Feasibility testing of an automated image-capture method to aid dietary recall

Gamma-Aminobutyric Acid in Rice Bran Extract Exerts Antistress Effects in Mouse Models with Depressive-Like Behaviors

Various neurotransmitters are involved in regulating stress systems. In this study, we investigated the effects of gamma-aminobutyric acid-rich rice bran extract (GRBe) in mice stressed by forced swimming and tail suspension tests. Four weeks of oral administration of GRBe (500-2000 mg/kg) reduced the levels of dopamine and corticosterone in the blood and brain while increasing serotonin levels. GRBe was involved not only in stress but also in regulating sleep and obesity-related genes. Modern society experiences diverse and tense lives because of urbanization and informatization, which cause excessive stress due to complicated interpersonal relationships, heavy work burden, and fatigue from the organized society. High levels of stress cause psychological instability and disrupt the balance in the autonomic nervous system, which maintains the body's equilibrium, resulting in cardiovascular and cerebrovascular diseases, hormonal imbalances, and sleep disorders. Therefore, our results suggest that GRBe is a useful substance that can relieve tension by ultimately influencing a depressive-like state by lowering the levels of neuronal substances, hormones, and cytokines involved in stress and sleep disorders.

Precision Nutrition Opportunities to Help Mitigate Nutrition and Health Challenges in Low- and Middle-Income Countries: An Expert Opinion Survey

Precision nutrition involves several data collection methods and tools that aim to better inform nutritional recommendations and improve dietary intake, nutritional status, and health outcomes. While the benefits of collecting precise data and designing well-informed interventions are vast, it is presently unclear whether precision nutrition is a relevant approach for tackling nutrition challenges facing populations in low- and middle-income countries (LMIC), considering infrastructure, affordability, and accessibility of approaches. The Swiss Food & Nutrition Valley (SFNV) Precision Nutrition for LMIC project working group assessed the relevance of precision nutrition for LMIC by first conducting an expert opinion survey and then hosting a workshop with nutrition leaders who live or work in LMIC. The experts were interviewed to discuss four topics: nutritional problems, current solutions, precision nutrition, and collaboration. Furthermore, the SFNV Precision Nutrition for LMIC Virtual Workshop gathered a wider group of nutrition leaders to further discuss precision nutrition relevance and opportunities. Our study revealed that precision public health nutrition, which has a clear focus on the stratification of at-risk groups, may offer relevant support for nutrition and health issues in LMIC. However, funding, affordability, resources, awareness, training, suitable tools, and safety are essential prerequisites for implementation and to equitably address nutrition challenges in low-resource communities.

Automated wearable cameras for improving recall of diet and time use in Uganda: a cross-sectional feasibility study

BACKGROUND

Traditional recall approaches of data collection for assessing dietary intake and time use are prone to recall bias. Studies in high- and middle-income countries show that automated wearable cameras are a promising method for collecting objective health behavior data and may improve study participants' recall of foods consumed and daily activities performed. This study aimed to evaluate the feasibility of using automated wearable cameras in rural Eastern Ugandan to collect dietary and time use data.

METHODS

Mothers of young children (n = 211) wore an automated wearable camera on 2 non-consecutive days while continuing their usual activities. The day after wearing the camera, participants' dietary diversity and time use was assessed using an image-assisted recall. Their experiences of the method were assessed via a questionnaire.

RESULTS

Most study participants reported their experiences with the automated wearable camera and image-assisted recall to be good (36%) or very good (56%) and would participate in a similar study in the future (97%). None of the eight study withdrawals could be definitively attributed to the camera. Fifteen percent of data was lost due to device malfunction, and twelve percent of the images were "uncodable" due to insufficient lighting. Processing and analyzing the images were labor-intensive, time-consuming, and prone to human error. Half (53%) of participants had difficulty interpreting the images captured by the camera.

CONCLUSIONS

Using an automated wearable camera in rural Eastern Uganda was feasible, although improvements are needed to overcome the challenges common to rural, low-income country contexts and reduce the burdens posed on both participants and researchers. To improve the quality of data obtained, future automated wearable camera-based image assisted recall studies should use a structured data format to reduce image coding time; electronically code the data in the field, as an output of the image review process, to eliminate ex post facto data entry; and, ideally, use computer-assisted personal interviews software to ensure completion and reduce errors. In-depth formative work in partnership with key local stakeholders (e.g., researchers from low-income countries, representatives from government and/or other institutional review boards, and community representatives and local leaders) is also needed to identify practical approaches to ensuring that the ethical rights of automated wearable camera study participants in low-income countries are adequately protected.

Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women's Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda

Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women’s time use in rural Eastern Ugandan. Women’s (n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland–Altman limits of agreement (LOA) method of analysis and Cronbach’s coefficient alpha (time allocation) or Cohen’s κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (−1.1 to 3.7) and AWC-IAR (−3.2 to 3.2). Cronbach’s alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women’s time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women’s time allocations and nutrition outcomes.

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

BACKGROUND

Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges.

OBJECTIVES

We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making.

METHODS

We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings.

RESULTS

We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data.

CONCLUSION

Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health.

