The Impact of Disability and Assistive Technology Use on Well-Being in Later Life: Findings From the National Health and Aging Trends Study

Abstract

Background and Objectives

Although assistive technologies have the potential to bridge the gap between personal capabilities and environmental demands, they may not always fully accommodate disability. This study examined the implications of change in the extent of accommodation provided by assistive technology for well-being in older adulthood.

Research Design and Methods

Data from 5 waves (2015–2019) of the National Health and Aging Trends Study provided information on disability and assistive technology use among older adults aged 65 and older in the United States (n = 7,057). An eight-level index that jointly characterized the spectrum of disability and assistive technology use was applied to 7 activities of daily living (ADLs). Fixed-effects panel model assessed within-person associations between well-being and the extent of assistive technology accommodation along different levels of the disability spectrum.

Results

At baseline, bathing (28.7%; 95% confidence interval [CI]: 27.6, 29.8) and toileting (37.9%; 95% CI: 36.2, 39.6) were the 2 activities in which most older adults successfully accommodated their limitations with assistive technologies. Longitudinally, the level of support provided by assistive technology changed widely across activities and over time. Within-person analyses showed that for all ADLs except for eating, there was a significant decline in well-being when the adopted assistive technology no longer supported users’ needs and successfully resolved their disabilities.

Discussion and Implications

Our findings highlight the utility of technology-based interventions and underscore the imperative that assistive technologies attend to the specific needs of older adults and support independence in everyday activities.

In the United States, nearly 40% of older adults aged 65 and older experienced disabilities, such as having difficulties or receiving help from others, when performing self-care and mobility activities in daily life (Freedman et al., 2021). Assistive technology, which includes a vast array of assistive devices (e.g., canes), special equipment (e.g., modified utensils), and environmental modifications (e.g., grab bars) designed to facilitate completion of tasks and daily activities, has the potential to enhance functioning and independence for older adults living with disabilities (Scherer, 2002; Scherer & Glueckauf, 2005). Indeed, research has shown that assistive technology use is associated with various health benefits such as improved functional capabilities, better management of chronic conditions, and increased activity and social participation among older adults (Borade et al., 2019; Pettersson et al., 2016; Sund et al., 2015).

Although prior studies suggest that assistive technology use should promote well-being, there is limited empirical evidence on the process by which assistive technology use promotes well-being in later life. Focusing on well-being is critical because it not only has the potential to improve health outcomes, but also reflects the subjective experiences of older adults with disabilities and chronic conditions (Diener et al., 2017; Lyubomirsky et al., 2005; Mitra et al., 2020; Mitra & Brucker, 2020). Previous research on well-being and assistive technology has focused primarily on individual differences in the number of technologies adopted. These studies have shown that, on average, assistive technologies mitigate declines in well-being for persons with disability (Lin & Wu, 2014; Veehof et al., 2006). However, these between-person comparisons mask fluctuations in both disability and the extent to which the adopted technology effectively accommodates users’ needs. As a result, little is known about the effects of using assistive technology on well-being when it fails to enable users to function fully within their environments.

Conceptual Framework

Lawton’s theory of person–environment fit (Lawton, 1983) provides a critical framework to assess the role of assistive technology use within the context of aging and disability. According to this theory, an older adult’s functioning and well-being are shaped by the dynamic interactions between their abilities and the environment in which they live in. Consistent with this notion, the Disablement Process model also posits that disability arises from the mismatch between personal capability and environmental demand (Verbrugge & Jette, 1994). Taken together, assistive technology as a part of the material environment has the potential to bridge the gap between individuals and their surroundings through the provision of contextual support, thereby accommodating a disability (Mitzner et al., 2018; Wahl & Gerstorf, 2020).

In alignment with the perspective of person–environment fit, disability is now recognized as an interplay between person, technology, and engagement in everyday activities (Freedman, 2009; Freedman & Martin, 2004). Particularly, seminal work by Freedman and colleagues portrayed disability as a five-level spectrum that not only captures activity limitations but also characterizes individuals’ responses to functional change (Freedman et al., 2014). Based on their definition, disability is operationalized as an index, with stages ranging from (1) fully able to (2) successful accommodation, (3) reduced activity frequency, (4) difficulty, and (5) receiving help from others. This classification suggests that individuals who use assistive technology and do not experience decreased activity levels, difficulty, or dependence on others have achieved successful accommodation (Freedman et al., 2014).

