Mobile-Based Cognitive Behavioral Therapy for Health Care Workers’ Mental Health in Ecuador: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Mental health challenges, including depression, anxiety, and burnout, have become increasingly prevalent among health care workers, who face high-stress environments, limited resources, and long working hours. The COVID-19 pandemic has intensified these issues, especially in regions like Latin America, where health care professionals experience heightened anxiety and depression. The urgent need for mental health support has prompted the development of mobile health (mHealth) solutions. These tools offer accessible, confidential interventions that help reduce stigma and encourage engagement. The "Psicovida" mobile app was designed to provide cognitive behavioral therapy (CBT)-based activities tailored to health care workers, supporting them in managing stress, anxiety, and depression. Objective: This study aims to evaluate the effectiveness of Psicovida, a mobile app that delivers CBT-based interventions, in reducing depressive symptoms and emotional distress among health care workers over a 3-month period. Methods: A quasi-experimental, nonrandomized controlled study was conducted with health care workers at a public hospital in Ecuador. Participants were recruited offline and assigned to either an intervention group that used the Psicovida app or a control group that received no intervention. The app provided weekly CBT-based tasks focused on stress management, cognitive restructuring, and emotional regulation. Data collection included demographic information, with mental health outcomes assessed pre- and postintervention using the Patient Health Questionnaire-9 (PHQ-9) to measure depression and the General Health Questionnaire-12 to assess overall psychological well-being. Results: A total of 211 health care workers participated, with 88 in the intervention group and 96 in the control group, and 29 participants dropped out. Among the intervention group, adherence varied: 34% (30/88) used the app consistently for 10-12 weeks, 42% (37/88) for 7-9 weeks, and 24% (21/88) for fewer than 6 weeks. Significant improvements in mental health outcomes were observed among app users. The intervention group exhibited a statistically significant reduction in depressive symptoms, with PHQ-9 scores decreasing significantly (P<.001; 95% CI 6.17-9.36). Within this group, 20% (18/88) achieved complete remission of depressive symptoms (PHQ-9 scores <5), 32% (28/88) showed mild symptoms (PHQ-9 scores=5-9), and 48% (42/88) remained in the range requiring treatment referral (PHQ-9 scores ≥10). General Health Questionnaire-12 scores similarly showed substantial improvement in psychological well-being (P<.001; 95% CI 3.99-5.58). Conclusions: The Psicovida mobile app demonstrates promise as an accessible, effective tool for reducing depression and anxiety among health care workers through CBT-based interventions. This study highlights the potential of mHealth technology to deliver targeted mental health support, especially in resource-limited settings. Future research should focus on evaluating long-term impacts and broader applications in varied health care environments.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".