Self-monitoring has been described as the """"""""cornerstone of the behavioral treatment of obesity"""""""" . Consistent self-monitoring of energy intake is associated with improved dietary adherence and weight loss and maintenance. The problem is that individuals are notoriously bad at self-monitoring their intake and suffer from an underreporting bias. The significance of the proposed work is that it will make a low-cost intake monitoring device widely available for use in behavioral weight loss treatment and research. Our innovative intake monitoring method uses sensors embedded into a watch-like device to automatically track wrist motion to count bites and provide intake feedback during a meal, allowing individuals to self-monitor intake anywhere and anytime. In Phase 1, the method was shown to accurately count bites across a wide variety of foods, utensils and subject demographics, and to provide an unbiased intake measurement. Additional studies show people will use the device long-term and prefer our method over a manual method. The proposed work will continue to improve the bite counting method by adapting to varying eating rates, develop a self-managed bite count based weight loss protocol, and perform an independent test of the protocol.
The significance of the proposed work is that it will make a low-cost intake monitoring device widely available for use in weight loss treatment and research. Dietary self-monitoring is critical to weight loss. However, automatic dietary self-monitoring tool are lacking. Thus individuals trying to lose weight are left to manually record what they eat, which results in transient dietary self-monitoring and ultimately failed weight loss. Our innovativ technology automates self-monitoring, providing objective feedback and promoting long-term use.
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|Yiru Shen; Salley, James; Muth, Eric et al. (2017) Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites Across Demographic and Food Variables. IEEE J Biomed Health Inform 21:599-606|
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