Connected Care in a Digital World: Engagement and Telehealth Use in Older Cancer Survivors

Abstract

Introduction

As the number of older cancer survivors (OCS) grows in number, telehealth can enhance their healthcare access, yet uneven digital engagement may exacerbate existing gaps in survivorship care. We aimed to describe digital technology and telehealth use among OCS and assess how sociodemographic and psychosocial factors are related to digital health and telehealth use.

Materials and Methods

We analyzed the Health Information National Trends Survey 7 (HINTS 7; March–September 2024) respondents aged ≥60 years (N = 691; analytic N = 638). Survey-weighted logistic regression models with multiple imputation were used to evaluate relationships between sociodemographic and psychosocial factors and digital engagement and telehealth use.

Results

Digital engagement was common but variable among OCS. Most reported frequent internet use (81.5%; n = 571) and smartphone use (78.9%; n = 545), while fewer used tablets (41.2%; n = 285) or participated in telehealth visits in the past year (33.1%; n = 229). However, 63.7% (n = 440) expressed willingness to use telehealth in the future if it were offered.

Compared with ≥$100k, income $50k–$99k and <$50k were associated with lower odds of broadband access (aOR 0.28; 95%CI: 0.11–0.75 and aOR 0.38; 95%CI: 0.17–0.86), and viewing test results online (aOR 0.20; 95%CI: 0.08–0.51 and aOR 0.14; 95% CI: 0.05–0.38). Lower education was associated with reduced desktop/laptop use (aOR 0.30; 95%CI: 0.11–0.79 and aOR 0.24; 95%CI: 0.08–0.71) and provider messaging (aOR 0.34; 95%CI: 0.18–0.64). Black OCS had lower odds of broadband access (aOR 0.43; 95%CI: 0.20–0.95) but higher odds of cellular internet use (aOR 2.70; 95%CI: 1.21–6.05). Depression/anxiety was associated with lower odds of viewing test results (aOR 0.40; 95%CI: 0.19–0.84) but higher odds of technology frustration (aOR 4.44; 95%CI: 1.79–11.03). Female participants had higher odds of willingness to use telehealth (aOR 2.15; 95% CI: 1.19–3.90).

Discussion

Digital engagement among OCS is widespread but uneven, with persistent gaps by income, race, and education. Addressing gaps in access, connectivity, and digital literacy will be essential to ensure improved telehealth-enabled survivorship care.

1. Introduction

As of 2025, there were an estimated 18.6 million people in the United States (U.S.) living with a history of cancer, with 79% ≥ 60 years.¹ The increase in the number of cancer survivors due to advances in early detection and treatment also necessitate the availability and accessibility of supportive care services responsive to the needs of an aging population.² This is particularly critical as the U.S. cancer survivor population is projected to reach 22 million by 2035, coinciding with a historic demographic shift in which older adults will outnumber children.^1,3,4 Especially post-COVID, digital health technologies, including telehealth, have rapidly proliferated, increasing access health information and care, and emerging as promising solutions to bridge care gaps, particularly for older adults who may face mobility limitations, geographic barriers, or complex care needs.^5,6

While promising, the adoption of digital health by older cancer survivors (OCS) face substantial challenges,^7,8 including limited digital literacy, inconsistent access to devices or high-speed internet, and varying levels of patient readiness, which all impede effective implementation.^9,10 Whereas prior studies have examined general patterns of telehealth use among older adults and patients with cancer, there is a dearth of literature focused on digital healthcare among OCS, as well as how sociodemographic (income, education, race and ethnicity), psychosocial (mental health and loneliness) and lifestyle (exercise and sleep) factors intersect with digital health engagement and telehealth use in this population.^11,12

Moreover, the accelerated expansion of telemedicine during and after the COVID-19 pandemic has raised critical questions about its evenhanded adoption and sustainability among OCS.^13–15. While telehealth use increased substantially during the pandemic, relatively few studies have focused specifically on post-COVID telemedicine use among OCS, particularly with respect to understanding persistent digital barriers and readiness for ongoing telehealth-based survivorship care.^13,16 Existing work has largely emphasized feasibility, acceptability, or provider experiences, with limited population-level evidence describing how older cancer survivors engage with digital technologies once emergency pandemic policies subsided.^17–21

We aimed to: (1) describe digital technology and telehealth use among OCS in the U.S. post COVID-19 pandemic, and (2) assess how sociodemographic, psychosocial, and lifestyle factors are related to digital health and telehealth use among OCS.

2. Methods

2.1. Study Design and Data Source

We utilized data from the Health Information National Trends Survey 7 (HINTS 7), a nationally representative, cross-sectional survey administered by the National Cancer Institute, based on a two-stage stratified sampling design.²² HINTS 7 data collection was conducted between March 25 and September 16, 2024, using a self-administered, mail-based survey with a push-to-web option to encourage online participation. The survey aimed to assess how U.S. adults’ access and use health information, including digital and telehealth tools.

2.2. Sampling and Participants

For this study, we focused on a subset of respondents aged ≥60 years who self-identified as cancer survivors. We further defined our cohort of OCS to those with solid tumors such as brain, breast, prostate, bladder, renal, liver, esophageal, lung and head and neck cancers, and excluded hematologic malignancies due to fundamental differences in treatment trajectories, survivorship care, and psychosocial burden.^23–25 A total of 691 participants were identified.

2.3. Outcomes, Exposures and Potential Confounders

Outcomes of interest captured multiple domains of digital engagement and telehealth use among OCS. These included measures of technology access and use (e.g., use of desktop/laptop computers, smartphones, tablets, and wearable devices), internet engagement and connectivity (e.g., frequent internet use, broadband or cellular access, satisfaction with internet connection), digital literacy and usability (e.g., ability to perform online health searches, ability to use videoconferencing platforms without assistance, perceived frustration with new technology), and healthcare-related digital behaviors (e.g., viewing test results, sending secure messages to healthcare providers, making appointments online, telehealth utilization in the past 12 months, and willingness to use telehealth in the future).

Primary exposures of interest included sociodemographic characteristics, specifically income, educational attainment, and race, selected a priori based on prior literature demonstrating their relation to digital technology and telehealth use.^16,26–35 Income was categorized as <$50,000, $50,000–$99,999, and ≥$100,000, and education was grouped into “college or vocational training”, “some college (no degree)” and “high school or less.” Race was analyzed as White and Black due to sample size considerations. These variables were treated as the main predictors in all regression models.

