Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.

OBJECTIVES: Dietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard. SUBJECTS/METHODS: Thirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences. RESULTS: Sensor-derived EI from regression modeling (R2 = 0.331) showed the closest agreement with EI from WFR, followed by diet diary estimates. EI from image analysis differed significantly from that by WFR. Bland-Altman analysis showed wide limits of agreement for all three test methods with WFR, with the sensor method overestimating at lower and underestimating at higher EI. Nutritionist error in portion size estimation and irreconcilable differences in portion size between food and nutrient databases used for WFR and image analyses were the greatest contributors to image analysis and WFR differences (44.4% and 44.8% of WFR EI, respectively). CONCLUSIONS: Estimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

Feasibility of the automatic ingestion monitor (AIM-2) for infant feeding assessment: a pilot study among breast-feeding mothers from Ghana.

OBJECTIVE: Passive, wearable sensors can be used to obtain objective information in infant feeding, but their use has not been tested. Our objective was to compare assessment of infant feeding (frequency, duration and cues) by self-report and that of the Automatic Ingestion Monitor-2 (AIM-2). DESIGN: A cross-sectional pilot study was conducted in Ghana. Mothers wore the AIM-2 on eyeglasses for 1 d during waking hours to assess infant feeding using images automatically captured by the device every 15 s. Feasibility was assessed using compliance with wearing the device. Infant feeding practices collected by the AIM-2 images were annotated by a trained evaluator and compared with maternal self-report via interviewer-administered questionnaire. SETTING: Rural and urban communities in Ghana. PARTICIPANTS: Participants were thirty eight (eighteen rural and twenty urban) breast-feeding mothers of infants (child age ≤7 months). RESULTS: Twenty-five mothers reported exclusive breast-feeding, which was common among those < 30 years of age (n 15, 60 %) and those residing in urban communities (n 14, 70 %). Compliance with wearing the AIM-2 was high (83 % of wake-time), suggesting low user burden. Maternal report differed from the AIM-2 data, such that mothers reported higher mean breast-feeding frequency (eleven v. eight times, P = 0·041) and duration (18·5 v. 10 min, P = 0·007) during waking hours. CONCLUSION: The AIM-2 was a feasible tool for the assessment of infant feeding among mothers in Ghana as a passive, objective method and identified overestimation of self-reported breast-feeding frequency and duration. Future studies using the AIM-2 are warranted to determine validity on a larger scale.

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.

Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.

Egg Intake Has No Adverse Association With Blood Lipids Or Glucose In Adolescent Girls.

OBJECTIVE: Longitudinal data on cardiometabolic effects of egg intake during adolescence are lacking. The current analyses aim to evaluate the impact of usual adolescent egg consumption on lipid levels, fasting glucose, and insulin resistance during late adolescence (age 17-20 years). METHODS: Data from 1392 girls, aged 9 to 10 at baseline and followed for 10 years, in the National Heart, Lung, and Blood Institute's National Growth and Health Study were used to examine the association between usual egg intake alone and in combination with other healthy lifestyle factors and late adolescent lipid levels, fasting glucose, and insulin resistance, measured as homeostasis model assessment of insulin resistance (HOMA-IR). Diet was assessed using 3-day food records during eight examination cycles. Girls were classified according to usual weekly egg intake, ages 9-17 years: <1 egg/wk (n = 361), 1 to <3 eggs/wk (n = 703), and ≥3 eggs/wk (n = 328). Analysis of covariance modeling was used to control for confounding by other behavioral and biological risk factors. RESULTS: Girls with low, moderate, and high egg intakes had adjusted low-density lipoprotein cholesterol levels of 99.7, 98.8, and 95.5 mg/dL, respectively (p = 0.0778). In combination with higher intakes of fiber, dairy, or fruits and vegetables, these beneficial effects were stronger and statistically significant. There was no evidence that ≥3 eggs/wk had an adverse effect on lipids, glucose, or HOMA-IR. More active girls who consumed ≥3 eggs/wk had the lowest levels of insulin resistance. CONCLUSION: These results suggest that eggs may be included as part of a healthy adolescent diet without adverse effects on glucose, lipid levels, or insulin resistance.

