The objective of this prospective cohort study was to evaluate if peak or cumulative musculoskeletal discomfort may predict future low-back, neck or shoulder pain among symptom-free workers. At baseline, discomfort per body region was rated on a 10-point scale six times during a working day. Questionnaires on pain were sent out three times during follow-up. Peak discomfort was defined as a discomfort level of 2 at least once during a day; cumulative discomfort was defined as the sum of discomfort during the day. Reference workers reported a rating of zero at each measurement. Peak discomfort was a predictor of low-back pain (relative risk (RR) 1.79), neck pain (RR 2.56), right or left shoulder pain (RR 1.91 and 1.90). Cumulative discomfort predicted neck pain (RR 2.35), right or left shoulder pain (RR 2.45 and 1.64). These results suggest that both peak and cumulative discomfort could predict future musculoskeletal pain.
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http://dx.doi.org/10.1080/00140130701743433 | DOI Listing |
JCO Precis Oncol
January 2025
Department of Urology, Kyoto University School of Medicine, Kyoto, Japan.
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PLoS One
January 2025
Faculty of Sport Sciences, University of Extremadura, Caceres, Spain.
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View Article and Find Full Text PDFPsychol Rep
January 2025
Ministry of National Education, Istanbul, Türkiye.
This study explores the role of mindfulness in marriage as a potential mediator in the relationship between infidelity tendencies and divorce anxiety among married individuals. Employing a sample of 415 married individuals, the research utilized a correlational model to examine the predictive capacity of marital mindfulness. The assessment involved the Mindfulness in Marriage Scale, Infidelity Tendency Scale, and Divorce Anxiety Scale, supplemented by a demographic information form.
View Article and Find Full Text PDFJ Clin Gastroenterol
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Diabetes Technol Ther
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Children's Mercy Kansas City, Endocrinology, Kansas City, Missouri, USA.
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S.
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