternatively, MOVES could be used by policy makersand practitioners hoping to better understand mobility.MOVES provides insight on how well older adults areable to engage with their communities, and would en-hance discussions around planning for driving cessationand maintaining mobility. Ultimately, MOVES repre-sents the quantitative embodiment of evidence and con-ceptual frameworks of mobility. By assigning numericvalues to these concepts, it further enhances discoursebetween various stakeholders around supports for olderadult mobility and opens new avenues of research.ConclusionGrounded in frameworks and qualitative research thatsupport conceptualizing mobility across physical, cogni-tive, transport and social domains, this study created aquantitative measurement tool (MOVES) for mobilitythat encompasses multiple domains. Descriptive data onMOVES in older adults from across Canada followed ex-pected sociodemographic, economic, and health patternsof mobility levels. MOVES appears useful for research,surveillance, evaluation, and interventions around thebroad factors influencing mobility in older adults. Futurework could use MOVES to examine determinants, con-sequences and changes in of mobility for older adultsacross a range of setting and populations.Fig. 3 Trend in MOVES with Age, by GenderHirsch et al. BMC Public Health  (2017) 17:513 Page 12 of 14Additional filesAdditional file 1: Figure S1. Trend in MOVES with age, by Region(PNG 119 kb)Additional file 2: Figure S2a. Trend in MOVES with age, by Region forMales (PNG 116 kb)Additional file 3: Figure S2b. Trend in MOVES with age, by Region forFemales (PNG 123 kb)AbbreviationsADL: Activities of Daily Living; ANOVA: Analysis of Variance; CCHS-HA: Canadian Community Health Survey – Health Aging; CI: 95% ConfidenceInterval; CIHR: Canadian Institutes of Health Research; HUI: Health UtilitiesIndex; IADL: Instrumental Activities of Daily Living; ICF: InternationalClassification of Functioning, Disability and Health; MOS: Medical OutcomesStudy; MOVES: Mobility Over Varied Environments Scale; NERI: New EnglandResearch Institutes; OARS: Older Americans Resources and Services;OMFAQ: OARS Multidimensional Functional Assessment Questionnaire©;PASE: Physical Activity Scale for the Elderly; PCA: Principal ComponentAnalysisAcknowledgementsStatistics Canada thanks all participants for their valuable input and adviceduring the development of the 2008/2009 Canadian Community HealthSurvey― Healthy Aging. Consultations included stakeholders from HumanResources and Social Development Canada and provincial and territorialhealth ministries. The authors would like to acknowledge the non-authors onthe expert panel who assisted in development and refinement of MOVES:Callista Haggis, Thea Franke, Christine Voss, Dawn Mackey, and SuzanneTherrien. This work was undertaken in the University of British Columbia andthe Simon Fraser University Statistics Canada Research Data Centres with theassistance of Lee Grenon and Lisa Oliver.FundingThe survey content was developed by the Health Statistics Division atStatistics Canada in consultation with Health Canada, the Public HealthAgency of Canada, and experts conducting the Canadian Longitudinal Studyon Aging (CLSA), a major strategic initiative of the Canadian Institutes ofHealth Research. The addition of 5000 respondents aged 45 to 54 wasfunded by the CLSA. Research was supported by the Canadian Institutes ofHealth Research (CIHR) grant number F14–03087. Hirsch is supported by thePopulation Research Training grant (T32 HD007168) and the PopulationResearch Infrastructure Program (R24 HD050924) awarded to the CarolinaPopulation Center at The University of North Carolina at Chapel Hill by theEunice Kennedy Shriver National Institute of Child Health and HumanDevelopment. Sims-Gould is supported by a CIHR New Investigator awardand a Michael Smith Foundation for Health Research Scholar award. MeghanWinters is supported by a Michael Smith Foundation for Health ResearchScholar award. Ashe is supported by the Canada Research Chairs Program.Availability of data and materialsThe data that support the findings of this study are available from StatisticsCanada but restrictions apply to the availability of these data, which wereused within the Statistics Canada secure Research Data Centre (RDC) for thecurrent study, and so are not publicly available. Data are, however, availablefor use within an RDC after appropriate clearance and approval by StatisticsCanada.Authors’ contributionsJH and HM conceived of the idea to create a holistic measure of mobility.JH, MW, JS, PC, and MA designed the MOVES, tailored the MOVES after PCA,and interpreted the results of the descriptive analysis. JH, MW, NS gainedaccess to, analyzed, and guided the release of the CHHS-HA data. JH, MW,and HM contributed to writing the manuscript. All authors reviewed andapproved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateThis study did not require ethics review as it was deemed exempt under theTri-Council Policy Statement: Ethical Conduct for Research Involving Humans(TCPS 2), Article 2.4, by Simon Fraser University’s Office of Research Ethics.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Department of Epidemiology and Biostatistics, Arnold School of PublicHealth, University of South Carolina, 915 Greene Street, Columbia, SC, USA.2Centre for Hip Health and Mobility, Robert H.N. Ho Research Centre,University of British Columbia, 5th Floor, 2635 Laurel St, Vancouver, BC,Canada. 3Faculty of Health Sciences, Simon Fraser University, 8888 UniversityDrive, Burnaby, BC, Canada. 4Department of Family Practice, University ofBritish Columbia, 3rd Floor David Strangway Building, 5950 UniversityBoulevard, Vancouver, BC, Canada. 5Department of Epidemiology, Universityof Michigan, 1415 Washington Heights, 4667 SPH I, Ann Arbor, MI, USA.6Institute for Social Research, University of Michigan, P.O. Box 1248426Thompson St, Ann Arbor, MI, USA. 7Division of Orthopaedic Surgery, McGillUniversity Health Centre, 1001 Boulevard Décarie, Montréal, QC, Canada.8Department of Orthopaedics, University of British Columbia, 3114 - 910West 10th Avenue, Vancouver, BC, Canada.Received: 2 November 2016 Accepted: 17 May 2017References1. Kinsella KG, Wan H. Census USBot: An aging world: 2008. In: US Departmentof Commerce, Economics and Statistics Administration, US Census Bureau;2009.2. 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Am J Epidemiol. 2008;167:240–8.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Hirsch et al. BMC Public Health  (2017) 17:513 Page 14 of 14


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