The ability of UK offshore workers of different body size and shape to egress through a restricted window space.

404 male offshore workers aged 41.4 (± 10.7 years) 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

Am J Ind Med

BackgroundDrivers of heavy and tractor-trailer trucks accounted for 56% of all production and nonsupervisory employees in the truck transportation industry in 2011. There are limited data for illness and injury in long-haul truck drivers, which prompted a targeted national survey.MethodsInterviewers collected data during 2010 from 1,670 long-haul truck drivers at 32 truck stops across the 48 contiguous United States that were used to compute prevalence estimates for self-reported health conditions and risk factors.ResultsObesity (69% vs. 31%, P <0.01) and current smoking (51% vs. 19%, P <0.01) were twice as prevalent in long-haul truck drivers as in the 2010 U.S. adult working population. Sixty-one percent reported having two or more of the risk factors: hypertension, obesity, smoking, high cholesterol, no physical activity, 6 or fewer hours of sleep per 24-hr period.ConclusionSurvey findings suggest a need for targeted interventions and continued surveillance for long-haul truck drivers.CC999999/Intramural CDC HHS/United States2015-07-22T00:00:00Z24390804PMC45111027184vault:2522

Ergonomics

This study evaluated the accuracy of self-reported body weight and height compared to measured values among firefighters and identified factors associated with reporting error. A total of 863 male and 88 female firefighters in four US regions participated in the study. The results showed that both men and women underestimated their body weight ( - 0.4 \uc2\ub1 4.1, - 1.1 \uc2\ub1 3.6 kg) and overestimated their height (29 \uc2\ub1 18 , 17 \uc2\ub1 16 mm). Women underestimated more than men on weight (p = 0.022) and men overestimated more than women on height (p < 0.001). Reporting errors on weight were increased with overweight status (p < 0.001) and were disproportionate among subgroups. About 27% men and 24% women had reporting errors on weight greater than \uc2\ub1 2.2 kg, and 59% men and 28% women had reporting errors on height greater than 25 mm.CC999999/Intramural CDC HHS/United States2016-02-09T00:00:00Z25198061PMC474703

Cardiometabolic risk factors and mental health status among truck drivers : a systematic review

The first author (AG) has received funding for their PhD Studentship from the Colt Foundation. The Colt Foundation had no role in study design; election, synthesis and interpretation of data; writing of the report; or the decision to submit the manuscript for publication. SC and JAK are in receipt of funding from the NIHR Public Health Research Programme (reference: NIHR PHR 15/190/42) for the evaluation of a multi-component health behaviour intervention in truck drivers. They are also supported by the NIHR Leicester Biomedical Research Centre – Lifestyle theme. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Peer reviewedPublisher PD

Anthropometry in physical performance and health.

Anthropometry is defined as 'the scientific procedures and processes of acquiring surface anatomical dimensional measurements such as lengths, breadths, girths and skinfolds of the human body by means of specialist equipment' (Stewart 2010). This approach has altered little if at all over the last hundred years, and even in ancient Greece, we hear of systematic body measurement in order to produce statues that were appropriately sized to real individuals. Sculptors would have appreciated that this approach demands painstaking detail, adherence to best practice, and diligence in reducing errors, and few scientists would argue with this. Anthropometry sits within the field of kinanthropometry — 'the academic discipline which involves the use of anthropometric measures in relation to other scientific parameters and/or thematic areas such as human movement, physiology or applied health sciences' (Stewart 2010). However, one of the issues for kinanthropometry, particularly in its applications for physical activity and sport, is that the tools have not advanced in parallel with those of other disciplines such as sports physiology and biomechanics. Researchers, therefore, may be persuaded to think that its relevance is reducing in a contemporary research context. Indeed, for publications in two main research journals, the prevalence of anthropometry as central to research (estimated from key word searches using similar terms) appears to have peaked a generation ago (Olds 2004). But perhaps kinanthropometry is on the verge of a renaissance for two reasons. First, the field has now largely embraced tightly defined standard procedures and error control, the lack of which previously diminished its ability to convince a research community becoming accustomed to more sophisticated methods. Second, recent advances in digital anthropometry, using three-dimensional (3D) body scanning, enable an unprecedented range of new measurement possibilities. These new measures can augment traditional anthropometry, and the combination of manual and digital anthropometry may allow new research questions to be addressed

Accuracy, precision and reliability in anthropometric surveys for ergonomics purposes in adult working populations: A literature review

Anthropometric surveys are the most common method of gathering human morphometric data, used to design clothing, products and workspaces. The aim of this paper was to assess how current peer reviewed literature addresses the accuracy, reliability and precision regarding manual anthropometric surveys applied to adult working populations in the field of ergonomics. A literature review was performed in two electronic databases for finding relevant papers. A total of 312 papers were reviewed, of which 79 met the inclusion criteria. The results shown that the subjects of these publications are poorly addressed, so that only 27 studies mentioned at least one of the terms and none of the studies evaluated all of the terms. Only one paper mentioned and assessed precision and reliability of the measurement procedure. Furthermore, none of the publications evaluated accuracy. Moreover, the reviewed papers presented large differences in the factors that affect precision, reliability and accuracy. This was particularly clear in the measurer technique/training, measurement tools, subject posture and clothing. Researchers in this area should take more rigorous approaches and explicit indicators with their results should be presented in any report. Relevance for industry: It is important that scientific literature related to manual anthropometric measurements uses methods for assessing measurement error, since these data are often used to design clothing and workspaces as well as to calibrate non manual methods such as 3D scanners.</p

