Within software engineering, social sustainability is the dimension of sustainability that focuses on the “support of current and future generations to have the same or greater access to social resources by pursuing social equity.” An important domain that strives to achieve social sustainability is e-Health, and more recently e-Health mobile apps.A wealth of e-Health mobile apps is available for many purposes, such as lifestyle improvement and mental coaching. 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., apps use fixed sets of rules or they are not sufficiently tailored toward individuals’ needs. 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 chapter presents a reference architecture for enabling AI-based personalization and self-adaptation of mobile apps for e-Health. The reference architecture makes use of a dedicated goal model and multiple MAPE loops operating at different levels of granularity and for different purposes. The proposed reference architecture is instantiated in the context of a fitness-based mobile application and exemplified through a series of typical usage scenarios extracted from our industrial collaborations.

Social Sustainability in the e-Health Domain via Personalized and Self-Adaptive Mobile Apps

De Sanctis, Martina
;
2021

Abstract

Within software engineering, social sustainability is the dimension of sustainability that focuses on the “support of current and future generations to have the same or greater access to social resources by pursuing social equity.” An important domain that strives to achieve social sustainability is e-Health, and more recently e-Health mobile apps.A wealth of e-Health mobile apps is available for many purposes, such as lifestyle improvement and mental coaching. 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., apps use fixed sets of rules or they are not sufficiently tailored toward individuals’ needs. 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 chapter presents a reference architecture for enabling AI-based personalization and self-adaptation of mobile apps for e-Health. The reference architecture makes use of a dedicated goal model and multiple MAPE loops operating at different levels of granularity and for different purposes. The proposed reference architecture is instantiated in the context of a fitness-based mobile application and exemplified through a series of typical usage scenarios extracted from our industrial collaborations.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12571/25142
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