Water is essential for survival, but one in three individuals worldwide (2.2 billion people) lacks access to safe drinking water. Water intake requirements largely reflect water turnover (WT), the water used by the body each day. We investigated the determinants of human WT in 5604 people from the ages of 8 days to 96 years from 23 countries using isotope-tracking ( ² H) methods. Age, body size, and composition were significantly associated with WT, as were physical activity, athletic status, pregnancy, socioeconomic status, and environmental characteristics (latitude, altitude, air temperature, and humidity). People who lived in countries with a low human development index (HDI) had higher WT than people in high-HDI countries. On the basis of this extensive dataset, we provide equations to predict human WT in relation to anthropometric, economic, and environmental factors.

Background: To accurately capture an individual’s food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client’s memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. Objective: This review aims to fill the gap by providing a current overview of AI’s integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems’ strengths and weaknesses and propose enhancements to improve IADA’s accuracy and adoption in the nutrition community. Methods: This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. Results: A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. Conclusions: This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.

Global economic development has been associated with an increased prevalence of obesity and related health problems. Increased caloric intake and reduced energy expenditure are both cited as development-related contributors to the obesity crisis, but their relative importance remains unresolved. Here, we examine energy expenditure and two measures of obesity (body fat percentage and body mass index, BMI) for 4,213 adults from 34 populations across six continents and a wide range of lifestyles and economies, including hunter-gatherer, pastoralist, farming, and industrialized populations. Economic development was positively associated with greater body mass, BMI, and body fat, but also with greater total, basal, and activity energy expenditure. Body size–adjusted total and basal energy expenditures both decreased approximately 6 to 11% with increasing economic development, but were highly variable among populations and did not correspond closely with lifestyle. Body size–adjusted total energy expenditure was negatively, but weakly, associated with measures of obesity, accounting for roughly one-tenth of the elevated body fat percentage and BMI associated with economic development. In contrast, estimated energy intake was greater in economically developed populations, and in populations with available data (n = 25), the percentage of ultraprocessed food in the diet was associated with body fat percentage, suggesting that dietary intake plays a far greater role than reduced energy expenditure in obesity related to economic development.

Date fruits (Phoenix dactylifera L.) are rich in sugar and also contain a substantial amount of phenolic compounds. Therefore, date fruits can be used to produce an alternative sweetener, having lower glycemic index than sucrose. This study investigated the effects of extraction and evaporation methods on various properties of the syrups prepared from Barhi dates. Extraction of date juice with the aid of pectinase or cellulase significantly enhanced the production yield, total phenolic content, and antioxidant activities determined by Ferric Reducing Antioxidant Power and Oxygen Radical Absorbance Capacity assays. Syrups prepared without enzyme application had about 6–7 times higher apparent viscosity than those prepared from the enzyme-assisted extracted juices. Vacuum evaporation produced syrups with significantly lighter color and inferior antioxidant properties than open heating. Properties of date syrups prepared with or without enzyme-assisted extraction followed by open heat evaporation were not different. They had a glucose-to-fructose ratio close to 1:1, received good sensory acceptability scores of above 6 on a 9-point hedonic scale, contained a safe level (<40 mg/kg) of 5-hydroxymethyl-2-furfuraldehyde, and exhibited similar glass transition and melting temperatures; while a greater inhibition on α-amylase activity was observed in syrups obtained from enzyme-assisted extraction. The in vivo glycemic measurement revealed that the syrup prepared with the aid of Pectinex and open heating was classified as low glycemic index (GI = 55) and medium glycemic load (GL = 11). Thus, enzyme-assisted extraction of date juice using Pectinex could be used to produce a healthy natural sweetener from low quality date fruits.

The Global Diet Quality Score (GDQS) was developed for monitoring nutrient adequacy and diet-related noncommunicable disease risk in diverse populations. A software application (GDQS app) was recently developed for the standardized collection of GDQS data. The application involves a simplified 24-h dietary recall (24HR) where foods are matched to GDQS-food groups using an onboard database, portion sizes are estimated at the food group level using cubic models, and the GDQS is computed. The study aimed to estimate associations between GDQS scores collected using the GDQS app and nutrient adequacy and metabolic risks. In this cross-sectional study of 600 Thai males and nonpregnant/nonlactating females (40-60 y), we collected 2 d of GDQS app and paper-based 24HR, food-frequency questionnaires (FFQs), anthropometry, body composition, blood pressure, and biomarkers. Associations between application scores and outcomes were estimated using multiple regression, and application performance was compared with that of metrics scored using 24HR and FFQ data: GDQS, Minimum Dietary Diversity-Women, Alternative Healthy Eating Index-2010, and Global Dietary Recommendations score. In covariate-adjusted models, application scores were significantly (P < 0.05) associated with higher energy-adjusted mean micronutrient adequacy computed using 24HR (range in estimated mean adequacy between score quintiles 1 and 5: 36.3%-44.5%) and FFQ (Q1-Q5: 40.6%-44.2%), and probability of protein adequacy from 24HR (Q1-Q5: 63%-72.5%). Application scores were inversely associated with BMI kg/m² (Q1-Q5: 26.3-24.9), body fat percentage (Q1-Q5: 31.7%-29.1%), diastolic blood pressure (Q1-Q5: 84-81 mm Hg), and a locally-developed sodium intake score (Q1-Q5: 27.5-24.0 points out of 100); positively associated with high-density lipoprotein cholesterol (Q1-Q5: 49-53 mg/dL) and 24-h urinary potassium (Q1-Q5: 1385-1646 mg); and inversely associated with high midupper arm circumference (Q5/Q1 odds ratio: 0.52) and abdominal obesity (Q5/Q1 odds ratio: 0.51). Significant associations for the application outnumbered those for metrics computed using 24HR or FFQ. The GDQS app effectively assesses nutrient adequacy and metabolic risk in population surveys.

Carbohydrate counting is essential for well-controlled blood glucose in people with type 1 diabetes, but to perform it precisely is challenging, especially for Thai foods. Consequently, we developed a deep learning-based system for automatic carbohydrate counting using Thai food images taken from smartphones. The newly constructed Thai food image dataset contained 256,178 ingredient objects with measured weight for 175 food categories among 75,232 images. These were used to train object detector and weight estimator algorithms. After training, the system had a Top-1 accuracy of 80.9% and a root mean square error (RMSE) for carbohydrate estimation of <10 g in the test dataset. Another set of 20 images, which contained 48 food items in total, was used to compare the accuracy of carbohydrate estimations between measured weight, system estimation, and eight experienced registered dietitians (RDs). System estimation error was 4%, while estimation errors from nearest, lowest, and highest carbohydrate among RDs were 0.7, 25.5, and 7.6%, respectively. The RMSE for carbohydrate estimations of the system and the lowest RD were 9.4 and 10.2, respectively. The system could perform with an estimation error of <10 g for 13/20 images, which placed it third behind only two of the best performing RDs: RD1 (15/20 images) and RD5 (14/20 images). Hence, the system was satisfactory in terms of accurately estimating carbohydrate content, with results being comparable with those of experienced dietitians.