Eating habits of lithuanian students and factors that influence them: survey results

Straipsnyje pristatomi masinės Lietuvos studen - tų (N=990) apklausos apie jų mitybos įpročius re - zultatai. Tyrimu siekta pabandyti bent iš dalies at - sakyti į esminį klausimą: kodėl egzistuoja priešta - ravimas tarp žinojimo apie sveiką mitybą ir fakti - nės elgsenos. Apie sveiką mitybą žino visi, tačiau didelis nuošimtis žmonių, tai pat ir studentų, svei - kos mitybos rekomendacijų visiškai nesilaiko. Mi - tybos įpročiams matuoti buvo naudojamas 21 pir - minis indikatorius. Faktorinės ir loginės validacijos derinimo būdu buvo suformuotos 7 poskalės, pasi - žyminčios tinkama psichometrine kokybe. Taikant K- vidurkių klasterinę analizę buvo identifikuoti trys studentų statistiniai tipažai, besiskiriantys mi - tybos įpročiais: 1. „Orientuoti į sveiką mitybą“ (pa - plitimas – 30,7 proc.). 2. „Tarpinė grupė, linkstan - ti į nesveiką mitybą“ (33,8 proc.). 3. „Ekonomiškai orientuoti, nesveikai besimaitinantys“ (35,5 proc.). Statistinio tipažo, orientuoto į sveiką mitybą, san - tykinis paplitimas tikslinėje populiacijoje nesiekia net vieno trečdalio. Aukščiausią santykinį papliti - mą turi ekonomiškai motyvuotas nesveikos mitybos statistinis tipas. Paaiškėjo, kad studentų priklausy - mą vienam ar kitam mitybos tipui paveikia: 1) ly - tis; 2) luomas (klasė); 3) studento tėvo ir motinos analogiški įpročiai ir 4) informavimasis apie sveiką mitybą per medijas. Studentų amžius ir jų tėvų išsi - lavinimas studentų mitybos įpročiams įtakos neturiThe article presents the survey about eating habits of Lithu - anian students (N = 990) and its results. The aim of the research was to try to at least partially answer the essential problem question: why is there a contradiction between the awareness and knowledge about healthy eating and the actual behavior. Everybody possess knowledge about the healthy diet, but a large percentage of people, including students, ignore recommendations about healthy eating. In order to measure eating habits there were used 21 primary indi - cators. While matching factorial and logical validation there were formed 7 subscales with appropriate psychometric quality. While using K-mean cluster analysis there were identified three statistical types of students with the expression of different eating habits. They are as follows: 1. “Focusing on a healthy diet” (prevalence - 30.7 percent.). 2. “Interim group tending towards an unhealthy diet” (33.8 percent.). 3. “Cost-oriented, eating unhealthy meals” (35.5 percent.). Statistical type of character, oriented to a healthy diet, does not reach even one-third of the relative prevalence of the target population. The highest rate is reached by economically motivated unhealthy diet statistical type. It turned out that the students belonging to one or another type of diet is affected by: 1) sex; 2) social layer; 3) The student‘s father or mother had analogous habits, and 4) getting informed about healthy eating through the media. Students’ age and the level of their parents’ education do not effect eating habits of the studentsKauno kolegijaKauno technologijos universitetasŠiaulių universiteta

Explainable Artificial Intelligence-Based Decision Support System for Assessing the Nutrition-Related Geriatric Syndromes

The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system’s prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician