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The increasing ubiquity of smartphones provides a potential new data source to capture physical activity behaviours. Though not designed as a research tool, these secondary data have the potential to capture a large population over a more extensive spatial area and with longer temporality than current methods afford. This paper uses one such secondary data source from a commercial app designed to incentivise activity. We explore the new insights these data provide, alongside the sociodemographic profile of those using physical activity apps, to gain insight into both physical activity behaviour and determinants of app usage in order to evaluate the suitability of the app in providing insights into the physical activity of the population. We find app usage to be higher in females, those aged 25–50, and users more likely to live in areas where a higher proportion of the population are of a lower socioeconomic status. We ascertain longer-term patterns of app usage with increasing age and more male users reaching physical activity guideline recommendations despite longer daily activity duration recorded by female users. Additionally, we identify key weekly and seasonal trends in physical activity. This is one of the first studies to utilise a large volume of secondary physical activity app data to co-investigate usage alongside activity behaviour captured.
Francesca Pontin; Nik Lomax; Graham Clarke; Michelle A. Morris. Socio-demographic determinants of physical activity and app usage from smartphone data. Social Science & Medicine 2021, 284, 114235 .
AMA StyleFrancesca Pontin, Nik Lomax, Graham Clarke, Michelle A. Morris. Socio-demographic determinants of physical activity and app usage from smartphone data. Social Science & Medicine. 2021; 284 ():114235.
Chicago/Turabian StyleFrancesca Pontin; Nik Lomax; Graham Clarke; Michelle A. Morris. 2021. "Socio-demographic determinants of physical activity and app usage from smartphone data." Social Science & Medicine 284, no. : 114235.
Survival analysis with cohort study data has been traditionally performed using Cox proportional hazards models. Random survival forests (RSFs), a machine learning method, now present an alternative method. Using the UK Women’s Cohort Study (n = 34,493) we evaluate two methods: a Cox model and an RSF, to investigate the association between Body Mass Index and time to breast cancer incidence. Robustness of the models were assessed by cross validation and bootstraping. Histograms of bootstrap coefficients are reported. C-Indices and Integrated Brier Scores are reported for all models. In post-menopausal women, the Cox model Hazard Ratios (HR) for Overweight (OW) and Obese (O) were 1.25 (1.04, 1.51) and 1.28 (0.98, 1.68) respectively and the RSF Odds Ratios (OR) with partial dependence on menopause for OW and O were 1.34 (1.31, 1.70) and 1.45 (1.42, 1.48). HR are non-significant results. Only the RSF appears confident about the effect of weight status on time to event. Bootstrapping demonstrated Cox model coefficients can vary significantly, weakening interpretation potential. An RSF was used to produce partial dependence plots (PDPs) showing OW and O weight status increase the probability of breast cancer incidence in post-menopausal women. All models have relatively low C-Index and high Integrated Brier Score. The RSF overfits the data. In our study, RSF can identify complex non-proportional hazard type patterns in the data, and allow more complicated relationships to be investigated using PDPs, but it overfits limiting extrapolation of results to new instances. Moreover, it is less easily interpreted than Cox models. The value of survival analysis remains paramount and therefore machine learning techniques like RSF should be considered as another method for analysis.
Georgios Aivaliotis; Jan Palczewski; Rebecca Atkinson; Janet E. Cade; Michelle A. Morris. A comparison of time to event analysis methods, using weight status and breast cancer as a case study. Scientific Reports 2021, 11, 1 -9.
AMA StyleGeorgios Aivaliotis, Jan Palczewski, Rebecca Atkinson, Janet E. Cade, Michelle A. Morris. A comparison of time to event analysis methods, using weight status and breast cancer as a case study. Scientific Reports. 2021; 11 (1):1-9.
Chicago/Turabian StyleGeorgios Aivaliotis; Jan Palczewski; Rebecca Atkinson; Janet E. Cade; Michelle A. Morris. 2021. "A comparison of time to event analysis methods, using weight status and breast cancer as a case study." Scientific Reports 11, no. 1: 1-9.