Potential confounders were selected based on theoretical relevance and prior evidence linking them to both socioeconomic position and digital health engagement.^{7,9,12,17,26,30,36–38} These included sex, and marital status, as well as health and psychosocial factors, including self-reported general health status (good or better vs fair/poor), psychological distress measured using the Patient Health Questionnaire-4 (PHQ-4), and loneliness assessed using a cumulative loneliness scale. Lifestyle factors, including exercise frequency and sleep duration, were also considered as potential confounders given their relation to both digital engagement behaviors and overall health status.^11,39–43

All variables were derived from the HINTS 7 questionnaire. Psychosocial measures were operationalized using validated scales, including the PHQ-4 for depression and anxiety symptoms and a multi-item loneliness scale, both of which were categorized based on established thresholds.

2.4. Mental Health Assessment

In the HINTS 7 survey, psychological distress was measured using the Patient Health Questionnaire-4 (PHQ-4), consisting of two depression items and two anxiety items.^44–46 Each item was scored on a 4-point Likert scale (0 = Not at all to 3 = Nearly every day), with a total score range of 0–12. Participants were classified as experiencing moderate to severe symptoms of distress if their PHQ-4 score exceeded 2. Subscale scores were calculated for depression and anxiety, with thresholds of ≥3 indicating moderate to severe symptoms.

Loneliness was measured using a four-item cumulative scale to assess perceived social isolation. Items were scored from 0 (Never) to 4 (Always), yielding a composite score range of 0–16. Participants were categorized as “not lonely” (<6) or “moderately to severely lonely” (≥6), based on prior literature and distributional characteristics of the data.^47,48 Psychosocial scales (PHQ-4 and loneliness) were constructed using HINTS-consistent scoring, whereby item-level missing or invalid responses(coded either −5, −7 or −9) resulted in the overall scale being set to missing, avoiding misclassification of symptom burden.

2.5. Handling of Missing Data

Missing, inapplicable, and invalid responses (e.g., “Not ascertained,” “Inapplicable,” or “Answered in error”) were standardized and recoded as missing. Variables with more than 40% missingness were excluded from imputation and subsequent analyses.

Multiple imputation was performed using an iterative chained equations approach (IterativeImputer with Bayesian ridge regression), generating five imputed datasets (m = 5). All variables included in the analytic models were incorporated into the imputation procedure to preserve underlying joint distributions. Categorical variables were encoded prior to imputation and subsequently restored to their original formats, while continuous variables were imputed on their native scales.

All regression analyses were conducted separately within each imputed dataset. Estimates from these models were subsequently combined using Rubin’s rules to account for both within and between imputation variability, ensuring valid statistical inference while retaining the full multiple imputation framework.

2.6. Statistical Analysis

Prior to analysis, categorical variables were recoded for consistency and interpretability. Age was categorized to 60–69, 70–79 and ≥80 years. Education was categorized into three levels: college or vocational training, some college (no degree), and high school graduate or less. Annual household income was categorized as <$50,000, $50,000–$99,999, and ≥$100,000. Alternative categorizations were explored in sensitivity analyses (Supplementary Methods & Results; Table S1 and Table S2). To reduce imbalance and instability in estimates, participants missing race or those identifying as multiracial or “other” were excluded from regression analyses, resulting in a final analytic sample of 638 older cancer survivors.

Descriptive statistics were used to summarize participant characteristics and patterns of technology and telehealth use. Survey-weighted logistic regression models were used to evaluate relationships between sociodemographic factors and outcomes including internet access, digital literacy, device use, and telehealth engagement. Models included main effects and interaction terms to examine intersectional relationships across race, income, education, and sex (including two-way and three-way interactions). White race and male sex served as reference categories to ensure stable comparisons. White race and male sex served as the reference groups for race and sex-at-birth categories to ensure stable estimates, as these groups had the largest sample sizes.

All regression analyses incorporated HINTS 7 full-sample survey weights and 50 replicate weights to account for the complex, stratified sampling design and to produce nationally representative estimates of OCS in the United States. To preserve valid variance estimation, a domain (subpopulation) analytic approach was implemented, whereby replicate weights were applied to the full survey design while restricting inference to the analytic subpopulation. Variance estimation was performed using replicate survey weights based on a jackknife replication approach; consistent with HINTS analytic recommendations and standard practices for variance estimation in complex survey designs using replicate weights.^49,50 Regression models were fit separately across each imputed dataset, and parameter estimates were pooled using Rubin’s rules to obtain final adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Final parameter estimates (aORs and 95% CIs) reflect the combined weighted estimates and replicate-based variance estimates, ensuring appropriate inference under the complex survey design.

Age was initially considered as a potential covariate but was not included in the primary adjusted models as the cohort was already age-restricted and inclusion of age categories substantially increased model complexity relative to the available sample size. However, we did run an age sensitivity analysis, which we report in the Results.

To address potential instability arising from sparse data and high-order interaction terms, ridge-penalized logistic regression models were used as a sensitivity approach when standard models failed to converge or produced extreme estimates. These penalized models were used to stabilize coefficient estimation and incorporated replicate weight variance estimation. Results exhibiting extreme or non-informative confidence intervals were classified as unstable and were not reported (see Supplementary Methods for more details).

All analyses were performed in Python (v3.12), using Pandas (v2.2.2), NumPy (v2.0.2), Scikit-learn (v1.6.1), Statsmodels (v0.14.5) libraries.

3. Results

3.1. Sociodemographic and Health Characteristics

Among 691 OCS (Table 1), 219 (31.7%) were age 60–69, 308 (44.6%) were 70–79 and 164 (23.7%) were ≥80. With regards to sex, just over half were female (53.0%; n=366), while 46.7% were male. Most participants identified as White (77.7%; n=537), followed by Black (14.6%), with 5.4% reporting other or multiple races. The majority were non-Hispanic (92.2%). Marital status was evenly distributed, with 50.9% married and 47.9% single.

Table 1. Patient Demographics of Older Cancer Survivors (N = 691).

