Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study.

BACKGROUND: Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population. OBJECTIVES: To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner. METHODS: In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models. RESULTS: The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods. CONCLUSIONS: Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

Integration of USDA Food Classification System and Food Composition Database for Image-Based Dietary Assessment among Individuals Using Insulin.

New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify food images. The objective of this study was to develop methods to create a food image database based on the most commonly consumed U.S. foods and those contributing the most to energy. The objective included using a systematic classification structure for foods based on the standardized United States Department of Agriculture (USDA) What We Eat in America (WWEIA) food classification system that can ultimately be used to link food images to a nutrition composition database, the USDA Food and Nutrient Database for Dietary Studies (FNDDS). The food image database was built using images mined from the web that were fitted with bounding boxes, identified, annotated, and then organized according to classifications aligning with USDA WWEIA. The images were classified by food category and subcategory and then assigned a corresponding USDA food code within the USDA's FNDDS in order to systematically organize the food images and facilitate a linkage to nutrient composition. The resulting food image database can be used in food identification and dietary assessment.

Accuracy and Cost-effectiveness of Technology-Assisted Dietary Assessment Comparing the Automated Self-administered Dietary Assessment Tool, Intake24, and an Image-Assisted Mobile Food Record 24-Hour Recall Relative to Observed Intake: Protocol for a Randomized Crossover Feeding Study.

BACKGROUND: The assessment of dietary intake underpins population nutrition surveillance and nutritional epidemiology and is essential to inform effective public health policies and programs. Technological advances in dietary assessment that use images and automated methods have the potential to improve accuracy, respondent burden, and cost; however, they need to be evaluated to inform large-scale use. OBJECTIVE: The aim of this study is to compare the accuracy, acceptability, and cost-effectiveness of 3 technology-assisted 24-hour dietary recall (24HR) methods relative to observed intake across 3 meals. METHODS: Using a controlled feeding study design, 24HR data collected using 3 methods will be obtained for comparison with observed intake. A total of 150 healthy adults, aged 18 to 70 years, will be recruited and will complete web-based demographic and psychosocial questionnaires and cognitive tests. Participants will attend a university study center on 3 separate days to consume breakfast, lunch, and dinner, with unobtrusive documentation of the foods and beverages consumed and their amounts. Following each feeding day, participants will complete a 24HR process using 1 of 3 methods: the Automated Self-Administered Dietary Assessment Tool, Intake24, or the Image-Assisted mobile Food Record 24-Hour Recall. The sequence of the 3 methods will be randomized, with each participant exposed to each method approximately 1 week apart. Acceptability and the preferred 24HR method will be assessed using a questionnaire. Estimates of energy, nutrient, and food group intake and portion sizes from each 24HR method will be compared with the observed intake for each day. Linear mixed models will be used, with 24HR method and method order as fixed effects, to assess differences in the 24HR methods. Reporting bias will be assessed by examining the ratios of reported 24HR intake to observed intake. Food and beverage omission and intrusion rates will be calculated, and differences by 24HR method will be assessed using chi-square tests. Psychosocial, demographic, and cognitive factors associated with energy misestimation will be evaluated using chi-square tests and multivariable logistic regression. The financial costs, time costs, and cost-effectiveness of each 24HR method will be assessed and compared using repeated measures analysis of variance tests. RESULTS: Participant recruitment commenced in March 2021 and is planned to be completed by the end of 2021. CONCLUSIONS: This protocol outlines the methodology of a study that will evaluate the accuracy, acceptability, and cost-effectiveness of 3 technology-enabled dietary assessment methods. This will inform the selection of dietary assessment methods in future studies on nutrition surveillance and epidemiology. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000209897; https://tinyurl.com/2p9fpf2s. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32891.

Dietary Aspects to Incorporate in the Creation of a Mobile Image-Based Dietary Assessment Tool to Manage and Improve Diabetes.

