The Short Questionnaire to Assess Health-Enhancing (SQUASH) Physical Activity in Adolescents: A Validation Using Doubly Labeled Water
Bibliographic record
Abstract
BACKGROUND: Accurate assessment of physical activity energy expenditure (PAEE) among adolescents is important for surveillance, evaluating interventions, and understanding the relation between energy balance and normal physiological and behavioral growth and development. The purpose of this study was to examine the validity of the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH)13 for measuring PAEE among adolescents. METHODS: The participants were seventeen adolescents (9 females; Mean age = 17.53; SD = 0.62). Energy expenditure was measured during a 9-day period with doubly labeled water (DLW). The SQUASH was self-administered on the morning of the 10th day and assessed commuting activities, leisure time activities, household activities, and activities at work and school over the previous 9 days. RESULTS: A Bland-Altman plot indicated that the SQUASH underestimated PAEE compared with DLW by a mean difference of 126 kcal·d-1 (95% limits of agreement: -1,207 to 1,459 kcal·d-1), representative of a 10% underestimation. The Spearman rank order correlation coefficient showed there was a significant association between the SQUASH and DLW (r = .50, P = .04), for estimating PAEE. CONCLUSION: When using a sample of highly active adolescents, the SQUASH is a valid self-report tool for measuring PAEE at the group and individual rank order level.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".