This table presents descriptive characteristics of older cancer survivors included in the analysis. Variables include sociodemographic factors (sex, marital status, race, ethnicity, education, and income), general health status, mental health (PHQ-4 depression/anxiety classification), loneliness, physical activity (weekly minutes and number of days of moderate exercise), and sleep patterns. Frequencies and percentages reflect unweighted counts from the analytic sample. Chi-square statistics assess differences across categories.

Patient Demographics	Frequency (%)	χ 2	p-value
Age
60–69	219 (31.7%)	45.85	<0.001
70–79	308 (44.6%)
>80	164 (23.7%)
Sex Distribution
Female	366 (53.0%)	343.54	<0.001
Male	323 (46.7%)
Missing	2 (0.3%)
Marital Status
Married	353 (50.9%)	0.65	0.42
Single	338 (47.9%)
Missing	8 (1.2%)
Race
White	537 (77.7%)	1046.74	<0.001
Black	101 (14.6%)
Other or Multiple	37 (5.4%)
Missing	16 (2.3%)
Ethnicity
Not Hispanic only	637 (92.2%)	861.54	<0.001
Hispanic	54 (7.8%)
Missing	43 (6.2%)
Education
College or Vocational graduate	368 (53.3%)	391.54	<0.001
Some College (no degree)	166 (24.0%)
High school only or less	157 (22.7%)
Missing	2 (0.3%)
Income
$100,000 or more	177 (25.6%)	81.26	<0.001
$50,000 to $99,999	172 (24.9%)
Less than $50,000	342 (49.5%)
General Health
Good or Better	502 (72.6%)	145.14	<0.001
Fair or Poor	186 (26.9%)
Missing	3 (0.4%)
PHQ-4
Not Endorsed	526 (76.1%)	213.43	<0.001
Depressed or Anxious	147 (21.3%)
Missing	18 (2.6%)
Loneliness
Not Endorsed	526 (76.1%)	219.27	<0.001
Lonely	143 (20.7%)
Missing	22 (3.2%)
Exercise Assessment
150 min or more	667 (96.5%)	584.40	<0.001
Below 150 min	24 (3.5%)
Missing	14 (2.0%)
Moderate Exercise
3 days or more	362 (52.4%)	134.54	<0.001
Less than 3 days	112 (15.9%)
Missing	219 (31.7%)
Sleep Assessment
7–9 hours	433 (62.7%)	54.05	<0.001
Abnormal sleep	242 (35.0%)
Missing	16 (2.3%)

More than half had completed college or vocational training (53.3%; n=368), with 24.0% reporting some college and 22.7% reporting a high school education or less. Income distribution was heterogeneous, with 25.6% reporting ≥$100,000, 24.9% reporting $50,000–$99,999, and nearly half (49.5%) reporting incomes below $50,000. Most participants reported good or better general health (72.6%), while 21.3% endorsed symptoms of depression or anxiety and 20.7% reported loneliness (Table 1).

Digital technology use was widespread but variable (Table 2). Most participants reported frequent internet use (82.6%; n=571) and use of smartphones (78.9%; n=545) and computers (70.6%; n=488), whereas fewer reported using tablets (41.2%; n=285) or smartwatches (19.8%; n=137). Use of digital health tools was common, including searching for health information online (73.7%; n=509) and viewing test results (67.4%; n=466), although fewer participants reported engaging in interactive functions such as messaging providers (56.7%; n=392) or making appointments online (48.8%; n=337). Telehealth engagement was more limited, with 33.1% (n=229) reporting a telehealth visit in the past 12 months, while a larger proportion (63.7%; n=440) expressed willingness to use telehealth in the future if it were offered.

Table 2. Patterns of Digital Technology Use and Telehealth Engagement Among Older Cancer Survivors.

This table summarizes the prevalence of digital technology access, device use, digital literacy, and telehealth engagement among older cancer survivors. Frequencies and percentages are presented for internet use, device ownership, digital health activities, and telehealth-related measures. Chi-square statistics assess differences across categories.

Digital Technology and Telehealth Use	Frequency (%)	χ2	p-value
Used Computer
Yes	488 (70.6%)	117.55	<0.001
No	203 (29.4%)
Used Tablet
Yes	285 (41.2%)	21.19	<0.001
No	406 (58.8%)
Used Smartphone
Yes	545 (78.9%)	230.39	<0.001
No	146 (21.1%)
Used Smartwatch
Yes	137 (19.8%)	251.65	<0.001
No	554 (80.2%)
Used Wearable for Health
Yes	178 (25.8%)	162.41	<0.001
No	513 (74.2%)
Used Health/Wellness Apps
Yes	287 (41.5%)	19.81	<0.001
No	404 (58.5%)
Internet via DSL/Cable/FiOS/Wifi/Satellite
Yes	520 (75.3%)	176.27	<0.001
No	171 (24.7%)
Internet via Cellular Network
Yes	397 (57.5%)	15.35	<0.001
No	294 (42.5%)
Internet Connection Satisfaction
Yes	563 (81.5%)	426.94	<0.001
No	51 (7.4%)
Missing	77 (11.1%)
Frequent Internet Use
Yes	571 (82.6%)	304.89	<0.001
No	114 (16.5%)
Missing	6 (0.9%)
Used Internet to Search for Health Info
Yes	509 (73.7%)	154.75	<0.001
No	182 (26.3%)
Found New Technology Frustrating
Yes	473 (68.5%)	102.19	<0.001
No	209 (30.2%)
Missing	9 (1.3%)
Can Use Technology Without Help
Yes	384 (55.6%)	13.11	<0.001
No	290 (42.0%)
Missing	17 (2.5%)
Has Health Search Skills
Yes	507 (73.4%)	170.25	<0.001
No	168 (24.3%)
Missing	16 (2.3%)
Willing to Use Telehealth in the Future if recommended
Yes	440 (63.7%)	74.9	<0.001
No	218 (31.5%)
Missing	33 (4.8%)
Received Telehealth Care in past 12 months
Yes	229 (33.1%)	73.02	<0.001
No	452 (65.4%)
Missing	10 (1.4%)
Messaged Doctor Online
Yes	392 (56.7%)	12.52	<0.001
No	299 (43.3%)
Viewed Test Results Online
Yes	466 (67.4%)	84.05	<0.001
No	225 (32.6%)
Made Appointments Online
Yes	337 (48.8%)	0.42	0.518
No	354 (51.2%)
Telehealth recommended by healthcare provider
No	691 (100.0%)	0	1
Used Telehealth to avoid exposure
Yes	29 (4.2%)	120.22	<0.001
No	192 (27.8%)
Missing	470 (68.0%)
Used Telehealth because it is convenient
Yes	102 (14.8%)	1.31	0.25
No	119 (17.2%)
Missing	470 (68.0%)
Used Telehealth to see a Healthcare Provider outside my area
Yes	11 (1.6%)	179.19	<0.001
No	210 (30.4%)
Missing	470 (68.0%)
Used Telehealth in order to Include family or other caregivers in my visit
Yes	12 (1.7%)	175.61	<0.001
No	209 (30.2%)
Missing	470 (68.0%)
Used Telehealth for another reason
Yes	12 (1.7%)	175.61	<0.001
No	209 (30.2%)
Missing	470 (68.0%)

