Learning network for Advanced Behavioural Data Analysis (LABDA)

最近，已经出现了一种范式转变，从孤立地关注单一行为（即PA，久坐行为或睡眠）对健康的影响，到这些24/7运动行为的组合，以获得最大的健康益处。然而，目前的公共卫生指南在很大程度上是基于不准确的自我报告数据，因此相当笼统（例如“多动少坐”）。技术进步已经导致可穿戴传感器技术在更长的时间段内提供丰富的时间序列数据。因此，需要新的分析方法来详细了解多维24/7运动行为概况与健康之间的联系;哪些亚组需要特别关注;以及什么样的行为特征是干预措施中最重要的目标。开发这种新的分析方法，对于创建最佳、量身定制的指南和反馈所需的证据基础至关重要，需要流行病学，数据科学，方法开发和公共卫生方面的知识和技能的特定组合，并彻底了解将知识转化为指南和改善可穿戴技术反馈所需的内容。因此，在LABDA，我们将培训10名博士研究员，以推动这一跨学科领域的发展，并为基于传感器的行为数据提供先进的分析方法工具箱，同时为其他研究人员和政策制定者提供指导，以决定使用哪种方法来解决哪一个（研究）问题。

Recently, there has been a paradigm shift from the isolated focus on the health impact of a single behaviour (i.e. PA, sedentary behaviour or sleep) to the combination of these 24/7 movement behaviours for maximum health benefits. However, current public health guidelines are largely based on inaccurate self-report data and are, therefore, rather general (e.g. "move more and sit less"). Technological advancements have led to wearable sensor techniques providing rich time-series data over longer periods. Consequently, novel analysis methods are required to provide detailed insight into the links between multi-dimensional 24/7 movement behaviour profiles and health; which subgroups need particular attention; and what behavioural profiles are most important to target in interventions.Developing such novel analysis methods, essential for creating the evidence base needed for optimal, tailored guidelines and feedback, requires a specific combination of knowledge and skills in epidemiology, data science, method development, and public health with a thorough understanding of what is needed to translate knowledge to guidelines and improve wearable technology feedback. In LABDA, we will therefore train 10 doctoral fellows to advance this interdisciplinary field and deliver a toolbox of advanced analysis methods for sensor-based behavioural data, together with a guide for other researchers and policy makers to decide which methods to use for which (research) question.