A blended- rather than whole-lentil meal with or without α-galactosidase mildly increases healthy adults' appetite but not their glycemic response.

BACKGROUND: Disrupting the physical structure of pulses by blending them or by using a digestive supplement (α-galactosidase) to reduce intestinal discomfort could potentially negate the previously observed beneficial effects of whole pulses of lowering appetitive and glycemic responses because of more rapid digestion. OBJECTIVE: We hypothesized that blended lentils, α-galactosidase, or both increase postprandial appetite and blood glucose responses vs. whole lentils. METHODS: Men and women [n = 12; means ± SDs body mass index (kg/m(2)): 23.3 ± 3.1; aged 28 ± 10 y] consumed breakfast meals containing whole (W), blended (B), or no lentils [control (C)], each with 3 α-galactosidase or placebo capsules in a randomized, crossover, double-blind placebo-controlled trial. Between each test day there was a 3- to 5-d washout period. RESULTS: Mixed-model ANOVA showed effects of meal on postprandial appetite and glucose (P = 0.0001-0.031). The B meal resulted in higher postprandial appetite ratings than did the W meal but not the C meal for hunger, desire to eat, and prospective consumption (Δ = 0.4-0.5 points; P = 0.002-0.044). Postprandial glucose concentration was 4.5 mg/dL lower for the B meal than for the C meal (P < 0.0001) but did not differ from the W meal. There were no main effects of α-galactosidase, but there were meal × α-galactosidase interaction effects, with a greater postprandial desire to eat and lower postprandial fullness with the B meal than with the 2 other meals in the placebo condition but not in the α-galactosidase condition. CONCLUSIONS: Blending lentils increased appetite (∼6%), but not glycemic response, compared with whole lentils, whereas α-galactosidase did not. Both B and W meals may be consumed (with or without an α-galactosidase supplement) with little impact on appetite, without increasing glycemic response. This trial was registered at clinicaltrials.gov as NCT02110511.

Integrated image and sensor-based food intake detection in free-living.

The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2). Thirty participants wore the AIM-2 for two days each (pseudo-free-living and free-living). The eating episodes were detected by three methods: (1) recognition of solid foods and beverages in images captured by AIM-2; (2) recognition of chewing from the AIM-2 accelerometer sensor; and (3) hierarchical classification to combine confidence scores from image and accelerometer classifiers. The integration of image- and sensor-based methods achieved 94.59% sensitivity, 70.47% precision, and 80.77% F1-score in the free-living environment, which is significantly better than either of the original methods (8% higher sensitivity). The proposed method successfully reduces the number of false positives in the detection of eating episodes.

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring.

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.

Objective assessment of shared plate eating using a wearable camera in urban and rural households in Ghana.

Background: Shared plate eating (SPE), defined as two or more individuals eating directly from the same plate or bowl, is a common household food consumption practice in many Low- and Middle-Income Countries (LMICs). Examination of household engagement in SPE remains largely unexplored, highlighting a gap in research when interpreting dietary information obtained from these settings. The dearth of research into SPE can be attributed to the inherent limitations of traditional dietary assessment methods which constrain their usability in settings where SPE is common. Objective: In this expository narrative, we describe what SPE is when it is practiced in an LMIC such as Ghana; and also compare the frequency of SPE versus individual plate eating (IPE) by different household members in rural and urban households using a wearable camera (Automatic Ingestion Monitor version 2: AIM-2). Methods: Purposive convenience sampling was employed to recruit and enroll 30 households each from an urban and a rural community (n = 60 households) in Ghana. The AIM-2 was worn on eyeglass frames for 3 days by selected household members. The AIM-2, when worn, automatically collects images to capture food consumption in participants' environments, thus enabling passive capture of household SPE dynamics. Results: A higher percentage of SPE occasions was observed for rural (96.7%) compared to urban (36.7%) households (p < 0.001). Common SPE dynamics included only adults sharing, adults and children sharing, only children sharing, and non-household member participation in SPE. Conclusion: The wearable camera captured eating dynamics within households that would have likely been missed or altered by traditional dietary assessment methods. Obtaining reliable and accurate data is crucial for assessing dietary intake in settings where SPE is a norm.

