Abstract
Objective:
To estimate readiness of older rehabilitation users in the United States to participate in video-based telerehabilitation and assess disparities in readiness among racial and ethnic minoritized populations, socioeconomically disadvantaged populations, and rural-dwelling older adults.
Design:
Retrospective cohort study using nationally representative survey data from the National Health and Aging Trends Study from 2015 and 2020. Survey-weighted regression models, accounting for complex survey design, were used to generate estimates of readiness and evaluate disparities across racial and ethnic, socioeconomic, and geographic subgroups. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for each comparison.
Setting:
Home or community rehabilitation environments
Participants:
A cohort of 5,274 home or community-based rehabilitation users aged 70 years or older, representing a weighted 33,576,313 older adults in the United States.
Interventions:
Not applicable.
Main Outcome Measure(s):
Video-based telerehabilitation readiness was defined consistent with prior work; unreadiness was defined as lacking ownership of internet-enabled devices, limited proficiency of use, or living with severe cognitive, visual, or hearing impairment. Telerehabilitation readiness was categorized as "Ready" or "Unready".
Results:
Approximately 2 in 3 of older rehabilitation users were categorized as ready to participate in video-based rehabilitation. Significantly lower rates of readiness were observed among those living in rural areas [OR=0.75, 95% CI: 0.60-0.94], financially strained individuals [OR=0.37, 95% CI: 0.26-0.53], and among individuals identifying as Black or Hispanic [as compared to non-Hispanic White older adults: Non-Hispanic Black [OR=0.23, 95% CI: 0.18-0.30; Hispanic [OR=0.17, 95% CI: 0.11, 0.27)].
Conclusions:
Our findings highlight significant disparities in the readiness to uptake video-based telerehabilitation. Policy and practice interventions to address telerehabilitation readiness should focus not only on improving broadband access but also on technology ownership and training to ensure equitable adoption in populations with lower baseline readiness.
Keywords: telemedicine, aged, social determinants of health
Delivery of rehabilitation services via telehealth has increased substantially in the United States over the past decade; use of telerehabilitation increased from 2% of all therapists prior to the pandemic to 48% in 2020.1 Telerehabilitation is perceived by patients as a beneficial resource for offering timely and convenient appointments2; these benefits were realized extensively during the COVID-19 pandemic when appointments were encouraged to be remote. Multiple systematic reviews have concluded telerehabilitation is non-inferior to traditional, in-person therapy for a range of conditions, from stroke recovery to management of chronic pain—findings that have accelerated calls to make telerehabilitation services billable across payers.3-5
Because telerehabilitation represents an especially important way to increase access to vital rehabilitation services, understanding how ready patient populations are to adopt video visits and identifying potential inequities is critical. Broadly, the concept of telemedicine readiness has emerged to describe whether delivery of care via a virtual environment would be feasible, safe, and effective. For older adults, these questions are especially important as this population has higher rates of visual, hearing, and cognitive impairments that may limit meaningful participation in video visits, as well as lower ownership and less comfort with internet-enabled devices such as smartphones.6-8 A 2021 study estimated that 13 million older adults in the United States are telemedicine unready and the authors of this report note that this disproportionately affects those who may need services most. However, rates are unclear among the population of older adults seeking rehabilitation care. Readiness may be lower for those seeking rehabilitation care virtually because geriatric vulnerabilities such as impairments in balance or mobility may limit the scope of evaluations and interventions that can be completed safely, even with a care partner present.9 Thus, proactive steps need to be taken to minimize the digital divide for older adults during expansions of telerehabilitation, especially video based-visits.
Among the older adults who may have lower likelihood of telemedicine readiness are those who identify as Black or Hispanic, those experiencing financial or other access barriers to technology ownership and broadband internet access, and those who live in rural areas.10,11 However, there has been little work in nationally representative samples evaluating inequities in telerehabilitation readiness. Evaluating disparities in telerehabilitation readiness is a critical step towards more equitable implementation among older adults. Thus the purpose of this report is to estimate, in a nationally representative cohort of older rehabilitation users, readiness to uptake video-based telerehabilitation and evaluate inequities in readiness. We also sought to explore whether rates of readiness are driven more by technological or disability-related factors.
