Biometrics and quality of life of lymphoma patients: A longitudinal mixed‐model approach

Abstract

Knowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health‐related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato‐oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real‐time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed‐effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life.