Mobility is a key predictor of change in well being among older adults who experience falls evidence from the vancouver falls prevention clinic cohort

Accepted Manuscript
Mobility is a key predictor of changes in wellbeing among older fallers: Evidence from
the Vancouver Falls Prevention Cohort
Jennifer C. Davis, John R. Best, Stirling Bryan, Linda C. Li, Chun Liang Hsu, Caitlin
Gomez, Kelly Vertes, Teresa Liu-Ambrose
PII:

S0003-9993(15)00292-0

DOI:

10.1016/j.apmr.2015.02.033

Reference:

YAPMR 56157

To appear in:

ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION

Received Date: 12 January 2015
Accepted Date: 24 February 2015

Please cite this article as: Davis JC, Best JR, Bryan S, Li LC, Hsu CL, Gomez C, Vertes K, Liu-Ambrose
T, Mobility is a key predictor of changes in wellbeing among older fallers: Evidence from the Vancouver
Falls Prevention Cohort, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2015), doi:
10.1016/j.apmr.2015.02.033.
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Mobility is a key predictor of changes in wellbeing among older fallers: Evidence from the
Vancouver Falls Prevention Cohort
Jennifer C. Davis* a,d, John R. Best* d,e,f,g, Stirling Bryan a, Linda C Li b,c, Chun Liang Hsu d,e,f, Caitlin Gomez

* Jennifer
a Centre

Kelly Vertes e,g, Teresa Liu-Ambrose d,e,f,g

C. Davis* a,d, John R. Best* d,e,f,g sharing first-authorship

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e,g,

for Clinical Epidemiology and Evaluation, 828 West 10th Avenue, University of British Columbia &

b Department

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Vancouver Coastal Health Research Institute (VCHRI), Vancouver, British Columbia, V6T 2B5, Canada
of Physical Therapy, 2177 Wesbrook Mall, University of British Columbia, Vancouver, British

c Arthritis

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Columbia, V6T 2B5, Canada

Research Centre of Canada, 5591 No. 3 Road, Richmond BC, British Columbia, V6X 2C7,

Canada
d

Aging, Mobility, and Cognitive Neuroscience Lab, 2211 Wesbrook Mall, University of British Columbia,

e Department

of Physical Therapy, 2177 Wesbrook Mall, University of British Columbia, Vancouver, British

Columbia, V6T 2B5, Canada

Research Center, 2211 Wesbrook Mall, University of British Columbia, Vancouver, British Columbia,

V6T 2B5, Canada

Center for Hip Health and Mobility, 311-2647 Willow Street, Vancouver, British Columbia, V5Z 1M9,

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g

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f Brain

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Vancouver, British Columbia, V6T 2B5, Canada

Canada

Funding: The Canadian Institute for Health Research Emerging Team Grant (CIHR, MOB-93373 to Karim
Khan, TLA, LL) provided funding for this study.
*Corresponding Author:
Teresa Liu-Ambrose, PhD, PT, Tel: 1-604-875-4111 ext. 69059, Fax: 1-604-875-4762, Email:



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Acknowledgement
We thank the Vancouver Falls Prevention Cohort study participants. The Canadian Institute for Health
Research Emerging Team Grant (CIHR, MOB-93373 to Karim Khan, TLA, LL) provided funding for this

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study. TLA is a Canada Research Chair in Physical Activity, Mobility, and Cognitive Neuroscience, a
Michael Smith Foundation for Health Research (MSFHR) Scholar, a Canadian Institutes of Health
Research (CIHR) New Investigator, and a Heart and Stroke Foundation of Canada’s Henry JM

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Barnett’s Scholarship recipient. JCD and JB are funded by a CIHR and MSFHR Postdoctoral
Fellowship. LL is a MSFHR Scholar and a Canada Research Chair. CLS is a CIHR Doctoral Trainee.

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These funding agencies did not play a role in study design. We obtained approval for the Vancouver
Falls Prevention Clinic Cohort study from UBC Clinical Ethics Review Board.

Conflict of Interest

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Jennifer C. Davis, Stirling Bryan, John R. Best, Linda C Li, Chun Liang Hsu, Caitlin Gomez, Kelly Vertes
and Teresa Liu-Ambrose declare that they have no competing interests.

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Author’s Contributions

TLA was principal investigator for the Vancouver Falls Prevention Clinic Cohort study. TLA and JCD

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were responsible for study concept and design, acquisition of data, data analysis and interpretation,
writing and reviewing of the manuscript. JCD and JB were responsible for data analysis. JCD, TLA, JB,
SB, CLH, LL, CG, and KV drafted and revised the manuscript. JCD, JB, TLA and SB acquired and
interpreted the data.


