The Use of Mobility Devices and Personal Assistance: A Joint Modeling Approach

The Use of Mobility Devices and Personal Assistance: A Joint Modeling Approach Objective: To examine whether mobility device use substitutes for personal assistance among U.S. older adults. Method: Using the National Health and Aging Trends Study, we identified 3,211 community-living older adults (aged 65 and older) who reported mobility difficulties at baseline. We used recursive bivariate probit models to simultaneously estimate the effect of covariates on the likelihood of using (a) mobility devices and (b) personal assistance to accommodate mobility difficulty. Independent variables included age, gender, race, physical/mental health status, cognition, and comorbidities. Results: Predictors of the use of personal assistance and mobility devices exhibit important similarities and differences. Device use reduced the odds of receiving personal assistance by 50% (odds ratio [OR] = 0.50, 95% confidence interval [CI] = [0.29, 0.86]). Discussion: Findings suggest device use substitutes for personal assistance. Practitioners and policymakers should promote the appropriate use of mobility devices while recognizing the importance of assistance with some groups and the potential of increasing mobility device use. Keywords assistive device, mobility, personal assistance, disability, accommodation Manuscript received: July 29, 2019; final revision received: September 30, 2019; accepted: October 4, assistive devices of all types has been rising for more Introduction than a decade (Spillman, 2005; Spillman, 2014). Mobility, the ability to move from place to place for the However, a recent national survey found that 29% of completion of daily tasks, is an essential component of U.S. adults 65 and older, or 11 million people, received quality of life among older adults. Mobility limitation is personal assistance because of an activity limitation often one of the early signs of functional decline and an (Freedman & Spillman, 2014). important component of frailty (Fried, Young, Rubin, Although both devices and personal assistance enable Bandeen-Roche, 2001). Individuals with mobility limi- individuals to accommodate their activity limitations, tations are more likely to be sedentary, to limit social researchers have focused on the greater potential of contact, and to experience chronic conditions such as device use to enable older adults to age in place with obesity, cardiovascular disease, diabetes, poor cognitive independence and to ease pressure on family caregivers function, and depression (Bohannon, 2011; Rosso, and a strained long-term care workforce. However, to Taylor, Tabb, & Michael, 2013; Saajanaho et al., 2016). date, evidence is mixed about the extent to which assis- For these individuals, compensation for the mobility tive devices are being used as substitutes for personal limitations by using devices (cane, walker, wheelchair, help. Studies have shown that basic mobility devices, and scooters) and/or personal assistance is important in such as canes, can reduce the need for personal help maintaining quality of life and social engagement (Freedman, Kasper, & Spillman, 2016; Giesbrecht, University of South Florida, Tampa, USA Smith, Mortenson, & Miller, 2017). Peking University, Beijing, China Studies have shown that the use of mobility devices Corresponding Author: can increase physical stability, confidence, and indepen- Lindsay J. Peterson, School of Aging Studies, University of South Florida, dence (Brown & Flood, 2013; Resnik, Allen, Isenstadt, 13301 Bruce B. Downs Blvd, MHC 1300, Tampa, FL 33612, USA. Wasserman, & Iezzoni, 2009); therefore, the use of Email: ljpeterson@usf.edu Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 Gerontology & Geriatric Medicine (Agree & Freedman, 2000; Allen, Foster, & Berg, 2001). If they reported difficulties in any one of the activities, Others found that device use was associated with fewer they were considered to have mobility difficulty. The hours of help for functional limitations (Hoenig, Taylor, final sample included 3,211 individuals. & Sloan, 2003). However, Agree, Freedman, Cornman, Wolf, and Marcotte (2005) found that devices did not Measures substitute for personal care for most older adults living in the community. Rather they were associated with a The two primary outcome variables about care arrange- higher probability of receiving personal care and using ment were as follows: (a) any use of mobility device and more caregivers and more care hours. Anderson and (b) any use of personal assistance for mobility during the Wiener (2015) found evidence that mobility devices month before the baseline interview. Mobility device use partially substituted for personal assistance. Therefore, was assessed using a yes/no question concerning use of a the potential substitution effect of mobility device use cane, walker, wheelchair, or scooter in the last month. To on personal assistance remains inconclusive. assess personal assistance, the survey asked respondents The objective of the present study was to examine yes/no questions concerning whether in the previous whether and to what extent mobility devices substitute month they had received personal help going outside for personal assistance. It expanded on prior studies with their home or building, getting around inside, or getting the use of data from a recent national survey. This is out of bed. They were considered to use personal assis- critical given evidence of the changes in disability rates tance if they received help with any of the mobility tasks. and use of accommodations over the past decades. Gender was measured dichotomously (male/female). Furthermore, we used a novel, joint modeling approach The sociodemographic predictors included age, race/ that enables us to directly measure the substitution ethnicity (White, Black, Hispanic), education (less than effect. It allows for the likelihood of using devices and high school, high school, some vocational training or of receiving personal assistance to be jointly determined. college, and bachelor’s degree or higher), and income Most previous studies have used single equation meth- divided into five groups (<US$14,000; US$14,000 ods that do not allow for a structural determination of -US$21,999; US$22,000-US$35,999; US$36,000-US$ the relationship between the two. The use of this 59,000; >US$60,000). approach with a recent cohort of older adults will We also included health insurance, having Medicare enhance our understanding of the relationship between supplemental insurance and having Medicaid (yes/no). mobility device use and personal assistance, two impor- Social/Physical Environment was assessed with ques- tant but critically different forms of accommodation. tions concerning living alone (yes/no) and living in a retirement community (yes/no). In addition, physical environment was measured with the inclusion of a ques- Method tion about the presence of stairs or a step (yes/no) at the entrance of the respondent’s home or building. Sample Physical and cognitive capacity and mental health We used the 2011 wave, baseline data, of the National measures also were used. A physical capacity score was Health and Aging Trend Study (NHATS), a nationally computed using six pairs of tasks (walking three or six representative sample of Medicare enrollees 65 and blocks, climbing 10 or 20 stairs, lifting and carrying 10 older (Kasper & Freedman, 2014). The survey inter- or 20 pounds, bending over or kneeling down, reaching viewed a total of 8,245 individuals with a response rate overhead or placing a heavy object overhead, grasping of 71%, including data on demographic characteristics, small objects, or opening a sealed jar). Participants mobility conditions, physical and cognitive health, eco- received a 1 or 2 depending on whether they could do nomic status, well-being, and quality of life. The cur- only one or both tasks for each pair. The total number rent study used the sample of community-dwelling was summed (1-12), with higher values indicating greater NHATS participants, excluding 468 nursing home resi- physical capacity (Freedman et al., 2011). Probable and dents and 168 participants in non-nursing residential possible dementia were determined based on the NHATS facilities who did not complete interviews. In addition, classification scheme, which consisted of self-reported 286 individuals with missing data on other covariates physician diagnosis of Alzheimer’s disease or dementia, were also excluded from the analysis, with the majority the AD8 dementia screening interview (administered to of them missing body mass index (BMI) data. Finally, proxy respondents), and tests of memory, orientation, to study the individuals with mobility difficulty, 4,112 and executive function (Kasper, Freedman, & Spillman, without mobility difficulty who did not use a mobility 2013). Depression measurements were based on ques- device or personal assistance were excluded. The sam- tions from the two-item depression screener (Patient ple included those who have mobility difficulty or those Health Questionnaire [PHQ]-2) that generated a symp- who used either a mobility device or personal assis- tom score ranging from 0 to 6, with depression at >3 tance. Mobility difficulty was measured by asking (Kroenke, Spitzer, & Williams, 2003). whether individuals had difficulty moving inside, mov- We used physical impairment and health variables ing outside, or getting out of bed in the previous month. found in previous research to be associated with mobility Meng et al. 3 device use (Peterson, Meng, Dobbs, & Hyer, 2016). use. Those who used a mobility device only were more These included participant reports (yes/no) of whether likely to have Medicare supplemental insurance (58.2%), they had pain, balance problems, limited lower body to live alone (45.6%), and to be obese (39.5%). Those strength, or limited upper body strength. Hospitalization who used personal assistance only were more likely to was measured with a question concerning whether par- have probable dementia (46.1%) and to have depression ticipants had been hospitalized overnight in the past 12 (58.7%). Those who used both were more likely to have months. BMI was calculated using measured height and physical impairments, low balance (74.1%), low lower weight (BMI = weight in kilograms divided by height in body strength (76.1%), and low upper body strength meter squared), with participants categorized as under- (60.2%). They also were more likely to have been hospi- weight (BMI < 18.5), overweight (25 ≤ BMI < 30), or talized (47.7%). As expected, those who used neither obese (BMI ≥30), with the default category of normal were younger, with higher income, and a low proportion weight. Comorbidities related to mobility device use of Black participants. This group also was less likely to were measured with yes/no questions concerning be on Medicaid and to be healthier. Interestingly, a large whether participants had been medically diagnosed with proportion of those who used neither accommodation stroke, arthritis, osteoporosis, or diabetes. reported pain (72.8%) and having arthritis (67.2%). Table 2 displays the results from the recursive bivari- ate probit model predicting the joint likelihood of using Statistical Analysis a mobility device and/or personal assistance. The likeli- All statistical analyses were performed using STATA hood ratio test comparing the likelihood of the joint version 13 (Stata Corp., College Station, TX). Bivariate bivariate models with the sum of the log likelihoods for analyses were used to examine the independent vari- the univariate probit models was statistically significant ables by accommodation (mobility device alone, per- at the .001 level, suggesting that the bivariate probit sonal assistance alone, both, and neither). We then used model was appropriate in modeling the joint distribution recursive bivariate probit models to jointly estimate the of the outcome variables. Results showed that using a effect of covariates on the likelihood of using mobility mobility device was significantly associated with a 50% devices and personal assistance (Greene, 2003). lower likelihood of receiving personal assistance (95% Bivariate probit models are suitable for the joint mod- confidence interval [CI] = [0.29, 0.86]). eling of two dichotomous dependent variables that are Predictors of personal assistance and mobility device correlated. An additional benefit of the bivariate probit use exhibited similarities and differences. Several vari- model is that the use of devices and personal assistance ables affected both the outcome variables in the same is not assumed to occur in any order. This approach has direction. Those with greater physical capacity were less been used in economic, health outcomes and other likely to receive personal assistance (odds ratio [OR] = studies (Gandelman, 2009; Liu, Chen, Chan, & Chen, 0.83, 95% CI = [0.80, 0.85]) and less likely to use a 2015). The model consists of two equations. To obtain mobility device (OR = 0.84, 95% CI = [0.83, 0.86]). By an estimate of the structural effect of mobility device contrast, those with a previous hospitalization were use on personal assistance use, the dependent variable more likely to receive personal assistance (OR = 1.41, of the second equation (device use) was entered into 95% CI = [1.27, 1.57]) and more likely to use a mobility the first equation (personal assistance) as an indepen- device (OR = 1.26, 95% CI = [1.13, 1.42]), as were dent variable, thereby linking the two equations to those with low balance and low lower body strength. form a recursive model. This joint modeling approach Other variables affected outcome variables in the helps to answer the question of whether mobility opposite direction. Women were more likely to use per- devices substitute for personal assistance. Independent sonal assistance (OR = 1.32, 95% CI = [1.15, 1.52]), but variables in the present study included sociodemo- were less likely to use mobility devices (OR = 0.75, 95% graphics, environment, physical/cognitive capacity, CI = [0.67, 0.85]). Likewise, those with probable demen- mental health status, and physical impairments and tia were more likely to use personal assistance (OR = health conditions. 