Assessment of the Acute Effects of Wearable Sensor Derived Auditory Biofeedback on Gross Lumbar Proprioception
Bibliographic record
Abstract
Lower back disorders (LBDs) affect a large proportion of the population, and treatment for LBDs have been shifting toward individualized, patient-centered approaches. LBDs are typically associated with poor proprioception. Therefore, there has been a recent uptake in the utilization of wearable sensors that can administer biofeedback in various industrial, clinical, and performance-based settings to improve lumbar proprioception. The aim of this study was to investigate whether wearable sensor-derived acute auditory biofeedback can be used to improve measures of gross lumbar proprioception. To assess this, healthy participants completed an active target repositioning protocol, followed by a training period where lumbar-spine posture referenced auditory feedback was provided for select targets. Target re-matching abilities were captured before and after acute auditory biofeedback training to extract measures related to accuracy and precision across spine flexion targets (i.e., 20%, 40%, 60%, 80% maximum). Results suggest a heterogenous response to proprioceptive training whereby certain individuals and spine flexion targets experienced positive effects (i.e., improved accuracy and precision). Specifically, results suggest that mid-range flexion targets (i.e., 40-60% maximum flexion) benefited most from the acute auditory feedback training. Further, individuals with poorer repositioning abilities in the pre-training assessment showed the greatest improvements from the auditory feedback training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".