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COVID-19 Vaccination Centre Accessibility

Yellow COVID-19 vaccination centre road sign

COVID-19 Vaccination Centre Accessibility

The need to rapidly rollout COVID-19 vaccinations in England brought issues of geographical accessibility to the fore. Ambitious targets to double-vaccinate every eligible member of the adult population, within a matter of months, presented considerable challenges. The Department of Health and Social Care’s ‘UK COVID-19 Vaccines Delivery Plan’ set ambitious targets for all households to be within 10 miles of their nearest vaccination site. Figures published periodically by the NHS (available here) suggested that almost 100% of households met this target, yet media reports frequently suggested that thousands of households faced challenges in accessing vaccination sites due to impracticably long journeys or lack of available public transport, especially in many rural areas.

Recently published CDRC research, carried out in conjunction with commercial partner HERE Technologies uncovered inequity in vaccination site accessibility, highlighting inequalities that are hidden by NHS-reported assessments of vaccination site coverage.  Our analysis reveals that over 90,000 households – all of which are inferred to lack access to public transport – face modelled journey times in excess of 1 hour to reach a vaccination site, with regional and urban-rural inequity evident (Figure 1).

Figure 1: Average journey times to closest 5 vaccination sites by all modes of transport.

Whilst mass-vaccination centres – some capable of vaccinating thousands of individuals in a day – offered operational efficiencies and economies of scale, these were largely confined to urban areas. Our research highlights the importance of local provision of COVID-19 vaccinations via consortia of GP-practices, and makes recommendations to support the continued delivery of booster jabs and other mass-vaccination programmes.

These analysis were only possible given the availability of open data from NHS England, coupled with comprehensive routing data from HERE Technologies. These data permitted calculation of validated travel times between residential neighbourhoods and vaccination sites, accounting for mode of transport and time of the day. The CDRC is uniquely placed to leverage domain-specific expertise, in this case from colleagues in the School of Geography, University of Leeds, in conjunction with high-quality commercial data such as those supplied by HERE.

The analysis reported here was undertaken by MSc student, Catherine Duffy, as part of the CDRC Masters Dissertation Scheme, which links Masters students with commercial sector partners. Catherine, a Geography Graduate from Leeds, was undertaking the MSc in Data Science and Analytics, and now works in a related role in the commercial sector.

You can read more about the analysis here: https://www.mdpi.com/2076-393X/10/1/50

For enquiries about this work, please contact:  Dr Andy Newing  

For more information on the CDRC Masters dissertation scheme please see: https://www.cdrc.ac.uk/education-and-training/masters-dissertation-scheme/

Physical activity behaviour over time – who, how much and when

Trainer clad feet moving right to left along a thin path in the middle of green grass

Physical activity behaviour over time – who, how much and when

A New Year can mean New Year’s resolutions. Commonly these revolve around weight loss, joining the gym or being more active. Though most people start with good intentions, many resolutions don’t last throughout the year (or even this far into the year!). Equally, many other factors influence how active we are at different points in the year.

With our research, we were interested in identifying whether there are different patterns of activity behaviour in different groups. And, in addition, how these patterns over time were linked to other characteristics such as age, gender and preference of activity type.

We investigated patterns of activity over both the week and the year using daily step count data. Interested in shorter term activity patterns, we wanted to know if people were more active mid-week or on weekends. We were also keen to understand whether activity behaviour was consistent throughout the year or whether it fluctuated across the seasons. Using a cohort of physical activity tracking app users (for more info see previous blog post) we utilised over 30,000 years’ worth of activity across more than 1,000,000 user recorded weeks.

Yearlong activity pattern insights

Looking at behaviour across the year, users fall into seven distinct patterns of behaviours, known as clusters (Figure 1). At the bottom of figure 1 we have the “inactive cluster” (turquoise) who were inactive pretty consistently across the year. On the flipside, the yellow ‘highly active yearlong cluster’ were consistently active throughout the year, on average exceeding 15,000 steps a day (equivalent to around 7 miles a day).

Figure 1: yearlong activity behaviour clusters

Several of the other clusters follow the same pattern as the highly active cluster, but at lower activity intensities (the orange and grey clusters). The spikes at the end of March and October indicate the start and end of UK daylight saving. The impact of daylight saving on activity is demonstrated with higher activity in the summer months (more hours of daylight and generally better weather) compared to the winter months. In contrast to the clusters showing seasonal variation in activity, we also see evidence of users showing both motivated activity behaviour – increasing activity as the year progresses (purple cluster) – and demotivated activity behaviour, where the reverse is true (brown cluster).

