Acceptability, User Satisfaction, and Feasibility of an App-Based Support Service During the COVID-19 Pandemic in a Psychiatric Outpatient Setting: Prospective Longitudinal Observational Study
Notice bibliographique
Résumé
BACKGROUND: Patients with mental disorders often have difficulties maintaining a daily routine, which can lead to exacerbated symptoms. It is known that apps can help manage mental health in a low-threshold way and can be used in therapeutic settings to complement existing therapies. OBJECTIVE: The aim of this study was to evaluate the acceptability, usability, and feasibility of an app-based support service specifically developed for outpatients with severe mental disorders in addition to regular face-to-face therapy during the COVID-19 pandemic. METHODS: Patients in a psychiatric outpatient department at a German university hospital were invited to use an app-based support service designed transdiagnostically for mental disorders for 4 weeks. The app included 7 relaxation modules, consisting of video, audio, and psychoeducational text; ecological momentary assessment-like questionnaires on daily mood answered via a visual smiley-face scale; and an activity button to record and encourage daily activities. Standardized questionnaires at baseline (T0; preintervention time point) and after 4 weeks (T2; postintervention time point) were analyzed. Feedback via the smiley-face scale was provided after using the app components (T1; during the intervention). Measures included depressive symptoms, quality of life, treatment credibility and expectancy, and satisfaction. Furthermore, participation rates, use of app modules and the activity button, and daily mood and the provided feedback were analyzed (T2). RESULTS: In total, 57 patients participated in the study, and the data of 38 (67%) were analyzed; 17 (30%) dropped out. Satisfaction with the app was high, with 53% (30/57) of the participants stating being rather satisfied or satisfied. Furthermore, 79% (30/38) of completers stated they would be more likely or were definitely likely to use an app-based support service again and recommend it. Feasibility and acceptability were high, with nearly half (18/38, 47%) of the completers trying relaxation modules and 71% (27/38) regularly responding to the ecological momentary assessment-like questionnaire between 15 and 28 times (mean 19.91, SD 7.57 times). The activity button was used on average 12 (SD 15.72) times per completer, and 58% (22/38) felt "definitely" or "rather" encouraged to perform the corresponding activities. Depressive symptomatology improved significantly at the postintervention time point (P=.02). Quality of life showed a nonsignificant increase in the physical, psychological, and social domains (P=.59, P=.06, and P=.42, respectively) and a significant improvement in the environment domain (P=.004). Treatment credibility and expectancy scores were moderate and significantly decreased at T2 (P=.02 and P<.001, respectively). Posttreatment expectancy scores were negatively associated with posttreatment depressive symptomatology (r=-0.36; P=.03). CONCLUSIONS: App-based programs seem to be an accessible tool for stabilizing patients with severe mental disorders, supporting them in maintaining a daily routine, complementing existing face-to-face treatments, and overall helping respond to challenging situations such as the COVID-19 pandemic.
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 | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».