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Mobility predicts wellbeing among older fallers
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Mobility is a key predictor of changes in wellbeing among older fallers: Evidence from the

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Vancouver Falls Prevention Cohort
ABSTRACT

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Objective: To determine the factors that predict change in wellbeing, over time among older men and

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women presenting to the Vancouver Falls Prevention Clinic.

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Design: 12-month prospective cohort study

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Setting: Vancouver Falls Prevention Clinic

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Participants: The study sample consisted of between 244 - 255 (depending on the analysis) community-

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dwelling older adults referred to the clinic after suffering a fall.

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Main Outcome Measure: The ICECAP-O, a measure of wellbeing/quality of life, was administered at

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baseline, 6-months, and 12-months. We constructed linear mixed models to determine whether baseline

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predictor variables were related to baseline wellbeing and/or changes in wellbeing over time. Additionally,

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we included interactions with sex to investigate difference for males versus females. Baseline predictors

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included two measures of mobility (Short Performance Physical Battery (SPPB) and Timed Up and Go

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(TUG)) and a measure of global cognitive function (Montreal Cognitive Assessment (MoCA)).

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Results: All three predictors were associated with wellbeing at baseline (p<0.05). Further, both SPPB and

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TUG interacted with sex (p<0.05) to predict changes in wellbeing over time. Follow-up analyses suggested

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that better mobility was protective against decline in wellbeing in males but was generally unrelated to

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changes in wellbeing in women.

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Conclusion: We found that two valid and reliable measures of mobility interacted with sex to predict

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changes in wellbeing overtime. This is a critical research area to develop in order to appropriately tailor

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future intervention strategies targeting wellbeing among older fallers – a population at high risk of functional

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decline.

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Trial Registration: ClinicalTrials.gov Identifier: NCT01022866

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Keywords: falls, mobility, wellbeing, quality of life, older adults

List of Abbreviations:

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ICECAP-O: Index of Capability for Older Adults

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TUG: Timed up and Go

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SPPB: Short Performance Physical Battery

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MoCA: Montreal Cognitive Assessment

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QALY: Quality Adjusted Life Year

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MMSE: Mini-Mental State Examination

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PPA: Physiological Profile Assessment

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MCI: Mild cognitive impairment

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DSST: Digit symbol substitution test

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Mobility predicts wellbeing among older fallers
Falls are a ‘geriatric giant’ and injuries resulting from falls in older adults represent a significant public

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health, personal and societal burden worldwide.[1] Non-fatal fall injuries are associated with increased

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morbidity, decreased functioning and increased healthcare resource utilization. Given the large financial

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burden imposed by falls and the scarcity of health care system resources, economic evaluations are

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essential to assist health care decision makers in allocating resources.

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Economic evaluations are long established tools essential for guiding health policy decisions. A critical

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component in economic evaluations is how health outcomes are assessed. This is a particularly relevant

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challenge within falls research because there is a large degree of heterogeneity in the denominator of cost-

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effectiveness studies. Health outcomes (i.e., effectiveness) are valued in a number of ways (i.e., falls,

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Quality Adjusted Life Year (QALY)). The QALY is most often used to evaluate health related quality of life in

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economic evaluations because it provides the benefit of a common metric across conditions.

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However, there is now increasing emphasis on wellbeing or quality of life more broadly as a critical

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outcome measure among specific populations such as older adults.[2] Wellbeing and quality of life are used

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interchangeable here and are distinct from health related quality of life because they are not focused on

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health alone. Many health care issues among older adults (i.e., falls, fracture, cognitive decline) [3] are

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accompanied by forms of care including health and social care. Hence, it seems logical that an outcome

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measure for older fallers should aim to measure these potential gains or losses from a broader perspective

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than health alone.[2] Measuring individuals capabilities rather than focusing strictly on their functional ability

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to perform specific tasks is a promising approach to capture these broader benefits among older adults.[4]

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The ICECAP-O, an index of capability, is a preference-based outcome measure designed to provide a

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broader assessment of an individual’s quality of life or wellbeing.[5,6] This instrument was designed for use

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in economic evaluations across different sectors and intervention types. It is conceptually linked to Sen's [7]

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capability approach, which defines wellbeing in terms of what individuals are able to do (i.e., capabilities),

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not what individuals actually do (i.e., functionings). Specifically, capabilities reflect an individual’s ability to

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do a specific task; whereas, a functioning reflects whether or not the individual does a specific task or is in

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a specific state. Sen [7] emphasizes that an individual’s capabilities are most useful in assessing and

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comparing impact of interventions.