METHODS
Data Source
We drew data from the National Health and Aging Trends Study (NHATS) data, rounds 5-10 conducted in 2015-2020. The NHATS cohort and weighting structure has been described elsewhere12, but briefly is designed as a nationally-representative longitudinal cohort study focusing on trends of disability and independent functioning of U.S. Medicare beneficiaries, including several key aspects of disability including presence of visual, cognitive, communication, or hearing disability. NHATS administers surveys annually, and oversamples those who identify as Non-Hispanic Black and older adults over the age of 85. NHATS employs a multistage, stratified, probability sampling design to select its participants, ensuring a representative sample of Medicare beneficiaries aged 65 and older living in the United States. Initially, the sampling frame is drawn from the Medicare enrollment database. NHATS is geographically clustered, with sampling frames first clustered into Primary Sampling Units (PSUs) based on county. PSUs were then sampled from strata that were created based on region of the country and variables reflecting demographic composition. Within sampled PSUs, zip codes (or clusters of zip codes) were then sampled.
NHATS provides analytic weights each round, which are designed to make the sample nationally representative by accounting for probability of selection into the sample and differential nonresponse. NHATS is sponsored by the National Institute on Aging (grant U01AG032947) through a cooperative agreement with the Johns Hopkins Bloomberg School of Public Health. Use of public NHATS data for this analysis is considered exempt, non-human subjects research by the University of Maryland Baltimore.
Identification of Home and Community Based Rehabilitation Users
During each annual interview between 2015 and 2020, participants were asked: “In the last year, have you received any rehabilitation services?” Participants who responded affirmatively were then asked which setting rehabilitation was received (e.g., inpatient, home-based, or outpatient/clinic-based). Those who reported rehabilitation use in home or outpatient settings were categorized as community rehabilitation users in this analysis. Reason for seeking rehabilitation was also captured from NHATS responses. The unit of analysis in our study was rehabilitation users from 2015-2020.
Defining Telerehabilitation Readiness:
The primary outcome of telerehabilitation readiness in this study was primarily defined using previously published work from Lam and colleagues using NHATS data.9 Briefly, in our study the criteria for determining if a participant was categorized as telemedicine ready was whether they did not report one of the following factors indicating routine telerehabilitation visits via video delivered directly to a patient would be unfeasible, difficult, or impossible: 1) Lacking ownership of internet-enabled devices (computer, tablet, and cellphone), 2) Reporting no recent internet use (going online, sending messages via email or text), 3) Reporting inability to participate in telephone conversations due to hearing loss (with or without hearing aids), 4) Reporting inability to clearly see television or read newspaper print with or without vision aids), 5) Reporting inability to verbally communicate (making oneself being understood), and 6) having patterns of survey responses indicative of cognitive impairment, as defined as a categorization of possible or probable dementia on the validated NHATS algorithm. Our focus was on video-based telerehabilitation, given that comprehensive initial evaluations of factors such as skin breakdown, fit and use of assistive devices, and home environments that are considered best practices in geriatric rehabilitation would be best completed virtually through video versus telephone. Using this criteria, we categorized each older rehabilitation user with one or more of these barriers as telerehabilitation “Unready”, and those with none as telerehabilitation “Ready.”
To reflect additional scenarios specific to telerehabilitation readiness, we also created an alternate definition—which we categorized as “Ready with Modifications”—that added two criteria related to safe mobility: patient report of a recent fall within the last 30 days and/or need for assistance from another person when walking inside the home.
Our rationale, supported by prior qualitative work with older adults13 and liability concerns of providers14, was that elevated fall risk and severe mobility limitations would both be factors that may require modifications in delivery of optimal video-based telerehabilitation (e.g., requiring caregivers to be present for sessions, potential changes in testing for balance and gait to accommodate safety concerns, such as deferring assessments of reactive balance or floor to stand transfers).
Key Health Disparity Populations of Interest:
We categorized older adults in NHATS survey by racial and ethnic identity, rurality, and poverty. Race and ethnicity were categorized as non-Hispanic White, non-Hispanic Black, Hispanic, or other identities (which were combined due to small sample sizes and included individuals identifying as Asian, Native American, Pacific Islander and Native Hawaiian, and those who identified as multiple races). Rurality was categorized as “Rural” or “Urban” using the available NHATS Metro/Nonmetro Residence Indicator generated from Rural-Urban Continuum Codes. Financial strain, our proxy measure for poverty, was measured using a validated variable in NHATS data, where financial strain was determined to be present if participants reported not having enough money for basic purchases for food, rent, utilities or medical needs.15
Covariates:
Other measures to characterize the sample or for use as covariates included age, sex, marital status, educational attainment, Activities of Daily Living (ADL) disability, Medicaid eligibility (self-reported), and Census region of the country where older adult resided.