1.70, 95% CI = [1.46, 1.98]), but were less likely to use mobility devices (OR = 0.77, 95% CI = [0.67, 0.89]). Conversely, those who lived alone were less likely to Results use personal assistance (OR = 0.72, 95% CI = [0.63, The final sample included 3,211 individuals with an 0.82]) and more likely to use mobility devices (OR = average age of 80.4 years (SD = 8.2; range = 65-106); 1.22, 95% CI = [1.08, 1.38]). 64.7% were female. The sample included four groups of Several other variables were significantly associated individuals based on their use of a mobility device and/ with the use of one accommodation but not with use of or personal assistance. Of the whole sample, 37.3% used the other. For example, having Medicare supplemental mobility devices only, 7.9% used personal assistance insurance was significantly associated with a greater only, 30.8% used both, and 24.1% used neither. Table 1 likelihood of using mobility devices. However, there displays the individual characteristics of each group, was no substitution effect because the likelihood of showing that participants differ based on the profile of receipt of personal assistance was not significant. 4 Gerontology & Geriatric Medicine Table 1. Baseline Characteristics of Sample Population, by Usage, National Health, and Aging Trends Study, 2011. Mobility device only Personal assistance only Both Neither (n = 1,198, 37.3%) (n = 254, 7.9%) (n = 988, 30.8%) (n = 771, 24.1%) Sociodemographics Age 80.6 79.2 83.5 76.4 Female 60.5% 74.0% 73.8% 56.5% Education 2.3 1.9 2.0 2.4 Income 2.6 2.3 2.1 2.9 Black 27.0% 24.0% 28.4% 21.3% Hispanics 5.3% 17.3% 8.0% 6.1% Medicare supplemental 58.2% 39.4% 48.1% 56.9% Medicaid 19.5% 31.9% 34.2% 15.2% Social/physical environment Lives alone 45.6% 23.2% 34.0% 30.7% Lives in retirement community 20.1% 13.4% 15.0% 10.1% Outside stairs 66.3% 75.6% 77.3% 66.2% Physical and cognitive capacity Physical capacity 5.9 5.0 2.6 8.8 Possible dementia 18.6% 13.0% 17.2% 13.4% Probable dementia 12.9% 46.1% 40.7% 7.9% Depression 33.6% 58.7% 52.3% 38.7% Physical impairment Pain 71.7% 65.0% 74.3% 72.8% Low balance 50.6% 54.7% 74.1% 39.0% Low lower body strength 59.6% 53.9% 76.1% 55.1% Low upper body strength 39.3% 44.9% 60.2% 42.2% Health conditions Hospitalization 32.7% 35.4% 47.7% 21.3% Underweight 1.8% 9.5% 6.7% 2.3% Overweight 31.9% 28.0% 32.8% 35.9% Obese 39.5% 24.0% 28.6% 34.5% Stroke 16.0% 18.1% 26.0% 11.7% Arthritis 73.9% 59.4% 75.2% 67.2% Osteoporosis 25.8% 26.8% 32.7% 21.5% Diabetes 32.0% 27.2% 34.7% 27.0% devices may substitute for personal assistance with this Discussion group and may enable individuals who are alone to con- This study simultaneously examined the use of per- tinue to live independently as they encounter mobility sonal assistance and mobility devices, finding that difficulties. These results support prior research con- among community-dwelling Medicare beneficiaries cerning device use among those who live alone (Elliott, with mobility difficulties, those who used a mobility Painter, & Hudson, 2009). device were less likely to use personal assistance. By contrast, those with a previous hospitalization or These findings provide support for previous research physical impairment (low balance or low lower body indicating that assistive devices have the potential to strength) were more likely to use both personal assis- substitute for personal assistance (Agree & Freedman, tance and a mobility device in the present research. 2000; Allen et al., 2001; Anderson & Wiener, 2015; Similarly, Agree and colleagues (2005) found a comple- Hoenig et al., 2003). mentary relationship between device use and personal A contribution of the present research is our finding care for those with more activity of daily living limita- that the multiple factors associated with personal assis- tions. It is possible that older adults with higher levels of tance and mobility device usage exhibited important illness or impairment may not be able to forego personal similarities and differences. Although we found evi- assistance, despite the use of a device. In addition, care- dence overall of a substitution effect, usage of one form givers may be facilitating assistive device use for those of accommodation or another differed depending on receiving care at home to complete their care tasks more participants’ conditions or situations. Those who lived safely or efficiently, as Anderson and Wiener (2015) sug- alone were less likely to use personal assistance and gested. In keeping with our results concerning hospital- more likely to use a mobility device, suggesting that ization or presence of an impairment, participants with Meng et al. 5 Table 2. Predictors of Personal Assistance and Mobility Device Use, Recursive Bivariate Probit Model. Personal assistance Mobility device use Variables Odds ratio p value 95% CI Odds ratio p value 95% CI Sociodemographics Age 0.83 .001 [0.74, 0.93] 0.91 .131 [0.80, 1.04] Female 1.32 <.001 [1.15, 1.52] 0.75 <.001 [0.67, 0.85] Education 1.04 .137 [0.99, 1.10] 1.08 .007 [1.02, 1.14] Income 0.99 .771 [0.95, 1.04] 1.00 .885 [0.95, 1.04] Black 0.92 .253 [0.81, 1.06] 1.24 .001 [1.09, 1.42] Hispanics 1.06 .583 [0.86, 1.31] 0.76 .012 [0.62, 0.94] Medicare supplemental 0.91 .107 [0.82, 1.02] 1.15 .013 [1.03, 1.28] Medicaid 1.35 <.001 [1.18, 1.54] 1.07 .355 [0.93, 1.24] Social/physical environment Lives alone 0.72 <.001 [0.63, 0.82] 1.22 <.001 [1.08, 1.38] Lives in retirement community 0.91 .270 [0.78, 1.07] 1.05 .581 [0.89, 1.24] Outside stairs 1.05 .437 [0.93-1.19] 0.81 .001 [0.72-0.92] Physical and cognitive capacity Physical capacity 0.83 <.001 [0.80, 0.85] 0.84 <.001 [0.83, 0.86] Possible dementia 1.08 .304 [0.93, 1.24] 1.05 .489 [0.91, 1.22] Probable dementia 1.70 <.001 [1.46, 1.98] 0.77 .001 [0.67, 0.89] Depression 1.14 .030 [1.01, 1.29] 0.75 <.001 [0.67, 0.84] Physical impairment Pain 0.89 .071 [0.79, 1.01] 1.00 .939 [0.89, 1.14] Low balance 1.21 .001 [1.08, 1.35] 1.15 .020 [1.02, 1.29] Low lower body strength 1.17 .012 [1.03, 1.31] 1.16 .016 [1.03, 1.31] Low upper body strength 0.99 .915 [0.88, 1.12] 0.81 <.001 [0.72, 0.91] Health conditions Hospitalization 1.41 <.001 [1.27, 1.57] 1.26 <.001 [1.13, 1.42] Underweight 1.33 .040 [1.01, 1.75] 0.77 .068 [0.59, 1.02] Overweight 1.00 .983 [0.87, 1.14] 