Increasing Non-Exercise Physical Activity With Training Reduces Chance Of Non-Response To Exercise
Bibliographic record
Abstract
Evidence of cardiorespiratory fitness (CRF) non-response is growing in both clinical and exercise training studies. Along with aerobic training, an increase in non-exercise physical activity may reduce CRF non-response contingency. PURPOSE: To determine if increases in non-exercise physical activity mitigates CRF non-response to exercise training among sedentary, overweight/obese adults. METHODS: Thirty-six adults (age: 54.19±7.14 years; BMI: 35.83±4.66 kg/m2; 77.8% female) were assessed from a previous exercise study (>70% adherence to 4 weekly sessions across 24 weeks). Participants were randomized to an aerobic training group or an aerobic training and increasing non-exercise physical activity group (increase 1,000 to 3,000 steps per day from baseline). Both groups performed the same supervised aerobic training (50-75% VO2 max) for 24 weeks at a dose of 12 kcals per kg per week. CRF non-response was determined via calculated delta (∆) values (follow-up minus baseline values) for absolute VO2 max (L/min) and participants were categorized as non-responders via technical error (TE) (∆<0.71 L/min) and classical measures (∆<0 L/min). Pearson Chi-square test of independence was conducted for categorical variables (i.e. responders vs. non-responders) in TE and classical non-responders, separately. A binary multivariable logistic regression was used to estimate odds of CRF non-response based on baseline demographic factors (age, race, BMI, fitness, waist circumference). RESULTS: Participants increasing non-exercise physical activity with aerobic training were significantly more likely to increase CRF based on TE analysis, X2 (2, N=36) =10.99, p=.004, compared to aerobic training alone. Whereas, classic non-response did not show a significant relationship X2 (2, N=36) =2.77, p=.251. Baseline age (p<.05) was a significant predictor of TE response, while baseline BMI (p<.05) was a significant predictor for classic response. CONCLUSION: Increasing non-exercise physical activity concurrent with aerobic training may improve likeliness of increasing CRF and, thus, reduce risk of cardiovascular disease and mortality. Supported by a grant from the American Heart Association (13SDG17140091).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".