Effectiveness of Mental Health Apps for Distress During COVID-19 in US Unemployed and Essential Workers: Remote Pragmatic Randomized Clinical Trial
Notice bibliographique
Résumé
Background During the COVID-19 pandemic, the general public was concerned about the mental health impacts of unemployment due to COVID-19 and the stress essential workers experienced during this time. Several reports indicated that people in distress were turning to digital technology, but there was little evidence about the impact of these tools on mitigating distress. Objective This study seeks to determine the acceptability, feasibility, usability, and effectiveness of mobile mental health apps for decreasing mental health symptoms in essential workers and unemployed individuals with suicide risk. Methods We recruited participants who indicated that they were unemployed because of COVID-19 or were COVID-19–designated essential workers. Participants were randomized to 1 of 4 free commercial mobile apps for managing distress that were (1) highly rated by PsyberGuide and (2) met the criteria for intervention features these participants indicated were desirable in a previous survey. Participants used the apps for 4 weeks and completed baseline and 4-week self-assessments of depression, anxiety emotional regulation, and suicide risk. Results We found no differences between the apps in any outcome but did find significant changes in depression and anxiety over time (Patient Health Questionnaire [PHQ]-9: estimate=–1.5, SE 0.2, 95% CI –1.1 to –1.8, P<.001; Generalized Anxiety Disorder Scale [GAD]-7: estimate=–1.3, SE 0.2, 95% CI –1.0 to –1.6, P<.001). We found no significant changes in suicidal behavior (Suicide Behaviors Questionnaire-Revised [SBQ-R]) or emotional regulation (Difficulties in Emotion Regulation Scale – Short Form [DERS-SF]) for the 4 weeks. We did find a significant dose-response pattern for changes in depression and anxiety. Using the app at least once a week resulted in greater improvements in treatment conditions over time on depression (estimate=–0.6, SE 0.2, 95% CI 1.0-0.2, P=.003) and anxiety (estimate=0.1, SE 0.2, 95% CI 0.4-0.6, P=.78). There was no association between app frequency and changes in suicidal behavior (SBQ-R) or emotional regulation (DERS-SF). We further found a significant difference between the conditions with regard to app usability, with the control app being the most usable (meanBeautiful Mood 72.9, SD 16.7; meanCOVID Coach 71.2, SD 15.4; meanCalm 66.8, SD 17.3; mean7 Cups 65.2, SD 17.7). We found no significant differences for app acceptability or appropriateness. Conclusions Few studies have evaluated prospectively the utility and usability of commercial apps for mood. This study found that free, self-guided commercial mobile mental health apps are seen as usable, but no one app is superior to the other. Although we found that regular use is indicated for effects on depression and anxiety to occur in those who are more symptomatic, regression to the mean cannot be ruled out. Trial Registration ClinicalTrials.gov NCT04536935; https://tinyurl.com/mr36zx3s
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 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 | 0,011 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».