The impact of balance training on static stability in a group of adults with autism and intellectual disability
Bibliographic record
Abstract
Context: Disruptions in motor functioning are characteristic of both Autism and Intellectual Disability (ID). Balance training is important for this population due to the risks associated with deficits in postural stability. Purpose: To investigate the impact of a 12-week motor control training intervention on static stability control in adults with Autism and ID (A-ID). Procedures: Eleven participants with A-ID (mean age: 35.6 ± 11.3 years; mean height: 1.7 ± 0.1 m; mean mass: 83.7 ± 19.3 kg; two females) performed 20-second quiet standing trials on a force platform, with eyes open (EO) and eyes closed (EC). Testing occurred at the start, mid-point, and end of the intervention, and after 4 weeks of detraining. Medial-lateral and anterior-posterior root-mean-square (RMS) displacement (mm) and velocity (mm/s) of the centre of pressure (COP) and sway area were calculated to assess static balance control. Statistical Analysis: Linear Mixed Modeling with compound symmetry repeated covariance structure was used to test for significant differences at each level of the dependent variables. Results: RMS displacements, velocities and sway areas did not change significantly during or after the 12 weeks of balance training. The group means and standard deviations of all three dependent measures were often lower in the EC condition than the EO condition. Discussion: Repetitive behaviours and distractions may be less problematic in the EC condition, which may partially explain this counterintuitive trend. Future studies should correlate measures of repetitive behaviour with measures of COP in order to more accurately assess balance in this population. Acknowledgments: Beth Anne Currie, Southern Network of Specialized Care
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".