Systematic Review of Sleep Characteristics in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Assessing sleep and pain among adults with myalgic encephalomyelitis/chronic fatigue syndrome: psychometric evaluation of the PROMIS® sleep and pain short forms

PURPOSE

To evaluate the psychometric properties of the patient-reported outcome measurement information system® (PROMIS) short forms for assessing sleep disturbance, sleep-related impairment, pain interference, and pain behavior, among adults with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

METHODS

Data came from the Multi-Site ME/CFS study conducted between 2012 and 2020 at seven ME/CFS specialty clinics across the USA. Baseline and follow-up data from ME/CFS and healthy control (HC) groups were used to examine ceiling/floor effects, internal consistency reliability, differential item functioning (DIF), known-groups validity, and responsiveness.

RESULTS

A total of 945 participants completed the baseline assessment (602 ME/CFS and 338 HC) and 441 ME/CFS also completed the follow-up. The baseline mean T-scores of PROMIS sleep and pain measures ranged from 57.68 to 62.40, about one standard deviation above the national norm (T-score = 50). All four measures showed high internal consistency (ω = 0.92 to 0.97) and no substantial floor/ceiling effects. No DIF was detected by age or sex. Known-groups comparisons among ME/CFS groups with low, medium, and high functional impairment showed significant small-sized differences in scores (η² = 0.01 to 0.05) for the two sleep measures and small-to-medium-sized differences (η² = 0.01 to 0.15) for the two pain measures. ME/CFS participants had significantly worse scores than HC (η² = 0.35 to 0.45) for all four measures. Given the non-interventional nature of the study, responsiveness was evaluated as sensitivity to change over time and the pain interference measure showed an acceptable sensitivity.

CONCLUSION

The PROMIS sleep and pain measures demonstrated satisfactory psychometric properties supporting their use in ME/CFS research and clinical practice.

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.