Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment
Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior,...
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| Published in | PloS one Vol. 11; no. 4; p. e0153085 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
06.04.2016
Public Library of Science (PLoS) |
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| Abstract | Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person's eating behavior, and the value of such approach compared to typical regression models.
Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject's activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments.
Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately.
A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement. |
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| AbstractList | Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person's eating behavior, and the value of such approach compared to typical regression models. Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject's activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments. Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately. A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement. Introduction Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person's eating behavior, and the value of such approach compared to typical regression models. Method Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject's activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments. Results Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately. Conclusion A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement. Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person's eating behavior, and the value of such approach compared to typical regression models. Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject's activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments. Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately. A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement. Introduction Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person’s eating behavior, and the value of such approach compared to typical regression models. Method Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject’s activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments. Results Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately. Conclusion A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement. |
| Audience | Academic |
| Author | Wu, Lemuel Li, Yan Shia, Victor Seto, Edmund Eom, Sue Hua, Jenna Wang, May |
| AuthorAffiliation | 5 Community Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America 2 Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America 3 Electrical Engineering and Computer Science, School of Engineering, University of California, Berkeley, California, United States of America 1 Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America University of North Carolina at Charlotte, UNITED STATES 4 Public Health Nutrition, School of Public Health, Seoul National University, Seoul, South Korea 6 Maternal and Child Health, School of Public Health, Kunming Medical University, Kunming, China |
| AuthorAffiliation_xml | – name: 5 Community Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America – name: 1 Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America – name: 2 Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America – name: 3 Electrical Engineering and Computer Science, School of Engineering, University of California, Berkeley, California, United States of America – name: 4 Public Health Nutrition, School of Public Health, Seoul National University, Seoul, South Korea – name: 6 Maternal and Child Health, School of Public Health, Kunming Medical University, Kunming, China – name: University of North Carolina at Charlotte, UNITED STATES |
| Author_xml | – sequence: 1 givenname: Edmund surname: Seto fullname: Seto, Edmund organization: Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America – sequence: 2 givenname: Jenna surname: Hua fullname: Hua, Jenna organization: Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America – sequence: 3 givenname: Lemuel surname: Wu fullname: Wu, Lemuel organization: Electrical Engineering and Computer Science, School of Engineering, University of California, Berkeley, California, United States of America – sequence: 4 givenname: Victor surname: Shia fullname: Shia, Victor organization: Electrical Engineering and Computer Science, School of Engineering, University of California, Berkeley, California, United