COVID-19 is a disease that has been shown to have outcomes that vary by certain socio-demographic and socio-economic groups. It is increasingly important that an understanding of these outcomes should be derived not from the consideration of one aspect, but by a more multi-faceted understanding of the individual. In this study use is made of a recent obesity driven classification of participants in the United Kingdom Biobank (UKB) to identify trends in COVID-19 outcomes. This classification is informed by a recently created obesity systems map, and the COVID-19 outcomes are: undertaking a test, a positive test, hospitalisation and mortality. It is demonstrated that the classification is able to identify meaningful differentials in these outcomes. This more holistic approach is recommended for identification and prioritisation of COVID-19 risk and possible long-COVID determination.
Stephen Clark; Michelle Morris; Nik Lomax; Mark Birkin. Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study? International Journal of Obesity 2021, 1 -5.
AMA StyleStephen Clark, Michelle Morris, Nik Lomax, Mark Birkin. Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study? International Journal of Obesity. 2021; ():1-5.
Chicago/Turabian StyleStephen Clark; Michelle Morris; Nik Lomax; Mark Birkin. 2021. "Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?" International Journal of Obesity , no. : 1-5.
Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.
Stephen Clark; Becky Shute; Victoria Jenneson; Tim Rains; Mark Birkin; Michelle Morris. Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles. Nutrients 2021, 13, 1481 .
AMA StyleStephen Clark, Becky Shute, Victoria Jenneson, Tim Rains, Mark Birkin, Michelle Morris. Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles. Nutrients. 2021; 13 (5):1481.
Chicago/Turabian StyleStephen Clark; Becky Shute; Victoria Jenneson; Tim Rains; Mark Birkin; Michelle Morris. 2021. "Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles." Nutrients 13, no. 5: 1481.
This work seeks to introduce improvements to the traditional variable selection procedures employed in the development of geodemographic classifications. It presents a proposal for shifting from a traditional approach for generating general-purpose one-size-fits-all geodemographic classifications to application-specific classifications. This proposal addresses the recent scepticism towards the utility of general-purpose applications by employing supervised machine learning techniques in order to identify contextually relevant input variables from which to develop geodemographic classifications with increased discriminatory power. A framework introducing such techniques in the variable selection phase of geodemographic classification development is presented via a practical use-case that is focused on generating a geodemographic classification with an increased capacity for discriminating the propensity for Library use in the UK city of Leeds. Two local classifications are generated for the city, one a general-purpose classification, and the other, an application-specific classification incorporating supervised Feature Selection methods in the selection of input variables. The discriminatory power of each classification is evaluated and compared, with the result successfully demonstrating the capacity for the application-specific approach to generate a more contextually relevant result, and thus underpins increasingly targeted public policy decision making, particularly in the context of urban planning.
Amanda Otley; Michelle Morris; Andy Newing; Mark Birkin. Local and Application-Specific Geodemographics for Data-Led Urban Decision Making. Sustainability 2021, 13, 4873 .
AMA StyleAmanda Otley, Michelle Morris, Andy Newing, Mark Birkin. Local and Application-Specific Geodemographics for Data-Led Urban Decision Making. Sustainability. 2021; 13 (9):4873.
Chicago/Turabian StyleAmanda Otley; Michelle Morris; Andy Newing; Mark Birkin. 2021. "Local and Application-Specific Geodemographics for Data-Led Urban Decision Making." Sustainability 13, no. 9: 4873.
New plans to restrict in‐store price and location‐based promotions of less healthy foods and drinks in the UK aimed to encourage healthier choices. With responsibility for implementation likely falling to food retailers, it is important to understand the feasibility of implementation and to ensure policy success. To ensure compliance, retailers will need to assess which products are restricted under the legislation. The large number of products in retailers’ portfolios poses a problem of scale. A recent research case study found the data available to retailers to be insufficient to accurately apply the rules‐based approach set out by the policy proposal. Misclassification would result in some less healthy products being incorrectly promoted and vice versa. Problems with implementation feasibility have the potential to undermine the public health goals of the legislation. Interviews were carried out with nutrition representatives from the UK food retail and manufacturing sector, to understand the real‐world implications of the proposed legislation. Industry nutritionists recommended a review of the use of the UK’s Nutrient Profiling Model as the legislative basis, proposed data‐related solutions to implementation problems and suggested a need for shared retailer‐manufacturer responsibility, given the context of data availability.