Building from a foundation of research that has established the impact of experiences across the disability spectrum for health outcomes (Freedman et al., 2017; Gill & Williams, 2017; Xiang et al., 2021), further development is needed to examine how change in accommodation provided by assistive technology affects well-being. First, current measurement approaches provide limited information on the extent of support provided by assistive technology when successful accommodation is unattainable. A comprehensive profile that outlines activity difficulty with and without assistive technology is essential to gauge our success in meeting individuals’ needs (Taylor & Hoenig, 2004; Verbrugge & Jette, 1994). Second, the need for assistive technology is confounded with between-person differences in physical function (Agree et al., 2004; Pressler & Ferraro, 2010). As a result, a within-person perspective that follows change in technology use and technology accommodation over time is needed to examine the consequences of using assistive technologies that no longer accommodate older adults’ evolving needs (Mitzner et al., 2018).

Guided by Lawton’s theory of person–environment fit, this study aimed to (1) provide an in-depth profile of disability and the extent of accommodation by assistive technology within older populations and to (2) define the implications of change in the extent of accommodation provided by assistive technology for well-being in later life. To accomplish the first goal, we drew on data from the National Health and Aging Trends Study (NHATS) and expanded the original five-level index to capture the use of assistive technology at all levels of disability. To achieve the second goal, we adopted a within-person perspective along with the fixed-effects (FE) approach to investigate whether changes in the level of support provided by assistive technology would have impacts on older adults’ well-being. The present study tested three hypotheses: (1) the extent of accommodation provided by assistive technology varies from year to year; (2) this accommodation is activity-specific, contingent on the daily activities the technology serves; and (3) declines in such accommodation are associated with worse subjective well-being among older adults. Findings from this study can enhance our understanding of person–technology interaction and have implications for advancing rehabilitation practices, specifically in assessing older adults’ needs and delivering technology-based services.

Method

Data and Study Population

NHATS is an ongoing population-based longitudinal study sponsored by the National Institute on Aging (NIA U01AG32947). The first NHATS cohort was recruited and interviewed in 2011 (n = 8,245), and a new cohort of older adults was included in 2015 (n = 4,182) to replenish the original panel size. Non-Hispanic Black and individuals at older ages were oversampled. Together, NHATS provides a nationally representative sample of Medicare beneficiaries aged 65 and older (n = 8,038 in 2015) in the United States. We chose NHATS as it offers thorough examinations of the disablement process and detailed measures of personal, behavioral, and environmental characteristics of older adults at a national level.

We analyzed data from the 2015 through 2019 rounds of the NHATS, encompassing the maximum number of waves preceding the coronavirus disease 2019 pandemic. The analytic sample was restricted to older adults who were alive at baseline, provided nonproxy interview responses, and were living in settings other than nursing homes. As such, a total of 7,057 respondents were eligible for inclusion at baseline. Altogether, the final analytic sample of the present study comprised 26,815 person-years of observations with each respondent having an average of 3.8 ± 1.6 rounds of valid interviews. The Office for the Protection of Research Subjects at the University of Illinois Urbana-Champaign determined the secondary analysis of deidentified publicly available data to not require ethical approval.

Measures

Subjective well-being

At each survey round, NHATS interviewers asked respondents four questions about their positive and negative emotions in the past month (i.e., frequency of feeling cheerful, bored, full of life, and upset) and three questions reflecting self-realization (i.e., the extent of agreement with the following three statements: My life has meaning and purpose; I feel confident and good about myself; and I like my living situation very much). Responses to questions on emotional well-being were rated on a 5-point Likert scale ranging from never (0) to 7 days a week (4), whereas responses to self-realization questions were rated on a 3-point scale ranging from agree not all (0) to agree a lot (2). Items assessing negative emotions were reverse-coded so that a higher total score indicates better well-being. We followed established recommendations and summed the seven items to create an overall well-being index (Freedman et al., 2017). Final scores range from 0 to 22, with higher scores representing higher levels of subjective well-being (Cronbach’s α = 0.74). The summary measure’s single-factor structure is established in the NHATS data, with all loadings at 0.47 or higher (Freedman et al., 2014).

