Physical activity preferences of individuals diagnosed with schizophrenia or bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Individuals with a severe mental illness (SMI) are at least two times more likely to suffer from metabolic co-morbidities, leading to excessive and premature deaths. In spite of the many physical and mental health benefits of physical activity (PA), individuals with SMI are less physically active and more sedentary than the general population. One key component towards increasing the acceptability, adoption, and long-term adherence to PA is to understand, tailor and incorporate the PA preferences of individuals. Therefore, the objective of this study was to determine if there are differences in PA preferences among individuals diagnosed with different psychiatric disorders, in particular schizophrenia or bipolar disorder (BD), and to identify PA design features that participants would prefer. METHODS: Participants with schizophrenia (n = 113) or BD (n = 60) completed a survey assessing their PA preferences. RESULTS: There were no statistical between-group differences on any preferred PA program design feature between those diagnosed with schizophrenia or BD. As such, participants with either diagnosis were collapsed into one group in order to report PA preferences. Walking (59.5 %) at moderate intensity (61.3 %) was the most popular activity and participants were receptive to using self-monitoring tools (59.0 %). Participants were also interested in incorporating strength and resistance training (58.5 %) into their PA program and preferred some level of regular contact with a fitness specialist (66.0 %). CONCLUSIONS: These findings can be used to tailor a physical activity intervention for adults with schizophrenia or BD. Since participants with schizophrenia or BD do not differ in PA program preferences, the preferred features may have broad applicability for individuals with any SMI.
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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) | 0.002 | 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".