Effectiveness, Usability, and Satisfaction of a Self-Administered Digital Intervention for Reducing Depression, Anxiety, and Stress in a University Community in the Andean Region of Peru: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: University communities, especially in low-resource settings like Peru's Andean region, experience high rates of depression, anxiety, and stress, which harm academic performance and well-being. Traditional mental health services often remain inaccessible, necessitating scalable, self-guided solutions. While digital mental health interventions have shown promise broadly, evidence is scarce for fully self-administered platforms in low-resource university environments. Objective: We evaluated the efficacy of a self-administered digital mental health service in members of a public university to reduce symptoms of depression, anxiety, and perceived stress. Methods: We conducted a double-blind, parallel-group randomized controlled trial with 1:1 allocation to the digital mental health self-care service or a 30-day waiting-list control. We recruited 427 participants (students, teachers, and administrative staff) in May-June 2024, reporting mild to moderate symptoms of depression, anxiety, and stress. Participants were randomized via simple randomization and blinded through automated platform assignment. The intervention comprised 6 sequential 5-day modules grounded in acceptance and commitment therapy, mindfulness, and behavioral activation, delivered via videos, daily text prompts, workbooks, and a responsive chatbot. SMS and WhatsApp (Meta) reminders promoted adherence. Depressive symptoms (Patient Health Questionnaire-9), anxiety symptoms (Generalized Anxiety Disorder-7), and perceived stress (PSS-10) were assessed at baseline and immediately post intervention (day 30). Secondary outcomes in the intervention arm included usability (Computer System Usability Questionnaire), satisfaction (Client Satisfaction Questionnaire-8), and subjective commitment (Twente Engagement with Ehealth Technologies Scales). Analysis of covariance (ANCOVA) adjusted for baseline scores, and multivariate ANCOVA accounted for correlations among outcomes. Effect sizes were quantified using Cohen d and partial epsilon-squared (ε²p). Results: Of 427 randomized, 85 (19.9%) completed all assessments (intervention: n=30; control: n=55). Baseline demographic and clinical characteristics were comparable between groups. Post intervention, the digital mental health self-care service group exhibited significantly greater reductions in mean Patient Health Questionnaire-9 scores (mean difference 2.78; Cohen d=0.64; P=.006), Generalized Anxiety Disorder-7 scores (mean difference 2.13; Cohen d=0.56; P=.015), and PSS-10 scores (mean difference 4.08; Cohen d=0.69; P=.003) than controls. ANCOVA confirmed robust group effects for depression (F₁,₈₂=9.78; P=.002; ε²p=0.31) and anxiety (F₁,₈₂=8.28; P=.005; ε²p=0.32), with a trend toward stress reduction (F₁,₈₂=3.73; P=.057; ε²p=0.46). Multivariate ANCOVA demonstrated a significant multivariate effect (F₄,₁₂=7.23; P=.015). Among intervention completers, 100% scored below the Client Satisfaction Questionnaire-8 satisfaction threshold (<24), 60% rated platform usability as low (Computer System Usability Questionnaire<64); yet, 96.7% reported high subjective commitment (Twente Engagement with Ehealth Technologies Scales ≥18), indicating strong engagement despite interface challenges. Conclusions: A self-administered digital self-care service effectively reduced depression, anxiety, and stress symptoms in a Peruvian university community. High user commitment underscores the platform's relevance, while low satisfaction and usability necessitate interface optimization-streamlined navigation, adaptive personalization, and feedback mechanisms-to enhance user experience and support scalable implementation in low-resource educational settings.
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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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
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 ».