Affiliations: The University of Texas School of Public Health,
Houston, TX, USA | The Institute for Work and Health, Toronto,
Canada | Harvard School of Public Health, Boston, MA, USA | Rice University, TX, USA | Liberty Mutual Research Institute for Safety,
Hopkinton, MA, USA | Orthopaedic and Arthritis Center for Outcomes
Research, Department of Orthopaedic Surgery and Division of Rheumatology,
Immunology and Allergy, Brigham and Women's Hospital, Harvard Medical School
and Harvard School of Public Health, Boston, MA, USA
Note: [] Address for correspondence: Cammie Chaumont Menéndez, 1095
Willowdale, MS 1811, Morgantown, WV 26505, USA. Tel.: +1 832 368 9191; Fax: +1
304 285 6235; E-mail: [email protected]
Abstract: Objective: The study examines temporal variations in upper-extremity
musculoskeletal symptoms throughout the day, over a week and throughout the
semester. Methods: 30 undergraduates were followed in a repeated measures
study throughout a semester. Upper extremity musculoskeletal symptoms data were
collected on handheld computers randomly throughout the day for seven days over
three data collection periods. Multilevel statistical models evaluated
associations between time-related predictors and symptoms. Results: In
adjusted models, pain reported at baseline was associated with increased odds
of experiencing both any symptoms (OR=15.64; 90% CI 7.22–33.88)
and moderate or greater symptoms (OR=16.44; 90% CI 4.57–29.99).
Any symptoms were less likely to be reported if responses occurred at
58–76 days (OR=0.66; 90% CI 0.49–0.86), 77–90 days (OR=0.29; 90%
CI 0.20–0.42) and 91–117 days (OR=0.54; 90% CI 0.39–0.75) into the
semester compared to 35–57 days. Similarly, responding after midnight was
associated with greater odds of reporting moderate or greater symptoms
(OR=21.33; 90% CI 6.49–65.97). There was no association observed for day
of week and symptoms. Conclusion: This pilot work suggests upper
extremity musculoskeletal symptoms exhibit temporal variations related to time
of day and days into semester. Understanding the natural history of
musculoskeletal symptoms and disorders is needed when designing epidemiologic
research and/or intervention studies using symptom outcome measures.