3.2. Logistic Regression Analyses

Compared with individuals earning ≥$100,000 annually, those with incomes <$50,000 had lower odds of using computers (aOR 0.32; 95% CI: 0.13–0.77), tablets (aOR 0.36; 95% CI: 0.18–0.72), and smartwatches (aOR 0.24; 95% CI: 0.11–0.51) (Table 3). Compared with college graduates, individuals with a high school education or less also had lower odds of computer use (aOR 0.24; 95% CI: 0.08–0.71) (Table 3). OCS reporting symptoms of depression or anxiety likewise had lower odds of computer use (aOR 0.38; 95% CI: 0.18–0.81) (Table 3). Moreover, female OCS had higher odds of using wearable devices than male OCS (aOR 1.99; 95% CI: 1.06–3.73) (Table 4).

Table 3: Logistic Regression Results for Use of Computers, Tablets, Smart Phones or Smart watches.

This table presents adjusted odds ratios (aOR), 95% confidence intervals (CI), and p-values from logistic regression models examining the association between demographic characteristics and whether in the last 12 months, older cancer survivors: (1) used a desktop or laptop, (2) used a tablet, (3) used a tablet, and/or (4) used a smartwatch (e.g. Fitbit, Apple Watch). Each model includes sex, marital status, race, education, income, general reported health, mental health, exercise, and sleep assessments as predictors. Odds ratios greater than 1 indicate higher odds of the outcome relative to the reference group; odds ratios less than 1 indicate lower odds. Statistically significant associations (p < 0.05) are shown in bold.

	Used Desktop or Laptop last 12 months				Used Smartphone last 12 months				Used Tablet last 12 months				Used smart watch to track activity last 12 months
Characteristic	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value
Sex
Male		1				1				1				1
Female	0.71	0.3	1.67	0.44	1.85	0.8	4.31	0.15	1.99	0.96	4.13	0.07	0.97	0.42	2.2	0.94
Marital Status
Married		1				1				1				1
Single	0.67	0.33	1.37	0.27	0.3	0.15	0.6	0.001	1.1	0.55	2.21	0.79	0.87	0.29	2.56	0.80
Race
White		1				1				1				1
Black	0.55	0.16	1.89	0.35	1.23	0.6	2.54	0.57	0.66	0.23	1.89	0.43	1.69	0.38	7.58	0.50
Education
College or Vocational graduate		1				1				1				1
Some College (no degree)	0.3	0.11	0.79	0.02	0.38	0.15	0.96	0.04	0.67	0.31	1.45	0.31	0.69	0.28	1.73	0.43
High School only or less	0.24	0.08	0.71	0.01	0.66	0.24	1.85	0.43	1.43	0.68	3.01	0.35	0.91	0.31	2.62	0.86
Income
$100,000 or more		1				1				1				1
$50,000 to $99,999	0.52	0.18	1.55	0.24	0.82	0.33	2.03	0.67	0.73	0.35	1.55	0.42	0.57	0.32	1.02	0.06
Less than $50,000	0.32	0.13	0.77	0.01	0.65	0.23	1.85	0.42	0.36	0.18	0.72	0.004	0.24	0.11	0.51	<0.001
General Health
Good or Better		1				1				1				1
Fair or Poor	1.2	0.61	2.35	0.60	1.21	0.48	3.05	0.68	0.6	0.28	1.29	0.19	0.32	0.1	1.05	0.06
PHQ-4
Not endorsed		1				1				1				1
Depressed or Anxious	0.38	0.18	0.81	0.01	0.73	0.32	1.65	0.45	1	0.38	2.61	>0.99	0.58	0.21	1.62	0.30
Loneliness
Not endorsed		1				1				1				1
Lonely	1.03	0.42	2.55	0.94	1.59	0.57	4.43	0.37	0.8	0.33	1.95	0.62	0.8	0.35	1.83	0.59
Exercise Assessment
150 min or more		1				1				1				1
Below 150 min	0.72	0.11	4.6	0.73	0.88	0.14	5.64	0.90	1.13	0.2	6.42	0.89	Unstable	Unstable	Unstable	Unstable
Moderate Exercise
3 days or more		1				1				1				1
Less than 3 days	0.81	0.37	1.79	0.60	0.52	0.24	1.14	0.11	0.67	0.25	1.81	0.43	0.98	0.34	2.83	0.97
Sleep Assessment
7–9 hours		1				1				1				1
Abnormal sleep	0.83	0.38	1.85	0.66	1.04	0.43	2.5	0.93	0.59	0.23	1.51	0.27	0.48	0.19	1.2	0.12

Table 4. Logistic Regression Results of Electronic Wearable and Health Apps.

This table presents adjusted odds ratios (aOR), 95% confidence intervals (CI), and p-values from logistic regression models examining the association between demographic characteristics and whether in the last 12 months, older cancer survivors: (1) used an electronic wearable device to monitor or track personal health, and/or (2) used health or wellness apps on one’s tablet or smartphone. Each model includes sex, marital status, race, education, income, general reported health, mental health, exercise, and sleep assessments as predictors. Odds ratios greater than 1 indicate higher odds of the outcome relative to the reference group; odds ratios less than 1 indicate lower odds. Statistically significant associations (p < 0.05) are shown in bold.

