Relationship of Consistency in Timing of Exercise Performance and Exercise Levels Among Successful Weight Loss Maintainers
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate whether consistency in time of day that moderate- to vigorous-intensity physical activity (MVPA) is performed relates to MVPA levels among successful weight loss maintainers in the National Weight Control Registry. METHODS: Participants (n = 375) reporting MVPA on ≥ 2 d/wk completed measures of temporal consistency in physical activity (PA) (> 50% of MVPA sessions per week occurring during the same time window: early/late morning, afternoon, or evening), PA levels, PA automaticity, and consistency in cues underlying PA habit formation (e.g., location). RESULTS: Most (68.0%) participants reported temporally consistent MVPA. These individuals reported higher MVPA frequency (4.8 ± 1.6 vs. 4.4 ± 1.5 d/wk; P = 0.007) and duration (median [IQR]: 350.0 [200.0-510.0] vs. 285.0 [140.0-460.0] min/wk; P = 0.03), and they were more likely to achieve the national MVPA guideline (≥ 150 min/wk) than temporally inconsistent exercisers (86.3% vs. 74.2%, P = 0.004). Among temporally consistent exercisers, 47.8% were early-morning exercisers; MVPA levels did not differ by time of day of routine MVPA performance (P > 0.05). Greater automaticity and consistency in several cues were related to greater MVPA among all participants. CONCLUSIONS: Most participants reported consistent timing of MVPA. Temporal consistency was associated with greater MVPA, regardless of the specific time of day of routine MVPA performance. Consistency in exercise timing and other cues might help explain characteristic high PA levels among successful maintainers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".