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Record W3191735482 · doi:10.1016/j.apergo.2021.103547

Evaluating a wearable biofeedback device for reducing end-range sagittal lumbar spine flexion among home caregivers

2021· article· en· W3191735482 on OpenAlexafffund
Megan Kamachi, Mohammadhasan Owlia, Tilak Dutta

Bibliographic record

VenueApplied Ergonomics · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSagittal planePhysical therapyPhysical medicine and rehabilitationMedicineBiofeedbackIntervention (counseling)Lumbar spineRepeated measures designLumbarNursingSurgery

Abstract

fetched live from OpenAlex

Caregivers who work in the home environment are at risk of back injury due to the awkward postures they have to adopt while providing care. Real-time biofeedback provided by a recently developed wearable device (PostureCoach) may be able to reduce this risk. The effectiveness of a two-day training intervention (including PostureCoach and an educational video) was evaluated for its ability to decrease the amount of time spent in end-range spine flexion. Twenty novice caregivers repeated a series of simulated care tasks. Real-time auditory biofeedback was provided to the intervention group (n = 10) when participants' sagittal lumbar spine flexion exceeded a preset threshold during training trials. Participants in the control group (n = 10) received no feedback. Participants repeated the tasks again two weeks and two months post-intervention. The intervention group maintained decreased end-range (80th and 95th percentile) spine flexion compared to controls at both post-intervention time points.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.308
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2021
Admission routes2
Has abstractyes

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