TusaRebecca E. Schap

The Dietary Patterns Methods Project: Synthesis of Findings across Cohorts and Relevance to Dietary Guidance

The Dietary Patterns Methods Project (DPMP) was initiated in 2012 to strengthen research evidence on dietary indices, dietary patterns, and health for upcoming revisions of the Dietary Guidelines for Americans, given that the lack of consistent methodology has impeded development of consistent and reliable conclusions. DPMP investigators developed research questions and a standardized approach to index-based dietary analysis. This article presents a synthesis of findings across the cohorts. Standardized analyses were conducted in the NIH-AARP Diet and Health Study, the Multiethnic Cohort, and the Womens Health Initiative Observational Study (WHI-OS). Healthy Eating Index 2010, Alternative Healthy Eating Index 2010 (AHEI-2010), alternate Mediterranean Diet, and Dietary Approaches to Stop Hypertension (DASH) scores were examined across cohorts for correlations between pairs of indices; concordant classifications into index score quintiles; associations with all-cause, cardiovascular disease (CVD), and cancer mortality with the use of Cox proportional hazards models; and dietary intake of foods and nutrients corresponding to index quintiles. Across all cohorts in women and men, there was a high degree of correlation and consistent classifications between index pairs. Higher diet quality (top quintile) was significantly and consistently associated with an 11-28% reduced risk of death due to all causes, CVD, and cancer compared with the lowest quintile, independent of known confounders. This was true for all diet index-mortality associations, with the exception of AHEI-2010 and cancer mortality in WHI-OS women. In all cohorts, survival benefit was greater with a higher-quality diet, and relatively small intake differences distinguished the index quintiles. The reductions in mortality risk started at relatively lower levels of diet quality. Higher scores on each of the indices, signifying higher diet quality, were associated with marked reductions in mortality. Thus, the DPMP findings suggest that all 4 indices capture the essential components of a healthy diet.

Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents

Background The development of a mobile telephone food record has the potential to ameliorate much of the burden associated with current methods of dietary assessment. When using the mobile telephone food record, respondents capture an image of their foods and beverages before and after eating. Methods of image analysis and volume estimation allow for automatic identification and volume estimation of foods. To obtain a suitable image, all foods and beverages and a fiducial marker must be included in the image. Objective To evaluate a defined set of skills among adolescents and adults when using the mobile telephone food record to capture images and to compare the perceptions and preferences between adults and adolescents regarding their use of the mobile telephone food record. Methods We recruited 135 volunteers (78 adolescents, 57 adults) to use the mobile telephone food record for one or two meals under controlled conditions. Volunteers received instruction for using the mobile telephone food record prior to their first meal, captured images of foods and beverages before and after eating, and participated in a feedback session. We used chi-square for comparisons of the set of skills, preferences, and perceptions between the adults and adolescents, and McNemar test for comparisons within the adolescents and adults. Results Adults were more likely than adolescents to include all foods and beverages in the before and after images, but both age groups had difficulty including the entire fiducial marker. Compared with adolescents, significantly more adults had to capture more than one image before (38% vs 58%, P = .03) and after (25% vs 50%, P = .008) meal session 1 to obtain a suitable image. Despite being less efficient when using the mobile telephone food record, adults were more likely than adolescents to perceive remembering to capture images as easy (P < .001). Conclusions A majority of both age groups were able to follow the defined set of skills; however, adults were less efficient when using the mobile telephone food record. Additional interactive training will likely be necessary for all users to provide extra practice in capturing images before entering a free-living situation. These results will inform age-specific development of the mobile telephone food record that may translate to a more accurate method of dietary assessment.

Comparison of Known Food Weights with Image-Based Portion-Size Automated Estimation and Adolescents' Self-Reported Portion Size

Background: Diet is a critical element of diabetes self-management. An emerging area of research is the use of images for dietary records using mobile telephones with embedded cameras. These tools are being designed to reduce user burden and to improve accuracy of portion-size estimation through automation. The objectives of this study were to (1) assess the error of automatically determined portion weights compared to known portion weights of foods and (2) to compare the error between automation and human. Methods: Adolescents (n = 15) captured images of their eating occasions over a 24 h period. All foods and beverages served were weighed. Adolescents self-reported portion sizes for one meal. Image analysis was used to estimate portion weights. Data analysis compared known weights, automated weights, and self-reported portions. Results: For the 19 foods, the mean ratio of automated weight estimate to known weight ranged from 0.89 to 4.61, and 9 foods were within 0.80 to 1.20. The largest error was for lettuce and the most accurate was strawberry jam. The children were fairly accurate with portion estimates for two foods (sausage links, toast) using one type of estimation aid and two foods (sausage links, scrambled eggs) using another aid. The automated method was fairly accurate for two foods (sausage links, jam); however, the 95% confidence intervals for the automated estimates were consistently narrower than human estimates. Conclusions: The ability of humans to estimate portion sizes of foods remains a problem and a perceived burden. Errors in automated portion-size estimation can be systematically addressed while minimizing the burden on people. Future applications that take over the burden of these processes may translate to better diabetes self-management.

