Abstract P440: Physical Activity Participation Increase in the Latina Population Using Evaluative Conditioning with Hypertrophy Training
Bibliographic record
Abstract
Purpose: Epidemiological data show that physical activity participation is lowest in minority women, particularly in Latinas. The purpose of this study was to examine the effect of hypertrophy (HT) and power training (PT) used concomitantly with evaluative (EC) or neutral conditioning (NC) on exercise adherence, lean body mass, and psychosocial variables in Latina women. EC is a behavioral method using paired stimuli to develop and strengthen associations in memory. Methods: 142 Latina women (mean ± SD, age 36.8±15.9 yrs.) were randomly assigned to HT or PT and then further stratified into EC or NC. Results: There was a significant training x conditioning interaction for exercise adherence (Fig. 1). For HT, the EC group demonstrated a significantly longer time spent training than the NC group, ( F 1, 184 = 11.874, P < 0.001,η p 2 = .061). For PT, there was no significant difference between EC and NC groups in time spent training. Additionally, a significant time x condition interaction was found for lean body mass, ( F 1, 76 = 5.138, P < 0.05,η p 2 = .063). A significant time x condition interaction was also observed for exercise self-efficacy ( p <0.05). For the EC condition, exercise self-efficacy significantly increased from pretest to post-test, ( F 1, 52 = 6.536, P < .05, η p 2 = .112). Conclusion: Both training modality and EC can impact exercise adherence in Latina women. Figure 1. Exercise adherence using hypertrophy and power training and evaluative or neutral conditioning. *Significantly greater than neutral conditioning during hypertrophy training, p < 0.01. ** p < 0.001.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".