1.14 .053 [1.00, 1.30] Obese 0.92 .251 [0.79, 1.06] 1.34 <.001 [1.16, 1.54] Stroke 1.14 .052 [1.00, 1.30] 1.12 .113 [0.97, 1.30] Arthritis 0.82 .002 [0.72, 0.93] 1.12 .061 [0.99, 1.27] Osteoporosis 1.02 .742 [0.90, 1.15] 1.12 .077 [0.99, 1.27] Diabetes 1.12 .064 [0.99, 1.25] 1.16 .015 [1.03, 1.30] Mobility device 0.50 .012 [0.29, 0.86] — — [—] Note. Boldface type indicates a significant association (<.05) between the independent variable and both personal assistance and mobility device use. CI = confidence interval. higher physical capacity, who are likely to be healthier associated with the use of fewer devices. Resnik and overall, were less likely to use both personal assistance colleagues (2009) reported in qualitative research a rela- and mobility devices. tionship between the need for mobility devices and feel- We found different patterns with other groups of par- ings of depression, such that device use was considered ticipants. Women were more likely to use personal assis- to signal a weakness or deficit. These studies and others tance and less likely to use a mobility device. Prior (Verbrugge, 2016) suggest there is a psychosocial aspect research found that women were less likely than men to to the use of assistive devices, which may help explain use a cane, and that receipt of personal assistance was a the lesser likelihood of using mobility devices among factor in the observed association (Peterson et al., 2016). some groups of participants. Some older adults may Research also has found that the use of mobility devices, reject assistive devices, Anderson and Wiener (2015) in particular canes and walkers, was aversive to women suggested, because using them could result in less per- who linked it to the idea of becoming older and more sonal interaction with their caregivers. They also sug- vulnerable (Porter, Benson, & Matsuda, 2011). gested the devices may not work equally well for Depression also was associated with a greater likelihood everyone who needs them. of receiving personal assistance and a lower likelihood The latter suggestion may partly explain our addi- of using a mobility device. Other research has linked tional finding that those with more severe cognitive depression and device use, with Tomita, Mann, Fraas, impairment were more likely to receive personal assis- and Stanton (2004) finding that depression was tance and less likely to use a mobility device. In their 6 Gerontology & Geriatric Medicine report on the technical difficulties of using some mobil- important given concerns about the availability of care- ity devices, Bateni and Maki (2005) highlighted the givers for older and disabled adults. Nevertheless, our need for new devices and device designs that place results show that many others continue to rely on per- fewer cognitive demands on their users. sonal assistance to accommodate their mobility diffi- Overall, the studies concerning the factors associated culties. This contributes to our understanding of some with lesser device use suggested that for certain indi- of the factors underlying the use of assistive devices viduals, device use was aversive and/or difficult. These and/or personal assistance for mobility. Greater knowl- obstacles, however, could be overcome through educa- edge of these factors may enable health care providers tion and training on the benefits of using devices, com- to better target recommendations for accommodations. bined with efforts to create more usable devices or In addition, research is needed into whether and how environments in which devices are easier to use. This is reluctance, aversion, or inability to use a mobility particularly important in light of previous findings that device can be modified to further increase indepen- devices are uniquely beneficial in easing the difficulty dence among those with mobility disabilities. of daily tasks (Verbrugge & Sevak, 2002). Increasing the use of assistive devices could increase the indepen- Declaration of Conflicting Interests dence of those who need help with their daily activities The authors declared no potential conflicts of interest with and reduce the strain on caregivers and the demand for respect to the research, authorship, and/or publication of this their services. article. Some limitations should be noted in the present study. This is a cross-sectional analysis. Although our Funding model controlled for some of the endogeneity, we can- The authors received no financial support for the research, not claim any causal relationships. In addition, although authorship, and/or publication of this article. we controlled for health conditions and functional limi- tations, we were not able to account for all the effects of ORCID iD physical and functional decline on choice of accommo- Lindsay J. Peterson https://orcid.org/0000-0003-1129-0931 dation. Longitudinal analysis is needed to study the dynamic change in choices of care arrangement and the References underlying mobility and health conditions overtime. Agree, E. M., & Freedman, V. A. (2000). Incorporating assis- Another limitation is that we could not conduct analyses tive devices into community-based long-term care: An by specific device type using the joint modeling analysis of the potential for substitution and supplementa- approach. The devices were not used mutually exclu- tion. Journal of Aging and Health, 12, 426-450. sively. Individuals in the study sample could have been Agree, E. M., Freedman, V. A., Cornman, J. 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Baltimore, MD: Johns Hopkins University frail elders. Journal of Applied Gerontology, 23, 141-155. School of Public Health. Verbrugge, L. M. (2016). Disability experience and measure- Kasper, J. D., Freedman, V. A., & Spillman, B. C. (2013). ment. Journal of Aging and Health, 28, 1124-1158. Classification of persons by dementia status in the National Verbrugge, L. M., & Sevak, P. (2002). Use, type, and effi- Health and Aging Trends Study (NHATS Technical Paper cacy of assistance for disability. Journals of Gerontology #5). Baltimore, MD: Johns Hopkins University School of Series B: Psychological Sciences and Social Sciences, 57, Public Health. S366-S379. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Gerontology and Geriatric Medicine Pubmed Central

The Use of Mobility Devices and Personal Assistance: A Joint Modeling Approach

Gerontology and Geriatric Medicine, Volume 5 – Oct 25, 2019

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Abstract

