TY - JOUR
T1 - The use of food images and crowdsourcing to capture real-time eating behaviors
T2 - Acceptability and usability study
AU - Harrington, Katharine
AU - Zenk, Shannon N.
AU - van Horn, Linda
AU - Giurini, Lauren
AU - Mahakala, Nithya
AU - Kershaw, Kiarri N.
N1 - Funding Information:
This work was supported by the National Institutes of Health (K01HL133531, UL1TR001422). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data collection was completed before SZ’s employment at the National Institutes of Health.
Publisher Copyright:
©Katharine Harrington, Shannon N Zenk, Linda Van Horn, Lauren Giurini, Nithya Mahakala, Kiarri N Kershaw.
PY - 2021/12
Y1 - 2021/12
N2 - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: Our approach offers a potentially time-efficient and cost-effective strategy for capturing eating events in real time.
AB - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: Our approach offers a potentially time-efficient and cost-effective strategy for capturing eating events in real time.
KW - Crowdsourcing
KW - Eating behaviors
KW - Ecological momentary assessment
KW - Food consumption images
KW - Food image processing
KW - Mobile phone
UR - http://www.scopus.com/inward/record.url?scp=85120952758&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120952758&partnerID=8YFLogxK
U2 - 10.2196/27512
DO - 10.2196/27512
M3 - Article
C2 - 34860666
AN - SCOPUS:85120952758
SN - 2561-326X
VL - 5
JO - JMIR Formative Research
JF - JMIR Formative Research
IS - 12
M1 - e27512
ER -