Usefulness of Interventions Using a Smartphone Cognitive Behavior Therapy Application for Children With Mental Health Disorders: Prospective, Single-Arm, Uncontrolled Clinical Trial
Notice bibliographique
Résumé
Background: The prevalence of mental health disorders among children in Japan has increased rapidly, and these children often show depressive symptoms and reduced quality of life (QOL). We previously developed a smartphone-based self-monitoring app to deliver cognitive behavioral therapy (CBT), implemented it in healthy children, and reported its effectiveness for health promotion. Objective: This study aims to examine the usefulness of the CBT app for improvement in depressive symptoms and QOL in children with mental health disorders. Methods: The participants were 115 children with mental health disorders (eg, school refusal, orthostatic hypotension, eating disorders, developmental disorders, among others) and aged 12-18 years. The CBT app-based program comprised 1 week of psychoeducation followed by 1 week of self-monitoring. After reading story-like scenarios, participants created a self-monitoring sheet with 5 panels: events, thoughts, feelings, body responses, and actions. All participants received regular mental health care from physicians in addition to the app-based program. To evaluate the participants' depressive symptoms and QOL, Patient Health Questionnaire for Adolescents (PHQ-9A), Depression Self-Rating Scale for Children (DSRS-C), and Pediatric Quality of Life Inventory (PedsQL) were measured at the beginning of the intervention, and at 2 and 6 months thereafter. Questionnaire for Triage and Assessment with 30 items (QTA30), and Rosenberg Self-Esteem Scale (RSES) were also used to measure their health and self-esteem. Participants were divided into 4 groups on the basis of the PHQ-9A score (above or below the cutoff; PHQ-9A≥5 or PHQ-9A<5) and completion or noncompletion of the CBT app-based program (app [+] or app [-]). The primary outcome was improvement in the DSRS-C score, and secondary outcomes were improvement in other psychometric scales including PedsQL, QTA30, and RSE. A paired-samples t test was used for statistical analysis. The Medical Ethics Committee of Fukuoka University Faculty of Medicine (approval U22-05-002) approved the study design. Results: There were 48, 18, 18, and 7 participants in the PHQ-9A≥5 app (+), PHQ-9A≥5 app (-), PHQ-9A<5 app (+), and PHQ-9A<5 app (-) groups, respectively. A total of 24 participants dropped out. No improvement in the DSRS-C score was observed in all groups. However, PedsQL scores improved significantly at 2 and 6 months in the PHQ-9A<5 app (+) group (t17=6.62; P<.001 and t17=6.11; P<.001, respectively). There was a significant positive correlation between the PHQ-9A scores and the number of self-monitoring sheets completed. Conclusions: The CBT app was useful for improving PedsQL scores of children with mental health disorders. However, a higher-intensity CBT program is necessary for more severely depressed children.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».