V. Jenneson; M. A. Morris. Data considerations for the success of policy to restrict in‐store food promotions: A commentary from a food industry nutritionist consultation. Nutrition Bulletin 2021, 46, 40 -51.
AMA StyleV. Jenneson, M. A. Morris. Data considerations for the success of policy to restrict in‐store food promotions: A commentary from a food industry nutritionist consultation. Nutrition Bulletin. 2021; 46 (1):40-51.
Chicago/Turabian StyleV. Jenneson; M. A. Morris. 2021. "Data considerations for the success of policy to restrict in‐store food promotions: A commentary from a food industry nutritionist consultation." Nutrition Bulletin 46, no. 1: 40-51.
In this short communication we demonstrate how an individual level classification built using a Whole Systems approach to an understanding of obesity can be used to profile individual’s exposure, treatment and mortality for COVID-19. The cohort is the UK Biobank and the information on COVID-19 test outcomes, hospitalisations and mortality are provided as part of this research initiative. We find that the cohort profiles accurately against the understood heightened risk factors for COVID-19, namely age, gender, ethnicity, obesity and deprivation. This confidence in these data then allows us to profile the participants in each of the classification clusters for these COVID-19 outcomes. We see that there is a large degree of differentiation between the classes. The article finishes by highlighting how this classification can help in prioritising care, treatments and vaccine delivery.
Stephen Clark; Mark Birkin; Nik Lomax; Michelle Morris. Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study? 2020, 1 .
AMA StyleStephen Clark, Mark Birkin, Nik Lomax, Michelle Morris. Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study? . 2020; ():1.
Chicago/Turabian StyleStephen Clark; Mark Birkin; Nik Lomax; Michelle Morris. 2020. "Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?" , no. : 1.
The UK government plans to limit price‐based and location‐based promotions for products high in saturated fat, salt and sugars. The 2004/2005 UK Nutrient Profiling Model (NPM) is the proposed legislative basis, but may be superseded by the draft 2018 NPM. This study develops an algorithm to apply both NPMs to a large food composition database (FCDB), and assesses implementation challenges. UK NPMs were applied algorithmically to the myfood24 FCDB, representing ~45 000 retail products. Pass rates – indicating free or restricted promotions – and micronutrient compositions were compared. Challenges were assessed, and recommendations addressed the legislation’s public consultation questions. For products in scope (75% of total), 6% fewer passed the 2018 NPM (36%, P < 0.001) compared with the 2004/2005 NPM (42%). Beverages showed the greatest reduction in pass rate (75%). Under both models, micronutrient contents (per 100 g of product) were generally lower for products that passed; except folate, vitamin C and vitamin D were no different for passed and failed products. Compared with products passing the 2004/2005 NPM, products passing the 2018 NPM on average had marginally higher amounts of iron (0.05 mg, 95% CI: 0.02, 0.08, P < 0.001) and magnesium (1.00 mg, 95% CI: 0.00, 1.17, P = 0.029), but marginally lower levels of calcium (−0.42 mg, 95% CI: −2.00, −0.40, P = 0.025). Missing ingredient information and heterogeneous product categories were challenges for both NPMs. Free sugars calculation further complicated 2018 NPM application. To balance feasibility and public health benefit, the proposed legislative basis may not be appropriate.
V. Jenneson; D. C. Greenwood; G. P. Clarke; N. Hancock; J. E. Cade; M. A. Morris. Restricting promotions of ‘less healthy’ foods and beverages by price and location: A big data application of UK Nutrient Profiling Models to a retail product dataset. Nutrition Bulletin 2020, 45, 389 -402.
AMA StyleV. Jenneson, D. C. Greenwood, G. P. Clarke, N. Hancock, J. E. Cade, M. A. Morris. Restricting promotions of ‘less healthy’ foods and beverages by price and location: A big data application of UK Nutrient Profiling Models to a retail product dataset. Nutrition Bulletin. 2020; 45 (4):389-402.
