Michelle A. Morris

Development of a UK online 24-h dietary assessment tool: myfood24

Assessment of diet in large epidemiological studies can be costly and time consuming. An automated dietary assessment system could potentially reduce researcher burden by automatically coding food records. myfood24 (Measure Your Food on One Day) an online 24-h dietary assessment tool (with the flexibility to be used for multiple 24 h-dietary recalls or as a food diary), has been developed for use in the UK population. Development of myfood24 was a multi-stage process. Focus groups conducted with three age groups, adolescents (11–18 years) (n = 28), adults (19–64 years) (n = 24) and older adults (≥65 years) (n = 5) informed the development of the tool, and usability testing was conducted with beta (adolescents n = 14, adults n = 8, older adults n = 1) and live (adolescents n = 70, adults n = 20, older adults n = 4) versions. Median system usability scale (SUS) scores (measured on a scale of 0–100) in adolescents and adults were marginal for the beta version (adolescents median SUS = 66, interquartile range (IQR) = 20; adults median SUS = 68, IQR = 40) and good for the live version (adolescents median SUS = 73, IQR = 22; adults median SUS = 80, IQR = 25). Myfood24 is the first online 24-h dietary recall tool for use with different age groups in the UK. Usability testing indicates that myfood24 is suitable for use in UK adolescents and adults.

What is the cost of a healthy diet? Using diet data from the UK Women's Cohort Study

Background A healthy diet is important to promote health and well-being while preventing chronic disease. However, the monetary cost of consuming such a diet can be a perceived barrier. This study will investigate the cost of consuming a range of dietary patterns. Methods A cross-sectional analysis, where cost of diet was assigned to dietary intakes recorded using a Food Frequency Questionnaire. A mean daily diet cost was calculated for seven data-driven dietary patterns. These dietary patterns were given a healthiness score according to how well they comply with the UK Department of Healths Eatwell Plate guidelines. This study involved ∼35 000 women recruited in the 1990s into the UK Womens Cohort Study. Results A significant positive association was observed between diet cost and healthiness of the diet (p for trend >0.001). The healthiest dietary pattern was double the price of the least healthy, £6.63/day and £3.29/day, respectively. Dietary diversity, described by the patterns, was also shown to be associated with increased cost. Those with higher education and a professional or managerial occupation were more likely to consume a healthier diet. Conclusions A healthy diet is more expensive to the consumer than a less healthy one. In order to promote health through diet and reduce potential inequalities in health, it seems sensible that healthier food choices should be made more accessible to all.

Cohort Profile: The UK Women’s Cohort Study (UKWCS)

Cohort Profile: The UK Women’s Cohort Study (UKWCS) Janet E Cade,* Victoria J Burley, Nisreen A Alwan, Jayne Hutchinson, Neil Hancock, Michelle A Morris, Diane E Threapleton and Darren C Greenwood School of Food Science and Nutrition, Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK, Department of Health Sciences, University of York, York, UK, School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, Academic Unit of Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK and School of Geography, University of Leeds, Leeds, UK

Using Geographic Information Systems to measure retail food environments: Discussion of methodological considerations and a proposed reporting checklist (Geo-FERN)

Abstract Geographic Information Systems (GIS) are widely used to measure retail food environments. However the methods used are hetrogeneous, limiting collation and interpretation of evidence. This problem is amplified by unclear and incomplete reporting of methods. This discussion (i) identifies common dimensions of methodological diversity across GIS‐based food environment research (data sources, data extraction methods, food outlet construct definitions, geocoding methods, and access metrics), (ii) reviews the impact of different methodological choices, and (iii) highlights areas where reporting is insufficient. On the basis of this discussion, the Geo‐FERN reporting checklist is proposed to support methodological reporting and interpretation. HighlightsGIS methods pertaining to the retail food environment (RFE) are varied.Reporting of GIS methods pertaining to the RFE are often unclear or incomplete.Evaluation and interpretation of research is limited due to inconsistent reporting.The Geo‐FERN checklist supports clear and comprehensive reporting.The Geo‐FERN checklist also supports critical appraisal of RFE research.

Comparability of methods assigning monetary costs to diets: derivation from household till receipts versus cost database estimation using 4-day food diaries

Background/Objectives:Diet cost could influence dietary patterns, with potential health consequences. Assigning a monetary cost to diet is challenging, and there are contrasting methods in the literature. This study compares two methods—a food cost database linked to 4-day diet diaries and an individual cost calculated from household till receipts.Subjects/Methods:The Diet and Nutrition Tool for Evaluation (DANTE) had supermarket prices (cost per 100 g) added to its food composition table. Agreement between diet costs calculated using DANTE from food diaries and expenditure recorded using food purchase till receipts for 325 individuals was assessed using correlation and Bland Altman (BA) plots.Results:The mean difference between the methods’ estimates was £0.10. The BA showed 95% limits of agreement of £2.88 and -£3.08. Excluding the highest 5% of diet cost values from each collection method reduced the mean difference to £0.02, with limits of agreement ranging from £2.31 to -£2.35. Agreement between the methods was stronger for males and for adults.Conclusions:Diet cost estimates using a food price database with 4-day food diaries are comparable to recorded expenditure from household till receipts at the population or group level. At the individual level, however, estimates differed by as much as £3.00 per day. The methods agreed less when estimating diet costs of children, females or those with more expensive diets.

