An Ontology-Based Framework for a Telehealthcare System to Foster Healthy Nutrition and Active Lifestyle in Older Adults

• Published in: Electronics (Basel) 2021-09, Vol.10 (17), p.2129
• Main Authors: Spoladore, Daniele, Colombo, Vera, Arlati, Sara, Mahroo, Atieh, Trombetta, Alberto, Sacco, Marco
• Format: Article
• Language: English
• Subjects: Adults; Artificial intelligence; Chronic conditions; Chronic illnesses; Continuity of care; Decision support systems; Diabetes mellitus; Diet; Disease; Exercise; Information retrieval; Interoperability; Knowledge bases (artificial intelligence); Lifestyles; Mortality; Nutrition; Nutrition research; Older people; Ontology; Patients; Physical exercise; Physical fitness; Quality of life; Recommender systems; Respiratory diseases; Semantic web; Semantics; Severe acute respiratory syndrome coronavirus 2; Telemedicine
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10172129

In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes an ontology-based TS, namely HeNuALs, aimed at fostering a healthy diet and an active lifestyle in older adults with chronic pathologies. The system is built on the formalization of users’ health conditions, which can be obtained by leveraging existing standards. This allows for modeling different pathologies via reusable knowledge, thus limiting the amount of information needed to retrieve nutritional indications from the system. HeNuALs is composed of (1) an ontological layer that stores patients and their data, food and its characteristics, and physical activity-related data, enabling the inference a series of suggestions based on the effects of foods and exercises on specific health conditions; (2) two applications that allow both the patient and the clinicians to access the data (with different permissions) stored in the ontological layer; and (3) a series of wearable sensors that can be used to monitor physical exercise (provided by the patient application) and to ensure patients’ safety. HeNuALs inferences have been validated considering two different use cases. The system revealed the ability to determine suggestions for healthy, adequate, or unhealthy dishes for a patient with respiratory disease and for a patient with diabetes mellitus. Future work foresees the extension of the HeNuALs knowledge base by exploiting automatic knowledge retrieval approaches and validation of the whole system with target users.