States of America – sequence: 5 givenname: Sue surname: Eom fullname: Eom, Sue organization: Public Health Nutrition, School of Public Health, Seoul National University, Seoul, South Korea – sequence: 6 givenname: May surname: Wang fullname: Wang, May organization: Community Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America – sequence: 7 givenname: Yan surname: Li fullname: Li, Yan organization: Maternal and Child Health, School of Public Health, Kunming Medical University, Kunming, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27049852$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1017/S0007114512001353 10.1016/S0749-3797(02)00470-1 10.1016/0022-3956(82)90016-4 10.1016/0023-9690(87)90017-8 10.1037/a0017813 10.1016/j.jecp.2010.10.007 10.1097/JCN.0b013e31827fcc4c 10.1186/1471-2288-12-75 10.1093/ajcn/82.1.236S 10.1001/archinternmed.2011.283 10.1016/j.amepre.2013.05.012 10.1016/j.jada.2009.10.010 10.1186/1479-5868-3-9 10.1016/j.eatbeh.2009.03.002 10.1111/j.1365-277X.1995.tb00296.x 10.1002/1098-108X(199309)14:2<171::AID-EAT2260140207>3.0.CO;2-U 10.1006/appe.1994.1015 10.1145/1839294.1839361 10.1016/j.jfca.2006.02.010 10.1186/1471-2458-12-793 10.1145/1899475.1899487 10.1046/j.1397-3142.2003.00117.x 10.1093/aje/kwp090 10.1109/JSTSP.2010.2051471 10.1016/j.jada.2004.01.006 10.1146/annurev.clinpsy.3.022806.091415 10.1016/j.appet.2008.03.018 10.1016/j.healthplace.2012.05.006 10.2105/AJPH.92.2.246 10.1177/193229681000400224 10.1016/j.pmcj.2011.07.003 10.1016/S0002-8223(03)00974-X 10.1023/B:PREV.0000037641.26017.00 10.2196/jmir.2470 10.1093/jn/135.4.905 10.1016/0272-4944(95)90001-2 10.1249/00005768-198806000-00002 10.2196/resprot.2205 10.4108/icst.pervasivehealth.2011.246105 10.1038/sj.ijo.0801938 10.3390/jsan1030217 10.1145/2047196.2047198 10.1111/obr.12384 10.2217/pme.11.7 10.1080/08964280009595752 10.1093/her/cyh049 |
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| Copyright | COPYRIGHT 2016 Public Library of Science 2016 Seto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Seto et al 2016 Seto et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: JH. Performed the experiments: JH. Analyzed the data: ES. Contributed reagents/materials/analysis tools: LW VS. Wrote the paper: ES. Developed the app: ES. Advised the study design: ES. Contributed to the study design: MW YL. Participated in the study data collection: SE. Contributed to the editing of the manuscript: JH MW YL. Managed the study data collection and diet assessment: JH. Contributed to the Google Places food environment analysis: LW VS. Competing Interests: The authors have declared that no competing interests exist. |
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| References | ref12 ref15 J Lakerveld (ref7) 2016; 17 R Gruber (ref58) 2010; 33 AC King (ref51) 2002; 23 LL Birch (ref57) 1987; 18 SL Godwin (ref27) 2004; 104 D Fleig (ref31) 2009; 10 DC Hughes (ref40) 2010; 4 L Fisher (ref48) 2000; 26 RG Barker (ref54) 1968; vi LV Moore (ref21) 2009; 170 S Kaplan (ref53) 1995; 15 M Swan (ref1) 2012; 1 ref46 PD Leathwood (ref59) 1982; 17 SP Kremers (ref8) 2006; 3 F Lucas (ref29) 1995; 8 T. Hernandez (ref26) 2006; 19 LR Young (ref24) 2002; 92 JA Ello-Martin (ref22) 2005; 82 F Kong (ref43) 2012; 8 O Newman (ref52) 1972; xvii u Bronfenbrenner (ref4) 1999; xvii CE Caspi (ref19) 2012; 18 L Hebden (ref39) 2012; 1 K Lynch (ref56) 1960 V Rivera-Pelayo (ref2) 2012 EO Lillie (ref6) 2011; 8 ref36 AA Stone (ref14) 1999 JS Silk (ref45) 2011; 110 ref30 DA Thompson (ref33) 1988; 20 DJ Hensel (ref47) 2012; 12 MC Carter (ref38) 2013; 109 FK McSweeney (ref3) 2014 RJ Shaw (ref50) 2003; 7 P Schoggen (ref55) 1989; xii LM Collins (ref5) 2004; 5 H Qiu (ref18) 2009; 4 F Zhu (ref42) 2010; 4 S Shiffman (ref13) 2008; 4 I Woo (ref44) 2010; 8 GA O'Reilly (ref37) 2013; 45 K Zhu (ref17) 2008; 16 D Donaire-Gonzalez (ref11) 2013; 15 S Kumanyika (ref9) 2002; 26 DA Williamson (ref28) 2003; 103 JH Ledikwe (ref23) 2005; 135 L Christensen (ref35) 1993; 14 HG Buck (ref49) 2013; 28 J Lakerveld (ref10) 2012; 12 GX Ayala (ref25) 2006; 21 DS Courvoisier (ref16) 2010; 10 J Boone-Heinonen (ref20) 2011; 171 E Schubert (ref32) 2008; 51 P Verger (ref34) 1994; 22 BL Six (ref41) 2010; 110 |
