Insomnia is reported by more than 50% of patients with chronic pain. In this study, the investigators aim to advance the understanding of physiological sleep in individuals with chronic pain. To do this the investigators will monitor at-home sleep with an ear-EEG over 20 nights in patients with chronic pain and collect self-reported measures of sleep and pain. The collected data will be used to explore and characterize intra-individual variations in sleep metrics (e.g. total sleep time, time in each sleep stage (N1, N2, N3, REM), sleep latency, REM stage latency, wake after sleep onset, sleep efficiency, number of arousals and arousal index) over 20 nights.
Study Type
OBSERVATIONAL
Enrollment
25
Pain Center, Department of Anesthesiology and Intensive Care Medicine, University Hospital Odense
Odense, Denmark
sleep period time (SPT) from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Qualitative sleep parameters obtained from sleep diary.
Sleep diary is completed in the morning
Time frame: Sleep diary is completed 5 mornings every week for 4 weeks
Pain intensity rating
Average pain intensity during the last day and current pain intensity in the morning will be assessed on an 11-point numeric rating scale (NRS) ranging from 0 (no pain) to 10 (worst imaginable pain)
Time frame: Pain intensity rating is completed 5 mornings every week for 4 weeks
Time from sleep onset until final awakening (TST) from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Sleep efficiency (SE) from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM). SE is the ratio of TST to time in bed / 100%
Time frame: 5 nights every week for 4 weeks
Sleep onset latency (SOL) from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Wake after sleep onset (WASO) from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
REM sleep latency from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Time from sleep onset until first epoch of REM stage sleep from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Amount of wake and stage N1, N2, N3, and R sleep as a percentage of SPT from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Number of awakenings within TST from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Arousal index which is number of arousals per hour from Ear EEG
Sleep metrics will be derived from the EEG assessments as recommended by the American Academy of Sleep Medicine (AASM).
Time frame: 5 nights every week for 4 weeks
Ease-of-use and Comfort with ear EEG
Three 0-10 questions are used: 1) How did you experience falling asleep with the ear EEG device, 2) How did you experience sleeping with the ear EEG device?, 3) How would you rate your experience of soreness or discomfort in your ears after sleeping with the device? A lower sum score is worse.
Time frame: Completed every morning after ear EEG first 2 weeks and at end of study
Adverse device effects
Any adverse device effect defined as an adverse effect related to the use of the ear EEG
Time frame: Baseline, after 2 weeks, after 6 weeks
Polysomnography
Polysomnography (PSG) is used in this study to ensure that the data that comes out of the automatic ear-EEG based sleep scoring matches the clinicians sleep scores based on the PSG. It will enable further development of the existing algorithm for automating the data analysis.
Time frame: Baseline
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.