Mobile-Based Cognitive Behavioral Therapy for Health Care Workers’ Mental Health in Ecuador: Quasi-Experimental Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».