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Record W2956074368 · doi:10.1080/24725838.2019.1634160

Individual, task, and environmental influences on balance recovery: a narrative review of the literature and implications for preventing occupational falls

2019· review· en· W2956074368 on OpenAlexaff
Vicki Komisar, William E. McIlroy, Carolyn A. Duncan

Bibliographic record

VenueIISE Transactions on Occupational Ergonomics and Human Factors · 2019
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of WaterlooSimon Fraser University
Fundersnot available
KeywordsBalance (ability)Task (project management)Occupational safety and healthHuman factors and ergonomicsContext (archaeology)Poison controlApplied psychologyJob designPsychologyRisk analysis (engineering)Physical medicine and rehabilitationMedicineSocial psychologyEngineeringEnvironmental healthJob satisfactionJob performance

Abstract

fetched live from OpenAlex

OCCUPATIONAL APPLICATIONSBalance recovery is a complex, multi-factorial task. When examining occupational environments for fall safety hazards, practitioners must be aware of how a worker’s ability to recover from balance loss and avoid a fall depends on their unique individual characteristics, the task they are performing, and their work environment. Balance recovery can be negatively affected by factors related to the individual (e.g., aging, obesity, arthritis, low back pain, fatigue, and peripheral neuropathies); the task (e.g., holding objects or performing multiple tasks); and the environment (e.g., slopes or stairs). Conversely, balance recovery can be enhanced by exposure to balance disturbances in the context of perturbation training, and by environmental design (e.g., appropriately-designed handrails). By understanding how individual, task, and environmental factors influence balance recovery and overall fall risk, occupational health and safety practitioners will be in a stronger position to design and implement safety controls to prevent occupational slips, trips, and falls.TECHNICAL ABSTRACT Rationale: Falls are a leading cause of injuries in occupational environments. Several factors related to the individual, the work task, and the work environment can influence a person’s ability to recover from balance loss and avoid a fall. Understanding how individual, task, and environmental factors affect balance recovery is important for ergonomic practitioners, engineers, managers and researchers, when developing appropriate solutions for preventing falls and improving the safety of the workplace. Purpose: This paper aims to provide a comprehensive, critical review of the individual, task, and environmental factors that influence balance recovery, and their implications for preventing falls in occupational environments. Methods: In this narrative review, we outline physical characteristics of balance recovery reactions, and describe how balance recovery depends on individual, task, and environmental factors. Results: Balance recovery can be negatively affected by individual (e.g., aging; obesity; musculoskeletal disorders; fatigue; peripheral neuropathies), task (e.g., holding small objects), and environmental (e.g., slopes; stairs) factors. Balance recovery can be enhanced by exposure to perturbations (in the context of perturbation training), and by environmental design (e.g., appropriately-designed handrails). Discussion: Several individual, task, and environmental factors must be considered when examining occupational fall risk. This risk can be mitigated through design, including installing appropriate handrails. Perturbation training shows promise as a fall prevention tool with clinical populations, but requires validation with healthy populations. As most of the published balance recovery literature involves laboratory studies of healthy (and mostly young) adults, future studies should examine balance recovery more directly in occupational contexts, particularly with subsets of the population who are under-represented in this literature.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.384
Teacher spread0.326 · 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.

Study designObservational
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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