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      SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding

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          Abstract

          As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

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          Most cited references39

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          BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

          We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
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            European Guidelines for Obesity Management in Adults

            Obesity is a chronic metabolic disease characterised by an increase of body fat stores. It is a gateway to ill health, and it has become one of the leading causes of disability and death, affecting not only adults but also children and adolescents worldwide. In clinical practice, the body fatness is estimated by BMI, and the accumulation of intra-abdominal fat (marker for higher metabolic and cardiovascular disease risk) can be assessed by waist circumference. Complex interactions between biological, behavioural, social and environmental factors are involved in regulation of energy balance and fat stores. A comprehensive history, physical examination and laboratory assessment relevant to the patient's obesity should be obtained. Appropriate goals of weight management emphasise realistic weight loss to achieve a reduction in health risks and should include promotion of weight loss, maintenance and prevention of weight regain. Management of co-morbidities and improving quality of life of obese patients are also included in treatment aims. Balanced hypocaloric diets result in clinically meaningful weight loss regardless of which macronutrients they emphasise. Aerobic training is the optimal mode of exercise for reducing fat mass while a programme including resistance training is needed for increasing lean mass in middle-aged and overweight/obese individuals. Cognitive behavioural therapy directly addresses behaviours that require change for successful weight loss and weight loss maintenance. Pharmacotherapy can help patients to maintain compliance and ameliorate obesity-related health risks. Surgery is the most effective treatment for morbid obesity in terms of long-term weight loss. A comprehensive obesity management can only be accomplished by a multidisciplinary obesity management team. We conclude that physicians have a responsibility to recognise obesity as a disease and help obese patients with appropriate prevention and treatment. Treatment should be based on good clinical care, and evidence-based interventions; should focus on realistic goals and lifelong multidisciplinary management.
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              Obesity in adults: a clinical practice guideline

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                Author and article information

                Contributors
                Journal
                Front Nutr
                Front Nutr
                Front. Nutr.
                Frontiers in Nutrition
                Frontiers Media S.A.
                2296-861X
                23 June 2022
                2022
                : 9
                : 870775
                Affiliations
                [1] 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University , Taipei, Taiwan
                [2] 2International Center for Health Information Technology, Taipei Medical University , Taipei, Taiwan
                [3] 3Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [4] 4Department of Health Behavior, Environment, and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [5] 5Center for Health Policy Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [6] 6Taipei Medical University Research Center of Cancer Translational Medicine , Taipei, Taiwan
                [7] 7Department of Dermatology, Wan Fang Hospital , Taipei, Taiwan
                Author notes

                Edited by: Jaimon Kelly, The University of Queensland, Australia

                Reviewed by: Naomi Kakoschke, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia; Yoo Jung Oh, University of California, Davis, United States

                *Correspondence: Yu-Chuan Jack Li jack@ 123456tmu.edu.tw

                This article was submitted to Clinical Nutrition, a section of the journal Frontiers in Nutrition

                †These authors have contributed equally to this work

                Article
                10.3389/fnut.2022.870775
                9260382
                35811989
                9926f512-c926-4247-aee2-1bf39ba63d23
                Copyright © 2022 Rahmanti, Yang, Bintoro, Nursetyo, Muhtar, Syed-Abdul and Li.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 February 2022
                : 12 May 2022
                Page count
                Figures: 4, Tables: 1, Equations: 0, References: 48, Pages: 13, Words: 9187
                Funding
                Funded by: Ministry of Science and Technology, Taiwan, doi 10.13039/501100004663;
                Award ID: MOST 110-2622-E-038 -003 -CC1
                Award ID: MOST 110-2221-E-038 -002 -MY2
                Award ID: MOST 110-2320-B-038 -029 -MY3
                Categories
                Nutrition
                Original Research

                artificial intelligence,artificial empathy,chatbot,diet bot,weight loss management,virtual diet assistant

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