Background & Aims

People with chronic musculoskeletal conditions often experience daily fluctuating pain [1], affected by various factors including sleep, fatigue, anxiety, depression and weather [2-6]. Not all fluctuations require clinical attention, but there are periods of significantly increased pain severity, known as pain flares, that are challenging to manage due to unpredictability [7]. Pain flares can be triggered by a complex interaction of biopsychosocial, behavioural and environmental factors [4,8,9], and vary greatly in intensity, duration and frequency [10]. Recent technological advancements allow easier collection of rich health information from multiple data sources over time with minimal user burden [11-13], enabling a more nuanced understanding of pain flare patterns and the opportunity to identify exposures that increase the risk of pain flares occurring. In this study, we aimed to investigate risk factors that lead to the onset of pain flares in people with rheumatoid arthritis (RA).

Methods

Our 30-day study [14] collected daily pain severity (score 1-5, no pain to very severe pain) via a smartphone app. Three pain flare types [15] were defined as 1) above average: pain above personal median score, 2) significant change: a two-point increase from yesterday, and 3) absolute change: pain above three following a two-point increase from yesterday. We tracked twelve exposures with the app and a wearable, including self-rated fatigue, mood, anxiety, disease control, well-being, concentration, challenge, sleepiness and sleep quality (score 1-5, higher are worse), alongside objective sleep efficiency (%), time in bed (hr) and sedentary time (%). A case-crossover analysis [16] compared within-person variability of exposures across pre-flare (three days before a flare) and control (three days which did not precede a flare) periods using mean and intraindividual standard deviation (iSD) [17]. Conditional logistic regression estimated the odds ratio (OR) for pain flare occurrence.

Results

190 patients contributed 5160 days of data (82.1% females; mean age 58.1, 12.4 average years with RA). 88% (168/190) had at least one above average pain flare, 41% (78/190) had a significant change flare, and 28% (53/190) had at least one absolute change flare. Half the patients had both pre-flare and control periods available for analysis (52%, 88/168 in above average; 51%, 40/78 in significant change; 60%, 32/53 in absolute change). Of the 12 exposures examined, higher variability in fatigue increased the risk of a significant change flare (iSD OR: 2.63, 95% CI: 1.08-6.39). Worsening sleepiness (Significant Change: Mean OR: 2.04, 95% CI: 1.1-3.8), and greater variability in sleepiness (Significant Change: iSD OR: 2.17, 95% CI: 1.04-4.5; Absolute Change: iSD OR: 2.5, 95% CI: 1.13-5.55) increased flare risk. Conversely, greater variability in sleep efficiency reduced flare risk (Significant Change: iSD OR: 0.77, 95% CI: 0.64-0.93; Absolute Change: iSD OR: 0.8, 95% CI: 0.64-0.99).

Conclusions

Our findings, leveraging mobile health data using three increasingly complex definitions, showed that pain flares commonly occurred in RA. Variations over a three-day window in self-rated symptoms such as fatigue and sleepiness predicted the onset of pain flares. This indicates the role of subjective pain-related experiences in triggering flares. Individual variability demonstrated contrasting patterns between subjective and objective measures. We observed that increased variability in perceived sleepiness heightened pain flare risk, whereas greater variability in objective sleep efficiency appeared to mitigate this risk. This highlights the importance of integrating both subjective symptom reports and objective health data in monitoring pain flare risk.

References

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Presenting Author

Chloe Hsu

Poster Authors

Chloe Hsu

MSc

University of Manchester

Lead Author

Belay Birlie Yimer

PhD

University of Manchester

Lead Author

Pauline Whelan

PhD

University of Manchester

Lead Author

Christopher J Armitage

PhD

University of Manchester

Lead Author

Katie Druce

PhD

University of Manchester

Lead Author

John McBeth

MA (Hons)

Centre for Epidemiology Versus Arthritis, University of Manchester

Lead Author

Topics

  • Assessment and Diagnosis