Key Subject Area 1: Health and Longevity in a Caring Community
Project Title: Combating Aging-Associated Sarcopenia: From Knowledge Discovery to Intervention Testing
Project Coordinator: Prof Huating WANG (CUHK)
Abstract
It is estimated that the proportion of elderly persons aged 65 and over will increase to 36.0% by 2046 in HK. Population aging during the next few decades is likely to become a major resource-consuming health crisis, both in HK and elsewhere in the world. Skeletal muscle, as a key organ of body homeostasis and mobility, suffers from age-associated sarcopenia, i.e. the decline of mass, function, and regenerating capacity. As a common geriatric condition with a prevalence of 10%–27% in adults, sarcopenia significantly impacts overall health and quality of life in older adults thus representing a major public health issue resulting in huge socioeconomic and healthcare burdens. Therefore, there is an urgent need to increase our collective efforts in understanding the underlying mechanisms of muscle aging and harnessing the knowledge to develop effective intervention strategies for combating sarcopenia. Here in this project, we gather a group of scientists and clinicians in the fields of biomedicine, geriatrics, AI engineering, bioinformatics to work around the same mission: to slow skeletal muscle aging and combat sarcopenia. Specifically, we propose three objectives: (1) To investigate molecular mechanisms underlying muscle aging and sarcopenia. (2) To establish AI-aided drug discovery system for sarcopenia. (3) To conduct pre-clinical and clinical testing for anti-sarcopenia drugs. Findings from the project will not only uncover new knowledge of muscle aging but also enable the development of intervention approaches for sarcopenia.
Key Subject Area 1: Health and Longevity in a Caring Community
Project Title: AI-assisted Infection Management in Elderly Hospitals and Nursing Homes: Safeguarding Health through Point-of-Care Technologies and Smart Materials
Project Coordinator: Prof I-ming HSING (HKUST)
Abstract
Many elderly individuals in Hong Kong are immunosuppressed, especially those residing in nursing homes or recovering in hospitals. Often in frail conditions, they are more susceptible to infections, which reduce their quality of life and increase the burden on public health. Current countermeasures for infections involve extensive use of antimicrobial agents such as antibiotics. However, their effectiveness in prolonging life or alleviating symptoms is questionable, and their use contributes to the undesirable development of multiple drug-resistant organisms (MDROs). Infection management in local elderly hospitals and nursing homes faces three main challenges. First, the insufficient capacity and efficiency of MDRO screening assays hinder patient classification and targeted antimicrobial treatments. Second, monitoring infection deterioration is difficult due to the altered mental and physical states of elderly patients, relying on unfavourable repetitive blood testing. Third, chronic wounds such as bedsores are common among dependent elderly patients, often related to biofilm infections. These wounds can take a long time to heal and require specialised management.
To tackle these challenges, this project aims to develop and implement whole-process infection management assisted by artificial intelligence (AI). The centre of this proposal is an integrated AI platform that generates personalized treatment and nursing plans from real-time and in-situ clinical data streams. We will create innovative point-of-care (PoC) and wearable biosensors to compose the real-time data stream facilitating the AI platform, including high-throughput on-site screening of MDROs, rapid antimicrobial susceptibility testing (AST), and continuous non-invasive vital sign and biomarker monitoring. In view of the specialized challenge on chronic wound-related infections, we will develop a subsystem integrated with the AI platform to manage the potential tissue infections due to prolonged wound healing. This subsystem encompasses functionalized smart responsive materials composing a multilayer patch for prevention, alert and management of chronic wounds. It will realize in-situ alert of pressure accumulation and inflammation while interfacing with the microorganism testing mentioned above for infection diagnosis. The sensing data will be processed by the AI platform for personalized nursing care plans to promote wound healing and prevent infections.
Together, the outcomes of the project will facilitate personalised, AI-assisted infection management for elderly individuals in hospitals and nursing homes. Our project deliverables include 1) a multimodal foundation AI platform for whole-process infection management; 2) onsite multi-level MDRO screening; 3) on-site rapid phenotypic AST; 4) continuous vital sign and biomarker monitoring by PoC and wearable devices; 5) On-demand management of chronic wounds by smart materials.
