Feasibility of a Mental Health App Intervention for Emergency Service Workers and Volunteers: Single-Arm Pilot Study
Bibliographic record
Abstract
Background: Emergency service workers are at an elevated risk for stressor-related mental health (MH) issues, such as anxiety, depression, and posttraumatic stress disorder. Barriers to help-seeking are widespread across this sector, necessitating interventions tailored to the unique needs of this population. Build Back Better is a smartphone-based intervention designed to provide evidence-based strategies for the prevention of anxiety, depression, and posttraumatic stress disorder among emergency service workers. Objective: This study aimed to evaluate the usability, acceptability, feasibility, and preliminary effectiveness of the Build Back Better app among emergency service workers. Methods: A single-group (N=67), 1-month pilot study assessing the impact of the Build Back Better app on MH outcomes, including general distress, anxiety, depression, and traumatic stress coping, was undertaken with emergency service workers. Participants completed baseline and 1-month follow-up assessments using the Kessler Psychological Distress Scale, 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, World Health Organization Well-Being Index, and the Trauma Coping Self-Efficacy Scale. The app's usability and acceptability were also evaluated through participant feedback and usage data. Results: Of the 71 participants enrolled, 67 completed the baseline assessment and downloaded the app, with 33 participants providing follow-up data. The mean age of participants was 44.73 (SD 11.4) years, and 64% (n=43) were male. The majority of respondents rated the app quality as very high (n=27, 79%), felt that the app was easy to use (n=20, 61%), easily understood (n=18, 55%), improved their mental fitness (n=27, 80%), and would recommend the app to others (n=20, 61%). Encouraging trends toward improvement were found across symptom and well-being outcomes; however, these trends were not significant: general distress (t32=0.65, P=.52), depression (t32=0.75, P=.46), anxiety (t32=1.08, P=.29), or traumatic stress coping (t32=-0.27, P=.79), with effect sizes ≤0.2, likely due to the small sample size. Conclusions: The Build Back Better app demonstrated satisfactory levels of usability and acceptability. While the pilot study showed encouraging trends toward improved MH, further research with a larger sample size is needed to determine its efficacy. Participants furthermore suggested improvements in app navigation and content clarity, emphasizing the need for a more intuitive user experience. Given the positive feedback and improvement in MH outcomes, a larger-scale efficacy trial is warranted to further assess the app's potential for MH support in this high-risk population.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".