An evaluation of the effectiveness of personalization and self-adaptation for e-Health apps

Journal article, 2022

Context: There are many e-Health mobile apps on the apps store, from apps to improve a user's lifestyle to mental coaching. Whilst these apps might consider user context when they give their interventions, prompts, and encouragements, they still tend to be rigid e.g., not using user context and experience to tailor themselves to the user.
Objective: To better engage and tailor to the user, we have previously proposed a Reference Architecture for enabling self-adaptation and AI personalization in e-Health mobile apps. In this work we evaluate the end users’ perception, usability, performance impact, and energy consumption contributed by this Reference Architecture.
Method: We do so by implementing a Reference Architecture compliant app and conducting two experiments: a user study and a measurement-based experiment.
Results: Although limited in the number of participants, the results of our user study show that usability of the Reference Architecture compliant app is similar to the control app. Users’ perception was found to be positively influenced by the compliant app when compared to the control group. Results of our measurement-based experiment showed some differences in performance and energy consumption measurements between the two apps. The differences are, however, deemed minimal.
Conclusions: Our experiments show promising results for an app implemented following our proposed Reference Architecture. This is preliminary evidence that the use of personalization and self-adaptation techniques can be beneficial within the domain of e-Health apps.