Food Habits: Insights from Food Diaries via Computational Recurrence Measures
| dc.citation.articleNumber | 2753 | en_US |
| dc.citation.issueNumber | 7 | en_US |
| dc.citation.journalTitle | Sensors | en_US |
| dc.citation.volumeNumber | 22 | en_US |
| dc.contributor.author | Pai, Amruta | en_US |
| dc.contributor.author | Sabharwal, Ashutosh | en_US |
| dc.contributor.org | Scalable Health Labs | en_US |
| dc.date.accessioned | 2022-04-28T14:29:15Z | en_US |
| dc.date.available | 2022-04-28T14:29:15Z | en_US |
| dc.date.issued | 2022 | en_US |
| dc.description.abstract | Humans are creatures of habit, and hence one would expect habitual components in our diet. However, there is scant research characterizing habitual behavior in food consumption quantitatively. Longitudinal food diaries contributed by app users are a promising resource to study habitual behavior in food selection. We developed computational measures that leverage recurrence in food choices to describe the habitual component. The relative frequency and span of individual food choices are computed and used to identify recurrent choices. We proposed metrics to quantify the recurrence at both food-item and meal levels. We obtained the following insights by employing our measures on a public dataset of food diaries from MyFitnessPal users. Food-item recurrence is higher than meal recurrence. While food-item recurrence increases with the average number of food-items chosen per meal, meal recurrence decreases. Recurrence is the strongest at breakfast, weakest at dinner, and higher on weekdays than on weekends. Individuals with relatively high recurrence on weekdays also have relatively high recurrence on weekends. Our quantitatively observed trends are intuitive and aligned with common notions surrounding habitual food consumption. As a potential impact of the research, profiling habitual behaviors using the proposed recurrent consumption measures may reveal unique opportunities for accessible and sustainable dietary interventions. | en_US |
| dc.identifier.citation | Pai, Amruta and Sabharwal, Ashutosh. "Food Habits: Insights from Food Diaries via Computational Recurrence Measures." <i>Sensors,</i> 22, no. 7 (2022) MDPI: https://doi.org/10.3390/s22072753. | en_US |
| dc.identifier.digital | sensors-22-02753-v2 | en_US |
| dc.identifier.doi | https://doi.org/10.3390/s22072753 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/112198 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | MDPI | en_US |
| dc.rights | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | Food Habits: Insights from Food Diaries via Computational Recurrence Measures | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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