%0 Journal Article
%@ 2291-5222
%I JMIR Publications
%V 8
%N 7
%P e16405
%T Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study
%A Dimitratos,Sarah M
%A German,J Bruce
%A Schaefer,Sara E
%+ Foods for Health Institute, University of California, 2141 Robert Mondavi Institute, North Building, 1 Shields Ave, Davis, CA, 95616, United States, 1 530 574 0797, seschaefer@ucdavis.edu
%K wearable technology
%K mobile health
%K mobile phone
%K food intake
%K validation study
%D 2020
%7 22.7.2020
%9 Original Paper
%J JMIR Mhealth Uhealth
%G English
%X Background: Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice, but few are reliable for obtaining accurate and precise measurements of diet and nutrition. Objective: This study aimed to assess the ability of wearable technology to monitor the nutritional intake of adult participants. This paper describes the development of a reference method to validate the wristband’s estimation of daily nutritional intake of 25 free-living study participants and to evaluate the accuracy (kcal/day) and practical utility of the technology. Methods: Participants were asked to use a nutrition tracking wristband and an accompanying mobile app consistently for two 14-day test periods. A reference method was developed to validate the estimation of daily nutritional intake of participants by the wristband. The research team collaborated with a university dining facility to prepare and serve calibrated study meals and record the energy and macronutrient intake of each participant. A continuous glucose monitoring system was used to measure adherence with dietary reporting protocols, but these findings are not reported. Bland-Altman tests were used to compare the reference and test method outputs (kcal/day). Results: A total of 304 input cases were collected of daily dietary intake of participants (kcal/day) measured by both reference and test methods. The Bland-Altman analysis had a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement between −1400 and 1189. The regression equation of the plot was Y=−0.3401X+1963, which was significant (P<.001), indicating a tendency for the wristband to overestimate for lower calorie intake and underestimate for higher intake. Researchers observed transient signal loss from the sensor technology of the wristband to be a major source of error in computing dietary intake among participants. Conclusions: This study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake, highlighting the need for reliable, effective measurement tools to facilitate accurate, precision-based technologies for personal dietary guidance and intervention. 
%M 32706729
%R 10.2196/16405
%U https://mhealth.jmir.org/2020/7/e16405
%U https://doi.org/10.2196/16405
%U http://www.ncbi.nlm.nih.gov/pubmed/32706729