What Matters Most to Veterans When Deciding to Use Technology for Health: Cross-Sectional Analysis of a National Survey
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».