Weekly activity patterns insights

Similar analysis investigating weekly behaviours, shown in figure 2, demonstrates how, across the week, users show six distinct patterns of behaviour. Three groups are consistent in their activity level at either a low, somewhat or high physical activity intensity (green, pink and orange clusters). Two groups were more active during the week with lower levels on activity on the weekends, again at different levels of activity intensity, indicative of activity associated with commuting (the brown and grey clusters). The final group, the ‘weekend warriors’, were more active on the weekend than weekdays (teal cluster).

Figure 2: weekly activity behaviour clusters

Consistent activity throughout the week was associated with recording more active minutes overall and was therefore more likely to meet physical activity guidelines than those just active on weekdays or weekends.

Demographic insights

Users in the different clusters also displayed different demographic characteristics. Older age was associated with being in the most active cluster of yearlong behaviour.  A user in the most active cluster was also more likely to be male, but the same is true as well for the inactive cluster. Female users were more likely to be in the moderately active groups, or show motivated or demotivated behaviour across the year, rather than the typical seasonal fluctuations in activity.

Moreover, users tended to record a variety of different weekly activity patterns across the year – for instance, three weeks of weekday active behaviour might be followed by a couple of highly active weeks. Different weekly activity clusters were more predominant in the overall yearlong activity behaviours. Unsurprisingly, the highly active yearlong cluster was made up of users who recorded a large proportion of weeks which were classed as highly active. Those who were active during weekdays were also more likely to be active throughout the year. This enables us to build up the picture of how shorter-term activity patterns contribute to longer term habitual activity.

By targeting policy towards the shorter- and longer-term clusters of behaviour we can increase overall activity. For example, guiding those usually only active during the week to accessible weekend activities such as Parkrun, or suggesting ways to incorporate activity mid-week for the ‘weekend warriors’. Looking at seasonal activity we can start to investigate interventions to stop activity levels dropping with the reduced hours of daylight in winter, such as better street lighting or greater provision of indoor activities.

The full paper is openly available: https://doi.org/10.3390/ijerph182111476

Fran Pontin is a CDRC Research Data Scientist and former Data Analytics and Society CDT student. Her PhD looks at the utility of secondary smartphone app data in capturing physical activity behaviour over a wide spatial scale. With a background in Food Science and Nutrition, her research interests lie in the use of consumer data to capture and better understand health behaviours and drive targeted policy change. Fran also provides Python training on behalf of the CDRC, aiming to make the analysis of large-scale consumer data accessible to a wider audience.

New ESRC funding secures CDRC future!

New ESRC funding secures CDRC future!

The Consumer Data Research Centre has been successful in applying to the Economic and Social Research Council (ESRC) to continue their data services from April 2022 to September 2024.

This officially marks the next phase for the CDRC, during which the Centre intends to build upon its proven vision and strategy, combining a unique, cloud-based data infrastructure with an established framework for supporting innovative research and capacity building.

The award will allow the CDRC to continue to increase the access, creation, use and awareness of DigitalFootprint Data (DFD) such as geo-spatial, commercial, transactional and sensor data, and advance the quality, quantity and impact of social science research.

This latest endorsement by the ESRC marks the most recent milestone in the continuing evolution of the CDRC since 2014. The award will allow the CDRC to build on our successes, supporting thriving interdisciplinary DFD communities to address the most urgent research and policy questions, which have been raised by both the challenges and opportunities of a post-COVID society.

We were thrilled that our bid was given particularly positive feedback for clearly providing “a strong asset and a foundational infrastructure” and “growing number of research outputs”, as well as “sustaining the foundations of innovative, creative and widely-used data infrastructure across the social sciences”.

The positive funding outcome means that, from April 2022, the Centre will be able to not only maintain its existing infrastructure, but also to foster innovation through partnerships that promote further collaboration across multiple disciplines, both within our local institutions and across a wider network of academic and external stakeholders.

This funding was provided via UKRI ESRC’s World Class Labs budget to support expansions and upgrades to existing social science data infrastructure.

Household mixing during COVID-19 lockdowns

Household mixing during COVID-19:
research suggests adherence to lockdowns in England declined over time

The grim prospect of COVID-19 stay-at-home orders is back in the news, with a number of European countries having either introduced new restrictions or reimposed full lockdowns amid rising cases. These developments inevitably raise questions around how we can best shape public health policy to reduce virus transmission.