Key Subject Area 1: Health and Longevity in a Caring Community
Project Title: An Integrated AI Solution for Large-Scale Improvement of Emotional Health among Older Populations: Risk Assessment of Depression and Intervention for Ageing Needs, Challenges, and Empowerment System (RADIANCE)
Project Coordinator: Prof Tatia LEE Mei-chun (HKU)
Abstract
Increased prevalence and incidence of depression among the rapidly ageing population in Hong Kong and the Greater Bay Area (GBA) is an increasingly challenging healthcare issue. Despite this pressing concern, there remains a critical lack of a large-scale digital solution to provide systematic, timely screening, monitoring, and contingent interventions for older adults at risk of depression. To address this imminent healthcare gap, we propose a multi-tiered, agentic, deep learning-based system that leverages multiple contemporary artificial intelligence approaches to process and learn from large-scale multimodal data. Building on our extensive research track record in late-life depression, our multidisciplinary team of scientists and practitioners from Hong Kong, Shenzhen, Guangzhou, and Beijing collaborated to propose the “Risk Assessment of Depression and Intervention for Ageing Needs, Challenges, and Empowerment System (RADIANCE)”. Our synergistic team, with a proven track record of collaboration, aims to develop a comprehensive AI solution that addresses critical gaps in current services for older adults’ emotional health and overcomes significant scalability and accessibility challenges in existing mental healthcare services. First, the AI system will perform population-wide screening, monitoring, and automatic referral to affiliated mental health professionals, significantly reducing the staffing and financial burdens placed on the healthcare system. Second, our system will be implemented on personal digital devices, thereby significantly reducing social barriers and stigma that discourage older adults from seeking help and substantially increasing access to mental health assistance. Third, in combination with our custom wearable devices, our system enables 24/7 continuous tracking of physiological, behavioural, and affective changes associated with depression. Fourth, personalised, digital agent-guided interventions will be delivered on our platform to protect older adults against developing depression. This will, in turn, help direct healthcare resources to those who have a genuine need for clinical assistance and, eventually, reduce the incidence and prevalence of geriatric depression in Hong Kong and the GBA. Our overarching goal is for RADIANCE to complement existing healthcare systems, enhancing scalability and accessibility to empower older people to take control of their emotional health. Our strong collaborations with community health and clinical translation platform providers (e.g., Virtus Medical Group in HK and the Chinese Mainland) and industry partners (e.g., Xiamen Amity Brain Health and A.I. Technology Co., Ltd. in the Chinese Mainland) provide platforms for rigorous research, validation, and translation of RADIANCE. Demonstrating RADIANCE’s success will unlock emerging opportunities in cross-border healthcare innovation, AI-enhanced mental health services, and large-scale preventive care strategies tailored to the rapidly ageing population across the GBA.
Key Subject Area 2: Smart Technology and an Ageing Population
Project Title: Building an Artificial Intelligence-Enhanced Platform for One-Stop Advance Care Planning Model
Project Coordinator: Prof Helen CHAN Yue-lai (CUHK)
Abstract
The aim of this project is to harness artificial intelligence (AI) to improve access to and enhance the quality of advance care planning (ACP) for older adults with life-limiting illnesses. Growing awareness of the importance of high-quality end-of-life care has led to rising demand for ACP services. Hong Kong is no exception, particularly following the recent enactment of legislation on Advance Medical Directives.
This project proposes an AI-enabled, comprehensive approach to promote equitable access to ACP services through the development of an ACP platform. The key deliverables of the platform include three components: (i) an AI-guided Decision Support Tool to empower older adults and their families to better understand end-of-life care options and reflect upon personal values; (ii) an automated Documentation Tool to reduce administrative burden of documenting ACP conversation; and (iii) an AI-powered ACP Communication Practice Tool to strengthen staff competence in emotionally challenging conversations, customised and refined through an iterative co-design process with stakeholders. The platform will underpin a one-stop ACP service model to be evaluated using an explanatory sequential mixed-methods study, including a stepped-wedged cluster randomised controlled trial and a qualitative study, across 24 care units in the community care and residential care homes across Hong Kong. To facilitate integration of the ACP model into existing services, train-the-trainer workshops will be provided to staff members of the study sites. Thereafter, a dyadic longitudinal cohort study will be conducted to assess the cost-effectiveness and sustained effects of the model on care outcomes, including documentation of end-of-life care decisions, decisional conflicts, ACP engagement, quality of life, death literacy and healthcare utilisation.
Led by a multidisciplinary team of experts in healthcare, AI technology, aged care, and policy, this project addresses a critical service gap in end-of-life care. Successful ACP implementation requires readiness of both individual and healthcare providers as the process requires a thorough clarification of medical and legal matters, and personal values regarding end-of-life care. Through rigorous empirical research, this project will generate evidence on the effects of the one-stop AI-enhanced ACP service model for enabling older adults with life-limiting illnesses and their family members to prepare for end-of-life care. The embedded implementation science is timely to drive a transformative shift in ACP service delivery, enhancing accessibility, sustainability, and scalability across diverse healthcare settings worldwide.