Creation of the Analytic Cohort:
Our sample included participants in NHATS who were community-dwelling, excluding those living in nursing homes or other institutional settings. From our cohort of community dwelling rehabilitation users, we restricted the sample to those who were over 70 years of age during the survey year to maintain nationally representative estimates across all 6 survey rounds, consistent with analytical guidelines outlined by NHATS.16
Statistical Approach
Unweighted demographic and clinical characteristics of the population were recorded, stratified by telerehabilitation readiness. Accounting for the complex design of the NHATS survey (using published annual analytic survey weights for each person-round) and also clustering to account for repeated patient observations, we then generated a nationally representative estimate of telerehabilitation readiness among all home and community based older rehabilitation users in the United States between 2015 and 2020.
Next, we evaluated differences in telerehabilitation readiness by racial and ethnic identity, rurality, and financial strain. We used multivariable logistic regression models to evaluate the relative odds of telerehabilitation readiness for Black, Hispanic, and Other older adults as compared to non-Hispanic White older adults, and then repeated these models for rurality (rural as compared to urban) and financial strain (financially strained versus not strained). Multivariable models were adjusted for age and sex to account for demographic differences. The resultant adjusted odds ratios (aOR) and 95% confidence intervals (CI) represent the relative differences in odds of telerehabilitation readiness.
As a sensitivity analysis, we assessed whether the results were consistent when considering the alternative definition of telerehabilitation readiness. We used a multinomial logistic regression model comparing the association of each domain with the odds of being telerehabilitation “unready” or “ready with modifications” as compared to “ready”. Missing data was minimal for exposures and outcomes (<1% for financial strain) and thus analysis was conducted using a complete case approach.
All analyses were done in SAS version 9.4 (SAS Institute, Cary NC). Data analysis was conducted between December 2022 and January 2024.
RESULTS
Our final analytic sample included 5,274 older rehabilitation users, which represents 33,576,313 total home and community based older rehabilitation users over 6 survey rounds between 2015 and 2020. The most common reasons for seeking rehabilitation were for a musculoskeletal condition (64%), a cardiopulmonary condition (12%), or a neurological condition (9%).
The weighted frequency of overall telerehabilitation readiness in the population was 66%, increasing over time (Figure 1). Telemedicine readiness was observed less frequently in older age groups, with notable decreases in readiness for those older than 90 years of age (Table 1). Additionally, telerehabilitation readiness was less commonly observed among members of minoritized racial and ethnic subgroups as compared to non-Hispanic White older adults. Notably, only 39% of Black older rehabilitation users, 49% of Hispanic older adults and 34% of those who identified as Asian, Pacific Islander, Native American, or multiple races were categorized as telerehabilitation ready. In comparison, 61% of White older rehabilitation users were categorized as telerehabilitation ready. For with financial strain, disparities in telerehabilitation readiness were also observed. Telerehabilitation readiness was observed among only a weighted 51% of older rehabilitation users who were financially strained as compared to 67% of those who were not. Telerehabilitation readiness was also modestly lower among rural rehabilitation users compared to urban dwelling older rehabilitation users (62% vs 67%).
Figure 1:
Overall population prevalence of telemedicine readiness from 2015 to 2020, drawn from National Health and Aging Trends Study (NHATS) data rounds 5 through 10, is depicted above. Blue bars indicate the overall weighted percentage of older rehabilitation users in the United States who were categorized as telemedicine ready. All prevalence values were estimated from survey weighted analysis of NHATS data, accounting for repeated observations of participants over time.
