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 ageingrelated 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 dataset is organised into two components: 1). one CSV file containing demographic information and baseline assessments related to mental health and cognitive ability for all participants; 2). individual folders that contain raw data, including sleep state and physiological features recorded by sleep mats, as well as step counts and heart rate from smart watches. More specifically, there are four tables included in each participant folder: ScanWatch Steps, ScanWatch HeartRate, Sleep States, Sleep Physiology. Due to differences in sampling frequencies, these variables are recorded in separate tables. Each folder is named after the participant's unique identifier (UID), allowing cross-referencing between the device data and the demographic information.