Assessing the Impact of Wearing a Weighted Vest on Balance, Stability, and Quality of Life in Older Adults: A Pilot Study
Bibliographic record
Abstract
Background and Aims: Falls are a significant public health concern among older adults, with profound impacts on health, independence, and healthcare costs. There is a growing need for innovative fall prevention tools and strategies, and weighted vests have the potential to be used as a fall-prevention tool. This study aimed to assess the effects of 10-pound and 20-pound weighted vests on balance and stability in adults aged 55 and older, as well as the usability and acceptability of these vests for daily use. Methods: A cross-sectional pilot study was conducted in 2024 among thirty-four participants who completed an in-person assessment of objective balance tests (4-Stage Balance Test, Timed Up and Go, Berg Balance Scale, Functional Reach) and subjective stability measures (Rate of Perceived Stability Scale, Functional Abilities Confidence Scale, Falls Efficacy Scale) under three conditions: no vest, 10-pound vest, and 20-pound vest. Seventeen participants volunteered to take the vests home for daily use for 2 weeks. Multiple linear and ordinal linear regression analyses were performed using SPSS 29. Results: Although the weighted vest conditions were not significantly correlated with measures of balance and stability, increased age and body weight were inversely related to these outcomes. In the in-lab condition, 73% of participants responded positively to the vests, citing improved posture, and feeling more stable. In the take-home phase, the vests were well-received, with 53% indicating willingness to continue use. Conclusion: This study suggests that while weighted vests may not directly enhance balance in the short-term, they could be beneficial as part of a broader strategy, particularly in weight management and exercise programs. Future research should explore the long-term effects of weighted vests, especially in more diverse populations, to better understand their potential in fall prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".