	Used electronic wearable device to monitor or track personal health				Used Health or Wellness Apps on your tablet or smartphone
Characteristic	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value
Sex
Male		1				1
Female	1.99	1.06	3.73	0.03	0.94	0.51	1.73	0.85
Marital Status
Married		1				1
Single	0.41	0.19	0.88	0.02	0.6	0.25	1.45	0.26
Race
White		1				1
Black	2.02	0.73	5.57	0.18	1.24	0.35	4.41	0.74
Education
College or Vocational graduate		1				1
Some College (no degree)	0.64	0.3	1.35	0.24	0.7	0.37	1.33	0.28
High School only or less	0.72	0.33	1.54	0.40	1.43	0.65	3.14	0.38
Income
$100,000 or more		1				1
$50,000 to $99,999	0.87	0.44	1.74	0.69	0.58	0.3	1.14	0.12
Less than $50,000	0.66	0.28	1.53	0.33	0.44	0.19	1.03	0.06
General Health
Good or Better		1				1
Fair or Poor	0.89	0.36	2.16	0.79	0.68	0.29	1.63	0.39
PHQ-4
Not endorsed		1				1
Depressed or Anxious	0.71	0.3	1.68	0.44	0.92	0.32	2.64	0.88
Loneliness
Not endorsed		1				1
Lonely	0.6	0.28	1.31	0.20	0.56	0.17	1.91	0.36
Exercise Assessment
150 min or more		1				1
Below 150 min	0.43	0.11	1.74	0.24	Unstable	Unstable	Unstable	Unstable
Moderate Exercise
3 days or more		1				1
Less than 3 days	0.69	0.29	1.65	0.41	0.9	0.29	2.78	0.86
Sleep Assessment
7–9 hours		1				1
Abnormal sleep	0.57	0.24	1.33	0.19	0.45	0.19	1.04	0.06

Income and education level were significantly associated with internet, broadband, and cellular access among OCS (Table 5). Lower income (<$50,000 vs ≥$100,000) was associated with reduced odds of frequent internet use (aOR 0.26; 95% CI: 0.08–0.90), broadband access (aOR 0.38; 95% CI: 0.17–0.86), and cellular internet use (aOR 0.41; 95% CI: 0.19–0.85). Compared with college graduates, individuals with a high school education or less also had lower odds of frequent internet use (aOR 0.25; 95% CI: 0.07–0.95). Black OCS had lower odds of broadband internet access (aOR 0.43; 95% CI: 0.20–0.95) but higher odds of cellular internet use (aOR 2.70; 95% CI: 1.21–6.05).

Table 5: Logistic Regression Results for Internet Use, Access and Connectivity.

This table presents adjusted odds ratios (aOR), 95% confidence intervals (CI), and p-values from four logistic regression models examining the association between demographic characteristics and digital outcomes: frequent internet use, broadband access, cellular internet use, and satisfaction with internet connection. Each model includes sex, marital status, race, education, and income as predictors. Odds ratios less than 1 indicate lower odds of the outcome relative to the reference group; odds ratios greater than 1 indicate higher odds. Statistically significant associations (p < 0.05) are highlighted in bold.

	Frequently used the Internet				Used DSL/Cable/FiOS/Wifi/Satellite for Internet				Used a cellular network for the Internet				Satisfied with his/her Internet Connection
Characteristic	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value
Sex
Male		1			1					1				1
Female	0.49	2.25	0.903	0.49	1.39	0.68	2.85	0.37	1.1	0.6	2	0.77	1.59	0.58	4.35	0.37
Marital Status
Married		1			1					1				1
Single	0.51	0.21	1.22	0.13	0.55	0.26	1.17	0.12	0.59	0.32	1.09	0.09	2.44	0.75	7.89	0.14
Race
White		1			1					1				1
Black	0.84	0.34	2.12	0.72	0.43	0.2	0.95	0.04	2.7	1.21	6.05	0.02	0.93	0.32	2.71	0.89
Education
College or Vocational graduate		1			1					1				1
Some College (no degree)	0.44	0.12	1.58	0.21	0.93	0.38	2.3	0.88	0.7	0.35	1.39	0.31	0.41	0.1	1.71	0.22
High School or less	0.25	0.07	0.95	0.04	0.73	0.32	1.68	0.46	0.7	0.33	1.51	0.37	0.92	0.28	3.03	0.89
Income
$100,000 or more		1			1					1				1
$50,000 to $99,999	0.21	0.08	0.51	0.001	0.28	0.11	0.75	0.01	0.72	0.35	1.47	0.36	0.35	0.04	2.93	0.34
Less than $50,000	0.26	0.08	0.9	0.03	0.38	0.17	0.86	0.02	0.41	0.19	0.85	0.02	0.47	0.05	4.18	0.50
General Health
Good or Better		1			1					1				1
Fair or Poor	0.9	0.38	2.15	0.81	0.5	0.26	0.99	0.048	1.16	0.55	2.42	0.70	0.61	0.1	3.6	0.59
PHQ-4
Not endorsed		1			1					1				1
Depressed or Anxious	0.61	0.25	1.51	0.29	0.68	0.24	1.95	0.48	0.89	0.35	2.27	0.81	0.64	0.19	2.16	0.47
Loneliness
Not endorsed		1			1					1				1
Lonely	0.55	0.21	1.45	0.23	0.76	0.29	2.03	0.59	0.81	0.31	2.13	0.66	0.42	0.18	0.99	0.047
Exercise Assessment
150 min or more		1			1					1				1
Below 150 min	1.47	0.19	11.61	0.72	1.45	0.26	8.19	0.67	0.96	0.2	4.64	0.96	Unstable	Unstable	Unstable	Unstable
Moderate Exercise
3 days or more		1			1					1				1
Less than 3 days	0.53	0.18	1.6	0.26	0.45	0.18	1.11	0.08	0.91	0.4	2.09	0.82	1	0.26	3.81	0.99
Sleep Assessment
7–9 hours		1			1					1				1
Abnormal sleep	0.45	0.16	1.22	0.12	0.66	0.26	1.62	0.36	0.94	0.5	1.78	0.85	0.42	0.13	1.37	0.15

Concerning digital literacy and technology attitudes (Table 6), lower income was also associated with reduced odds of using the internet to look for health or medical information (aOR 0.22; 95% CI: 0.09–0.53). Lower educational attainment was linked to reduced digital literacy, including lower odds of reporting the ability to use videoconferencing tools without assistance (aOR 0.36; 95% CI: 0.15–0.84) and having health search skills (aOR 0.18; 95% CI: 0.08–0.40). OCS reporting symptoms of depression or anxiety had substantially higher odds of reporting that learning new technology was frustrating (aOR 4.44; 95% CI: 1.79–11.03), and loneliness was associated with lower odds of having health search skills (aOR 0.31; 95% CI: 0.12–0.80). Black OCS had lower odds of reporting that technology use was frustrating (aOR 0.23; 95% CI: 0.06–0.84) (Table 6), whereas female OCS had higher odds of reporting technology-related frustration (aOR 2.02; 95% CI: 1.04–3.89).