Table 1:
Sample Demographics
| Variable, n(%) | All, n=5274 | Likely Telerehabilitation Ready, n=2907 |
Likely Telerehabilitation Unready, n=2367 |
|---|---|---|---|
| Age 70-74 | 1206(22.9%) | 903(31.1%) | 303(12.8%) |
| Age 75-79 | 1401(26.6%) | 929(32.0%) | 472(19.94%) |
| Age 80-84 | 1190(22.6%) | 601(20.7%) | 589(24.9%) |
| Age 85-89 | 862(16.3%) | 339(11.7%) | 523(22.1%) |
| Age 90+ | 615(11.7%) | 135(2.6%) | 480(20.3%) |
| Female Sex | 3271(62.0%) | 1739(59.8%) | 1532(64.7%) |
| Married/Partnered | 2573(48.8%) | 1701(58.5%) | 872(36.8%) |
| Below High School Education | 848(16.3%) | 135(4.7%) | 713(30.8%) |
| Medicaid Eligible | 715(14.0%) | 155(5.4%) | 560(24.8%) |
| ≥1 ADL Disabilities | 2048(38.8%) | 644(22.1%) | 1404(59.3%) |
| Midwest Census Region | 1385(26.3%) | 743(25.6%) | 642(27.1%) |
| Northeast Census Region | 907(17.2%) | 498(17.1%) | 409(17.2%) |
| Southern Census Region | 1969(37.3%) | 1023(35.2%) | 946(40.0%) |
| Western Census Region | 1013(19.2%) | 643(12.2%) | 370(15.6%) |
| Urban Dwelling | 4311(81.7%) | 2408(82.8%) | 1903(90.4%) |
| Rural Dwelling | 963(18.3%) | 499(17.2%) | 464(19.6%) |
| Non-Hispanic White | 3901(74.0%) | 2467(84.9%) | 1434(60.6%) |
| Non-Hispanic Black | 930(17.6%) | 295(10.15%) | 635(26.8%) |
| Hispanic | 254(4.8%) | 61(2.1%) | 193(8.2%) |
| Other Race or Ethnicity | 189(3.6%) | 84(2.9%) | 105(4.4%) |
| Financial Strain1 | 327(6.3%) | 124(4.3%) | 203(8.8%) |
Data was missing for financial strain on 49 participants
Disparities in telerehabilitation readiness persisted in weighted models that further adjusted for age and sex(Figure 2). As compared to non-Hispanic White older adults, older adults identifying as members of minoritized race or ethnicity groups had significantly lower likelihood of readiness. Similarly, financially strained older adults had nearly threefold lower odds in telerehabilitation readiness as compared to those who were not financially strained, and rural dwelling older adults had a 25% lower odds of readiness as compared to urban dwelling peers. Similar significant patterns of telerehabilitation readiness across racial and ethnic identities, financial strain, and rurality were observed when applying the alternative definition of unreadiness (Supplemental Figures S1 & S2). Across all groups, the main driver of unreadiness was low technology ownership and proficiency versus sensory or cognitive impairments (Supplemental Figure S3)
Figure 2:
Odds ratios and 95% confidence intervals (CIs) are depicted. Rurality was defined by rural-urban continuum codes and dichotomized as rural or urban dwelling (reference group=urban). Financial strain represents those with documented difficulty meeting basic expenses such as rent, utilities, or food during annual surveys (reference=not financially strained). For race and ethnicity (reference=non-Hispanic White), those categorized as other due to small sample sizes included those identifying as American Indian, Asian, Native Hawaiian, Pacific Islander, Alaska Native, or those who identified as more than one race.
DISCUSSION
In this nationally representative cohort of older home and community-based rehabilitation users in the United States between 2015 and 2020, we found inequities in telerehabilitation readiness by racial and ethnic identity, socioeconomic disadvantage, and rurality. Two key aspects of our findings warrant comment. First, only 2 in 3 older rehabilitation users in the United States were considered ready to participate in video-based telerehabilitation visits, suggesting nearly 11 million rehabilitation episodes over the 6 years would not have been able to easily completed virtually. This is concerning, as telerehabilitation has been touted as a feasible non-inferior option for rehabilitation after common surgeries in older adults such as total joint arthroplasty17 and is being adopted or recommended as a standard care pathway in some clinical situations without clear equity considerations—potentially exacerbating disparities. Second, while substantial media attention has endorsed the benefits of telerehabilitation to bridge digital divides in rural communities18, rural dwelling older adults had only small differences in telerehabilitation readiness as compared to urban populations in our results. Disparities by race and socioeconomic status were substantially larger, suggesting these interventions focused on bridging the digital divide in rural areas alone may be insufficient to address telerehabilitation readiness in populations receiving home and community-based rehabilitation care.
