Active Video Games to Improve Behavioral Intentions and Cognitive Function in Patients With Schizophrenia: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is a severe mental illness that affects the cognitive, social, and daily functions of patients. Physical activity has been found to be important for maintaining these functions in patients with schizophrenia, but many lack the motivation to participate in physical activities. OBJECTIVE: This study aimed to explore the efficacy of active video games (AVGs) on the behavioral intention and cognitive function of patients with schizophrenia. METHODS: In this experimental study, 103 participants were recruited from 2 medical centers. All participants were randomly assigned to the experimental or control group, and 82 participants (n=41, 50% in the experimental group and n=41, 50% in the control group) completed all the processes of our protocol. The experimental group was provided with AVGs for 30 minutes twice per week for 6 weeks. The Mini Mental State Examination and a behavioral intention questionnaire were administered before and after playing the AVGs. Data were collected between April 2021 and January 2022. Generalized estimating equations and 2-tailed paired t tests were used for data analysis. RESULTS: The experimental group showed significant improvements in behavioral intention to participate in AVGs compared with the control group at both T1 (β=4.88; P=.009) and T2 (β=4.24; P=.04). In addition, the experimental group experienced significant improvements in orientation (T2: β=0.66; P=.04) and language (T2: β=0.28; P=.03) among cognitive functions compared to the control group. In contrast, there was no significant change in these variables in the control group. CONCLUSIONS: Playing AVGs can effectively enhance the behavioral intention of patients with schizophrenia to participate in physical activity and exercise and significantly improve their orientation and language. AVGs are inexpensive and easily operated tools for people with mental or physical disabilities. TRIAL REGISTRATION: ClinicalTrials.gov NCT05933356; https://clinicaltrials.gov/study/NCT05933356.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".