Self-Guided Digital Intervention for Depression in Adolescents: Feasibility and Preliminary Efficacy Study
Bibliographic record
Abstract
BACKGROUND: Depression in adolescents is a large and growing problem; however, access to effective mental health care continues to be a challenge. Digitally based interventions may serve to bridge this access gap for adolescents in need of care. Digital interventions that deliver components of cognitive behavioral therapy (CBT) have been shown to reduce symptoms of depression, and virtual reality (VR) may be a promising adjunctive component. However, research on these types of treatments in adolescents and young adults is limited. OBJECTIVE: This study aims to evaluate the feasibility, acceptability, and preliminary efficacy of Spark (v1.0), a 5-week, self-guided, CBT-based digital program using a mobile app and VR experiences to target symptoms of depression in adolescents. METHODS: A single-arm, open-label study of the Spark program was conducted with a community sample of 30 adolescents and young adults aged 12 to 21 years with self-reported moderate to severe depression symptoms. Participants completed a weekly depression assessment (Patient Health Questionnaire-8) in the app during the 5-week intervention period as well as web-based baseline, postintervention, and 1-month follow-up self-report assessments. The participants also completed a qualitative postintervention interview. For participants aged <18 years, caregivers completed assessments at baseline and postintervention time points. Feasibility outcomes included recruitment rate (the proportion of participants who enrolled in the study divided by the total number of participants screened for eligibility) and retention rate (the proportion of participants who completed postintervention assessments divided by the total number of participants who received the intervention). Acceptability outcomes included engagement with the program and quantitative and qualitative feedback about the program. Preliminary efficacy was evaluated based on the Patient Health Questionnaire-8. RESULTS: The study recruitment (31/66, 47%) and retention (29/30, 97%) rates were high. Participants provided higher ratings for the ease of use of the Spark program (8.76 out of 10) and their enjoyment of both the mobile app (7.00 out of 10) and VR components (7.48 out of 10) of the program, whereas they provided lower ratings for the program's ability to improve mood (4.38 out of 10) or fit into their daily routines (5.69 out of 10). We observed a clinically and statistically significant reduction in depression scores at postintervention (mean difference 5.36; P<.001) and 1-month follow-up (mean difference 6.44; P<.001) time points. CONCLUSIONS: The Spark program was found to be a feasible and acceptable way to deliver a self-guided CBT-focused intervention to adolescents and young adults with symptoms of depression. Preliminary data also indicated that the Spark program reduced the symptoms of depression in adolescents and young adults. Future studies should evaluate the efficacy of this intervention in an adequately powered randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04165681; https://classic.clinicaltrials.gov/ct2/show/NCT04165681.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".