Oncokompas is a web-based self-management application that supports cancer survivors to monitor their health-related quality of life (HRQOL) and symptoms, and to obtain personalised feedback and tailored options for supportive care. In a large randomised controlled trial among survivors of head and neck cancer, colorectal cancer, and breast cancer and (non-)Hodgkin lymphoma, Oncokompas proved to improve HRQOL, and to reduce several tumour-specific symptoms. Effect sizes were however small, and no effect was observed on the primary outcome patient activation. Therefore, this study aims to explore which subgroups of cancer survivors may especially benefit from Oncokompas.
The intervention effect on HRQOL was the largest among cancer survivors with low to moderate self-efficacy, and among those with high personal control and those with high health literacy scores. Cancer survivors with higher baseline symptom scores benefitted more on head and neck (pain in the mouth, social eating, swallowing, coughing, trismus), and colorectal cancer (weight) specific symptoms.
Oncokompas seems most effective in reducing symptoms in head and neck cancer and colorectal cancer survivors who report a higher burden of tumour-specific symptoms. Oncokompas seems most effective in improving HRQOL in cancer survivors with lower self-efficacy, and in cancer survivors with higher personal control, and higher health literacy.
Cancer survivors (n = 625) were randomly assigned to the intervention group (access to Oncokompas, n = 320) or control group (6 months waiting list, n = 305). Outcome measures were HRQOL, tumour-specific symptoms, and patient activation. Potential moderators included socio-demographic (sex, age, marital status, education, employment), clinical (tumour type, stage, time since diagnosis, treatment modality, comorbidities), and personal factors (self-efficacy, personal control, health literacy, Internet use), and patient activation, mental adjustment to cancer, HRQOL, symptoms, and need for supportive care, measured at baseline. Linear mixed models were performed to investigate potential moderators.