Use of the Brief-BESTest partially instrumented with accelerometry to detect balance deterioration in middle-age
Bibliographic record
Abstract
BACKGROUND: Most standardized balance tests cannot detect subtle balance deterioration in middle age, or identify those at higher risk for accelerated balance decline due to a ceiling effect. AIMS: To determine whether the Brief Balance Evaluation Systems Test (Brief-BESTest), partially instrumented with accelerometry, can detect balance deterioration in middle age and identify individuals with poor balance. METHODS: We studied young (25.3 ± 2.3 years), early middle-aged (47.7 ± 2.6 years), and late middle-aged adults (60.6 ± 3.6 years), with 25 participants in each age group. Subjects wore an accelerometer on their lower back while performing the Brief-BESTest. Balance measurements included the Brief-BESTest total and sub-measures scores, and postural sway during the Brief-BESTest standing tasks, calculated by the 95% confidence ellipse trajectory of the center of mass (COM-95% ellipse). RESULTS: Compared to the two middle-aged groups, young adults had better total Brief-BESTest and sub-measures scores, apart from the Stability-in-Gait sub-measure, and less postural sway during the Sensory-Orientation sub-measure. The total Brief-BESTest scores as well as the Biomechanical-Constraints and Sensory-Orientation sub-measures differed also between early and late middle-aged adults. Both the Brief-BESTest total scores and the Sensory-Orientation postural sway values demonstrated increased variation with age, allowing to identify subjects with poor balance. A moderate negative correlation (r = -0.43) was found between the Brief-BESTest total score and the COM-95% ellipse size, and a moderate agreement (k = 0.56) in identifying subjects with poor performance in the early but not the late middle age group. CONCLUSIONS: The Brief-BESTest test combined with accelerometry could be a suitable screening tool to identify middle-aged people with early balance deterioration and potentially identify those with poor balance and a possible higher risk for falls. Clinicians and policymakers can use our findings to implement balance assessment programs in patients < 65 years, leading to preventive strategies before the risk increases.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".