BACKGROUND
Self-management can increase self-efficacy and quality of life and improve disease outcomes in patients with chronic conditions. Effective self-management may also help to reduce the pressure on healthcare systems. However, patients need support in dealing with their disease and in developing skills to manage the symptoms, treatment, physical consequences, psychological consequences, and lifestyle changes associated with their condition. Online self-management support programs have helped patients with cardiovascular disease (CVD) and rheumatoid arthritis (RA) but program use was low.
OBJECTIVE
This study aims to identify the patient-, disease- and program characteristics that determine whether patients use online self-management support programs or not.
METHODS
A realist evaluation methodology was used to give a comprehensive oversight of context (patient- and disease characteristics), mechanism (program characteristics), and outcome (program use). The relation between context (sex, age, education, employment status, living situation, self-management [measured using PAM-13], quality of life [measured using RAND-36], interaction efficacy [measured using PEPPI-5], diagnosis, physical comorbidity, and time since diagnosis) and outcome (program use) was analyzed through logistic regression analyses. The relation between mechanism (program design, implementation strategies, behavior change techniques) and outcome was analyzed through a qualitative interview study.
RESULTS
For this study, 68 non-users and 111 users of the online self-management support programs were included, of which 57% were diagnosed with CVD and 43% with RA. Younger age and a lower level of education were associated with program use. An interaction effect was found between program use and diagnosis (CVD or RA) and four quality of life subscales (social functioning, physical role limitations, vitality, and bodily pain). CVD patients with higher self-management and quality of life scores were less likely to use the program, while RA patients with higher self-management and quality of life scores were more likely to use the program. Interviews with ten non-users, ten low-users, and 18 high-users were analyzed to give insight into the relation between mechanisms and outcome. Program use was encouraged by an easy to use, clear, and transparent design and by recommendations from a professional and email reminders. Five behavior change techniques were identified as potential mechanisms to promote program use: tailored information, self-reporting behavior with delayed feedback, giving information on peer behavior, and modeling.
CONCLUSIONS
This realist evaluation showed that certain patient-, disease-, and program characteristics are associated with the use of online self-management support programs. These results can help developers of future online self-management support programs to tailor the interventions to increase use and effectiveness.