Chicago/Turabian StyleV. Jenneson; D. C. Greenwood; G. P. Clarke; N. Hancock; J. E. Cade; M. A. Morris. 2020. "Restricting promotions of ‘less healthy’ foods and beverages by price and location: A big data application of UK Nutrient Profiling Models to a retail product dataset." Nutrition Bulletin 45, no. 4: 389-402.
The number of people who are obese and overweight presents a global challenge, and the development of effective interventions is hampered by a lack of research which takes in to account a joined up, whole systems approach to understanding the drivers of the phenomena. We need to better understand the collective characteristics and behaviours of the overweight and obese population and how these differ from those who maintain a healthy weight. Using the UK Biobank cohort of 500 000 adults, we develop an obesity classification system using k-means clustering. Variable selection from UK Biobank is informed by the Foresight whole system obesity map across key domains (Societal Influences, Individual Psychology, Individual Physiology, Individual Physical Activity, Physical Activity Environment). This paper presents the first study of UK Biobank participants to adopt this whole systems approach. Our classification identifies six groups of people, similar in respect to their exposure to known drivers of obesity: ‘Younger, active and working hard’, ‘Retirees with good lifestyle’ , ‘Stressed, sedentary and struggling’, Older with poor lifestyle’, ‘Younger, busy professionals’ and ‘Younger, fitter families’. Pen portraits are developed to describe the characteristics of these different groups. Multinomial logistic regression is used to demonstrate that the classification can effectively detect groups of individuals more likely to be overweight or obese. The group identified as ‘Younger, fitter families’ are observed to have a higher proportion of healthy weight, while three groups have increased relative risk of being overweight or obese: ‘Younger, active and working hard’, ‘Stressed, sedentary and struggling’ and ‘Older with poor lifestyles’. This work presents an innovative new approach to better understand the whole systems drivers of obesity which has the potential to produce meaningful tools for policy makers to better target interventions across the whole system to reduce overweight and obesity.
Stephen Clark; Mark Birkin; Nik Lomax; Michelle Morris. Developing a whole systems obesity classification for the UK Biobank Cohort. 2020, 1 .
AMA StyleStephen Clark, Mark Birkin, Nik Lomax, Michelle Morris. Developing a whole systems obesity classification for the UK Biobank Cohort. . 2020; ():1.
Chicago/Turabian StyleStephen Clark; Mark Birkin; Nik Lomax; Michelle Morris. 2020. "Developing a whole systems obesity classification for the UK Biobank Cohort." , no. : 1.
BACKGROUND Novel consumer and lifestyle data, for example those collected by supermarket loyalty cards or mobile phone exercise tracking apps, offer numerous benefits for researchers wishing to understand diet and exercise related risk factors for diseases. Yet, limited research has addressed public attitudes towards linking these data with individual health records for research purposes. OBJECTIVE The aim of this research was to identify key barriers for data linkage and recommend safeguards and procedures that would encourage individuals to share these data for potential future research. METHODS The LifeInfo Survey consulted the public on their attitudes towards sharing consumer and lifestyle data for research purposes. Where barriers to data sharing existed, participants provided unstructured survey responses detailing what would make them more likely to share data for linkage with their health record in the future. The topic modelling technique Latent Dirichlet Allocation (LDA) was used to analyse these textual responses to uncover common thematic topics within the texts. RESULTS Participants provided responses related to sharing their store loyalty card data (n = 2,338) and health/fitness app data (n = 1,531). Key barriers to data sharing identified through topic modelling included: data safety and security, personal privacy, requirements of further information, fear of data being accessed by others, problems with data accuracy, not understanding the reason for data linkage and not using data production services. We provide recommendations for addressing these issues to establish best practice for future researchers wishing to utilise these data. CONCLUSIONS This study formulates large-scale consultation of public attitudes towards data linkage of this kind, as such, it is an important first step in understanding and addressing barriers to participation for research utilising novel consumer and lifestyle data.
Holly Clarke; Stephen Clark; Mark Birkin; Heather Iles-Smith; Adam Glaser; Michelle Morris. Understanding barriers to linking novel consumer and lifestyle data for health research, results from the LifeInfo Survey: a topic modelling approach (Preprint). 2020, 1 .