Objective: To examine whether mobility device use substitutes for personal assistance among U.S. older adults. Method: Using the National Health and Aging Trends Study, we identified 3,211 community-living older adults (aged 65 and older) who reported mobility difficulties at baseline. We used recursive bivariate probit models to simultaneously estimate the effect of covariates on the likelihood of using (a) mobility devices and (b) personal assistance to accommodate mobility difficulty. Independent variables included age, gender, race, physical/mental health status, cognition, and comorbidities. Results: Predictors of the use of personal assistance and mobility devices exhibit important similarities and differences. Device use reduced the odds of receiving personal assistance by 50% (odds ratio [OR] = 0.50, 95% confidence interval [CI] = [0.29, 0.86]). Discussion: Findings suggest device use substitutes for personal assistance. Practitioners and policymakers should promote the appropriate use of mobility devices while recognizing the importance of assistance with some groups and the potential of increasing mobility device use. Keywords assistive device, mobility, personal assistance, disability, accommodation Manuscript received: July 29, 2019; final revision received: September 30, 2019; accepted: October 4, assistive devices of all types has been rising for more Introduction than a decade (Spillman, 2005; Spillman, 2014). Mobility, the ability to move from place to place for the However, a recent national survey found that 29% of completion of daily tasks, is an essential component of U.S. adults 65 and older, or 11 million people, received quality of life among older adults. Mobility limitation is personal assistance because of an activity limitation often one of the early signs of functional decline and an (Freedman & Spillman, 2014). important component of frailty (Fried, Young, Rubin, Although both devices and personal assistance enable Bandeen-Roche, 2001). Individuals with mobility limi- individuals to accommodate their activity limitations, tations are more likely to be sedentary, to limit social researchers have focused on the greater potential of contact, and to experience chronic conditions such as device use to enable older adults to age in place with obesity, cardiovascular disease, diabetes, poor cognitive independence and to ease pressure on family caregivers function, and depression (Bohannon, 2011; Rosso, and a strained long-term care workforce. However, to Taylor, Tabb, & Michael, 2013; Saajanaho et al., 2016). date, evidence is mixed about the extent to which assis- For these individuals, compensation for the mobility tive devices are being used as substitutes for personal limitations by using devices (cane, walker, wheelchair, help. Studies have shown that basic mobility devices, and scooters) and/or personal assistance is important in such as canes, can reduce the need for personal help maintaining quality of life and social engagement (Freedman, Kasper, & Spillman, 2016; Giesbrecht, University of South Florida, Tampa, USA Smith, Mortenson, & Miller, 2017). Peking University, Beijing, China Studies have shown that the use of mobility devices Corresponding Author: can increase physical stability, confidence, and indepen- Lindsay J. Peterson, School of Aging Studies, University of South Florida, dence (Brown & Flood, 2013; Resnik, Allen, Isenstadt, 13301 Bruce B. Downs Blvd, MHC 1300, Tampa, FL 33612, USA. Wasserman, & Iezzoni, 2009); therefore, the use of Email: ljpeterson@usf.edu Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 Gerontology & Geriatric Medicine (Agree & Freedman, 2000; Allen, Foster, & Berg, 2001). If they reported difficulties in any one of the activities, Others found that device use was associated with fewer they were considered to have mobility difficulty. The hours of help for functional limitations (Hoenig, Taylor, final sample included 3,211 individuals. & Sloan, 2003). However, Agree, Freedman, Cornman, Wolf, and Marcotte (2005) found that devices did not Measures substitute for personal care for most older adults living in the community. Rather they were associated with a The two primary outcome variables about care arrange- higher probability of receiving personal care and using ment were as follows: (a) any use of mobility device and more caregivers and more care hours. Anderson and (b) any use of personal assistance for mobility during the Wiener (2015) found evidence that mobility devices month before the baseline interview. Mobility device use partially substituted for personal assistance. Therefore, was assessed using a yes/no question concerning use of a the potential substitution effect of mobility device use cane, walker, wheelchair, or scooter in the last month. To on personal assistance remains inconclusive. assess personal assistance, the survey asked respondents The objective of the present study was to examine yes/no questions concerning whether in the previous whether and to what extent mobility devices substitute month they had received personal help going outside for personal assistance. It expanded on prior studies with their home or building, getting around inside, or getting the use of data from a recent national survey. This is out of bed. They were considered to use personal assis- critical given evidence of the changes in disability rates tance if they received help with any of the mobility tasks. and use of accommodations over the past decades. Gender was measured dichotomously (male/female). Furthermore, we used a novel, joint modeling approach The sociodemographic predictors included age, race/ that enables us to directly measure the substitution ethnicity (White, Black, Hispanic), education (less than effect. It allows for the likelihood of using devices and high school, high school, some vocational training or of receiving personal assistance to be jointly determined. college, and bachelor’s degree or higher), and income Most previous studies have used single equation meth- divided into five groups (<US$14,000; US$14,000 ods that do not allow for a structural determination of -US$21,999; US$22,000-US$35,999; US$36,000-US$ the relationship between the two. The use of this 59,000; >US$60,000). approach with a recent cohort of older adults will We also included health insurance, having Medicare enhance our understanding of the relationship between supplemental insurance and having Medicaid (yes/no). mobility device use and personal assistance, two impor- Social/Physical Environment was assessed with ques- tant but critically different forms of accommodation. tions concerning living alone (yes/no) and living in a retirement community (yes/no). In addition, physical environment was measured with the inclusion of a ques- Method tion about the presence of stairs or a step (yes/no) at the entrance of the respondent’s home or building. Sample Physical and cognitive capacity and mental health We used the 2011 wave, baseline data, of the National measures also were used. A physical capacity score was Health and Aging Trend Study (NHATS), a nationally computed using six pairs of tasks (walking three or six representative sample of Medicare enrollees 65 and blocks, climbing 10 