0208 Daytime alertness quantification and modelling: results from a large observational study

Abstract

Abstract Introduction Subjective alertness variations throughout the day can be characterized using the two-process model (TPM) of sleep regulation, which combines sleep homeostasis and the circadian rhythm to derive a theoretical daytime alertness curve. The TPM has been adopted to model the effect of sleep deprivation on memory, circadian misalignment, temperature regulation, and brain function; however, despite its broad influence, evidence supporting the TPM-derived alertness comes largely from small-scale, controlled studies. Here, we show that a similar three-parameter alertness measure can scale to a large study sample under real-world conditions. Methods Subjective alertness was voluntarily rated on a scale from 1 to 10 by Sleep Number smart bed users (N=22 499) through the SleepIQ app. Three age groups (18–40, 41–65, and 66–90 years) were analyzed. A 3-parameter version of the TPM-derived alertness curve was fit to the self-rated alertness responses using nonlinear least-squares fitting. Results A total of 65 528 sleep sessions were gathered over 95 days and analyzed. Overall, subjective alertness followed a similar trend to that reported in published literature: mean hourly alertness increased in the morning, dipped slightly in the afternoon, increased during the evening, and dropped again during the night. In contrast to previous studies, mean alertness ratings only changed by approximately 1 unit from low to high, and a greater increase in alertness occurred from afternoon to evening. Age-group analyses found that youngest sleepers’ mean daily alertness was more stable throughout the day, and the amplitude of alertness variation decreased with age. These experimental results showed high agreement with model prediction (R2=0.96, P<0.001). Conclusion Overall, our results were similar to previous reports, with the exception of a small absolute change over the course of the day (about 1 unit) and an evening peak in alertness that was more pronounced in our data. These results show that the TPM-derived alertness can effectively predict daily alertness trends in a large sample under real-world conditions. Support (If Any) This study was funded by Sleep Number Corporation. Medical writing support, provided by Rachel C. Brown, PhD, from Oxford PharmaGenesis Inc., Newtown, PA, USA, was funded by Sleep Number Corporation.