Diabetes is the seventh leading cause of death in United States. Dietary intake and behaviors are essential components of diabetes management. Growing evidence suggests dietary components beyond carbohydrates may critically impact glycemic control. Assessment tools on mobile platforms have the ability to capture multiple aspects of dietary behavior in real-time throughout the day to inform and improve diabetes management and insulin dosing. The objective of this narrative review was to summarize evidence related to dietary behaviors and composition to inform a mobile image-based dietary assessment tool for managing glycemic control of both diabetes types (type 1 and type 2 diabetes). This review investigated the following topics amongst those with diabetes: (1) the role of time of eating occasion on indicators of glycemic control; and (2) the role of macronutrient composition of meals on indicators of glycemic control. A search for articles published after 2000 was completed in PubMed with the following sets of keywords "diabetes/diabetes management/diabetes prevention/diabetes risk", "dietary behavior/eating patterns/temporal/meal timing/meal frequency", and "macronutrient composition/glycemic index". Results showed eating behaviors and meal macronutrient composition may affect glycemic control. Specifically, breakfast skipping, late eating and frequent meal consumption might be associated with poor glycemic control while macronutrient composition and order of the meal could also affect glycemic control. These factors should be considered in designing a dietary assessment tool, which may optimize diabetes management to reduce the burden of this disease.

Utility of self-rated adherence for monitoring dietary and physical activity compliance and assessment of participant feedback of the Healthy Diet and Lifestyle Study pilot.

BACKGROUND: We examined the utility of self-rated adherence to dietary and physical activity (PA) prescriptions as a method to monitor intervention compliance and facilitate goal setting during the Healthy Diet and Lifestyle Study (HDLS). In addition, we assessed participants' feedback of HDLS. HDLS is a randomized pilot intervention that compared the effect of intermittent energy restriction combined with a Mediterranean diet (IER + MED) to a Dietary Approaches to Stop Hypertension (DASH) diet, with matching PA regimens, for reducing visceral adipose tissue area (VAT). METHODS: Analyses included the 59 (98%) participants who completed at least 1 week of HDLS. Dietary and PA adherence scores were collected 8 times across 12 weeks, using a 0-10 scale (0 = not at all, 4 = somewhat, and 10 = following the plan very well). Adherence scores for each participant were averaged and assigned to high and low adherence categories using the group median (7.3 for diet, 7.1 for PA). Mean changes in VAT and weight from baseline to 12 weeks are reported by adherence level, overall and by randomization arm. Participants' feedback at completion and 6 months post-intervention were examined. RESULTS: Mean ± SE, dietary adherence was 6.0 ± 0.2 and 8.2 ± 0.1, for the low and high adherence groups, respectively. For PA adherence, mean scores were 5.9 ± 0.2 and 8.5 ± 0.2, respectively. Compared to participants with low dietary adherence, those with high adherence lost significantly more VAT (22.9 ± 3.7 cm2 vs. 11.7 ± 3.9 cm2 [95% CI, - 22.1 to - 0.3]) and weight at week 12 (5.4 ± 0.8 kg vs. 3.5 ± 0.6 kg [95% CI, - 3.8 to - 0.0]). For PA, compared to participants with low adherence, those with high adherence lost significantly more VAT (22.3 ± 3.7 cm2 vs. 11.6 ± 3.6 cm2 [95% CI, - 20.7 to - 0.8]). Participants' qualitative feedback of HDLS was positive and the most common response, on how to improve the study, was to provide cooking classes. CONCLUSIONS: Results support the use of self-rated adherence as an effective method to monitor dietary and PA compliance and facilitate participant goal setting. Study strategies were found to be effective with promoting compliance to intervention prescriptions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03639350 . Registered 21st August 2018-retrospectively registered.

Food Security and Diet Quality in Native Hawaiian, Pacific Islander, and Filipino Infants 3 to 12 Months of Age.

Food insecurity and other nutritional risks in infancy pose a lifelong risk to wellbeing; however, their effect on diet quality in Native Hawaiian, Pacific Islander, and Filipino (NHPIF) infants in Hawai'i is unknown. In this cross-sectional analysis, the association between various indicators of food security and NHPIF infant diet quality were investigated in 70 NHPIF infants aged 3-12 months residing on O'ahu, Hawai'i. The dietary assessments of the infants were collected using a mobile food recordTM. Foods consumed across four days were categorized into seven food groups. Indicators for food security were examined through an adapted infant food security index and other indicators. Data were analyzed using chi-square tests, independent sample t-tests, multinomial logistic regression, and linear regression models. In models adjusting for age and sex, infants defined as food insecure by the adapted index were found to consume foods from more food groups and consume flesh foods on a greater proportion of days. Of the indicators examined, the adapted index was shown to be the best indicator for food group consumption. Further work is needed on a more representative sample of NHPIF infants to determine the impact that food security has on nutritional status and other indicators of health.

