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BackgroundShared 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.ObjectiveIn 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).MethodsPurposive 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.ResultsA higher percentage of SPE occasions was observed for rural (96.7%) compared to urban (36.7%) households (p

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.

Aims: The objective of the study was to compare screening performances of HbA1c, fasting plasma glucose (FPG), and two-hour plasma glucose (2hPG) in heart failure (HF) patients. Methods: We included 237 HF patients aged >20 years without history of diabetes, using National Health and Nutrition Examination Survey data (2005–2016). American Diabetes Association diabetes screening criteria were used: (1) HbA1c ≥6.5%, (2) FPG ≥126 mg/dL, and (3) 2hPG ≥200 mg/dL. Sensitivity, specificity, predictive values, and Receiver Operating Characteristic (ROC) curves for HbA1c and FPG were examined against reference methods. Results: N = 50 patients (20.5%) met at least 1 of 3 clinical criteria for diabetes. 2hPG alone identified 70.5% of patients, whereas HbA1c alone identified only 27.0% of patients. Sensitivity and specificity using a HbA1c cutoff at ≥6.5% were 24.4% and 97.6%, respectively. The Youden's J statistic for HbA1c was maximized at 6.1%. The area under the ROC curve of HbA1c against 2hPG was significantly lower compared to FPG (0.79, 95% CI 0.70-0.88; 0.89, 95% CI 0.84-0.94, respectively; p = 0.04). Conclusions: Blood glucose criteria are more sensitive than HbA1c when screening HF patients for diabetes. Future studies should test performance of a HbA1c cutoff at 6.1% when FPG or 2hPG cannot be completed.

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.

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.

The authors analyzed data from a trial assessing the efficacy of breastfeeding peer counseling (PC) for increasing exclusive breastfeeding (EBF) to (1) examine whether different ethnic groups responded differently to the intervention and (2) document the determinants of EBF. At 2 months postpartum, the prevalence of EBF in the intervention group was 11.4% among Puerto Ricans compared to 44.4% among non-Puerto Ricans (P = .008). Multivariate logistic regression analyses showed that women who had the intention prenatally to engage in EBF were more likely to do so and those whose mothers lived in the United States were less likely to engage in EBF at hospital discharge. At 2 months postpartum, mothers who were breastfed as children were more likely to engage in EBF, whereas non-Puerto Ricans had a significantly greater response to the intervention than Puerto Ricans (odds ratio, 6.40; 95% confidence interval, 1.45-28.33). There is a need for further studies to determine why different ethnic groups respond differently to EBF promotion interventions. [ABSTRACT FROM AUTHOR]

To examine reasons for not breastfeeding, and to identify factors associated with ever breastfeeding among Puerto Rican women, a retrospective study was done using a convenience sample of 161 low-income Latino women with children younger than 6 years. Women were recruited from the Hispanic Health Council (43.5%), the Supplemental Nutrition Program for Women, Infants, and Children (29.8%), and other places (26.7%). Participants were interviewed using a pretested questionnaire specific to the target community. Most (73%) of the respondents chose to be interviewed in Spanish. Chi-square analyses were used to examine the bivariate association between ever breastfeeding and the independent variables. Multivariate logistic regression was conducted to estimate the independent association between acculturation, social capital, and breastfeeding. Women with more social capital were more likely (odds ratio = 2.25, 95% confidence interval, 1.02-4.95) to have breastfed the index child, suggesting that social capital is an important predictor of breastfeeding initiation in this community. J Hum Lact. 20(1):39-45. [ABSTRACT FROM AUTHOR]