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Key risk factors for falls include impaired mobility [8] and cognition.[9] Prior to intervening, we must first

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ensure that we are appropriately capturing gains/losses in this population. Given that behavioural

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interventions aimed at promoting mobility may have benefits that extend beyond health, it is critical to

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explore measures (i.e., the ICECAP-O) that capture these changes. Previous cross-sectional studies have

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demonstrated an association between balance, mobility and cognition with wellbeing.[10,11] However, the

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literature remains relatively devoid of longitudinal data that explains determinants of wellbeing or changes

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in wellbeing over time.[12] Hence, our primary objective was to determine the factors that predict change in

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wellbeing, as measured by the ICECAP-O, over time (i.e., from baseline to 12-month follow-up) among

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older men and women presenting to the Vancouver Falls Prevention Clinic. Similarities and differences

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among the relationship between predictor variables that explain the change in wellbeing over time for men

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versus women.are not well established.[10,11] Given that men and women’s cognitive and mobility

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function progress and respond differently, we hypothesize that their wellbeing would be differentially

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affected as well. Therefore, our secondary objective was to determine whether sex moderated the

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relationship between the identified predictors and changes in wellbeing.

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Methods

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Study design

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We conducted a longitudinal analysis of data from a 12-month prospective cohort study at the Vancouver

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Falls Prevention Clinic (www.fallclinic.com) from June 7, 2010 through October 24, 2013. Participants

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received a comprehensive assessment at the Vancouver Falls Prevention Clinic at baseline and 12-

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months.

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Participants

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Individuals presenting to the Vancouver Falls Prevention Clinic have all sustained a previous fall in the past

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12 months and are at high risk of mobility impairments that may result from subsequent falls, fracture, and

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functional decline.[10,13]. Community dwelling women and men who lived in the lower mainland region of

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British Columbia were eligible for study entry if they:

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were adults ≥ 70 years of age referred by a medical professional to the Falls Prevention Clinic as a

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result of seeking medical attention for a non-syncopal fall in the previous 12 months;

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understood, spoke, and read English proficiently;

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had a Physiological Profile Assessment (PPA) [14] score of at least 1.0 SD above age-normative

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value or Timed Up and Go Test (TUG) [15] performance of greater than 15 seconds or one

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additional non-syncopal fall in the previous 12 months;

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were expected to live greater than 12 months (based on the geriatricians’ expert opinion);

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were able to walk 3 metres with or without an assistive device; and

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were able to provide written informed consent.

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We excluded those with a neurodegenerative disease or dementia, patients who recently had a stroke,

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those with clinically significant peripheral neuropathy or severe musculoskeletal or joint disease, and

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anyone with a history indicative of carotid sinus sensitivity. We highlight that exclusions for this study were

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based on clinical grounds.

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Ethical approval was obtained from the Vancouver Coastal Health Research Institute and the University of

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British Columbia’s Clinical Research Ethics Board (H09-02370). All participants provided written informed

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consent.

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Vancouver Falls Prevention Clinic Measures at Baseline, 6 Months and 12 Months

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Primary Outcome Measure

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The primary outcome was the ICECAP-O [16,17], which assessing quality of life/wellbeing [5,18,19].

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Patients completed the ICECAP-O using paper versions that were given to them upon their initial visit to the

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Vancouver Falls Prevention Clinic. Telephone interviews were used to complete the ICECAP-O at 6 and 12

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months. No cards were used to aid interpretation of the questions. The ICECAP-O is a five item multiple

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choice questionnaire that measures an individual’s wellbeing and quality of life more broadly according to

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five attributes: attachment (love and friendship), security (thinking about the future without concern), role

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(doing things that make you feel valued), enjoyment (enjoyment and pleasure) and control (independence).

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Each domain has four possible response options. The ICECAP-O can be used to calculate a global

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capability index score on a zero to one scale where zero represents no capability and one represents full

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capability. QALYs can also be estimated from the ICECAP-O for use in economic evaluation [2,17].

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Vancouver Falls Prevention Clinic Measures at Baseline

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A comprehensive set of measurements relating to mobility and cognitive function that were collected at

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baseline are described below.

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Predictor Variables
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Mobility

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Mobility was assessed using the Short Physical Performance Battery (SPPB) [20] and the Timed-Up-and-

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Go Test (TUG).[21] For the Short Physical Performance Battery, participants were assessed on

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performances of standing balance, walking, and sit-to-stand. Each component is rated out of four points, for

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a maximum of 12 points; a score < 9/12 predicts subsequent disability.[22] For the TUG, participants rose

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from a standard chair, walked a distance of three meters, turned, walked back to the chair and sat

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down.[21] We recorded the time (s) to complete the TUG, based on the average of two separate trials. A

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TUG performance time of > 13.5 seconds correctly classified persons as fallers in 90% of cases.[21]

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Cognition

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The Montreal Cognitive Assessment (MoCA), a brief screening tool for mild cognitive impairment (MCI)

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[23]. It is more sensitive than the MMSE in detecting MCI.[23] It is a 30-point test covering eight cognitive

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domains: 1) attention and concentration; 2) executive functions; 3) memory; 4) language; 5) visuo-

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constructional skills; 6) conceptual thinking; 7) calculations; and 8) orientation. Scores below 26 are

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considered to be indicative of possible MCI.

