Making the best use of new technologies in the National Diet and Nutrition Survey: a review

.Background Dietary assessment is of paramount importance for public health monitoring. Currently in the UK, the population’s diets are examined by the National Diet and Nutrition Survey Rolling Programme (NDNS RP). In the survey, diet is assessed by a four-day paper-based dietary diary, with accompanying interviews, anthropometric measurements and blood and urine sampling. However, there is growing interest worldwide in the potential for new technologies to assist in data collection for assessment of dietary intake. Published literature reviews have identified the potential of new technologies to improve accuracy, reduce costs, and reduce respondent and researcher burden by automating data capture and the nutritional coding process. However, this is a fast-moving field of research, with technologies developing at a rapid pace, and an updated review of the potential application of new technologies in dietary assessment is warranted. This review was commissioned to identify the new technologies employed in dietary assessment and critically appraise their strengths and limitations in order to recommend which technologies, if any, might be suitable to develop for use in the NDNS RP and other UK population surveys. Objectives The overall aim of the project was to inform the Department of Health of the range of new technologies currently available and in development internationally that have potential to improve, complement or replace the methods used in the NDNS RP. The specific aims were: to generate an itinerary of new and emerging technologies that may be suitable; to systematically review the literature and critically appraise new technologies; and to recommend which of these new technologies, if any, would be appropriate for future use in the NDNS RP. To meet these aims, the project comprised two main facets, a literature review and qualitative research. Literature review data sources The literature review incorporated an extensive search of peer-reviewed and grey literature. The following sources were searched: Cochrane Database of Systematic Reviews (CDSR), Database of Abstracts of Reviews of Effectiveness (DARE), Web of Science Core Collection, Ovid MEDLINE, Ovid MEDLINE In-Process, Embase, NHS EED (Economic Evaluation Database), National Cancer Institute (NCI) Dietary Assessment Calibration/Validation Register, OpenGrey, EPPI Centre (TRoPHI), conference proceedings (ICDAM 2012, ISBNPA 2013, IEEE Xplore, Nutrition Society Irish Section and Summer Meetings 2014), recent issues of journals (Journal of Medical Internet Research, International Journal of Medical Informatics), grants registries (ClinicalTrials.gov, BBSRC, report), national surveys, and mobile phone application stores. In addition, hand-searching of relevant citations was performed. The search also included solicitation of key authors in the field to enquire about Making the best use of new technologies in the NDNS: a review 4 as-yet unpublished articles or reports, and a Bristol Online Survey publicised via social media, society newsletters and meetings. Literature review eligibility criteria Records were screened for eligibility using a three-stage process. Firstly, keyword searches identified obviously irrelevant titles. Secondly, titles and abstracts were screened against the eligibility criteria, following which full-text copies of papers were obtained and, in the third stage of screening, examined against the criteria. Two independent reviewers screened each record at each stage, with discrepancies referred to a third reviewer. Eligibility criteria were pre-specified and agreed by the project Steering Group (Section 1.6). Eligible records included: studies involving technologies, new to the NDNS RP, which can be used to automate or assist the collection of food consumption data and the coding of foods and portion sizes, currently available or beta versions, public domain or commercial; studies that address the development, features, or evaluation of new technology; technologies appropriate for the requirements of the NDNS RP in terms of nutritional analysis, with capacity to collect quantifiable consumption data at the food level; primary sources of information on a particular technology; and journal articles published since the year 2000 or grey literature available from 2011 onwards. The literature search was not limited to Englishlanguage publications, which are included in the itinerary, although data were not extracted from non-English studies. Literature synthesis and appraisal New technologies were categorised into eleven types of technology, and an itinerary was generated of tools falling under each category type. Due to the volume of eligible studies identified by the literature searches, data extraction was limited to the literature focussing on selected exemplar tools of five technology categories (web-based diet diary, web-based 24- hour recall, handheld devices (personal digital assistants and mobile phones), nonautomated cameras to complement traditional methods, and non-automated cameras to replace traditional methods). For each category, at least two exemplars were chosen, and all studies involving the exemplar were included in data extraction and synthesis. Exemplars were selected on the basis of breadth of evidence available, using pre-specified criteria agreed by the Steering Group. Data were extracted by a single reviewer and an evidence summary collated for each exemplar. A quality appraisal checklist was developed to assess the quality of validation studies. The checklist was piloted and applied by two independent reviewers. Studies were not excluded on the basis of quality, but study quality was taken into account when judging the strength of evidence. Due to the heterogeneity of the literature, meta-analyses were not performed. References were managed and screened using the EPPI Reviewer 4 systematic review software. EPPI Reviewer was also used to extract data

Embedding a Grid of Load Cells into a Dining Table for Automatic Monitoring and Detection of Eating Events

