Abstract
Objectives:
Telehealth can improve access to geriatric care, especially for vulnerable older adults facing transportation, mobility, and/or health-related barriers to in-person care. However, integrating telehealth into geriatric care requires consideration of the different residence types where older adults use telehealth. This study aimed to examine whether telehealth use among older adults using long-term services and supports (LTSS) differs by residence type, after adjusting for demographic and health-related factors, using data from the National Core Indicators-Aging and Disabilities (NCI-AD) Adult Consumer Survey (ACS).
Design:
Cross-sectional study.
Setting and Participants:
We used data from the 2021–22 NCI-AD ACS for LTSS users aged ≥65 years without intellectual or developmental disability.
Methods:
We compared the proportion of telehealth users across residence types (community-dwelling, residential care, and nursing home), adjusting for sociodemographic and health-related factors, using multivariable logistic regression with state random effects.
Results:
Of the 6,925 respondents in the analytic sample, 4,789 (69%) lived in the community, 1,381 (20%) resided in residential care, and 755 (11%) were in nursing homes. Telehealth use was reported by 39% of community-dwelling respondents, 34% in residential care, and 20% in nursing homes. The regression model revealed that individuals in residential care and nursing homes had significantly lower adjusted odds of telehealth use than their community-dwelling counterparts (residential care: OR, 0.80 [95% CI, 0.69–0.92]; nursing homes: OR, 0.37 [95% CI, 0.29–0.47]). Notably, nursing home residents had lower adjusted odds of telehealth use than those in residential care (OR, 0.46 [95% CI, 0.36–0.60]).
Conclusions and Implications:
Older LTSS users in residential care and nursing homes have significantly lower adjusted odds of using telehealth compared to those living in the community. These findings underscore the importance of considering residential context when evaluating telehealth access and delivery among older adults, particularly those using LTSS.
Keywords: telehealth, older adults, long-term services and supports, residence type, nursing homes, institutional care
Brief Summary:
Older adults in residential care and nursing facilities are less likely to use telehealth than those living in the community. Promoting telehealth in these settings may improve access to care.
Introduction
Telehealth—defined as the use of telecommunications technology to provide health care from a distance1—is a promising strategy for enhancing the accessibility of health care and long-term services for America’s aging population. Telehealth acceptance increased dramatically during the COVID-19 pandemic, leading to a surge in telehealth use, as well as technological innovations and reimbursement changes that continue to support its use.2–6 Concurrently, a growing literature shows that telehealth is affordable, feasible, and effective for a wide range of health care services for both younger and older adults.6–10 In particular, telehealth can improve healthcare access for vulnerable populations—including older adults who face transportation, mobility, and/or health-related barriers to in-person care.11–12 Notably, older adults themselves express interest in using telehealth. Recent data show that between one-half and two-thirds of older adults are interested in telehealth, depending on the service and/or appointment type.13–14
Integrating telehealth into geriatric care requires consideration of the different places older adults live and use telehealth services. Traditionally, telehealth is associated with home and community-based care; however, an estimated 15% of older Americans reside in residential and nursing homes.15 These individuals often have complex health and functional limitations that require evaluation and treatment by off-site specialists. Transporting residents to off-site health care appointments is costly, time-consuming, and cumbersome for both residents and staff/administrators.16–17 Telehealth can ameliorate these challenges, resulting in timely, efficient care that has been shown to lower healthcare costs and reduce hospitalizations among nursing home residents.18–19 Despite the perception that telehealth is primarily a tool for home- and community-based care, evidence demonstrates its value in nursing homes, where telehealth can improve access to specialists, enhance continuity of care, and support timely decision-making for acutely ill or medically complex residents.16, 18–19 In turn, equitable access to telehealth requires that telehealth be accessible not only in the community but also in residential and nursing homes.
As an initial step toward establishing equitable access to telehealth, it is essential to characterize telehealth use among older adults in different places of residence, focusing on the post-pandemic period when the most recent data are available. To address this need, we conducted an exploratory analysis to determine whether telehealth use among older adults using long-term services and supports (LTSS) differs by residence type, after adjusting for demographic and health characteristics, using data from the National Core Indicators-Aging and Disabilities (NCI-AD) Adult Consumer Survey (ACS). This exploratory analysis aims to contribute preliminary evidence on patterns of telehealth use by residence type, informing future research and policy efforts toward equitable, effective telehealth access across long-term care settings.