The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device.

Introduction: The aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options. Methods: Data from 25 participants and 116 total days (7 men, 18 women, Mage = 44 ± 12 years, BMI 34.3 ± 5.2 kg/mm2), who wore the passive capture device for at least 7 consecutive days (≥12h waking hours/d) were analyzed. Data were analyzed at the participant level and stratified amongst meal type into breakfast, lunch, dinner, and snack categories. Out of 116 days, 68.1% included breakfast, 71.5% included lunch, 82.8% included dinner, and 86.2% included at least one snack. Results: The most prevalent eating environment among all eating occasions was at home and with one or more screens in use (breakfast: 48.1%, lunch: 42.2%, dinner: 50%, and snacks: 55%), eating alone (breakfast: 75.9%, lunch: 89.2%, dinner: 74.3%, snacks: 74.3%), in the dining room (breakfast: 36.7%, lunch: 30.1%, dinner: 45.8%) or living room (snacks: 28.0%), and in multiple locations (breakfast: 44.3%, lunch: 28.8%, dinner: 44.8%, snacks: 41.3%). Discussion: Results suggest a passive capture device can provide accurate detection of food intake in multiple eating environments. To our knowledge, this is the first study to classify eating occasions in multiple eating environments and may be a useful tool for future behavioral research studies to accurately codify eating environments.

I2N: image to nutrients, a sensor guided semi-automated tool for annotation of images for nutrition analysis of eating episodes.

Introduction: Dietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user. Methods: This paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data). Results: In both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images. Discussion: I2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.

Evaluation of Acceptability, Functionality, and Validity of a Passive Image-Based Dietary Intake Assessment Method in Adults and Children of Ghanaian and Kenyan Origin Living in London, UK.

BACKGROUND: Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs. METHODS: Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records. RESULTS: The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. CONCLUSIONS: Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.

Dietary patterns are associated with dietary recommendations but have limited relationship to BMI in the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort.

OBJECTIVE: Traditional food systems in indigenous groups have historically had health-promoting benefits. The objectives of the present study were to determine if a traditional dietary pattern of Pacific Northwest Tribal Nations (PNwT) could be derived using reduced rank regression and if the pattern would be associated with lower BMI and current Dietary Reference Intakes. DESIGN: The baseline data from the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort were used to derive dietary patterns for the total sample and those with plausibly reported energy intakes. SETTING: Pacific Northwest Coast of Washington State, USA. SUBJECTS: Adult PNwT members of the CoASTAL cohort with laboratory-measured weight and height and up to 4 d of dietary records (n 418). RESULTS: A traditional dietary pattern did not evolve from the analysis. Moderate consumption of a sweet drinks dietary pattern was associated with lower BMI while higher consumption of a vegetarian-based dietary pattern was associated with higher BMI. The highest consumers of the vegetarian-based dietary pattern were almost six times more likely to meet the recommendations for dietary fibre. CONCLUSIONS: Distinct dietary patterns were found. Further exploration is needed to confirm whether the lack of finding a traditional pattern is due to methodology or the loss of a traditional dietary pattern among this population. Longitudinal assessment of the CoASTAL cohort's dietary patterns needs to continue.

Body mass index and variability in meal duration and association with rate of eating.

Background: A fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day. Methods: Using an observational cross-sectional study design, non-smoking participants aged 18-45 years (N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating. Results: Participants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex (p = 0.03). Faster rate of eating was associated with higher BMI across all meals (p = 0.04) and higher energy intake for all meals (p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner (p = 0.96). Conclusion: Shorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors.

Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices.

