Does light-intensity physical activity moderate the relationship between sitting time and adiposity markers in adolescents?
Bibliographic record
Abstract
BACKGROUND: While the relationship between sedentary time and adiposity markers may be independent of moderate-to-vigorous intensity physical activity (MVPA) among adolescents, little is known about the role of light-intensity physical activity (LIPA) in this relationship. The aim of this cross-sectional study was to examine whether device-measured LIPA and MVPA moderate the associations between objectively measured sitting time and adiposity markers (body mass index (BMI)) and waist circumference (WC)) among adolescents. METHODS: This study included accelerometer and inclinometer data obtained from 219 adolescents (age = 14.9 ± 1.6 years, mean ± SD), collected during 2014 and 2015 in Melbourne, Australia. ActiGraph GT3X accelerometers were used to determine time spent in total-LIPA (101 counts/min to 3.99 metabolic equivalents (METs)) was dichotomized into low-LIPA (101-799 counts/min) and high LIPA (800 counts/min to 3.99 METs), and MVPA (≥ 4 METs). The average time spent sitting was obtained from activPAL inclinometers. Anthropometric measures were assessed by trained staff. Interactions between sitting and total-LIPA, low-LIPA, high-LIPA, and MVPA on BMI z-score (zBMI) and WC z-score (zWC), respectively, were examined using linear regression, adjusting for age and sex; and moderation by total-LIPA, low-LIPA, high-LIPA, and MVPA were examined by adding interaction terms. Significant interaction effects were probed by comparing associations at the mean and at 1 SD below and above the mean. RESULTS: Total-LIPA significantly moderated the association between sitting time and zBMI, and low-LIPA significantly moderated the association between sitting time and zBMI and zWC. No other associations were found for total-LIPA, high-LIPA, or MVPA. Specifically, at high levels of total-LIPA (+1 SD), there is a negative association between sitting time and zBMI. In addition, at high levels of low-LIPA (+1 SD), there is a negative association between sitting time and zBMI and zWC. CONCLUSION: Associations between sitting and adiposity depended on time spent in total-LIPA and low-LIPA, but not high-LIPA or MVPA. Results suggest that increasing time spent in LIPA may provide protection from the deleterious effects of sitting on adiposity markers among adolescents. Experimental evidence is needed to support these conclusions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".