AMA StyleHolly Clarke, Stephen Clark, Mark Birkin, Heather Iles-Smith, Adam Glaser, Michelle Morris. Understanding barriers to linking novel consumer and lifestyle data for health research, results from the LifeInfo Survey: a topic modelling approach (Preprint). . 2020; ():1.
Chicago/Turabian StyleHolly Clarke; Stephen Clark; Mark Birkin; Heather Iles-Smith; Adam Glaser; Michelle Morris. 2020. "Understanding barriers to linking novel consumer and lifestyle data for health research, results from the LifeInfo Survey: a topic modelling approach (Preprint)." , no. : 1.
Novel consumer and lifestyle data, such as those collected by supermarket loyalty cards or mobile phone exercise tracking apps, offer numerous benefits for researchers seeking to understand diet- and exercise-related risk factors for diseases. However, limited research has addressed public attitudes toward linking these data with individual health records for research purposes. Data linkage, combining data from multiple sources, provides the opportunity to enhance preexisting data sets to gain new insights. The aim of this study is to identify key barriers to data linkage and recommend safeguards and procedures that would encourage individuals to share such data for potential future research. The LifeInfo Survey consulted the public on their attitudes toward sharing consumer and lifestyle data for research purposes. Where barriers to data sharing existed, participants provided unstructured survey responses detailing what would make them more likely to share data for linkage with their health records in the future. The topic modeling technique latent Dirichlet allocation was used to analyze these textual responses to uncover common thematic topics within the texts. Participants provided responses related to sharing their store loyalty card data (n=2338) and health and fitness app data (n=1531). Key barriers to data sharing identified through topic modeling included data safety and security, personal privacy, requirements of further information, fear of data being accessed by others, problems with data accuracy, not understanding the reason for data linkage, and not using services that produce these data. We provide recommendations for addressing these issues to establish the best practice for future researchers interested in using these data. This study formulates a large-scale consultation of public attitudes toward this kind of data linkage, which is an important first step in understanding and addressing barriers to participation in research using novel consumer and lifestyle data.
Holly Clarke; Stephen Clark; Mark Birkin; Heather Iles-Smith; Adam Glaser; Michelle Morris. Understanding barriers to novel data linkages, results from the LifeInfo Survey: a topic modelling approach (Preprint). Journal of Medical Internet Research 2020, 23, e24236 .
AMA StyleHolly Clarke, Stephen Clark, Mark Birkin, Heather Iles-Smith, Adam Glaser, Michelle Morris. Understanding barriers to novel data linkages, results from the LifeInfo Survey: a topic modelling approach (Preprint). Journal of Medical Internet Research. 2020; 23 (5):e24236.
Chicago/Turabian StyleHolly Clarke; Stephen Clark; Mark Birkin; Heather Iles-Smith; Adam Glaser; Michelle Morris. 2020. "Understanding barriers to novel data linkages, results from the LifeInfo Survey: a topic modelling approach (Preprint)." Journal of Medical Internet Research 23, no. 5: e24236.
Starting university is an important time with respect to dietary changes. This study reports a novel approach to assessing student diet by utilising student-level food transaction data to explore dietary patterns. First-year students living in catered accommodation at the University of Leeds (UK) received pre-credited food cards for use in university catering facilities. Food card transaction data were obtained for semester 1, 2016 and linked with student age and sex. k-Means cluster analysis was applied to the transaction data to identify clusters of food purchasing behaviours. Differences in demographic and behavioural characteristics across clusters were examined using χ2 tests. The semester was divided into three time periods to explore longitudinal changes in purchasing patterns. Seven dietary clusters were identified: ‘Vegetarian’, ‘Omnivores’, ‘Dieters’, ‘Dish of the Day’, ‘Grab-and-Go’, ‘Carb Lovers’ and ‘Snackers’. There were statistically significant differences in sex (P < 0·001), with women dominating the Vegetarian and Dieters, age (P = 0·003), with over 20s representing a high proportion of the Omnivores and time of day of transactions (P < 0·001), with Dieters and Snackers purchasing least at breakfast. Many students (n 474, 60·4 %) changed dietary cluster across the semester. This study demonstrates that transactional data present a feasible method for dietary assessment, collecting detailed dietary information over time and at scale, while eliminating participant burden and possible bias from self-selection, observation and attrition. It revealed that student diets are complex and that simplistic measures of diet, focusing on narrow food groups in isolation, are unlikely to adequately capture dietary behaviours.
