Usefulness of Interventions Using a Smartphone Cognitive Behavior Therapy Application for Children With Mental Health Disorders: Prospective, Single-Arm, Uncontrolled Clinical Trial
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".