What Matters Most to Veterans When Deciding to Use Technology for Health: Cross-Sectional Analysis of a National Survey
Bibliographic record
Abstract
BACKGROUND: There is an increasingly diverse range of mobile apps and digital health devices available to help patients manage their health. Despite evidence for the effectiveness of such technologies, their potential has not been fully realized because adoption remains low. Such limited uptake can have direct implications for the intended benefits of these technologies. OBJECTIVE: This study aimed to understand what matters most to US military veterans when deciding whether to use digital health technologies (DHTs) such as mobile health apps or devices to manage their health and compare these factors between veterans with and without prevalent chronic physical and mental health conditions. METHODS: We conducted a cross-sectional analysis of survey data collected from a national sample of veterans who receive care from the Veterans Health Administration (VHA), which was predominantly gathered as part of the last wave of a larger longitudinal data collection effort. RESULTS: Among respondents (n=857), 86.7% (736/849) reported currently using or having previously used ≥1 devices to manage their health, and 78.4% (639/815) also reported using either VHA or non-VHA health apps. Considerations most frequently endorsed as "very important" by veterans when deciding whether to use DHTs included receiving secure messages from their health care team about DHTs, knowing data from DHTs would be used to inform their care, and receiving recommendations from providers to use DHTs. Conversely, considerations most frequently endorsed as "not at all important" included seeing information about DHTs on social media, having community support to use DHTs, and receiving encouragement from peers to use DHTs. Considerations did not significantly differ between veterans with or without prevalent chronic health conditions; however, a greater proportion of veterans with prevalent mental health conditions reported the following considerations to be "very important:" seeing information about DHTs on social media, having community support to use DHTs, having other veterans encourage DHT use, and having help from family, friends, or other important people to use DHTs. CONCLUSIONS: Understanding what matters most to patients when they are deciding to adopt a technology for their health can, and should, inform implementation strategies and other approaches to enhance health-related technology use. Our results suggest that, for veterans, recommendations from health care team members and knowing that the data from DHTs will be used in clinical care are more important than information from social media, community sources, or peers when deciding to use DHTs, although perceptions of importance regarding the latter may differ among patients with different conditions. Our findings suggest that communication from health care team members to patients, perhaps either in-person or electronically, could help encourage DHT adoption and use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".