Designing and implementing a physical exercise intervention for people with first episode psychosis using experience-based co-design: A pilot study from Chennai, India
Bibliographic record
Abstract
BACKGROUND: Physical exercise can improve outcomes for people with first-episode psychosis (FEP). Co-designing physical exercise interventions with end users has the potential to enhance their acceptability, feasibility, and long-term viability. This study's objective was to use experience-based co-design (EBCD) methodology to develop a physical exercise intervention for FEP, and pilot test it. METHODS: The study was conducted at the Schizophrenia Research Foundation's FEP program in Chennai, India. Participants(N=36) were individuals with FEP and their caregivers, mental health professionals (MHPs, and physical training experts. EBCD methodology included one-to-one interviews, focus group discussions, joint conferences, and co-design workshops. Two instructional videos were developed. Twelve FEP patients engaged in physical exercise with help of the videos over three months. They were followed up through weekly phone calls and in-person interviews to capture data on regularity, frequency, location of exercise, and comfort levels. RESULTS: Several touch points emerged from the interviews, focus groups, and joint meetings including lack of motivation, knowledge about physical exercise; differing perspectives about physical exercise; limited resource, and time constraints. Two instructional videos demonstrating activities for participants incorporated strategies that addressed these touch points. Pilot data indicated that participants engaged with the physical exercise intervention over 3 months. CONCLUSION: This was the first study to use co-design methodology to design a physical exercise intervention for first-episode psychosis. The intervention may have therefore been responsive to stakeholder needs and preferences. Results of this study highlight the potential of co-design in designing and adapting interventions. There is need for rigorous testing with larger samples.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".