Reducing lumbar spine flexion using real-time biofeedback during patient handling tasks
Bibliographic record
Abstract
BACKGROUND: Patient handling activities require caregivers to adopt postures that increase the risk of back injury. Training programs relying primarily on didactic methods have been shown to be ineffective at reducing this risk. The use of real-time biofeedback has potential as an alternative training method. OBJECTIVE: To investigate the effect of real-time biofeedback on time spent by caregivers in end-range lumbar spine flexion. METHODS: Novice participants were divided into intervention (n = 10) and control (n = 10) groups and were asked to perform a set of simulated care activities eight times on two consecutive days. Individuals in the intervention group watched a training video on safer movement strategies and received real-time auditory feedback from a wearable device (PostureCoach) in four training trials whenever their lumbar spine flexion exceeded a threshold (70% of maximum flexion). Changes in end-range lumbar spine flexion were compared between groups and across trials. RESULTS: Participants in the intervention group saw reductions in end-range lumbar spine flexion during the simulated patient handling tasks at the end of the training compared to their baseline trials while there was no change for the control group. CONCLUSIONS: The training program including PostureCoach has the potential to help caregivers learn to use safer postures that reduce the risk of back injury.
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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) | 0.000 | 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) | 0.002 | 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".