Background/objectives:Household-level food spending data are not suitable for population-based studies of the economics of nutrition. This study compared three methods of deriving diet cost at the individual level.Subjects/methods:Adult men and women (n=164) completed 4-day diet diaries and a food frequency questionnaire (FFQ). Food expenditures over 4 weeks and supermarket prices for 384 foods were obtained. Diet costs (US$/day) were estimated using: (1) diet diaries and expenditures; (2) diet diaries and supermarket prices; and (3) FFQs and supermarket prices. Agreement between the three methods was assessed on the basis of Pearson correlations and limits of agreement. Income-related differences in diet costs were estimated using general linear models.Results:Diet diaries yielded mean (s.d.) diet costs of $10.04 (4.27) based on Method 1 and $8.28 (2.32) based on Method 2. FFQs yielded mean diet costs of $7.66 (2.72) based on Method 3. Correlations between energy intakes and costs were highest for Method 3 (r2=0.66), lower for Method 2 (r2=0.24) and lowest for Method 1 (r2=0.06). Cost estimates were significantly associated with household incomes.Conclusion:The weak association between food expenditures and food intake using Method 1 makes it least suitable for diet and health research. However, merging supermarket food prices with standard dietary assessment tools can provide estimates of individual diet cost that are more closely associated with food consumed. The derivation of individual diet cost can provide insights into some of the economic determinants of food choice, diet quality and health.
There was no evidence that commercial cola beverages sweetened with either sucrose or HFCS have significantly different effects on hunger, satiety, or short-term energy intakes.
Lifestyle interventions with the focus on diet are crucial in self-management and prevention of many chronic conditions, such as obesity, cardiovascular disease, diabetes, and cancer. Such interventions require a diet monitoring approach to estimate overall dietary composition and energy intake. Although wearable sensors have been used to estimate eating context (e.g., food type and eating time), accurate monitoring of dietary intake has remained a challenging problem. In particular, because monitoring dietary intake is a self-administered task that involves the end-user to record or report their nutrition intake, current diet monitoring technologies are prone to measurement errors related to challenges of human memory, estimation, and bias. New approaches based on mobile devices have been proposed to facilitate the process of dietary intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. Such approaches, however, suffer from errors due to low adherence to technology adoption and time sensitivity to the dietary intake context. In this article, we introduce EZNutriPal , 1 an interactive diet monitoring system that operates on unstructured mobile data such as speech and free-text to facilitate dietary recording, real-time prompting, and personalized nutrition monitoring. EZNutriPal features a natural language processing unit that learns incrementally to add user-specific nutrition data and rules to the system. To prevent missing data that are required for dietary monitoring (e.g., calorie intake estimation), EZNutriPal devises an interactive operating mode that prompts the end-user to complete missing data in real-time. Additionally, we propose a combinatorial optimization approach to identify the most appropriate pairs of food names and food quantities in complex input sentences. We evaluate the performance of EZNutriPal using real data collected from 23 human subjects who participated in two user studies conducted in 13 days each. The results demonstrate that EZNutriPal achieves an accuracy of 89.7% in calorie intake estimation. We also assess the impacts of the incremental training and interactive prompting technologies on the accuracy of nutrient intake estimation and show that incremental training and interactive prompting improve the performance of diet monitoring by 49.6% and 29.1%, respectively, compared to a system without such computing units.
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