One key challenge is reducing “risky” interactions between people, such as household visits indoors. We know close proximity and inadequate ventilation increase the chances of the virus spreading, leading to rising rates of illness. Yet our understanding of these household visitation behaviours, and the effectiveness of policy to reduce them, is lacking.

So in our latest research, we analysed mobility data collected from almost one million people in England between January 2020 and May 2021, seeking to understand trends in home visits during the pandemic.

This data was collected via location-based mobile phone apps by the data company Cuebiq, who obtained consent from users for their anonymised data to be used for research purposes. Working with Cuebiq we were able to generate aggregate analyses without obtaining any individual or household data (that is, none of the data we used could be linked to specific people).

Our interest here was in regional and national trends in mobility, and how populations moved around during the pandemic. For each region we developed indicators of visitation rates to residential areas outside of usual home areas, and assessed how these rates varied from baseline levels set in January and February 2020.

What we found

We saw a rapid reduction in people visiting other residential areas during the first lockdown in March 2020. The average decrease over the duration of the first lockdown was 39.3%, while at the lowest point, this activity was 56.4% below baseline levels. Rates of interaction increased prior to the end of the lockdown on May 12, and continued increasing through the spring and summer. But this was a gradual return.

The allowance of support bubbles in June 2020 brought no significant increase in home visits, although a flattening of the rate during August 2020 may indicate that social gatherings moved to restaurants during the operation of the Eat Out to Help Out scheme, or to public outdoor areas with warmer weather. While visitation rates exceed those seen in our baseline months, we can’t tell whether these were indoors or visits to front and back gardens, nor whether the rule of six was followed.

Later national lockdowns saw higher levels of mixing compared to what we observed in March 2020. The second lockdown in November 2020 saw a 15.3% reduction from baseline on average. Activity increased quickly after the end of the November lockdown, potentially due to the run-up to Christmas. The third lockdown, in January 2021, saw around a 26.2% reduction until mid-February.

We observed a significant rise in visitation from mid-February onwards while the third national lockdown continued – within two weeks rising to 23.3% above baseline levels. This increase in activity aligns with the announcement that the UK had offered vaccinations to the first four priority groups, which may have given people confidence to return to social activities at this time.

Graph showing trends in visits to other residential areas.
In this figure, the dots represent the daily mobility data, while the orange line represents a seven-day average. Scientific Reports, Author provided

Taken together, the evidence suggests a slowly declining adherence to the stay-at-home rules as the pandemic went on. The underlying reasons for this will be multifaceted, summarised neatly as “lockdown fatigue”, but more precisely relate to increasing perceptions of safety in the face of the vaccine rollout, a need to re-engage in social activity, declining trust in government, and other personal stresses. These trends tell us we can’t simply pull the same policy leavers and expect to achieve the same outcomes as those seen in March 2020.

There is further variation in our findings when we look at different areas. In general, we observed lower levels of household visitation in rural areas, while some cities (London, Manchester and Cambridge, for example) regularly exceeded pre-COVID activity levels.

The reason for these differences is not clear. It could be linked to factors like household composition and personal circumstances, but further work is needed to better understand the complex demographic and household factors influencing these trends.

The implications

While there are some intriguing patterns of activity in this data, we must also apply plenty of caution in drawing conclusions. We can only speculate on the causes underlying the trends we observe, and the trends we see in England don’t necessarily reflect what has happened, or might happen, elsewhere.

Nevertheless, these findings add to our understanding of the impacts of pandemic policy, and highlight the need for nuance in crafting future interventions.

The patterns of household visitation we observed reflect the social complexities of the pandemic period. We must remember that household visitation does not equate to malicious noncompliance, and instead may point to the need for people to see each other for their emotional wellbeing.

While there are clear public health reasons to encourage caution in social mixing, this must be balanced against the negative outcomes of lockdowns and their potentially diminishing returns. Policy must be crafted to account for these nuances – supporting opportunities to socialise while avoiding higher risk interactions, responding locally, and adapting with the changing attitudes and circumstances faced by the population.

Ed Manley, Professor of Urban Analytics, University of Leeds and Mengdie Zhuang, Lecturer in Data Science, University of Sheffield

This article is republished from The Conversation under a Creative Commons license. Read the original article.

