Recorded and Reported Sleepiness: The Association Between Brain Arousal in Resting State and Subjective Daytime Sleepiness. - Jawinski, P.; Kittel, J.; Sander, C.; Huang, J.; Spada, J.; Ulke, C.; Wirkner, K.; Hensch, T.; Hegerl, U. (Sleep, 2017)
OBJECTIVES: Daytime sleepiness is a significant public health concern. Early evidence points toward the computerized VIGALL (Vigilance Algorithm Leipzig) as time-efficient tool to assess sleepiness objectively. In the present study, we investigated the association between VIGALL variables of EEG vigilance (indicating brain arousal in resting state) and subjective daytime sleepiness in the LIFE cohort study. Additionally, we validated VIGALL against the self-rated likelihood of having fallen asleep during the conducted resting EEG and against heart periods. METHODS: Participants of the primary sample LIFE 60+ (N = 1927, 60-79 years) and replication sample LIFE 40+ (N = 293, 40-56 years) completed the Epworth Sleepiness Scale (ESS). After an average interval of 3 weeks (LIFE 60+) and 65 weeks (LIFE 40+), respectively, participants underwent a single 20-minute resting EEG, analyzed using VIGALL 2.1. RESULTS: Analyses revealed significant associations between ESS and EEG vigilance in LIFE 60+ (rho = -0.17, p = 1E-14) and LIFE 40+ (rho = -0.24, p = 2E-5). Correlations between EEG vigilance and self-rated sleep likelihood reached rho = -0.43 (p = 2E-91) in LIFE 60+ and rho = -0.50 (p = 5E-20) in LIFE 40+. Overall, strongest correlations were obtained for EEG vigilance variable "slope index." Furthermore, lower EEG vigilance was consistently associated with longer heart periods. CONCLUSIONS: The present study contributes to the validation of VIGALL. Despite the considerable interval between ESS and EEG assessment dates, the strength of ESS-VIGALL association approximates prior ESS-Multiple Sleep Latency Test results. In this light, VIGALL might constitute an economical choice for the objective assessment of daytime sleepiness in large cohort studies. The discriminative power to identify disorders of hypersomnolence, however, remains to be addressed.
Keywords: Adult; Aged; Algorithms; Arousal/*physiology; Brain/*physiology; Cohort Studies; Disorders of Excessive Somnolence/physiopathology; Electroencephalography; Female; Humans; Male; Middle Aged; Reproducibility of Results; Rest/*physiology; Sleep Stages/*physiology; Wakefulness/physiology