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Record W4412726501 · doi:10.7759/cureus.88736

Real-Time Postural Feedback to Optimize Ergonomics and Musculoskeletal Health in Ophthalmology Residents: A Canadian Pilot Quality Improvement Study

2025· article· en· W4412726501 on OpenAlexaffabout
M Bolis, Anubhav Garg, Brian J. Chan

Bibliographic record

VenueCureus · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePhysical therapyPhysical medicine and rehabilitationOphthalmoscopyTrainerSession (web analytics)Human factors and ergonomicsOphthalmologyPoison controlMedical emergency

Abstract

fetched live from OpenAlex

Background Musculoskeletal (MSK) pain is a common occupational concern in ophthalmology, often associated with the sustained and ergonomically demanding positions required during clinical and surgical activities. Tasks such as slit-lamp examinations, indirect ophthalmoscopy, and microscope-assisted procedures may contribute to postural strain. Despite this, ergonomics remains an underemphasized component of resident education, even though physical strain during training can influence long-term clinical performance and physician well-being. This pilot study investigates whether the UPRIGHT GO 2, a wearable posture trainer, can improve posture and reduce MSK pain in ophthalmology residents. Methodology This prospective, interventional, proof-of-concept case series recruited five postgraduate year (PGY) 2 to 5 ophthalmology residents at McMaster University. Each participant wore the UPRIGHT GO 2 device over the following four distinct two-week phases: baseline, training, short-term testing, and long-term testing. During the baseline phase, the vibration mode was turned off to establish baseline posture data. In the training phase, the vibration mode was activated with a 30-second delay to provide real-time posture feedback. The short-term testing phase was conducted with the vibration turned off to assess short-term retention effects. The long-term testing phase, also with vibration off, was performed six weeks after short-term testing to evaluate long-term effects. All participants attended a brief standardized educational session before device use, which reviewed ergonomic principles relevant to ophthalmology, including optimal posture during slit-lamp examinations, indirect ophthalmoscopy, and microscope-guided procedures. The primary outcome was the percent time spent in an upright posture. The secondary outcome was MSK pain, assessed using a modified Nordic musculoskeletal and numerical pain rating scale. Results All participants (N = 5, 100%) completed the baseline and training phases, with two residents (n = 2, 40%) completing the full study through long-term testing. The mean proportion of time spent in an upright posture increased from 68.9% at baseline to 78.5% during the training phase, coinciding with the activation of vibration-based feedback. This improvement declined to 66.8% during short-term testing and further to 52.4% at long-term follow-up (n = 2, 40%), suggesting a potential attenuation of effect in the absence of continued reinforcement. MSK pain scores followed a similar pattern: mean scores increased slightly from 6.6 to 7.4 post-baseline, then declined post-training (6.4), post-short-term testing (6.5), and reached their lowest average at long-term follow-up (3.5, n = 2, 40%). All participants demonstrated either stable or improved pain scores, with two residents exhibiting concordant improvements in both posture and pain. These findings suggest that wearable feedback devices may enhance ergonomics and mitigate MSK symptoms among ophthalmology residents when incorporated into clinical training environments. Conclusions The UPRIGHT GO 2, combined with an educational intervention, may provide short-term ergonomic benefit for posture and MSK pain in ophthalmology residents. However, long-term posture retention varied. Limitations include the small sample size and device data fidelity. Larger studies are needed to validate these findings and guide ergonomic strategies in medical training.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.909

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.355
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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