M. A. Morris; E. L. Wilkins; M. Galazoula; S. D. Clark; M. Birkin. Assessing diet in a university student population: a longitudinal food card transaction data approach. British Journal of Nutrition 2020, 123, 1406 -1414.
AMA StyleM. A. Morris, E. L. Wilkins, M. Galazoula, S. D. Clark, M. Birkin. Assessing diet in a university student population: a longitudinal food card transaction data approach. British Journal of Nutrition. 2020; 123 (12):1406-1414.
Chicago/Turabian StyleM. A. Morris; E. L. Wilkins; M. Galazoula; S. D. Clark; M. Birkin. 2020. "Assessing diet in a university student population: a longitudinal food card transaction data approach." British Journal of Nutrition 123, no. 12: 1406-1414.
Background/objectiveObesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of ‘big data’ presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). ‘Additional computing power’ introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.Methods and resultsThree case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.ConclusionsThe case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.
Emma Wilkins; Ariadni Aravani; Amy Downing; Adam Drewnowski; Claire Griffiths; Stephen Zwolinsky; Mark Birkin; Seraphim Alvanides; Michelle A. Morris. Evidence from big data in obesity research: international case studies. International Journal of Obesity 2020, 44, 1028 -1040.
AMA StyleEmma Wilkins, Ariadni Aravani, Amy Downing, Adam Drewnowski, Claire Griffiths, Stephen Zwolinsky, Mark Birkin, Seraphim Alvanides, Michelle A. Morris. Evidence from big data in obesity research: international case studies. International Journal of Obesity. 2020; 44 (5):1028-1040.
Chicago/Turabian StyleEmma Wilkins; Ariadni Aravani; Amy Downing; Adam Drewnowski; Claire Griffiths; Stephen Zwolinsky; Mark Birkin; Seraphim Alvanides; Michelle A. Morris. 2020. "Evidence from big data in obesity research: international case studies." International Journal of Obesity 44, no. 5: 1028-1040.
This study examines nutritional intakes in Gestational diabetes mellitus piloting the myfood24 tool, to explore frequency of meals/snacks, and daily distribution of calories and carbohydrates in relation to glycaemic control. A total of 200 women aged 20–43 years were recruited into this prospective observational study between February 2015 and February 2016. Diet was assessed using myfood24, a novel online 24-h dietary recall tool. Out of 200 women 102 completed both ≥1 dietary recalls and all blood glucose measurements. Blood glucose was self-measured as part of usual care. Differences between groups meeting and exceeding glucose targets in relation to frequency of meal/snack consumption and nutrients were assessed using chi-squared and Mann–Whitney tests. Women achieving a fasting glucose target
Michelle A. Morris; Jayne Hutchinson; Carla Gianfrancesco; Nisreen A. Alwan; Michelle C. Carter; Eleanor M. Scott; Janet E. Cade. Relationship of the Frequency, Distribution, and Content of Meals/Snacks to Glycaemic Control in Gestational Diabetes: The myfood24 GDM Pilot Study. Nutrients 2019, 12, 3 .
AMA StyleMichelle A. Morris, Jayne Hutchinson, Carla Gianfrancesco, Nisreen A. Alwan, Michelle C. Carter, Eleanor M. Scott, Janet E. Cade. Relationship of the Frequency, Distribution, and Content of Meals/Snacks to Glycaemic Control in Gestational Diabetes: The myfood24 GDM Pilot Study. Nutrients. 2019; 12 (1):3.
Chicago/Turabian StyleMichelle A. Morris; Jayne Hutchinson; Carla Gianfrancesco; Nisreen A. Alwan; Michelle C. Carter; Eleanor M. Scott; Janet E. Cade. 2019. "Relationship of the Frequency, Distribution, and Content of Meals/Snacks to Glycaemic Control in Gestational Diabetes: The myfood24 GDM Pilot Study." Nutrients 12, no. 1: 3.