Using Wearable Cameras to Investigate Health-Related Daily Life Experiences: A Literature Review of Precautions and Risks in Empirical Studies

Automated, wearable cameras can benefit health-related research by capturing accurate and objective information about individuals' daily experiences. However, wearable cameras present unique privacy- and confidentiality-related risks due to the possibility of the images capturing identifying or sensitive information from participants and third parties. Although best practice guidelines for ethical research with wearable cameras have been published, limited information exists on the risks of studies using wearable cameras. The aim of this literature review was to survey risks related to using wearable cameras, and precautions taken to reduce those risks, as reported in empirical research. Forty-five publications, comprising 36 independent studies, were reviewed, and findings revealed that participants' primary concerns with using wearable cameras included physical inconvenience and discomfort in certain situations (e.g., public settings). None of the studies reviewed reported any serious adverse events. Although it is possible that reported findings do not include all risks experienced by participants in research with wearable cameras, our findings suggest a low level of risk to participants. However, it is important that investigators adopt recommended precautions, which can promote autonomy and reduce risks, including participant discomfort.

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.

Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system "mentally" gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an "intelligent guess", implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

A Novel Food Record App for Dietary Assessments Among Older Adults With Type 2 Diabetes: Development and Usability Study

Background

More than 1 in 4 people in the United States aged 65 years and older have type 2 diabetes. For diabetes care, medical nutrition therapy is recommended as a clinically effective intervention. Previous researchers have developed and validated dietary assessment methods using images of food items to improve the accuracy of self-reporting over traditional methods. Nevertheless, little is known about the usability of image-assisted dietary assessment methods for older adults with diabetes.

Objective

The aims of this study were (1) to create a food record app for dietary assessments (FRADA) that would support image-assisted dietary assessments, and (2) to evaluate the usability of FRADA for older adults with diabetes.

Methods

For the development of FRADA, we identified design principles that address the needs of older adults and implemented three fundamental tasks required for image-assisted dietary assessments: capturing, viewing, and transmitting images of food based on the design principles. For the usability assessment of FRADA, older adults aged 65 to 80 years (11 females and 3 males) were assigned to interact with FRADA in a lab-based setting. Participants’ opinions of FRADA and its usability were determined by a follow-up survey and interview. As an evaluation indicator of usability, the responses to the survey, including an after-scenario questionnaire, were analyzed. Qualitative data from the interviews confirmed the responses to the survey.

Results

We developed a smartphone app that enables older adults with diabetes to capture, view, and transmit images of food items they consumed. The findings of this study showed that FRADA and its instructions for capturing, viewing, and transmitting images of food items were usable for older adults with diabetes. The survey showed that participants found FRADA easy to use and would consider using FRADA daily. The analysis of the qualitative data from interviews revealed multiple categories, such as the usability of FRADA, potential benefits of using FRADA, potential features to be added to FRADA, and concerns of older adults with diabetes regarding interactions with FRADA.

Conclusions

This study demonstrates in a lab-based setting not only the usability of FRADA by older adults with diabetes but also potential opportunities using FRADA in real-world settings. The findings suggest implications for creating a smartphone app for an image-assisted dietary assessment. Future work still remains to evaluate the feasibility and validity of FRADA with multiple stakeholders, including older adults with diabetes and dietitians.

Validating an automated image identification process of a passive image-assisted dietary assessment method: proof of concept

OBJECTIVE

To validate an automated food image identification system, DietCam, which has not been validated, in identifying foods with different shapes and complexities from passively taken digital images.

DESIGN

Participants wore Sony SmartEyeglass that automatically took three images per second, while two meals containing four foods, representing regular- (i.e., cookies) and irregular-shaped (i.e., chips) foods and single (i.e., grapes) and complex (i.e., chicken and rice) foods, were consumed. Non-blurry images from the meals' first 5 min were coded by human raters and compared with DietCam results. Comparisons produced four outcomes: true positive (rater/DietCam reports yes for food), false positive (rater reports no food; DietCam reports food), true negative (rater/DietCam reports no food) or false negative (rater reports food; DietCam reports no food).

SETTING

Laboratory meal.

PARTICIPANTS

Thirty men and women (25·1 ± 6·6 years, 22·7 ± 1·6 kg/m2, 46·7 % White).

RESULTS

Identification accuracy was 81·2 and 79·7 % in meals A and B, respectively (food and non-food images) and 78·7 and 77·5 % in meals A and B, respectively (food images only). For food images only, no effect of food shape or complexity was found. When different types of images, such as 100 % food in the image and on the plate, <100 % food in the image and on the plate and food not on the plate, were analysed separately, images with food on the plate had a slightly higher accuracy.

CONCLUSIONS

DietCam shows promise in automated food image identification, and DietCam is most accurate when images show food on the plate.

Counting Bites With Bits: Expert Workshop Addressing Calorie and Macronutrient Intake Monitoring

BACKGROUND

Conventional diet assessment approaches such as the 24-hour self-reported recall are burdensome, suffer from recall bias, and are inaccurate in estimating energy intake. Wearable sensor technology, coupled with advanced algorithms, is increasingly showing promise in its ability to capture behaviors that provide useful information for estimating calorie and macronutrient intake.

OBJECTIVE

This paper aimed to summarize current technological approaches to monitoring energy intake on the basis of expert opinion from a workshop panel and to make recommendations to advance technology and algorithms to improve estimation of energy expenditure.

METHODS

A 1-day invitational workshop sponsored by the National Science Foundation was held at Northwestern University. A total of 30 participants, including population health researchers, engineers, and intervention developers, from 6 universities and the National Institutes of Health participated in a panel discussing the state of evidence with regard to monitoring calorie intake and eating behaviors.

RESULTS

Calorie monitoring using technological approaches can be characterized into 3 domains: (1) image-based sensing (eg, wearable and smartphone-based cameras combined with machine learning algorithms); (2) eating action unit (EAU) sensors (eg, to measure feeding gesture and chewing rate); and (3) biochemical measures (eg, serum and plasma metabolite concentrations). We discussed how each domain functions, provided examples of promising solutions, and highlighted potential challenges and opportunities in each domain. Image-based sensor research requires improved ground truth (context and known information about the foods), accurate food image segmentation and recognition algorithms, and reliable methods of estimating portion size. EAU-based domain research is limited by the understanding of when their systems (device and inference algorithm) succeed and fail, need for privacy-protecting methods of capturing ground truth, and uncertainty in food categorization. Although an exciting novel technology, the challenges of biochemical sensing range from a lack of adaptability to environmental effects (eg, temperature change) and mechanical impact, instability of wearable sensor performance over time, and single-use design.

CONCLUSIONS

Conventional approaches to calorie monitoring rely predominantly on self-reports. These approaches can gain contextual information from image-based and EAU-based domains that can map automatically captured food images to a food database and detect proxies that correlate with food volume and caloric intake. Although the continued development of advanced machine learning techniques will advance the accuracy of such wearables, biochemical sensing provides an electrochemical analysis of sweat using soft bioelectronics on human skin, enabling noninvasive measures of chemical compounds that provide insight into the digestive and endocrine systems. Future computing-based researchers should focus on reducing the burden of wearable sensors, aligning data across multiple devices, automating methods of data annotation, increasing rigor in studying system acceptability, increasing battery lifetime, and rigorously testing validity of the measure. Such research requires moving promising technological solutions from the controlled laboratory setting to the field.

Compliance Rates, Advantages, and Drawbacks of a Smartphone-Based Method of Collecting Food History and Foodborne Illness Data

HIGHLIGHTS

The use of wearable cameras in assessing children's dietary intake and behaviours in China

The use of lifelogging device in dietary assessments can reduce misreporting and underreporting, which are common in the previous studies conventional methods. We performed the first study in Chinese children (primary school Grade 4) that applied the wearable cameras in assisting dietary recall. Children (n = 52) wore the wearable cameras (Narrative Clip 2) for seven consecutive days, during which they completed a 3-day 24-h dietary recall at home. Then children modified their dietary recalls at school by reviewing the photos taken by the wearable camera at school, with the assistance of the investigator, and generated the camera-assisted 24-h dietary recalls. Compared with camera-assisted dietary recalls, 8% (n = 160) and 1% (n = 11) of food items were underreported (i.e. not reported at all) and misreported (i.e. reported in an incorrect amount) by dietary recalls without camera-assistance, respectively. Dietary recalls without camera assistance underestimated daily energy intake by 149 ± 182 kcal/d (8%) in comparison to the camera-assisted dietary recalls. Foods consumed on the snacking occasions (40%) were more likely to be underreported than those consumed at main meals (P < 0.001). Beverages (37%), fruits (30%), snacks and desserts (16%) were foods most likely to be inaccurately reported. Children were satisfied with the wearable cameras, with a median score 5.0 (IQR: 5.0-5.0) for most features. Wearable cameras hold promise for improving accuracy of dietary intake assessment in children, providing rich objective information on dietary behaviours, and received high level of satisfaction and compliance of the users. Our results suggest that the accuracy of dietary recall among Chinse school-aged children could be improved by wearable camera, especially avoiding underreporting in the snacking occasions.

Dietary Intake Reporting Accuracy of the Bridge2U Mobile Application Food Log Compared to Control Meal and Dietary Recall Methods

Mobile technology introduces opportunity for new methods of dietary assessment. The purpose of this study was to compare the reporting accuracy of a mobile food log application and 24 h recall method to a controlled meal among a convenience sample of adults (18 years of age or older). Participants were recruited from a community/university convenience sample. Participants consumed a pre-portioned control meal, completed mobile food log entry (mfood log), and participated in a dietary recall administered by a registered dietitian (24R). Height, weight, and application use survey data were collected. Sign test, Pearson’s correlation, and descriptive analyses were conducted to examine differences in total and macronutrient energy intake and describe survey responses. Bland Altman plots were examined for agreement between energy intake from control and 24R and mfood log. The 14 included in the analyses were 78.6% female, 85.7% overweight/obese, and 64.3% African American. Mean total energy, protein, and fat intakes reported via the mfood log were significantly (p < 0.05) lower compared to the control, by 268.31kcals, 20.37 g, and 19.51 g, respectively. Only 24R mean fat intake was significantly (p < 0.01) lower than the control, by 6.43 g. Significant associations (r = 0.57–0.60, p < 0.05) were observed between control and mfood log mean energy, carbohydrate, and protein intakes, as well as between control and 24R mean energy (r = 0.64, p = 0.01) and carbohydrate (r = 0.81, p < 0.001) intakes. Bland Altman plots showed wide limits of agreement, which were not statistically significant but may have practical limitations for individual dietary assessment. Responses indicated the ease of and likelihood of daily mfood log use. This study demonstrates that the Bridge2U mfood log is valid for the assessment of group level data, but data may vary too widely for individual assessment. Further investigation is warranted for nutrition intervention research.

Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review

Background

Self-management is a critical component of chronic disease management and can include a host of activities, such as adhering to prescribed medications, undertaking daily care activities, managing dietary intake and body weight, and proactively contacting medical practitioners. The rise of technologies (mobile phones, wearable cameras) for health care use offers potential support for people to better manage their disease in collaboration with their treating health professionals. Wearable cameras can be used to provide rich contextual data and insight into everyday activities and aid in recall. This information can then be used to prompt memory recall or guide the development of interventions to support self-management. Application of wearable cameras to better understand and augment self-management by people with chronic disease has yet to be investigated.

Objective

The objective of our review was to ascertain the scope of the literature on the use of wearable cameras for self-management by people with chronic disease and to determine the potential of wearable cameras to assist people to better manage their disease.

Methods

We conducted a scoping review, which involved a comprehensive electronic literature search of 9 databases in July 2017. The search strategy focused on studies that used wearable cameras to capture one or more modifiable lifestyle risk factors associated with chronic disease or to capture typical self-management behaviors, or studies that involved a chronic disease population. We then categorized and described included studies according to their characteristics (eg, behaviors measured, study design or type, characteristics of the sample).

Results

We identified 31 studies: 25 studies involved primary or secondary data analysis, and 6 were review, discussion, or descriptive articles. Wearable cameras were predominantly used to capture dietary intake, physical activity, activities of daily living, and sedentary behavior. Populations studied were predominantly healthy volunteers, school students, and sports people, with only 1 study examining an intervention using wearable cameras for people with an acquired brain injury. Most studies highlighted technical or ethical issues associated with using wearable cameras, many of which were overcome.

Conclusions

This scoping review highlighted the potential of wearable cameras to capture health-related behaviors and risk factors of chronic disease, such as diet, exercise, and sedentary behaviors. Data collected from wearable cameras can be used as an adjunct to traditional data collection methods such as self-reported diaries in addition to providing valuable contextual information. While most studies to date have focused on healthy populations, wearable cameras offer promise to better understand self-management of chronic disease and its context.

Automatic food detection in egocentric images using artificial intelligence technology

OBJECTIVE

To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.

DESIGN

To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.

RESULTS

A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.

CONCLUSIONS

The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

Scaling up Dietary Data for Decision-Making in Low-Income Countries: New Technological Frontiers

Dietary surveys in low-income countries (LICs) are hindered by low investment in the necessary research infrastructure, including a lack of basic technology for data collection, links to food composition information, and data processing. The result has been a dearth of dietary data in many LICs because of the high cost and time burden associated with dietary surveys, which are typically carried out by interviewers using pencil and paper. This study reviewed innovative dietary assessment technologies and gauged their suitability to improve the quality and time required to collect dietary data in LICs. Predefined search terms were used to identify technologies from peer-reviewed and gray literature. A total of 78 technologies were identified and grouped into 6 categories: 1) computer- and tablet-based, 2) mobile-based, 3) camera-enabled, 4) scale-based, 5) wearable, and 6) handheld spectrometers. For each technology, information was extracted on a number of overarching factors, including the primary purpose, mode of administration, and data processing capabilities. Each technology was then assessed against predetermined criteria, including requirements for respondent literacy, battery life, requirements for connectivity, ability to measure macro- and micronutrients, and overall appropriateness for use in LICs. Few technologies reviewed met all the criteria, exhibiting both practical constraints and a lack of demonstrated feasibility for use in LICs, particularly for large-scale, population-based surveys. To increase collection of dietary data in LICs, development of a contextually adaptable, interviewer-administered dietary assessment platform is recommended. Additional investments in the research infrastructure are equally important to ensure time and cost savings for the user.

Yum-Me: A Personalized Nutrient-Based Meal Recommender System

Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people’s food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals’ nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist’s superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from itemwise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

When Personal Tracking Becomes Social: Examining the Use of Instagram for Healthy Eating

Many people appropriate social media and online communities in their pursuit of personal health goals, such as healthy eating or increased physical activity. However, people struggle with impression management, and with reaching the right audiences when they share health information on these platforms. Instagram, a popular photo-based social media platform, has attracted many people who post and share their food photos. We aim to inform the design of tools to support healthy behaviors by understanding how people appropriate Instagram to track and share food data, the benefits they obtain from doing so, and the challenges they encounter. We interviewed 16 women who consistently record and share what they eat on Instagram. Participants tracked to support themselves and others in their pursuit of healthy eating goals. They sought social support for their own tracking and healthy behaviors and strove to provide that support for others. People adapted their personal tracking practices to better receive and give this support. Applying these results to the design of health tracking tools has the potential to help people better access social support.