Table 6: Logistic Regression Results of Digital Literacy and Technology Usability.

This table presents adjusted odds ratios (aOR), 95% confidence intervals (CI), and p-values from logistic regression models examining the association between demographic characteristics and three outcomes: (1) Finding learning new technology difficult, (2) Use videoconference programs like Zoom (3) Have the skills to lookup health information online. Each model includes sex, marital status, race, education, income, general reported health, mental health, exercise, and sleep assessments as predictors. Odds ratios greater than 1 indicate higher odds of the outcome relative to the reference group; odds ratios less than 1 indicate lower odds. Statistically significant associations (p < 0.05) are shown in bold.

	Found learning new technology frustrating				Able to use applications/programs like Zoom on computer or cell phone without help				Has skills to find health information on the Internet				Used Internet to look for health or medical information
Characteristic	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value
Sex
Male		1				1				1				1
Female	2.02	1.04	3.89	0.04	1.12	0.58	2.15	0.73	1.3	0.66	2.57	0.44	0.82	0.37	1.82	0.63
Marital Status
Married		1				1				1				1
Single	1.59	0.64	3.96	0.32	1.27	0.67	2.4	0.46	1.12	0.57	2.17	0.74	0.67	0.31	1.44	0.30
Race
White		1				1				1				1
Black	0.23	0.06	0.84	0.03	1.07	0.32	3.54	0.92	1.02	0.5	2.06	0.96	0.42	0.15	1.15	0.09
Education
College or Vocational graduate		1				1				1				1
Some College (no degree)	1.98	0.96	4.1	0.07	0.58	0.27	1.24	0.16	0.31	0.13	0.73	0.007	0.27	0.12	0.62	0.002
High School only or less	3.1	1.01	9.55	0.049	0.36	0.15	0.84	0.02	0.18	0.08	0.4	<0.001	0.38	0.15	0.96	0.04
Income
$100,000 or more		1				1				1				1
$50,000 to $99,999	1.36	0.63	2.95	0.43	0.89	0.48	1.65	0.71	0.39	0.11	1.41	0.15	0.38	0.14	1.01	0.05
Less than $50,000	1.38	0.5	3.79	0.54	0.45	0.19	1.05	0.07	0.61	0.18	2.02	0.42	0.22	0.09	0.53	0.001
General Health
Good or Better		1				1				1				1
Fair or Poor	0.98	0.43	2.28	0.97	0.64	0.32	1.25	0.19	0.43	0.22	0.85	0.02	0.95	0.42	2.14	0.90
PHQ-4
Not endorsed		1				1				1				1
Depressed or Anxious	4.44	1.79	11.03	0.001	0.6	0.26	1.4	0.24	0.99	0.37	2.64	0.99	0.86	0.36	2.02	0.73
Loneliness
Not endorsed		1				1				1				1
Lonely	0.88	0.38	2	0.76	0.48	0.2	1.13	0.09	0.31	0.12	0.8	0.02	0.85	0.32	2.25	0.74
Exercise Assessment
150 min or more		1				1				1				1
Below 150 min	0.46	0.1	2.1	0.32	0.88	0.2	3.89	0.86	1.97	0.3	12.89	0.48	0.61	0.07	5.69	0.66
Moderate Exercise
3 days or more		1				1				1				1
Less than 3 days	1.01	0.38	2.72	0.98	1.24	0.53	2.86	0.62	0.97	0.44	2.18	0.95	0.63	0.21	1.84	0.39
Sleep Assessment
7–9 hours		1				1				1				1
Abnormal sleep	1.23	0.6	2.56	0.57	0.33	0.16	0.65	0.001	0.71	0.39	1.31	0.28	0.69	0.28	1.72	0.43

In healthcare-related digital behaviors (Table 7), lower-income OCS had lower odds of viewing test results online (aOR 0.14; 95% CI: 0.05–0.38), sending secure messages to providers (aOR 0.36; 95% CI: 0.16–0.81), making appointments online (aOR 0.37; 95% CI: 0.17–0.79), and reporting willingness to use telehealth in the future (aOR 0.36; 95% CI: 0.17–0.76). Compared with college graduates, individuals with a high school education or less had lower odds of viewing test results online (aOR 0.36; 95% CI: 0.15–0.85) and making appointments online (aOR 0.39; 95% CI: 0.16–0.99). Female OCS had higher odds of expressing willingness to use telehealth in the future (aOR 2.15; 95% CI: 1.19–3.90).

Table 7: Logistic Regression Results for Telemedicine Use and Digital Health Communication.

This table displays adjusted odds ratios (aOR), 95% confidence online, (4) Had a intervals (CI), and p-values from logistic regression models examining the association between demographic characteristics and three digital health outcomes: (1) Used Internet to view test results, (2) Used Internet to send a message to health care provider, (3) Made healthcare appointments telehealth visit in the past 12 months, and (4) willing to use telehealth in the future if offered. Each model includes sex, marital status, race, education, income, general reported health, mental health, exercise, and sleep assessments as predictors. Odds ratios greater than 1 indicate higher odds of the outcome relative to the reference group; odds ratios less than 1 indicate lower odds. Statistically significant associations (p < 0.05) are shown in bold.

