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

Context: There are many e-Health mobile apps on the apps store, from apps to improve a user\u27s 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

Self-adaptation in mobile apps: A systematic literature study

With their increase, smartphones have become more integral components of our lives but due to their mobile nature it is not possible to develop a mobile application the same way another software system would be built. In order to always provide the full service, a mobile application needs to be able to detect and deal with changes of context it may be presented with. A suitable method to achieve this goal is self-adaptation. However, as of today it is difficult to have a clear view of existing research on self-adaptation in the context of mobile applications. In this paper, we apply the systematic literature review methodology on selected peer-reviewed papers focusing on selfadaptability in the context of mobile applications. Out of 607 potentially relevant studies, we select 44 primary studies via carefully-defined exclusion and inclusion criteria. We use known modelling dimensions for self-adaptive software systems as our classification framework, which we apply to all selected primary studies. From the synthesized data we obtained, we produce an overview of the state of the art. The results of this study give a solid foundation to plan for future research and practice on engineering self-adaptive mobile applications

Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing

Personalisation has become omnipresent in society. For the domain of health and wellbeing such personalisation can contribute to better interventions and improved health states of users. In order for personalisation to be effective in this domain, it needs to be performed quickly and with minimal impact on the users. Reinforcement learning is one of the techniques that can be used to establish such personalisation, but it is not known to be very fast at learning. Cluster-based reinforcement learning has been proposed to improve the learning speed. Here, users who show similar behaviour are clustered and one policy is learned for each individual cluster. An important factor in this effort is the method used for clustering, which has the potential to influence the benefit of such an approach. In this paper, we propose three distance metrics based on the state of the users (Euclidean distance, Dynamic Time Warping, and high-level features) and apply different clustering techniques given these distance metrics to study their impact on the overall performance. We evaluate the different methods in a simulator with users spawned from very distinct user profiles as well as overlapping user profiles. The results show that clustering configurations using high-level features significantly outperform regular reinforcement learning without clustering (which either learn one policy for all or one policy per individual)

Reinforcement learning for personalization:A systematic literature review

The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work

A Framework for the Automatic Execution of Measurement-based Experiments on Android Devices

Conducting measurement-based experiments is fundamental for assessing the quality of Android apps in terms of, e.g., energy consumption, CPU, and memory usage. However, orchestrating such experiments is not trivial as it requires large boilerplate code, careful setup of measurement tools, and the adoption of various empirical best practices scattered across the literature. All together, those factors are slowing down the scientific advancement and harming experiments' replicability in the mobile software engineering area. In this paper we present Android Runner (AR), a framework for automatically executing measurement-based experiments on native and web apps running on Android devices. In AR, an experiment is defined once in a descriptive fashion, and then its execution is fully automatic, customizable, and replicable. AR is implemented in Python and it can be extended with third-party profilers. AR has been used in more than 25 scientific studies primarily targeting performance and energy efficiency

A reference architecture for personalized and self-adaptive e-health apps

A wealth of e-Health mobile apps are available for many purposes, such as life style improvement, mental coaching, etc. The interventions, prompts, and encouragements of e-Health apps sometimes take context into account (e.g., previous interactions or geographical location of the user), but they still tend to be rigid, e.g., by using fixed rule sets or being not sufficiently tailored towards individuals. Personalization to the different users’ characteristics and run-time adaptation to their changing needs and context provide a great opportunity for getting users continuously engaged and active, eventually leading to better physical and mental conditions. This paper presents a reference architecture for enabling AI-based personalization and self-adaptation of mobile apps for e-Health. The reference architecture makes use of multiple MAPE loops operating at different levels of granularity and for different purposes