| References_xml | – volume: 109 start-page: 539 issue: 3 year: 2013 ident: ref38 article-title: 'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss publication-title: The British journal of nutrition doi: 10.1017/S0007114512001353 contributor: fullname: MC Carter – volume: 16 start-page: 696 issue: 6 year: 2008 ident: ref17 article-title: Epidemiological survey on simple obesity in 0~ 7 years old children in Kunming city publication-title: Chin J Child Health Care contributor: fullname: K Zhu – start-page: 26 year: 1999 ident: ref14 article-title: Well-being: The foundations of hedonic psychology contributor: fullname: AA Stone – volume: 23 start-page: 15 issue: 2 Suppl year: 2002 ident: ref51 article-title: Theoretical approaches to the promotion of physical activity: forging a transdisciplinary paradigm publication-title: American journal of preventive medicine doi: 10.1016/S0749-3797(02)00470-1 contributor: fullname: AC King – volume: 17 start-page: 147 issue: 2 year: 1982 ident: ref59 article-title: Diet-induced mood changes in normal populations publication-title: Journal of psychiatric research doi: 10.1016/0022-3956(82)90016-4 contributor: fullname: PD Leathwood – volume: 18 start-page: 301 issue: 3 year: 1987 ident: ref57 article-title: Clean up Your Plate—Effects of Child Feeding Practices on the Conditioning of Meal Size publication-title: Learn Motiv doi: 10.1016/0023-9690(87)90017-8 contributor: fullname: LL Birch – volume: 10 start-page: 115 issue: 1 year: 2010 ident: ref16 article-title: Psychometric properties of a computerized mobile phone method for assessing mood in daily life publication-title: Emotion doi: 10.1037/a0017813 contributor: fullname: DS Courvoisier – volume: 110 start-page: 241 issue: 2 year: 2011 ident: ref45 article-title: Daily emotional dynamics in depressed youth: A cell phone ecological momentary assessment study publication-title: Journal of experimental child psychology doi: 10.1016/j.jecp.2010.10.007 contributor: fullname: JS Silk – volume: 28 start-page: E37 issue: 6 year: 2013 ident: ref49 article-title: Dyadic heart failure care types: qualitative evidence for a novel typology publication-title: The Journal of cardiovascular nursing doi: 10.1097/JCN.0b013e31827fcc4c contributor: fullname: HG Buck – volume: 12 start-page: 75 issue: 1 year: 2012 ident: ref47 article-title: The feasibility of cell phone based electronic diaries for STI/HIV research publication-title: BMC medical research methodology doi: 10.1186/1471-2288-12-75 contributor: fullname: DJ Hensel – volume: 82 start-page: 236S issue: 1 Suppl year: 2005 ident: ref22 article-title: The influence of food portion size and energy density on energy intake: implications for weight management publication-title: The American journal of clinical nutrition doi: 10.1093/ajcn/82.1.236S contributor: fullname: JA Ello-Martin – volume: xvii start-page: 264 year: 1972 ident: ref52 article-title: Defensible space; crime prevention through urban design contributor: fullname: O Newman – volume: 171 start-page: 1162 issue: 13 year: 2011 ident: ref20 article-title: Fast food restaurants and food stores: longitudinal associations with diet in young to middle-aged adults: the CARDIA study publication-title: Archives of internal medicine doi: 10.1001/archinternmed.2011.283 contributor: fullname: J Boone-Heinonen – volume: 45 start-page: 501 issue: 4 year: 2013 ident: ref37 article-title: Current mHealth technologies for physical activity assessment and promotion publication-title: American journal of preventive medicine doi: 10.1016/j.amepre.2013.05.012 contributor: fullname: GA O'Reilly – ident: ref36 – volume: 110 start-page: 74 issue: 1 year: 2010 ident: ref41 article-title: Evidence-based development of a mobile telephone food record publication-title: Journal of the American Dietetic Association doi: 10.1016/j.jada.2009.10.010 contributor: fullname: BL Six – volume: 3 start-page: 9 year: 2006 ident: ref8 article-title: Environmental influences on energy balance-related behaviors: a dual-process view publication-title: Int J Behav Nutr Phys Act doi: 10.1186/1479-5868-3-9 contributor: fullname: SP Kremers – volume: 10 start-page: 115 issue: 2 year: 2009 ident: ref31 article-title: Association between chronotype and diet in adolescents based on food logs publication-title: Eat Behav doi: 10.1016/j.eatbeh.2009.03.002 contributor: fullname: D Fleig – year: 2012 ident: ref2 article-title: A framework for applying quantified self approaches to support reflective learning publication-title: Mobile Learning contributor: fullname: V Rivera-Pelayo – volume: 8 start-page: 65 year: 1995 ident: ref29 article-title: Estimation of food portion size using photographs: Relative validity, strengths, weaknesses and recommendations publication-title: J Hum Nutr Diet doi: 10.1111/j.1365-277X.1995.tb00296.x contributor: fullname: F Lucas – volume: 14 start-page: 171 issue: 2 year: 1993 ident: ref35 article-title: Effects of eating behavior on mood: a review of the literature