Methods
Data Source and Study Sample
This cross-sectional study used data from the 2021–2022 wave of the NCI-AD ACS, the most recent data available that captures telehealth use after the onset of the COVID-19 pandemic. This survey wave included approximately 14,000 respondents from 15 states. Each state administers the NCI-AD survey annually to a sample of at least 400 older adults and adults with disabilities using LTSS. Sampling and recruitment strategies vary by state, with samples typically drawn from Medicaid, state programs, and nursing homes, as long as sampling methods align with the requirements of the NCI-AD protocol.20 For the present analysis, we applied two additional inclusion criteria, limiting our sample to respondents ≥65 years without an intellectual or developmental disability who had a residence type listed as anything other than missing, other, or homeless/temporary shelter.
The NCI-AD ACS is a survey designed to assess and track the performance of publicly funded home and community-based services (HCBS) and other long-term services and supports for older adults and adults with disabilities. State participation in the NCI-AD ACS is voluntary. The survey is administered annually to a sample of state-funded HCBS users and nursing home residents. The NCI-AD ACS includes two sections: 1) background information, including demographics, health, and service-use data typically populated from state administrative records; and 2) a direct-conversation consumer survey which gathers person-centered data regarding the service user’s health, functional abilities, and care experiences. These surveys are conducted in-person or via secure videoconferencing or telephone depending on respondent preference or state protocol. Whenever possible, the service user completes the interview. However, if the individual is unable to respond (e.g., due to cognitive or communication challenges), a proxy familiar with the participant may answer a subset of questions that are considered observable and objective. Proxy use is determined at the time of administration based on interviewer assessment and participant preference.
Telehealth Use
Telehealth use, the primary dependent variable, was assessed through an interview question asking respondents if they had ever communicated with health professionals via videoconferencing or telehealth. The possible responses were “yes,” “no,” “don’t know,” or “unclear; refused; no response.” We classified all “yes” responses as telehealth users and all “no” responses as non-telehealth users. All other responses were classified as missing and excluded (n = 728).
Residence Type
Residence type, the predictor variable of primary interest, was obtained from the background information section of the NCI-AD ACS. Residence data were classified into the following response categories: “own or family house or apartment (owned or rented),” “assisted living facility or residential care facility,” “senior living,” “group home; adult family home; foster home; or host home,” “nursing facility/home,” “homeless or temporary shelter,” “other,” “don’t know,” “unclear; refused; no response,” or “unknown.” We classified those living in their own or family house/apartment as community-dwelling older adults and those residing in nursing homes as a separate category. Respondents with missing, other, or homeless/temporary shelter responses were excluded. All other responses were grouped into a category called residential care, which includes any non-nursing home congregate residential setting (e.g., assisted living).
Covariates
Based on the telehealth and healthcare literature,21–23 we included the following covariates: ZIP Code rural-urban commuting area code (RUCA), age, race/ethnicity, gender, number of known non-ADRD diagnoses, known diagnosis of Alzheimer’s disease and related dementias (ADRD), and response type (self vs. proxy). We classified ZIP Code RUCA into four categories: metropolitan, micropolitan, rural, and small town. Age was treated as a categorical variable split into the following ranges in years: 65–74, 75–84, and ≥85. Race/ethnicity was defined as Black or African American, Hispanic or Latino, White, and other or multiracial. The number of known non-ADRD diagnoses was grouped into three levels: zero diagnoses, one diagnosis, or more than one diagnosis. The following nine diagnoses were included in this count: traumatic or acquired brain injury, cancer, chronic obstructive pulmonary disease, stroke, diabetes, hypertension, heart disease, physical disability, and chronic psychiatric or mental health condition. Gender, ADRD diagnosis, and response type were coded using binary indicators: female vs. male, yes vs. no, and self vs. proxy response.
All covariates except response type were obtained from the NCI-AD ACS background information section, which is populated via state-reported administrative records, or by self-report. Administrative data include provider-documented diagnoses (e.g., ICD-10 diagnosis codes or I4200 or I4800 Minimum Data Set fields). For all categorical variables included in the regression model, the following reference categories were used: community-dwelling, metropolitan area, age 65–74 years, White race/ethnicity, female gender, no known non-ADRD diagnosis, no known ADRD diagnosis, and self-response.