Disability and assistive technology spectrum

For each of the seven ADLs (going outside, getting around inside, getting out of bed, eating, bathing, toileting, and dressing), respondents reported (1) their corresponding use of assistive technologies such as canes, walkers, wheelchairs, eating utensils, grab bars, raised chairs, and button hook in the past month; and (2) changes in activity frequency, experience of activity difficulty, and receipt of assistance from others during the last month. Following the original disability spectrum procedures (Freedman et al., 2014), this information was combined to develop an eight-level index that characterized assistive technology use at all levels of activity limitations. The eight categorical levels were operationalized as: (Level 1) fully able: does not use technology but can perform the activity without difficulty, reduced frequency, or assistance from another person; (Level 2) successful accommodation: uses technology to accommodate limitations and performs the activity without difficulty, reduced frequency, or assistance; (Level 3) reduced activity and no technology use: performs the activity without technology, difficulty, or assistance but reduces the frequency of performing that activity compare to a year ago; (Level 4) reduced activity despite technology use: reduces activity frequency despite the use of technology but without difficulty or assistance; (Level 5) difficulty and no technology use: has difficulty performing the activity alone but does not use technology or receive assistance; (Level 6) difficulty despite technology use: has difficulty performing the activity alone even when using technology but no assistance; (Level 7) assistance and no technology use: receives help from another person but no technology use or, rarely, not doing that particular activity; (Level 8) assistance despite technology use: receives help from others despite technology use. Due to the nature of certain daily activities, Levels 3 and 4 (indicating reduced activities with and without assistive technology) were omitted for eating, toileting, and getting out of bed. The fine-grained classification allowed specific contrast between pairs of levels along the spectrum.

Time-varying covariates

To account for alternative explanations, our analysis included a number of covariates that may have affected both disability and well-being and varied over time (Cavanaugh et al., 2018; Emerson et al., 2021; Meeks et al., 2011). Adjusting for these factors also allowed us to interpret fluctuations in the disability index as changes in the extent of support provided by assistive technology use. First, the Short Physical Performance Battery (SPPB) measured physical function (Guralnik et al., 1994) and included assessments of performance in balance tests, 3-m walking test, rapid chair stands, grip strength, and peak air flow. Scores were summed to create a performance-based measure of physical capacity (range: 0–12; Kasper et al., 2012). Second, count of chronic health conditions was calculated as the number of self-reported diagnosis of heart disease, high blood pressure, osteo or rheumatoid arthritis, osteoporosis, diabetes, lung disease, stroke, broken bones or fractured hips, and cancer. Third, participants’ dementia status (0 = possible or no dementia; 1 = probably dementia) was determined based on: (1) self-reported physician diagnosis of dementia or Alzheimer’s disease; (2) AD8 Dementia Screening Interview scores; and (3) cognitive tests assessing respondents’ memory, orientation, and executive function (Kasper et al., 2013, 2015). Fourth, a social isolation index (range: 0–5) was constructed from respondents’ reports of living status (1 = living alone), number of confidants (1 = less than two confidants), participation in religious services (1 = no participation), clubs/classes/organized activities (1 = no participation), and volunteer work in the past month (1 = no volunteering; Cudjoe et al., 2020). Time-invariant individual characteristics such as gender, race and ethnicity, educational attainment, baseline age groups, and household income were not included in the estimation process of the FE analysis.

Analytic Strategy

Missing data were assessed prior to formal analyses. Among the 7,057 qualified respondents at baseline, a total of 196 (2.8%) individuals moved to nursing home settings, whereas 1,124 (15.9%) participants passed away over the follow-up years. Logistic regressions showed that attrition was more prominent among people aged 80 and older, living in residential care settings, with lower functional capacity and probable dementia, and among individuals who were more socially isolated. Age, gender, health conditions, residential status, and marital status were associated with missingness due to moving to nursing homes or decease. To account for the unequal probabilities of selection and differential nonresponse, the analytic weights developed by NHATS were used in the analyses.

We reported baseline characteristics of survey respondents as means and standard deviations (SDs) for continuous variables. Percentages and 95% confidence intervals (CIs) were presented for categorical variables and each level of the disability spectrum at baseline. Clusters, stratum, and weights reflecting the complex sampling design of NHATS were accounted for to make national estimates in 2015.