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Descriptive Variables

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Comorbidity, activities of daily living and depression

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Functional comorbidity index (FCI) was calculated to estimate the degree of comorbidity associated with

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physical functioning.[24] This scale’s score is the total number of comorbidities. We used the 15-item

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Geriatric Depression Scale (GDS) [25,26] to indicate the presence of depression; a score of ≥ 5 indicates

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depression.[27]

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Physiological Falls Risk
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Physiological falls risk was assessed using the short form of the Physiological Profile Assessment (PPA).

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The PPA is a valid and reliable [60] measure of falls risk. Based on a participant’s performance in five

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physiological domains – postural sway, reaction time, strength, proprioception, and vision – the PPA

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computes a falls risk score (standardized score) that has a 75% predictive accuracy for falls in older

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people.[28,29] A PPA Z-score of ≥ 0.60 indicates high physiological falls risk.[30]

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Cognitive Function

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We assessed global cognition using the Mini Mental State Examination (MMSE) and the Montreal Cognitive

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Assessment (MoCA). The MMSE is a widely used and well-known questionnaire used to screen for

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cognitive impairment (i.e., MMSE <24).[31] It is scored on a 30-point scale with a median score of 28 for

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healthy community dwelling octogenarians with more than 12 years of education.[31] The MMSE may

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underestimate cognitive impairment for frontal system disorders because it has no items specifically

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addressing executive function.[31]

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Statistical Analyses

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Data distributions were initially examined using visual inspection of histograms and computation of skew

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and kurtosis values. Bivariate correlations were computed to determine the strength of association among

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the three predictors (i.e., MoCA, SPPB, and TUG). Within-subjects t-tests determined whether wellbeing

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changed significantly over time.

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For the main analyses, linear mixed models were constructed using the SPSS 22.0 MIXED procedure (IBM

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Corporation, 2013). Assessment month (0, 6, 12) was entered as a continuous, within-subjects repeated

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measure, the intercept was specified as a random effect, and predictors and covariates were entered as
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between-subjects fixed effects. A first-order auto-regressive covariance matrix provided superior model fit

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compared to an unstructured covariance matrix (based on the Bayesian Information Criterion) and allowed

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for model convergence across the models. Denominator degrees of freedom were calculated from the

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Satterthwaite approximation.[32] Maximum likelihood estimation utilized all individuals who completed at

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least one of the ICECAP-O assessments and who had data for the predictor of interest. Because

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completion rates for the three predictors varied slightly, the number of individuals included in each linear

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mixed model differed slightly (as noted below). Visual analysis of histograms of the model residual values

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demonstrated that the residuals were normally distributed and that there were no outlier cases that would

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unduly influence the model parameters.

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A separate linear mixed model was constructed for each predictor variable. In addition to the specific

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predictor and its interaction with time, models included participant age and sex and their interactions with

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time. To investigate sex differences in the relations between the predictor variable and ICECAP-O score,

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the predictor X sex and predictor X sex X time interaction terms were also included. If not statistically

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significant, these terms involving an interaction with sex were dropped. Additionally, in the examination of

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SPPB and TUG as predictors, the use of armrest was included as a covariate, along with its interaction with

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time. (Note: the use of armrest did not interact with the main variables of interest the model, and therefore

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these interaction terms were excluded). In the text, we report the unstandardized beta estimate (B), its 95%

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confidence interval, and its significance value. Given a significant interaction with sex, computation of

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simple slopes was used to examine the association between the predictor and ICECAP-O score separately

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for males and females. To visualize significant interaction effects, we used model-based estimated

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marginal means at low (-1 SD), average (0 SD), and high (+1 SD) levels of the predictor.[33] When a

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higher-order interaction was significant (e.g., 3-way interaction), we do not report significant lower-order

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interactions (2-way interactions) or main effects.

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We included two sets of sensitivity analyses to examine additional approaches for dealing with missing data

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in the follow-up ICECAP-O assessments. One approach was to conduct the linear mixed models on only

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those individuals with complete data across the three assessment time points (complete case analyses).

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The second approach was to use multiple imputation to impute the missing data. Briefly, multiple imputation

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using the ICE (Imputation by Chained Equations) procedure in STATA 10.0 was used to create five

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complete data sets (MI analysis). We followed recommendations by Oostenbrink [34,35] and Briggs [36,37]

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for multiple imputation of missing effectiveness data. We imputed missing ICECAP-O values at each time

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point (i.e., 6 and 12 months). For each missing value, we generated five possible values using multiple

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linear regression. Covariates included age, FCI, TUG, PPA and baseline ICECAP-O score, and the weight

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and value of the missing variable in the preceding period. The final imputed value was the mean value from

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the five data sets created.

