The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data. For each study, we calculated the mean Body Mass Index (BMI) and number of people in each BMI level. Also, we defined and calculated scores (1–100 scale) regarding global health, BMI, alimentation, physical activity and social network. Moreover, we graphically represented obesity risk for living areas and the social network with nodes colored by BMI.
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. Our 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. With this work, we have shown that 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. You can access to this work here S. Asensio-Cuesta et al.
A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study. JMIR Med Inform 2021;9(4):e17503
Our team VALENCIA IA4COVID, coleaded by Nuria Oliver and me, has won 500k Pandemic Response Challenge, organized by XPRIZE Foundation and supported by Cognizant. The $500K Pandemic Response Challenge, required teams to build effective data-driven AI systems capable of accurately predicting COVID-19 transmission rates and prescribing intervention and mitigation measures that, with testing in “what-if” scenarios, were shown to minimize infection rates as well as negative economic impacts.
Our group is made up of fourteen experts from the Universities and research centers of the Valencian Community. Our model successfully forecasted epidemiological evolution through their use of AI and data science and provided decision makers with the best prescriptor models to produce non-pharmaceutical intervention plans that minimize the number of infections while minimizing the stringency of the interventions.
Our team VALENCIA IA4COVID has progressed to the 2nd phase of the Pandemic Response Challenge, organized by XPRIZE Foundation and supported by Cognizant. This is a $500K, a four-month challenge that focuses on the development of data-driven AI systems to predict COVID-19 infection rates and prescribe Intervention Plans (IPs) that regional governments, communities, and organizations can implement to minimize harm when reopening their economies. Our group is made up of fourteen experts from the Universities and research centers of the Valencian Community and it is leaded by Nuria Oliver and me. We have all been working intensively since the beginning of the pandemic, altruistically and using the resources available to us in our respective institutions and with the occasional philanthropic collaboration of some companies.
Our model is among the three best models in the competition in MAE Mean Rank, leading in ASIA and in the top 5 of EUROPE in MAE per 100k habitants.
You can see our predictions here. The model has not been updated since its release on December 22nd.
Our recent paper Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset has been accepted for publication in J Am Med Inform Assoc. (JAMIA, IF 4.112). We study whether the lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. Our results are based in the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. We show that cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.
Article d’opinió al voltant de la disponibilitat de dades al voltant de la evolució de la pandèmia del COVID-19. Després de portar uns mesos analitzant dades sobre l’evolució de la COVID-19 hem comprovat que algunes coses han millorat i que ha augmentat la disponibilitat de les dades i l’accés a aquests.
Comptar cada vegada amb ciutadans amb una major cultura de l’ús de les dades ens permet ser més corresponsables en la presa de decisions individuals i poder recolzar amb major criteri les decisions dels organismes oficials.
Pots trobar l’article sencer en https://www.viaempresa.cat/opinio/conejero-upv-dades-dades-dades_2142814_102.html
We Alberto Conejero (Instituto Universitario de Matemática Pura y Aplicada, IUMPA) and Miguel Rebollo (of the Valencian Research Institute for Artificial Intelligence, VRAIN) of UPV are part of the Data Science Working Group in the Fight against COVID-19 , of the Commissioner for the Presidency of the Generalitat Valenciana on Strategy for Artificial Intelligence and Data Sciences against COVID-19. News appeared in UPV, in El Periodic newspaper and in RUVID website.
Since April 2020, I have been involved in the Data Science Group COVID-19 of the València Region under the supervision of Nuria Oliver. This is a multidisciplinary team of volunteers that work side by side with the General Director of Analysis and Public Policies of the Presidency of the RValencia Region Government. The analysis for COVID-19 is coordinated with the Ministry of Health and the rest of the Councils involved. This working group is led by Nuria Oliver, Commissioner of the Presidency of the Generalitat for the Valencian Strategy for Artificial Intelligence and, especially, for the coordination of data intelligence before the COVID-19 epidemic in the Valencia Region.
They are part of the group of experts from the Jaume I University, the University of Valencia, the Polytechnic University of Valencia, the Miguel Hernández University, the University of Alacant, the CEU Cardenal Herrera University, Fisabio, and Microsoft, with the collaboration of Esri, the INE, the Secretary of State for Artificial Intelligence and the three most important mobile phone companies in the country.
This group is divided into three priority areas with their respective work coordinators: (1) analysis, visualization, and modeling of mobility data, (2) epidemiological models and (3) data science applied to COVID-19. There, I work in epidemiological models with Antonio Falcó, Miguel Rebollo, Miguel A. Lozano, Emilio Sansano, Xavier Barber, and Francisco Escolano.
This is the web page of our data research group http://infocoronavirus.gva.es/es/grup-de-ciencies-de-dades-del-covid-19-de-la-comunitat-valenciana