This dissertation describes a “smart dining table” that can detect and measure consumption events. This work is motivated by the growing problem of obesity, which is a global problem and an epidemic in the United States and Europe. Chapter 1 gives a background on the economic burden of obesity and its comorbidities. For the assessment of obesity, we briefly describe the classic dietary assessment tools and discuss their drawback and the necessity of using more objective, accurate, low-cost, and in-situ automatic dietary assessment tools. We explain in short various technologies used for automatic dietary assessment such as acoustic-, motion-, or image-based systems. This is followed by a literature review of prior works related to the detection of weights and locations of objects sitting on a table surface. Finally, we state the novelty of this work. In chapter 2, we describe the construction of a table that uses an embedded grid of load cells to sense the weights and positions of objects. The main challenge is aligning the tops of adjacent load cells to within a few micrometer tolerance, which we accomplish using a novel inversion process during construction. Experimental tests found that object weights distributed across 4 to 16 load cells could be measured with 99.97±0.1% accuracy. Testing the surface for flatness at 58 points showed that we achieved approximately 4.2±0.5 um deviation among adjacent 2x2 grid of tiles. Through empirical measurements we determined that the table has a 40.2 signal-to-noise ratio when detecting the smallest expected intake amount (0.5 g) from a normal meal (approximate total weight is 560 g), indicating that a tiny amount of intake can be detected well above the noise level of the sensors. In chapter 3, we describe a pilot experiment that tests the capability of the table to monitor eating. Eleven human subjects were video recorded for ground truth while eating a meal on the table using a plate, bowl, and cup. To detect consumption events, we describe an algorithm that analyzes the grid of weight measurements in the format of an image. The algorithm segments the image into multiple objects, tracks them over time, and uses a set of rules to detect and measure individual bites of food and drinks of liquid. On average, each meal consisted of 62 consumption events. Event detection accuracy was very high, with an F1-score per subject of 0.91 to 1.0, and an F1 score per container of 0.97 for the plate and bowl, and 0.99 for the cup. The experiment demonstrates that our device is capable of detecting and measuring individual consumption events during a meal. Chapter 4 compares the capability of our new tool to monitor eating against previous works that have also monitored table surfaces. We completed a literature search and identified the three state-of-the-art methods to be used for comparison. The main limitation of all previous methods is that they used only one load cell for monitoring, so only the total surface weight can be analyzed. To simulate their operations, the weights of our grid of load cells were summed up to use the 2D data as 1D. Data were prepared according to the requirements of each method. Four metrics were used to evaluate the comparison: precision, recall, accuracy, and F1-score. Our method scored the highest in recall, accuracy, and F1-score; compared to all other methods, our method scored 13-21% higher for recall, 8-28% higher for accuracy, and 10-18% higher for F1-score. For precision, our method scored 97% that is just 1% lower than the highest precision, which was 98%. In summary, this dissertation describes novel hardware, a pilot experiment, and a comparison against current state-of-the-art tools. We also believe our methods could be used to build a similar surface for other applications besides monitoring consumption

Early detection of morbidity in feedlot cattle using pattern recognition techniques

Computer algorithms are routinely used to aid in the identification of biological patterns not easily detected with standard statistics. Currently, observed changes in normal patterns of feeding behavior (FB) are used to identify morbid feedlot cattle. The objective of this study was to use pattern classification techniques to develop algorithms capable of identifying morbid (M) cattle earlier than traditional pen checking methods. In two separate studies, individual feeding behaviour was obtained from 384 feedlot steers (228 ± 22.7 kg, initial BW) in a 226 d trial (model dataset), and 384 feedlot heifers (322 ± 34.7 kg, initial BW) in a 142 d trial (naive dataset). Data was collected using an automated feed bunk monitoring system. FB variables calculated included feeding duration, inter-meal interval (min., max., avg., SD and total; min/d) and feeding frequency (visits/d). Animal health records including the number of times treated, d in the hospital and d on feed were also collected. Ninety-three and 53 morbid (M) animals were identified in each trial respectively, and were categorized into low, moderate and high groups, based on severity of sickness. FB data for 68 cattle from the model dataset (45 classified as Moderate and 25 classified as High) was analyzed to develop an algorithm which would aid in identifying morbid FB. This algorithm was later tested on 18 M animals (12 classified as Moderate and 6 as High) in the naive dataset. The pattern recognition procedure involved reducing data dimensionality via Principal Component Analysis, followed by K-means clustering and finally the development of a binary string to aid in the classification of M feeding behaviour. The developed procedure resulted in an overall classification accuracy of 84 % (82.5 and 85 % accuracy for H and M, respectively) for the model dataset, and 75 % overall (100 and 50 % accuracy for H and M, respectively) for the naive dataset. The model predicted morbidity on average 3.3 and 1.2 d earlier than pen checkers could for each trial respectively. The application of pattern recognition algorithms to FB shows value as a method of identifying morbid cattle in advance of overt physical signs of morbidity