Understanding Adoption and Preliminary Effectiveness of a Mobile App for Chronic Pain Management Among US Military Veterans: Pre-Post Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: The Veterans Health Administration Pain Coach mobile health app was developed to support veterans with chronic pain. OBJECTIVE: Our objective was to evaluate early user experiences with the Pain Coach app and preliminary impacts of app use on pain-related outcomes. METHODS: Following a sequential, explanatory, mixed methods design, we mailed surveys to veterans at 2 time points with an outreach program in between and conducted semistructured interviews with a subsample of survey respondents. We analyzed survey data using descriptive statistics among veterans who completed both surveys and examined differences in key outcomes using paired samples t tests. We analyzed semistructured interview data using thematic analysis. RESULTS: Of 1507 veterans invited and eligible to complete the baseline survey, we received responses from 393 (26.1%). These veterans received our outreach program; 236 (236/393, 60.1%) completed follow-up surveys. We conducted interviews with 10 app users and 10 nonusers. Among survey respondents, 10.2% (24/236) used Pain Coach, and 58% (14/24) reported it was easy to use, though interviews identified various app usability issues. Veterans who used Pain Coach reported greater pain self-efficacy (mean 23.1 vs mean 16.6; P=.01) and lower pain interference (mean 34.6 vs mean 31.8; P=.03) after (vs before) use. The most frequent reason veterans reported for not using the app was that their health care team had not discussed it with them (96/212, 45.3%). CONCLUSIONS: Our findings suggest that future efforts to increase adoption of Pain Coach and other mobile apps among veterans should include health care team endorsement. Our findings regarding the impact of Pain Coach use on outcomes warrant further study.
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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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".