	Used Internet to view test results				Used Internet to send a message to health care provider				Made healthcare appointments online				Had a telehealth visit in the past 12 months				Willing to use telehealth in the future if offered
Characteristic	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value	Adjusted Odds Ratio (aOR)	95% CI Lower	95% CI Upper	p-value
Sex
Male		1				1				1				1				1
Female	1.85	0.96	3.55	0.07	1.08	0.55	2.14	0.82	0.52	0.27	0.99	0.048	1.73	0.85	3.52	0.13	2.15	1.19	3.9	0.01
Marital Status
Married		1				1				1				1				1
Single	0.96	0.41	2.25	0.93	0.69	0.37	1.28	0.24	0.84	0.43	1.62	0.60	0.59	0.27	1.29	0.19	0.55	0.25	1.21	0.14
Race
White		1				1				1				1				1
Black	0.47	0.17	1.3	0.15	0.53	0.2	1.39	0.20	0.89	0.41	1.94	0.77	2.35	0.77	7.13	0.13	2.31	0.98	5.49	0.06
Education
College or Vocational graduate			1			1				1				1				1
Some college (no degree)	0.38	0.16	0.89	0.03	0.34	0.18	0.64	0.001	0.52	0.24	1.13	0.10	0.9	0.43	1.9	0.78	0.79	0.39	1.61	0.53
High School or less	0.36	0.15	0.85	0.02	0.49	0.21	1.15	0.10	0.39	0.16	0.99	0.047	0.54	0.24	1.22	0.14	1.1	0.54	2.27	0.79
Income
$100,000 or more		1				1				1				1				1
$50,000 to $99,999	0.2	0.08	0.51	0.001	0.39	0.18	0.83	0.02	0.37	0.19	0.73	0.004	1.32	0.6	2.9	0.49	0.79	0.35	1.75	0.55
Less than $50,000	0.14	0.05	0.38	<0.001	0.36	0.16	0.81	0.01	0.37	0.17	0.79	0.01	0.84	0.41	1.72	0.63	0.36	0.17	0.76	0.007
General Health
Good or Better		1				1				1				1				1
Fair or Poor	1.3	0.62	2.72	0.49	1.22	0.56	2.65	0.62	1.24	0.58	2.64	0.57	2.27	0.96	5.38	0.06	0.93	0.45	1.94	0.85
PHQ-4
Not endorsed		1				1				1				1				1
Depressed or Anxious	0.4	0.19	0.84	0.02	0.65	0.32	1.32	0.23	0.75	0.36	1.55	0.43	0.61	0.27	1.39	0.24	0.79	0.36	1.74	0.57
Loneliness
Not endorsed		1				1				1				1				1
Lonely	0.6	0.23	1.59	0.30	1.16	0.5	2.7	0.72	1.41	0.64	3.14	0.40	1.09	0.42	2.84	0.87	0.98	0.47	2.04	0.96
Exercise Assessment
150 min or more		1				1				1				1				1
Below 150 min	0.8	0.14	4.55	0.80	1.1	0.22	5.64	0.91	1.2	0.1	14.68	0.89	1.17	0.23	5.96	0.85	0.99	0.18	5.39	0.99
Moderate Exercise
3 days or more		1				1				1				1				1
Less than 3 days	0.77	0.26	2.33	0.65	0.36	0.15	0.84	0.02	0.4	0.18	0.9	0.03	1.2	0.45	3.21	0.71	1.58	0.6	4.14	0.35
Sleep Assessment
7–9 hours		1				1				1				1				1
Abnormal sleep	0.46	0.19	1.11	0.08	1.22	0.56	2.66	0.61	1.2	0.57	2.52	0.62	0.86	0.38	1.92	0.70	1.15	0.45	2.93	0.78

Notably, in age-sensitivity analysis (Table S3), compared with survivors aged 60–69 years, those aged ≥80 years had significantly lower odds of smartphone use (aOR=0.13, 95% CI: 0.03–0.59), cellular internet access (aOR=0.06, 95% CI: 0.02–0.17), use of programs such as Zoom without assistance (aOR=0.16, 95% CI: 0.04–0.56), online health information search skills (aOR=0.19, 95% CI: 0.06–0.68), viewing test results online (aOR=0.17, 95% CI: 0.04–0.73), and making appointments online (aOR=0.25, 95% CI: 0.07–0.86). Conversely, survivors aged ≥80 years reported higher odds of finding technology frustrating (aOR=6.73, 95% CI: 1.68–27.02). Survivors aged 70–79 years also demonstrated lower odds of cellular internet access (aOR=0.31, 95% CI: 0.13–0.73), viewing test results online (aOR=0.39, 95% CI: 0.16–0.98), and using programs such as Zoom without assistance (aOR=0.39, 95% CI: 0.16–0.99), as well as higher odds of reporting technology frustration (aOR=2.62, 95% CI: 1.12–6.13).

3.3. Ridge-Penalized Interaction models

Among lower-income participants, Black OCS had lower odds of messaging healthcare providers online (aOR 0.06; 95% CI: 0.00–0.78), while Black OCS with Some College education were less likely to use the internet via cell phone (aOR 0.07; 95%CI: 0.01–0.99) compared with their White counterparts (Table S4).

4. Discussion

Our overall objective was to examine post-pandemic patterns of digital engagement and telehealth use among older cancer survivors (OCS) in the United States and to identify sociodemographic, psychosocial, and behavioral factors associated with digital access, literacy, and telehealth readiness. As the population of older cancer survivors continues to grow rapidly in the United States^1,3,4, understanding how digital tools shape access to survivorship care is increasingly important. Using recent, nationally representative HINTS 7 data, we provide an updated characterization of how older adults with a history of cancer interact with digital tools increasingly embedded within oncology care.

