Engineering approaches to energy balance and obesity: opportunities for novel collaborations and research: report of a joint national science foundation and national institutes of health workshop

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health.

Enhanced gastrointestinal expression of cytosolic malic enzyme (ME1) induces intestinal and liver lipogenic gene expression and intestinal cell proliferation in mice

The small intestine participates in lipid digestion, metabolism and transport. Cytosolic malic enzyme 1 (ME1) is an enzyme that generates NADPH used in fatty acid and cholesterol biosynthesis. Previous work has correlated liver and adipose ME1 expression with susceptibility to obesity and diabetes; however, the contributions of intestine-expressed ME1 to these conditions are unknown. We generated transgenic (Tg) mice expressing rat ME1 in the gastrointestinal epithelium under the control of the murine villin1 promoter/enhancer. Levels of intestinal ME1 protein (endogenous plus transgene) were greater in Tg than wildtype (WT) littermates. Effects of elevated intestinal ME1 on body weight, circulating insulin, select adipocytokines, blood glucose, and metabolism-related genes were examined. Male Tg mice fed a high-fat (HF) diet gained significantly more body weight than WT male littermates and had heavier livers. ME1-Tg mice had deeper intestinal and colon crypts, a greater intestinal 5-bromodeoxyuridine labeling index, and increased expression of intestinal lipogenic (Fasn, Srebf1) and cholesterol biosynthetic (Hmgcsr, Hmgcs1), genes. The livers from HF diet-fed Tg mice also exhibited an induction of cholesterol and lipogenic pathway genes and altered measures (Irs1, Irs2, Prkce) of insulin sensitivity. Results indicate that gastrointestinal ME1 via its influence on intestinal epithelial proliferation, and lipogenic and cholesterologenic genes may concomitantly impact signaling in liver to modify this tissue’s metabolic state. Our work highlights a new mouse model to address the role of intestine-expressed ME1 in whole body metabolism, hepatomegaly, and crypt cell proliferation. Intestinal ME1 may thus constitute a therapeutic target to reduce obesity-associated pathologies.

Improving the recognition of eating gestures using intergesture sequential dependencies

This paper considers the problem of recognizing eating gestures by tracking wrist motion. Eating gestures are activities commonly undertaken during the consumption of a meal, such as sipping a drink of liquid or using utensils to cut food. Each of these gestures causes a pattern of wrist motion that can be tracked to automatically identify the activity. Previous works have studied this problem at the level of a single gesture. In this paper, we demonstrate that individual gestures have sequential dependence. To study this, three types of classifiers were built: 1) a K-nearest neighbor classifier which uses no sequential context, 2) a hidden Markov model (HMM) which captures the sequential context of subgesture motions, and 3) HMMs that model intergesture sequential dependencies. We built first-order to sixth-order HMMs to evaluate the usefulness of increasing amounts of sequential dependence to aid recognition. On a dataset of 25 meals, we found that the baseline accuracies for the KNN and the subgesture HMM classifiers were 75.8% and 84.3%, respectively. Using HMMs that model intergesture sequential dependencies, we were able to increase accuracy to up to 96.5%. These results demonstrate that sequential dependencies exist between eating gestures and that they can be exploited to improve recognition accuracy.

Detecting periods of eating during free-living by tracking wrist motion

This paper is motivated by the growing prevalence of obesity, a health problem affecting over 500 million people. Measurements of energy intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require a considerable manual effort, leading to underreporting of consumption, noncompliance, and discontinued use over the long term. The purpose of this paper is to describe a new method that uses a watch-like configuration of sensors to continuously track wrist motion throughout the day and automatically detect periods of eating. Our method uses the novel idea that meals tend to be preceded and succeeded by the periods of vigorous wrist motion. We describe an algorithm that segments and classifies such periods as eating or noneating activities. We also evaluate our method on a large dataset (43 subjects, 449 total h of data, containing 116 periods of eating) collected during free-living. Our results show an accuracy of 81% for detecting eating at 1-s resolution in comparison to manually marked event logs of periods eating. These results indicate that vigorous wrist motion is a useful indicator for identifying the boundaries of eating activities, and that our method should prove useful in the continued development of body-worn sensor tools for monitoring energy intake.

Body composition and military performance--many things to many people

Soldiers are expected to maintain the highest possible level of physical readiness because they must be ready to mobilize and perform their duties anywhere in the world at any time. The objective of Army body composition standards is to motivate physical training and good nutrition habits to ensure a high state of readiness. Establishment of enforceable and rational standards to support this objective has been challenging even at extremes of body size. Morbidly obese individuals are clearly not suited to military service, but very large muscular individuals may be superbly qualified for soldier performance demands. For this reason, large individuals are measured for body fat using a waist circumference-based equation (female soldiers are also measured for hip circumference). The main challenge comes in setting appropriate fat standards to support the full range of Army requirements. Military appearance ideals dictate the most stringent body fat standards, whereas health risk thresholds anchor the most liberal standards, and physical performance associations fall on a spectrum between these 2 poles. Standards should not exclude or penalize specialized performance capabilities such as endurance running or power lifting across a spectrum of body sizes and fat. The full integration of women into the military further complicates the issue because of sexually dimorphic characteristics that make gender-appropriate standards essential and where inappropriately stringent standards can compromise both health and performance of this segment of the force. Other associations with body composition such as stress effects on intraabdominal fat distribution patterns and metabolic implications of a fat reserve for survival in extreme environments are also relevant considerations. This is a review of the science that underpins the U.S. Army body composition standards.