The Kunming CalFit study: modeling dietary behavioral patterns using smartphone data

Human behavioral interventions aimed at improving health can benefit from objective wearable sensor data and mathematical models. Smartphone-based sensing is particularly practical for monitoring behavioral patterns because smartphones are fairly common, are carried by individuals throughout their daily lives, offer a variety of sensing modalities, and can facilitate various forms of user feedback for intervention studies. We describe our findings from a smartphone-based study, in which an Android-based application we developed called CalFit was used to collect information related to young adults' dietary behaviors. In addition to monitoring dietary patterns, we were interested in understanding contextual factors related to when and where an individual eats, as well as how their dietary intake relates to physical activity (which creates energy demand) and psychosocial stress. 12 participants were asked to use CalFit to record videos of their meals over two 1-week periods, which were translated into nutrient intake by trained dietitians. During this same period, triaxial accelerometry was used to assess each subject's energy expenditure, and GPS was used to record time-location patterns. Ecological momentary assessment was also used to prompt subjects to respond to questions on their phone about their psychological state. The GPS data were processed through a web service we developed called Foodscoremap that is based on the Google Places API to characterize food environments that subjects were exposed to, which may explain and influence dietary patterns. Furthermore, we describe a modeling framework that incorporates all of these information to dynamically infer behavioral patterns that may be used for future intervention studies.

A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing

Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.

Exploring the opportunities for food and drink purchasing and consumption by teenagers during their journeys between home and school: a feasibility study using a novel method

OBJECTIVE

To investigate the feasibility and acceptability of using wearable cameras as a method to capture the opportunities for food and drink purchasing/consumption that young people encounter on their regular journeys to and from school.

DESIGN

A qualitative study using multiple data-collection methods including wearable cameras, global positioning system units, individual interviews, food and drink purchase and consumption diaries completed by participants over four days, and an audit of food outlets located within an 800 m Euclidean buffer zone around each school.

SETTING

A community setting.

SUBJECTS

Twenty-two students (fourteen girls and eight boys) aged 13-15 years recruited from four secondary schools in two counties of England.

RESULTS

Wearable cameras offered a feasible and acceptable method for collecting food purchase and consumption data when used alongside traditional methods of data collection in a small number of teenagers. We found evidence of participants making deliberate choices about whether or not to purchase/consume food and drink on their journeys. These choices were influenced by priorities over money, friends, journey length, travel mode and ease of access to opportunities for purchase/consumption. Most food and drink items were purchased/consumed within an 800 m Euclidean buffer around school, with items commonly selected being high in energy, fat and sugar. Wearable camera images combined with interviews helped identify unreported items and misreporting errors.

CONCLUSIONS

Wearable camera images prompt detailed discussion and generate contextually specific information which could offer new insights and understanding around eating behaviour patterns. The feasibility of scaling up the use of these methods requires further empirical work.

Acceptability and feasibility of smartphone-assisted 24 h recalls in the Chinese population

OBJECTIVE

To examine the acceptability and feasibility of using smartphone technology to assess beverage intake and evaluate whether the feasibility of smartphone use is greater among key sub-populations.

DESIGN

An acceptability and feasibility study of recording the video dietary record, the acceptability of the ecological momentary assessment (EMA), wearing smartphones and whether the videos helped participants recall intake after a cross-over validation study.

SETTING

Rural and urban area in Shanghai, China.

SUBJECTS

Healthy adults (n 110) aged 20-40 years old.

RESULTS

Most participants reported that the phone was acceptable in most aspects, including that videos were easy to use (70%), helped with recalls (77%), EMA reminders helped them record intake (75%) and apps were easy to understand (85%). However, 49% of the participants reported that they had trouble remembering to take videos of the beverages before consumption or 46% felt embarrassed taking videos in front of others. Moreover, 72% reported that the EMA reminders affected their consumption. When assessing overall acceptability of using smartphones, 72% of the participants were favourable responders. There were no statistically significant differences in overall acceptability for overweight v. normal-weight participants or for rural v. urban residents. However, we did find that the overall acceptability was higher for males (81%) than females (61%, P=0·017).

CONCLUSIONS

Our study did not find smartphone technology helped with dietary assessments in a Chinese population. However, simpler approaches, such as using photographs instead of videos, may be more feasible for enhancing 24 h dietary recalls.

The case for diet: a safe and efficacious strategy for secondary stroke prevention

Diet is strongly associated with risk for first stroke. In particular, observational and experimental research suggests that a Mediterranean-type diet may reduce risk for first ischemic stroke with an effect size comparable to statin therapy. These data for first ischemic stroke suggest that diet may also be associated with risk for recurrent stroke and that diet modification might represent an effective intervention for secondary prevention. However, research on dietary pattern after stroke is limited and direct experimental evidence for a therapeutic effect in secondary prevention does not exist. The uncertain state of science in this area is reflected in recent guidelines on secondary stroke prevention from the American Heart Association, in which the Mediterranean-type diet is listed with only a class IIa recommendation (level of evidence C). To change guidelines and practice, research is needed, starting with efforts to better define current nutritional practices of stroke patients. Food frequency questionnaires and mobile applications for real-time recording of intake are available for this purpose. Dietary strategies for secondary stroke prevention are low risk, high potential, and warrant further evaluation.

