Predictors of Resistance Training Behavior among Glucagon-like Peptide 1 Medication Users
Bibliographic record
Abstract
ABSTRACT Introduction Obesity is a chronic condition affecting around 650 million adults globally, with significant health implications such as cardiovascular disease and type 2 diabetes. Glucagon-like peptide 1 (GLP-1) medications have shown efficacy in promoting weight loss among obese individuals, although the weight loss is associated with undesired muscle breakdown. In this study, we investigate the psychosocial determinants of resistance training (RT) behavior among GLP-1 medication users, utilizing the Capability, Opportunity, and Motivation Behavior (COM-B) model. Methods A 1-wk longitudinal study design was adopted, involving members of a medical weight loss program (95.3% female; body mass index, 32.8 ± 7.0 kg·m −2 , 48.8 ± 9.9 yr old), assessing correlates of RT participation through an online survey. Results Psychological capabilities ( b = 0.39, standard error (SE) = 0.14; P = 0.004) and reflective motivation had direct effects on frequency ( b = 1.27, SE = 0.52; P = 0.02). Automatic motivation ( b = 3.40, SE = 1.22; P = 0.005) and physical opportunities ( b = 2.05, SE = 0.92; P = 0.02) had direct effects on duration, and psychological capabilities ( b = 0.41, SE = 0.18; P = 0.03) and automatic motivation ( b = 0.27, SE = 0.13; P = 0.04) had direct effects on intensity. Psychological capabilities directly or indirectly influenced all RT participation characteristics, suggesting a critical role of planning and self-monitoring in fostering RT adherence. Conclusion For GLP-1 users engaging in RT, targeted behavioral interventions may be useful to mitigate muscle loss. Behavior change strategies should focus on psychological capabilities integrating planning and self-monitoring to enhance RT participation, with future research needed to confirm these results in more diverse and larger populations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".