Objectives: To investigate the relationship between self-reported physical workload and neck trouble (NT) in twins. Additionally, to explore whether the relationship between physical workload and NT is influenced by genetic factors.
Methods: A twin control study was performed within a population-based, cross-sectional questionnaire study using 3,208 monozygotic (MZ) and same-sexed dizygotic (DZ) twins aged 19-70. Twin pairs discordant for self-reported NT during the past year ("Any NT") were included. Self-reported physical workload in four categories was used as exposure ("sitting," "sitting and walking," "light physical," and "heavy physical" work). Paired analyses including conditional logistic regression were made for all participants and for each sex, and MZ and DZ pairs separately.
Results: No marked associations between physical workload and NT were seen. A moderate risk elevation in "heavy physical" work was seen in DZ men (odds ratio 2.3, 95% confidence intervals 1.3-4.0), but not in MZ men or the MZ or DZ women.
Conclusions: The findings in some degree supported that "heavy physical" work is a determinant of NT, perhaps only in men, but hardly of any greater importance. The different results between DZ and MZ men suggest that genetic factors influence the relationship between physical workload and NT.
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http://dx.doi.org/10.1007/s00420-012-0755-7 | DOI Listing |
J Int Soc Prev Community Dent
December 2024
Department of Pedodontics and Preventive Dentistry, Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand.
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Materials And Methods: A cross-sectional analytical study was conducted among Thai dental students from first to sixth year who had been engaged in online learning during the COVID-19 period. A total of 440 students participated.
Cureus
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Physiotherapy and Physical Medicine, University of Dschang, Dschang, CMR.
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Intelligent Control Laboratory, Xi'an Research Institute of High Technology, Xi'an 710025, China.
For public security purposes, distributed surveillance systems are widely deployed in key areas. These systems comprise visual sensors, edge computing boxes, and cloud servers. Resource scheduling algorithms are critical to ensure such systems' robustness and efficiency.
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