Doctor: Theodora Oikonomidi
Title : Willingness to adopt digital behavior change interventions in people with chronic conditions
Supervisors: Philippe Ravaud, Viet-Thi Tran
Doctoral school: ED 393 Epidemiology and Biomedical Information Sciences, Université Paris Cité
Date of thesis defense: 07/12/2021
Jury: Frances Mair, Nathalie Pelletier-Fleury, Emmanuel Cosson, Florence Canoui-Poitrine, Philippe Ravaud, Viet-Thi Tran
Summary:
Digital behavior change interventions can help fulfill the unmet need for timely, personalized behavior-change support for patients with chronic conditions. However, many components of digital behavior change interventions, such as enacting continuous monitoring and receiving frequent feedback, can become intrusive to patients’ lives. How patients might weigh this impact against the promised benefits of digital behavior change interventions, to decide whether they are willing to adopt them or not, is unknown. In this thesis, we sought to identify the intervention components and patient characteristics that contribute to patients’ perception of digital behavior change interventions as intrusive, and assess how intrusiveness relates to patients’ willingness to adopt digital behavior change interventions in their usual care.
In our first project, we conducted a vignette study with 1010 patients with type 1 and type 2 diabetes from 30 countries. We assessed the perceived intrusiveness of digital behavior change interventions composed of different modalities. We collected qualitative data regarding why patients considered specific modalities as intrusive. Our study focused on monitoring and feedback, two of the most commonly used behavior-change techniques. We identified a positive, significant association between intrusiveness and: the addition of food monitoring, compared with glucose- and PA-monitoring alone, permanent monitoring with real-time physician-generated feedback, compared with monitoring for a week with feedback in consultation, and private-sector data handling compared with public-sector data handling. The qualitative analysis identified 4 drivers of intrusiveness: burden, control, data safety/misuse, and dehumanization of care. We subsequently analyzed the data collected in the first study to assess the relationship between intervention characteristics, intrusiveness and patients’ willingness to adopt digital behavior change interventions. Specifically, we sought to identify the minimum effectiveness at improving different health outcomes, for which patients would adopt digital behavior change interventions with varying degrees of intrusiveness. We found that patients require greater minimum effectiveness to adopt interventions perceived to be more intrusive. Our third project was motivated by the shift towards digital care imposed by the COVID-19 pandemic. In a mixed-methods survey with 1599 patients with chronic conditions, we quantified patients’ ideal post-pandemic balance of digital and traditional care, and identified the appropriate circumstances in which digital care modalities could replace traditional care modalities, according to patients. We found that patients would be willing to replace traditional care with digital care modalities for 22 to 52% of their future needs, and we identified 67 care activities, patient characteristics, and characteristics of digital care modalities, for which patients considered it appropriate to replace traditional with digital care. We discuss how our findings inform the post-pandemic integration of digital behavior change interventions in patients’ care. This work helps us to understand how we can reduce the intrusiveness of digital behavior change interventions, to ensure patients are capable and willing to adopt them. The next steps in this line of work are to measure patients’ experience of using digital behavior change interventions longitudinally, including perceived intrusiveness and treatment burden, and to develop shared decision-making aids for digital health and behavior change interventions.