An FE panel model, an established statistical method for analyzing longitudinal data, provided unbiased estimates of within-person associations between changes in the levels on the disability spectrum and changes in subjective well-being (Croissant & Millo, 2008). More specifically, a formal FE model can be written as:

$${\displaystyle \begin{array}{l}{\displaystyle \begin{array}{lll}{Y}_{ti}=& {\displaystyle \rho {(eight\text{-}leveldisabilityspectrum)}_{ti}+X_{ti}\beta +{\alpha }_i+{\epsilon }_{ti}}& {\displaystyle \begin{array}{lll}\mathrm{f}\mathrm{o}\mathrm{r}t=1,\dots ,T& {\displaystyle and}& {\displaystyle i=1,\dots ,N}\end{array}}\end{array}}\end{array}}$$

where Y_ti represents the score of subjective well-being for person i at time t in this study. The regression coefficient ρ denotes the impact on the outcome when individuals’ levels on the disability spectrum changed across survey rounds (e.g., moving from fully able to successful accommodation with assistive technology). X_ti is the vector of time-varying explanatory variables, whereas β is the associated matrix of regression coefficients. α_i, without the subscript t, indicates all observed and unobserved individual or contextual characteristics that are constant over time. Unlike a random-effects model, α_i is allowed to have arbitrary correlations with X_ti in an FE framework. This flexibility makes FE a more convincing method for our analysis because a person’s state of disability and assistive technology use could be influenced by individual characteristics such as gender, race, and ethnicity. Furthermore, using FE models enables us to eliminate the omitted variable bias due to unmeasured individual differences.

We first examined whether transitions across the disability spectrum were associated with changes in well-being. Next, we included the set of covariates described previously to adjust for these potential time-varying confounders. Indicators of survey rounds (2015–2019) were included in all models to account for the temporal trend. Because the nature of each activity and assistive technology is distinct, we conducted the analyses for each ADL separately. Finally, we performed multiple planned comparisons to investigate the impact on well-being when the extent of accommodation provided by assistive technology changes. The comparisons were determined a priori and were achieved by comparing subjective well-being scores for a particular individual when they transitioned from Level 2 “successful accommodation” to lower levels on the disability spectrum, wherein assistive technology was also employed (Levels 4, 6, and 8). We performed linear contrasts using likelihood ratio tests and applied Bonferroni corrections to prevent inflation of Type I error. We applied a robust covariance matrix to produce unbiased standard errors and account for the presence of heteroskedasticity in the residuals. Statistical analyses were conducted using R (version 4.1.0) and Stata 17 (Stata Corp., College Station, TX).

Results

Weighted descriptive statistics showed that our study population was mostly female (55%), non-Hispanic Whites (78%), married or partnered (57%), community-dwelling (96%), and received some college degrees or higher (84%). The average subjective well-being score was 17.3 (SD = 3.3; range = 0–22) at baseline. The average scores of physical capacity, social isolation, and number of chronic conditions at baseline were 7.2 (0–12), 2.5 (0–5), and 2.3 (0–8), respectively. Around 5% of the study population had probable dementia (Table 1).

Table 1.

Weighted Study Sample Characteristics at Baseline (N = 7,057)

Characteristics	Weighted mean/percentage (SD)
Subjective well-being (0–22)	17.3 (3.3)
Age groups at baseline (%)
65–69 years	30%
70–74 years	27%
75–79 years	19%
80–84 years	12%
85–89 years	8%
90 years+	4%
Female (%)	55%
Married/partnered (%)	57%
Living alone (%)	30%
Race/ethnicity (%)
Non-Hispanic White	78%
Non-Hispanic Black	8%
Hispanic	7%
Other	4%
Education (%)
Less than high school	16%
Some college	55%
College degree or higher	29%
Residential status (%)
Community	96%
Residential care settings	4%
Household income in 2015
First quartile (less than $17,000)	19%
Second quartile ($17,001–$32,000)	22%
Third quartile ($32,001–$60,000)	27%
Fourth quartile ($60,001+)	32%
Dementia status (%)
Probable dementia	5%
Possible/no dementia	95%
Chronic health conditions (0–9)	2.3 (1.4)
Physical capacity (0–12)	7.2 (3.3)
Social isolation (0–5)	2.5 (1.3)

Notes: NHATS = National Health and Aging Trends Study; SD = standard deviation. Complex sample design of NHATS including clusters, stratum, and weights was accounted for in the analysis.

