A Real-Time Eating Detection System for Capturing Eating Behaviors In-the-Wild (Preprint)

2020 ◽  
Author(s):  
Mehrab Bin Morshed ◽  
Samruddhi Shreeram Kulkarni ◽  
Richard Li ◽  
Koustuv Saha ◽  
Lama Nachman ◽  
...  
Keyword(s):  
College Students ◽  
Eating Behavior ◽  
Ecological Momentary Assessment ◽  
Detection System ◽  
Contextual Information ◽  
Contextual Factors ◽  
Survey Study ◽  
In The Wild ◽  
Ecological Momentary ◽  
Momentary Assessment

BACKGROUND This paper describes a semi-automated eating detection system that leverages Ecological Momentary Assessment (EMA) questions to capture contextual factors upon detecting when an individual is eating. Our validation study demonstrates the efficacy of the system by deploying it in-the-wild among college students. OBJECTIVE This study builds a semi-automated eating detection system that leverages Ecological Momentary Assessment (EMA) questions to capture contextual factors upon detecting when an individual is eating. It also demonstrates the efficacy of the system by deploying it in-the-wild among college students. METHODS The eating detection system was deployed among 28 college students at a US institution over a period of three weeks. The participants reported various contextual information through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS Among the total consumed meals, 90% of breakfast, 99% of lunch, and 98% of dinner episodes were detected by our novel eating detection system. The eating detection system showed a high accuracy by capturing 95.67% of the meals out of 1,259 meals consumed by the participants. The eating detection classifier shows a precision of 80%, recall of 96%, and F1 of 87%. We found that over 99% of the meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. Significant portions of meals were consumed alone (54.09%) in dorm rooms or apartment housing (31.19%). Our participants self-reported 63% of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS The presented eating detection system is the first of its kind to leverage EMAs to capture eating context, which has significant implications for wellbeing research. We reflect on the contextual data that has been gathered by our system and discuss how these insights can be used to design individual-specific interventions.

scholarly journals A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study

10.2196/20625 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e20625
Author(s):  
Mehrab Bin Morshed ◽  
Samruddhi Shreeram Kulkarni ◽  
Richard Li ◽  
Koustuv Saha ◽  
Leah Galante Roper ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Detection System ◽  
System Development ◽  
Contextual Factors ◽  
Well Being ◽  
Survey Study ◽  
Dominant Hand ◽  
Contextual Data ◽  
The Individual ◽  
Ecological Momentary

Background Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. Objective The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. Methods The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. Results Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. Conclusions The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

scholarly journals A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Mehrab Bin Morshed ◽  
Samruddhi Shreeram Kulkarni ◽  
Richard Li ◽  
Koustuv Saha ◽  
Leah Galante Roper ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Detection System ◽  
System Development ◽  
Contextual Factors ◽  
Well Being ◽  
Survey Study ◽  
Dominant Hand ◽  
Contextual Data ◽  
The Individual ◽  
Ecological Momentary

BACKGROUND Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. OBJECTIVE The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. METHODS The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

scholarly journals Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol

2021 ◽  
Vol 7 ◽  
pp. 205520762098821
Author(s):  
Stephanie P Goldstein ◽  
Adam Hoover ◽  
E Whitney Evans ◽  
J Graham Thomas
Keyword(s):  
Weight Loss ◽  
Eating Behavior ◽  
Ecological Momentary Assessment ◽  
Structured Interview ◽  
Self Report ◽  
Significant Weight Loss ◽  
Clinically Significant ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Lapse Behavior

Objectives Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse. Method We are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed. Results While data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work. Conclusion This protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions. Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151

scholarly journals A Systematic Review of Ecological Momentary Assessment of Diet: Implications and Perspectives for Nutritional Epidemiology

Nutrients ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2696 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta
Keyword(s):  
Systematic Review ◽  
Ecological Momentary Assessment ◽  
Eating Behaviors ◽  
Dietary Habits ◽  
Contextual Information ◽  
Dietary Assessment ◽  
Nutritional Epidemiology ◽  
Future Research ◽  
Ecological Momentary ◽  
Momentary Assessment

The ecological momentary assessment (EMA) of eating behaviors represents an innovative, detailed and valid approach to capture the complexity of food intake and to overcome limitations of traditional dietary assessment methods. Moreover, EMA studies might generate a large variety of data (e.g., dietary, behavioral, physical, sociopsychological, and contextual information), thereby enabling to examine concurrent exposures and events. Due to the increasing number of studies in this field of research, here we systematically reviewed EMA methods for the assessment of dietary intake in epidemiological studies, and discussed implications and perspectives for future research. Our study summarized several protocols and platforms that may be applied to assess diet in terms of eating frequency, choices, and habits. Nearly 38% of studies used an event-contingent strategy by asking participants to report foods and beverages consumed in real-time at each eating occasion. Instead, approximately 55% of studies used a signal-contingent prompting approach that notified the participants to record their dietary consumption. The remaining studies used a combination of event- and signal-contingent protocols to compare their accuracy or to improve the assessment of dietary data. Although both approaches might improve the accuracy and ecological validity of dietary assessment—also reducing the burden for participants—some limitations should nevertheless be considered. Despite these limitations, our systematic review pointed out that EMA can be applied in various fields of nutritional epidemiology, from the identification of determinants of dietary habits in healthy people to the management of patients with eating or metabolic disorders. However, more efforts should be encouraged to improve the validity and the reliability of EMA and to provide further technological innovations for public health research and interventions.