Statistical Analysis
Descriptive statistics were used to characterize the analytic sample. We compared telehealth use between residence types using multivariable logistic regression on the complete-case subset, adjusting for the covariates described above and state-specific random effects. This approach controls for factors that could explain unadjusted differences in telehealth use by residence type. P < 0.05 was considered statistically significant.
All analyses were completed using R version 4.4.2 (R Project for Statistical Computing). This study was deemed exempt from review by the governing Institutional Review Board.
Results
Respondent Characteristics
Of the approximately 14,000 respondents in the full dataset, 7,083 were aged ≥65 years without intellectual or developmental disability. After excluding 158 individuals with missing residence type, the descriptive sample included 6,925 respondents, with 4,789 (69%) living in the community, 1,381 (20%) residing in residential care, and 755 (11%) living in nursing homes. Irrespective of residence type, the majority of respondents resided in metropolitan areas and were female. Racial composition, age, and health status differed across residence types. Those living in residential and nursing homes were more likely to be White, older, and have a known ADRD diagnosis than those living in the community. Table 1 displays the full characteristics of the analytic sample.
Table 1:
Sample characteristics and predictor frequencies from NCI-AD 2021–2022 for adults ages 65+ without intellectual or developmental disability conditional on residence type (n = 6,925)
| Characteristic | Community Dwelling, N = 4,7891 | Residential Care, N = 1,3811 | Nursing Home, N = 7551 |
|---|---|---|---|
| Telehealth Usage | 1,696 (39%) | 398 (34%) | 138 (20%) |
| Missing/Unknown | 459 | 194 | 75 |
| Zip Code RUCA | |||
| Metropolitan | 3,327 (71%) | 959 (73%) | 515 (69%) |
| Micropolitan | 701 (15%) | 184 (14%) | 101 (13%) |
| Rural | 279 (6.0%) | 75 (5.7%) | 38 (5.1%) |
| Small Town | 348 (7.5%) | 102 (7.7%) | 96 (13%) |
| Missing/Unknown | 134 | 61 | 5 |
| Gender | |||
| Female | 3,452 (72%) | 995 (73%) | 521 (69%) |
| Male | 1,326 (28%) | 377 (27%) | 233 (31%) |
| Missing/Unknown | 11 | 9 | 1 |
| Age (years) | |||
| 65 to 74 | 2,279 (48%) | 546 (40%) | 261 (35%) |
| 75 to 84 | 1,596 (33%) | 477 (35%) | 280 (37%) |
| 85 and older | 914 (19%) | 358 (26%) | 214 (28%) |
| Race/Ethnicity | |||
| White | 2,492 (54%) | 918 (71%) | 579 (80%) |
| Black | 1,072 (23%) | 200 (15%) | 98 (14%) |
| Hispanic/Latino | 493 (11%) | 54 (4.2%) | 7 (1.0%) |
| Other | 531 (12%) | 128 (9.8%) | 37 (5.1%) |
| Missing/Unknown | 201 | 81 | 34 |
| Number of Known Non-ADRD Diagnoses | |||
| Mean (SD) | 3.00 (1.64) | 2.79 (1.62) | 2.65 (1.60) |
| Median (Q1 - Q3) | 3.00 (2.00–4.00) | 3.00 (2.00–4.00) | 3.00 (1.00–4.00) |
| Number of Known Non-ADRD Diagnoses (categorical) | |||
| 0 | 241 (5.0%) | 104 (7.5%) | 74 (9.8%) |
| 1 | 704 (15%) | 219 (16%) | 117 (15%) |
| More than 1 | 3,844 (80%) | 1,058 (77%) | 564 (75%) |
| Known ADRD Diagnosis | 660 (14%) | 261 (19%) | 250 (33%) |
| Proxy Response | 984 (21%) | 310 (22%) | 249 (33%) |
n (%)
Note: Respondents are aged 65 years and older without intellectual or developmental disability with reported residence type, telehealth usage data, and not experiencing homelessness or living in a temporary shelter.
A higher proportion of community-dwelling respondents reported telehealth use (39%) compared to those living in residential care (34%) and nursing homes (20%). To examine whether these differences persisted after accounting for demographic and health-related factors, adjusted analyses (described below) were fit on the complete-case subset (n = 5,854). Covariate missingness ranged from 0.3% to 4.6%. In total, 85% of respondents had complete cases and were included in the logistic regression model.