Usability of a smartphone food picture app for assisting 24-hour dietary recall: a pilot study

BACKGROUND/OBJECTIVES

The Recaller app was developed to help individuals record their food intakes. This pilot study evaluated the usability of this new food picture application (app), which operates on a smartphone with an embedded camera and Internet capability.

SUBJECTS/METHODS

Adults aged 19 to 28 years (23 males and 22 females) were assigned to use the Recaller app on six designated, nonconsecutive days in order to capture an image of each meal and snack before and after eating. The images were automatically time-stamped and uploaded by the app to the Recaller website. A trained nutritionist administered a 24-hour dietary recall interview 1 day after food images were taken. Participants' opinions of the Recaller app and its usability were determined by a follow-up survey. As an evaluation indicator of usability, the number of images taken was analyzed and multivariate Poisson regression used to model the factors determining the number of images sent.

RESULTS

A total of 3,315 food images were uploaded throughout the study period. The median number of images taken per day was nine for males and 13 for females. The survey showed that the Recaller app was easy to use, and 50% of the participants would consider using the app daily. Predictors of a higher number of images were as follows: greater interval (hours) between the first and last food images sent, weekend, and female.

CONCLUSIONS

The results of this pilot study provide valuable information for understanding the usability of the Recaller smartphone food picture app as well as other similarly designed apps. This study provides a model for assisting nutrition educators in their collection of food intake information by using tools available on smartphones. This innovative approach has the potential to improve recall of foods eaten and monitoring of dietary intake in nutritional studies.

Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall

Preliminary research has suggested that wearable cameras may reduce under-reporting of energy intake (EI) in self-reported dietary assessment. The aim of the present study was to test the validity of a wearable camera-assisted 24 h dietary recall against the doubly labelled water (DLW) technique. Total energy expenditure (TEE) was assessed over 15 d using the DLW protocol among forty adults (n 20 males, age 35 (sd 17) years, BMI 27 (sd 4) kg/m2 and n 20 females, age 28 (sd 7) years, BMI 22 (sd 2) kg/m2). EI was assessed using three multiple-pass 24 h dietary recalls (MP24) on days 2-4, 8-10 and 13-15. On the days before each nutrition assessment, participants wore an automated wearable camera (SenseCam (SC)) in free-living conditions. The wearable camera images were viewed by the participants following the completion of the dietary recall, and their changes in self-reported intakes were recorded (MP24+SC). TEE and EI assessed by the MP24 and MP24+SC methods were compared. Among men, the MP24 and MP24+SC measures underestimated TEE by 17 and 9%, respectively (P< 0.001 and P= 0.02). Among women, these measures underestimated TEE by 13 and 7%, respectively (P< 0.001 and P= 0.004). The assistance of the wearable camera (MP24+SC) reduced the magnitude of under-reporting by 8% for men and 6% for women compared with the MP24 alone (P< 0.001 and P< 0.001). The increase in EI was predominantly from the addition of 265 unreported foods (often snacks) as revealed by the participants during the image review. Wearable cameras enhance the accuracy of self-report by providing passive and objective information regarding dietary intake. High-definition image sensors and increased imaging frequency may improve the accuracy further.

Image-assisted dietary assessment: a systematic review of the evidence

Images captured during eating episodes provide objective information to assist in the assessment of dietary intake. Images are captured using handheld devices or wearable cameras, and can support traditional self-report or provide the primary record of dietary intake. A diverse range of image-assisted methods have been developed and evaluated but have not been previously examined together. Therefore, a review was undertaken to examine all studies that have evaluated or validated image-assisted methods of dietary assessment for assessing dietary energy intake. Identified image-assisted methods that employ similar methodologies were grouped for comparison. English-language full-text research articles published between January 1998 and November 2013 were searched using five electronic databases. A search of reference lists and associated websites was also conducted. Thirteen studies that evaluated 10 unique image-assisted methods among adults aged 18 to 70 years were included. Ten studies used handheld devices and three studies used wearable cameras. Eight studies evaluated image-based food records, two studies explored the use of images to enhance written food records, and three studies evaluated image-assisted 24-hour dietary recalls. Results indicate images enhance self-report by revealing unreported foods and identify misreporting errors not captured by traditional methods alone. Moreover, when used as the primary record of dietary intake, images can provide valid estimates of energy intake. However, image-assisted methods that rely on image analysis can be prone to underestimation if users do not capture images of satisfactory quality before all foods are consumed. Further validation studies using criterion measures are warranted. The validity among children, adolescents, and elderly persons as well as the feasibility of using image-assisted methods in large samples needs to be examined. Additional research is also needed to better understand the potential applications and pitfalls of wearable cameras.