BACKGROUND Digital health is an important part of the future of health care, prevention and management of disease and innovative monitoring solutions. With an aging population and rising health related costs, digital health is an essential part of the solution, alongside the emerging big data and associated analytics. To varying extents, digital health and big data are present worldwide. However, consistency in terminology, regulation and implementation differ. As an international network of interdisciplinary experts we review and discuss the digital health and big data landscape. OBJECTIVE We firstly identify current challenges and solutions in digital health development, research, deployment in the management of non-communicable disease and regulation and then go on to establish an ongoing and international collaboration of multidisciplinary researchers and educators; creating opportunities for research and education. METHODS The Digital Health Research Network was established using the Worldwide Universities Network as a platform and a funding resource. The newly formed network harnesses expertise from a wide array of academic disciplines within applications of digital health and big data for health. Meetings took place both electronically and face to face, with a Research Open Day in Sydney and the International Symposium for Digital Health in Hong Kong facilitating wider networking and discussion. RESULTS Many challenges working across disciplines in the digital health area have been identified. These include inconsistent definitions for digital health and big data, a diverse range of digital technologies available across the globe, differences in regulation of such technologies. There is not equity in resources and standards globally. He range of stakeholders involved in digital health and big data relating to health are extensive. It is important that these stakeholders can communicate effectively, with a common technical language. Continued development, education and widening engagement are integral components of developing digital health worldwide. CONCLUSIONS Digital Health is a necessary and sufficient factor in achieving health gains. However, in is critical that digital health is leveraged appropriately and that transformation of interdisciplinary practices can intelligently link digital health with care management processes to make a difference. The new interdisciplinary, International Society for Digital Health aims to provide a platform to facilitate this. CLINICALTRIAL n/a
Simon Poon; Mark Latt; Michelle A Morris; Owen Johnson; Nicholas Fuggle; Zhiyao Duan; Allen Lee; Rachel Oldroyd; Jonathan Penm; Bryant Lin; Martin Zand; Chin Hur; Jeremy Wyatt; Kelvin Tsoi. Overview of Digital Health Research: A Global Multi-Disciplinary Collaborative Alliance from the Worldwide Universities Network (Preprint). 2019, 1 .
AMA StyleSimon Poon, Mark Latt, Michelle A Morris, Owen Johnson, Nicholas Fuggle, Zhiyao Duan, Allen Lee, Rachel Oldroyd, Jonathan Penm, Bryant Lin, Martin Zand, Chin Hur, Jeremy Wyatt, Kelvin Tsoi. Overview of Digital Health Research: A Global Multi-Disciplinary Collaborative Alliance from the Worldwide Universities Network (Preprint). . 2019; ():1.
Chicago/Turabian StyleSimon Poon; Mark Latt; Michelle A Morris; Owen Johnson; Nicholas Fuggle; Zhiyao Duan; Allen Lee; Rachel Oldroyd; Jonathan Penm; Bryant Lin; Martin Zand; Chin Hur; Jeremy Wyatt; Kelvin Tsoi. 2019. "Overview of Digital Health Research: A Global Multi-Disciplinary Collaborative Alliance from the Worldwide Universities Network (Preprint)." , no. : 1.
Big data are part of the future in obesity research. The ESRC funded Strategic Network for Obesity has together generated a series of papers, published in the International Journal for Obesity illustrating various aspects of their utility, in particular relating to the large social and environmental drivers of obesity. This article is the final part of the series and reflects upon progress to date and identifies four areas that require attention to promote the continued role of big data in research. We additionally include a ‘getting started with big data’ checklist to encourage more obesity researchers to engage with alternative data resources.
Mark Birkin; Emma Wilkins; Michelle A. Morris. Creating a long-term future for big data in obesity research. International Journal of Obesity 2019, 43, 2587 -2592.
AMA StyleMark Birkin, Emma Wilkins, Michelle A. Morris. Creating a long-term future for big data in obesity research. International Journal of Obesity. 2019; 43 (12):2587-2592.