Objective: To describe best practices for manual nutritional analyses of data from passive capture wearable devices in free-living conditions. Method: 18 participants (10 female) with a mean age of 45 ± 10 years and mean BMI of 34.2 ± 4.6 kg/m2 consumed usual diet for 3 days in a free-living environment while wearing an automated passive capture device. This wearable device facilitates capture of images without manual input from the user. Data from the first nine participants were used by two trained nutritionists to identify sources contributing to inter-nutritionist variance in nutritional analyses. The nutritionists implemented best practices to mitigate these sources of variance in the next nine participants. The three best practices to reduce variance in analysis of energy intake (EI) estimation were: (1) a priori standardized food selection, (2) standardized nutrient database selection, and (3) increased number of images captured around eating episodes. Results: Inter-rater repeatability for EI, using intraclass correlation coefficient (ICC), improved by 0.39 from pre-best practices to post-best practices (0.14 vs 0.85, 95% CI, respectively), Bland-Altman analysis indicated strongly improved agreement between nutritionists for limits of agreement (LOA) post-best practices. Conclusion: Significant improvement of ICC and LOA for estimation of EI following implementation of best practices demonstrates that these practices improve the reproducibility of dietary analysis from passive capture device images in free-living environments.

Nutritional quality of calorie restricted diets in the CALERIE™ 1 trial.

OBJECTIVES: The aim was to determine the nutritional adequacy of calorie restricted (CR) diets during CR interventions up to 12 months. METHODS: The Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE™) phase 1 trial consisted of 3 single-site studies to test the feasibility and effectiveness of CR in adults without obesity. After baseline assessments, participants who were randomized to a CR intervention received education and training from registered dietitians on how to follow a healthful CR diet. Food diaries were completed at baseline and during the CR interventions (~6, 9, and 12 months) when participants were self-selecting CR diets. Diaries were analyzed for energy, macronutrients, fiber, 11 vitamins, and 9 minerals. Nutritional adequacy was defined by sex- and age-specific Estimated Average Requirement (EAR) or Adequate Intake (AI) criteria for each nutrient. Diet quality was evaluated using the PANDiet diet quality index. RESULTS: Eighty-eight CR participants (67% women, age 40 ± 9 y, BMI 27.7 ± 1.5 kg/m2) were included in the analysis. Dietary intake of fiber and most vitamins and minerals increased during CR. More than 90% of participants achieved 100% of EAR or AI during CR for 2 of 4 macronutrients (carbohydrate and protein), 6 of 11 vitamins (A, B1, B2, B3, B6, B12), and 6 of 9 minerals assessed (copper, iron, phosphorus, selenium, sodium, zinc). Nutrients for which <90% of participants achieved adequacy included fiber, omega-3 fatty acids, vitamins B5, B9, C, E, and K, and the minerals calcium, magnesium, and potassium. The PANDiet diet quality index improved from 72.9 ± 6.0% at baseline to 75.7 ± 5.2% during CR (p < 0.0001). CONCLUSION: Long-term, calorie-restricted diets were nutritionally equal or superior to baseline ad libitum diets among adults without obesity. Our results support modest calorie restriction as a safe strategy to promote healthy aging without compromising nutritional adequacy or diet quality.

Effects of eating frequency, snacking, and breakfast skipping on energy regulation: symposium overview.

The ASN hosted a symposium entitled "Eating Patterns and Energy Balance: A Look at Eating Frequency, Snacking, and Breakfast Omission" at the Experimental Biology 2009 annual meeting on April 19, 2009, in New Orleans, LA. The symposium was chaired by Megan McCrory and co-chaired by Wayne Campbell, both from Purdue University. The goal of the symposium was to bring together experts to provide an overview of research on the potential role of eating patterns in the development of overweight and obesity. Studies on eating frequency, snacking, and breakfast skipping were highlighted. In particular, evidence both for and against their roles were discussed, methodological issues that underlie controversies were addressed, and suggested future directions for research were outlined. Appetite regulation and hormonal effects were also reviewed. Megan McCrory introduced the session then discussed studies on eating frequency and energy regulation in free-living adults consuming self-selected diets. Heather Leidy summarized the state of the research on eating frequency and energy regulation in adults from controlled feedings studies. Didier Chapelot discussed various usages of "snack" and argued for a physiological basis to distinguish snacks from meals. Mark Pereira presented information on the effects of breakfast skipping and the macronutrient composition of breakfast in energy regulation and mood. A panel discussion/question and answer session ended the symposium. The symposium was videotaped and can be viewed at www.nutrition.org.

