Physical Activity Level Using Doubly-Labeled Water in Relation to Body Composition and Physical Fitness in Preschoolers
Bibliographic record
Abstract
Background and objectives: There is a lack of studies investigating associations of physical activity level (PAL) and activity energy expenditure (AEE) using the doubly-labeled water (DLW) method with body composition and physical fitness in young children. Thus, we aimed to examine cross-sectional associations of PAL and AEE with body composition indices and physical fitness components in Swedish preschool children. Materials and methods: PAL was calculated as total energy expenditure measured using DLW divided by the predicted basal metabolic rate in 40 children aged 5.5 (standard deviation 0.2) years. AEE was calculated as total energy expenditure minus basal metabolic rate and the thermic effect of food, and divided by fat-free mass. Body composition was assessed using the 3-component model by combining measurements based on isotope dilution and air-displacement plethysmography. Physical fitness (muscular strength, motor fitness, and cardiorespiratory fitness) was evaluated using the PREFIT test battery. Multiple linear regression models were conducted. Results: PAL and AEE were negatively associated with body mass index, percent body fat, and fat mass index (PAL: standardized β −0.35, −0.41, and −0.45, all p < 0.036; AEE: standardized β −0.44, −0.44, and −0.47, all p < 0.006, respectively). Furthermore, PAL and AEE were positively associated with the standing long jump test (PAL: standardized β 0.37, p = 0.017; AEE: standardized β 0.38, p = 0.014). There were no statistically significant associations found regarding PAL or AEE with fat-free mass index or any other physical fitness test. Conclusions: Greater PAL and AEE at the age 5.5 were significantly associated with body fatness and improved lower-body muscular strength. Therefore, increasing physical activity, and thus energy expenditure, at young ages may be beneficial for preventing overweight/obesity. However, further studies with larger sample sizes are needed to confirm the results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".