Key Subject Area 2: Smart Technology and an Ageing Population
Project Title: PMAI-PD: A Personalized Multimodal AI Platform for Integrated Life-Cycle Management of Parkinson’s Disease in Aging Populations
Project Coordinator: Prof Qian ZHANG (HKUST)
Abstract
Parkinson’s disease (PD) presents a rapidly growing public health challenge in aging societies. China has over 3 million older adults living with PD, and around 60% of patients experience diagnostic delays of more than one year because early symptoms are subtle and easily overlooked. As PD progresses, patients frequently require weekly and even daily caregiving resources, making PD management both clinical and service scaling challenge: today’s hospital-centric model is not scalable for population-level screening, continuous monitoring, and sustained rehabilitation. As a result, PD screening remains late, monitoring relies on episodic clinic visits that miss daily fluctuations, and rehabilitation depends heavily on therapist-led programs with limited accessibility. These gaps contribute to substantial economic burden and caregiver stress in rapidly aging regions such as Hong Kong and the Greater Bay Area (GBA).
While prior work has explored isolated aspects of PD care, comprehensive life-cycle management is fundamentally hard. It requires clinically consistent outputs across modules, realtime robustness to real-world data, and designs that work under operational constraints. To advance a new consensus for modern PD management in the era of AI, this project will partner with frontline physicians and elderly-care industry partners in the GBA to propose PMAI-PD, an AI-based PD care platform that shifts the PD management landscape from a hospital-centric model to a “hospital + home” care pathway. The central idea is to extend screening, monitoring, and rehabilitation into daily environments using low-cost sensors and clinically grounded AI models, ensuring reliable end-to-end integration while keeping clinicians involved in critical decision-making. PMAI-PD includes three integrated modules: (1) PMAI-PDscreen provides early screening by combining passive daily behaviour signals (e.g., gait and speech) with clinical profiles (e.g., blood proteomic and neuroimaging signatures) linked to early PD. (2) PMAI-PD assess agent supports continuous homebased assessment by estimating PD symptoms comparable to standard clinical scales (UPDRS/MoCA/MMSE) from contactless and wearable sensing during everyday activities, enabling ecologically valid tracking over time. (3) PMAI-PD rehab delivers personalized rehabilitation through an LLM-enabled agent that guides exercises using the same home sensors, improving patient adherence and therapist efficiency.
The project will validate technical performance and clinical utility through multi-center studies across the GBA, followed by a longitudinal end-to-end PD service delivery study with community deployment involving 1,000 older adults. The evidence will quantify the feasibility and benefits on life-cycle PD management. Lessons and implementation guidelines from this deployment will inform how similar home-extended pathways could be adapted to other chronic, aging-related conditions.
Key Subject Area 3: Silver Society, Economy and Finance
Project Title: Empowering an Inclusive, Age-Friendly Silver Economy in Hong Kong: From Longitudinal Evidence to Intergenerational Multi-stakeholder Action
Project Coordinator: Prof Xue BAI (PolyU)
Abstract
Hong Kong is rapidly becoming a super-aged society, with adults aged 65+ projected to comprise over one-third of the population by 2043. This demographic shift presents urgent challenges and significant opportunities for the silver economy—a $15 trillion global market driven by the needs and spending power of ageing consumers (aged 50+). Our research shows that age-friendly market practices can increase silver spending intent by 2.46 times; however, fewer than a quarter of ageing consumers are satisfied with current market offerings. Financial insecurity and inadequate preparedness for later life remain widespread, while the market lacks the evidence and capacity to serve this growing segment with diverse needs.
This project unites leading scholars in longevity and wellbeing, economics and finance, social policy and social work, behavioural sciences and marketing, and built environment and design innovation, alongside key industry and policy partners (e.g., IFEC, HKMCA, Consumer Council). We deliver integrated silver economy solutions by generating evidence, co-creating across generations and sectors, and empowering the silver economy’s dual engines. On the demand side, we equip ageing consumers with financial capability, preparedness, and wellbeing, enabling them to make smart, confident, and value-driven choices. On the supply side, we strengthen the market with age-friendly capacities to meet informed demand.
Guided by Empowerment Theory and market co-creation logic, our pioneering ‘Silver Evidence-to-Action Empowerment’ framework adopts three core strategies. The Discover Strategy leverages the Project Coordinator’s Panel Study of Active Ageing and Society (N=5,007, aged 50+) to conduct a representative, longitudinal survey, analysing silver consumption, financial profiles, and age-friendliness. Insights identify needs, gaps, and market drivers, and assess how age-friendliness affects spending intent, decision autonomy, satisfaction, and consumption outcomes. The Define and Develop Strategy uses Delphi studies with interdisciplinary experts to prioritise challenges and opportunities to empower ageing consumers and the silver market, then translate them into evidence-based prototype interventions through co-creation workshops with intergenerational ambassadors and multi-stakeholders. The Deliver Strategy implements targeted interventions using Motivational Interviewing, grounded in stages of change and social cognitive theories, and leverages a digitalised approach for flexible, scalable learning. Effectiveness is evaluated through two cluster randomised controlled trials: one on financial empowerment for ageing consumers and one on age-friendly empowerment for the silver market. This project will establish Hong Kong as a global leader in the age-friendly silver economy. It will inform policy, education, and industry practices to empower ageing consumers and the market, drive inclusive growth, and foster an age-friendly society.