Strategies to prevent falls in the elderly: effect of a 10-week Taiji training program on proprioception, functional strength and mobility, and postural adaptation
Bibliographic record
Abstract
The impact of elderly falls on the Canadian health care system is widespread. Balance and motor coordination are commonly affected during the aging process due to declining proprioception (Ribeiro & Oliveira, 2007). In addition, there is slower walking speed and shorter stride length among fallers (Wolfson, Judge, Whipple, & King, 1995). Robinovitch et al. (2013) reported that 41% of falls in long term care homes were attributed to incorrect weight shifting. Considering the strong relationship between falls in the elderly and declining proprioception (Mion et al. 1989), the purpose of this study was to examine the effects of a 10-week Taiji training program on ankle proprioception, functional lower extremity strength and mobility and postural adaptation of older adults at risk of falls. \nA sample of 32 older adults (M = 66.5, SD = 4.94) participated in this study. Sixteen participants were conveniently assigned to the Taiji group; practiced Taiji Quan \n6-form twice weekly for 60 minutes for 10-weeks, and completed their weekly Taiji logbook. The remaining 16 participants in the control group; continued their regular activities except Taiji and completed their weekly logbook. All the participants completed pre and post assessments of postural control on an AMTI force platform, functional mobility on the Adapted Timed Up and Go Test (ATGUG), ankle joint proprioception i.e., perception of joint movement sensation, on a tilting platform, and functional strength of lower extremities on the Chair Stand test. A two by two mixed factorial ANOVA indicated significant changes with large effect size for proprioception (backward angle), lower extremity strength (repetitions), functional mobility (ATGUG 5 \nand ATGUG 4) and medium effect size for functional mobility (ATGUG 2). Changes in the proprioception variable suggest that Taiji may be a valuable alternative to traditional exercise programs. As Taiji exercises are beneficial in enhancing ankle joint backward movement perception and it also increases the efficacy of body movement by promoting protective effects against declining physical functions. Future studies should implement \nrandomized controlled design and a larger sample size.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".