First Findings From IGD Research Trials Announced

Bowl of delicious looking fruit

First Findings From IGD Research Trials Announced

The research results from the first of our behaviour change trials working with the Institute of Grocery Distribution have been announced this week!  Further blogs from members of the team involved will follow in the next few weeks…

IGD press release

Collaboration between IGD, food and consumer goods industry and the University of Leeds helps shift people towards healthier, more sustainable diets

As part of its ambition to make healthy and sustainable diets easy for everyone, IGD is today launching the first results from its in-store behaviour change trials, testing what strategies at point of sale could shift consumers towards making healthier, more sustainable food and drink choices.

To find out what truly drives long-term behaviour change, IGD has joined forces with leading retailers, manufacturers and researchers at the University of Leeds, to put theory into practice with millions of people through a series of real-life behaviour change trials. These first results were taken from promotions across 101 Sainsbury’s stores during a four-week-period in both January 2020 and January 2021.

With 37% of consumers saying that cost prevents them from eating a healthy, sustainable diet1 , the trials tested the hypothesis: reducing the price of fruit and vegetables to 60p in stores across the country, for four weeks, should increase portions sold and variety of products purchased2 using three behaviour change levers. Sales data analysed by the team at The University of Leeds found the number of promoted fruit and vegetable portions sold increased by 78% when the price was reduced.

Susan Barratt, IGD CEO, said: “Obesity is one of the biggest health problems this country faces. Just 1% of the UK population currently meets government healthy eating guidance.3 With our diets having such a huge impact on our health and our planet, now is the time for government, the food and consumer goods industry and shoppers to take collective action. The most impactful way to make a difference is to change what we eat and drink.

“This report explores our initial findings, which already shows a positive impact through nudge tactics, pricing and product placement. This is a hugely exciting project, demonstrating the genuine opportunity our industry has to make healthy and sustainable diets easier and more accessible for everyone.”

As well as the number of promoted fruit or vegetables purchased, fruit and vegetable sales also increased beyond the items on offer. The findings show that promoted fruit and vegetable sales did decline after the promotions ended, although the rate of decline reduced year-on-year, suggesting some consumers carried their healthier eating habits forward.

Further findings from Sainsbury’s – looking at whether consumers continued to eat a greater variety of fruit and vegetables in the year after the trial – will be reported on in 2022.

IGD is leading the way and bringing industry together to collaboratively drive change by implementing the trials, with support from their research partner, the University of Leeds, through its Leeds Institute for Data Analytics (LIDA) and Consumer Data Research Centre (CDRC). LIDA is capturing and measuring sales data from each intervention to assess what levers drive long-term behaviour change to adopting healthier and more sustainable food and drink choices. With learnings from these and further trials that are underway with several UK retailers, which will be shared in 2022, IGD will recommend how industry can effectively shift consumer behaviour towards healthy and sustainable diets.

Dr Michelle Morris, who leads the Nutrition and Lifestyle Analytics team at LIDA/CDRC, said: “Using anonymous sales data at scale, over an extended period of time to understand consumer behaviours and evaluate interventions, is unique and exciting. The collaborative approach to study design, independent analysis and wide dissemination strategy means that we can share learnings across the sector to make the best changes to help consumers purchase healthier and more sustainable choices.”

Use this report to understand how, by working together, the food and consumer goods industry can drive change and trial real-life solutions to inspire others. As part of this work, IGD has also developed a hub of inspiring industry insight, bringing together a wealth of resources to help deliver change in your organisation. Visit the hub to find out why healthy, sustainable diets should be central to your business strategy and see how you can get involved.

For media enquiries please contact Sarah Burns sarah.burns@igd.com / t: 07483 094027.

  1. IGD (2021), Appetite for Change
  2. IGD, Healthy Sustainable Diets: Driving Change, Behavioural Insights Report 2021 – An adult portion of fruit and vegetables is 80g, according to Government guidance
  3. https://bmjopen.bmj.com/content/10/8/e037554

Population movement in Greater London during the first lockdown

Population movement in Greater London during the first lockdown

Researchers from UCL Geography with the Consumer Data Research Centre (CDRC) have published a new study that uses smartphone location data to explore activity patterns in Greater London during the first lockdown.

Professor James Cheshire and PhD researcher Terje Trasberg used anonymised smartphone data from over 300,000 devices to measure population mobility from the early period of the pandemic last March to the easing of the first lockdown last summer.

The study aimed to identify socioeconomic characteristics that could explain the differing rates of decline in population movement across neighbourhoods.

