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.

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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.

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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.

The results from this study in 12 healthy adults do not support the popularized notion that small, frequent meals help to decrease overall appetite. This trial was registered at clinicaltrials.gov as NCT02548026.

The effect of monosodium glutamate (MSG) supplementation in soup or broth on satiety is not well understood. In the present study, the relative effects of four chicken broths with or without added MSG on motivational ratings and energy intakes at the next meal were compared using a double-blinded, within-subject design. A total of thirty-five normal-weight women, aged 20-40 years, took part in four study sessions. The four broths were base chicken broth (63 kJ), broth with added MSG (1·19 g) and nucleotides (0·03 g), broth with added MSG (1·22 g), and broth with added fat (BAF; 681 kJ). The preloads were presented twice at 09.00 and 11.15 hours for a maximum cumulative dose of 2·44 g MSG. Motivational ratings were collected before and at 15 min intervals post-ingestion for a total of 210 min. A test lunch meal was served at 12.00 hours, and plate waste was measured. The addition of MSG to chicken broth did not increase energy intakes at lunch or affect motivational ratings over the entire testing session. Both hunger and desire to snack between the second preload exposure and the test meal were significantly reduced in the MSG condition relative to the base broth condition (both, P ¼ 0·03). However, only the BAF significantly suppressed energy intakes at lunch compared with the base broth control condition. Supplementing chicken broth with MSG can increase subjective ratings for satiety but does not alter energy intake at the next meal relative to an equal energy broth without added MSG.Key words: Satiety: Appetite: Monosodium glutamate: SodiumThe amino acid L-glutamate is primarily consumed in the form of monosodium glutamate (MSG) and is known to elicit a distinguishable fifth taste termed 'umami'. MSG has been shown to improve the sensory characteristics of many different foods (1) and is commonly used as a flavour enhancer. The highly palatable savoury umami taste can also be achieved by pairing MSG with the nucleotides inosine 5 0 -monophosphate and guanosine 5 0 -monophosphate (MSG þ ) (1,2) .Enhancing the palatability of foods could potentially lead to overeating; however, evidence that MSG is associated with excess energy intake is mixed. Whereas some short-term experimental studies on satiation in relation to MSG intake have reported that MSG supplementation could lead to a more rapid recovery of hunger, no significant differences in hunger ratings or differences in subsequent energy intake were observed (3) . Other behavioural studies have reported that adding MSG to some foods can lead to higher energy intakes at a subsequent meal compared with an equal energy control without MSG, even when no effects on hunger ratings were observed (4) . In addition, that study reported that the addition of MSG in combination with the nucleotide ionosine monophosphate-5 (IMP-5) did not lead to higher energy consumption at a subsequent meal relative to the control and concluded that further investigation into the impact of MSG, alone as well as in combination with nucleotides, was warranted (4) . The results o...

Background Eating frequency may influence obesity-related disease risk by attenuating post-prandial fluctuations in hormones involved in metabolism, appetite regulation, and inflammation. Materials/Methods This randomized crossover intervention trial tested the effects of eating frequency on fasting plasma insulin-like growth factor-I (IGF-1) and leptin. Fifteen participants (4 males, 11 females) completed two eucaloric intervention phases lasting 21 days each: low eating frequency (“low-EF”; 3 eating occasions/day) and high eating frequency (“high-EF”; 8 eating occasions/day). Participants were free-living and consumed their own meals using individualized structured meal plans with instruction from study staff. Participants completed fasting blood draws and anthropometry on the first and last day of each study phase. The GEE modification of linear regression tested the intervention effect on fasting serum insulin-like growth factor I (IGF-I) and leptin. Results Mean (± SD) age was 28.5 ± 8.70 years, and mean (± SD) BMI was 23.3 (3.4) kg/m2. We found lower mean serum IGF-1 following the high-EF condition compared to the low-EF condition (p<0.001). There was no association between EF and plasma leptin (p=0.83). Conclusion These results suggest that increased eating frequency may lower serum IGF-1, which is a hormonal biomarker linked to increased risk of breast, prostate and colorectal cancer.