Association of Telehealth Use with Residence Type
The complete-case regression sample included 5,854 respondents after excluding 1,071 individuals due to missing telehealth or covariate data. Table 2 presents the pairwise residence type contrasts for the adjusted odds ratio of telehealth use vs. no telehealth use from the complete-case logistic regression model. After adjusting for all other covariates, we found that respondents living in residential care and nursing homes had significantly lower odds of using telehealth compared to those living in the community (Residential Care vs. Community Dwelling: OR = 0.80 [95% CI: 0.69–0.92]; Nursing Home vs. Community Dwelling: OR = 0.37 [95% CI: 0.29–0.47]; Table 2). Additionally, when comparing nursing homes to residential care, we found that those living in nursing homes had significantly lower adjusted odds of using telehealth than their counterparts in residential care (OR = 0.46 [95% CI: 0.36–0.60]; Table 2).
Table 2:
Residence type contrasts from the fully adjusted complete-case logistic regression model (n = 5,854). Values are odds ratios (OR) and 95% confidence intervals (CI) for telehealth use versus the reference level of no telehealth use. For each contrast, the first residence type is the comparison group and the second is the reference group. P-values have not been adjusted for multiple comparisons.
| Contrast (comparison - reference) | OR1 |
|---|---|
| Residential Care - Community Dwelling | 0.80 (95% CI: 0.69–0.92)** |
| Nursing Home - Community Dwelling | 0.37 (95% CI: 0.29–0.47)*** |
| Nursing Home - Residential Care | 0.46 (95% CI: 0.36–0.60)*** |
*p<0.05;
p<0.01;
p<0.001
Note: Results are adjusted for the following covariates: ZIP Code rural-urban commuting area code (RUCA), age, race/ethnicity, gender, known diagnosis of Alzheimer disease and related dementias (ADRD), number of known non-ADRD diagnoses, and response type (self vs. proxy).
Table 3 displays estimated odds ratios for all covariates in the fully adjusted regression model. Several significant associations emerged. Adjusting for all covariates, the following characteristics were associated with lower odds of using telehealth: non-metropolitan ZIP Code RUCA (micropolitan and small town), male gender, and older age. The presence of more than one known non-ADRD diagnosis was associated with higher odds of telehealth use. Black LTSS users had lower odds of using telehealth compared to White users, while the odds of telehealth use among Hispanic or Latino LTSS users did not differ significantly from White users. We found no significant association of a known ADRD diagnosis or response type (self vs. proxy) with telehealth use.
Table 3:
Logistic regression model for associations between telehealth use and relevant covariates (n = 5,854). Values are odds ratios (OR) and 95% confidence intervals (CI) for telehealth use versus the reference level of no telehealth use.
| Characteristic | OR1,2 |
|---|---|
| Residence Type | |
| Community Dwelling | — |
| Residential Care | 0.80 (95% CI: 0.69–0.92)** |
| Nursing Home | 0.37 (95% CI: 0.29–0.47)*** |
| Zip Code RUCA | |
| Metropolitan | — |
| Micropolitan | 0.84 (95% CI: 0.71–0.99)* |
| Rural | 0.78 (95% CI: 0.61–1.01) |
| Small Town | 0.66 (95% CI: 0.53–0.83)*** |
| Gender | |
| Female | — |
| Male | 0.85 (95% CI: 0.75–0.96)** |
| Age (years) | |
| 65 to 74 | — |
| 75 to 84 | 0.67 (95% CI: 0.59–0.76)*** |
| 85 and older | 0.51 (95% CI: 0.43–0.60)*** |
| Race/Ethnicity | |
| White | — |
| Black | 0.80 (95% CI: 0.69–0.94)** |
| Hispanic/Latino | 0.88 (95% CI: 0.69–1.13) |
| Other | 1.02 (95% CI: 0.84–1.22) |
| Number of Known Non-ADRD Diagnoses (categorical) | |
| 0 | — |
| 1 | 1.12 (95% CI: 0.83–1.52) |
| More than 1 | 1.47 (95% CI: 1.12–1.93)** |
| Known ADRD Diagnosis | |
| No | — |
| Yes | 1.16 (95% CI: 0.99–1.37) |
| Proxy Response | |
| No | — |
| Yes | 1.08 (95% CI: 0.92–1.25) |
*p<0.05;
p<0.01;
p<0.001
OR = Odds Ratio
Discussion
Our results show that older LTSS users in residential care and nursing homes have significantly lower odds of using telehealth than their community-dwelling counterparts (20% and 63% lower adjusted odds, respectively). Individuals in nursing homes had the lowest telehealth use, with 54% lower adjusted odds compared to those in residential care. The confidence intervals for these estimates, reported in Table 3, were relatively narrow, indicating that the adjusted associations by residence were estimated with moderate precision. Importantly, our model adjusted for a range of sociodemographic and health-related variables that might otherwise account for observed unadjusted differences in telehealth use by residence type, suggesting a unique association of residence type with telehealth use in our sample.
