Many breast cancer survivors face long-term post-operative challenges as a result of suffering from breast cancer related lymphedema (hereafter, lymphedema), which causes abnormal swelling and multiple distressing and sometimes extremely painful symptoms. These symptoms have been linked to clinically detrimental outcomes, such as disability and psychological distress, both of which are known risk factors for breast cancer survivors' poor quality of life. More importantly, lymphedema symptoms may indicate an early stage of lymphedema that constitutes only minimal changes in objective measures of limb volume. Without timely intervention in this early disease stage, lymphedema can progress into a chronic condition that no surgical or medical interventions at present can cure. Yet, current clinical practice largely relies on clinicians' observation of swelling and current research methods for detecting and assessing lymphedema are cumbersome and not effective in detecting early stage of lymphedema. Thus, early detection of lymphedema based on symptoms and early intervention for lymphedema symptom management may play an important role in reducing the patient's risk for chronic lymphedema. The first primary goal of this project is to use machine learning to understand the association between symptoms and other relevant personal and clinical factors and the presence of lymphedema, and develop a web-based self-assessment platform that enables patients to assess their risk for lymphedema from anywhere. The second goal is to develop a Kinect-sensor based training system to improve the effectiveness of training for patients to learn and practice the intervention lymphatic exercises developed by the Pl Fu, which have shown great promise in reducing the risk of chronic lymphedema by maintaining pre-surgery limb volume, and relieving lymphedema symptoms. The proposed solutions empower breast cancer survivors to take control of their progression path of lymphedema, and will be integrated into our current IT-based self-care platform for lymphedema symptom management and lymphedema risk reduction, which focuses on preventive, proactive, evidence-based, person-centered approach to improve the quality of life of cancer survivors.
Lymphedema, which causes multiple painful symptoms, is a major health problem that affects more than 40% of 3.1 million breast cancer survivors in the US. This project will develop a self-assessment platform for . early detection of lymphedema and a Kinect-based exercise training system to enhance early intervention. The project has the potential to relieve lymphedema symptoms and reduce the risk for chronic lymphedema.
| Chiang, An-Ti; Chen, Qi; Wang, Yao et al. (2018) Motion Sequence Alignment for A Kinect-Based In-Home Exercise System for Lymphatic Health and Lymphedema Intervention. Conf Proc IEEE Eng Med Biol Soc 2018:2072-2075 |
| Fu, Mei R; Wang, Yao; Li, Chenge et al. (2018) Machine learning for detection of lymphedema among breast cancer survivors. Mhealth 4:17 |
| Fu, Mei R; Axelrod, Deborah; Guth, Amber A et al. (2016) Usability and feasibility of health IT interventions to enhance Self-Care for Lymphedema Symptom Management in breast cancer survivors. Internet Interv 5:56-64 |
