Abstract
This paper discusses the use of the HORUS.AI solution, an AI-enabled persuasive personal health assistant built upon the integration of semantic web technologies and persuasive techniques, for motivating people to adopt a healthy lifestyle and for supporting them to cope with the self-management of chronic diseases associated with bad lifestyle habits. The solution collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g., facts of the world), and interacts with users by using a goal-based metaphor. Persuasive dialogues are used for proposing persuasion goals to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. In this paper, we focus on how behavioral change strategies have been exploited for providing a personalized support concerning the adoption of healthy lifestyle or the management of their chronic diseases based on the results of personal data processing. Such results are produced by reasoning operations, briefly mentioned in this paper, and coded into motivational strategies and messages by a dialogue-based persuasive layer. This layer manages dialogues and generates persuasive messages based on (i) the information provided by the reasoner, (ii) the user’s behavior and profile, and (iii) the implemented behavioral change strategies. This way, messages are tailored to specific users. HORUS.AI has been validated within the context of the Key To Health project. Results demonstrated how the use of proposed approach supported users about improving their habits from the health perspectives as well as the overall good acceptability of the system by the users involved in the pilot study. Finally, the analysis of system’s efficiency shows how HORUS.AI can be deployed within a real-world scenarios.
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Notes
The architecture of the HORUS.AI solution is briefly summarized in Sect. 3.
Luxembourg Declaration on workplace health promotion in the European Union. 1997.
The current version of PerSEO supports the generation of messages in English and Italian.
Deployment machine: 2 CPUs Intel Xeon X5690 3.47GHz, 8GB RAM, 160GB HDD.
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Appendix
Appendix
1.1 Screenshots of the provided mobile application
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The users interact with the mobile applications by using different chats replicating the main functionalities of the system. The Meddie bot supports the acquisition of food information. The Cleo bot supports the acquisition of the physical activities information. The “Regole d’oro” (Golden rules) bot provides educational information about healthy foods and activities. The “Valutazione” (Evaluation) bot provides information about the current adherence of a user with respect to the assigned guidelines
Interface for inserting a new meal. The user can select the type of meal from a closed list in order to ease and to speed up the insertion process
For each meal, the user can add a new food by specifying the quantity consumed. The application automatically adjusts the amount of calories intake
To add a new food, the user can exploits a category-based view to speed up the insertion process
The user can ask in the chat with the Meddie bot detailed information about a food. The bot asks for the food name and it checks the label provided by the user with the ones contained in the ontology. If the provided label is not found, similarity measures are applied for suggesting a possible candidate. The details of such measures are out of scope of the paper
The user can check anytime the log of consumed foods. For each meal, it is possible to see the amount consumed for each food and the total calories intake
Interface for inserting a new activity. The user can select the type of activity from a closed list in order to ease and to speed up the insertion process
For each activity, the user can specifying the amount of time spent on that activity. The provided amount of time will automatically adjust the quantity of calories consumed
The tracker widget provides the users a visual description of his/her adherence to a goal, e.g., eating at least two portions of vegetables every day (left). The doughnut chart (right) provides a visual description about the user’s adherence to the prescriptions of the Mediterranean diet in a certain day of a week. Satisfied rules are in green (e.g., a maximum amount of sweets), violated rules are in red (e.g., too much meat)
1.2 Box plots about the violation evolution
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Figure 19 shows the box plots about the average number of violations sampled during the time span project. We considered three sample weeks: after the first week, the fourth week and the last week.
Box plots about the average number of violations sampled during the time span project. Interv. e Ctrl stand for the intervention and control group, respectively
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Donadello, I., Dragoni, M. AI-enabled persuasive personal health assistant. Soc. Netw. Anal. Min. 12, 106 (2022). https://doi.org/10.1007/s13278-022-00935-3
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DOI: https://doi.org/10.1007/s13278-022-00935-3