Motivated, fit, and strong: changing fitness-fatness associations to increase physical activity in individuals with obesity
Bibliographic record
Abstract
Background: Regularly engaging in physical activity (PA) is related to health benefits regardless of body size. Yet, individuals with obesity frequently experience weight stigma, which can lead to PA avoidance when internalized. This study will determine if evaluative conditioning (EC) using positive images of persons with obesity increases PA in individuals living with obesity (primary outcome), and affects implicit and explicit attitudes about PA and internalized weight stigma (secondary outcomes). Methods: Sixty adults that self-identify as living with obesity will be randomly assigned to an experimental or control group, each completing four online sessions one week apart. The experimental group will complete EC tasks to retrain automatic fitness-fatness associations. The control group will read Canada’s PA Guidelines and complete PA goal-setting tasks. PA attitudes, internalized weight stigma, and PA behaviour will be measured pre-test, post-test, and at one-week follow-up. Multivariate analysis of variance will be used to determine between-group differences. Expected results: It is hypothesized that participants in the EC group will have increased PA, and that implicit and explicit PA attitudes, and internalized weight stigma will mediate the relationship between the intervention and PA. Current stage: Ethical approval has been obtained and data collection will begin in March 2017. Discussion: The results of this study could demonstrate a way to reduce internalized weight stigma and increase PA in persons with obesity, a typically inactive population. Media and health promotion practitioners may choose to portray individuals with obesity in a non-stereotypical/positive way to promote PA to persons at every size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".