Merging dietary assessment with the adolescent lifestyle

The use of image-based dietary assessment methods shows promise for improving dietary self-report among children. The Technology Assisted Dietary Assessment (TADA) food record application is a self-administered food record specifically designed to address the burden and human error associated with conventional methods of dietary assessment. Users would take images of foods and beverages at all eating occasions using a mobile telephone or mobile device with an integrated camera [e.g. Apple iPhone, Apple iPod Touch (Apple Inc., Cupertino, CA, USA); Nexus One (Google, Mountain View, CA, USA)]. Once the images are taken, the images are transferred to a back-end server for automated analysis. The first step in this process is image analysis (i.e. segmentation, feature extraction and classification), which allows for automated food identification. Portion size estimation is also automated via segmentation and geometric shape template modeling. The results of the automated food identification and volume estimation can be indexed with the Food and Nutrient Database for Dietary Studies to provide a detailed diet analysis for use in epidemiological or intervention studies. Data collected during controlled feeding studies in a camp-like setting have allowed for formative evaluation and validation of the TADA food record application. This review summarises the system design and the evidence-based development of image-based methods for dietary assessment among children.

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.

Index-based dietary patterns and risk of lung cancer in the NIH–AARP diet and health study

Background/Objectives:Dietary pattern analysis considers combinations of food intake and may offer a better measure to assess diet–cancer associations than examining individual foods or nutrients. Although tobacco exposure is the major risk factor for lung cancer, few studies have examined whether dietary patterns, based on preexisting dietary guidelines, influence lung cancer risk. After controlling for smoking, we examined associations between four diet quality indices—Healthy Eating Index–2010 (HEI-2010), Alternate Healthy Eating Index–2010 (AHEI-2010), alternate Mediterranean Diet score (aMED) and Dietary Approaches to Stop Hypertension (DASH)—and lung cancer risk in the NIH–AARP (National Institutes of Health-American Association of Retired Persons) Diet and Health study.Subjects/Methods:Baseline dietary intake was assessed in 460 770 participants. Over a median of 10.5 years of follow-up, 9272 incident lung cancer cases occurred. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and confidence intervals (CIs).Results:Comparing highest to lowest quintiles, HRs (95% CIs) for lung cancer were as follows: HEI-2010=0.83 (0.77–0.89), AHEI-2010=0.86 (0.80–0.92), aMED=0.85 (0.79–0.91) and DASH=0.84 (0.78–0.90). Among the individual components of the dietary indices, higher consumption of whole grains and fruits was significantly inversely associated with lung cancer risk for several of the diet indices. Total index score analyses stratified by smoking status showed inverse associations with lung cancer for former smokers; however, only HEI-2010 was inversely associated in current smokers and no index score was inversely associated among never smokers.Conclusions:Although smoking is the factor most strongly associated with lung cancer, this study adds to a growing body of evidence that diet may have a modest role in reducing lung cancer risk, especially among former smokers.

Development of a mobile user interface for image-based dietary assessment

In this paper, we present a mobile user interface for image-based dietary assessment. The mobile user interface provides a front end to a client-server image recognition and portion estimation software. In the client-server configuration, the user interactively records a series of food images using a built-in camera on the mobile device. Images are sent from the mobile device to the server, and the calorie content of the meal is estimated. In this paper, we describe and discuss the design and development of our mobile user interface features. We discuss the design concepts, through initial ideas and implementations. For each concept, we discuss qualitative user feedback from participants using the mobile client application. We then discuss future designs, including work on design considerations for the mobile application to allow the user to interactively correct errors in the automatic processing while reducing the user burden associated with classical pen-and-paper dietary records.

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 anal- ysis 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 ex- periments 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 commu- nity and can be used for data mining to extract diet patterns of individuals and/or entire social groups.

Importance of user interaction with automated dietary assessment methods

The utility of using images, as described by Arab et al.,1 which uses automated image-capture to aid completion of a web-based dietary recall to enhance recall of food and beverage intakes, is indeed promising. We have verified that individuals can correctly identify foods and beverages using an image up to 14 h after an eating occasion.2 However, we do want to clarify an incorrect description of our system described on page 1157 in Arab et al.1 The system in Boushey et al.3 and Six et al.4 referred to as the Mobile Phone Food Record (mpFR) is automated with image upload followed by ‘decoding of images by automation’. In addition, the system also includes a user confirmation step, which Arab et al.1 implied was not a part of the mpFR. The user provides the final confirmation of foods and beverages identified by the automated system; thus, the images are used directly by the participant. This step is clearly illustrated in the mpFR diagram4 and in the process diagram.2 This step is important because an automated system would need a method to identify new foods (whether new to the system or new to market), as well as correct foods that are misclassified. In addition to the advantages of soliciting contextual help from the user as described by Arab et al.,1 this step provides a novel approach for selecting foods to add to an existing food composition database.

Analysis, Presentation and Interpretation of Dietary Data

Dietary assessment methods provide valuable data to measure dietary exposure in nutritional epidemiology. This chapter focuses on research applications for the interpretation of dietary data that includes the examination of the dietary data to determine the nutritional composition of the participants’ diets, presentation and communication of the data and results, and interpretation and translation of the data. Underpinning dietary assessment is how best to minimize measurement error. Improving dietary intake methodology is critical to the credibility of nutrition research, and appropriate use of technology may play an important role in the future in reducing the burden for the participant and the researcher.