TIHM: An open dataset for remote healthcare monitoring in dementia

Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal observational and measurement data within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days. We have provided raw data and guidelines on how to access, visualise, manipulate and predict health-related events within the dataset, available on the Github repository. The Jupyter Notebooks have been developed using Python 3.9. <strong>The dataset is provided for research and patient benefit purposes.<br> Please acknowledge the Surrey and Borders Partnership NHS Foundation Trust in any publication or use of this dataset.</strong>痴呆症是一种会损害认知与功能能力的进行性病症。当前亟需针对痴呆症患者（People Living with Dementia，PLWD）开展可靠且持续的健康监测，以提升其生活质量并支持其独立生活。医疗服务机构通常聚焦于诊治已确诊的、会影响痴呆症患者的健康问题。对这类健康问题开展持续管理，能够更早地为痴呆症患者的高质量照护管理提供更科学的决策依据。技术整合健康管理（Technology Integrated Health Management，TIHM）项目开发了一款新型数字平台，可在居家场景下常态化收集纵向观测与测量数据，并应用机器学习与分析模型来检测、预测影响痴呆症患者健康福祉的不良健康事件。本工作介绍了在TIHM项目第二阶段（即可行性研究阶段）期间采集的TIHM数据集。该数据集采集自56名痴呆症患者的家庭，并关联了各类事件与临床观测数据（涵盖日常活动、生理监测信息以及健康相关病症的标注标签）。本研究中每名参与者的平均数据记录时长为50天，总数据时长共计2803天。我们已在GitHub代码仓库中提供了该数据集的原始数据，以及关于如何访问、可视化、处理该数据集并预测其中健康相关事件的操作指南。本研究所用的Jupyter Notebooks基于Python 3.9开发。<strong>本数据集仅用于研究与患者福祉相关的用途。<br>若在任何出版物或使用场景中使用本数据集，请注明萨里与边境合作国民保健信托基金会（Surrey and Borders Partnership NHS Foundation Trust）。</strong>