On Modeling the Quality of Nutrition for Healthy Ageing Using Fuzzy Cognitive Maps
Modelling dietary intake of older adults can prevent nutritional deficiencies and diet-related diseases, improving their quality of life. Towards such direction, a Fuzzy Cognitive Map (FCM)-based modelling approach that models the interdependencies between the factors that affect the Quality of Nutrition (QoN) is presented here. The proposed FCM-QoN model uses a FCM with seven input-one output concepts, i.e., five food groups of the UK Eatwell Plate, Water (H2O), and older adult’s Emotional State (EmoS), outputting the QoN. The weights incorporated in the FCM structure were drawn from an experts’ panel, via a Fuzzy Logic-based knowledge representation process. Using various levels of analysis (causalities, static/feedback cycles), the role of EmoS and H2O in the QoN was identified, along with the one of Fruits/Vegetables and Protein affecting the sustainability of effective food combinations. In general, the FCM-QoN approach has the potential to explore different dietary scenarios, helping health professionals to promote healthy ageing and providing prognostic simulations for diseases effect (such as Parkinson’s) on dietary habits, as used in the H2020 i-Prognosis project (www.i-prognosis.eu).
KeywordsOlder adults Healthy ageing Emotional state Fuzzy cognitive maps (FCMs) Quality of nutrition (QoN) H2020 i-Prognosis
This work has received funding from the EU H2020-PHC-2014-2015/H2020-PHC-2015, grant agreement No. 690494: ‘i-Prognosis’ project (www.i-prognosis.eu). Moreover, Dr. Dias (first author) acknowledges the financial support by the Foundation for Science and Technology (FCT, Portugal) (Postdoctoral Grant SFRH/BPD/496004/20) and the Interdisciplinary Centre for the Study of Human Performance (CIPER, Portugal). Finally, the authors would like to thank the four nutritionists/dieticians that served as experts in this study.
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