Symptom monitoring in cancer and fully automated advice on supportive care: Patients' perspectives on self-management strategies and the eHealth self-management application Oncokompas.

OBJECTIVE: The web-based application Oncokompas was developed to support cancer patients to self-manage their symptoms. This qualitative study was conducted to obtain insight in patients' self-management strategies to cope with cancer and their experiences with Oncokompas as a fully automated behavioural intervention technology. METHODS: Data were collected from semi-structured interviews with 22 participants (10 head and neck cancer survivors and 12 incurably ill patients). Interview questions were about self-management strategies and experiences with Oncokompas. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. RESULTS: Participants applied several self-management strategies, among which trying to stay in control and make the best of their situation. They described Oncokompas' added value: being able to monitor symptoms and having access to a personal online library. Main reasons for not using Oncokompas were concentration problems, lack of time or having technical issues. Recommendations were made for further development of Oncokompas, relating to its content, technical and functional aspects. CONCLUSIONS: Survivors and incurably ill patients use various self-management strategies to cope with cancer. The objectives of self-management interventions as Oncokompas correspond well with these strategies: taking a certain responsibility for your well-being and being in charge of your life as long as possible by obtaining automated information (24/7) on symptoms and tailored supportive care options.

Understanding Drivers of Resistance Toward Implementation of Web-Based Self-Management Tools in Routine Cancer Care Among Oncology Nurses: Cross-Sectional Survey Study.

BACKGROUND: Supporting patients to engage in (Web-based) self-management tools is increasingly gaining importance, but the engagement of health care professionals is lagging behind. This can partly be explained by resistance among health care professionals. OBJECTIVE: The aim of this study was to investigate drivers of resistance among oncology nurses toward Web-based self-management tools in cancer care. METHODS: Drawing from previous research, combining clinical and marketing perspectives, and several variables and instruments, we developed the Resistance to Innovation model (RTI-model). The RTI-model distinguishes between passive and active resistance, which can be enhanced or reduced by functional drivers (incompatibility, complexity, lack of value, and risk) and psychological drivers (role ambiguity, social pressure from the institute, peers, and patients). Both types of drivers can be moderated by staff-, organization-, patient-, and environment-related factors. We executed a survey covering all components of the RTI-model on a cross-sectional sample of nurses working in oncology in the Netherlands. Structural equation modeling was used to test the full model, using a hierarchical approach. In total, 2500 nurses were approached, out of which 285 (11.40%) nurses responded. RESULTS: The goodness of fit statistic of the uncorrected base model of the RTI-model (n=239) was acceptable (χ21=9.2; Comparative Fit Index=0.95; Tucker Lewis index=0.21; Root Mean Square Error of Approximation=0.19; Standardized Root Mean Square=0.016). In line with the RTI-model, we found that both passive and active resistance among oncology nurses toward (Web-based) self-management tools were driven by both functional and psychological drivers. Passive resistance toward Web-based self-management tools was enhanced by complexity, lack of value, and role ambiguity, and it was reduced by institutional social pressure. Active resistance was enhanced by complexity, lack of value, and social pressure from peers, and it was reduced by social pressure from the institute and patients. In contrast to what we expected, incompatibility with current routines was not a significant driver of either passive or active resistance. This study further showed that these drivers of resistance were moderated by expertise (P=.03), managerial support (P=.004), and influence from external stakeholders (government; P=.04). CONCLUSIONS: Both passive and active resistance in oncology nurses toward Web-based self-management tools for patients with cancer are driven by functional and psychological drivers, which may be more or less strong, depending on expertise, managerial support, and governmental influence.