WeightMentor: A New Automated Chatbot for Weight Loss Maintenance

Holmes, Samuel; Moorhead, Anne; Bond, Raymond; Zheng, Huiru; Coates, Vivien; McTear, Mike

doi:10.14236/ewic/hci2018.103

Cited by 20 publications

(14 citation statements)

References 12 publications

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“…In this paper, we describe a study that assessed the usability of a healthcare focused chatbot called WeightMentor, which we have developed at Ulster University. This chatbot is a bespoke self-help motivational tool for weight loss maintenance, with the purpose of supports weight loss maintenance by encouraging self-reporting, personalized feedback, and motivational dialogues [13]. Self-reporting, personalized feedback and motivation have been shown to be beneficial for weight loss maintenance in the short term [9] [11].…”

Section: The Weightmentor Chatbotmentioning

confidence: 99%

Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

Holmes

Moorhead

Bond

et al. 2019

Proceedings of the 31st European Conference on Cognitive Ergonomics

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137

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Chatbots are becoming increasingly popular as a human-computer interface. The traditional best practices normally applied to User Experience (UX) design cannot easily be applied to chatbots, nor can conventional usability testing techniques guarantee accuracy. WeightMentor is a bespoke self-help motivational tool for weight loss maintenance. This study addresses the following four research questions: How usable is the WeightMentor chatbot, according to conventional usability methods?; To what extend will different conventional usability questionnaires correlate when evaluating chatbot usability?; And how do they correlate to a tailored chatbot usability survey score?; What is the optimum number of users required to identify chatbot usability issues?; How many task repetitions are required for a first-time chatbot users to reach optimum task performance (i.e. efficiency based on task completion times)? This paper describes the procedure for testing the WeightMentor chatbot, assesses correlation between typical usability testing metrics, and suggests that conventional wisdom on participant numbers for identifying usability issues may not apply to chatbots. The study design was a usability study. WeightMentor was tested using a predetermined usability testing protocol, evaluating ease of task completion, unique usability errors and participant opinions on the chatbot (collected using usability questionnaires). WeightMentor usability scores were generally high, and correlation between questionnaires was strong. The optimum number of users for identifying chatbot usability errors was 26, which challenges previous research. Chatbot users reached optimum proficiency in tasks after just one repetition. Usability test outcomes confirm what is already known

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Section: The Weightmentor Chatbotmentioning

confidence: 99%

Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

Holmes

Moorhead

Bond

et al. 2019

Proceedings of the 31st European Conference on Cognitive Ergonomics

Self Cite

137

View full text Add to dashboard Cite

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“…People with certain health problems feel embarrassed about their body, so with a talking agent this problem will be solved as people won"t feel shy to talk freely about their problems by providing companionship and support [3].This talking agent will suggest the perfect diet and exercises based on the information provided by the user. The BMI will be calculated and the user will be categorized as overweight or underweight [6] & [8]. Even if the user writes sentences that are not correct or makes some grammatical mistakes, the chatbot will be able to identify the meaning of the words by considering the words as individual morphemes and it will give the required response.…”

Section: Related Workmentioning

confidence: 99%

“…It stimulates human conversation and fulfills a variety of roles, from customer service to virtual providing and fulfills a variety of roles, from customer to virtual consultant, but the functionality is always the same. Various kinds of conversational chatbots have been invented but from those agents, NLP based chatbots are in demand since they are lightweight and simple to construct [8]. The architecture combines a language model and calculation algorithm to change informal conversation between a user and computer.…”

Section: Introductionmentioning

confidence: 99%

Ai Healthcare Interactive Talking Agent using Nlp

Praba-M.E.,

Sen,

Chauhan

et al. 2019

IJITEE

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Good nutrition plays an important part in leading an active lifestyle. Combined with physical exercises, the diet can benefit people to maintain their weight, reduce the possibility of diseases and improve overall health. A self-help motivational tool for weight maintenance is a good option. This paper presents an interactive talking agent that is a chatbot. A chatbot is a piece of software that operates a conversation using textual methods. Chatbot will start communication with the user and help to solve the concern by initiating a human way conversation using Language Understanding Intelligence Service (LUIS) concept. Natural language processing (NLP) is the capability of a computer application for understanding human dialect. It is one of the part of Artificial Intelligence (AI). Each language has a different morphology the chatbot has to be able to separate words into individual morphemes. Morphology is one of the tasks that NLP should be able to handle.

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“…Previous studies suggest that chatbots may have the potential to contribute to obesity and overweight prevention and management [10]. In 2017, Kowatsch et al [11] designed a text-based health care chatbot to effectively support patients and health professionals in therapeutic settings beyond on-site consultations and applied to childhood obesity control.…”

Section: Introductionmentioning

confidence: 99%

“…In 2018, Huang et al [12] developed a chatbot integrated in the SWITCHes app, where the chatbot helps monitor users' health; users also talk to the chatbot and get information in a real-time manner or take a bot's advice, including diet and exercise plans, in the context of healthy recommendation. In 2018, Holmes et al [10] described the design and development of a chatbot (WeightMentor), a self-help motivational tool for weight loss maintenance. In 2019, Stephens et al [13] implemented a behavioral coaching chatbot (Tess) to help support teens in a weight management program.…”

Section: Introductionmentioning

confidence: 99%

A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study

Asensio-Cuesta¹,

Blanes-Selva²,

Conejero³

et al. 2021

JMIR Med Inform

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Background Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps. Objective This study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations. Methods We first studied the users’ needs and gathered users’ graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility. Results We collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m2 (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities. Conclusions The Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives.

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WeightMentor: A New Automated Chatbot for Weight Loss Maintenance

Cited by 20 publications

References 12 publications

Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

Ai Healthcare Interactive Talking Agent using Nlp

A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study

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