Abstract TP319: Randomized Pilot Trial of Usual Care versus LIFE (Lifestyle Intervention using Functional Exercise to Reduce Falls) in Those with Mild Stroke
Bibliographic record
Abstract
Purpose/Objectives: Falls are a health concern post-stroke with 30% of those who return home reporting loss of balance in the first month. Individuals with “mild” stroke are especially vulnerable as they are often discharged home quickly without exposure to balance improvement/falls prevention programs. It is important to identify effective interventions that can be offered in the naturalistic home setting. We assessed, for individuals with a first mild stroke, the impact of a home-based falls prevention program - LiFE (Lifestyle Intervention using Functional Exercise) vs STRUCTURED vs CONTROL intervention on rate of falls (self-report) and secondary outcomes including static and dynamic balance, etc.. This pilot RCT data is from a larger RCT (n=317) comparing these three interventions in older individuals (>70) at high risk for falls. Methods: Three group parallel RCT with all interventions taught at home. The LiFE group received teaching on principles of balance and strength training; applied while the individual performed common daily activities. STRUCTURED received balance and lower limb strength exercises while CONTROLS received gentle exercise. Blind assessments of the main outcomes of interest were performed before randomisation, after intervention (six months) and at one year (follow-up). Results: Of 39 participants 14 were randomized to LiFE; 14 to STRUCTURED; and, 11 to CONTROL. There were fewer fallers in the group receiving LiFE (n=6/14) vs the other two groups: (RR = 0.67; LiFE vs CONTROL); but not between STRUCTURED vs CONTROL (RR= 1.01). However, when number of falls was analyzed, LiFE had the greatest number - 26 vs 13 vs 20 - STRUCTURED and CONTROL respectively. Further data exploration revealed that LiFE was the only group with participants (n=3) who had >5 falls. Conclusion: Although LiFE was associated with a reduction in fallers; there was a disconcertingly higher rate of frequent falls in a few participants. These findings, while on a small sample, suggest that LiFE, which requires judgment and problem solving, may be suitable for a specified sub-set. The LiFE intervention “how to” and patient selection criteria will be discussed during the presentation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".