Weight status and breast cancer incidence in the UK Women's Cohort Study: a survival analysis

Abstract Background Breast cancer is the most common type of cancer in the UK, even though it predominantly affects women. Some research suggests that obesity before menopause can be protective against breast cancer, whereas postmenopausal obesity poses increased risk. Because of a period of latency in the development of cancer, longitudinal cohort studies are essential. The aim of this research was to investigate the association between body-mass index (BMI) and breast cancer incidence. Methods The UK Womens Cohort Study was established in the 1990s to investigate patterns between diet and health. 35 000 women were recruited and followed up via cancer incidence and mortality data reports from the National Health Service information centre. Self-reported height and weight were used to calculate BMI. WHO cut-off points were used to define BMI categories. Age-adjusted Cox proportional hazards regression was applied with Stata (version IC12). Findings 1445 (4%) of 35 372 women developed breast cancer during a median time to follow-up of 14·9 years (IQR 1·4). Cases prevalent at baseline were excluded. There were significant differences between the observed and expected outcomes by BMI category (p=0·0003, log-rank test), with underweight and normal weight having lower observed than expected incidence. Overweight and obese women had higher observed than expected incidence. Test for trend of survivor functions showed a significant trend of increasing breast cancer incidence with increasing BMI for the whole cohort (p=0·0001) and the postmenopausal subgroup (p=0·0001), but not for premenopausal women. Hazard ratios in an age-adjusted model were significant in postmenopausal women: underweight 1·0 (reference); normal weight 1·6, 95% CI 0·8–3·2; overweight 2·0, 1·0–4·1; obese 2·1, 1·0–4·3. Interpretation We found a clear association between BMI and breast cancer incidence, especially in postmenopausal women. We did not see an association in premenopausal women. These results are supportive of existing scientific literature and highlight the importance of maintaining a healthy weight in adulthood, especially after the menopause. Health-care professionals should consider referral of overweight and obese women to weight loss services as a protective measure before menopause. Funding This research was funded as part of an Economic and Social Research Council/Medical Research Council interdisciplinary PhD studentship.

How has big data contributed to obesity research? A review of the literature

There has been growing interest in the potential of ‘big data’ to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are ‘found’ (as opposed to ‘made’), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of ‘found’ data to obesity research to date, and describe the benefits and challenges encountered. A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which ‘found’ data can complement conventional ‘made’ data: firstly, in moving beyond constraints in scope (coverage, size and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard-to-access population groups; and lastly in the potential for evaluating real-world interventions. Alongside these opportunities, ‘found’ data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage. Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.

Classification of Westminster Parliamentary constituencies using e-petition data

In a representative democracy it is important that politicians have knowledge of the desires, aspirations and concerns of their constituents. Opportunities to gauge these opinions are however limited and, in the era of novel data, thoughts turn to what alternative, secondary, data sources may be available to keep politicians informed about local concerns. One such source of data are signatories to electronic petitions (e-petitions). Such e-petitions have risen greatly in popularity over the past decade and allow members of the public to initiate and sign an e-petition online, with popular e-petitions resulting in media attention, a response from the government or ultimately a debate in parliament. These data are thus novel in their availability and have not yet been widely used for research purposes. In this article we will use the e-petition data to show how semantic classes of Westminster Parliamentary constituencies, fitted as Gaussian finite mixture models via EM algorithm, can be used to typify constituencies. We identify four classes: Domestic Liberals; International Liberals; Nostalgic Brits and Rural Concerns, and illustrate how they map onto electoral results. The findings and the utility of this approach to incorporate new e-petitions and adapt to changes in electoral geography are discussed.

Development and usability of myfood24: an online 24-hour dietary assessment tool

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Exploring the Feasibility of Use of An Online Dietary Assessment Tool (myfood24) in Women with Gestational Diabetes

myfood24 is an online 24 hr dietary recall tool developed for nutritional epidemiological research. Its clinical application has been unexplored. This mixed methods study explores the feasibility and usability of myfood24 as a food record in a clinical population, women with gestational diabetes (GDM). Women were asked to complete five myfood24 food records, followed by a user questionnaire (including the System Usability Scale (SUS), a measure of usability), and were invited to participate in a semi-structured interview. Of the 199 participants, the mean age was 33 years, mean booking body mass index (BMI) 29.7 kg/m2, 36% primiparous, 57% White, 33% Asian. Of these, 121 (61%) completed myfood24 at least once and 73 (37%) completed the user questionnaire; 15 were interviewed. The SUS was found to be good (mean 70.9, 95% CI 67.1, 74.6). Interviews identified areas for improvement, including optimisation for mobile devices, and as a clinical management tool. This study demonstrates that myfood24 can be used as an online food record in a clinical population, and has the potential to support self-management in women with GDM. However, results should be interpreted cautiously given the responders’ demographic characteristics. Further research to explore the barriers and facilitators of uptake in people from ethnic minority and lower socioeconomic backgrounds is recommended.