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Food Frequency Questionnaire Personalisation Using Multi-Target Regression

Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salu...

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Detalles Bibliográficos
Autores principales: Reščič, Nina, Mayora, Oscar, Eccher, Claudio, Luštrek, Mitja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571126/
https://www.ncbi.nlm.nih.gov/pubmed/36235596
http://dx.doi.org/10.3390/nu14193943
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author Reščič, Nina
Mayora, Oscar
Eccher, Claudio
Luštrek, Mitja
author_facet Reščič, Nina
Mayora, Oscar
Eccher, Claudio
Luštrek, Mitja
author_sort Reščič, Nina
collection PubMed
description Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned interventions are based on promoting behaviour change in various domains such as physical activity, mental health and nutrition. In particular, the nutrition component is a self-monitoring module that collects dietary habits to analyse them and recommend healthier eating behaviours. Dietary assessment is completed using a Food Frequency Questionnaire on the Mediterranean diet that is presented to the user as a grid of images. The questionnaire returns feedback on 11 aspects of nutrition. Although the questionnaire used in the application only consists of 24 questions, it still could be a bit overwhelming and a bit crowded when shown on the screen. In this paper, we tried to find a machine-learning-based solution to reduce the number of questions in the questionnaire. We proposed a method that uses the user’s previous answers as additional information to find the goals that need more attention. We compared this method with a case where the subset of questions is randomly selected and with a case where the subset is chosen using feature selection. We also explored how large the subset should be to obtain good predictions. All the experiments are conducted as a multi-target regression problem, which means several goals are predicted simultaneously. The proposed method adjusts well to the user in question and has the slightest error when predicting the goals.
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spelling pubmed-95711262022-10-17 Food Frequency Questionnaire Personalisation Using Multi-Target Regression Reščič, Nina Mayora, Oscar Eccher, Claudio Luštrek, Mitja Nutrients Article Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned interventions are based on promoting behaviour change in various domains such as physical activity, mental health and nutrition. In particular, the nutrition component is a self-monitoring module that collects dietary habits to analyse them and recommend healthier eating behaviours. Dietary assessment is completed using a Food Frequency Questionnaire on the Mediterranean diet that is presented to the user as a grid of images. The questionnaire returns feedback on 11 aspects of nutrition. Although the questionnaire used in the application only consists of 24 questions, it still could be a bit overwhelming and a bit crowded when shown on the screen. In this paper, we tried to find a machine-learning-based solution to reduce the number of questions in the questionnaire. We proposed a method that uses the user’s previous answers as additional information to find the goals that need more attention. We compared this method with a case where the subset of questions is randomly selected and with a case where the subset is chosen using feature selection. We also explored how large the subset should be to obtain good predictions. All the experiments are conducted as a multi-target regression problem, which means several goals are predicted simultaneously. The proposed method adjusts well to the user in question and has the slightest error when predicting the goals. MDPI 2022-09-23 /pmc/articles/PMC9571126/ /pubmed/36235596 http://dx.doi.org/10.3390/nu14193943 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Reščič, Nina
Mayora, Oscar
Eccher, Claudio
Luštrek, Mitja
Food Frequency Questionnaire Personalisation Using Multi-Target Regression
title Food Frequency Questionnaire Personalisation Using Multi-Target Regression
title_full Food Frequency Questionnaire Personalisation Using Multi-Target Regression
title_fullStr Food Frequency Questionnaire Personalisation Using Multi-Target Regression
title_full_unstemmed Food Frequency Questionnaire Personalisation Using Multi-Target Regression
title_short Food Frequency Questionnaire Personalisation Using Multi-Target Regression
title_sort food frequency questionnaire personalisation using multi-target regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571126/
https://www.ncbi.nlm.nih.gov/pubmed/36235596
http://dx.doi.org/10.3390/nu14193943
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