A smartphone application for semi-controlled collection of objective eating behavior data from multiple subjects.

Autor: Maramis C; Department of Medicine, School of Life Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: chmaramis@med.auth.gr., Moulos I; Department of Medicine, School of Life Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece., Ioakimidis I; Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, Sweden., Papapanagiotou V; Department of Electrical & Computer Engineering, School of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece., Langlet B; Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, Sweden., Lekka I; Department of Medicine, School of Life Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece., Bergh C; Mando Group AB, Stockholm, Sweden., Maglaveras N; Department of Medicine, School of Life Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2020 Oct; Vol. 194, pp. 105485. Date of Electronic Publication: 2020 May 01.
DOI: 10.1016/j.cmpb.2020.105485
Abstrakt: Background & Objective: The study of eating behavior has made significant progress towards understanding the association of specific eating behavioral patterns with medical problems, such as obesity and eating disorders. Smartphones have shown promise in monitoring and modifying unhealthy eating behavior patterns, often with the help of sensors for behavior data recording. However, when it comes to semi-controlled deployment settings, smartphone apps that facilitate eating behavior data collection are missing. To fill this gap, the present work introduces ASApp, one of the first smartphone apps to support researchers in the collection of heterogeneous objective (sensor-acquired) and subjective (self-reported) eating behavior data in an integrated manner from large-scale, naturalistic human subject research (HSR) studies.
Methods: This work presents the overarching and deployment-specific requirements that have driven the design of ASApp, followed by the heterogeneous eating behavior dataset that is collected and the employed data collection protocol. The collected dataset combines objective and subjective behavior information, namely (a) dietary self-assessment information, (b) the food weight timeseries throughout an entire meal (using a portable weight scale connected wirelessly), (c) a photograph of the meal, and (d) a series of quantitative eating behavior indicators, mainly calculated from the food weight timeseries. The designed data collection protocol is quick, straightforward, robust and capable of satisfying the requirement of semi-controlled HSR deployment.
Results: The implemented functionalities of ASApp for research assistants and study participants are presented in detail along with the corresponding user interfaces. ASApp has been successfully deployed for data collection in an in-house testing study and the SPLENDID study, i.e., a real-life semi-controlled HSR study conducted in the cafeteria of a Swedish high-school in the context of an EC-funded research project. The two deployment studies are described and the promising results from the evaluation of the app with respect to attractiveness, usability, and technical soundness are discussed. Access details for ASApp are also provided.
Conclusions: This work presents the requirement elucidation, design, implementation and evaluation of a novel smartphone application that supports researchers in the integrated collection of a concise yet rich set of heterogeneous eating behavior data for semi-controlled HSR.
Competing Interests: Declaration of Competing Interest The authors have no financial or personal conflict of interest to disclose.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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