Predictive Accuracy of the 3-m Backward Walk Test for Fall Risk in Older Adults
Bibliographic record
Abstract
OBJECTIVES: Falls are a leading cause of injury in older adults, and accurate tools to predict fall risk are essential. This study evaluates the predictive accuracy of the 3-m Backward Walk Test (3 MBWT) for fall risk, comparing it with the Short Physical Performance Battery (SPPB), 10-m walk test (10 MWT), and fall-related questionnaires. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: The study was conducted in 2 nursing homes in Vienna, Austria. A total of 217 participants (65.9% female, median age, 80 years) were included. METHODS: Fall history was recorded retrospectively over 12 months and prospectively over 4 months using interviews and a Cogvis 3D sensor system. Predictive accuracy was assessed with receiver operating characteristic curves and area under the curve (AUC) analyses, whereas Poisson regression was used to analyze fall incidence. RESULTS: Among participants, 86 (39.6%) reported a fall within the previous 12 months. For retrospective falls, the SPPB (AUC, 0.76), 3 MBWT (AUC, 0.75), and 10 MWT (AUC, 0.73) showed similar predictive accuracy. For prospective falls, the SPPB (AUC, 0.75) and 3 MBWT (AUC, 0.74) remained similarly accurate, whereas the 10 MWT had lower predictive power (AUC, 0.67). For the 3 MBWT, cutoff points were ≥9.31 seconds for retrospective falls (32% higher incidence; 95% CI, 1.05-1.95) and ≥8.15 seconds for prospective falls (27% higher incidence; 95% CI, 1.02-1.66). Each additional second in 3 MBWT performance increased fall incidence (retrospective, 7%; prospective, 5%). SPPB cutoffs of <7.02 and <8.00 were associated with 49% (95% CI, 1.06-2.21) and 30% (95% CI, 1.02-1.74) higher fall incidence, respectively. CONCLUSIONS AND IMPLICATIONS: The 3 MBWT shows comparable accuracy with the SPPB and moderately better performance than the 10 MWT for prospective falls. Its efficiency, predictive value, and time savings make it a practical and viable tool for clinical fall risk assessment in older adults.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".