Analysis of the Relationship Between Rural-Urban Status and Use of Digital Health Technology Among Older Cancer Survivors Based on the Health Information National Trends Survey: Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Though telehealth has been a promising avenue for engaging cancer survivors with health care and lifestyle programming, older and rural-dwelling cancer survivors may have additional challenges in accessing digital devices and tools that have not yet been described. This study aimed to use a robust, nationally representative sample collected in 2022 to provide an updated view of digital technology use and the use of technology for health in this population. Objective: This study aimed to examine the prevalence of digital technology use for health-related activities among older cancer survivors in both rural and urban settings. The primary outcomes of interest included (1) internet access and use for health-related activities, (2) digital device ownership and use as a tool for health behaviors, (3) use of social media for health, and (4) use of telehealth. Methods: A cross-sectional analysis of the National Cancer Institute's Health Information National Trends Survey Cycle 6 (HINTS 6) was completed to examine the prevalence of digital technology use among older cancer survivors. For analysis, the sample was restricted to cancer survivors over the age of 60 years (n=710). Unadjusted and adjusted logistic regression models were used to test the association between rurality and digital health tool use. Results: Overall, 17% (125/710) of the sample lived in a rural area of the United States and the mean sample age was 73 (SD 8.2) years. Older cancer survivors, regardless of rural-urban status, reported a high prevalence of internet usage (n=553, 79.9%), digital device ownership (n=676, 94.9%), and social media use (n=448, 66.6%). In unadjusted models, rural survivors were less likely than urban survivors to report that they had used a health or wellness application in the previous year (odds ratio [OR] 0.56, 95% CI 0.32-0.97; P=.04). In adjusted models, rural survivors were more likely to report that they had shared personal health information on social media (OR 2.64, 95% CI 1.13-6.19; P=.03). There were no differences in the proportion of rural and urban respondents who reported receiving health services through telehealth in the previous year. Conclusions: Regardless of the residential status, older cancer survivors report high internet and technology use for health-related activities. These results show promise for the feasibility of using digital technologies to implement supportive care and wellness programming with older cancer survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".