Renske Lok1, Dwijen Chawra1, Flora Hon2, Michelle Ha3, Katherine A Kaplan1, Jamie M Zeitzer4. 1. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA. 2. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; College of Literature, Science, and The Arts, University of Michigan, Ann Arbor, MI 48109, USA. 3. Department of Mathematics and Statistics, San Jose State University, San Jose, CA 95112, USA. 4. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Mental Illness Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA. Electronic address: jzeitzer@stanford.edu.
Abstract
STUDY OBJECTIVES: The measurable aspects of brain function (polysomnography, PSG) that are correlated with sleep satisfaction are poorly understood. Using recent developments in automated sleep scoring, which remove the within- and between-rater error associated with human scoring, we examine whether PSG measures are associated with sleep satisfaction. DESIGN AND SETTING: A single night of PSG data was compared to contemporaneously collected measures of sleep satisfaction with Random Forest regressions. Whole and partial night PSG data were scored using a novel machine learning algorithm. PARTICIPANTS: Community-dwelling adults (N = 3165) who participated in the Sleep Heart Health Study. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Models explained 30% of sleep depth and 27% of sleep restfulness, with a similar top four predictors: minutes of N2 sleep, sleep efficiency, age, and minutes of wake after sleep onset (WASO). With increasing self-reported sleep quality, there was a progressive increase in N2 and decrease in WASO of similar magnitude, without systematic changes in N1, N3 or REM sleep. In comparing those with the best and worst self-reported sleep satisfaction, there was a range of approximately 30 min more N2, 30 min less WASO, an improvement of sleep efficiency of 7-8%, and an age span of 3-5 years. Examination of sleep most proximal to morning awakening revealed no greater explanatory power than the whole-night data set. CONCLUSIONS: Higher N2 and concomitant lower wake is associated with improved sleep satisfaction. Interventions that specifically target these may be suitable for improving the self-reported sleep experience. Published by Elsevier B.V.
STUDY OBJECTIVES: The measurable aspects of brain function (polysomnography, PSG) that are correlated with sleep satisfaction are poorly understood. Using recent developments in automated sleep scoring, which remove the within- and between-rater error associated with human scoring, we examine whether PSG measures are associated with sleep satisfaction. DESIGN AND SETTING: A single night of PSG data was compared to contemporaneously collected measures of sleep satisfaction with Random Forest regressions. Whole and partial night PSG data were scored using a novel machine learning algorithm. PARTICIPANTS: Community-dwelling adults (N = 3165) who participated in the Sleep Heart Health Study. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Models explained 30% of sleep depth and 27% of sleep restfulness, with a similar top four predictors: minutes of N2 sleep, sleep efficiency, age, and minutes of wake after sleep onset (WASO). With increasing self-reported sleep quality, there was a progressive increase in N2 and decrease in WASO of similar magnitude, without systematic changes in N1, N3 or REM sleep. In comparing those with the best and worst self-reported sleep satisfaction, there was a range of approximately 30 min more N2, 30 min less WASO, an improvement of sleep efficiency of 7-8%, and an age span of 3-5 years. Examination of sleep most proximal to morning awakening revealed no greater explanatory power than the whole-night data set. CONCLUSIONS: Higher N2 and concomitant lower wake is associated with improved sleep satisfaction. Interventions that specifically target these may be suitable for improving the self-reported sleep experience. Published by Elsevier B.V.
Authors: Katherine A Kaplan; Jason Hirshman; Beatriz Hernandez; Marcia L Stefanick; Andrew R Hoffman; Susan Redline; Sonia Ancoli-Israel; Katie Stone; Leah Friedman; Jamie M Zeitzer Journal: Biol Psychol Date: 2016-11-24 Impact factor: 3.251
Authors: Guo-Qiang Zhang; Licong Cui; Remo Mueller; Shiqiang Tao; Matthew Kim; Michael Rueschman; Sara Mariani; Daniel Mobley; Susan Redline Journal: J Am Med Inform Assoc Date: 2018-10-01 Impact factor: 4.497
Authors: R Lok; T Woelders; M C M Gordijn; M J van Koningsveld; K Oberman; S G Fuhler; D G M Beersma; R A Hut Journal: J Biol Rhythms Date: 2022-06-22 Impact factor: 3.649