Tai Chi Rivals Therapy for Lasting Sleep Relief

A research team led by the School of Public Health at the LKS Faculty of Medicine, the University of Hong Kong (HKUMed), has discovered that tai chi, a traditional Chinese mind-body exercise, offers long-term benefits for chronic insomnia comparable to cognitive behavioural therapy for insomnia (CBT-I), the current first-line non-pharmacological treatment for chronic insomnia. The study supports the therapeutic use of tai chi as an accessible, culturally relevant, low-cost alternative for managing chronic insomnia among middle-aged and older adults. The findings were published in The BMJ .

Insomnia is a common sleep disorder in older adults and is linked to serious disorder risks, including cardiovascular disease, mental illness, and cognitive decline. CBT-I, also known as talking therapy, is a psychological intervention designed to help individuals recognise and alter harmful thought patterns and behaviours that interfere with sleep. CBT-I is considered to be an effective treatment option for chronic insomnia but its accessibility is limited by high costs and a shortage of trained therapists, restricting its availability in the community.

Tai chi is a Chinese martial art and an ancient form of mind-body exercise. As a low-impact, moderate-intensity exercise, tai chi is popular among older adults for regular practice to promote health. While previous studies hinted at its positive effects on sleep, rigorous scientific evidence comparing tai chi to standard clinical treatment was lacking.

Professor Parco Siu Ming-fai, Professor and Head of Division of Kinesiology of the School of Public Health, HKUMed, emphasised the importance of validating tai chi as an evidence-based option. This study was designed to fill this important knowledge gap on the therapeutic role of tai chi in relieving chronic insomnia.

Between May 2020 and July 2022, HKUMed researchers conducted a rigorous trial in Hong Kong involving 200 Chinese adults aged 50 or above who were suffering from chronic insomnia. The participants were randomly assigned to either the tai chi or the CBT-I group. The tai chi group practiced the widely recognised 24-form Yang style in one-hour, instructor-led workout sessions twice a week for three months. To rigorously assess outcomes, insomnia severity was measured using the Insomnia Severity Index (ISI) at three critical points: before the programme (baseline), at three months (post-intervention) and at 15 months (12-month follow-up). Researchers set a benchmark to assess tai chi's potential as an alternative therapy for insomnia. If tai chi's improvement on the ISI fell within four points of the standard CBT-I treatment, it would be recognised as delivering comparable clinical effectiveness.

The results revealed a fascinating dynamic between short and long-term outcomes. At the three-month checkpoint, CBT-I demonstrated a clear advantage, reducing ISI scores by 11.19 points compared to tai chi's 6.67, resulting in a between-group difference of 4.52, underscoring CBT-I's faster impact. However, the picture shifted dramatically over time. By month 15, tai chi had nearly caught up, achieving a reduction of 9.51 points versus CBT-I's 10.18, narrowing the gap to just 0.68 points and meeting the non-inferiority criteria.

"While CBT-I delivers rapid relief, tai chi offers sustained, long-term improvement without the barriers of cost or therapist availability," said Professor Siu. "For many middle-aged and older adults struggling with chronic insomnia, access to CBT-I often means long waits and high expenses. Our research provides strong evidence that tai chi can serve as a practical alternative — providing a lifestyle-based intervention that improves sleep while promoting overall physical and mental well-being. This is more than just good news for insomnia sufferers. Instead of relying solely on clinical referrals, patients can integrate lifestyle-based interventions into insomnia care, empowering individuals to take control of their health."

Professor Parco Siu Ming-fai said that tai chi offers lifestyle-based interventions for integration into insomnia care, empowering individuals to take control of their health and improve both their sleep and overall well-being.

The HKUMed study supports the therapeutic use of tai chi as an alternative for managing chronic insomnia among middle-aged and older adults.

A research team from the Department of Pharmacology and Pharmacy at the LKS Faculty of Medicine of the University of Hong Kong (HKUMed) has developed an innovative AI-based cardiovascular risk prediction tool, called CardiOmicScore. With a single blood test, the system can accurately forecast the future risk of six major cardiovascular diseases (CVDs): coronary artery disease, stroke, heart failure, atrial fibrillation, peripheral artery disease and venous thromboembolism. It can also provide early warning signals up to 15 years before clinical onset. The findings were published in Nature Communications [link to the publication].

HKUMed develops a cardiovascular risk prediction tool that can accurately predict the future risk of six major cardiovascular diseases with a single blood test. The system can provide early warning signals up to 15 years before clinical onset. The research is led by Professor Zhang Qingpeng (left).

AI-based multiomics integration reflects the body’s real-time health status

CVDs remain the leading cause of death worldwide, accounting approximately 19.8 million fatalities in 2022 alone. In routine health assessments, physicians typically evaluate cardiovascular risk based on age, blood pressure, smoking and other conventional clinical indicators. However, these measures often fail to capture subtle and early biological changes before the disease becomes clinically apparent, leading to many patients missing the optimal window for preventive intervention. Although polygenic risk scores have become popular in recent years, genetic predisposition is largely fixed at birth and does not change over time. Consequently, polygenic risk scores cannot reflect the immediate impact on health conditions resulting from lifestyle or environmental changes. This creates an urgent need for tools that can capture a person’s current biological state and provide accurate, early warnings for CVDs.

To address this problem, the HKUMed research team applied deep learning techniques to integrate multiomics data, including genomics, metabolomics and proteomics, to develop the CardiOmicScore tool. The study was based on large-scale population data from the UK Biobank, analysing 2,920 circulating proteins and 168 metabolites measured from blood samples. These molecular signals act as ‘real-time recorders’ of the body, sensitively reflecting subtle changes in the immune system, metabolism, and vascular health.

Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy at HKUMed, explained, ‘Genes determine where we start—they define our baseline health risk. However, proteins and metabolites reflect our current physical health. Our AI tool is designed to decode these complex molecular signals, enabling doctors and patients to identify risks much earlier, which can potentially change the trajectory of disease through timely lifestyle modifications and early prevention.’

Accurate prediction of six major cardiovascular diseases with 15-year advance warning in high-risk groups

The results showed that CardiOmicScore transforms complex multiomics measurements into personalised risk scores with substantially improved predictive performance compared with conventional polygenic risk scores. When combined with clinical information such as age and gender, the model significantly enhanced the risk prediction accuracy of six common CVDs and can even flag elevated risk up to 15 years before symptoms appear.

This study marks a shift in precision medicine from a static, gene-centric paradigm towards a more dynamic, multiomics-based approach. In the future, a small-volume blood sample may be sufficient to generate a comprehensive cardiovascular risk profile for multiple diseases.

Professor Zhang added, ‘We aim to leverage technology to identify and prevent diseases before they develop. By shifting health management from reactive treatment to proactive prediction and intervention, we aim to create a lasting impact for both public health and individual patient care.’

About the research team

The study was led by Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy, HKUMed, and the HKU Musketeers Foundation Institute of Data Science (IDS). The first author is Luo Yan from the HKU IDS.