Abstract

BackgroundObesity 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.ObjectiveThis 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.

Highlights

  • The percentage of overweight people has not stopped increasing worldwide since the 1980s [1]

  • We addressed the panel with three research questions: (1) what data would be relevant for study of obesity and overweight, according to current knowledge, (2)

  • Analyzing the data obtained from the pilot, we found a percentage of people with overweight of 7% (5/74), while the percentage of people with underweight was higher (8/74, 11%); no obesity cases were detected in the sample

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Summary

Introduction

The percentage of overweight people has not stopped increasing worldwide since the 1980s [1]. According to the World Health Organization, in Europe, more than 50% of the population is overweight, and 20% is obese [3]. Obesity is a complex problem with individual, socioeconomic, and environmental factors [4]. Fowler and Christakis conducted a study about the spread of obesity in a large social network over 32 years (Framingham Heart Study) [5] and found evidence of the “contagion” of obesity among people in close social circles. A person's chances of becoming obese increased by 57% if he or she had a friend who became obese in a given interval. Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Chatbots are becoming increasingly popular as a way to interact with users in mobile health apps

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