Obstacle course training can improve mobility and prevent falls in people with intellectual disabilities
Bibliographic record
Abstract
BACKGROUND: Persons with intellectual disabilities (ID) constitute a special-needs population at high risk of falling. This is the first study to evaluate whether obstacle course training can improve mobility and prevent falls in this population. METHODS: The intervention was implemented as part of an institution-wide health care improvement plan aimed at reducing falls at a residential facility for people with ID. It comprised an annual screening of each resident for his or her individual fall risk. Subsequently, the group of ambulatory persons with a moderate to high fall risk (n = 39) were offered 10-session obstacle course training to improve their balance and gait abilities. Mobility was assessed pre-intervention, mid-term and post-intervention with the Performance Oriented Mobility Assessment (POMA), the Timed Up and Go (TUG) and the 10-meter walking test. The number of falls was compared between the year before and after intervention. RESULTS: The number of falls decreased by 82% (P < 0.001). POMA scores significantly improved from pre-intervention to mid-term (mean difference ± SD, 1.8 ± 2.9, P = 0.001), from mid-term to post-intervention (2.0 ± 2.9, P < 0.001), and from pre-intervention to post-intervention (3.8 ± 4.3, P < 0.001). Participants completed the 10-meter walking test faster at the post-intervention compared with the pre-intervention assessment (difference ± SD, 2.1 ± 5.1 s, P = 0.022). TUG scores did not improve significantly. CONCLUSIONS: The present study provides preliminary evidence for the effectiveness of obstacle course training in improving mobility and preventing falls in people with ID. As falls are a significant health concern in this population, further research is advocated to provide conclusive evidence for the suggested beneficial effects of exercise interventions.
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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 | 0.000 | 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) | 0.003 | 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".