scholarly journals Pilot Ecological Momentary Assessment Study of Subjective and Contextual Factors Surrounding E-Cigarette and Combustible Tobacco Product Use among Young Adults

2021 ◽  
Vol 18 (21) ◽  
pp. 11005
Author(s):  
Deepa R. Camenga ◽  
Angela M. Haeny ◽  
Suchitra Krishnan-Sarin ◽  
Stephanie S. O’Malley ◽  
Krysten W. Bold
Keyword(s):  
Young Adults ◽  
Ecological Momentary Assessment ◽  
Contextual Factors ◽  
Tobacco Product ◽  
Cigarette Use ◽  
Tobacco Products ◽  
Product Use ◽  
Cigarette Dependence ◽  
Ecological Momentary ◽  
Momentary Assessment

Background: Dual use of e-cigarettes and combustible tobacco products is common in young adults. We aimed to explore how ratings of subjective and contextual factors differed between discrete episodes of e-cigarette use vs. combustible tobacco product smoking among a sample of young adults. Methods: Young adults (N = 29, ages 18–30) who used e-cigarettes and ≥1 combustible tobacco product at least once weekly completed a 1-week smartphone-based ecological momentary assessment (EMA). Twice daily random prompts assessed past-15-min use of tobacco products, ratings of subjective factors (e.g., negative affect, craving), and contextual factors related to activity, location, and companionship. A multivariable GEE model assessed whether subjective or contextual factors were associated with e-cigarette vs. combustible tobacco product episodes. Results: 184 tobacco use episodes were reported (39.7% e-cigarette, 60.3% combustible tobacco product). High baseline cigarette dependence, as measured by the Fagerström Test for Cigarette Dependence, was associated with lower odds of e-cigarette vs. combustible tobacco product episodes (aOR 0.01, 95% CI (0.002–0.08); p < 0.001). Neither between- or within-subjects negative affect or craving scores were associated with e-cigarette use. Activities of eating/drinking (aOR 0.20, 95% CI (0.08–0.49); p = 0.001) and being in the companionship of a person who smoked cigarettes (aOR 0.13, 95% CI (0.04–0.43); p = 0.001) were associated with lower odds of e-cigarette vs. combustible tobacco product use episodes. However, traveling (aOR 12.02, 95% CI (3.77–38.26); p ≤ 0.001) and being in a public space (aOR 2.76, 95% CI (1.10–6.96); p = 0.03) were associated with higher odds of e-cigarette than combustible tobacco product use episodes. Conclusions: This pilot data suggests that unique contextual factors may be associated with e-cigarette use, compared to combustible tobacco smoking in a sample of young adults who use both e-cigarettes and combustible tobacco products. Future research with larger samples is needed to better characterize varying contexts and cues for tobacco use among young adults who are dual users.

Momentary associations between affect and alcohol use in the daily lives of college students

2021 ◽  
Author(s):  
Farah Sayed ◽  
Amanda Lee McGowan ◽  
Mia Jovanova ◽  
Danielle Cosme ◽  
Yoona Kang ◽  
...  
Keyword(s):  
College Students ◽  
Alcohol Use ◽  
Negative Affect ◽  
Positive Affect ◽  
Ecological Momentary Assessment ◽  
Social Contexts ◽  
Drinking Motives ◽  
Daily Lives ◽  
Ecological Momentary ◽  
Momentary Assessment

Objective: Alcohol is theorized to be motivated by desires to regulate negative affect and/or to enhance positive affect. We tested the association between momentary affect and alcohol use in the daily lives of college students, hypothesizing that alcohol use would be more likely to follow increases in positive affect and that alcohol use would not be strongly associated with negative affect. Method: Using two ecological momentary assessment (EMA) studies consisting of two prompts per day for 28 days, we used multilevel hurdle models to test for lagged associations between positive and negative affect and alcohol use. There were 108 participants (60.19%; mean age = 20.20, SD=1.69) in EMA study 1 and 268 participants (60.03%women, mean age = 20.22, SD=1.96) in EMA study 2. To provide context for the affect-alcohol associations, we collected data on whether participants drank alone or with others at each drinking occasion and the drinking motives of participants using the Drinking Motives Questionnaire. Results: Alcohol use was more likely to occur following increases in positive affect. No significant associations emerged between fluctuations in negative affect and alcohol use. This pattern of findings was observed across both ecological momentary assessment studies. The majority of alcohol use occurred in social contexts. Conclusions: College students who report primarily social and enhancement motives for drinking and who seldom drink alone are more likely to drink following increases in positive affect.

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