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Work-related low-back disorders (LBDs) continue to be one of the single largest sources of compensation costs. The relative contributions of personal, workplace, organizational, and environmental variables to the development and severity of LBDs are not completely understood. The inclusion of personal variables in epidemiologic studies of LBDs has been inconsistent, and different authors have different opinions concerning the importance of such variables. Personal variables either known or suspected to influence outcomes are discussed to elucidate the importance of these variables with respect to understanding LBDs and conducting epidemiological studies in industry. The authors suggest that age, gender, injury history, relative strength, smoking, and psychosocial variables be studied further, and that height, weight, pathologies, genetic factors, maximum oxygen uptake, and absolute strength are unlikely to produce significant effects in industrial populations.

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A three-level experiment was developed to validate a 3-D hand scanning and dimension extraction method with dimension data. At the first level, a resin hand model of a participant was fabricated to test the repeatability of the dimension data obtained by the 3-D method. At the second level, the actual hand of that participant was measured repeatedly using both the 3-D method and the traditional manual measurement method. The repeatability for both methods was investigated and compared. The influence of posture keeping, surface deformation and other human issues were also examined on the second level. At the third level, a group of participants were recruited and their hands were measured using both methods to examine any differences between the two methods on statistical descriptives. Significant differences, which varied among dimension types (length, depth/breadth, and circumference), were found between the 3-D method and the traditional method. 3-D anthropometric measurement and dimension extraction has become a prospective technology. The proposed three-level experiment provides a systematic method for validation of the repeatability of a 3-D method and compatibility between dimension data from a 3-D method and a traditional method.

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