The Association between Movement Behaviors and Mental Health Issues in Adolescents with Neurodevelopmental Disorders
Bibliographic record
Abstract
PURPOSE: To examine movement behavior with stress-related biomarkers alongside self-reported mental health issues in adolescents with neurodevelopmental disorders (NDD). METHODS: One hundred fifty-one adolescents with clinically diagnosed NDD and aged between 12 and 17 yr were recruited in Hong Kong secondary schools. Salivary cortisol as a stress-related biomarker and self-reported mental health variables including anxiety, depression, and stress were collected. Physical activity (PA) levels (light PA [LPA], moderate PA [MPA], moderate-to-vigorous PA [MVPA], and vigorous PA [VPA]) and sedentary behavior [SB] were measured using a tri-axial waist-worn accelerometer. Data were analyzed using bootstrapping regression models (bias-corrected method), adjusted for age, gender, and body mass index. RESULTS: A significant negative association between MPA and MVPA with self-reported stress and a negative association between MVPA and self-reported anxiety were observed in adolescents with attention-deficit/hyperactivity disorder (ADHD). A significant positive association between SB and salivary cortisol and a negative association between VPA and salivary cortisol in adolescents with comorbid autism spectrum disorder (ASD)-ADHD were observed. CONCLUSIONS: Study results demonstrated the association of PA levels and SB with mental ill-being, including stress-related biomarkers in both adolescents with ADHD and comorbid ASD-ADHD. Participation in PA, in particular at MVPA intensity, may be essential for mitigating mental health issues in this population. Alternatively, our results could show that mitigating stress and mental health issues is critical for PA participation in adolescents with NDD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".