SummaryThe 24-hour day comprises four main activities: sleep, sedentary time, light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA). Time-based recommendations for adults are only available for sleep duration (7–9 hours/night) and MVPA (150-300 minutes/week). In contrast, only general advice is given to minimize sedentary time and perform more LPA. This is mainly because current public health recommendations deal separately with sleep, sedentary time, LPA, and MVPA, neglecting the interactions among these activities. It remains unclear how time over a 24-hour cycle should be distributed among the activity behaviors for better health. In addition to the duration, accumulation patterns of movement behaviors may also be related to health and disease profiles. For instance, limiting sedentary time to shorter durations is shown to be associated with better cardiovascular health and several other health indicators. However, more research is needed to understand better whether and how patterns of movement behaviors are related to different health indicators. In this study, we aim to utilize NAKO raw accelerometry data to obtain time spent asleep in sedentary and physical activity, as well as variables describing the accumulation patterns of daily activities, such as the number and duration of sedentary breaks for each participant. These data will then be analyzed using innovative time-use epidemiology, data-driven, and machine-learning approaches to explore the combined associations of durations and patterns of 24-hour movement behaviors with cardiometabolic health, physical fitness, and performance in a cross-sectional setup.

Keywords Profile-analysis Time-use machine-learning

InstitutionsTU Dortmund University, Leibniz Institut für Präventionsforschung und Epidemiologie - BIPS, Deutsches Krebsforschungszentrum, Universität Regensburg, Studienzentrum Berlin-Mitte (Charité), Leibniz-Institut für Präventionsforschung und Epidemiologie BIPS