Weight management interventions in adults with intellectual disabilities and obesity: a systematic review of the evidence

o evaluate the clinical effectiveness of weight management interventions in adults with intellectual disabilities (ID) and obesity using recommendations from current clinical guidelines for the first line management of obesity in adults. Full papers on lifestyle modification interventions published between 1982 to 2011 were sought by searching the Medline, Embase, PsycINFO and CINAHL databases. Studies were evaluated based on 1) intervention components, 2) methodology, 3) attrition rate 4) reported weight loss and 5) duration of follow up. Twenty two studies met the inclusion criteria. The interventions were classified according to inclusion of the following components: behaviour change alone, behaviour change plus physical activity, dietary advice or physical activity alone, dietary plus physical activity advice and multi-component (all three components). The majority of the studies had the same methodological limitations: no sample size justification, small heterogeneous samples, no information on randomisation methodologies. Eight studies were classified as multi-component interventions, of which one study used a 600 kilocalorie (2510 kilojoule) daily energy deficit diet. Study durations were mostly below the duration recommended in clinical guidelines and varied widely. No study included an exercise program promoting 225–300 minutes or more of moderate intensity physical activity per week but the majority of the studies used the same behaviour change techniques. Three studies reported clinically significant weight loss (≥ 5%) at six months post intervention. Current data indicate weight management interventions in those with ID differ from recommended practice and further studies to examine the effectiveness of multi-component weight management interventions for adults with ID and obesity are justified

Context-aware food recommendation system

Recommendation systems are commonly used in websites with large datasets, frequently used in e-commerce or multimedia streaming services. These systems effectively help users in the task of finding items of their interest, while also being helpful from the perspective of the service or product provider. However, successful applications to other domains are less common, and the number of personalized food recommendation systems is surprisingly small although this particular domain could benefit significantly from recommendation knowledge. This work proposes a contextaware food recommendation system for well-being care applications, using mobile devices, beacons, medical records and a recommender engine. Users passing near a food place receives food recommendation based on available offers order by appropriate foods for everyone’s health at the table in real time. We also use a new robust recipe recommendation method based on matrix factorization and feature engineering, both supported by contextual information and statistical aggregation of information from users and items. The results got from the application of this method to three heterogeneous datasets of recipe’s user ratings, showed that gains are achieved regarding recommendation performance independently of the dataset size, the items textual properties or even the rating values distribution.info:eu-repo/semantics/publishedVersio

Human Behavior-based Personalized Meal Recommendation and Menu Planning Social System

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social-affect has some significant benefits of the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person's affect is detected using electroencephalography signals. EEG allows to capture the brain signals and analyze them to anticipate affective toward a food. In this study, we have used a 14-channel wireless Emotive Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person's energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters

The capability approach and school food education and culture in England: ‘gingerbread men ain’t gonna get me very far’

This study examines the role of school food education and school food culture in England and their potential to support pupils’ capabilities to adopt health protecting and promoting behaviours. Drawing on Amartya Sen’s capability approach, and Susan Michie’s COM-B model, the research was conducted for the Food Education Learning Landscape project. Methods included national surveys of food teachers (N = 1503), senior school leaders and class teachers (N = 684), parents and carers (N = 573) and a mixed methods study of pupils in primary and secondary schools (N = 240). Findings indicate that adequate curriculum time, teaching facilities, budget, class size, subject status and teacher training are key factors for successful curriculum implementation. Monitoring and evaluation of school food provision and development of wider health supporting school food practices were found to be critical in supporting pupils’ health capabilities. The research insights can inform future policies and practices to support children’s potential to lead healthy, flourishing lives

Bargaining agents based system for automatic classification of potential allergens in recipes

The automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users’ attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations