The Effect of Individualized Fall Prevention Programs on Community-Dwelling Older Adults: A Scoping Review
Bibliographic record
Abstract
An alarming rate of injurious falls among older adults warrants proactive measures to reduce falls and fall risk. The purpose of this article was to examine and synthesize the literature as it relates to programmatic components and clinical outcomes of individualized fall prevention programs on community-dwelling older adults. A literature search of four databases was performed using search strategies and terms unique to each database. Title, abstract, and full article reviews were performed to assure inclusion and exclusion criteria were met. Data were analyzed for type of study, program providers, interventions and strategies used to deliver the program, assessments used, and statistically significant outcomes. Queries resulted in 410 articles and 32 met all inclusion criteria (19 controlled trials and 13 quasi-experimental). Physical therapists were part of the provider team in 23 (72%) studies and the only provider in 10 (31%). There was substantial heterogeneity in procedures and outcome measures. Most common procedures were balance assessments (n=30), individualized balance exercises (n=29), cognition (n=21), home and vision assessments (n=16), specific educational modules (n=15), referrals to other providers/community programs (n=8), and motivational interviewing (n=7). Frequency of falls improved for eight of 13 (61.5%) controlled trials and four of five (80%) quasi-experimental studies. Balance and function improved in six of 11 (54.5%) controlled trials and in each of the six (100%) quasi-experimental studies. Strength improved in three of seven (43%) controlled trials and four of five (75%) quasi-experimental studies. While many programs improved falls and balance of older adults, there was no conclusive evidence as to which assessments and interventions were optimal to deliver as individualized fall prevention programming. The skill of a physical therapist and measures of fall frequency, balance, and function were common among the majority of studies reviewed. Despite the variability among programs, there is emerging evidence that individualized, multimodal fall prevention programs may improve fall risk of community-dwelling older adults and convenient access to these programs should be emphasized.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".