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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:

  1. To identify state-of-the-art factors that predict non-adherence based on a literature review and original data from longitudinal studies.
  2. 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.
  3. To build a taxonomy of intervention components that trigger intended use of DHIs.
  4. To develop and evaluate an early warning system that is able to predict and prevent non-adherence.

Related Work

Baumel, A., & Yom-Tov, E. (2018). Predicting user adherence to behavioral eHealth interventions in the real world: examining which aspects of intervention design matter mostTranslational behavioral medicine8(5), 793–798. https://doi.org/10.1093/tbm/ibx037

Hesser, H. (2020). Estimating causal effects of internet interventions in the context of nonadherenceInternet interventions21, 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 AnalysisAmerican journal of preventive medicine50(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 PredictorsJournal of medical Internet research21(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 AdherenceJournal of medical Internet research19(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

CDHI Research Team

M.Sc. Robert Jakob, Prof. Dr. Elgar Fleisch & Prof. Dr. Tobias Kowatsch

Runtime

Sep 2020 – Aug 2024

Funding
CSS Insurance
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