Managing Fall Prevention through Exercise in Older Adults Afflicted by Cognitive and Strength Impairment
Bibliographic record
Abstract
INTRODUCTION: A growing trend in medicine is older adults and increased need for geriatric services. Falls contribute heavily to hospitalizations and worsening of overall health in this frail demographic. There are numerous biological and physical culprits which, if targeted, can prevent falls. The objective was to review benefits of different types of exercises for fall prevention for older persons who are community-dwelling or living in long-term care facilities. METHODS: A systematic review was conducted to determine the different types of exercises for fall prevention. Data extraction via a standardized protocol was performed to assess study design, outcomes, limitations, and author's conclusions. Corroborative themes were identified and the authors responsible for the contributing research were cited as they came up. Nineteen randomized controlled trials were identified, between 1990 and 2018, using MEDLINE, PubMed, Cochrane, CINAHL, and Web of Science databases. Studies involving adults greater than age 60 in high-risk community or nursing home populations in the English language with a duration longer than or equal to 6 weeks with focuses on either low-risk balance, strength, or combination of both and whole-body vibration. RESULTS: Balance exercise training increased balance at 6 and 12-month intervals involving balance, strength, and cognitive training. DISCUSSION: Insignificant results were seen in whole-body vibration and differing results existed for Tai Chi. It is important to recognize that although exercises help reduce the risk of falling and play a significant role in improving mobility safely, there will always be a risk of falls.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".