To present the cross-sectional profile of disability and assistive technology use, we first reported the proportion of respondents at different stages on the eight-level disability spectrum across seven ADLs in 2015 (Table 2). The proportion of participants at Level 1 “fully able” ranged from 56.3% (toileting) to 95.4% (eating). Of all activities, bathing (28.7%) and toileting (37.9%) were most likely to be fully accommodated by assistive technologies (Level 2). Notably, the incorporation of assistive technology did not always fully resolve activity limitations and lead to the expected state of successful accommodation. As an illustration, in the case of going outside, 8% of participants successfully accommodated with assistive technology, but 1.7% exhibited reduced activity frequency, 4% experienced difficulty, and 6.2% still received assistance from other people, despite the use of assistive technologies.

Table 2.

Weighted Percentages of NHATS Respondents at Distinct Stages of the Disability Spectrum for Seven Activities of Daily Living in 2015 (N = 7,057)

	Mobility activities			Self-care activities
	Going outside	Getting around inside	Getting out of bed	Eating	Bathing	Toileting	Dressing
Disability spectrum	% (95% confidence interval)
Fully able (Level 1)	69.3 (68.3, 70.2)	74.7 (73.5, 75.9)	79.0 (77.7, 80.3)	95.4 (94.9, 95.9)	56.6 (55.4, 57.8)	56.3 (54.6, 57.9)	81.3 (80.4, 82.2)
Successful accommodation with AT (Level 2)	8.1 (7.4, 8.8)	7.9 (7.2, 8.5)	3.3 (2.8, 3.8)	0.1 (0.1, 0.2)	28.7 (27.6, 29.8)	37.9 (36.2, 39.6)	1.5 (1.2, 1.8)
Reduced activity and no AT use (Level 3)	5.0 (4.4, 5.6)	1.4 (1.2, 1.7)	—	—	1.2 (0.9, 1.5)	—	1.1 (0.8, 1.4)
Reduced activity despite AT use (Level 4)	1.7 (1.5, 2.0)	1.1 (0.8, 1.4)	—	—	1.3 (1.0, 1.6)	—	0.0 (0, 0.0)
Difficulty and no AT use (Level 5)	4.4 (3.8, 5.0)	6.1 (5.3, 6.9)	11.0 (10.0, 12.0)	1.9 (1.6, 2.2)	3.2 (2.7, 3.7)	1.5 (1.1, 1.8)	7.1 (6.3, 7.8)
Difficulty despite AT use (Level 6)	4.0 (3.5, 4.5)	4.7 (4.2, 5.2)	3.4 (2.9, 3.9)	0.1 (0, 0.1)	4.1 (3.5, 4.6)	3.0 (2.6, 3.5)	0.7 (0.5, 0.9)
Assistance and no AT use (Level 7)	1.3 (1.0, 1.6)	1.0 (0.7, 1.3)	1.9 (1.5, 2.3)	2.4 (2.0, 2.9)	2.0 (1.7, 2.4)	0.1 (0, 0.2)	7.2 (6.7, 7.8)
Assistance despite AT use (Level 8)	6.2 (5.6, 6.8)	3.1 (2.8, 3.5)	1.4 (1.1, 1.7)	0.1 (0.0, 0.2)	2.9 (2.5, 3.3)	1.3 (1.0, 1.5)	1.1 (0.8, 1.3)
Using AT during the activity	20.0%	16.8%	8.1%	0.3%	37.0%	42.2%	3.3%

Notes: AT = assistive technology; NHATS = National Health and Aging Trends Study. For mobility activities, the associated assistive technologies included canes, walkers, wheelchairs, and scooters. For self-care activities, the associated assistive technologies included eating utensils, grab bars, bath seats, raised chairs, button hook, reacher, and grabber. Complex sampling designs including clusters, stratum, and weights were used to make national estimates.

