Effects of Physical Activity on Disordered Eating Behaviours in Individuals With a Psychotic Disorder
Bibliographic record
Abstract
AIM: This study aims to determine the effect of physical activity on cognitive restraint, uncontrolled eating and emotional eating in individuals with a psychotic disorder. METHODS: Twenty-seven participants with a psychotic disorder (55% male; mean age: 30 ± 7.5 years; Caucasian: 66.7%; schizophrenia spectrum disorders: 44.4%; bipolar disorder with psychotic features: 29.6%) took part in a 6-month bi-weekly physical activity program (walking, running, yoga, cycling and dancing). The Three-Factor Eating Questionnaire was used to assess participant's eating behaviours, and the frequency of completed physical activity sessions was compiled. RESULTS: The mixed models analysis approach revealed that the level of cognitive restraint remained unchanged (pre: 39.2 ± 18.7 vs. post: 44.1 ± 18.3; p = 0.24), while the levels of uncontrolled eating (pre: 39.7 ± 19 vs. post: 31.6 ± 19.7; p = 0.02) and emotional eating (pre: 45.5 ± 22.3 vs. post: 32.2 ± 22.2; p < 0.001) decreased at the end of the 6-month physical activity program. DISCUSSION: This study showed that physical activity has positive effects on disordered eating behaviours in individuals with a psychotic disorder, similarly to previous studies on other populations (e.g., overweight and obese participants, postmenopausal women). CONCLUSION: Further studies are warranted to better understand the role of physical activity in moderating eating behaviours.
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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 | 0.001 |
| 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) | 0.001 | 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".