Veterans’ Attitudes Toward Smartphone App Use for Mental Health Care: Qualitative Study of Rurality and Age Differences
Notice bibliographique
Résumé
BACKGROUND: Mental health smartphone apps provide support, skills, and symptom tracking on demand and come at minimal to no additional cost to patients. Although the Department of Veterans Affairs has established itself as a national leader in the creation of mental health apps, veterans' attitudes regarding the use of these innovations are largely unknown, particularly among rural and aging populations who may benefit from increased access to care. OBJECTIVE: The objective of our study was to examine veterans' attitudes toward smartphone apps and to assess whether openness toward this technology varies by age or rurality. METHODS: We conducted semistructured qualitative interviews with 66 veterans from rural and urban areas in Maine, Arkansas, and California. Eligible veterans aged 18 to 70 years had screened positive for postraumatic stress disorder (PTSD), alcohol use disorder, or major depressive disorder, but a history of mental health service utilization was not required. Interviews were digitally recorded, professionally transcribed, and coded by a research team using an established codebook. We then conducted a thematic analysis of segments pertaining to smartphone use, informed by existing theories of technology adoption. RESULTS: Interviews revealed a marked division regarding openness to mental health smartphone apps, such that veterans either expressed strongly positive or negative views about their usage, with few participants sharing ambivalent or neutral opinions. Differences emerged between rural and urban veterans' attitudes, with rural veterans tending to oppose app usage, describe smartphones as hard to navigate, and cite barriers such as financial limitations and connectivity issues, more so than urban populations. Moreover, rural veterans more often described smartphones as being opposed to their values. Differences did not emerge between younger and older (≥50) veterans regarding beliefs that apps could be effective or compatible with their culture and identity. However, compared with younger veterans, older veterans more often reported not owning a smartphone and described this technology as being difficult to use. CONCLUSIONS: Openness toward the use of smartphone apps in mental health treatment may vary based on rurality, and further exploration of the barriers cited by rural veterans is needed to improve access to care. In addition, findings indicate that older patients may be more open to integrating technology into their mental health care than providers might assume, although such patients may have more trouble navigating these devices and may benefit from simplified app designs or smartphone training. Given the strong opinions expressed either for or against smartphone apps, our findings suggest that apps may not be an ideal adjunctive treatment for all patients, but it is important to identify those who are open to and may greatly benefit from this technology.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
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| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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
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