The dynamics in health-related quality of life of patients with stable coronary artery disease were revealed: a network analysis

• Published in: Journal of clinical epidemiology 2019-03, Vol.107, p.116-123
• Main Authors: Oreel, Tom H., Borsboom, Denny, Epskamp, Sacha, Hartog, Iris D., Netjes, Justine E., Nieuwkerk, Pythia T., Henriques, José P.S., Scherer-Rath, Michael, van Laarhoven, Hanneke W.M., Sprangers, Mirjam A.G.
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Arthritis; Cardiovascular disease; Chronic illnesses; Coronary artery; Coronary artery disease; Coronary Artery Disease - psychology; Coronary vessels; Data analysis; Data processing; Dynamic structural analysis; Dynamics; Epidemiology; Experience sampling; Female; Health Status; Health-related quality of life; Heart diseases; Humans; Male; Middle Aged; Mood; Multilevel Analysis; Network analysis; Pain; Patients; Personalized treatment; Psychometrics; Quality of life; Quality of Life - psychology; Sample Size; Sampling methods; Signs and symptoms; Stable coronary artery disease; Surveys and Questionnaires; Variation
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/j.jclinepi.2018.11.022

Health-related quality of life (HRQoL) is a dynamic construct. Experience sampling methods (ESM) are becoming increasingly popular to capture within-person fluctuations in HRQoL. An emerging approach to analyze such momentary data is network analysis. Our aim was to explore the use of network analysis for investigating the dynamics within individual's HRQoL. We analyzed ESM data of 30 patients with stable coronary artery disease (CAD). Patients completed eight HRQoL items representing four scales (i.e., positive mood, negative mood, CAD symptoms, and physical state) at nine times a day for seven consecutive days. Network analysis was used to analyze the data at group level to estimate the average HRQoL dynamics and at patient level to estimate HRQoL dynamics of individual patients. Group-level analysis showed that, on average, feeling “tired” and feeling “anxious” are the most central items in patients' HRQoL. Patient-level analysis revealed differences in patients’ network structures, indicating within-person differences in HRQoL dynamics. This study is one of the first to apply network analysis to momentary HRQoL data. To the extent that network models are meaningful representations of HRQoL dynamics, they may help deepening our insight into experienced HRQoL and provide targets for personalized treatment.