Consistent with our expectations, transitions between different levels on the disability spectrum across the seven ADLs were observed (Supplementary Figures 1 and 2). Overall, the observed patterns of transitions varied greatly in the study population. Beyond patterns of stability among those who were at Level 1 “fully able” and Level 2 “successful accommodation” at baseline, intraindividual variability in disability and the extent of accommodation provided by assistive technology use were also evident in most activities. Percent agreement was calculated to describe the extent of intraindividual variability in respondents’ transitions between different stages, where a 0% agreement would indicate that all participants transitioned into different stages during every wave of the study period and a 100% agreement indicates no transitions at all. The percentages ranged from 48.8% (going outside) to 55% (getting out of bed) for the three mobility activities and ranged from 39.5% (bathing) to 87% (eating) for the four self-care activities. In essence, we found that older adults adopted or abandoned assistive technologies during the study period, and that the extent of support provided by assistive technology use differed across activities and varied across waves. These results highlight the intraindividual variability within as well as interplay between disability and assistive technology use in later life.

Table 3 presents the within-person associations between changes in the levels on the disability spectrums and changes in well-being scores. As expected, in the years when technologies were not utilized, there was a significant decrease in subjective well-being as individuals transitioned from fully able (Level 1) to reduced activity and no technology use (Level 3), difficulty and no technology use (Level 5), and assistance and no technology use (Level 7) in all ADLs. In parallel, when assistive technologies were newly adopted in the subsequent years but unable to support successful accommodation, there was also a decrease in subjective well-being. However, when the assistive technology successfully resolved users’ difficulty and promoted independence, it was observed that transitioning from fully able (Level 1) to successful accommodation (Level 2) did not generally result in a systematic change in subjective well-being. Exceptions to this pattern were found for getting around inside (b = −0.39; 95% CI: −0.54, −0.24) and getting out of bed (b = −0.22; 95% CI: −0.40, −0.04).

Table 3.

Within-Person Association Between Transitions Across the Disability Spectrum and Changes in Subjective Well-Being

	Mobility activities			Self-care activities
	Going outside	Getting around inside	Getting out of bed	Eating	Bathing	Toileting	Dressing
Disability spectrum	Unstandardized regression coefficients (95% confidence interval)
Fully able (Level 1)	Reference group
Successful Accommodation with AT (Level 2)	−0.06 (−0.21, 0.09)	−0.39 (−0.54, −0.24)	−0.22 (−0.40, −0.04)	−0.54 (−1.22, 0.13)	0.02 (−0.07, 0.11)	−0.09 (−0.18, 0.01)	−0.10 (−0.36, 0.15)
Reduced activity and no AT use (Level 3)	−0.31 (−0.47, −0.15)	−0.56 (−0.82, −0.30)	—	—	−0.56 (−0.90, −0.23)	—	−0.67 (−1.06, −0.27)
Reduced activity despite AT use (Level 4)	−1.00 (−1.29, −0.71)	−0.75 (−1.12, −0.38)	—	—	−0.34 (−0.67, −0.02)	—	−0.68 (−2.12, 0.75)
Difficulty and no AT use (Level 5)	−0.51 (−0.70, −0.32)	−0.56 (−0.69, −0.43)	−0.34 (−0.45, −0.22)	−0.46 (−0.70, −0.22)	−0.49 (−0.71, −0.27)	−0.14 (−0.48, 0.20)	−0.44 (−0.58, −0.30)
Difficulty despite AT use (Level 6)	−0.48 (−0.67, −0.29)	−0.87 (−1.07, −0.67)	−0.42 (−0.64, −0.21)	−1.31 (−2.35, −0.27)	−0.39 (−0.57, −0.20)	−0.54 (−0.76, −0.32)	−0.92 (−1.31, −0.53)
Assistance and no AT use (Level 7)	−0.53 (−0.83, −0.22)	−0.90 (−1.35, −0.45)	−0.49 (−0.85, −0.13)	−0.63 (−0.97, −0.30)	−0.80 (−1.11, −0.49)	−0.38 (−1.19, 0.43)	−0.52 (−0.69, −0.36)
Assistance despite AT use (Level 8)	−0.80 (−1.00, −0.59)	−1.41 (−1.67, −1.16)	−1.18 (−1.53, −0.82)	−0.79 (−1.44, −0.14)	−0.72 (−0.99, −0.46)	−0.88 (−1.28, −0.47)	−0.97 (−1.27, −0.68)
No. of persons	6,821	6,816	6,820	6,822	6,819	6,815	6,819
Person-years	24,117	24,075	24,126	24,125	24,111	24,110	24,123

Notes: AT = assistive technology. All regression coefficients were relative to the reference group (Level 1 fully able). Models were adjusted for the count of chronic health conditions, physical functioning, dementia status, and social isolation. Values in bold are statistically significant (p < .05).

