Prediction and Prevention of Non-Adherence to Digital Health Interventions
Digital Health Interventions show great potential in supporting health care systems with the globally increasing prevalence and economic costs of chronic diseases – the leading causes of death and disability worldwide. However, despite the availability of evidence-based DHIs, a substantial proportion of users does not adhere to them and may consequently not receive treatment (Hesser, 2020). Non-Adherence is a significant issue because it jeopardizes treatment success and leads to a significant amount of hospitalizations (Morrissey et al. 2016; Sieverink et al., 2017). Most DHIs are only beneficial if people use them regularly. Therefore, identifying users at risk and preventing them from dropping out is a fundamental concern for the efficiency and quality of DHIs (Pedersen et al., 2019; Trinh at al., 2018).
Scientific literature still lacks concise conceptualizations and measures for intended use and evidence-based intervention components that trigger intended use are still to be explored (Sieverink et al., 2017; Baumel & Yom-Tov, 2018). To this end, we will use our technical expertise in state of receptivity to fill this research gap and apply it to the prediction and prevention of non-adherence also including dropouts (Kramer et al., 2019; Künzler et al., 2019). This project has the following objectives:
- To identify state-of-the-art factors that predict non-adherence based on a literature review and original data from longitudinal studies.
- To identify intervention components (e.g. based on existing behavior change literature) that effectively intervene in critical situations to prevent non-adherence and to reduce dropouts.
- To build a taxonomy of intervention components that trigger intended use of DHIs.
- To develop and evaluate an early warning system that is able to predict and prevent non-adherence.
Baumel, A., & Yom-Tov, E. (2018). Predicting user adherence to behavioral eHealth interventions in the real world: examining which aspects of intervention design matter most. Translational behavioral medicine, 8(5), 793–798. https://doi.org/10.1093/tbm/ibx037
Hesser, H. (2020). Estimating causal effects of internet interventions in the context of nonadherence. Internet interventions, 21, 100346. https://doi.org/10.1016/j.invent.2020.100346
Kramer, J. N., Künzler, F., Mishra, V., Presset, B., Kotz, D., Smith, S., Scholz, U., & Kowatsch, T. (2019). Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial. JMIR research protocols, 8(1), e11540. https://doi.org/10.2196/11540
Künzler, F., Mishra, V., Kramer, J.-N., Fleisch, E., Kotz, D.F. & T. Kowatsch (2019) Exploring the State-of-Receptivity for mHealth Interventions, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Issue 4, December. https://doi.org/10.1145/3369805
Morrissey, E. C., Corbett, T. K., Walsh, J. C., & Molloy, G. J. (2016). Behavior Change Techniques in Apps for Medication Adherence: A Content Analysis. American journal of preventive medicine, 50(5), e143–e146. https://doi.org/10.1016/j.amepre.2015.09.034
Pedersen, D. H., Mansourvar, M., Sortsø, C., & Schmidt, T. (2019). Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors. Journal of medical Internet research, 21(9), e13617. https://doi.org/10.2196/13617
Sieverink, F., Kelders, S. M., & van Gemert-Pijnen, J. E. (2017). Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence. Journal of medical Internet research, 19(12), e402. https://doi.org/10.2196/jmir.8578
Trinh, H., Shamekhi, A., Kimani, E., Bickmore, T. (2018). Predicting User Engagement in Longitudinal Interventions with Virtual Agents. In Proceedings of the 18th International Conference on Intelligent Virtual Agents (IVA ’18). Association for Computing Machinery, New York, NY, USA, 9–16. https://doi.org/10.1145/3267851.3267909