publication-title: The International journal of eating disorders doi: 10.1002/1098-108X(199309)14:2<171::AID-EAT2260140207>3.0.CO;2-U contributor: fullname: L Christensen – volume: 22 start-page: 159 issue: 2 year: 1994 ident: ref34 article-title: Free food choice after acute exercise in men publication-title: Appetite doi: 10.1006/appe.1994.1015 contributor: fullname: P Verger – volume: xii start-page: 419 year: 1989 ident: ref55 article-title: Behavior settings: a revision and extension of Roger G. Barker's Ecological psychology contributor: fullname: P Schoggen – ident: ref12 doi: 10.1145/1839294.1839361 – volume: 4 start-page: 037 year: 2009 ident: ref18 article-title: Study on the Epidemiological Characteristics of Overweight and Obesity in Kunming publication-title: Chinese Primary Health Care contributor: fullname: H Qiu – start-page: 194 year: 1960 ident: ref56 article-title: The image of the city contributor: fullname: K Lynch – volume: 19 start-page: S14 issue: suppl year: 2006 ident: ref26 article-title: Portion size estimation and expectation of accuracy publication-title: J Food Comp Anal doi: 10.1016/j.jfca.2006.02.010 contributor: fullname: T. Hernandez – volume: 12 start-page: 793 year: 2012 ident: ref10 article-title: Sustainable prevention of obesity through integrated strategies: The SPOTLIGHT project's conceptual framework and design publication-title: BMC Public Health doi: 10.1186/1471-2458-12-793 contributor: fullname: J Lakerveld – ident: ref30 doi: 10.1145/1899475.1899487 – volume: 7 start-page: 489 issue: 6 year: 2003 ident: ref50 article-title: A typology of non-adherence in pediatric renal transplant recipients publication-title: Pediatric transplantation doi: 10.1046/j.1397-3142.2003.00117.x contributor: fullname: RJ Shaw – volume: xvii start-page: 419 year: 1999 ident: ref4 article-title: Measuring environment across the life span: emerging methods and concepts contributor: fullname: u Bronfenbrenner – volume: 170 start-page: 29 issue: 1 year: 2009 ident: ref21 article-title: Fast-food consumption, diet quality, and neighborhood exposure to fast food: the multi-ethnic study of atherosclerosis publication-title: American journal of epidemiology doi: 10.1093/aje/kwp090 contributor: fullname: LV Moore – volume: 4 start-page: 756 issue: 4 year: 2010 ident: ref42 article-title: The use of mobile devices in aiding dietary assessment and evaluation publication-title: Selected Topics in Signal Processing, IEEE Journal of doi: 10.1109/JSTSP.2010.2051471 contributor: fullname: F Zhu – volume: 104 start-page: 585 issue: 4 year: 2004 ident: ref27 article-title: Accuracy of reporting dietary intake using various portion-size aids in-person and via telephone publication-title: J Am Diet Assoc doi: 10.1016/j.jada.2004.01.006 contributor: fullname: SL Godwin – volume: 4 start-page: 1 year: 2008 ident: ref13 article-title: Ecological momentary assessment publication-title: Annu Rev Clin Psychol doi: 10.1146/annurev.clinpsy.3.022806.091415 contributor: fullname: S Shiffman – volume: 51 start-page: 501 issue: 3 year: 2008 ident: ref32 article-title: Association between chronotype and the constructs of the Three-Factor-Eating-Questionnaire publication-title: Appetite doi: 10.1016/j.appet.2008.03.018 contributor: fullname: E Schubert – volume: vi start-page: 242 year: 1968 ident: ref54 article-title: Ecological psychology; concepts and methods for studying the environment of human behavior contributor: fullname: RG Barker – volume: 18 start-page: 1172 issue: 5 year: 2012 ident: ref19 article-title: The local food environment and diet: a systematic review publication-title: Health & place doi: 10.1016/j.healthplace.2012.05.006 contributor: fullname: CE Caspi – volume: 92 start-page: 246 issue: 2 year: 2002 ident: ref24 article-title: The contribution of expanding portion sizes to the US obesity epidemic publication-title: American journal of public health doi: 10.2105/AJPH.92.2.246 contributor: fullname: LR Young – volume: 4 start-page: 429 issue: 2 year: 2010 ident: ref40 article-title: BALANCE (Bioengineering Approaches for Lifestyle Activity and Nutrition Continuous Engagement): developing new technology for monitoring energy balance in real time publication-title: Journal of diabetes science and technology doi: 10.1177/193229681000400224 contributor: fullname: DC Hughes – volume: 8 start-page: 147 issue: 1 year: 2012 ident: ref43 article-title: DietCam: Automatic dietary assessment with mobile camera phones publication-title: Pervasive and Mobile Computing doi: 10.1016/j.pmcj.2011.07.003 contributor: fullname: F Kong – volume: 103 