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<p>The growing ageing population and prevalence of comorbidities pose significant healthcare challenges, from increasing hospitalisations to dementia risk. 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 study provides technical foundations and pilot validation for constructing virtual wards to support and complement healthcare services.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月22日

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  • Nathalia Céspedes Gómez,
  • Yu Chen,
  • Samaneh Kouchaki,
  • Mahan Heydari,
  • Alexandra Cairns,
  • Sergio David Sierra Marín,
  • Alexander Capstick,
  • Jaye Somers,
  • Kirsty Harris,
  • Wilson Wen Bin Goh,
  • Chloe Walsh,
  • Jessica True,
  • Olga Balazikova,
  • Ramin Nilforooshan,
  • Payam Barnaghi

摘要

The growing ageing population and prevalence of comorbidities pose significant healthcare challenges, from increasing hospitalisations to dementia risk. 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 study provides technical foundations and pilot validation for constructing virtual wards to support and complement healthcare services.