Abstract
This is a descriptive, exploratory and cross-sectional study aimed at determining the predictive value of esthesiometry for diagnosing compressive neuropathies of the upper limbs in freezer warehouse workers. Participated in the study 300 workers aged from 20 to 50 years who work for more than 12 months in poultry slaughterhouses, whose work activities are characterized by repetitive movements and prevalence of biomechanical requirements. Data collection used esthesiometry resources to determine the sensitivity of the hand of workers; the use of a dynamometer to verify and assess the handgrip strength, and the use of a questionnaire to record socio-demographic characteristics of participants. As statistical criterion for analysis, descriptive statistics and the ROC curve (Receiver Operating Characteristic) were selected in order to identify the best cutoff point handgrip strength value and the result of hand esthesiometry to indicate the presence of RSI in the upper limbs. The results indicated that the handgrip strength is efficient in diagnosing RSI by compression syndromes in 77.67 % of cases for the right side of the body and in 71 % of cases for the left side of the body. Thus, considering the analysis by statistical method by applying the ROC curve, the esthesiometry procedure used as predictive diagnosis was characterized as the best criterion for the early identification of RSI by compression syndromes in the upper limbs. Thus, esthesiometry seems to be an easy-to-apply quantitative tool able to identify changes in sensitivity even before the onset of RSI, providing more accurate objective results when compared to the handgrip strength, both for its diagnosis and follow-up, which can be used in slaughterhouses safely and effectively.
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Reis, P., Moro, A., Reis, D., Ramos, E. (2016). Repetitive Working in Slaughterhouses: The Esthesiometry Use of the Hand as a Proposal for Assessing Levels of RSI Compression Syndromes of the Upper Limbs. In: Goonetilleke, R., Karwowski, W. (eds) Advances in Physical Ergonomics and Human Factors. Advances in Intelligent Systems and Computing, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-319-41694-6_13
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