(Credit: Mary Hinkley)

Their analysis revealed the division between areas dominated by white- and blue-collar jobs, the latter showing a much smaller reduction in activity during the lockdown.

This finding highlights a divide between those who can work from home and those with jobs that must be carried out on-site. This has important implications for transport, retail and post-COVID-19 recovery policy.

Terje Trasberg said: “By linking the mobile location data to the broader demographic characteristics, we were able to provide additional insights into the impacts of mobility restrictions in different demographic groups across Greater London.

“We hoped that our analysis can offer a more nuanced insight into why the effectiveness of social distancing interventions appeared to vary between areas. The data also signals those areas likely to require the most support during a post-pandemic recovery phase as activity is slower to return.”

For more information on the study, read the CDRC’s data story. The full study can be accessed here.

(First published at www.geog.ucl.ac.uk/news-events/news/new-study-explores-population-movement-in-first-lockdown, written by Samuel Kelly, Communications & Marketing Officer, UCL Geography)

Smartphone Apps and Activity – tracking trends in who, how and when we move

Someone doing up a shoelace and wearing a fitness tracker watch

Smartphone apps and activity – tracking trends in who, how and when we move

The advantages of being physically active have never been more apparent, with proven benefits across a wide range of health conditions. Traditionally, we might consider the beneficial role of physical activity to be in reducing obesity incidence and preventing non-communicable diseases, such as cardiovascular disease and type 2 diabetes. However, the COVID-19 pandemic has thrown further positives into the spotlight, as being physically active has been shown to reduce the risk of severe COVID-19 outcomes. Moreover, lockdowns and state-sanctioned time for exercise highlighted the importance of physical activity to mental health and wellbeing.

Physical inactivity is responsible for 1 in 6 deaths in the UK (equivalent to the risk from smoking1), with 1 in 3 men and 1 in 2 women not meeting the recommended 150 minutes of moderate to vigorous activity a week1. To reduce physical inactivity, we need to identify and remove the barriers to being active. These barriers are diverse and wide ranging, varying from person to person. Examples include, but are not limited to: increasingly sedentary occupations, time or monetary constraints and environments that do not support activity.

To best identify what and where these barriers to being active are, we need to establish a good understanding of where, when and how people are active. However, studies investigating physical activity behaviour are typically limited by sample sizes, small study areas and shorter study durations.

Increasingly, individuals are monitoring their own activity and fitness levels using smartphone apps or wearable trackers such as Fitbit, Garmin or smartwatches. Secondary use of these consumer data can provide researchers with new insights into physical activity behaviour. In this research, we use secondary app data provided by FUELL Ltd‘s Bounts app (available for use by researchers via application to the CDRC). We evaluate how useful secondary smartphone data are in providing insight into how active the public are. To do this, we first need to assess how representative app users are of the population as a whole. Finally we uncover key activity behaviours associated with different age and gender user profiles.

The app – who is using it?

The Bounts app was commercially available on all major app provider stores, with users earning points for activities which could later be exchanged for vouchers and prizes. All user data is pseudonymised and no identifiable user information is shared with the researchers. Additionally, data is only accessible to those with data security training and in a data secure environment.

We used the data of 30,804 app users who recorded seven or more days of activity in 2016. With an average user age of 39, women make up a significantly larger proportion of app users (77.7% of users). 43.8% of users provided a postcode district which we linked to the Office for National Statistics socioeconomic classification. Unlike traditional studies, which tend to underrepresent lower socio-economic groups, we found there was no substantial socioeconomic difference in the areas where Bounts users lived compared to the general population.

Research highlights

Seasonal and weekly trends in physical activity behaviour

Users recorded on average 218 days of activity, which is substantially longer than the typical seven-day data collection period in traditional physical activity studies. Thanks to this long monitoring period, we were able to observe distinct patterns in activity behaviour across weekly and seasonal timeframes.

Across the year, we can see the role daylight saving plays, with a higher number of activities recorded by users over the summer months when evenings are longer, dropping off in autumn as the days get shorter (Figure 1).

Figure 1 – Seasonal trend heatmap of total daily activity recorded by all app users
Figure 2 – Heatmap of total daily activity recorded by all app users standardised by week, highlighting weekly patterns of behaviour

We can also see a weekly pattern in activity behaviour with the highest number of activities recorded mid-week, peaking on Tuesdays (Figure 2). Higher weekday activity levels are suspected to be functional activity around commuting behaviours. This goes against the ‘weekend warrior’ theory that individuals tend to exercise more on the weekends and less on weekdays.

