TY - JOUR
T1 - A user-centered Chatbot (Wakamola) to collect linked data in population networks to support studies of overweight and obesity causes
T2 - Design and pilot study
AU - Asensio-Cuesta, Sabina
AU - Blanes-Selva, Vicent
AU - Alberto Conejero, J.
AU - Frigola, Ana
AU - Portolés, Manuel G.
AU - Merino-Torres, Juan Francisco
AU - Almanza, Matilde Rubio
AU - Syed-Abdul, Shabbir
AU - Li, Yu Chuan
AU - Vilar-Mateo, Ruth
AU - Fernandez-Luque, Luis
AU - García-Gómez, Juan M.
N1 - Funding Information:
The authors gratefully acknowledge designers María Dolores Blanco, Ángel Esteban, and Marta Lavall for their contribution as graphical designers for the Wakamola chatbot, as well as the support to this research provided by Salvador Tortajada from the Scientific Unit of Business Innovation at the Institute of Corpuscular Physics.
Funding Information:
Moreover, the authors acknowledge the funding support for this study provided by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies, 727560).
Publisher Copyright:
© Sabina Asensio-Cuesta, Vicent Blanes-Selva, J Alberto Conejero, Ana Frigola, Manuel G Portolés, Juan Francisco Merino-Torres, Matilde Rubio Almanza, Shabbir Syed-Abdul, Yu-Chuan (Jack) Li, Ruth Vilar-Mateo, Luis Fernandez-Luque, Juan M García-Gómez.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Assessment
KW - Chatbot
KW - MHealth
KW - Obesity
KW - Overweight
KW - Public health
KW - Social network analysis
KW - Telegram
KW - User-centered design
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UR - http://www.scopus.com/inward/citedby.url?scp=85104895601&partnerID=8YFLogxK
U2 - 10.2196/17503
DO - 10.2196/17503
M3 - Article
AN - SCOPUS:85104895601
SN - 2291-9694
VL - 9
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
IS - 4
M1 - e17503
ER -