Effects Of A 30-day Fitness Challenge On Body Composition And Markers Of Health In Women
Bibliographic record
Abstract
Numerous studies have documented the value of exercise in controlled clinical trials. However, results achieved in clinical trials may not be realized when applied to the general public. PURPOSE: To determine the effects of a second international 30-day fitness challenge on fitness and markers of health in women. METHODS: 29,220 sedentary women (44.8±13 yrs, 84.0±20 kg, 31.3±7 kg/m2 BMI, 38.3±7% fat) at 3,446 Curves® clubs in the US and Canada participated in the study. Subjects gave online consent and then completed questionnaires and baseline measures. Participants then followed the Curves 30-min circuit training program 3 d/wk plus a 30-min walk 4 d/wk. After 4-wks, subjects repeated testing and questionnaires. Data were analyzed by dependent T-test and are presented as mean±SD changes from baseline. RESULTS: Post-study results were obtained from 2,967 clubs with 14,535 participants. Participants experienced significant (p<0.05) decreases in weight (-1.8±18 lbs, -0.9%; n=14,535), percent fat (-0.7±1.9%, -1.8%; n=14,396), total inches (-3.9±5.3 in, -1.9%; n=13,548), BMI (-0.31±2.8 kg/m2, -0.9%; n=14,432), systolic BP (-2.6±11 mmHg, -1.6%; n=3,870), and diastolic BP (-2.2±8 mmHg, -2.1%; n=3,863). Participants also reported significantly less weekly (-10%) and monthly (-19%) alcohol consumption, sugar intake (-24%), and fat intake (-22.4%) with greater calcium intake (6.6%), and fiber intake (7%). CONCLUSIONS: Results corroborate previous findings that significant improvements in body composition, markers of health, and positive behaviors can be achieved through short-term fitness initiatives. Supported by Curves International, Waco TX
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".