Article Text

Original research
Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation
  1. Ipek Ensari1,
  2. Sharon Lipsky-Gorman2,
  3. Emma N Horan2,
  4. Suzanne Bakken1,2,3,
  5. Noémie Elhadad1,2
  1. 1Data Science Institute, Columbia University, New York, New York, USA
  2. 2Department of Biomedical Informatics, Columbia University, New York, New York, USA
  3. 3School of Nursing, Columbia University, New York, New York, USA
  1. Correspondence to Dr Ipek Ensari; ie2145{at}columbia.edu

Abstract

Objectives This 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.

Participants The sample included 90 382 days of data from 1009 participants (~85% non-Hispanic white) living with endometriosis across 38 countries.

Study design This 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 measures The 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.

Results The 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.

Conclusions Regular 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.

  • pain management
  • health informatics
  • preventive medicine
  • epidemiology
  • complementary medicine

Data availability statement

Data are available upon reasonable request. Study data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Data availability statement

Data are available upon reasonable request. Study data are available upon reasonable request.

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Footnotes

  • Contributors IE conceptualised the study, conducted the data analyses, prepared the first draft of the manuscript, and is responsible for the overall content as guarantor. SL-G and ENH were responsible for data acquisition, curation and management. NE acquired the funding and provided the mHealth infrastructure for the study (Phendo app). NE and SB provided guidance on the study design and data analyses. SB provided guidance on the copyediting of the manuscript. SB, NE, SL-G and ENH reviewed and provided feedback on the manuscript.

  • Funding Funding for the work is provided by a postdoctoral fellowship from the Data Science Institute at Columbia University and an award from the National Library of Medicine (R01 LM013043).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.