The Use of Food Images and Crowdsourcing to Capture Real-time Eating Behaviors: Acceptability and Usability Study

The Use of Food Images and Crowdsourcing to Capture Real-time Eating Behaviors: Acceptability and Usability Study

As poor diet quality is a significant risk factor for multiple noncommunicable diseases prevalent in the United States, it is important that methods be developed to accurately capture eating behavior data. There is growing interest in the use of ecological momentary assessments to collect data on he...

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Bibliographic Details
Published in:JMIR formative research Vol. 5; no. 12; p. e27512
Main Authors: Harrington, Katharine, Zenk, Shannon N, Van Horn, Linda, Giurini, Lauren, Mahakala, Nithya, Kershaw, Kiarri N
Format: Journal Article
Language:English
Published: Canada JMIR Publications 02-12-2021
Subjects:
Crowdsourcing
Data collection
Eating behavior
Food
Fruits
Heart rate
Meals
Original Paper
Real time
Recall
Smartphones
State court decisions
Sugar
Text messaging
Trans fats
United States > US
Chicago Illinois
eating behaviors
crowdsourcing
food consumption images
food image processing
mobile phone
ecological momentary assessment
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Summary:As poor diet quality is a significant risk factor for multiple noncommunicable diseases prevalent in the United States, it is important that methods be developed to accurately capture eating behavior data. There is growing interest in the use of ecological momentary assessments to collect data on health behaviors and their predictors on a micro timescale (at different points within or across days); however, documenting eating behaviors remains a challenge. This pilot study (N=48) aims to examine the feasibility-usability and acceptability-of using smartphone-captured and crowdsource-labeled images to document eating behaviors in real time. Participants completed the Block Fat/Sugar/Fruit/Vegetable Screener to provide a measure of their typical eating behavior, then took pictures of their meals and snacks and answered brief survey questions for 7 consecutive days using a commercially available smartphone app. Participant acceptability was determined through a questionnaire regarding their experiences administered at the end of the study. The images of meals and snacks were uploaded to Amazon Mechanical Turk (MTurk), a crowdsourcing distributed human intelligence platform, where 2 Workers assigned a count of food categories to the images (fruits, vegetables, salty snacks, and sweet snacks). The agreement among MTurk Workers was assessed, and weekly food counts were calculated and compared with the Screener responses. Participants reported little difficulty in uploading photographs and remembered to take photographs most of the time. Crowdsource-labeled images (n=1014) showed moderate agreement between the MTurk Worker responses for vegetables (688/1014, 67.85%) and high agreement for all other food categories (871/1014, 85.89% for fruits; 847/1014, 83.53% for salty snacks, and 833/1014, 81.15% for sweet snacks). There were no significant differences in weekly food consumption between the food images and the Block Screener, suggesting that this approach may measure typical eating behaviors as accurately as traditional methods, with lesser burden on participants. Our approach offers a potentially time-efficient and cost-effective strategy for capturing eating events in real time.
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ISSN:2561-326X
2561-326X
DOI:10.2196/27512