Chicago/Turabian StyleMark Birkin; Emma Wilkins; Michelle A. Morris. 2019. "Creating a long-term future for big data in obesity research." International Journal of Obesity 43, no. 12: 2587-2592.
Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed. Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out. A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research. Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.
Michelle A. Morris; Emma Wilkins; Kate A. Timmins; Maria Bryant; Mark Birkin; Claire Griffiths. Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. International Journal of Obesity 2018, 42, 1963 -1976.
AMA StyleMichelle A. Morris, Emma Wilkins, Kate A. Timmins, Maria Bryant, Mark Birkin, Claire Griffiths. Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. International Journal of Obesity. 2018; 42 (12):1963-1976.
Chicago/Turabian StyleMichelle A. Morris; Emma Wilkins; Kate A. Timmins; Maria Bryant; Mark Birkin; Claire Griffiths. 2018. "Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map." International Journal of Obesity 42, no. 12: 1963-1976.
Michelle A. Morris; Mark Birkin. The ESRC Strategic Network for Obesity: tackling obesity with big data. International Journal of Obesity 2018, 42, 1948 -1950.
AMA StyleMichelle A. Morris, Mark Birkin. The ESRC Strategic Network for Obesity: tackling obesity with big data. International Journal of Obesity. 2018; 42 (12):1948-1950.
Chicago/Turabian StyleMichelle A. Morris; Mark Birkin. 2018. "The ESRC Strategic Network for Obesity: tackling obesity with big data." International Journal of Obesity 42, no. 12: 1948-1950.
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.
Carla Gianfrancesco; Zoe Darwin; Linda McGowan; Debbie M. Smith; Roz Haddrill; Michelle Carter; Eleanor M. Scott; Nisreen A. Alwan; Michelle A. Morris; Salwa A. Albar; Janet E. Cade. Exploring the Feasibility of Use of An Online Dietary Assessment Tool (myfood24) in Women with Gestational Diabetes. Nutrients 2018, 10, 1147 .
AMA StyleCarla Gianfrancesco, Zoe Darwin, Linda McGowan, Debbie M. Smith, Roz Haddrill, Michelle Carter, Eleanor M. Scott, Nisreen A. Alwan, Michelle A. Morris, Salwa A. Albar, Janet E. Cade. Exploring the Feasibility of Use of An Online Dietary Assessment Tool (myfood24) in Women with Gestational Diabetes. Nutrients. 2018; 10 (9):1147.
Chicago/Turabian StyleCarla Gianfrancesco; Zoe Darwin; Linda McGowan; Debbie M. Smith; Roz Haddrill; Michelle Carter; Eleanor M. Scott; Nisreen A. Alwan; Michelle A. Morris; Salwa A. Albar; Janet E. Cade. 2018. "Exploring the Feasibility of Use of An Online Dietary Assessment Tool (myfood24) in Women with Gestational Diabetes." Nutrients 10, no. 9: 1147.
The United Kingdom’s 2016 referendum on membership of the European Union is perhaps one of the most important recent electoral events in the UK. This political sentiment has confounded pollsters, media commentators and academics alike, and has challenged elected Members of the Westminster Parliament. Unfortunately, for many areas of the UK this referendum outcome is not known for Westminster Parliamentary Constituencies, rather it is known for the coarser geography of counting areas. This study uses novel data and machine learning algorithms to estimate the Leave vote percentage for these constituencies. The results are seen to correlate well with other estimates.
Stephen D Clark; Michelle A Morris; Nik Lomax. Estimating the outcome of UKs referendum on EU membership using e-petition data and machine learning algorithms. Journal of Information Technology & Politics 2018, 15, 344 -357.
AMA StyleStephen D Clark, Michelle A Morris, Nik Lomax. Estimating the outcome of UKs referendum on EU membership using e-petition data and machine learning algorithms. Journal of Information Technology & Politics. 2018; 15 (4):344-357.
Chicago/Turabian StyleStephen D Clark; Michelle A Morris; Nik Lomax. 2018. "Estimating the outcome of UKs referendum on EU membership using e-petition data and machine learning algorithms." Journal of Information Technology & Politics 15, no. 4: 344-357.