Using Mobile Assessments to Characterize Mental and Physical Health Behaviors in Youth: Protocol for a Pilot Intensive Longitudinal Study
Bibliographic record
Abstract
Background: Promoting healthy behaviors including adequate sleep, regular physical activity, balanced diet, and abstinence from substance use, alongside nurturing affective functioning, may crucially support physical and mental well-being in youth. Yet, little is known about how these domains interact dynamically in their daily lives. Objective: This pilot study evaluates the feasibility of prolonged, multiwave ambulatory assessment in school-recruited adolescents as well as in clinical and school-recruited preadolescents. In addition, it will examine the dynamic interplay of different health behavior domains and affective functioning in adolescent samples recruited in schools. Methods: The initial target sample size was 100 youths (ages 9-17 years) across 2 assessment protocols. Adolescents complete three 3-week waves with 4 daily ecological momentary assessments and parallel actigraphy, and preadolescents follow two 10-day assessment waves. Feasibility aims will be assessed via equivalence tests targeting an a priori level of missing data and dropout at 70% each. Dynamic modeling in adolescent datasets will be addressed by a planned multilevel analysis leveraging idiographic effects as predictors of symptom outcomes, as well as state-of-the-art exploratory frameworks. Results: By July 2025, we enrolled 113 school adolescents and 27 school preadolescents exceeding the a priori target. Recruitment and data collection are still ongoing in the clinical preadolescent group of currently 13 preadolescents. Conclusions: This concise, intensive longitudinal protocol provides the basis for examining the feasibility of long-term intensive longitudinal paradigms in youth and lays a critical foundation for future large-scale studies aimed at unpacking the evolving interplay of health behaviors and emotional well-being.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".