Relationships between adolescent body mass index, physical activity, and sensory-processing patterns
Bibliographic record
Abstract
Background. Trends show a significant decline with adolescent physical activity (PA). Knowledge regarding how sensory-processing patterns and body mass index (BMI) relate to adolescents’ PA participation is scarce. Purpose. This study investigated if relationships exist between adolescent BMI, sensory processing, and PA participation. Method. This correlational study collected data from 141 adolescents who completed the Adolescent/Adult Sensory Profile and the Physical Activity Questionnaire–Adolescent. Their BMIs were calculated using self-reported age, height, and weight. The data were analyzed using descriptive statistics and two-tailed Spearman’s rank correlation coefficients. Findings. Adolescents with different sensory-processing patterns reported participation in both similar and distinct PAs. Participation in PA and BMI shared no significant correlation. Sensory sensitivity and BMI total ( r s = –.171, p = .044) and BMI percentile ( r s = –.191, p = .024) demonstrated significant correlations. Analysis revealed a correlation between sensory seeking patterns and PA ( r s = .224, p = .008) as well as correlations among sensory quadrants and participation in specific PAs. Implications. Occupational therapists should consider an adolescent’s sensory preferences when recommending PA interventions.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".