Effectiveness and safety of Nintendo Wii Fit PlusTM training in children with migraine without aura: a preliminary study
Bibliographic record
Abstract
BACKGROUND: Migraine without aura (MoA) is a painful syndrome, particularly in childhood; it is often accompanied by severe impairments, including emotional dysfunction, absenteeism from school, and poor academic performance, as well as issues relating to poor cognitive function, sleep habits, and motor coordination. MATERIALS AND METHODS: The study population consisted of 71 patients affected by MoA (32 females, 39 males) (mean age: 9.13±1.94 years); the control group consisted of 93 normally developing children (44 females, 49 males) (mean age: 8.97±2.03 years) recruited in the Campania school region. The entire population underwent a clinical evaluation to assess total intelligence quotient level, visual-motor integration (VMI) skills, and motor coordination performance, the later using the Movement Assessment Battery for Children (M-ABC). Children underwent training using the Wii-balance board and Nintendo Wii Fit Plus™ software (Nintendo Co, Ltd, Kyoto, Japan); training lasted for 12 weeks and consisted of three 30-minute sessions per week at their home. RESULTS: The two starting populations (MoA and controls) were not significantly different for age (P=0.899) and sex (P=0.611). M-ABC and VMI performances at baseline (T0) were significantly different in dexterity, balance, and total score for M-ABC (P<0.001) and visual (P=0.003) and motor (P<0.001) tasks for VMI. After 3 months of Wii training (T1), MoA children showed a significant improvement in M-ABC global performance (P<0.001), M-ABC dexterity (P<0.001), M-ABC balance (P<0.001), and VMI motor task (P<0.001). CONCLUSION: Our study reported the positive effects of the Nintendo Wii Fit Plus™ system as a rehabilitative device for the visuomotor and balance skills impairments among children affected by MoA, even if further research and longer follow-up are needed.
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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) | 0.001 | 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".