Veterans’ Attitudes Toward Smartphone App Use for Mental Health Care: Qualitative Study of Rurality and Age Differences
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".