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Change in BMI Accurately Predicted by Social Exposure to Acquaintances
Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased towa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835855/ https://www.ncbi.nlm.nih.gov/pubmed/24278122 http://dx.doi.org/10.1371/journal.pone.0079238 |
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author | Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai Madan, Anmol Pentland, Alex (Sandy) Khayal, Inas |
author_facet | Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai Madan, Anmol Pentland, Alex (Sandy) Khayal, Inas |
author_sort | Oloritun, Rahman O. |
collection | PubMed |
description | Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends. |
format | Online Article Text |
id | pubmed-3835855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38358552013-11-25 Change in BMI Accurately Predicted by Social Exposure to Acquaintances Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai Madan, Anmol Pentland, Alex (Sandy) Khayal, Inas PLoS One Research Article Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends. Public Library of Science 2013-11-20 /pmc/articles/PMC3835855/ /pubmed/24278122 http://dx.doi.org/10.1371/journal.pone.0079238 Text en © 2013 Oloritun et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai Madan, Anmol Pentland, Alex (Sandy) Khayal, Inas Change in BMI Accurately Predicted by Social Exposure to Acquaintances |
title | Change in BMI Accurately Predicted by Social Exposure to Acquaintances |
title_full | Change in BMI Accurately Predicted by Social Exposure to Acquaintances |
title_fullStr | Change in BMI Accurately Predicted by Social Exposure to Acquaintances |
title_full_unstemmed | Change in BMI Accurately Predicted by Social Exposure to Acquaintances |
title_short | Change in BMI Accurately Predicted by Social Exposure to Acquaintances |
title_sort | change in bmi accurately predicted by social exposure to acquaintances |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835855/ https://www.ncbi.nlm.nih.gov/pubmed/24278122 http://dx.doi.org/10.1371/journal.pone.0079238 |
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