While telehealth offers a promising strategy for improving access to care—particularly for populations with mobility limitations or limited local providers11–12—our findings highlight notable differences in telehealth use by place of residence. Across our sample, unadjusted telehealth use was modest: only 32% of respondents reported any telehealth use, with telehealth use highest among community-dwelling older adults (39%), followed by those in residential care (34%), and lowest among nursing home residents (20%). These residence-type differences may reflect variation in infrastructure, staffing, and resident autonomy across settings. Older adults in residential care or nursing homes may rely on staff to determine whether and when telehealth is used, with limited ability to initiate or independently participate in virtual care.24 Organizational workflows, competing staff demands, and the availability of on-site providers may further constrain use.24–25 Nevertheless, telehealth can offer substantial benefits in congregate care settings, especially in expanding access to specialty care. Prior studies have shown that telehealth can improve care outcomes for nursing home residents in areas such as dementia (including neuropsychiatric symptoms), urinary incontinence, wound care, and cardiovascular risk monitoring by enabling more timely, cost-effective care that helps prevent avoidable hospitalizations and complications.16,26–28 Thus, even if the structure and care context of residential or institutional care differs from those in the community, telehealth may still have an important role to play.
Our preliminary findings of relatively low telehealth use in nursing homes among older LTSS users suggest a potentially unrealized and underexplored opportunity to expand telehealth in these settings to improve care coordination, increase access to specialty care, and support better resident health outcomes. While ongoing research continues to address barriers to telehealth use among older adults,29–30 future work should examine how residence type shapes telehealth access, delivery, and effectiveness. Identifying setting-specific determinants—such as staffing models, technology infrastructure, care complexity, and resident autonomy—will be critical for better understanding when, in what settings, and for whom telehealth is most appropriate. Such work is essential for identifying and addressing potential disparities in telehealth use across the LTSS continuum and informing policies and interventions that promote equitable access for all older adults who can benefit from telehealth.
Limitations
Several limitations of this preliminary exploratory study should be noted. First, the NCI-AD ACS dataset relies on self-reported lifetime telehealth use, which may be subject to recall bias and limits insight into the timing or recency of telehealth use. Second, the dataset does not include detailed telehealth use data, such as the type of service(s) used, frequency of use, satisfaction levels, reasons for non-use, and intention to continue using telehealth. Third, although we adjusted for health diagnoses, we could not determine whether respondents’ care needs were appropriate for telehealth, whether telehealth was offered, or whether needs were already met by on-site or in-person providers, particularly in residential and nursing home settings. Fourth, state participation in the NCI-AD program is voluntary, and each state has its own service structure with varying eligibility criteria. States also choose their sampling and recruitment procedures, provided they meet NCI-AD protocol requirements,20 introducing additional variability. These factors may limit generalizability beyond the populations served and sampled by participating states. Finally, the findings are cross-sectional and should be interpreted as exploratory and associative rather than causal.
Conclusions and Implications
Using cross-sectional survey data from the NCI-AD ACS, we identified a significant association of residence type and telehealth use among older adults who use LTSS. Individuals in residential care and nursing homes had substantially lower odds of using telehealth compared to community-dwelling older adults. Of note, nursing home residents had markedly lower odds of telehealth use compared to those in residential care. These findings highlight the importance of considering residential context when evaluating telehealth access and delivery among older adults using LTSS. Future research should clarify the conditions under which telehealth is most accessible and effective in different care environments to inform tailored strategies for promoting equitable telehealth access for all older adults.
Acknowledgments
We thank Stephanie Giordano and Nilufer Isvan of Human Services Research Institute for their partnership, support, and feedback on this manuscript, and Wyatt Tarter for providing valuable statistical assistance.
Funding
This work was supported by the National Institute on Aging of the National Institutes of Health under Grant [1R01AG069771/1R01AG060871]. All content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Conflict of Interest Disclosure
Eric Jutkowitz is a co-founder and on the board of directors of Plans4Care, Inc., a digital health company that provides personalized dementia care on-demand.
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