scholarly journals Ecological Momentary Assessment of Obesogenic Eating Behavior: Combining Person-Specific and Environmental Predictors

Obesity ◽  
2011 ◽  
Vol 19 (8) ◽  
pp. 1574-1579 ◽  
Author(s):  
J. Graham Thomas ◽  
Sapna Doshi ◽  
Ross D. Crosby ◽  
Michael R. Lowe
Keyword(s):  
Eating Behavior ◽  
Ecological Momentary Assessment ◽  
Environmental Predictors ◽  
Ecological Momentary ◽  
Momentary Assessment

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

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 Evaluating the Ecological Validity of the Dutch Eating Behavior Questionnaire Among Obese Adults Using Ecological Momentary Assessment

Assessment ◽  
2017 ◽  
Vol 26 (5) ◽  
pp. 907-914 ◽  
Author(s):  
Tyler B. Mason ◽  
Carly R. Pacanowski ◽  
Jason M. Lavender ◽  
Ross D. Crosby ◽  
Stephen A. Wonderlich ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Ecological Momentary Assessment ◽  
Ecological Validity ◽  
Obese Adults ◽  
Ecological Momentary ◽  
Momentary Assessment

scholarly journals Ecological momentary assessment of food perceptions and eating behavior using a novel phone application in adults with or without obesity

2018 ◽  
Vol 30 ◽  
pp. 35-41 ◽  
Author(s):  
Kholoud Alabduljader ◽  
Marion Cliffe ◽  
Francesco Sartor ◽  
Gabriele Papini ◽  
W. Miles Cox ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Ecological Momentary Assessment ◽  
Food Perceptions ◽  
Ecological Momentary ◽  
Momentary Assessment

Emotionsregulation bei Depression

2013 ◽  
Vol 64 (4) ◽  
pp. 235-243 ◽  
Author(s):  
Sven Barnow ◽  
Maren Aldinger ◽  
Ines Ulrich ◽  
Malte Stopsack
Keyword(s):  
Ecological Momentary Assessment ◽  
Ecological Momentary ◽  
Momentary Assessment

Die Anzahl der Studien, die sich mit dem Zusammenhang zwischen Emotionsregulation (ER) und depressiven Störungen befassen, steigt. In diesem Review werden Studien zusammengefasst und metaanalytisch ausgewertet, die den Zusammenhang zwischen ER und Depression mittels Fragebögen bzw. Ecological Momentary Assessment (EMA) erfassen. Dabei zeigt sich ein ER-Profil welches durch die vermehrte Nutzung von Rumination, Suppression und Vermeidung bei gleichzeitig seltenerem Einsatz von Neubewertung und Problemlösen gekennzeichnet ist. Mit mittleren bis großen Effekten, ist der Zusammenhang zwischen Depression und maladaptiven Strategien besser belegt als bei den adaptiven Formen, wo die Effekte eher moderat ausfielen. EMA-Messungen bestätigen dieses Profil. Da EMA-Studien neben der Häufigkeit des Strategieeinsatzes auch die Erfassung anderer ER-Parameter wie Effektivität und Flexibilität ermöglichen, sollten solche Designs in der ER-Forschung zukünftig vermehrt Einsatz finden.

Using Personal Cell Phones for Ecological Momentary Assessment

2013 ◽  
Vol 18 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Emmanuel Kuntsche ◽  
Florian Labhart
Keyword(s):  
Data Collection ◽  
Ecological Momentary Assessment ◽  
Communication Systems ◽  
Cell Phones ◽  
Personal Digital Assistants ◽  
Natural Environments ◽  
Mobility Patterns ◽  
Standard Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Ecological Momentary Assessment (EMA) is a way of collecting data in people’s natural environments in real time and has become very popular in social and health sciences. The emergence of personal digital assistants has led to more complex and sophisticated EMA protocols but has also highlighted some important drawbacks. Modern cell phones combine the functionalities of advanced communication systems with those of a handheld computer and offer various additional features to capture and record sound, pictures, locations, and movements. Moreover, most people own a cell phone, are familiar with the different functions, and always carry it with them. This paper describes ways in which cell phones have been used for data collection purposes in the field of social sciences. This includes automated data capture techniques, for example, geolocation for the study of mobility patterns and the use of external sensors for remote health-monitoring research. The paper also describes cell phones as efficient and user-friendly tools for prompt manual data collection, that is, by asking participants to produce or to provide data. This can either be done by means of dedicated applications or by simply using the web browser. We conclude that cell phones offer a variety of advantages and have a great deal of potential for innovative research designs, suggesting they will be among the standard data collection devices for EMA in the coming years.

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