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Results

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Preliminary Analyses

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Table 1 reports descriptive statistics of all available cases at baseline for our variables of interest for this

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cohort. Due to significant positive skew (skew = 3.16), TUG scores were log10 transformed. The remaining

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variables of interest (MoCA, SPPB, and ICECAP-O) showed normal distributions (skew and kurtosis < |1|).

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Bivariate correlation analysis of the primary predictor variables revealed that faster TUG performance

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correlated with higher composite SPPB scores (r = -0.76, p < 0.001), and with better performance for each

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SPPB component: standing balance (r = -0.43, p < .001), sit-to-stand performance (r = -0.54, p < 0.001),

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and walking performance (r = -0.79, p < .001). MoCA scores correlated modestly with TUG performance (r

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= -0.23, p < 0.001), composite SPPB scores (r = 0.18, p = .002) and SPPB walking performance (r = 0.26,

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p < .001), but not with SPPB sit-to-stand performance (r = 0.06, p = 0.298) or SPPB standing balance (r =

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0.12, p = 0.052). At baseline, 244 individuals completed the ICECAP-O (mean score 0.814±.124); at 6-

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month follow-up, 178 individuals completed the ICECAP-O (mean score 0.809±.142); and at 12-month

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follow-up, 154 individuals completed the ICECAP-O (mean score 0.787±.149). A within-subjects t test

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indicated that wellbeing decreased significantly over time, t (190.4) = -2.04, p = .04.

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Primary Analyses: Predictors of change in ICECAP-O score

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The results of the linear mixed models are summarized in Table 2.

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Short Performance Physical Battery (n=245)

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For all individuals, SPPB was associated with wellbeing at baseline (p<0.001). Further, a SPPB by time by

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sex interaction was observed (p=0.034). To parse this interaction, sex-stratified follow-up analyses were

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conducted. For males (n = 80), there was a trend for higher SPPB scores to predict better maintenance of

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ICECAP-O scores over time (B = .02, p = .077). Alternatively, for females (n = 165), there was no evidence

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that SPPB predicted change in ICECAP-O over time (B = -.01, p = .417). These effects among men and

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women are graphed in Figure 1a (with men and women overlaid), using model-based estimated marginal

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means for average, low (-1 SD) and high (+1 SD) SPPB scores.

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Timed Up and Go (n=246)

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For all individuals, TUG performance predicted baseline ICECAP-O (p<0.001). Further, a significant TUG

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by time by sex interaction was observed (p<0.001). The sex-stratified simple slopes analyses showed that

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for males (n = 81), TUG performance was negatively related to change in ICECAP-O over time; that is,
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better baseline TUG performance predicted better maintenance of ICECAP-O scores from baseline to 1-

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year follow-up (B= -0.32, p=0.011). In contrast, for females (n = 165), the relationship between TUG

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performance and change in ICECAP-O over time was positive (B = 0.18, p = .022), meaning that poorer

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baseline TUG performance predicted greater decreases in ICECAP-O over time. These effects among men

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and women are graphed in Figures 1b, using model-based estimated marginal means for average, low (-1

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SD) and high (+1 SD) TUG scores.

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MoCA (n=255)

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There was no evidence that MoCA performance interacted with sex to predict baseline ICECAP-O scores

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or their change over time (ps > 0.45); therefore, these terms were dropped from the model. In the reduced

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model, higher MoCA scores predicted higher ICECAP-O score at baseline (p=0.026); however, MoCA

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scores did predict change in ICECAP-O over time (p = 0.78).

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Sensitivity Analysis

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Sensitivity analyses are summarized in Table 3.

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Complete Case Analysis

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The findings from the complete case analyses are consistent with the primary results described above;

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indeed, the parameter estimates were generally equal to, or larger than, the estimates described above.

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Multiple Imputation Analysis

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The findings from the multiple imputation analyses are generally consistent with the previous analyses, with

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the exception that the SPPB X sex X time interaction was not significant.

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Discussion

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This is the first longitudinal study examining key factors that explain variation in wellbeing over time among

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older fallers. This is a critical research area to develop in order to appropriately tailor future intervention

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strategies targeting wellbeing among older fallers – a population at high risk of both functional and cognitive

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decline. We found that two valid and reliable measures of mobility interacted with sex to predict changes in

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wellbeing overtime. Interestingly, cognition and specifically executive function explained variation in

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wellbeing at baseline only, not over time.

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There is limited longitudinal data that examines factors that explain variation in wellbeing among older

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adults [12]. Previously, cross-sectional data demonstrated that the ICECAP-O described between

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measures of depression, instrumental activities of daily living and the presence or absence of social activity

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limitations. The ICECAP-O has also demonstrated discriminative ability between multi-morbid elderly and

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those with high or low health related quality of life scores (measured using the EQ-5D). Mobility is affected

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by all of the above items (depression, instrumental activities of daily living, mood, social activities and

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health related quality of life). As such, our findings that mobility is a key factor accounting for variation in

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wellbeing over time builds on existing literature in this field. Interestingly, the ICECAP-O does not have a

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specific physical dimension; however, previous research has demonstrated its capacity to capture the

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effects of decreased physical function on wellbeing through the control and role dimensions [38,39].

