Impacts of the Mindfulness Meditation Mobile App Calm on Undergraduate Students’ Sleep and Emotional State: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
Background: Undergraduate students frequently experience negative emotional states and sleep quality, which is believed to have worsened following the COVID-19 pandemic. Objective: This study piloted the use of a popular mobile mindfulness app (Calm) as a potential intervention to improve state depression, anxiety, stress, and sleep quality in undergraduate students attending a Canadian university, following the COVID-19 pandemic. Methods: Undergraduate students were randomly assigned to a control or treatment group and completed a series of 3 questionnaires to evaluate baseline state emotional health (Depression Anxiety Stress Scale 42-Item Version [DASS-42], Perceived Stress Scale 10-Item Version [PSS-10], and Pittsburgh Sleep Quality Index). Treatment group participants were instructed to engage with the fully-automated Calm app's sleep section for 30 days: 20 minutes daily, 5 days a week, along with an additional 30 minutes of interaction with other app sections each week, resulting in a goal of 130 minutes per week. The control participants were instructed to continue with everyday life and refrain from the use of mindfulness-based apps for 30 days. Following the 30-day treatment period, all participants repeated the 3 questionnaires. The impact of the treatment on all outcomes was examined using linear mixed model analyses. Independent samples t tests were used to determine if psychosocial health or sleep scores differed between baseline and follow-up and if differences in such scores were present between the groups. Results: A total of 80 students met the inclusion criteria and were randomly assigned to the control (n=40) or treatment (n=40) group. One control participant was lost to follow-up and 3 treatment participants discontinued engaging with the Calm app. Both control (n=39) and treatment (n=37) groups began with similar demographic, emotional state, and sleep characteristics. Treatment participants engaged with the Calm app's sleep section for an average of 234 minutes per week; however, 54% (20/37) met the minimum prescribed interaction time across all 4 weeks. Following the 30-day treatment period, compared to the control group, the treatment group's state anxiety (mean 14, SD 7.4 vs mean 12, SD 7.8; P=.002), state stress (DASS-42: mean 20, SD 8.8 vs mean 15, SD 8.5; P<.001; PSS-10: mean 22, SD 5.9 vs mean 19, SD 5.9; P=.02), and sleep quality (mean 7.7, SD 2.7 vs mean 6.4, SD 3.5; P<.001) improved. Posttreatment, state stress and perceived stress severity was lower in the treatment versus control group (DASS-42: P=.02; PSS-10: P=.03, respectively). Conclusions: These pilot findings indicate that a mindfulness app may be an effective tool for reducing state anxiety and stress, as well as enhancing sleep quality among undergraduate university students. A larger, randomized controlled trial should confirm these findings.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| 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 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».