The Healthy Aging Data Science Group

Our group leverages digital health data to better understand and promote healthy aging. We are led by Dr. Collin Sakal and located at the City University of Hong Kong (Dongguan), 香港城市大学(东莞). We have openings for PhD students. For more information, please see the “Recruitment” page.

Recent News

  • 2026-08 Dr. Sakal gave an invited talk at the 2nd APEC Healthy Aging Policy Dialogue in Dalian, China.

  • 2026-08 We published a paper in BMC Medicine! [paper]

  • 2026-07 We published a paper in Translational Psychiatry! [paper]

  • 2026-04 Dr. Sakal and Dr. LI Xinyue were awarded a ¥1,200,000 grant from the Guangdong and Hong Kong Universities “1+1+1” Joint Research Collaboration Scheme for their proposal titled “利用人工智能和可穿戴技术推动数字健康管理与健康老龄化.“

  • 2026-04 We published a paper in the IEEE Journal of Biomedical and Health Informatics! [paper].

  • 2026-02 Dr. Sakal gave an invited talk at the APEC Population Aging and Environmental Health Policy Dialogue in Guangzhou, China.

Research Areas

  • The bulk of our current research involves analyzing health data derived from wearables. In our work, we examine how wearable-derived sleep, walking patterns, physical activity, and circadian rhythms associate with adverse health outcomes in older adult populations.

  • Our research is centered around understanding and promoting longevity among older adults through the use of digital health technologies.

  • We are also interested in identifying how wearables could be used for personal health monitoring. More specifically, we explore how wearable device data can be used to train machine learning models for tasks like forecasting sleep quality, monitoring cognitive function, and predicting risk of adverse health outcomes.

  • Functional Data Analysis is an emerging area of statistical methodology that has direct applications to wearable device data analysis. In our lab we leverage functional data analytic methods in digital health research.

Selected Publications

  • Sakal C, Xu W, Zhang W, Li X. Associations of physical activity bout duration and fragmentation with future dementia, depression, and anxiety: a multi-modal UK Biobank study. Translational Psychiatry (2026). https://doi.org/10.1038/s41398-026-04272-3

  • Sakal C, Chen T, Xu W, Zhang W, Yang Y, Li X. Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4-8 hours before sleep onset. SLEEP (2025). https://doi.org/10.1093/sleep/zsaf113

  • Sakal C, Li T, Li J, Li X. Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study. npj Aging (2024). https://doi.org/10.1038/s41514-024-00177-x