Background & Aims
Sleep quality and pain intensity are critical aspects of health and well-being, particularly among middle to older-aged individuals experiencing chronic pain. While self-reported measures like the Pittsburgh Sleep Quality Index (PSQI) are commonly used, the advent of wearable technology like the OURA© Ring offers objective sleep data collection. This study aims to compare these two contrasting yet complementary methods of evaluating sleep quality. Furthermore, it seeks to investigate the correlation between subjective and objective measures of sleep and pain, providing additional insights into the relationship between these critical health parameters.
Methods
The present investigation uses baseline data from a pilot clinical trial that employed comprehensive objective and self-reported sleep assessments in persons with chronic musculoskeletal pain (i.e., pain greater than 4 out 10 during the past 3 months on most days) and self-reported sleep disturbance (PSQI score>5, n=24). We estimated measures and components equivalent to the PSQI questionnaire (e.g., 30-day average sleep hours, latency, or overall sleep quality) based on the objective OURA© ring data to evaluate whether self-reports in the PSQI reflect objective sleep states and patterns. Pearson’s coefficient was used to assess the correlation between PSQI measures and their OURA© Ring equivalents. Partial correlations were conducted between objective and subjective measures of sleep and pain. Statistical significance was set at 0.05 after correcting for multiple comparisons.
Results
There were statistically significant correlations between several pairs of sleep measures from the Pittsburgh Sleep Quality Index (PSQI) and their equivalents from the OURA© Ring. There was a positive correlation for Sleep Timing (r=0.503, p=0.012, p’=0.049), a strong positive correlation for Total Hours of Sleep (r=0.705, p=0.000, p’=0.00048), a very strong positive correlation for Time to Wake Up – Time to Go to Bed (r=0.833, p=0.000, p’=1.70E-06), and a strong positive correlation for Sleep Duration (r=0.646, p=0.001, p’=0.0033). Also, WOMAC Pain showed a moderate positive correlation with PSQI Total (r=0.534, p=0.002, p’=0.0024). MPQ Total also showed a moderate positive correlation with PSQI Total (r=0.497, p=0.008, p’=0.0084). Furthermore, the CPM showed a strong positive correlation with OURA Sleep Latency Mean (r=0.626, p=0.007, p’=0.0071). There were no significant associations regarding objective sleep and subjective pain measures after correcting for multiple comparisons.
Conclusions
Associations between self-reported subjective and objective sleep quality measures were found in middle-aged to older adults with chronic pain, suggesting that the PSQI could provide reliable surrogates for the average latency, total sleep, and total hours in bed. However, the PSQI appears to fail in capturing the number of nights where sleep latency exceeded 30 minutes. Consequently, the PSQI may provide reliable surrogates of C1 and C3, which measure the severity of the poor sleep duration and poor quality overall but fail in the rest of the components, i.e., C2, C4 and C5, which measure the severity of poor sleep latency, sleep efficiency and sleep disturbances, respectively. Our research underscores the importance of integrating subjective and objective measures in sleep research and contributes to a comprehensive understanding of sleep quality assessment among aging populations experiencing chronic pain. Future studies are necessary to determine the causes of these discrepancies.
References
(Husak & Bair, 2020)
(O’Donoghue et al., 2009)
(Altini & Kinnunen, 2021)
(De Zambotti et al., 2019)
(Buysse Charles F Reynolds Ill et al., 1988)