Associations of Low- and High-Intensity Light Activity with Cardiometabolic Biomarkers
Bibliographic record
Abstract
PURPOSE: Light-intensity physical activity (LIPA) accounts for much of adults' waking hours (≈40%) and substantially contributes to overall daily energy expenditure. Encompassing activity behaviors of low intensity (standing with little movement) to those of higher intensity (slow walking), LIPA is ubiquitous, yet little is known about how associations with health may vary depending on its intensity. We examined the associations of objectively assessed LIPA (categorized as either low LIPA [LLPA] or high LIPA [HLPA]) and moderate- to vigorous-intensity activity with cardiometabolic risk biomarkers. METHODS: Cardiometabolic biomarkers were measured in 4614 US adults (47 ± 17 yr) who participated in the 2003-2004 and 2005-2006 National Health and Nutrition Examination Survey cycles. Multiple linear regression analyses examined the associations of three accelerometer-derived physical activity (SD increment per day) intensity categories (LLPA, 100-761 counts per minute; HLPA, 762-1951 counts per minute; moderate-intensity physical activity [MPA], 1952-5724 counts per minute; vigorous-intensity physical activity [VPA], ≥5725 counts per minute) with cardiometabolic biomarkers, adjusting for potential sociodemographic, behavioral, and medical confounders. RESULTS: All intensities of physical activity were beneficially associated with waist circumference, C-reactive protein, triglycerides, fasting insulin, β-cell function, and insulin sensitivity (P < 0.05); only some activity intensities showed significant associations with systolic blood pressure (LLPA), body mass index, HDL cholesterol, fasting glucose, and 2-h plasma glucose (HLPA, MPA, and VPA). Generally, effect size increased with intensity of physical activity. Overall, further adjustment for waist circumference attenuated associations with MPA and VPA to a greater extent than associations with LLPA and HLPA. CONCLUSIONS: The cross-sectional findings provide novel evidence for the potential benefits of increasing both LLPA and HLPA. They further reinforce the established importance of moderate- to vigorous-intensity activity, the mainstay of public health recommendations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".