Impact of Digital Phenotypes and Question-Asking on Emotional Disorders in Adolescents: 4-Week Field Study
Bibliographic record
Abstract
Background: Adolescence is the period with the highest incidence of mental disorders, with approximately one-third, half, and two-thirds of cases emerging by ages 14, 18, and 25 years, respectively. Proactive interventions are essential, and digital phenotyping has emerged as a promising approach for timely detection and management. However, passive digital phenotyping is limited to sensor-detectable behaviors, while active phenotyping is often confined to clinical scales, missing the opportunity to capture users' subjective perspectives and emotional nuances. Furthermore, the potential therapeutic effect of the data collection process itself on emotional disorder management remains underexplored. Objective: This study developed and tested a mobile app that collects passive and active digital phenotypes related to adolescents' emotions and daily behaviors. The study aimed to assess the app's impact on managing emotional disorders through self-monitoring and to identify daily lifestyle indicators that can predict and track the development of such disorders. Methods: A 4-week parallel, nonequivalent control group design was employed. The intervention group installed a digital phenotype collection tool on their mobile devices for 28 days. Passive data (location, sleep, and screen time) were continuously recorded. Active data were collected through ecological momentary assessments delivered randomly up to 8 times daily, prompting participants to report their current mood and levels of depression, anxiety, and stress. The control group received no intervention. Both groups were assessed at time points on emotional disorders, self-efficacy, and time management. Postintervention interviews were conducted with the intervention group. Results: Thirty-six Korean adolescents participated (19 control, 17 intervention). The intervention group showed significant reductions in depression (P=.04, d=0.42) and stress (P=.03, d=0.46) and improvements in self-efficacy (P=.002, d=0.50) and time management abilities (P<.001, d=0.39), with small to large effect sizes. No significant change was observed in anxiety levels (P=.11). Correlational analysis revealed weak but significant links between passive digital phenotypes and daily emotional states. Conclusions: Integrating active and passive digital phenotypes through a mobile collection tool can help manage emotional disorders in adolescents. Use of the tool was associated with moderate reductions in depression and stress, as well as improvements in self-efficacy and time management, while anxiety levels remained unchanged, possibly due to adolescents' differing perceptions of anxiety. Passive digital phenotypes such as location variability and phone usage showed modest correlations with daily emotional states, supporting their potential as ecological markers. These findings suggest that digital phenotype collection not only aids in monitoring but may also have therapeutic benefits by promoting self-reflection on mood and behavior. High adherence rates further support the practicality and acceptability of this approach for long-term emotional disorder management in adolescents.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".