0137 Phase response curve for effects of school start times on students’ diurnal learning rhythm

Abstract

Abstract Introduction School start times impose constraints on sleep-wake behaviour that can result in social jet lag. Here, we used university archived datasets (1) to test whether social jet lag was associated with lower grades, and (2) to assess the phase resetting effects of school start times on students’ diurnal learning behaviour. Methods Social jet lag was estimated in 33,645 university students by measuring the phase shift in their Learning Management System (LMS) login rhythm on school days relative to non-school days (LMS social jet lag). ANCOVA was used to test the association between LMS social jet lag and grade point average, adjusting for demographic variables. We constructed a phase response curve by plotting phase shifts on school days (LMS social jet lag) against the initial phase when students’ first class of the day took place. The initial phase was expressed relative to students’ LMS login rhythm on non-school days (LMS chronotype). Results Students with greater LMS social jet lag had a lower grade point average (ANCOVA: F9,32269 = 44.8, P < 0.001). Social jet lag was larger in students with a later LMS login rhythm on non-school days (later chronotype) and for earlier school start times. The phase response curve revealed that the direction and magnitude of social jet lag were strongly dependent on the phase of students’ diurnal rhythm when their first class of the day took place. Phase shifts of up to 12 h were observed when school start times occurred out of phase with students’ diurnal rhythm. Conclusion School start times have a profound impact on students’ diurnal behaviour. Students whose diurnal patterns of LMS logins were similar on both school days and non-school days obtained better grades than their peers with LMS social jet lag. Universities can potentially improve learning by scheduling classes at times that are better aligned with students’ diurnal learning rhythm. Support (If Any) Data storage and management were supported by the NUS Office of the Senior Deputy President & Provost and ALSET. The work was funded by the Ministry of Education, Singapore (MOE2019-T2-2-074) and the National Research Foundation, Singapore (NRF2016-SOL002-001).