Pricing through health apps generated data—Digital dividend as a game changer: Discrete choice experiment

Abstract

Objectives The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. Methods We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. Results Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. Conclusion A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes.

Enrollment and Retention of Participants in Remote Digital Health Studies: Scoping Review and Framework Proposal

“…The higher study completion could be due to interventional studies providing more incentives or nudges to participants than observational studies. The provision of incentives in studies is consistent with recent approaches that use willingness to accept estimations or incentive matching with local wages to enable higher study enrollment and completion [97][98][99][100][101]. Furthermore, the choice of offering nudges to study participants is supported by recent adaptations to the nudge theory, which claims that providing motivational elements in digital health research may affect decision making in study participants [102].…”

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment

“…Other studies examine factors of communication (eg, information giving, information transfer, and contact) [ 41 - 43 ]. Most of the studies do not include health-related goal variables [ 39 , 44 ]. Thus, there is a gap in holistic evidence on preference research on digital interventions.…”

“…Most of the studies do not include health-related goal variables [39,44]. Thus, there is a gap in holistic evidence on preference research on digital interventions.…”

Decision Trade-Offs in Ecological Momentary Assessments and Digital Wearables Uptake: Protocol for a Discrete Choice Experiment

“…The process is repeated with different combinations of attributes, and the resulting data are used to calculate the relative importance each participant places on each attribute [44]. In previous studies, this method has been used to elicit preferences for health mobile apps [45][46][47], sharing health data [48,49], and health interventions [50]. In this study, 2 DCEs will elucidate preferences for EMAs and DWs, examine how these preferences vary across demographics (ie, sex, age, and race and ethnicity) and behaviors (ie, substance use, physical activity, dietary behavior, and sleep duration), and assess whether specific preferences are associated with attitudes toward or intentions to use EMAs and DWs in the future.…”