Different parameters of biological monitoring were applied to 26 benzene-exposed car mechanics. Twenty car mechanics worked in a work environment with probably high benzene exposures (exposed workers); six car mechanics primarily involved in work organization were classified as non-exposed. The maximum air benzene concentration at the work places of exposed mechanics was 13 mg/m3 (mean 2.6 mg/m3). Elevated benzene exposure was associated with job tasks involving work on fuel injections, petrol tanks, cylinder blocks, gasoline pipes, fuel filters, fuel pumps and valves. The mean blood benzene level in the exposed workers was 3.3 micrograms/l (range 0.7-13.6 micrograms/l). Phenol proved to be an inadequate monitoring parameter within the exposure ranges investigated. The muconic and S-phenylmercapturic acid concentrations in urine showed a marked increase during the work shift. Both also showed significant correlations with benzene concentrations in air or in blood. The best correlations between the benzene air level and the mercapturic and muconic acid concentrations in urine were found at the end of the work shift (phenylmercapturic acid concentration: r = 0.81, P < 0.0001; muconic acid concentration: r = 0.54, P < 0.05). In conclusion, the concentrations of benzene in blood and mercapturic and muconic acid in urine proved to be good parameters for monitoring benzene exposure at the workplace even at benzene air levels below the current exposure limits. Today working as a car mechanic seems to be one of the occupations typically associated with benzene exposure.
Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.
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