Literature DB >> 23415128

Using a wearable camera to increase the accuracy of dietary analysis.

Gillian O'Loughlin1, Sarah Jane Cullen, Adrian McGoldrick, Siobhan O'Connor, Richard Blain, Shane O'Malley, Giles D Warrington.   

Abstract

BACKGROUND: Food diaries are commonly used to assess individual dietary intake in both the general and sporting populations. Despite the widespread use of such diaries, evidence suggests that individuals' self-reported energy intake frequently and substantially underestimate true energy intake.
PURPOSE: To examine the use of the Microsoft SenseCam wearable camera to help more accurately report dietary intake within various sporting populations.
METHODS: In 2011, a total of 47 participants were recruited to take part in this study (17 trainee jockeys, 15 elite Gaelic footballers, and 15 healthy physically active university students). Participants wore a SenseCam for 1 day (from morning until night) while simultaneously keeping a 1-day food diary. Comparisons were made between the energy intake reported in the food diary alone and the food diary in conjunction with information gathered from the SenseCam. Data analysis was conducted in 2012.
RESULTS: Mean total calorie intake using diary alone and diary and SenseCam were 2349±827.9 kcals vs 2631±893.4 kcal for the trainee jockeys; 2600±521.9 kcal vs 3191±770.2 kcal for the Gaelic footballers, and 2237±318.5 kcal vs 2487±404.6 kcal for the university students. This represented a difference of 10.7% (p≤0.001); 17.7% (p≤0.001); and 10.1% (p≤0.01) among measurement methods for trainee jockeys, Gaelic footballers, and university students, respectively.
CONCLUSIONS: Results from this first-generation study suggest that a more accurate estimate of total energy intake is provided when combining the use of a conventional food diary and a SenseCam. Additional information on portion size, forgotten foods, leftovers, and brand names can be obtained by using this novel sensing technology in conjunction with the diary, with improved dietary assessment a potential outcome.
Copyright © 2013 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2013        PMID: 23415128     DOI: 10.1016/j.amepre.2012.11.007

Source DB:  PubMed          Journal:  Am J Prev Med        ISSN: 0749-3797            Impact factor:   5.043


  34 in total

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2.  "Are You Sure?": Lapses in Self-Reported Activities Among Healthy Older Adults Reporting Online.

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Review 3.  Use of technology when assessing adherence to diabetes self-management behaviors.

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4.  Physical and Lifestyle Factors Influencing Bone Density in Jockeys: A Comprehensive Update of the Bone Density Status of Irish Jockeys.

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Journal:  Int J Exerc Sci       Date:  2021-04-01

5.  Advances and Controversies in Diet and Physical Activity Measurement in Youth.

Authors:  Donna Spruijt-Metz; Cheng K Fred Wen; Brooke M Bell; Stephen Intille; Jeannie S Huang; Tom Baranowski
Journal:  Am J Prev Med       Date:  2018-08-19       Impact factor: 5.043

6.  Predicting Daily Activities From Egocentric Images Using Deep Learning.

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Journal:  Proc Int Symp Wearable Comput       Date:  2015-08

7.  NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions.

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Journal:  Proc ACM Interact Mob Wearable Ubiquitous Technol       Date:  2020-06

8.  Automatic food detection in egocentric images using artificial intelligence technology.

Authors:  Wenyan Jia; Yuecheng Li; Ruowei Qu; Thomas Baranowski; Lora E Burke; Hong Zhang; Yicheng Bai; Juliet M Mancino; Guizhi Xu; Zhi-Hong Mao; Mingui Sun
Journal:  Public Health Nutr       Date:  2018-03-26       Impact factor: 4.022

9.  A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing.

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10.  Accuracy of Wearable Cameras to Track Social Interactions in Stroke Survivors.

Authors:  Amar Dhand; Alexandra E Dalton; Douglas A Luke; Brian F Gage; Jin-Moo Lee
Journal:  J Stroke Cerebrovasc Dis       Date:  2016-09-09       Impact factor: 2.136

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