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Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.

ABSTRACT: 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.

SUBMITTER: Seto E

PROVIDER: S-EPMC4822823 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.

Seto Edmund E Hua Jenna J Wu Lemuel L Shia Victor V Eom Sue S Wang May M Li Yan Y

PloS one 20160406 4

<h4>Introduction</h4>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 behavio ...[more]

PMID: 27049852

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Project description:BACKGROUND:Our understanding of how food choices are affected by exposure to the food environment is limited, and there are important gaps in the literature. Recently developed smartphone-based technologies, including global positioning systems and ecological momentary assessment, enable these gaps to be filled. OBJECTIVE:We present the FoodTrack study design and methods, as well as participants' compliance with the study protocol and their experiences with the app. We propose future analyses of the data to examine individual food environmental exposure taking into account the accessible food environment and individual time constraints; to assess people's food choices in relation to food environmental exposure; and to examine the moderating role of individual and contextual determinants of food purchases and consumption. METHODS:We conducted a 7-day observational study among adults (25-45 years of age) living in urban areas in the Netherlands. Participants completed a baseline questionnaire, used an app (incorporating global positioning system tracking and ecological momentary assessment) for 7 days, and then completed a closing survey. The app automatically collected global positioning system tracking data, and participants uploaded information on all food purchases over the 7-day period into the app. Participants also answered questions on contextual or individual purchase-related determinants directly after each purchase. During the final 3 days of the study, the participants also uploaded data on fruit, vegetable, and snack consumption and answered similar ecological momentary assessment questions after each intake. RESULTS:In total, 140 participants completed the study. More than half of the participants said they liked the app (81/140, 57.9%) and found it easy to use (75/140, 53.6%). Of the 140 participants, 126 (90.0%) said that they had collected data on all or almost all purchases and intakes during the 7-day period. Most found the additional ecological momentary assessment questions "easy to answer" (113/140, 80.7%) with "no effort" (99/140, 70.7%). Of 106 participants who explored their trips in the app, 20 (18.8%) had trouble with their smartphone's global positioning system tracking function. Therefore, we will not be able to include all participants in some of the proposed analyses, as we lack these data. We are analyzing data from the first study aim and we expect to publish the results in the spring of 2020. CONCLUSIONS:Participants perceived the FoodTrack app as a user-friendly tool. The app is particularly useful for observational studies that aim to gain insight into daily food environment exposure and food choices. Further analyses of the FoodTrack study data will provide novel insights into individual food environmental exposure, evidence on the individual food environment-diet interaction, and insights into the underlying individual and contextual mechanisms of food purchases and consumption. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/15283.

Project description:Introduction: Many type 2 diabetes patients show insufficient levels of physical activity and are often unmotivated to change physical activity behaviors. This study investigated whether a newly developed smartphone game delivering individualized exercise and physical activity promotion through an elaborate storyline can generate sustained improvements in daily physical activity (steps/day).Study design: Thirty-six participants were enrolled in this 24-week RCT between August 2016 and April 2018. After baseline assessment, participants were randomized in equal numbers to the intervention or control condition. Data analysis was performed in May-June 2018.Setting/participants: Inactive, overweight type 2 diabetes patients, aged 45-70 years, were recruited through advertising and from hospitals and diabetes care centers in the Basel, Switzerland, metropolitan area.Intervention: Participants were instructed to play the innovative smartphone game (intervention group) or to implement the recommendations from the baseline lifestyle counseling (control group) autonomously during the 24-week intervention period.Main outcome measures: Primary outcomes were changes in daily physical activity (steps/day); changes in aerobic capacity, measured as oxygen uptake at the first ventilatory threshold; and changes in glycemic control, measured as HbA1c.Results: Daily physical activity increased by an average of 3,998 (SD=1,293) steps/day in the intervention group and by an average of 939 (SD=1,156) steps/day in the control group. The adjusted difference between the two groups was 3,128 steps/day (95% CI=2,313, 3,943, p<0.001). The increase in daily physical activity was accompanied by an improved aerobic capacity (adjusted difference of oxygen uptake at the first ventilatory threshold of 1.9 mL/(kg·min), 95% CI=0.9, 2.9, p<0.001). Glycemic control (HbA1c) did not change over the course of the intervention.Conclusions: A novel, self-developed smartphone game, delivering multidimensional home-based exercise and physical activity promotion, significantly increases daily physical activity (steps/day) and aerobic capacity in inactive type 2 diabetes patients after 24 weeks. The ability of the game to elicit a sustained physical activity motivation may be relevant for other inactive target groups with chronic diseases.Trial registration: This study is registered at www.clinicaltrials.gov NCT02657018.

Project description:Leading-following behavior as a way of transferring information about the location of resources is wide-spread in many animal societies. It represents active information transfer that allows a given social species to reach collective decisions in the presence of limited information. Although leading-following behavior has received much scientific interest in the form of field studies, there is a need for systematic methods to quantify and study the individual contributions in this information transfer, which would eventually lead us to hypotheses about the individual mechanisms underlying this behaviour. In this paper we propose a general methodology that allows us to (a) infer individual leading-following behaviour from discrete observational data and (b) quantify individual influence based on methods from social network analysis. To demonstrate our methodology, we analyze longitudinal data of the roosting behavior of two different colonies of Bechstein's bats in different years. Regarding (a) we show how the inference of leading-following events can be calibrated from data making it a general approach when only discrete observations are available. This allows us to address (b) by constructing social networks in which nodes represent individual bats and directed and weighted links-the leading-following events. We then show how social network theory can be used to define and quantify individual influence in a way that reflects the dynamics of the specific social network. We find that individuals can be consistently ranked regarding their influence in the information transfer. Moreover, we identify a small set of individuals that play a central role in leading other bats to roosts. In the case of Bechstein's bats this finding can direct future studies on the individual-level mechanisms that result in such collective pattern. More generally, we posit that our data-driven methodology can be used to quantify leading-following behavior and individual impact in other animal systems, solely based on discrete observational data.

Project description:Many authors have proposed that facial expressions, by conveying emotional states of the person we are interacting with, influence the interaction behavior. We aimed at verifying how specific the effect is of the facial expressions of emotions of an individual (both their valence and relevance/specificity for the purpose of the action) with respect to how the action aimed at the same individual is executed. In addition, we investigated whether and how the effects of emotions on action execution are modulated by participants' empathic attitudes. We used a kinematic approach to analyze the simulation of feeding others, which consisted of recording the "feeding trajectory" by using a computer mouse. Actors could express different highly arousing emotions, namely happiness, disgust, anger, or a neutral expression. Response time was sensitive to the interaction between valence and relevance/specificity of emotion: disgust caused faster response. In addition, happiness induced slower feeding time and longer time to peak velocity, but only in blocks where it alternated with expressions of disgust. The kinematic profiles described how the effect of the specificity of the emotional context for feeding, namely a modulation of accuracy requirements, occurs. An early acceleration in kinematic relative-to-neutral feeding profiles occurred when actors expressed positive emotions (happiness) in blocks with specific-to-feeding negative emotions (disgust). On the other hand, the end-part of the action was slower when feeding happy with respect to neutral faces, confirming the increase of accuracy requirements and motor control. These kinematic effects were modulated by participants' empathic attitudes. In conclusion, the social dimension of emotions, that is, their ability to modulate others' action planning/execution, strictly depends on their relevance and specificity to the purpose of the action. This finding argues against a strict distinction between social and nonsocial emotions.

Dataset Information

Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.

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Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.

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