Eating frequency and energy regulation in free-living adults consuming self-selected diets.

The relative importance of eating frequency to weight control is poorly understood. This review examines the evidence to date on the role of eating frequency in weight control in free-living adults. The majority of cross-sectional studies in free-living adults show an inverse relationship between eating frequency and adiposity; however, this is likely an artifact produced by the underreporting of eating frequency concurrent with underreporting of energy intake. When implausible energy intake reporting (which is mostly underreporting) is taken into account, the association between eating frequency and adiposity becomes positive. In studies in which eating frequency is prescribed and food intake is mostly self-selected, there is either no effect or a minor positive effect of eating frequency on energy intake. Most of those studies have been short-term and lack the necessary dietary biomarkers to validate reported energy intakes and eating frequencies. In conclusion, there is some suggestion from cross-sectional studies in which energy intake underreporting is taken into account and from experimental studies to date that greater eating frequency may promote positive energy balance. However, experimental studies of longer-term duration that include objective dietary biomarkers are necessary before firm conclusions about the relative importance of eating frequency in weight control can be made.

Accuracy of stated energy contents of restaurant foods.

CONTEXT: National recommendations for the prevention and treatment of obesity emphasize reducing energy intake. Foods purchased in restaurants provide approximately 35% of the daily energy intake in US individuals but the accuracy of the energy contents listed for these foods is unknown. OBJECTIVE: To examine the accuracy of stated energy contents of foods purchased in restaurants. DESIGN AND SETTING: A validated bomb calorimetry technique was used to measure dietary energy in food from 42 restaurants, comprising 269 total food items and 242 unique foods. The restaurants and foods were randomly selected from quick-serve and sit-down restaurants in Massachusetts, Arkansas, and Indiana between January and June 2010. MAIN OUTCOME MEASURE: The difference between restaurant-stated and laboratory-measured energy contents, which were corrected for standard metabolizable energy conversion factors. RESULTS: The absolute stated energy contents were not significantly different from the absolute measured energy contents overall (difference of 10 kcal/portion; 95% confidence interval [CI], -15 to 34 kcal/portion; P = .52); however, the stated energy contents of individual foods were variable relative to the measured energy contents. Of the 269 food items, 50 (19%) contained measured energy contents of at least 100 kcal/portion more than the stated energy contents. Of the 10% of foods with the highest excess energy in the initial sampling, 13 of 17 were available for a second sampling. In the first analysis, these foods contained average measured energy contents of 289 kcal/portion (95% CI, 186 to 392 kcal/portion) more than the stated energy contents; in the second analysis, these foods contained average measured energy contents of 258 kcal/portion (95% CI, 154 to 361 kcal/portion) more than the stated energy contents (P <.001 for each vs 0 kcal/portion difference). In addition, foods with lower stated energy contents contained higher measured energy contents than stated, while foods with higher stated energy contents contained lower measured energy contents (P <.001). CONCLUSIONS: Stated energy contents of restaurant foods were accurate overall. However, there was substantial inaccuracy for some individual foods, with understated energy contents for those with lower energy contents.

A Church-based Culturally Sensitive Physical Activity Intervention in African American Women.

The purpose of this pilot study was to test a church-based, culturally sensitive, six-week intervention called GET FIT DON'T QUIT. The intervention aimed to increase knowledge and change beliefs about physical activity, and to improve social facilitation to increase self-regulation, in order to promote physical activity in African-American women. A two-group pretest/posttest, quasi-experimental design was conducted in a convenience sample (N = 37) of African-American women. Participants were randomly assigned to the intervention or control group by church affiliation. The six-week intervention consisted of teaching and roundtable discussions, and email reminders to be physically active. There were significant differences (p < .05) in the level of self-efficacy, self-regulation, and friend social support. There were no significant differences in knowledge of physical activity guidelines, beliefs, and family social support. These pilot study results suggested that multiple factors are associated with physical activity engagement in African-American women.

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.