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We did observe sex specific differences over time; however, there were subtle differences between the

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SPPB and TUG findings. Overall, our data suggest that a higher baseline level of mobility is related to

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better maintenance of wellbeing in males. In contract, baseline level of mobility in women is not significantly

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related to changes in wellbeing in females. In summary for the SPPB, although the trends for males and

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females were different in explaining wellbeing over time, these trends were non-significant. In contrast, the
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TUG was significant for males and females in explaining variation in wellbeing over time. This is a new

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finding compared with previous cross-sectional research that did not demonstrate any significant

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associations between ICECAP-O and sex [39]. Because these analyses are exploratory, it is too early to

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draw strong conclusions regarding the effect of sex on wellbeing over time. One recent cross-sectional

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study supporting the link between mobility and wellbeing examined what factors explain mobility

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among men and women [40]. Mobility in men was associated with higher ratings of subjective

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health, lower levels of depression and more engagement in sport activities. For women, better

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motility was associated with higher ratings of subjective health and higher levels instrumental

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activities of daily living outside the home. Of note, these findings do highlight that future intervention

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strategies aimed at improving wellbeing among older adults may need to consider different mobility related

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interventions for males and females.

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Study Limitations

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First, there was missing data could influence the interpretation of the results. It is possible that individuals

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who did not complete the cohort study were different that those who did. To investigate the impact of the

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missing data on our conclusions, we conducted two sensitivity analyses with the multiply imputed case set

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and the complete case set and found comparable results. Importantly, there are also limited longitudinal

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ICECAP-O data published to date. As such, we had limited data to compare our findings with. Additional

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longitudinal studies are needed to understand the sensitivity of the ICECAP-O to change among older

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adults at risk of mobility impairment. Further, future research should explore whether these overall findings

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are restricted to those with impaired mobility or if these findings can be extended to a general population.

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Lastly, we also used the UK ICECAP-O valuations because there are no Canadian valuations published to

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date.

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Conclusions & future directions

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This study is the first to investigate predictors of wellbeing over time. This study highlighted that mobility is

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a critical factor in explaining wellbeing at baseline and over time. Cognition at baseline did not explain

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wellbeing over time. Further, this study highlights the unique contribution of mobility to wellbeing over time

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between men and women. Specifically, men in the average or lower functioning mobility tertiles

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demonstrated decline over time regardless; whereas women regardless of their baseline status

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demonstrated a regression to the mean trend. This study provides an initial benchmark that mobility is an

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important factor contributing to older adults wellbeing. As such, future intervention strategies aimed at

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improving wellbeing should consider mobility as a primary target.

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References

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5.
6.
7.
8.
9.
10.

11.

12.
13.

14.

15.

16.

17.
18.

RI
PT

SC

4.

M
AN
U

3.

TE
D

2.

Tinetti, M. E., & Williams, C. S. (1997) Falls, injuries due to falls, and the risk of admission to a
nursing home. N Engl J Med, 337(18), 1279-1284.

