Learning network for Advanced Behavioural Data Analysis (LABDA)

高级行为数据分析学习网络 (LABDA)

• 批准号: EP/X042464/1
• 负责人: Alex Rowlands
• 金额: $ 33.8万
• 依托单位: University of Leicester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX042464%2F1
• 关键词: Learning; network; Advanced; Behavioural; Data

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.

最近，范式发生了转变，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，转向将这些 24/7 运动行为结合起来以获得最大的健康益处。然而，当前的公共卫生指南主要基于不准确的自我报告数据，因此相当笼统（例如“多动少坐”）。技术进步催生了可穿戴传感器技术，可以在更长的时间内提供丰富的时间序列数据。因此，需要新颖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；开发这种新颖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，全面了解将知识转化为指南和改进可穿戴技术反馈所需的内容。因此，在 LABDA 中，我们将培训 10 名博士研究员来推进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，并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（研究） 问题。