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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2014
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| Online Access: | http://hdl.handle.net/1721.1/85998 https://orcid.org/0000-0002-8053-9983 |
| _version_ | 1826202861806026752 |
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| author | Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai T. Madan, Anmol Khayal, Inas Pentland, Alex Paul |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai T. Madan, Anmol Khayal, Inas Pentland, Alex Paul |
| author_sort | Oloritun, Rahman O. |
| collection | MIT |
| 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[superscript 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. |
| first_indexed | 2024-09-23T12:21:32Z |
| format | Article |
| id | mit-1721.1/85998 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:21:32Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/859982022-09-28T07:51:52Z Change in BMI Accurately Predicted by Social Exposure to Acquaintances Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai T. Madan, Anmol Khayal, Inas Pentland, Alex Paul Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Moturu, Sai T. Madan, Anmol Pentland, Alex Paul Khayal, Inas 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[superscript 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. MIT Masdar Program MIT Media Lab Consortium 2014-04-03T17:25:53Z 2014-04-03T17:25:53Z 2013-11 2013-08 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/85998 Oloritun, Rahman O., Taha B. M. J. Ouarda, Sai Moturu, Anmol Madan, Alex (Sandy) Pentland, and Inas Khayal. “Change in BMI Accurately Predicted by Social Exposure to Acquaintances.” Edited by Manlio Vinciguerra. PLoS ONE 8, no. 11 (November 20, 2013): e79238. https://orcid.org/0000-0002-8053-9983 en_US http://dx.doi.org/10.1371/journal.pone.0079238 PLoS ONE Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLoS |
| spellingShingle | Oloritun, Rahman O. Ouarda, Taha B. M. J. Moturu, Sai T. Madan, Anmol Khayal, Inas Pentland, Alex Paul 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 |
| url | http://hdl.handle.net/1721.1/85998 https://orcid.org/0000-0002-8053-9983 |
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