The main challenges in creating a successful digital wellness program, from a behavioural perspective

23 September 2020

23 September 2020

Over recent years, institutional investors and large corporates, such as insurers, have started investing an increasing amount of capital into digital healthcare. Additionally, insurers incentivise their customers towards a healthier lifestyle by providing relevant rewards. Nevertheless, people tend not to follow healthy habits, even though they understand that a healthy lifestyle can improve their overall well-being, because of a variety of behavioural biases (such as Tasty=Unhealthy Intuition, Risk Aversion and Time Preference). What is more, most of the digital wellness programs build their applications on setting goals, providing feedback and rewarding on performance, and motivating social interactions (i.e. compare progress with others). However, the design of such applications quite often ignores consumers’ psychology and motivations for using digital wellness programs. As a result, the widespread digital wellness programs face significant challenges to succeed, which can be summarized as (a) the unintended reasons that people give up on their goals, and (b) the irrational aspect of people’s decision making.

Regarding the first challenge, Kahneman (2011) described, at his book “Thinking fast and slow”, the existence of 2 systems in humans’ brains: system 1 is unconscious and fast (e.g. driving a car), while system 2 is conscious, slow and requires effort (e.g. sustain a higher than normal walking rate). Most of the humans’ decision-making and behaviour is driven by system 1. The digital wellness programs are trying to modify behaviour, which requires much more effort and utilization of system 2. Hence, it’s really important to understand how to design and communication (i.e. frame) each feature of a digital wellness program, so to persuade and eventually change users’ habits. Behavioural change interventions often only work during the period that an intervention is active. To achieve actual behavioural change beyond the intervention phase you have to create habits, which are behavioural patterns that people do automatically, repeatedly and consistently (Verplanken & Aarts, 1999).

With respect to the second challenge, the majority of digital health interventions take for granted that human’s decision-making is rational. However, according to the Prospect Theory of Amos Tversky and Daniel Kahneman (1992), people assess their losses and gains in an asymmetric way and focus on the latter, which highlights the importance of framing. As a result, in the beginning, digital wellness programs should motivate their users by focusing on the positive outcomes of changing their behaviour, but later on and when the user is close to reaching his/her goal, the driver to increase effort should be on the fear of losing this gain (Louro, Pieters & Zeelenberg, 2007) and the negative emotions (e.g. guilt) that a potential failure would cause. It would be useful to mention that personalized feedback is also important (Thaler & Sustein, 2008). For example, in a smoking cessation program, in the beginning, the messaging should be related to how many cigarettes were not smoked and how much money the user saved, as well as how his health has been improved. After this phase, the messaging could focus on how much regret the user would feel if he/she start smoking again.




Louro, M. J., Pieters, R., & Zeelenberg, M. (2007). Dynamics of multiple-goal pursuit. Journal of personality and social psychology93(2), 174.

Thaler, R. H., & Sunstein, C. R. (2009). Nudge: improving decisions about health, wealth, and happiness. Rev. and expanded ed. New York: Penguin Books.

Tversky, A., Kahneman, D. Advances in prospect theory: Cumulative representation of uncertainty. J Risk Uncertainty 5, 297–323 (1992). https://doi.org/10.1007/BF00122574

Verplanken, B., & Aarts, H. (1999). Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity?. European review of social psychology10(1), 101-13

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