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

Area of Science:

• Gerontology
• Digital Health
• Biomedical Data Science

Background:

• The ageing population and rising comorbidity rates present significant healthcare challenges, including increased hospitalizations and dementia risk.
• Current healthcare systems often focus on single conditions, neglecting the complex interactions between chronic diseases.
• Wearable technology and remote monitoring offer promising avenues for managing comorbidities and enabling early interventions.

Purpose of the Study:

• To introduce the RESILIENT dataset, comprising physiological, sleep, and mental health data from an ageing-related comorbidities and dementia study.
• To detail the development of a digital platform for integrating data from wearable and in-home monitoring technologies.
• To validate the dataset by exploring correlations between various health indicators.

Main Methods:

• Collection of physiological, sleep, and mental health data using wearable devices and in-home monitoring.
• Development of a digital platform to integrate and track diverse health patterns.
• Validation analysis to identify correlations between cognitive function, mental health, physical activity, and sleep.

Main Results:

• The RESILIENT dataset was successfully compiled, integrating multi-modal health data.
• Validation analysis confirmed significant correlations between cognitive function, mental health, physical activity, and sleep patterns.
• The study established technical foundations and pilot validation for virtual ward construction.

Conclusions:

• The RESILIENT dataset provides a valuable resource for researchers studying ageing-related comorbidities and dementia.
• Leveraging this data can facilitate the development of predictive models for early detection and personalized interventions.
• The findings support the potential of virtual wards to enhance healthcare services for the ageing population.