JMIR Publications

ABSTRACT

Background:

Conventional diet assessment approaches such as the 24-hour self-reported recall are burdensome, suffer from recall-bias, and are inaccurate in estimating energy intake. Wearable sensor technology, coupled with advanced algorithms, is increasingly showing promise in its ability to capture behaviors that provide useful information for estimating calorie and macronutrient intake.

Objective:

The aim of this paper is to summarize current technological approaches to monitoring energy intake based on expert opinion from a workshop panel and to make recommendations to advance technology and algorithms to improve estimation of energy expenditure.

Methods:

A one-day invitational workshop sponsored by the National Science Foundation was held at Northwestern University. Forty participants, including population health researchers, engineers, and intervention developers, from six universities and the National Institutes of Health participated in a panel discussing the state of evidence with regard to monitoring calorie intake and eating behaviors.

Results:

Caloric monitoring using technological approaches can be characterized into three domains: (1) image-based sensing (eg, wearable and smartphone-based cameras combined with machine-learning algorithms); (2) eating action unit (EAU) sensors (eg, to measure feeding gesture, chewing rate); and (3) biochemical measures (eg, serum/plasma metabolite concentrations). We discuss how each domain functions, provide examples of promising solutions, and highlight potential challenges and opportunities in each domain. Image-based sensor research requires improved “ground truth” (context and known information about the foods), accurate food image segmentation and recognition algorithms, and reliable methods of estimating portion size. EAU-based domain research is limited by understanding of when their systems (device and inference algorithm) succeed and fail, need for privacy-protecting methods of capturing “ground truth,” and uncertainty in food categorization. Although an exciting novel technology, biochemical sensing challenges range from lack of adaptability to environmental effects (eg, temperature change) and mechanical impact, instability of wearable sensor performance over time, and single-use design.

Conclusions:

Conventional approaches to caloric monitoring rely predominantly on self-report. These approaches can gain contextual information from image-based and EAU-based domains that can map automatically captured food images to a food database and detect proxies that correlate with food volume and caloric intake. While the continued development of advanced machine-learning techniques will advance the accuracy of such wearables, biochemical sensing provides electrochemical analysis of sweat using soft bioelectronics on human skin, enabling noninvasive measures of chemical compounds that provide insight into the digestive and endocrine systems of the body. Future computing-based researchers should focus on reducing the burden of wearable sensors, aligning data across multiple devices, automating methods of data annotation, increasing rigor in studying system acceptability, increasing battery lifetime, and rigorously testing validity of the measure. Such research requires moving promising technological solutions from the controlled laboratory setting to the field.