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BACKGROUND: Dietary intake assessment is an integral part of addressing suboptimal dietary intakes. Existing food-based methods are time-consuming and burdensome for users to report the individual foods consumed at each meal. However, ease of use is the most important feature for individuals choosing a nutrition or diet app. Intakes of whole meals can be reported in a manner that is less burdensome than reporting individual foods. No study has developed a method of dietary intake assessment where individuals report their dietary intakes as whole meals rather than individual foods. OBJECTIVE: This study aims to develop a novel, meal-based method of dietary intake assessment and test its ability to estimate nutrient intakes compared with that of a web-based, 24-hour recall (24HR). METHODS: Participants completed a web-based, generic meal-based recall. This involved, for each meal type (breakfast, light meal, main meal, snack, and beverage), choosing from a selection of meal images those that most represented their intakes during the previous day. Meal images were based on generic meals from a previous study that were representative of the actual meal intakes in Ireland. Participants also completed a web-based 24HR. Both methods were completed on the same day, 3 hours apart. In a crossover design, participants were randomized in terms of which method they completed first. Then, 2 weeks after the first dietary assessments, participants repeated the process in the reverse order. Estimates of mean daily nutrient intakes and the categorization of individuals according to nutrient-based guidelines (eg, low, adequate, and high) were compared between the 2 methods. P values of less than .05 were considered statistically significant. RESULTS: In total, 161 participants completed the study. For the 23 nutrient variables compared, the median percentage difference between the 2 methods was 7.6% (IQR 2.6%-13.2%), with P values ranging from <.001 to .97, and out of 23 variables, effect sizes for the differences were small for 19 (83%) variables, moderate for 2 (9%) variables, and large for 2 (9%) variables. Correlation coefficients were statistically significant (P<.05) for 18 (78%) of the 23 variables. Statistically significant correlations ranged from 0.16 to 0.45, with median correlation of 0.32 (IQR 0.25-0.40). When participants were classified according to nutrient-based guidelines, the proportion of individuals who were classified into the same category ranged from 52.8% (85/161) to 84.5% (136/161). CONCLUSIONS: A generic meal-based method of dietary intake assessment provides estimates of nutrient intake comparable with those provided by a web-based 24HR but with varying levels of agreement among nutrients. Further studies are required to refine and improve the generic recall across a range of nutrients. Future studies will consider user experience including the potential feasibility of incorporating image recognition of whole meals into the generic recall.

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Recent work has focused on understanding the link between diet quality and environmental impact, however it is also important to consider the role food processing plays in this relationship. Using model meal plans, this paper examines the link between nutrient content, environmental impact, and processing. Four distinct meal plans were considered - 'Healthy', 'Unhealthy', 'Healthy (plant-based)', 'Healthy (plant-based, processed)'. For each a variety of environmental impact, processing and nutritional composition metrics were compared. Alternative healthy eating index (AHEI) score for the Unhealthy diet was significantly lower than the other three diets. The 'Healthy (plant-based)' diet had the highest AHEI score but was not significantly different to the 'Healthy (plant-based, processed)' and 'Healthy' diet scores. The greenhouse gas emissions for the two plant based diets were not significantly different to each other or to the 'Healthy' diet but were significantly lower than the 'Unhealthy' diet. The 'Healthy', 'Unhealthy', and 'Healthy (plant-based)' diets had similar processing specific energy consumption values however, the 'Healthy (plant-based, processed)' diets had significantly greater specific energy consumption. There was no clear link between diet quality and food processing when considered using processing specific energy value. When the number of processes in each diet was estimated, the unhealthier diet had considerably more processes associated with it. Examining the interaction of nutritional quality, environmental impact and processing of diets in this way highlights the complexity of the inter-relationships. Understanding these interactions is necessary to support the transition to healthy diets from sustainable sources.

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BACKGROUND: Examination of meal intakes can elucidate the role of individual meals or meal patterns in health not evident by examining nutrient and food intakes. To date, meal-based research has been limited to focus on population rather than individual intakes, without considering portions or nutrient content when characterizing meals. OBJECTIVES: We aimed to characterize meals commonly consumed, incorporating portions and nutritional content, and to determine the accuracy of nutrient intake estimates using these meals at both population and individual levels. METHODS: The 2008-2010 Irish National Adult Nutrition Survey (NANS) data were used. A total of 1500 participants, with a mean ± SD age of 44.5 ± 17.0 y and BMI of 27.1 ± 5.0 kg/m2, recorded their intake using a 4-d weighed food diary. Food groups were identified using k-means clustering. Partitioning around the medoids clustering was used to categorize similar meals into groups (generic meals) based on their Nutrient Rich Foods Index (NRF9.3) score and the food groups that they contained. The nutrient content for each generic meal was defined as the mean content of the grouped meals. Seven standard portion sizes were defined for each generic meal. Mean daily nutrient intakes were estimated using the original and the generic data. RESULTS: The 27,336 meals consumed were aggregated to 63 generic meals. Effect sizes from the comparisons of mean daily nutrient intakes (from the original compared with generic meals) were negligible or small, with P values ranging from <0.001 to 0.941. When participants were classified according to nutrient-based guidelines (high, adequate, or low), the proportion of individuals who were classified into the same category ranged from 55.3% to 91.5%. CONCLUSIONS: A generic meal-based method can estimate nutrient intakes based on meal rather than food intake at the sample population and individual levels. Future work will focus on incorporating this concept into a meal-based dietary intake assessment tool.

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There is a scarcity of dietary intake research focusing on the intake of whole meals rather than on the nutrients and foods of which those meals are composed. This growing area of research has recently begun to utilize advanced statistical techniques to manage the large number of variables and permutations associated with these complex meal patterns. The aim of this narrative review was to evaluate those techniques and the meal patterns they detect. The 10 observational studies identified used techniques such as principal components analysis, clustering, latent class analysis, and decision trees. They examined meal patterns under 3 categories: temporal patterns (relating to the timing and distribution of meals), content patterns (relating to combinations of foods within a meal and combinations of those meals over a day), and context patterns (relating to external elements of the meal, such as location, activities while eating, and the presence or absence of others). The most common temporal meal patterns were the 3 meals/d pattern, the skipped breakfast pattern, and a grazing pattern consisting of smaller but more frequent meals. The 3 meals/d pattern was associated with increased diet quality compared with the other 2 patterns. Studies identified between 7 and 12 content patterns with limited similarities between studies and no clear associations between the patterns and diet quality or health. One study simultaneously examined temporal and context meal patterns, finding limited associations with diet quality. No study simultaneously examined other combinations of meal patterns. Future research that further develops the statistical techniques required for meal pattern analysis is necessary to clarify the relations between meal patterns and diet quality and health.

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Recent interest in seaweeds as a source of macronutrients, micronutrients, and bioactive components has highlighted prospective applications within the functional food and nutraceutical industries, with impetus toward the alleviation of risk factors associated with noncommunicable diseases such as obesity, type 2 diabetes, and cardiovascular disease. This narrative review summarizes the nutritional composition of edible seaweeds; evaluates the evidence regarding the health benefits of whole seaweeds, extracted bioactive components, and seaweed-based food products in humans; and assesses the potential adverse effects of edible seaweeds, including those related to ingestion of excess iodine and arsenic. If the potential functional food and nutraceutical applications of seaweeds are to be realized, more evidence from human intervention studies is needed to evaluate the nutritional benefits of seaweeds and the efficacy of their purported bioactive components. Mechanistic evidence, in particular, is imperative to substantiate health claims.

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