or 20 stairs, lifting and carrying 10 older (Kasper & Freedman, 2014). The survey inter- or 20 pounds, bending over or kneeling down, reaching viewed a total of 8,245 individuals with a response rate overhead or placing a heavy object overhead, grasping of 71%, including data on demographic characteristics, small objects, or opening a sealed jar). Participants mobility conditions, physical and cognitive health, eco- received a 1 or 2 depending on whether they could do nomic status, well-being, and quality of life. The cur- only one or both tasks for each pair. The total number rent study used the sample of community-dwelling was summed (1-12), with higher values indicating greater NHATS participants, excluding 468 nursing home resi- physical capacity (Freedman et al., 2011). Probable and dents and 168 participants in non-nursing residential possible dementia were determined based on the NHATS facilities who did not complete interviews. In addition, classification scheme, which consisted of self-reported 286 individuals with missing data on other covariates physician diagnosis of Alzheimer’s disease or dementia, were also excluded from the analysis, with the majority the AD8 dementia screening interview (administered to of them missing body mass index (BMI) data. Finally, proxy respondents), and tests of memory, orientation, to study the individuals with mobility difficulty, 4,112 and executive function (Kasper, Freedman, & Spillman, without mobility difficulty who did not use a mobility 2013). Depression measurements were based on ques- device or personal assistance were excluded. The sam- tions from the two-item depression screener (Patient ple included those who have mobility difficulty or those Health Questionnaire [PHQ]-2) that generated a symp- who used either a mobility device or personal assis- tom score ranging from 0 to 6, with depression at >3 tance. Mobility difficulty was measured by asking (Kroenke, Spitzer, & Williams, 2003). whether individuals had difficulty moving inside, mov- We used physical impairment and health variables ing outside, or getting out of bed in the previous month. found in previous research to be associated with mobility Meng et al. 3 device use (Peterson, Meng, Dobbs, & Hyer, 2016). use. Those who used a mobility device only were more These included participant reports (yes/no) of whether likely to have Medicare supplemental insurance (58.2%), they had pain, balance problems, limited lower body to live alone (45.6%), and to be obese (39.5%). Those strength, or limited upper body strength. Hospitalization who used personal assistance only were more likely to was measured with a question concerning whether par- have probable dementia (46.1%) and to have depression ticipants had been hospitalized overnight in the past 12 (58.7%). Those who used both were more likely to have months. BMI was calculated using measured height and physical impairments, low balance (74.1%), low lower weight (BMI = weight in kilograms divided by height in body strength (76.1%), and low upper body strength meter squared), with participants categorized as under- (60.2%). They also were more likely to have been hospi- weight (BMI < 18.5), overweight (25 ≤ BMI < 30), or talized (47.7%). As expected, those who used neither obese (BMI ≥30), with the default category of normal were younger, with higher income, and a low proportion weight. Comorbidities related to mobility device use of Black participants. This group also was less likely to were measured with yes/no questions concerning be on Medicaid and to be healthier. Interestingly, a large whether participants had been medically diagnosed with proportion of those who used neither accommodation stroke, arthritis, osteoporosis, or diabetes. reported pain (72.8%) and having arthritis (67.2%). Table 2 displays the results from the recursive bivari- ate probit model predicting the joint likelihood of using Statistical Analysis a mobility device and/or personal assistance. The likeli- All statistical analyses were performed using STATA hood ratio test comparing the likelihood of the joint version 13 (Stata Corp., College Station, TX). Bivariate bivariate models with the sum of the log likelihoods for analyses were used to examine the independent vari- the univariate probit models was statistically significant ables by accommodation (mobility device alone, per- at the .001 level, suggesting that the bivariate probit sonal assistance alone, both, and neither). We then used model was appropriate in modeling the joint distribution recursive bivariate probit models to jointly estimate the of the outcome variables. Results showed that using a effect of covariates on the likelihood of using mobility mobility device was significantly associated with a 50% devices and personal assistance (Greene, 2003). lower likelihood of receiving personal assistance (95% Bivariate probit models are suitable for the joint mod- confidence interval [CI] = [0.29, 0.86]). eling of two dichotomous dependent variables that are Predictors of personal assistance and mobility device correlated. An additional benefit of the bivariate probit use exhibited similarities and differences. Several vari- model is that the use of devices and personal assistance ables affected both the outcome variables in the same is not assumed to occur in any order. This approach has direction. Those with greater physical capacity were less been used in economic, health outcomes and other likely to receive personal assistance (odds ratio [OR] = studies (Gandelman, 2009; Liu, Chen, Chan, & Chen, 0.83, 95% CI = [0.80, 0.85]) and less likely to use a 2015). The model consists of two equations. To obtain mobility device (OR = 0.84, 95% CI = [0.83, 0.86]). By an estimate of the structural effect of mobility device contrast, those with a previous hospitalization were use on personal assistance use, the dependent variable more likely to receive personal assistance (OR = 1.41, of the second equation (device use) was entered into 95% CI = [1.27, 1.57]) and more likely to use a mobility the first equation (personal assistance) as an indepen- device (OR = 1.26, 95% CI = [1.13, 1.42]), as were dent variable, thereby linking the two equations to those with low balance and low lower body strength. form a recursive model. This joint modeling approach Other variables affected outcome variables in the helps to answer the question of whether mobility opposite direction. Women were more likely to use per- devices substitute for personal assistance. Independent sonal assistance (OR = 1.32, 95% CI = [1.15, 1.52]), but variables in the present study included sociodemo- were less likely to use mobility devices (OR = 0.75, 95% graphics, environment, physical/cognitive capacity, CI = [0.67, 0.85]). Likewise, those with probable demen- mental health status, and physical impairments and tia were more likely to use personal assistance (OR = health conditions. 