Quantifying Actual and Perceived Inaccuracy When Estimating the Sugar, Energy Content and Portion Size of Foods

In order to adhere to dietary guidelines and manage health risks, consumers need to be able to estimate with some accuracy the sugar and energy content of foods. The present study compared how well participants could estimate the sugar and energy content of foods, the weight of foods, and approximate portion size (using a hand measure estimation aid). The study had three aims. First, it aimed to investigate differences in accuracy across the four measures. Second, it aimed to examine whether these differences in accuracy between estimation measures were accurately perceived by the participants. Third, it aimed to test if estimation accuracy was related to food journaling experience, body-mass index or gender. One hundred and ninety-seven participants took part in an estimation task and filled in a questionnaire. While the participants were inaccurate when using all four estimation measures, inaccuracy was most pronounced for sugar content (ds ≥ 0.39), which was consistently overestimated by between 62.1% and 98.5% of the sample. None of the other measures showed a consistent pattern of under- or overestimation. Participants’ perceived accuracy did not match their actual accuracy (rs ≤ |0.20|, ps ≥ 0.005). Actual accuracy showed only marginal covariation with food journaling experience (ts ≤ 2.01, ps ≥ 0.049, ds ≤ 0.14), body-mass index (rs ≤ |0.15|, ps ≥ 0.041) or gender (ts ≤ 3.17, ps ≥ 0.002, ds ≤ 0.46). It is particularly challenging for consumers to estimate the sugar content of food, which might have negative consequences for health and well-being. Thus, more education about sugar content and misperceptions is needed to support consumers so that they can make healthy food choices.

Nutrition Monitoring and Calorie Estimation using Internet of Things (IoT)

Diet observation is one of the principal aspect in precautionary health care that aims to cut back varied health risks. The various recent advancements in smartphone and wearable sensing element technologies have paved way to a proliferation of food observation applications that are based on automated image processing and intake detection, with an aim to beat drawbacks of the standard manual food journaling that's time overwhelming, inaccurate, underreporting, and low adherent. The currently developed food logging methods are very much time consuming and inconvenient that limits their effectiveness. The proposed work presents an Internet of Things (IoT) based mobile-controlled calorie estimation system to make technical advancements in healthcare industry. The proposed system operates on mobile environment, which allow the user to acquire the food image and quantify the calorie intake mechanically. The Mqtt protocol based MyMqtt broker is used to connect the application and the edge device and also to store the data in the Thingspeak cloud. A deep convolutional network is employed to classify the food accurately within the system. The volume estimation is done using sensors and the calorie approximation is done using formula

An Assessment of the Validity of an Audio-Video Method of Food Journaling for Dietary Quantity and Quality

Objective. To validate an audio-video (AV) method of food journaling, in a free-living scenario, compared to direct, weighed food assessment. Design and Setting. Data were collected in a cafeteria. Meals, selected by participants (n=30), were documented using the AV method: participants video-recorded their tray while audio-recording a description of their selected meal, after which the research team digitally weighed each food item and created an itemized diary record of the food. Variables Measured. Data from the AV method and from the weighed food diaries were transcribed and entered into a nutrition software analysis program (Nutribase Pro 10.0). Nutrient outputs were compared between the two methods including kilocalories, macronutrients, and selected micronutrients. Analyses. Using mean scores for each variable, Wilcoxon signed-rank test and Spearman’s correlation coefficients were conducted. Interclass correlation coefficient (ICC) was calculated for absolute agreement between the two methods to assess interrater reliability. Results. With the exception of Vitamin E and total weight, nutrient values were highly correlated between methods and were statistically significant given alpha = 0.05, power = 0.95, and effect size of 0.70. Conclusions. The AV method may be a meaningful alternative to diary recording in a free-living setting.