Type, Timing, and Diversity of Complementary Foods Among Native Hawaiian, Pacific Islander, and Filipino Infants.

Prevention is the recommended strategy for addressing childhood obesity and may be particularly important for minority groups such as Native Hawaiians, Pacific Islanders, and Filipinos (NHPIF) who display poorer health outcomes than other race/ethnic groups. Complementary feeding is a critical milestone in the first 1,000 days of life and plays a critical role in growth and eating habit formation. This cross-sectional study recruited NHPIF infants between 3 - 12 months of age residing on O'ahu, Hawai'i to examine timing and types of complementary foods introduced first as well as the dietary diversity of those infants 6 - 12 months of age. Basic demographic information and early feeding practices were assessed via online questionnaire. Diet was evaluated using the image-based mobile food record completed over 4-days. Images were evaluated to derive the World Health Organization's minimum dietary diversity (MDD) score. Data were analyzed using descriptive statistics and linear regressions. Seventy participants completed the study with a majority being between the ages of 6 - 12 months (n=56). About half of the participants were provided a complementary food prior to 6 months of age with the most common first complementary food being poi (steamed, mashed taro). Grains were the most commonly reported food group while the high protein food groups was the least commonly reported. Approximately 25% of infants 6 - 12 months of age met MDD all four days. Meeting MDD was significantly associated with age. Findings illuminate opportunities for improvement (eg, delayed introduction) and for promotion (eg, cultural foods) in NHPIF complementary feeding.

Image-Based Dietary Assessment and Tailored Feedback Using Mobile Technology: Mediating Behavior Change in Young Adults.

Assessing the implementation of nutrition interventions is important to identify characteristics and dietary patterns of individuals who benefit most. The aim was to report on young adults' experiences of receiving dietary feedback text messaging intervention. Diet was captured using an image-based 4-day mobile food recordTM application (mFRTM) and assessed to formulate two tailored feedback text messages on fruit and vegetables and energy-dense nutrient-poor (EDNP) foods and beverages. At 6-months 143 participants completed a second mFRTM and a questionnaire evaluating the dietary feedback. Participants who agreed the text messages made them think about how much vegetables they ate were more likely to increase their intake by at least half a serve than those who disagreed [odds ratio (OR) = 4.28, 95% Confidence Interval (CI): 1.76 to 10.39]. Those who agreed the text messages made them think about how much EDNP foods they ate, were twice as likely to decrease their intake by over half a serve (OR = 2.39, 95%CI: 1.12 to 5.25) than those who disagreed. Undertaking detailed dietary assessment ensured the tailored feedback was constructive and relevant. Personal contemplation about vegetable and EDNP food intake appears to be a mediator of dietary change in young adults.

Technology-Based Dietary Assessment in Youth with and Without Developmental Disabilities.

Obesity prevalence is higher in children with developmental disabilities as compared to their typically developing peers. Research on dietary intake assessment methods in this vulnerable population is lacking. The objectives of this study were to assess the feasibility, acceptability, and compare the nutrient intakes of two technology-based dietary assessment methods in children with-and-without developmental disabilities. This cross-sectional feasibility study was an added aim to a larger pilot study. Children (n = 12; 8⁻18 years) diagnosed with spina bifida, Down syndrome, or without disability were recruited from the larger study sample, stratified by diagnosis. Participants were asked to complete six days of a mobile food record (mFR™), a 24-h dietary recall via FaceTime® (24 HR-FT), and a post-study survey. Analysis included descriptive statistics for survey results and a paired samples t-test for nutrient intakes. All participants successfully completed six days of dietary assessment using both methods and acceptability was high. Energy (kcal) and protein (g) intake was significantly higher for the mFR™ as compared to the 24 HR-FT (p = 0.041; p = 0.014, respectively). Each method had strengths and weaknesses. The two technology-based dietary assessment tools were well accepted and when combined could increase accuracy of self-reported dietary assessment in children with-and-without disability.

Context Based Image Analysis With Application in Dietary Assessment and Evaluation.