Makai, P., Looman, W., Adang, E., Melis, R., Stolk, E., & Fabbricotti, I. (2014) Cost-effectiveness of
integrated care in frail elderly using the ICECAP-O and EQ-5D: does choice of instrument matter?
Eur J Health Econ.
Liu-Ambrose, T., Nagamatsu, L. S., Graf, P., Beattie, B. L., Ashe, M. C., & Handy, T. C. (2010)
Resistance training and executive functions: a 12-month randomized controlled trial. Arch Intern
Med, 170(2), 170-178.
Al-Janabi, H., Flynn, T. N., & Coast, J. (2012) Development of a self-report measure of capability
wellbeing for adults: the ICECAP-A. Qual Life Res, 21(1), 167-176.
Coast, J., Flynn, T. N., Natarajan, L., Sproston, K., Lewis, J., Louviere, J. J., & Peters, T. J. (2008)
Valuing the ICECAP capability index for older people. Soc Sci Med, 67(5), 874-882.
Rowen, D., Brazier, J., Tsuchiya, A., & Alava, M. H. (2011) Valuing states from multiple measures
on the same visual analogue sale: a feasibility study. Health Econ.
Sen, A., & Nussbaum, M. (1993) Capability and well-being. Oxford: Oxford University Press.
Lord, S. R., Clark, R. D., & Webster, I. W. (1991) Physiological factors associated with falls in an
elderly population. J Am Geriatr Soc, 39(12), 1194-1200.
Tinetti, M. E., Speechley, M., & Ginter, S. F. (1988) Risk factors for falls among elderly persons
living in the community. N Engl J Med, 319(26), 1701-1707.
Davis, J. C., Bryan, S., McLeod, R., Rogers, J., Khan, K., & Liu-Ambrose, T. (2012) Exploration of
the association between quality of life, assessed by the EQ-5D and ICECAP-O, and falls risk,
cognitive function and daily function, in older adults with mobility impairments. BMC Geriatr, 12, 65.
Davis, J. C., Liu-Ambrose, T., Richardson, C. G., & Bryan, S. (2012) A comparison of the ICECAPO with EQ-5D in a falls prevention clinical setting: are they complements or substitutes? Qual Life
Res.
Comans, T. A., Peel, N. M., Gray, L. C., & Scuffham, P. A. (2013) Quality of life of older frail
persons receiving a post-discharge program. Health Qual Life Outcomes, 11, 58.
Davis, J. C., Liu-Ambrose, T., Richardson, C. G., & Bryan, S. (2013) A comparison of the ICECAPO with EQ-5D in a falls prevention clinical setting: are they complements or substitutes? Qual Life
Res, 22(5), 969-977.
Lord, S., Sherrington, C., & Menz, H. (2001). A physiological profile approach for falls prevention.
In Falls in older people. Risk factors and strategies for prevention. (pp. 221-238). Cambridge:
Cambridge University Press.
Whitney, J. C., Lord, S. R., & Close, J. C. (2005) Streamlining assessment and intervention in a

falls clinic using the Timed Up and Go Test and Physiological Profile Assessments. Age Ageing,
34(6), 567-571.
Flynn, T. N., Chan, P., Coast, J., & Peters, T. J. (2011) Assessing quality of life among British older
people using the ICEPOP CAPability (ICECAP-O) measure. Appl Health Econ Health Policy, 9(5),
317-329.
Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2010) Using discrete choice experiments to
understand preferences for quality of life. Variance-scale heterogeneity matters. Soc Sci Med,
70(12), 1957-1965.
Coast, J., & Al-Janabi, H. (2008). />
EP

1.

AC
C

335
336
337
338
339
340
341
342
343
344
345
346
347
348

349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377

16


ACCEPTED MANUSCRIPT
Mobility predicts wellbeing among older fallers
Coast, J., Peters, T. J., Natarajan, L., Sproston, K., & Flynn, T. (2008) An assessment of the
construct validity of the descriptive system for the ICECAP capability measure for older people.
Qual Life Res, 17(7), 967-976.
20.
Guralnik, J. M., Ferrucci, L., Simonsick, E. M., Salive, M. E., & Wallace, R. B. (1995) Lowerextremity function in persons over the age of 70 years as a predictor of subsequent disability. N
Engl J Med, 332(9), 556-561.
21.
Shumway-Cook, A., Brauer, S., & Woollacott, M. (2000) Predicting the probability for falls in
community-dwelling older adults using the Timed Up & Go Test. Phys Ther, 80(9), 896-903.
22.
Guralnik, J. M., Ferrucci, L., Simonsick, E. M., Salive, M. E., & Wallace, R. B. (1995) LowerExtremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability
10.1056/NEJM199503023320902. N Engl J Med, 332(9), 556-562.
23.
Nasreddine, Z. S., Phillips, N. A., Bedirian, V., Charbonneau, S., Whitehead, V., Collin, I.,
Cummings, J. L., & Chertkow, H. (2005) The Montreal Cognitive Assessment, MoCA: a brief
screening tool for mild cognitive impairment. J Am Geriatr Soc, 53(4), 695-699.
24.
Groll, D. L., To, T., Bombardier, C., & Wright, J. G. (2005) The development of a comorbidity index
with physical function as the outcome. J Clin Epidemiol, 58(6), 595-602.
25.
Yesavage, J. A. (1988) Geriatric Depression Scale. Psychopharmacol Bull, 24(4), 709-711.
26.
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982)
Development and validation of a geriatric depression screening scale: a preliminary report. J
Psychiatr Res, 17(1), 37-49.