Virtual delivery of medical services, including rehabilitation, has been increasing over the past decade, especially among older adults during the COVID-19 pandemic when the Center for Medicare and Medicaid services provided additional flexibilities for billing during the public health emergency.11,19,20 While virtual delivery of care has shown promising clinical efficacy for a wide range of conditions8,17,21,22, our results indicate that more than a third of older adults have risk factors for telerehabilitation unreadiness and millions of others would require substantial modifications or presence of a care partner to maintain safety (such as those with severe mobility impairments). While much of the focus related to delivery of telerehabilitation more broadly has been related to broadband internet access, the main driver of telerehabilitation unreadiness in our data was a lack of technology ownership and/or proficiency with use. This disparity, while known prior to 2020, became alarmingly apparent during the COVID-19 pandemic as communities and families often struggled to connect older adults with peers and family supports virtually to stave off loneliness and social isolation.23
Our results indicate that telerehabilitation access is clearly not equitable among older adults. Policy interventions to address disparities in access have largely focused on improving rural broadband access,24 but there has been a relative lack of policy urgency to address telemedicine access more broadly for the comparatively triple the number of urban dwelling older adults who are far more likely to additionally identify as a member of minoritized racial or ethnic group. Disparities among populations, such as Black older adults already less likely to receive traditional rehabilitation even after serious fall-related injuries25, may grow if telerehabilitation services expand without a clear strategy for ensuring equitable opportunities for adoption.
Our findings provide clear targets for future interventions. To date, interventions to improve technology ownership and proficiency are less commonly implemented, but a recent study showed trainings led by volunteer community members may improve comfort with engaging in medical visits virtually among older adults.26 However, equity was not addressed in this study. Embedding technology training in clinical practice and finding ways to provide older adults with low-cost or free technology to participate in care may be important first steps towards bridging the digital divides observed in our work.
This work has several key strengths and some modest limitations. First, this work leverages a unique nationally representative dataset with information on use of rehabilitation care, technology ownership, and sensory function among older adults. The estimates of telerehabilitation readiness, therefore, are representative of Medicare beneficiaries aged 65 and older in the United States.
Study Limitations
Our study only included those who received rehabilitation over the study period in a home or community-based setting—which may underestimate the true prevalence of telerehabilitation unreadiness. Our definition of readiness focused namely on video visits, the primary but certainly not singular way to deliver telerehabilitation services. Other supports, like phone calls, wearable devices or smart home technology, may have fewer barriers to adoption. Our definition of telerehabilitation readiness is intended for use as a population health measure to monitor population level characteristics and trends, and thus may not reflect an individual’s actual feeling regarding whether they are ready to participate in telerehabilitation. For example, some individuals with vision or hearing loss may have technology solutions or social supports that enhance their comfort with virtual technologies such as telerehabilitation. Moreover, this study does not address whether individuals may feel “ready” for telerehabilitation after an initial in-person rehabilitation visit. Our results, therefore, are intended to provide guidance to clinical practitioners and health systems as to which populations may be left behind if telerehabilitation is adopted as part of usual care practice and to implement proactive solutions to maximize equity of adoption. Also, the majority of our data reflect prepandemic trends (with inclusion of some 2020 participants who were surveyed during the early COVID-19 waves)—follow up work should determine if readiness and adoption rates of telerehabilitation continue to show similar disparity patterns. Last, small samples sizes precluded the evaluation of intersectionality in this work—for example, financial strain may have disproportionately greater impact on telerehabilitation readiness among older adults who also identify as Black or Hispanic, or those living in rural areas. This is an important area of investigation for future work.
CONCLUSION
In conclusion, telerehabilitation is a valuable service for older adults, provides access to critical rehabilitation services during public health emergencies27, and has tremendous potential for bridging care gaps. Prior work indicates telemedicine use generally increased during the COVID-19 pandemic, the majority of older adults in the United States still relied on in-person visits to receive medical care and uptake was lower among socially vulnerable populations.28 Our findings further indicate that widespread implementation of video-based telerehabilitation into clinical practice may widen disparities in care if equity is not centered in these models of care. Finding ways to support equitable wider adoption of telerehabilitation services among older adults would be a fruitful avenue for future investigations.
Supplementary Material
Funding Disclosure:
Dr. Falvey was supported during the work by the National Institute on Aging and the Maryland Claude D. Pepper Center (grant numbers K76AG074926 and P30AG028747). Dr. Miller’s time is supported in part by funding from the National Institutes of Health (grant numbers KL2TR001870 and P30AG044281). NHATS is sponsored by the National Institute on Aging (NIA; grant U01AG032947) through a cooperative agreement with the Johns Hopkins Bloomberg School of Public Health.
ABBREVIATIONS
- NHATS
National Health and Aging Trends Study
- ADL
Activities of Daily Living
- OR
Odds ratio
- CI
Confidence Interval
Footnotes
Conflicts of Interest:
Dr. Mullins reports consulting relationships with Bayer Healthcare, Rocket, and Takeda outside of the submitted work. The other authors have nothing to disclose.
Prior Presentation of Work:
Preliminary findings from this work were presented at the AcademyHealth Annual Research Meeting in Seattle, WA (June 2023).
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