The RESILIENT Dataset: Multimodal Monitoring of Ageing-Related Comorbidities and Cognitive Decline

The growing ageing population and prevalence of comorbidities pose significant healthcare challenges, from increasing hospitalisations to dementia risk. Current healthcare systems primarily treat single conditions, overlooking the complex interplay of chronic diseases. Advances in wearable technology and remote healthcare monitoring technologies offer opportunities to enhance management of comorbidities and early intervention to improve healthcare outcomes. This study presents the RESILIENT dataset, a collection of physiological, sleep, and mental health assessment data conducted as part of an ageing-related comorbidities and dementia study. The RESILIENT study has developed a digital platform to integrate data from wearable devices and in-home monitoring technologies to track physiological, sleep, and cognitive patterns. The validation analysis using the Resilient data highlights correlations between cognitive function, mental health, physical activity, and sleep, aligning with existing literature. By leveraging this dataset, researchers can develop predictive models for early detection and personalised interventions aimed at reducing unplanned hospital admissions and improving health outcomes. The Resilient digital platform, an open-source repository that provides software for collecting, storing, and analyzing in-home monitoring data is available at: https://github.com/tmi-lab/resilient. The open-source software for aggregating and analysing the dataset, including summary statistics, stratified analyses by gender and age group, and data visualizations, can be found at: https://github.com/tmi-lab/Resilient-Dataset. The RESILIENT dataset is organised into four main components: 1) A CSV file containing demographic information and baseline assessments related to mental health (PHQ-9, GAD-7, GDS-12) and cognitive functioning (ACE-III) for all participants. For ACE-III, both baseline and 6-month follow-up scores are included; 2) a metadata CSV files describing variables present in the demographic and devices data; 3) a CSV summary file providing per-participant data coverage statistics, including the number of recorded days, average records per day, and the earliest and latest timestamps; and 4) individual participant folders containing raw time-series data, including sleep states and physiological features captured by sleep mats, as well as step counts and heart rate data recorded by smart watches. More specifically, there are four tables included in each participant folder: ScanWatch Steps, ScanWatch HeartRate, Sleep States, and Sleep Physiology. Each folder is named after the participant's unique identifier (UID), allowing cross-referencing between the device data and the demographic information. Ethics statement: The RESILIENT study has been reviewed and approved by the London-Surrey Borders Research Ethics Committee and the Health Research Authority and is registered on the Integrated Research Application System (IRAS) under reference number 321104. This publicly available dataset includes remote healthcare monitoring data and baseline mental health and cognitive assessments conducted throughout the monitoring period, providing a comprehensive resource for analysing health trends and detecting early signs of cognitive and physiological decline. Dataset anonymisation: A two-stage de-identification process was applied to the data. In the first stage, the data was pseudo-anonymised to develop analytical methods for the study. In the second stage, data was fully anonymised by removing all personally identifying information and any identifiable attributes. Participants are randomly assigned a Universally Unique Identifier (UID) to enhance security during de-identification. This ensures demographics and raw monitoring data from sleep mats and scan watches cannot be traced back to individuals while preserving the data’s utility for analysis. *Note (13/07/2025) : ACE-III scores taken at 6 months are subject to a second quality control. If any changes are identified, an updated version will be issued.日益增长的人口老龄化与共病（comorbidities）患病率给全球医疗保健体系带来了严峻挑战，具体表现为住院人次攀升、痴呆风险上升等。当前医疗体系多聚焦单一病症的诊疗，忽视了慢性病之间的复杂相互作用。可穿戴技术与远程医疗监测技术的进步，为优化共病管理、开展早期干预以改善医疗结局提供了全新机遇。 本研究推出RESILIENT数据集，该数据集采集自一项针对老龄化相关共病与痴呆的研究，涵盖生理、睡眠及心理健康评估数据。RESILIENT研究搭建了数字化平台，整合可穿戴设备与居家监测技术的数据，以追踪生理、睡眠与认知模式。基于该数据集开展的验证分析揭示了认知功能、心理健康、身体活动与睡眠之间的相关性，与现有文献结论相符。借助该数据集，研究人员可开发预测模型，实现早期检测与个性化干预，旨在减少非计划住院人次并改善健康结局。 RESILIENT数字化平台作为开源仓库，提供居家监测数据的采集、存储与分析软件，其开源地址为：https://github.com/tmi-lab/resilient。 用于该数据集聚合与分析的开源软件（涵盖汇总统计、按性别与年龄组分层分析及数据可视化功能）可通过以下地址获取：https://github.com/tmi-lab/Resilient-Dataset。 RESILIENT数据集共分为四大核心模块：1） 涵盖所有受试者人口统计学信息与基线评估数据的CSV文件，其中基线评估包含心理健康量表（PHQ-9、GAD-7、GDS-12）与认知功能量表（ACE-III）的得分；针对ACE-III，同时包含基线与6个月随访的测评结果；2） 描述人口统计学与设备数据中变量信息的元数据CSV文件；3） 提供每名受试者数据覆盖情况统计的CSV汇总文件，涵盖记录天数、日均记录条数以及最早与最晚时间戳；4） 以受试者唯一标识符（Universally Unique Identifier, UID）命名的个体受试者文件夹，内含原始时序数据，包括睡眠垫采集的睡眠状态与生理特征数据，以及智能手表记录的步数与心率数据。具体而言，每个受试者文件夹包含四张数据表：ScanWatch Steps、ScanWatch HeartRate、Sleep States与Sleep Physiology。通过受试者UID可实现设备数据与人口统计学信息的交叉关联。 伦理声明：RESILIENT研究已通过伦敦-萨里边界研究伦理委员会与健康研究管理局的审查批准，并已在综合研究申请系统（Integrated Research Application System, IRAS）注册，注册号为321104。本公开数据集包含远程医疗监测数据以及整个监测周期内开展的基线心理健康与认知评估数据，可为分析健康趋势、检测认知与生理衰退的早期迹象提供全面的研究资源。 数据集匿名化处理：本数据采用两阶段去标识化流程。第一阶段为伪匿名化，用于开发研究分析方法；第二阶段为完全匿名化，移除所有个人身份信息与可识别属性。受试者被随机分配唯一标识符（UID）以增强去标识化过程中的安全性。此举可确保睡眠垫与智能手表采集的人口统计学信息与原始监测数据无法追溯至个体受试者，同时保留数据用于分析的可用性。 *备注（2025年7月13日）：6个月随访采集的ACE-III得分需经过第二轮质量控制。若发现任何变更，将发布更新版本。