Self-compassion is a supportive style of self-relating in times of suffering. For people with cancer, self-compassion is associated with less anxiety, depression and self-criticism. Self-compassion can be trained with interventions such as Compassionate Mind Training (CMT). Yet face-to-face interventions can be too intensive for people with cancer, and eHealth may offer a low-threshold form of support. Given the absence of compassion-based eHealth for people with cancer, it is important to create interventions based on daily life experiences of people with cancer as well as on theory and evidence. To that end, a top-down bottom-up co-design process was applied in this project to develop a mobile self-compassion intervention for people with newly diagnosed cancer. First, theory and evidence regarding self-compassion (interventions) were investigated. Second, this top-down input was integrated with wishes, needs and experiences of people with cancer and oncology nurses in an extensive co-design process. Third, the resulting mobile intervention Compas-Y was evaluated in a mixed methods pilot study. A wide range of interdisciplinary research methods were applied, including systematic literature review, semi-structured interviews, co-design workshops and correlational research with mixed analysis. Results indicated a highly appreciated and promising intervention to support people with cancer in their mental health. The thesis also offered insights regarding the relevance of self-compassion in the context of cancer, developing compassion-based eHealth and conducting top-down bottom-up co-design. Further research is needed to evaluate the proposed co-design model in other development studies, and to evaluate the intervention with controlled studies. Overall, this thesis illustrated the relevance of 1) self-compassion (training) in adjusting to a cancer diagnosis and 2) developing eHealth based on a systematic integration of theory and evidence with user wishes, needs and experiences.
|Qualification||Doctor of Philosophy|
|Award date||1 Sept 2023|
|Place of Publication||Enschede|
|Publication status||Published - 2023|
- mixed methods