Finally, as shown in Figure 1, results from our planned comparisons exemplified the implications for an older person’s subjective well-being when the extent of accommodation provided by the assistive technology changes. In the years where assistive technology continued to be utilized, it was for going outside that moving away from successful accommodation to reduced activity despite technology use (Level 2 vs Level 4) was associated with a significant decrease in well-being (Δβ = −0.94, χ²(1) = 43.44, p < .001). Likewise, for going outside, getting around inside, bathing, toileting, and dressing, moving from successful accommodation to difficulty despite technology use (Level 2 vs Level 6) was associated with lower well-being (all p < .001). Similarly, for all activities except eating, moving away from successful accommodation to assistance despite technology use (Level 2 vs Level 8) was also linked to a significant decline in well-being (eating: Δβ = −0.25, χ²(1) = 0.29, p = .59). Full reports of the planned comparison tests and unadjusted models are presented in Supplementary Tables 1–3.

Figure 1.

Within-person associations between changes in the extent of support provided by assistive technology and changes in subjective well-being. Changes in subjective well-being from successful accommodation to reduced activity frequency are not applicable to eating, toileting, and getting out of bed due to the nature of these daily activities.

Discussion and Implications

This study used a nationally representative sample of older adults to provide an in-depth profile of disability and examine the role of using assistive technology in supporting well-being in later life. First, our study documented the extent to which assistive technologies accommodate disability across various daily activities. Aside from bathing and toileting, where assistive technologies such as grab bars or raised toilet seats can assist with actual or feared balance and mobility issues, we found that for most participants who utilized assistive technologies, the device did not fully accommodate their needs. Our results show that assistive technology does not necessarily result in successful accommodation, that disability can persist if the gap between task demands and individual’s capacity is not mitigated, and that this gap has consequences for well-being.

By examining the year-to-year fluctuations in the extent of accommodation provided by assistive technologies, this study found a considerable amount of variability in older adults’ state of disability and assistive technology use over a 5-year time frame. Despite the longer 1-year measurement interval in this research, our findings align with previous studies on short-term changes in disability and support the notion that disability is highly dynamic even in the later life course (Gill et al., 2010; Hardy & Gill, 2004; Stolz et al., 2019; Verbrugge et al., 1994). Moreover, this study contributes to a better understanding of the disablement process by adding granularity to the original five-level disability spectrum. Specifically, our findings demonstrate that using assistive technology is a common strategy employed by older adults facing functional limitations (Carpentieri et al., 2017; Pressler & Ferraro, 2010; Wrosch et al., 2006). Further, by demonstrating that both assistive technology use and effectiveness fluctuate across waves, our findings highlight the dynamic nature of not only the uptake of assistive technology, but also in the level of support provided by those technologies. We suggest that in addition to facilitating long-term use of assistive technology, it is critical to develop and assign technology-based solutions that meet the changing abilities, needs, and expectations of older populations (Garçon et al., 2016; Phillips & Zhao, 1993).

In accordance with prior studies (Kendig et al., 2000; Mitra et al., 2020), our findings indicate that subjective well-being was highest when a person was fully able to perform the activity and declined as disability progressed to a more severe stage. However, we also found that well-being among people who later used assistive technology and achieved successful accommodation was almost comparable to that of when they were fully able. This finding highlights that using assistive technology that fully accommodates one’s needs could compensate for declines or losses in physical capacity and support mental health outcomes in later stages of life (Freedman et al., 2017; Xiang et al., 2021). The potential for assistive technology to facilitate accommodation and support well-being was identified in most ADLs in this study. However, in the context of getting around inside and getting out of bed, the adoption of assistive technologies that fully accommodated needs was still associated with decreased well-being. We speculate that greater psychological distress along with a lower sense of self-mastery could result from the use of assistive technology in essential daily activities. More careful attention is required to ensure that assistive technology not only attends to the functional needs of older-adult users but also preserves their agency and self-esteem when performing valued activities.

