BACKGROUND: Prevention of Work-Related Musculoskeletal Disorders (WRMSDs) is crucial as they jeopardize the well-being of the workforce in workplaces and are prevalent in developed and developing countries. OBJECTIVE: This study aimed to determine th

BACKGROUND: Musculoskeletal disorders are very prevalent among dentists. Of these disorders, carpal tunnel syndrome (CTS) is notable. OBJECTIVE: The aim of this study was to investigate the relationship between the occupational and non-occupational risk factors and CTS among dentists. METHODS: It is a cross-sectional study. We evaluated 106 dentists from dental schools in Tehran. We collected data by a general questionnaire, a hand diagram, and physical examination. A nerve conduction velocity (NCV) test was used to confirm the diagnosis of CTS. The analysis was performed using an independent T-test and logistic regressions on SPSS software. RESULTS: The prevalence of CTS among dentists was 17.9%. Significant risk factors of CTS included: vibration exposure greater than two hours per day (OR: 2.5), a wrist diameter ratio of greater than 0.7 (OR: 10.14), and female sex (OR: 13.38). CONCLUSIONS: Exposure to the hand-arm vibration should be considered as an occupational risk factor for CTS in dentists. Female dentists should take more preventive measures.

Sleep duration, sleep efficiency, and sleep timing have been shown to have potential effects on metabolic functions relevant to circadian rhythms. It is not clear if the impact of sleep patterns on metabolic risk factors is through sociocultural and environmental factors or circadian misalignment. We investigated the associations of sleep patterns, chronotype, and social jet lag with metabolic syndrome among non-shift worker Hispanic/Latino adults. We used cross-sectional data from the Sueño Ancillary Study of The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Data from a subsample of 2189 participants aged 18–64 years were used in the analysis. Mean nightly sleep duration, mean sleep onset time, mean sleep offset time, mean sleep midpoint time, sleep efficiency, sleep variability (standard deviation (SD) of sleep duration, and SD of sleep midpoint), and time spent above light exposure threshold (1000 lux) in a day were assessed by wrist actigraphy (Acti-watch Spectrum). Chronotype was determined by the reduced Morningness-Eveningness Questionnaire. Medical conditions including dyslipidemia, hypertension, and diabetes mellitus were determined from a fasting blood specimen and physical exam at the baseline visit. To determine whether sleep patterns, light levels, chronotype, and social jetlag are associated with metabolic syndrome, multivariable logistic regression models were fitted, including variables with P < .15 in the univariate analysis. The results of the multivariable analysis demonstrated that in participants older than 40 years, intermediate chronotype (vs early) was significantly associated with a higher risk of metabolic syndrome (Odds ratio (95%CI): 1.33 (1.04,1.7)), while later chronotype (vs. early) in participants younger than 40 years was significantly associated with a lower risk of metabolic syndrome (Odds ratio (95%CI): 0.37 (0.14, 0.96)). Also, higher sleep efficiency was significantly associated with decreased odds of metabolic syndrome (Odds ratio (95%CI): 0.98 (0.96, 0.99)). Nightly sleep duration was not significantly different between two groups of participants with and without metabolic syndrome in multivariable analyses. There was no significant association between social jet lag and metabolic syndrome in multivariable analysis (p = .286). Moreover, there was no significant association between chronotype and social jet lag in multivariable analysis. The association between metabolic syndrome and chronotype is age-dependent. While early chronotype is associated with metabolic syndrome in younger individuals, it tended to be associated with lower odds for metabolic syndrome in older individuals.

One of the most important ways to prevent accidents is to consider safety climate or culture. Moreover, some studies suggest that behavior contributes to 86%-96% of all injuries. This cross-sectional study took place in an Iranian petrochemical company in 2010. Vinodkumar and Bhasi’s safety climate questionnaire and an ergonomic behavior sampling checklist were the data collection tools. Cronbach’s a for questionnaire reliability was .928. With reference to the results of a pilot study, a sample of 1755 was determined for behavior sampling. We used principal component analysis (PCA) to derive the coefficient of paths in the path model and the Anderson-Rabin method to calculate factor scores. The results showed that safety climate was an effective predictor of ergonomic behavior (p < .01). They also showed the importance of decreasing the number of workers with negative safety climate. Moreover, it is necessary to promote workers’ ergonomic behaviors in the workplace.