Cross-sectional analysis reveals links between pain frequency, sleep latency, and health in individuals with hEDS, highlighting implications for care.
Background: Individuals with hypermobile Ehlers–Danlos syndrome (hEDS) frequently report chronic pain, sleep-related complaints, and reduced mental and physical health. While sleep disturbance has been described in this population, less is known about how pain frequency relates to sleep initiation and broader health burden. This study aimed to examine the associations between pain frequency, sleep latency, and mental and physical health burden in individuals with hEDS. Methods: This analysis used cross-sectional survey data collected from adults with self-reported EDS. Pain frequency was treated as an ordinal variable. Sleep disturbance was assessed via self-reported sleep latency, and mental and physical health burden were defined as reporting 14 or more days of poor health in the prior 30 days. Ordinal logistic regression was used to examine associations between pain frequency and sleep latency, adjusting for age group. Binary logistic regression models examined associations between pain frequency and mental and physical health burden. Results: A total of 2365 respondents with self-reported hEDS were included in the analysis. Increasing pain frequency was associated with progressively longer sleep latency, demonstrating a clear dose–response relationship (p < 0.001). Greater pain frequency was also independently associated with increased odds of reporting frequent poor mental health days (OR = 1.35, 95% CI [1.26, 1.45], p < 0.001) and poor physical health days (OR = 1.82, 95% CI [1.66, 1.99], p < 0.001). Conclusions: Pain frequency was significantly associated with longer sleep latency and overall health burden in individuals with hEDS. These findings suggest that pain and sleep disturbance are interconnected components of a broader symptom burden rather than isolated clinical features. Routine assessment of sleep disturbance in individuals with frequent pain may help identify those at greater risk for broader health burden.
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Tudini et al. (2026) studied this question.
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