1.70, 95% CI = [1.46, 1.98]), but were less likely to use mobility devices (OR = 0.77, 95% CI = [0.67, 0.89]). Conversely, those who lived alone were less likely to Results use personal assistance (OR = 0.72, 95% CI = [0.63, The final sample included 3,211 individuals with an 0.82]) and more likely to use mobility devices (OR = average age of 80.4 years (SD = 8.2; range = 65-106); 1.22, 95% CI = [1.08, 1.38]). 64.7% were female. The sample included four groups of Several other variables were significantly associated individuals based on their use of a mobility device and/ with the use of one accommodation but not with use of or personal assistance. Of the whole sample, 37.3% used the other. For example, having Medicare supplemental mobility devices only, 7.9% used personal assistance insurance was significantly associated with a greater only, 30.8% used both, and 24.1% used neither. Table 1 likelihood of using mobility devices. However, there displays the individual characteristics of each group, was no substitution effect because the likelihood of showing that participants differ based on the profile of receipt of personal assistance was not significant. 4 Gerontology & Geriatric Medicine Table 1. Baseline Characteristics of Sample Population, by Usage, National Health, and Aging Trends Study, 2011. Mobility device only Personal assistance only Both Neither (n = 1,198, 37.3%) (n = 254, 7.9%) (n = 988, 30.8%) (n = 771, 24.1%) Sociodemographics Age 80.6 79.2 83.5 76.4 Female 60.5% 74.0% 73.8% 56.5% Education 2.3 1.9 2.0 2.4 Income 2.6 2.3 2.1 2.9 Black 27.0% 24.0% 28.4% 21.3% Hispanics 5.3% 17.3% 8.0% 6.1% Medicare supplemental 58.2% 39.4% 48.1% 56.9% Medicaid 19.5% 31.9% 34.2% 15.2% Social/physical environment Lives alone 45.6% 23.2% 34.0% 30.7% Lives in retirement community 20.1% 13.4% 15.0% 10.1% Outside stairs 66.3% 75.6% 77.3% 66.2% Physical and cognitive capacity Physical capacity 5.9 5.0 2.6 8.8 Possible dementia 18.6% 13.0% 17.2% 13.4% Probable dementia 12.9% 46.1% 40.7% 7.9% Depression 33.6% 58.7% 52.3% 38.7% Physical impairment Pain 71.7% 65.0% 74.3% 72.8% Low balance 50.6% 54.7% 74.1% 39.0% Low lower body strength 59.6% 53.9% 76.1% 55.1% Low upper body strength 39.3% 44.9% 60.2% 42.2% Health conditions Hospitalization 32.7% 35.4% 47.7% 21.3% Underweight 1.8% 9.5% 6.7% 2.3% Overweight 31.9% 28.0% 32.8% 35.9% Obese 39.5% 24.0% 28.6% 34.5% Stroke 16.0% 18.1% 26.0% 11.7% Arthritis 73.9% 59.4% 75.2% 67.2% Osteoporosis 25.8% 26.8% 32.7% 21.5% Diabetes 32.0% 27.2% 34.7% 27.0% devices may substitute for personal assistance with this Discussion group and may enable individuals who are alone to con- This study simultaneously examined the use of per- tinue to live independently as they encounter mobility sonal assistance and mobility devices, finding that difficulties. These results support prior research con- among community-dwelling Medicare beneficiaries cerning device use among those who live alone (Elliott, with mobility difficulties, those who used a mobility Painter, & Hudson, 2009). device were less likely to use personal assistance. By contrast, those with a previous hospitalization or These findings provide support for previous research physical impairment (low balance or low lower body indicating that assistive devices have the potential to strength) were more likely to use both personal assis- substitute for personal assistance (Agree & Freedman, tance and a mobility device in the present research. 2000; Allen et al., 2001; Anderson & Wiener, 2015; Similarly, Agree and colleagues (2005) found a comple- Hoenig et al., 2003). mentary relationship between device use and personal A contribution of the present research is our finding care for those with more activity of daily living limita- that the multiple factors associated with personal assis- tions. It is possible that older adults with higher levels of tance and mobility device usage exhibited important illness or impairment may not be able to forego personal similarities and differences. Although we found evi- assistance, despite the use of a device. In addition, care- dence overall of a substitution effect, usage of one form givers may be facilitating assistive device use for those of accommodation or another differed depending on receiving care at home to complete their care tasks more participants’ conditions or situations. Those who lived safely or efficiently, as Anderson and Wiener (2015) sug- alone were less likely to use personal assistance and gested. In keeping with our results concerning hospital- more likely to use a mobility device, suggesting that ization or presence of an impairment, participants with Meng et al. 5 Table 2. Predictors of Personal Assistance and Mobility Device Use, Recursive Bivariate Probit Model. Personal assistance Mobility device use Variables Odds ratio p value 95% CI Odds ratio p value 95% CI Sociodemographics Age 0.83 .001 [0.74, 0.93] 0.91 .131 [0.80, 1.04] Female 1.32 <.001 [1.15, 1.52] 0.75 <.001 [0.67, 0.85] Education 1.04 .137 [0.99, 1.10] 1.08 .007 [1.02, 1.14] Income 0.99 .771 [0.95, 1.04] 1.00 .885 [0.95, 1.04] Black 0.92 .253 [0.81, 1.06] 1.24 .001 [1.09, 1.42] Hispanics 1.06 .583 [0.86, 1.31] 0.76 .012 [0.62, 0.94] Medicare supplemental 0.91 .107 [0.82, 1.02] 1.15 .013 [1.03, 1.28] Medicaid 1.35 <.001 [1.18, 1.54] 1.07 .355 [0.93, 1.24] Social/physical environment Lives alone 0.72 <.001 [0.63, 0.82] 1.22 <.001 [1.08, 1.38] Lives in retirement community 0.91 .270 [0.78, 1.07] 1.05 .581 [0.89, 1.24] Outside stairs 1.05 .437 [0.93-1.19] 0.81 .001 [0.72-0.92] Physical and cognitive capacity Physical capacity 0.83 <.001 [0.80, 0.85] 0.84 <.001 [0.83, 0.86] Possible dementia 1.08 .304 [0.93, 1.24] 1.05 .489 [0.91, 1.22] Probable dementia 1.70 <.001 [1.46, 1.98] 0.77 .001 [0.67, 0.89] Depression 1.14 .030 [1.01, 1.29] 0.75 <.001 [0.67, 0.84] Physical impairment Pain 0.89 .071 [0.79, 1.01] 1.00 .939 [0.89, 1.14] Low balance 1.21 .001 [1.08, 1.35] 1.15 .020 [1.02, 1.29] Low lower body strength 1.17 .012 [1.03, 1.31] 1.16 .016 [1.03, 1.31] Low upper body strength 0.99 .915 [0.88, 1.12] 0.81 <.001 [0.72, 0.91] Health conditions Hospitalization 1.41 <.001 [1.27, 1.57] 1.26 <.001 [1.13, 1.42] Underweight 1.33 .040 [1.01, 1.75] 0.77 .068 [0.59, 1.02] Overweight 1.00 .983 [0.87, 1.14] 1.14 .053 [1.00, 1.30] Obese 0.92 .251 [0.79, 1.06] 1.34 <.001 [1.16, 1.54] Stroke 1.14 .052 [1.00, 1.30] 1.12 .113 [0.97, 1.30] Arthritis 0.82 .002 [0.72, 0.93] 1.12 .061 [0.99, 1.27] Osteoporosis 1.02 .742 [0.90, 1.15] 1.12 .077 [0.99, 1.27] Diabetes 1.12 .064 [0.99, 1.25] 1.16 .015 [1.03, 1.30] Mobility device 0.50 .012 [0.29, 0.86] — — [—] Note. Boldface type indicates a significant association (<.05) between the independent variable and both personal assistance and mobility device use. CI = confidence interval. higher physical