NIOSH science awards 2014

The annual NIOSH Science Awards are an opportunity for NIOSH to honor researchers for excellence in science. The awards include the following:\u2022 The Alice Hamilton Award, for scientific excellence of technical and instructional materials by NIOSH scientists and engineers.\u2022 The James P. Keogh Award, for outstanding service by an individual in the occupational safety and health field.\u2022 The Bullard-Sherwood Research-to-Practice Award, for exceptional efforts by NIOSH researchers and partners in applying occupational safety and health research to the prevention of workplace fatalities, illnesses, or injuries.\u2022 The Director\u2019s Award for Extraordinary Intramural Science.NIOSH Presents 2014 Awards for Significant Scientific Contributions -- Alice Hamilton Awards for Occupational Safety and Health -- Bullard-Sherwood Research-to-Practice (r2p) Award -- Director\u2019s Intramural Award for Extraordinary Science (DIA) -- James P. Keogh Award for Outstanding Service in Occupational Safety and Health -- NIOSH Nominations for the Charles C. Shepard Science Award Nominations.201

Hum Factors

ObjectivesThis article presented four anthropometric theories (univariate, bivariate/probability distribution, multivariate, and shape-based methods) for protective equipment design decisions.BackgroundWhile the significance of anthropometric information for product design is well recognized, designers continue to face challenges in selecting efficient anthropometric data processing methods and translating the acquired information into effective product designs.MethodsFor this study, 100 farm tractor operators, 3,718 respirator users, 951 firefighters, and 816 civilian workers participated in four studies on the design of tractor roll-over protective structures (ROPS), respirator test panels, fire truck cabs, and fall-arrest harnesses, respectively. Their anthropometry and participant-equipment interfaces were evaluated.ResultsStudy 1 showed a need to extend the 90-cm vertical clearance for tractor ROPS in the current industrial standards to 98.3 to 101.3 cm. Study 2 indicated that current respirator test panel would have excluded 10% of the male firefighter population; a systematic adjustment to the boundaries of test panel cells was suggested. Study 3 provided 24 principal component analysis-based firefighter body models to facilitate fire truck cab design. Study 4 developed an improved gender-based fall-arrest harness sizing scheme to supplant the current unisex system.ConclusionsThis article presented four anthropometric approaches and a six-step design paradigm for ROPS, respirator test panel, fire truck cab, and fall-arrest harness applications, which demonstrated anthropometric theories and practices for defining protective equipment fit and sizing schemes.ApplicationsThe study provided a basis for equipment designers, standards writers, and industry manufacturers to advance anthropometric applications for product design and improve product efficacy.CC999999/Intramural CDC HHS/United States2015-09-08T00:00:00Z23516791PMC45623327184vault:263

Clarity of View: An Analytic Hierarchy Process (AHP)-Based Multi-Factor Evaluation Framework for Driver Awareness Systems in Heavy Vehicles

Several emerging technologies hold great promise to improve the situational awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. The average commercial vehicle driver in the USA is 54 years old with many drivers continuing past retirement age. Current methods for evaluating visibility systems only consider field of view and do not incorporate measures of the cognitive elements critical to drivers, especially the older demographic. As a result, industry is challenged to evaluate new technologies in a way that provides enough information to make informed selection and purchase decisions. To address this problem, we introduce a new multi-factor evaluation framework, “Clarity of View,” that incorporates several important factors for visibility systems including: field of view, image detection time, distortion, glare discomfort, cost, reliability, and gap acceptance accuracy. It employs a unique application of the Analytic Hierarchy Process (AHP) that involves both expert participants acting in a Supra-Decision Maker role alongside driver-level participants giving both actual performance data as well as subjective preference feedback. Both subjective and objective measures have been incorporated into this multi-factor decision-making model that will help industry make better technology selections involving complex variables. A series of experiments have been performed to illustrate the usefulness of this framework that can be expanded to many types of automotive user-interface technology selection challenges. A unique commercial-vehicle driving simulator apparatus was developed that provides a dynamic, 360-degree, naturalistic driving environment for the evaluation of rearview visibility systems. Evaluations were performed both in the simulator and on the track. Test participants included trucking industry leadership and commercially licensed drivers with experience ranging from 1 to 40 years. Conclusions indicated that aspheric style mirrors have significant viability in the commercial vehicle market. Prior research on aspheric mirrors left questions regarding potential user adaptation, and the Clarity of View framework provides the necessary tools to reconcile that gap. Results obtained using the new Clarity of View framework were significantly different than that which would have previously been available using current industry status-quo published test methods. Additional conclusions indicated that middle-aged drivers performed better in terms of image detection time than young and elderly age categories. Experienced drivers performed better than inexperienced drivers, regardless of age. This is an important conclusion given the demographic challenges faced by the commercial vehicle industry today that is suffering a shortage of new drivers and may be seeking ways to retain its aging driver workforce. The Clarity of View evaluation framework aggregates multiple factors critical to driver visibility system effectiveness into a single selection framework that is useful for industry. It is unique both in its multi-factor approach and custom-developed apparatus, but also in its novel approach to the application of the AHP methodology. It has shown significance in ability to discern more well-informed technology selections and is flexible to expand its application toward many different types of driver interface evaluations