title: Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America.
authors.forename: Edmund
authors.initials: E
authors.lastname: Seto
authors.affiliation: Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America.
authors.forename: Jenna
authors.initials: J
authors.lastname: Hua
authors.affiliation: Electrical Engineering and Computer Science, School of Engineering, University of California, Berkeley, California, United States of America.
authors.forename: Lemuel
authors.initials: L
authors.lastname: Wu
authors.affiliation: Electrical Engineering and Computer Science, School of Engineering, University of California, Berkeley, California, United States of America.
authors.forename: Victor
authors.initials: V
authors.lastname: Shia
authors.affiliation: Public Health Nutrition, School of Public Health, Seoul National University, Seoul, South Korea.
authors.forename: Sue
authors.initials: S
authors.lastname: Eom
authors.affiliation: Community Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.
authors.forename: May
authors.initials: M
authors.lastname: Wang
authors.affiliation: Maternal and Child Health, School of Public Health, Kunming Medical University, Kunming, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
mesh_terms: D000293:Adolescent; D000328:Adult; D015331:Cohort Studies; D004032:Diet; D004644:Emotions; D005260:Female; D006801:Humans; D008297:Male; D063731:Mobile Applications; D009043:Motor Activity; D000068997:Smartphone; D055815:Young Adult