Search Results (4)

Dietary fats, consisting of fatty acids (FAs), have diverse implications for disease prevention and treatment. Understanding the quality of dietary lipids is essential for managing chronic conditions and establishing food-based dietary guidelines. FAs naturally occur as mixtures of saturated (SFAs), monounsaturated (MUFAs), and polyunsaturated FAs (PUFAs), and their nutritional and medicinal values are evaluated using specific indices. This study aimed to assess the FA profiles and lipid quality indices of daily meals served in kindergartens located in Novi Sad, Serbia. During the autumn, winter, and spring seasons of the 2022/2023 year, meal (breakfast, snack, and lunch) sampling was conducted in a randomized manner on 15 unannounced days in each season. The nutritional composition and energy value of the sampled meals were determined, as well as their FA composition (GC-FID). The findings indicated that the average energy value of the daily meals met the recommendations of national regulations, as well as the daily fat intake, with a total fat intake amounting to 24.5 g/day during both the autumn and winter seasons and 23.4 g/day in the spring season. The predominant FAs were SFAs; their average intake was 11.9, 13.4, and 12.1 g/day during autumn, winter, and spring, respectively. MUFA intake exhibited minor variations across the seasons, with mean intakes of 7.8, 7.6, and 7.4 g/day, respectively. The highest mean PUFA intake was observed during autumn (4.8 g/day), while the winter and spring seasons displayed intakes of 3.5 and 4.0 g/day, respectively. Furthermore, regarding the lipid quality indices, the highest average values of PUFAs/SFAs, considered desirable, were identified during autumn (0.51 ± 0.31), whereas the lowest values were observed in winter (0.32 ± 0.27). The atherogenicity (IA) and thrombogenicity (IT) indices consistently exceeded the recommended value of one across all seasons, indicating an unfavorable lipid quality. The lowest IA (1.07 ± 0.66) and IT values (1.11 ± 0.49) were recorded during autumn. These results have significant implications for establishing national guidelines and nutrition standards, particularly for preschool-aged children, aiming to enhance health outcomes and mitigate the burden of chronic diseases on the healthcare system in the Republic of Serbia. Improving the lipid quality of meals provided in kindergartens can contribute to these objectives. Full article

This narrative review describes the observational approaches used to study infant and young child feeding (IYCF) practices in low- and middle-income countries (LMICs) published between 2001 and 2021. Articles were included in this narrative review if they were (1) original peer-reviewed articles published in English in PubMed and Web of Science; (2) published between 1 January 2001, and 31 December 2021; (3) conducted in an LMIC; and (4) employed observations and focused on IYCF practices among children aged 6–59 months. The studies (n = 51) revealed a wide-ranging application of direct meal and full-day observations, as well as indirect spot checks, to study IYCF. The findings revealed that meal observations were typically conducted during a midday meal using precise recording approaches such as video and aimed to understand child–caregiver interactions or specialized nutritious food (SNF) usage. Conversely, full-day observations lasted between 6 and 12 h and often used a field notes-based recording approach. Behaviors occurring outside of mealtime, such as snacking or interhousehold food sharing, were also a primary focus. Finally, spot checks were conducted to indirectly assess SNF compliance during both announced and unannounced visits. This review highlights the adaptability of observations across contexts and their versatility when used as a primary data collection tool to help monitor and evaluate nutrition programs. Full article

Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation. Full article

The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the same meal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 ± 2%, 93 ± 5%, and 47 ± 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes. Full article