This study aligns with Latwon’s theory of person–environment fit and offers a within-person perspective to the environmental docility hypothesis (Lawton & Simon, 1968). In complement to the hypothesis’s between-person approaches, our study utilizes within-person analyses and reveals that reduced independence during activities has implications for older adults’ well-being. More broadly, findings provide crucial evidence to the current discourse on aging and technology research. From a technology perspective, for instance, the TechSAge Technology Intervention Model developed by Mitzner and colleagues (2018) emphasizes the potential of technology in closing the gap between one’s intrinsic capacity and functional ability. Our study suggests that older adults’ well-being may be affected when the assistive technology used fails to bridge the capacity–ability gap. From a user perspective, the present study also sheds light on the human development model of disability, health, and well-being (Mitra & Brucker, 2020). In this model, the contributions of resources, agency, and conversion functions were underscored in relation to older adults’ well-being. Our study elucidates this standpoint by characterizing assistive technology as an external resource and suggests that when an older person is unable to convert technology into actual capabilities and functioning, the benefits of using such resource could be attenuated. Findings highlight the importance of technological supports and stress the necessity to assess person–technology fit regularly, informing the clinical practices of rehabilitation professionals, and potentially influencing the broader reimbursement systems or policies governing the provision of assistive technology. It should be noted, however, that in this study well-being varied within a high range of magnitude across the disability spectrum. Recognizing that well-being may remain relatively stable among older adults (Diener et al., 2006; Kunzmann et al., 2000; Larson, 1978), the implications of person–technology interaction should be further examined among other critical outcomes such as or long-term care utilization or risk of falling (Das Gupta et al., 2023).

Although the study benefits from a within-person analysis of longitudinal data, the findings should be interpreted within the context of its limitations. First, compared to the original five-level scale, our proposed eight-level disability index divided older-adult samples into smaller subsets, which could reduce the statistical power to detect differences across levels. However, by integrating assistive technology use across the full range of activity limitations, this eight-level classification scheme contributes to a more holistic picture of disability in later life. Second, although the FE analysis effectively controlled for time-invariant confounding factors, allowing for an unbiased estimate of the effects of assistive technology use on well-being, employing other longitudinal analytic strategies is necessary to better understand the between-person differences in these within-person changes. Third, reported assistive technology was limited to seven ADLs in the current study. Additional research efforts are needed to examine the implications of a more diverse set of activities and the corresponding assistive technologies (Pressler & Ferraro, 2010). This study focused largely on community-dwelling older adults with relatively high physical and cognitive functioning. An important next step is to focus on the oldest-old, frail, and persons living in long-term care facilities. In addition, to complement findings from the annual assessments in this study, future research should consider leveraging longitudinal data with measurement intervals that align with theory, policy, and/or practice (e.g., quarterly, monthly, or even daily) to characterize the coupling of disability and assistive technology use and its impact among older adults.

In summary, this study utilizes a sophisticated eight-level disability spectrum and demonstrates both the dynamics and nuances of disability and assistive technology use in the later life course. Our findings indicate that while assistive technology is frequently adopted by older adults, it does not always result in successful accommodation of disability. The present study highlights that a person’s well-being is best preserved when the assistive technology possessed meets their specific needs and supports participation fully in everyday activities. Tailored patient education, continued monitoring programs, and public health policies are needed to target older adults who have already or are planning to use assistive technologies. These efforts will provide a crucial next step toward realizing the full potential of technological solutions, which in turn will benefit the lives of the fast-growing aging population.

Funding

None.

Conflict of Interest

None.

Data Availability

This study was not preregistered. The data used in this study are publicly available from the Sample Person and Other Person files of the National Health and Aging Trends Study. Detailed analytic procedures are provided in the main text. Analytic code is available from the corresponding author upon request.

Author Contributions

Tai-Te Su (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Project administration [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [equal]) and Shannon Mejía (Conceptualization [equal], Data curation [supporting], Formal analysis [supporting], Investigation [equal], Methodology [equal], Project administration [supporting], Writing—original draft [supporting], Writing—review & editing [equal])