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ORIGINAL RESEARCH article

Front. Pain Res.

Sec. Pain Research Methods

Volume 6 - 2025 | doi: 10.3389/fpain.2025.1626589

Short-term variability of chronic musculoskeletal pain

Provisionally accepted
  • 1Department of Engineering, University of Cambridge, Cambridge, United Kingdom
  • 2Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
  • 3Department of Medicine, University of Cambridge, Cambridge, United Kingdom

The final, formatted version of the article will be published soon.

Chronic musculoskeletal (MSK) pain can be characterized by its temporal variability and evolution, affecting both pain management and treatment outcomes. While pain variability is traditionally studied over long timescales (e.g. days or weeks), few studies have explored short-term fluctuations (e.g. minutes to seconds) and their clinical relevance. This study investigated the short-term variability of chronic musculoskeletal pain across consecutive days, examining whether these fluctuations are stable, exhibit consistent temporal patterns, and relate to clinical severity. We also explored whether individuals with chronic MSK pain could predict their pain intensity on the following day, suggesting an ability to learn about their pain's levels. Eighty-one participants with chronic MSK pain to the back, neck, leg or arm (22-65 years, 72% females, 28% males) rated their pain continuously over two days, using a smartphone-based app. Results indicated that pain ratings were stable and exhibited consistent temporal patterns across days, with a temporally correlated structure. High mean pain levels were associated with lower variability, possibly reflecting a stabilized pain state. Short-term pain variability negatively correlated with clinical severity, indicating that greater variability is linked to milder pain. These findings highlight the importance of short-term variability as a distinct and clinically relevant feature of chronic MSK pain, with implications for personalized pain management strategies.

Keywords: Chronic Pain, Pain monitoring, Temporal variability, Clinical severity, Pain prediction

Received: 12 May 2025; Accepted: 27 Aug 2025.

Copyright: © 2025 Zheng, Rajwal, Ashworth, Ho, Seymour, Shenker and Mancini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Xuanci Zheng, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Flavia Mancini, Department of Engineering, University of Cambridge, Cambridge, United Kingdom

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