Overall, digital engagement among OCS was widespread but uneven. While most participants reported frequent internet and smartphone use, fewer engaged in telehealth or more interactive digital health activities, with only OCS aged ≥80 years showing consistent lower use of technology and telehealth use. This pattern is consistent with prior work demonstrating that access to technology does not necessarily translate into meaningful engagement.^5,6 Telehealth adoption in cancer care has expanded rapidly, particularly during the COVID-19 pandemic, yet sustained use remains influenced by usability, digital literacy, and structural factors.^9,13 Recent evidence further suggests that satisfaction with internet access and confidence in using digital tools are central to sustained engagement.⁵¹ Together, these findings reinforce that digital engagement reflects not only availability of technology but also readiness and capacity to use it effectively.⁵²

Socioeconomic factors, particularly income and education, emerged as the most consistent correlates of digital engagement in this study. Lower-income OCS had reduced odds of device use, online health information access, and engagement with patient portal functionalities, including viewing test results and communicating with providers. Education showed similar gradients across domains of digital literacy and healthcare-related use. These findings align with prior evidence demonstrating persistent digital divides in cancer survivorship populations.^53–55 More broadly, gaps in digital health engagement reflect structural differences in access to broadband, digital devices, and educational resources.^56,57 Recent studies have similarly shown that adverse social determinants of health are strongly related to telehealth nonuse among cancer survivors.⁵⁸ Collectively, these findings support the concept of “digital readiness,” in which access, literacy, and socioeconomic context interact to shape engagement with digital health tools.

Psychosocial and health-related factors were also important. Symptoms of depression and anxiety were linked to lower engagement with digital tools, consistent with prior work indicating that psychological distress can reduce digital self-efficacy and increase barriers to participation in health-related technologies.^59,60 Loneliness, which is highly prevalent among cancer survivors,^61–63 may further limit motivation or capacity to engage with digital platforms. At the same time, individuals reporting poorer overall health were more likely to complete telehealth visits, suggesting that perceived healthcare need may drive utilization. These findings align with prior evidence indicating that telehealth is often more readily adopted among patients with higher healthcare needs or mobility limitations.^10,18

Behavioral and technology-related factors also appear to play a role in shaping digital engagement. Use of digital devices, including smartphones and wearable technologies, was associated with broader engagement across digital health domains. Evidence suggests that familiarity with digital tools may facilitate uptake of more complex platforms such as telehealth.^43,64,65 This supports the idea that engagement in one aspect of digital health may enhance readiness for others, offering potential entry points for intervention.^{36,59,66–71} Additionally, prior work has demonstrated that targeted education and digital skill-building interventions can improve both confidence and willingness to use telehealth among older adults.⁷²

4.1. Clinical Relevance

These findings have important implications for survivorship care delivery. Expanding telehealth services alone may not be sufficient to ensure equal access. Health systems may benefit from incorporating structured assessments of digital readiness into routine care, including evaluation of device access, internet connectivity, and digital literacy. Interventions such as patient navigation, structured onboarding to patient portals, and simplified telehealth interfaces may improve engagement among OCS facing barriers. Additionally, given the observed relationships between psychosocial factors and digital engagement, integrating mental health support into digital care strategies may further enhance uptake and sustained use. (Figure S1).^{6,36,68,73,74}

Clinic-level strategies supported by our findings include default offering (rather than opt-in) of telehealth visits, structured patient-portal onboarding at the point of care, use of teach-back methods during telehealth encounters, and coordination with community or aging-service organizations to facilitate device access and digital skills training (Figure S2).^72,75,76 These approaches move beyond access alone and directly address the behavioral and psychosocial determinants of telehealth readiness identified in this study.^36,74

4.2. Implications for Research

From a research perspective, these findings highlight the need to shift from descriptive assessments of telehealth differences toward intervention-focused and implementation science approaches.^6,77 Future studies should prioritize pragmatic trials evaluating the impact of portal onboarding, digital coaching, and clinic-facilitated telehealth workflows on engagement and survivorship outcomes among OCS.^33,54 Longitudinal research is also needed to determine whether improvements in digital literacy and access reduce loneliness, distress, or unmet supportive care needs over time.^63,78 Finally, qualitative and mixed-methods studies could help elucidate trust, usability, and health-system factors that appear to differentially shape telehealth use across racial and socioeconomic groups, even when access is similar.^19,43,79

4.3. Limitations and Strengths

There are several limitations in this study. First, this cross-sectional analysis cannot establish causality. Second, all measures are self-reported, introducing potential recall or social desirability bias. Additionally, to stabilize model estimates, respondents identifying as multiracial/“other” were excluded from adjusted models, which may limit generalizability. We observed differences across racial groups in selected domains of digital engagement; however, these findings were less consistent than those related to socioeconomic factors. Importantly, the current dataset does not include provider-level or healthcare system variables, nor does it capture whether telehealth services were offered and declined. Prior work has shown that gaps in telehealth use may reflect multiple layers, including access, system-level processes, and patient preferences.^29,79 Given these limitations, our findings should be interpreted cautiously and viewed as hypothesis-generating. Future studies incorporating multilevel data will be critical for identifying mechanisms underlying these differences. Lastly, network quality (speed/latency), plan affordability, device age/accessibility features, and neighborhood infrastructure were not measured in HINTS 7, preventing tests of several plausible mechanisms.

Despite these limitations, this study has important strengths. It uses recent, nationally representative data to provide a timely post-pandemic assessment of digital health use among OCS. By examining multiple domains, including access, digital literacy, and healthcare engagement, it offers a comprehensive view of digital readiness.^33,53,54 Furthermore, the integration of psychosocial and behavioral factors provides new insight into the broader context influencing digital health use in this population.^6,40,42,43

Moreover, our study builds on foundational work on national patterns of telehealth conducted by Fareed et al. by using more recent HINTS 7 data and focusing on a clinically vulnerable subgroup, OCS aged ≥60 years.⁵³ This narrower focus allowed us to examine age and cancer-specific barriers, including cognitive and sensory limitations, loneliness, and psychological distress, which were not addressed in prior work. Additionally, by incorporating measures of digital literacy, device ownership, exercise, and sleep, our findings provide a more nuanced understanding of factors influencing telehealth readiness among OCS and inform targeted strategies to promote impartial digital health engagement.

5. Conclusion

In conclusion, this study identifies gaps in digital engagement among OCS based on sociodemographic differences. Future interventions should prioritize improving access to affordable broadband and devices, enhancing digital literacy through targeted education, and integrating psychosocial support into telehealth delivery. Policy-level actions, including demographic focused telehealth guidelines, will be essential to ensure that telehealth fulfills its promise of improving care access and quality for all OCS.^6,9

Availability of Data and Materials

HINTS 7 survey is publicly available and may be found via the following url: https://hints.cancer.gov/data/download-data.aspx

Code availability

Analysis code is available through https://github.com/joabodera/OCS_Telehealth