27.
van Marwijk, H. W., Wallace, P., de Bock, G. H., Hermans, J., Kaptein, A. A., & Mulder, J. D.
(1995) Evaluation of the feasibility, reliability and diagnostic value of shortened versions of the
geriatric depression scale. Br J Gen Pract, 45(393), 195-199.
28.
Lord, S., Clark, R., & Webster, I. (1991) Physiological factors associated with falls in an elderly
population. Journal of American Geriatrics Society, 39, 1194-1200.
29.
Lord, S., Ward, J., Williams, P., & Anstey, K. (1994) Physiological factors associated with falls in
older community-dwelling women. Journal of American Geriatrics Society, 42, 1110-1117.
30.
Delbaere, K., Close, J. C. T., Brodaty, H., Sachdev, P., & Lord, S. R. (2010) Determinants of
disparities between perceived and physiological risk o falling among elderly people: cohort study.
British Medical Journal.
31.
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) "Mini-mental state". A practical method for
grading the cognitive state of patients for the clinician. J Psychiatr Res, 12(3), 189-198.
32.
Satterthwaite, F. E. (1946) An Approximate Distribution of Estimates of Variance Components.
Biometrics Bulletin, 2(6), 110-114.
33.
Wolitzky-Taylor, K. B., Arch, J. J., Rosenfield, D., & Craske, M. G. (2012) Moderators and NonSpecific Predictors of Treatment Outcome for Anxiety Disorders: A Comparison of Cognitive
Behavioral Therapy to Acceptance and Commitment Therapy. Journal of Consulting and Clinical
Psychology.
34.
Oostenbrink, J. B., & Al, M. J. (2005) The analysis of incomplete cost data due to dropout. Health
Econ, 14(8), 763-776.
35.
Oostenbrink, J. B., Al, M. J., & Rutten-van Molken, M. P. (2003) Methods to analyse cost data of
patients who withdraw in a clinical trial setting. Pharmacoeconomics, 21(15), 1103-1112.

36.
Briggs, A., Clark, T., Wolstenholme, J., & Clarke, P. (2003) Missing... presumed at random: costanalysis of incomplete data. Health Econ, 12(5), 377-392.
37.
Briggs, A. H., & Gray, A. M. (1999) Handling uncertainty when performing economic evaluation of
healthcare interventions. Health Technol Assess, 3(2), 1-134.

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Makai, P., Beckebans, F., van Exel, J., & Brouwer, W. B. (2014) Quality of life of nursing home
residents with dementia: validation of the German version of the ICECAP-O. PLOS ONE, 9(3),
e92016.
Makai, P., Koopmanschap, M. A., Brouwer, W. B., & Nieboer, A. A. (2013) A validation of the
ICECAP-O in a population of post-hospitalized older people in the Netherlands. Health Qual Life
Outcomes, 11, 57.
Fristedt, S., Dahl, A. K., Wretstrand, A., Bjorklund, A., & Falkmer, T. (2014) Changes in community
mobility in older men and women. A 13-year prospective study. PLoS One, 9(2), e87827.

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Figure Legend

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Figure 1a: SPPB by time by sex interaction over 12 months. Error bars represent the standard error of

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the mean.

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Figure 1b: TUG by time by sex interaction over 12 months. Error bars represent the standard error of

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the mean. Because TUG is scored in seconds, low TUG indicates good mobility, whereas high TUG

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indicates poor mobility.

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Table 1. Baseline Characteristics of the Vancouver Falls Prevention Cohort
Mean (SD) or Number (%)

Age (years) (n=315)

82.5 (6.5)

Sex (Male/Female) (n=308)

112 (36.4%) / 196 (63.4%)

Living status (n=253)
100 (39.5%)

Lives with others

122 (48.2%)

Assisted living

31 (12.3%)

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Lives alone

Education (n=299)
< Grade 9

33 (11.0%)

Grades 9-13, no diploma

59 (19.7%)

High school with diploma

Some university

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GDS (n=315)

58 (19.4%)

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FCI (n=320)

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23 (7.7%)
36 (12.0%)
90 (30.1%)
2.5 (1.9)
3.1 (2.6)

ICECAP-O (n=244)

0.805 (0.124)

SPPB (n=303)

7.3 (2.5)

TUG (n=296), seconds

19.7 (10.5)

PPA (n=311)

1.7 (1.1)

MMSE (n=315)

26.4 (3.2)

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MoCA (n=303)

22.1 (4.6)

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Table 2: Summary of primary linear mixed models to examine predictors of change in wellbeing.
Maximum Likelihood

Model 1 - SPPB, N=245
.02 (.01, .03)***

SPPB X time

-.004 (-.02, .01)

SPPB X sex

-.01 (-.02, .003)

SPPB X sex X time

.02 (.001, .04)*

Model 2 - TUG, N=244
TUG
TUG X time

Model 3 - MoCA, N=255

MoCA X time

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MoCA X sex X time

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.576
.142
.034

-.25 (-.36, -.14)***

<.001

.16 (.02, .31)*

.025

.18 (-.01, .38)

.066

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TUG X sex X time

<.001

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P value

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Predictor

-.48 (-.73, -.23)***

<.001

.004 (.001, .01)*

.026

.0001 (-.004, .01)

.782

--

--

--

--

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Notes. Only terms relevant to the study hypotheses are shown. Not shown are: age, age x time, sex,

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and sex X time. In model 3, the MoCA X sex, and MoCA X sex X time effects were removed due to

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*p<0.05; **p<0.01; ***p<.001

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