Abstract
ObjectivesThis study investigates the association of daily physical exercise with pain symptoms in endometriosis. We also examined whether an individual’s typical weekly (ie, habitual) exercise frequency influences (ie, moderates) the relationship between their pain symptoms on a given day (day t) and previous-day (day t-1) exercise.ParticipantsThe sample included 90 382 days of data from 1009 participants (~85% non-Hispanic white) living with endometriosis across 38 countries.Study designThis was an observational, retrospective study conducted using data from a research mobile app (Phendo) designed for collecting self-reported data on symptoms and self-management of endometriosis.Primary outcome measuresThe two primary outcomes were the composite day-level pain score that includes pain intensity and location, and the change in this score from previous day (Δ-score). We applied generalised linear mixed-level models to examine the effect of previous-day exercise and habitual exercise frequency on these outcomes. We included an interaction term between the two predictors to assess the moderation effect, and adjusted for previous-day pain, menstrual status, education level and body mass index.ResultsThe association of previous-day (day t-1) exercise with pain symptoms on day t was moderated by habitual exercise frequency, independent of covariates (rate ratio=0.96, 95% CI=0.95 to 0.98, p=0.0007 for day-level pain score, B=−0.14, 95% CI=−0.26 to −0.016, p=0.026 for Δ-score). Those who regularly engaged in exercise at least three times per week were more likely to experience favourable pain outcomes after having a bout of exercise on the previous day.ConclusionsRegular exercise might influence the day-level (ie, short-term) association of pain symptoms with exercise. These findings can inform exercise recommendations for endometriosis pain management, especially for those who are at greater risk of lack of regular exercise due to acute exacerbation in their pain after exercise.
Funder
U.S. National Library of Medicine
Columbia University Data Science Institute Postdoctoral Fellowship
Cited by
10 articles.
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