title: The ability of UK offshore workers of different body size and shape to egress through a restricted window space.
abstract: 404 male offshore workers aged 41.4 ± 10.7 y underwent 3D body scanning and an egress task simulating the smallest helicopter window emergency exit size. The 198 who failed were older (P < 0.01), taller (P < 0.05) and heavier (P < 0.0001) than the 206 who passed. Using all extracted dimensions from the scans, binary logistic regression identified a model (refined using backward elimination) which predicted egress outcome with 75.2% accuracy. Using only weight, bideltoid breadth and maximum chest depth, the model achieved ∼70% accuracy. When anatomical dimensions categorise individuals for small window egress, 25% or more will be misclassified, with false positives (those predicted to fail, but pass) slightly outnumbering false negatives (those predicted to pass, but fail), highlighting the limitations of a predictive approach which treats the body as a rigid object. Differences in flexibility and technique may explain these observations, which may be important considerations for future research.
authors.affiliation: Faculty of Health and Social Care, Robert Gordon University, Aberdeen, UK. Electronic address: a.d.stewart@rgu.ac.uk.
authors.forename: Arthur
authors.initials: A
authors.lastname: Stewart
authors.affiliation: Faculty of Health and Social Care, Robert Gordon University, Aberdeen, UK. Electronic address: r.ledingham1@rgu.ac.uk.
authors.forename: Robert
authors.initials: R
authors.lastname: Ledingham
authors.affiliation: Medical Advisor, Oil & Gas UK, The Exchange 2, 62 Market Street, Aberdeen, AB11 5PJ, UK. Electronic address: g.furnace@btinternet.com.
authors.forename: Graham
authors.initials: G
authors.lastname: Furnace
authors.affiliation: Alliance for Research in Exercise, Nutrition and Activity (ARENA), School of Health Sciences, University of South Australia, City East Campus, Australia. Electronic address: natasha.schranz@mymail.unisa.edu.au.
authors.forename: Natasha
authors.initials: N
authors.lastname: Schranz
authors.affiliation: University of Wolverhampton, Faculty of Education, Health and Wellbeing Walsall Campus, Gorway Road, Walsall, WS1 3BD, UK. Electronic address: a.m.nevill@wlv.ac.uk.
authors.forename: Alan
authors.initials: A
authors.lastname: Nevill
mesh_terms: D000328:Adult; D000401:Aircraft; D000886:Anthropometry / Q000379:methods; D049628:Body Size; D001835:Body Weight; D020378:Confined Spaces; D019652:Extraction and Processing Industry; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009068:Movement; D013647:Task Performance and Analysis; D006113:United Kingdom
keywords: 3D scanning; Binary logistic regression; Offshore workers; Window egress