Evaluation of an mHealth App (DeStressify) on University Students’ Mental Health: Pilot Trial
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Notice bibliographique
Résumé
BACKGROUND: One in five Canadians experience mental health issues with those in the age range of 15 to 24 years being most at risk of a mood disorder. University students have shown significantly higher rates of mental health problems than the general public. Current university support services are limited by factors such as available staff and finances, and social stigma has frequently been identified as an additional barrier that prevents students from accessing these resources. Mobile health (mHealth) apps are one form of alternative health support that is discrete and accessible to students, and although they are recognized as a promising alternative, there is limited research demonstrating their efficacy. OBJECTIVE: The aim of this study was to evaluate a mindfulness-based app's ("DeStressify") efficacy on stress, anxiety, depressive symptomology, sleep behavior, work or class absenteeism, work or school productivity, and quality of life (QoL) among university students. METHODS: Full-time undergraduate students at a Canadian university with smartphones and Internet access were recruited through in-class announcements and on-campus posters. Participants randomized into an experimental condition were given and instructed to use the DeStressify app 5 days a week for 4 weeks. Control condition participants were wait-listed. All participants completed pre- and postintervention Web-based surveys to self-assess stress, anxiety, depressive symptomatology, sleep quality, and health-related QoL. RESULTS: A total of 206 responses were collected at baseline, with 163 participants completing the study (86 control, 77 experimental). Using DeStressify was shown to reduce trait anxiety (P=.01) and improve general health (P=.001), energy (P=.01), and emotional well-being (P=.01) in university students, and more participants in the experimental condition believed their productivity improved between baseline and postintervention measurements than the number of participants expected to believe so randomly by chance (P=.01). The app did not significantly improve stress, state anxiety, physical and social functioning, and role limitations because of physical or emotional health problems or pain (P>.05). CONCLUSIONS: Mindfulness-based apps may provide an effective alternative support for university students' mental health. Universities and other institutions may benefit from promoting the use of DeStressify or other mindfulness-based mHealth apps among students who are interested in methods of anxiety management or mindfulness-based self-driven health support. Future steps include examining DeStressify and similar mHealth apps over a longer period and in university staff and faculty.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle