Asensio-Cuesta Sabina, Blanes-Selva Vicent, Conejero J Alberto, Frigola Ana, Portolés Manuel G, Merino-Torres Juan Francisco, Rubio Almanza Matilde, Syed-Abdul Shabbir, Li Yu-Chuan Jack, Vilar-Mateo Ruth, Fernandez-Luque Luis, García-Gómez Juan M
Instituto de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Valencia, Spain.
Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Valencia, Spain.
JMIR Med Inform. 2021 Apr 14;9(4):e17503. doi: 10.2196/17503.
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.
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.
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.
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/m (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.
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.
肥胖和超重是一个全球性的严重健康问题，其成因多样且相互关联。与此同时，聊天机器人作为移动健康应用中与用户交互的一种方式正变得越来越受欢迎。
本研究报告了一个聊天机器人以用户为中心的设计及可行性研究，该聊天机器人用于收集关联数据，以支持对人群中个体及社会层面超重和肥胖成因的研究。
我们首先通过对150名设计专业学生设计的52个线框图进行公开调查，研究用户需求并收集用户的图形偏好；调查还包括有关社会人口统计学、饮食和活动习惯、对超重和肥胖应用程序的需求以及期望功能等问题。我们还采访了一个专家小组。然后我们设计并开发了一个聊天机器人。最后，我们进行了一项试点研究以测试可行性。
我们收集到了452份调查问卷答案，并采访了4位专家。基于这项研究，我们开发了一个名为Wakamola的Telegram聊天机器人，它分为六个部分：个人信息、饮食、身体活动、社交网络、用户状态评分和项目信息。我们将用户状态评分定义为关于饮食（50种食物的进食频率）、身体活动、体重指数（BMI）和社交网络的分数的标准化总和（0 - 100）。我们在85名健康志愿者中进行了一项试点，以评估聊天机器人的应用情况。在74名完成所有部分的参与者中，我们发现有8人体重过轻（11%），5人超重（7%），没有肥胖病例。平均BMI为21.4 kg/m²（正常体重）。最常食用的食物是橄榄油、牛奶及其制品、谷物、蔬菜和水果。人们每周有5.8天步行10分钟，每天睡眠7.02小时，每周坐着的时间为30.57小时。此外，我们能够创建一个由74名用户、178个关系和12个社区组成的社交网络。
Telegram聊天机器人Wakamola是一种可行的工具，可以从人群中收集有关社会人口统计学、饮食模式（饮食模式）、身体活动、BMI和特定疾病的数据。此外，该聊天机器人允许在社交网络中连接用户，以便从个体和社会角度研究超重和肥胖的成因。
