Preoperative 24-hour movement behaviors and early weight loss after metabolic bariatric surgery: a compositional analysis
Bibliographic record
Abstract
This study used compositional data techniques that address the interdependence of 24-h movement behaviors (sleep, sedentary behavior [SB], light-intensity physical activity [LPA], moderate-to-vigorous intensity physical activity [MVPA]) to examine: (1) how patients undergoing metabolic bariatric surgery (MBS) allocate time among these behaviors before MBS, and (2) whether overall time-use composition and modeled reallocation patterns relate to early weight loss after MBS. Participants wore an accelerometer 24 h/day for 10 days before MBS to measure time in sleep, SB, LPA, and MVPA. Isotemporal substitution models estimated differences in 6-month post-MBS percentage total weight loss (%TWL) associated with reallocations of these pre-surgery movement behaviors. Forty-five participants provided valid data. Pre-MBS time-use composition was associated with %TWL (23.8 ± 5.1%; F = 2.66, p = 0.047). Reallocating 15-60 SB or LPA minutes/day to MVPA was estimated to relate to 0.9-3.5% greater %TWL. Reallocating 15-30 MVPA minutes/day to SB or LPA was estimated to relate to 1.4-5.0% less %TWL (all comparisons p < 0.05). Other reallocations were non-significant. In conclusion, modeled shifts in time from SB or LPA to MVPA and vice versa were associated with estimated increases or decreases in early post-surgical weight loss, respectively. Experimental research is needed to clarify causal relationships and inform interventions to improve MBS outcomes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".