Imaged based estimation of food volume using circular referents in dietary assessment

Measuring food volume (portion size) is a critical component in both clinical and research dietary studies. With the wide availability of cell phones and other camera-ready mobile devices, food pictures can be taken, stored or transmitted easily to form an image based dietary record. Although this record enables a more accurate dietary recall, a digital image of food usually cannot be used to estimate portion size directly due to the lack of information about the scale and orientation of the food within the image. The objective of this study is to investigate two novel approaches to provide the missing information, enabling food volume estimation from a single image. Both approaches are based on an elliptical reference pattern, such as the image of a circular pattern (e.g., circular plate) or a projected elliptical spotlight. Using this reference pattern and image processing techniques, the location and orientation of food objects and their volumes are calculated. Experiments were performed to validate our methods using a variety of objects, including regularly shaped objects and food samples.

Nanotechnology in elevation of the worldwide impact of obesity and obesity-related diseases: potential roles in human health and disease

Current worldwide data show epidemics of obesity and type 2 diabetes with no real solutions apart from continuous calls to changing lifestyle and food habits. Despite health messages that are communicated by health authorities, the epidemic is growing. More people are affected with health consequences that are usually frightening as more resources are wasted, especially in areas where health care and resources are lacking. Nanotechnology applications in food industry present practical approaches that help produce more tasty food with little calories, functional foods, and nutritional supplements and alter the fats and sugar contents of our foods with potential for many more applications. Consequently, this opens more windows to better control of many nutritional deficiencies as well as obesity and type 2 diabetes, especially among children and young adults who are addicted to fast food. With such potential, food producers, policy makers, health authorities, food scientists, and governments need to collaborate and make all possible efforts to fund and support research in different areas of food produced using nanotechnology. So far, consumers are not prepared to accept food produced using nanotechnology, mainly because information on the safety of such products are not enough. This issue needs to be addressed and researched well using suitable risk assessment methodologies. Consumers need to be assured, and involved as well, to avoid the "refusal state" that still exists against many safe products such as genetically modified organisms and irradiated food. There is the possibility that consumers could perceive that they will bear the potential risks posed by nanotechnology applications while the benefits will accrue mainly to others, such as food processors or farmers.

Recognizing physical activity from ego-motion of a camera

A new image based activity recognition method for a person wearing a video camera below the neck is presented in this paper. The wearable device is used to capture video data in front of the wearer. Although the wearer never appears in the video, his or her physical activity is analyzed and recognized using the recorded scene changes resulting from the motion of the wearer. Correspondence features are extracted from adjacent frames and inaccurate matches are removed based on a set of constraints imposed by the camera model. Motion histograms are defined and calculated within a frame and we define a new feature called accumulated motion distribution derived from motion statistics in each frame. A Support Vector Machine (SVM) classifier is trained with this feature and used to classify physical activities in different scenes. Our results show that different types of activities can be recognized in low resolution, field acquired real-world video.

Smart phones are useful for food intake and physical activity surveys

Current self-report methods of recording food intake and Physical Activity (PA) are cumbersome and inaccurate. Food and activity surveys implemented on a smart phone will allow for immediate entry, data transfer to a researcher, and feedback to the user. Ten subjects followed a script, representative of one day, to enter food intake and PA on a smart phone. In the follow-up report, all subjects were interested in using the tested program to compare food intake with PA to predict weight gain and loss.

BALANCE (Bioengineering Approaches for Lifestyle Activity and Nutrition Continuous Engagement): developing new technology for monitoring energy balance in real time

Methods that measure energy balance accurately in real time represent promising avenues to address the obesity epidemic. We developed an electronic food diary on a mobile phone that includes an energy balance visualization and computes and displays the difference between energy intake from food entries and energy expenditure from a multiple-sensor device that provides objective estimates of energy expenditure in real time. A geographic information system dataset containing locations associated with activity and eating episodes is integrated with an ArcPad mapping application on the phone to provide users with a visual display of food sources and locations associated with physical activity within their proximal environment. This innovative tool captures peoples' movement through space and time under free-living conditions and could potentially have many health-related applications in the future.

Data upload capability of 3G mobile phones

Mobile phones are becoming an important platform to measure free-living energy balance and to support weight management therapies. Sensor data, camera images and user input are needed by clinicians and researchers in close to real time. We assessed upload (reverse link) data transport rates for 2007-2008 model mobile phones on two major US wireless systems. Even the slowest phone (EVDO Rev 0) reliably uploaded 40 MB of data in less than 1 h. More than 95% of file uploads were successful in tests that simulated normal phone use over 3 d. Practical bandwidth and data currency from typical smart phones will likely keep pace with the data needs of energy balance studies and weight management therapy.