start-page: 1139 issue: 9 year: 2003 ident: ref28 article-title: Comparison of digital photography to weighed and visual estimation of portion sizes publication-title: J Am Diet Assoc doi: 10.1016/S0002-8223(03)00974-X contributor: fullname: DA Williamson – volume: 5 start-page: 185 issue: 3 year: 2004 ident: ref5 article-title: A conceptual framework for adaptive preventive interventions publication-title: Prevention science: the official journal of the Society for Prevention Research doi: 10.1023/B:PREV.0000037641.26017.00 contributor: fullname: LM Collins – volume: 15 start-page: e111 issue: 6 year: 2013 ident: ref11 article-title: Comparison of physical activity measures using mobile phone-based CalFit and Actigraph publication-title: J Med Internet Res doi: 10.2196/jmir.2470 contributor: fullname: D Donaire-Gonzalez – volume: 135 start-page: 905 issue: 4 year: 2005 ident: ref23 article-title: Portion sizes and the obesity epidemic publication-title: The Journal of nutrition doi: 10.1093/jn/135.4.905 contributor: fullname: JH Ledikwe – volume: 15 start-page: 169 issue: 3 year: 1995 ident: ref53 article-title: The Restorative Benefits of Nature—toward an Integrative Framework publication-title: J Environ Psychol doi: 10.1016/0272-4944(95)90001-2 contributor: fullname: S Kaplan – volume: 20 start-page: 222 issue: 3 year: 1988 ident: ref33 article-title: Acute effects of exercise intensity on appetite in young men publication-title: Med Sci Sports Exerc doi: 10.1249/00005768-198806000-00002 contributor: fullname: DA Thompson – volume: 1 start-page: e9 issue: 2 year: 2012 ident: ref39 article-title: Development of smartphone applications for nutrition and physical activity behavior change publication-title: JMIR research protocols doi: 10.2196/resprot.2205 contributor: fullname: L Hebden – year: 2014 ident: ref3 article-title: The Wiley-Blackwell handbook of operant and classical conditioning [text] contributor: fullname: FK McSweeney – ident: ref46 doi: 10.4108/icst.pervasivehealth.2011.246105 – volume: 26 start-page: 425 issue: 3 year: 2002 ident: ref9 article-title: Obesity prevention: the case for action publication-title: Int J Obes Relat Metab Disord doi: 10.1038/sj.ijo.0801938 contributor: fullname: S Kumanyika – volume: 1 start-page: 217 issue: 3 year: 2012 ident: ref1 article-title: Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0 publication-title: Journal of Sensor and Actuator Networks doi: 10.3390/jsan1030217 contributor: fullname: M Swan – ident: ref15 doi: 10.1145/2047196.2047198 – volume: 33 start-page: A80 year: 2010 ident: ref58 article-title: The Association between Chronotype and Sleep Patterns in Children publication-title: Sleep contributor: fullname: R Gruber – volume: 17 start-page: 5 issue: Suppl 1 year: 2016 ident: ref7 article-title: Individual and contextual correlates of obesity-related behaviours and obesity: the SPOTLIGHT project publication-title: Obesity reviews: an official journal of the International Association for the Study of Obesity doi: 10.1111/obr.12384 contributor: fullname: J Lakerveld – volume: 8 start-page: 161 issue: 2 year: 2011 ident: ref6 article-title: The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? publication-title: Personalized medicine doi: 10.2217/pme.11.7 contributor: fullname: EO Lillie – volume: 26 start-page: 53 issue: 2 year: 2000 ident: ref48 article-title: Disease management status: a typology of Latino and Euro-American patients with type 2 diabetes publication-title: Behavioral medicine doi: 10.1080/08964280009595752 contributor: fullname: L Fisher – volume: 8 start-page: 1 issue: 7533 year: 2010 ident: ref44 article-title: Automatic portion estimation and visual refinement in mobile dietary assessment publication-title: Computational Imaging contributor: fullname: I Woo – volume: 21 start-page: 133 issue: 1 year: 2006 ident: ref25 article-title: An experimental evaluation of a group- versus computer-based intervention to improve food portion size estimation skills publication-title: Health Educ Res doi: 10.1093/her/cyh049 contributor: fullname: GX Ayala |
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| Snippet | Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for... Introduction Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline... INTRODUCTION:Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline... Introduction Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline... |
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| Title | Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment |
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