Evidence map›Paper›PMID 41019708›Full record

ArticleFrontiers in pain research (Lausanne, Switzerland)2025

Short-term variability of chronic musculoskeletal pain.

Xuanci Zheng, Swati Rajwal, Carl Ashworth, Sharon Yuen Shan Ho, Ben Seymour, Nicholas Shenker, Flavia Mancini

Abstract read
In one paragraph

Article in Frontiers in pain research (Lausanne, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xuanci ZhengComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Swati RajwalComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Carl AshworthComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Sharon Yuen Shan HoComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Ben SeymourWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Nicholas ShenkerRheumatology Research Unit, Cambridge University Hospitals, Cambridge, United Kingdom.
Flavia ManciniComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

chronic painclinical severitypain monitoringpain predictiontemporal variability

Identifiers

PMID41019708
PMCPMC12460470

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.