Development and pilot testing of 24-hour multiple-pass recall to assess dietary intake of toddlers of Somali- and Iraqi-born mothers living in Norway

The aim of this study was to develop, test, and evaluate a 24-h recall procedure to assess the dietary intake of toddlers of Somali- and Iraqi-born mothers living in Norway. A protocol for a 24-h multiple-pass recall procedure, registration forms, and visual tools (a picture library for food identification and portion size estimation) was developed and tested in 12 mothers from Somalia and Iraq with children aged 10-21 months. Five female field workers were recruited and trained to conduct the interviews. Evaluation data for the 24-h recall procedure were collected from both the mothers and the field workers. Nutrient intake was calculated using a Norwegian dietary calculation system. Each child's estimated energy intake was compared with its estimated energy requirement. Both the mothers and the field workers found the method feasible and the visual tools useful. The estimated energy intake corresponded well with the estimated energy requirement for most of the children (within mean ± 2 SD, except for three). The pilot study identified the need for additional foods in the picture library and some crucial aspects in training and supervising the field workers to reduce sources of error in the data collection.

Feasibility and validity of mobile phones to assess dietary intake

Current limitations of conventional dietary assessment methods restrict the establishment of diet-disease relationships and efficacy of dietary interventions. Technology, in particular the use of mobile phones, may help resolve methodologic limitations, in turn improving the validity of dietary assessment and research and associated findings. This review aims to evaluate the validity, feasibility, and acceptability of dietary assessment methods that have been deployed on mobile phone platforms. In August 2013, electronic databases for health sciences were searched for English, peer-reviewed, full-text articles, published from January 1, 2001 onward; and accompanied by a hand search of available relevant publications from universities and government bodies. Studies were not limited by design, length, setting, or population group. Of 194 articles, 12 met eligibility criteria: mobile phone as the dietary recording platform and validation of energy and/or macronutrient intake against another dietary or biological reference method. Four dietary recoding methods had been validated on mobile phone platforms: electronic food diary, food photograph-assisted self-administered, 24 h recall, food photograph analysis by trained dietitians, and automated food photograph analysis. All mobile phone dietary assessment methods showed similar, but not superior, validity or reliability when compared with conventional methods. Participants' satisfaction and preferences for mobile phone dietary assessment methods were higher than those for conventional methods, indicating the need for further research. Validity testing in larger and more diverse populations, over longer durations is required to evaluate the efficacy of these methods in dietary research.

A Mobile Phone User Interface for Image-Based Dietary Assessment

Many chronic diseases, including obesity and cancer, are related to diet. Such diseases may be prevented and/or successfully treated by accurately monitoring and assessing food and beverage intakes. Existing dietary assessment methods such as the 24-hour dietary recall and the food frequency questionnaire, are burdensome and not generally accurate. In this paper, we present a user interface for a mobile telephone food record that relies on taking images, using the built-in camera, as the primary method of recording. We describe the design and implementation of this user interface while stressing the solutions we devised to meet the requirements imposed by the image analysis process, yet keeping the user interface easy to use.

Dietary approaches to assessing iron-related nutrition

PURPOSE OF REVIEW

Both dietary and nondietary factors contribute to iron deficiency, the most common nutritional deficiency worldwide. Identifying dietary factors associated with iron deficiency is challenging due to the number of components in food affecting iron absorption. This review describes recent advances in dietary approaches to assessing iron-related nutrition.

RECENT FINDINGS

Most research investigating the relationship between dietary intake and iron deficiency has focussed on individual foods and nutrients, despite several components in foods influencing iron absorption. More recently, studies have considered the overall diet and combinations of foods eaten, through the analysis of dietary patterns and practices. This includes the development and validation of dietary assessment tools to assess iron-related dietary patterns.

SUMMARY

Dietary pattern analysis which considers the whole diet and combinations of foods eaten may enhance our understanding of how diet impacts on iron deficiency. The analysis of dietary patterns offers an alternative and complementary approach to the traditional focus on individual foods and nutrients when investigating dietary factors associated with iron deficiency.

Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration

Dietary assessment is important in health maintenance and intervention in many chronic conditions, such as obesity, diabetes, and cardiovascular disease. However, there is currently a lack of convenient methods for measuring the volume of food (portion size) in real-life settings. We present a computational method to estimate food volume from a single photographical image of food contained in a typical dining plate. First, we calculate the food location with respect to a 3D camera coordinate system using the plate as a scale reference. Then, the food is segmented automatically from the background in the image. Adaptive thresholding and snake modeling are implemented based on several image features, such as color contrast, regional color homogeneity and curve bending degree. Next, a 3D model representing the general shape of the food (e.g., a cylinder, a sphere, etc.) is selected from a pre-constructed shape model library. The position, orientation and scale of the selected shape model are determined by registering the projected 3D model and the food contour in the image, where the properties of the reference are used as constraints. Experimental results using various realistically shaped foods with known volumes demonstrated satisfactory performance of our image based food volume measurement method even if the 3D geometric surface of the food is not completely represented in the input image.

Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake

BACKGROUND/OBJECTIVES

The SenseCam is a camera worn on a lanyard around the neck that automatically captures point-of-view images in response to movement, heat and light (every 20-30 s). This device may enhance the accuracy of self-reported dietary intake by assisting participants' recall of food and beverage consumption. It was the objective of this study to evaluate if the wearable camera, SenseCam, can enhance the 24-h dietary recall by providing visual prompts to improve recall of food and beverage consumption.

SUBJECT/METHODS

Thirteen volunteer adults in Oxford, United Kingdom, were recruited. Participants wore the SenseCam for 2 days while continuing their usual daily activities. On day 3, participants' diets were assessed using an interviewer-administered 24-h recall. SenseCam images were then shown to the participants and any additional dietary information that participants provided after viewing the images was recorded. Energy and macronutrient intakes were compared between the 24-h recall and 24-h recall+SenseCam.

RESULTS

Data from 10 participants were included in the final analysis (8 males and 2 females), mean age 33 ± 11 years, mean BMI 25.9 ± 5.1 kg/m(2). Viewing the SenseCam images increased self-reported energy intake by approximately 1432 ± 1564 kJ or 12.5% compared with the 24-h recall alone (P=0.02). The increase was predominantly due to reporting of 41 additional foods (241 vs 282 total foods) across a range of food groups. Eight changes in portion size were made, which resulted in a negligible change to energy intake.

CONCLUSIONS

Wearable cameras are promising method to enhance the accuracy of self-reported dietary assessment methods.

The smartphone as a platform for wearable cameras in health research

BACKGROUND

The Microsoft SenseCam, a small camera that is worn on the chest via a lanyard, increasingly is being deployed in health research. However, the SenseCam and other wearable cameras are not yet in widespread use because of a variety of factors. It is proposed that the ubiquitous smartphones can provide a more accessible alternative to SenseCam and similar devices.

PURPOSE

To perform an initial evaluation of the potential of smartphones to become an alternative to a wearable camera such as the SenseCam.

METHODS

In 2012, adults were supplied with a smartphone, which they wore on a lanyard, that ran life-logging software. Participants wore the smartphone for up to 1 day and the resulting life-log data were both manually annotated and automatically analyzed for the presence of visual concepts. The results were compared to prior work using the SenseCam.

RESULTS

In total, 166,000 smartphone photos were gathered from 47 individuals, along with associated sensor readings. The average time spent wearing the device across all users was 5 hours 39 minutes (SD=4 hours 11 minutes). A subset of 36,698 photos was selected for manual annotation by five researchers. Software analysis of these photos supports the automatic identification of activities to a similar level of accuracy as for SenseCam images in a previous study.

CONCLUSIONS

Many aspects of the functionality of a SenseCam largely can be replicated, and in some cases enhanced, by the ubiquitous smartphone platform. This makes smartphones good candidates for a new generation of wearable sensing devices in health research, because of their widespread use across many populations. It is envisioned that smartphones will provide a compelling alternative to the dedicated SenseCam hardware for a number of users and application areas. This will be achieved by integrating new types of sensor data, leveraging the smartphone's real-time connectivity and rich user interface, and providing support for a range of relatively sophisticated applications.

An ethical framework for automated, wearable cameras in health behavior research

Technologic advances mean automated, wearable cameras are now feasible for investigating health behaviors in a public health context. This paper attempts to identify and discuss the ethical implications of such research, in relation to existing guidelines for ethical research in traditional visual methodologies. Research using automated, wearable cameras can be very intrusive, generating unprecedented levels of image data, some of it potentially unflattering or unwanted. Participants and third parties they encounter may feel uncomfortable or that their privacy has been affected negatively. This paper attempts to formalize the protection of all according to best ethical principles through the development of an ethical framework. Respect for autonomy, through appropriate approaches to informed consent and adequate privacy and confidentiality controls, allows for ethical research, which has the potential to confer substantial benefits on the field of health behavior research.

Innovations in national nutrition surveys

The aim of this paper is to describe innovations taking place in national nutrition surveys in the UK and the challenges of undertaking innovations in such settings. National nutrition surveys must be representative of the overall population in characteristics such as socio-economic circumstances, age, sex and region. High response rates are critical. Dietary assessment innovations must therefore be suitable for all types of individuals, from the very young to the very old, for variable literacy and/or technical skills, different ethnic backgrounds and life circumstances, such as multiple carers and frequent travel. At the same time, national surveys need details on foods consumed. Current advances in dietary assessment use either technological innovations or simplified methods; neither lend themselves to national surveys. The National Diet and Nutrition Survey (NDNS) rolling programme, and the Diet and Nutrition Survey of Infants and Young Children (DNSIYC), currently use the 4-d estimated diary, a compromise for detail and respondent burden. Collection of food packaging enables identification of specific products. Providing space for location of eating, others eating, the television being on and eating at a table, adds to eating context information. Disaggregation of mixed dishes enables determination of true intakes of meat and fruit and vegetables. Measurement of nutritional status requires blood sampling and processing in DNSIYC clinics throughout the country and mobile units were used to optimise response. Hence, innovations in national surveys can and are being made but must take into account the paramount concerns of detail and response rate.