capacity, who are likely to be healthier associated with the use of fewer devices. Resnik and overall, were less likely to use both personal assistance colleagues (2009) reported in qualitative research a rela- and mobility devices. tionship between the need for mobility devices and feel- We found different patterns with other groups of par- ings of depression, such that device use was considered ticipants. Women were more likely to use personal assis- to signal a weakness or deficit. These studies and others tance and less likely to use a mobility device. Prior (Verbrugge, 2016) suggest there is a psychosocial aspect research found that women were less likely than men to to the use of assistive devices, which may help explain use a cane, and that receipt of personal assistance was a the lesser likelihood of using mobility devices among factor in the observed association (Peterson et al., 2016). some groups of participants. Some older adults may Research also has found that the use of mobility devices, reject assistive devices, Anderson and Wiener (2015) in particular canes and walkers, was aversive to women suggested, because using them could result in less per- who linked it to the idea of becoming older and more sonal interaction with their caregivers. They also sug- vulnerable (Porter, Benson, & Matsuda, 2011). gested the devices may not work equally well for Depression also was associated with a greater likelihood everyone who needs them. of receiving personal assistance and a lower likelihood The latter suggestion may partly explain our addi- of using a mobility device. Other research has linked tional finding that those with more severe cognitive depression and device use, with Tomita, Mann, Fraas, impairment were more likely to receive personal assis- and Stanton (2004) finding that depression was tance and less likely to use a mobility device. In their 6 Gerontology & Geriatric Medicine report on the technical difficulties of using some mobil- important given concerns about the availability of care- ity devices, Bateni and Maki (2005) highlighted the givers for older and disabled adults. Nevertheless, our need for new devices and device designs that place results show that many others continue to rely on per- fewer cognitive demands on their users. sonal assistance to accommodate their mobility diffi- Overall, the studies concerning the factors associated culties. This contributes to our understanding of some with lesser device use suggested that for certain indi- of the factors underlying the use of assistive devices viduals, device use was aversive and/or difficult. These and/or personal assistance for mobility. Greater knowl- obstacles, however, could be overcome through educa- edge of these factors may enable health care providers tion and training on the benefits of using devices, com- to better target recommendations for accommodations. bined with efforts to create more usable devices or In addition, research is needed into whether and how environments in which devices are easier to use. This is reluctance, aversion, or inability to use a mobility particularly important in light of previous findings that device can be modified to further increase indepen- devices are uniquely beneficial in easing the difficulty dence among those with mobility disabilities. of daily tasks (Verbrugge & Sevak, 2002). Increasing the use of assistive devices could increase the indepen- Declaration of Conflicting Interests dence of those who need help with their daily activities The authors declared no potential conflicts of interest with and reduce the strain on caregivers and the demand for respect to the research, authorship, and/or publication of this their services. article. Some limitations should be noted in the present study. This is a cross-sectional analysis. Although our Funding model controlled for some of the endogeneity, we can- The authors received no financial support for the research, not claim any causal relationships. In addition, although authorship, and/or publication of this article. we controlled for health conditions and functional limi- tations, we were not able to account for all the effects of ORCID iD physical and functional decline on choice of accommo- Lindsay J. Peterson https://orcid.org/0000-0003-1129-0931 dation. Longitudinal analysis is needed to study the dynamic change in choices of care arrangement and the References underlying mobility and health conditions overtime. Agree, E. M., & Freedman, V. A. (2000). Incorporating assis- Another limitation is that we could not conduct analyses tive devices into community-based long-term care: An by specific device type using the joint modeling analysis of the potential for substitution and supplementa- approach. The devices were not used mutually exclu- tion. Journal of Aging and Health, 12, 426-450. sively. Individuals in the study sample could have been Agree, E. M., Freedman, V. A., Cornman, J. C., Wolf, D. combining the use of two or more of the four devices. A., & Marcotte, J. E. (2005). Reconsidering substitution However, other research has found evidence that type of in long-term care: When does assistive technology take device (e.g., cane or walker) does affect the likelihood the place of personal care? The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60, of receiving personal assistance (Agree & Freedman, S272-S280. 2000; Allen et al., 2001). Future research assessing Allen, S. M., Foster, A., & Berg, K. (2001). Receiving device use in more specific detail would produce impor- help at home: The interplay of human and techno- tant additional information about how to provide appro- logical assistance. The Journals of Gerontology Series priate services to those with mobility difficulty. In B: Psychological Sciences and Social Sciences, 56, addition, we did not use measures of the community S374-S382. environment (e.g., neighborhood disorder and safety) Anderson, W. L., & Wiener, J. M. (2015). The impact of assis- that may affect the availability of personal assistance. tive technologies on formal and informal home care. The Such variables should be included in future work con- Gerontologist, 55, 422-433. cerning devise use and personal assistance. It is notable Bateni, H., & Maki, B. E. (2005). Assistive devices for bal- that our results do show a device substitution effect for ance and mobility: Benefits, demands, and adverse consequences. Archives of Physical Medicine and those who may have less access to personal assistance Rehabilitation, 86, 134-145. because they live alone. Bohannon, R. W. (2011). Body mass index and mobility of In all, using a nationally representative sample of home care patients. 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Journal

Gerontology and Geriatric MedicinePubmed Central

Published: Oct 25, 2019

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