Dietary assessment is essential for understanding the link between diet and health. We develop a context based image analysis system for dietary assessment to automatically segment, identify and quantify food items from images. In this paper, we describe image segmentation and object classification methods used in our system to detect and identify food items. We then use context information to refine the classification results. We define contextual dietary information as the data that is not directly produced by the visual appearance of an object in the image, but yields information about a user's diet or can be used for diet planning. We integrate contextual dietary information that a user supplies to the system either explicitly or implicitly to correct potential misclassifications. We evaluate our models using food image datasets collected during dietary assessment studies from natural eating events.

A Mobile Food Record For Integrated Dietary Assessment.

This paper presents an integrated dietary assessment system based on food image analysis that uses mobile devices or smartphones. We describe two components of our integrated system: a mobile application and an image-based food nutrient database that is connected to the mobile application. An easy-to-use mobile application user interface is described that was designed based on user preferences as well as the requirements of the image analysis methods. The user interface is validated by user feedback collected from several studies. Food nutrient and image databases are also described which facilitates image-based dietary assessment and enable dietitians and other healthcare professionals to monitor patients dietary intake in real-time. The system has been tested and validated in several user studies involving more than 500 users who took more than 60,000 food images under controlled and community-dwelling conditions.

Multiple hypotheses image segmentation and classification with application to dietary assessment.

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

A Printer Indexing System for Color Calibration with Applications in Dietary Assessment.

In image based dietary assessment, color is a very important feature in food identification. One issue with using color in image analysis in the calibration of the color imaging capture system. In this paper we propose an indexing system for color camera calibration using printed color checkerboards also known as fiducial markers (FMs). To use the FM for color calibration one must know which printer was used to print the FM so that the correct color calibration matrix can be used for calibration. We have designed a printer indexing scheme that allows one to determine which printer was used to print the FM based on a unique arrangement of color squares and binarized marks (used for error control) printed on the FM. Using normalized cross correlation and pattern detection, the index corresponding to the printer for a particular FM can be determined. Our experimental results show this scheme is robust against most types of lighting conditions.

Image Enhancement and Quality Measures for Dietary Assessment Using Mobile Devices.

Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. We are developing a system, known as the mobile device food record (mdFR), to automatically identify and quantify foods and beverages consumed based on analyzing meal images captured with a mobile device. The mdFR makes use of a fiducial marker and other contextual information to calibrate the imaging system so that accurate amounts of food can be estimated from the scene. Food identification is a difficult problem since foods can dramatically vary in appearance. Such variations may arise not only from non-rigid deformations and intra-class variability in shape, texture, color and other visual properties, but also from changes in illumination and viewpoint. To address the color consistency problem, this paper describes illumination quality assessment methods implemented on a mobile device and three post color correction methods.

Segmentation Assisted Food Classification for Dietary Assessment.

Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed.

Multilevel Segmentation for Food Classification in Dietary Assessment.

Given a dataset of images, we seek to automatically identify and locate perceptually similar objects. We combine two ideas to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of each image and then learning the object class by combining different segmentations to generate optimal segmentation. We demonstrate that the proposed method can be used as part of a new dietary assessment tool to automatically identify and locate the foods in a variety of food images captured during different user studies.

COMBINING GLOBAL AND LOCAL FEATURES FOR FOOD IDENTIFICATION IN DIETARY ASSESSMENT.

Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a "voting" based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.

INTEGRATED DATABASE SYSTEM FOR MOBILE DIETARY ASSESSMENT AND ANALYSIS.

Of the 10 leading causes of death in the US, 6 are related to diet. Unfortunately, methods for real-time assessment and proactive health management of diet do not currently exist. There are only minimally successful tools for historical analysis of diet and food consumption available. In this paper, we present an integrated database system that provides a unique perspective on how dietary assessment can be accomplished. We have designed three interconnected databases: an image database that contains data generated by food images, an experiments database that contains data related to nutritional studies and results from the image analysis, and finally an enhanced version of a nutritional database by including both nutritional and visual descriptions of each food. We believe that these databases provide tools to the healthcare community and can be used for data mining to extract diet patterns of individuals and/or entire social groups.

The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation.

There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.

AN IMAGE ANALYSIS SYSTEM FOR DIETARY ASSESSMENT AND EVALUATION.

There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that provides a measure of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.