A Comparison of Solo and Multiplayer Active Videogame Play in Children with Unilateral Cerebral Palsy
Bibliographic record
Abstract
OBJECTIVE: Active videogames (AVGs) have potential in terms of physical activity and therapy for children with cerebral palsy. However, the effect of social interaction on AVG play has not yet been assessed. The objective of this study is to determine if multiplayer AVG versus solo affects levels of energy expenditure and movement patterns. SUBJECTS AND METHODS: Fifteen children (9.77 [standard deviation (SD) 1.78] years old) with hemiplegic cerebral palsy (Gross Motor Function Classification System Level I) participated in solo and multiplayer Nintendo(®) "Wii™ Boxing" (Nintendo, Inc., Redmond, WA) AVG play while energy expenditure and punching frequency were monitored. RESULTS: Moderate levels of physical activity were achieved with no significant differences in energy measures during multiplayer and solo play. Dominant arm punching frequency increased during the multiplayer session from 95.75 (SD 37.93) punches/minute to 107.77 (SD 36.99) punches/minute. Conversely, hemiplegic arm punching frequency decreased from 39.05 (SD 29.57) punches/minutes to 30.73 (SD 24.74) punches/minutes during multiplayer game play. Children enjoyed multiplayer more than solo play. CONCLUSIONS: Opportunities to play AVGs with friends and family may translate to more frequent participation in this moderate physical activity. Conversely, increased hemiplegic limb use during solo play may have therapeutic advantages. As such, new strategies are recommended to promote use of the hemiplegic hand during multiplayer AVG play and to optimize commercial AVG systems for applications in virtual reality therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".