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Grant Details

Grant Number: 1R21CA172864-01A1 Interpret this number
Primary Investigator: Baranowski, Tom
Organization: Baylor College Of Medicine
Project Title: Minimizing Memory Errors in Child Diet Assessment
Fiscal Year: 2013
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Abstract

DESCRIPTION (provided by applicant): Sun ASA24-Kids Accurate assessment of dietary intake is important for 1) evaluating the outcome of dietary change interventions, 2) assessing the relationship between diet and health outcomes (e.g., obesity, metabolic syndrome), 3) identifying factors (e.g. environmental, psychosocial) influencing dietary intake which could be targeted in change programs, and 4) assessing diet changes over time (called surveillance). Our research revealed 35% of the foods children reported consuming the previous day were imaginary (called intrusions: observers did not see those foods consumed) and 15% were forgotten (called omissions: foods observers recorded, but were not reported by the children). This level of error needs to be minimized to enhance children's dietary intake assessment accuracy. Pictures of actual meals served, and after consumption, should minimize intrusions and omissions. Problems with the smart phone method of taking meal pictures for children, however, are it requires remembering to carry and use the smart phone correctly at the right time, even in social circumstances (e.g., with certain friends) when child reticence, even embarrassment, may minimize some children's willingness to draw that kind of attention to themselves. Dr. Mingui Sun (U Pittsburgh) has developed a multisensory unit, the e.button, attached to the shirt, which includes a camera, battery and storage to record pictures of everything in front of a child at 2 to 10 second intervals throughout the day. The Sun System requires a trained research assistant to identify the foods, and use of an on screen malleable wire mesh feature to estimate amounts consumed (taking about 10 min/day of images), which are in turn verified by a nutrition/dietetics professional (taking about five min/day of images). Limits of this system are that many foods are hard to recognize in a photo (e.g., poor lighting, odd angle, unusual food, unusual preparation or presentation of the food). Similar problems will occur with an automated system using pattern recognition to identify the foods. The child who consumed the foods, alternatively, should be able to recognize an image of the foods without much difficulty. The ASA24-Kids, adapted to children's abilities from the Automated Self-Administered 24 hour recall (ASA24) for adults, enables children to self-report what they consumed the previous day. Combining the Sun System with ASA24-Kids may permit recording children's dietary intake that minimizes the limitations of child's 24 hour memory by providing images of the foods consumed, yet allows child review of foods in the images. While the feasibility of using an automated image capture method for dietary assessment has been established among adults, parallel work has not been done among children. As a result, we propose creating Sun-ASA24-Kids, adapting it to child abilities, and validating it in a field study. The Sun-ASA24-Kids system could minimize two major sources of child reporting error, and unclear picture errors.

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Publications

Saliency-aware food image segmentation for personal dietary assessment using a wearable computer.
Authors: Chen HC, Jia W, Sun X, Li Z, Li Y, Fernstrom JD, Burke LE, Baranowski T, Sun M
Source: Meas Sci Technol, 2015 Feb;26(2), p. null.
PMID: 26257473
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A FPGA Implementation of JPEG Baseline Encoder for Wearable Devices.
Authors: Li Y, Jia W, Luan B, Mao ZH, Zhang H, Sun M
Source: Proc IEEE Annu Northeast Bioeng Conf, 2015 Apr;2015, p. null.
PMID: 26190911
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Assessing Physical Performance in Free-Living Older Adults with a Wearable Computer.
Authors: Zhao Q, Wang J, Feng W, Jia W, Burke LE, Zgibor JC, Sun M
Source: Proc IEEE Annu Northeast Bioeng Conf, 2015 Apr;2015, p. null.
PMID: 26190910
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SIFT-Based Indoor Localization for Older Adults Using Wearable Camera.
Authors: Zhang B, Zhao Q, Feng W, Sun M, Jia W
Source: Proc IEEE Annu Northeast Bioeng Conf, 2015 Apr;2015, p. null.
PMID: 26190909
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A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation.
Authors: Li Y, Jia W, Yu T, Luan B, Mao ZH, Zhang H, Sun M
Source: Proc IEEE Annu Northeast Bioeng Conf, 2015 Apr;2015, p. null.
PMID: 26185409
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Cross-trees, Edge and Superpixel Priors-based Cost aggregation for Stereo matching.
Authors: Cheng F, Zhang H, Sun M, Yuan D
Source: Pattern Recognit, 2015 Jul 1;48(7), p. 2269-2278.
PMID: 26034314
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Are active video games useful to combat obesity?
Authors: Baranowski T
Source: Am J Clin Nutr, 2015 Jun;101(6), p. 1107-8.
EPub date: 2015 Mar 25.
PMID: 25809852
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An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.
Authors: Li Z, Wei Z, Yue Y, Wang H, Jia W, Burke LE, Baranowski T, Sun M
Source: J Med Syst, 2015 May;39(5), p. 57.
EPub date: 2015 Mar 19.
PMID: 25787786
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An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle.
Authors: Sun M, Burke LE, Baranowski T, Fernstrom JD, Zhang H, Chen HC, Bai Y, Li Y, Li C, Yue Y, Li Z, Nie J, Sclabassi RJ, Mao ZH, Jia W
Source: J Healthc Eng, 2015;6(1), p. 1-22.
PMID: 25708374
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A Novel Binocular Vision System for Wearable Devices.
Authors: Zhai H, Li H, Bai Y, Jia W, Sun M
Source: Proc IEEE Annu Northeast Bioeng Conf, 2014 Apr 25;2014, p. 1-3.
PMID: 25525322
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eButton: A Wearable Computer for Health Monitoring and Personal Assistance.
Authors: Sun M, Burke LE, Mao ZH, Chen Y, Chen HC, Bai Y, Li Y, Li C, Jia W
Source: Proc Des Autom Conf, 2014;2014, p. 1-6.
PMID: 25340176
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How to engage children in self-administered dietary assessment programmes.
Authors: Lu AS, Baranowski J, Islam N, Baranowski T
Source: J Hum Nutr Diet, 2014 Jan;27 Suppl 1, p. 5-9.
EPub date: 2012 May 18.
PMID: 22594618
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