A higher level of functional activity is associated with women and those in less affluent socioeconomic groups. This corresponds to our user sample which has a high proportion of women and captures users from less affluent socioeconomic groups, who are usually underrepresented in physical activity studies.

Who is meeting the physical activity guidelines?

For each week that a user recorded activity, we calculated whether the culmination of this activity was enough to meet physical activity guidelines of 150 minutes of moderate to vigorous activity per week. This includes any activity with greater or equal intensity to brisk walking.

Despite the known health benefits, the overall proportion of weeks meeting these physical activity guidelines was low. The youngest and oldest users were the least likely to meet the guidelines, with those aged 35 to 44 most likely to meet the sufficiently active threshold.

Men were almost twice as likely to meet the guidelines, with 24.2% of weeks recorded by male users classed as adequately active compared to 12.4% of weeks recorded by female users. Additionally, living in the most affluent area compared to the least affluent (in terms of employment), improved the odds of recording an active week by almost 5%.

How useful are secondary smartphone data?

Secondary smartphone data are an invaluable tool to provide new insights into physical activity and other health behaviours, as they give a breadth and depth of detailed data not available from other methods.

On the flip side, using these data requires careful consideration, including meticulous implementation of data anonymity and ethics, attention to data handling and cleaning processes, and skilled training to be able to handle such a large detailed dataset. Used in tandem with more traditional primary data collection studies, secondary smartphone app data have the capability to address some of the most complex questions around physical activity behaviour.  We are still very much in the infancy of using these data and have just scratched the surface of their full potential.

Read the full paper: Pontin F, Lomax N, Clarke G, et al. Socio-demographic determinants of physical activity and app usage from smartphone data. Social Science & Medicine 2021: 114235.


1. Public Health England. Physical activity: applying All Our Health.  2019.

New partnership pilots trials to help change eating habits

New partnership pilots trials to help change eating habits

What we choose to put into our shopping baskets and how we make those choices will come under the microscope in a series of pilot trials designed to encourage healthy and sustainable diets.

Data analysts from the University of Leeds have joined forces with social impact organisation, the Institute of Grocery Distribution (IGD), to test different ways to encourage healthy and sustainable eating.

They are working in partnership with 20 leading retailers and manufacturers, including Morrison’s, Sainsbury’s and Aldi, to trial different strategies, including signposting better choices, the positioning of products in shops and online and the use of influencers and recipe suggestions.

Some have already begun to use some of those techniques in real-life settings as part of the research designed and implemented by the Leeds Institute for Data Analytics (LIDA) and the Consumer Data Research Centre (CDRC).

Researchers from LIDA and CDRC will analyse the results by capturing and measuring sales data from each intervention, enabling the project group to see exactly what is going on in people’s shopping baskets and assess what truly drives long-term behaviour change.

Dr Michelle Morris, who leads the Nutrition and Lifestyle Analytics team at LIDA and is a CDRC Co-Investigator, said: “I am passionate about helping our population move towards a diet that is both healthier and more sustainable. I believe that unlocking the power of anonymous consumer data, collected by retailers and manufacturers, is a really important step towards this goal.

“Working with the IGD and its members to evaluate their healthy and sustainable diets programme is very exciting – testing strategies to change purchasing behaviour and evaluating the wider impact of these changes.”

The pilot trials have been funded by IGD and form a key part of the charity’s Social Impact ambition to make healthy and sustainable diets easy for everyone.

Hannah Pearse, Head of Nutrition at IGD, said: “We want to lead industry collaboration and build greater knowledge of what really works. Our Appetite for Change research tells us that 57% of people are open to changing their diets to be healthy and more sustainable, and they welcome help to do it. But we also know that people don’t like to be told what to do and information alone is unlikely to change behaviour.

“We believe consumers will make this transition if we make it easier for them; that’s why we are delighted to be partnering with our industry project group and our research partners at the University of Leeds, to pilot this series of interventions over the coming months. The team at LIDA are experts in capturing, storing and analysing big data and have a variety of academic specialties that will be critical for this work.”

The work being carried out by CDRC researchers at the University of Leeds is unique because it will use the secure infrastructure at LIDA to allow retailers and manufacturers to share anonymised transaction data over a sustained period of time.

It is hoped that the results of the first pilot trial will be published towards the end of this year.

Celebrating collaboration: the CDRC Masters Dissertation Scheme

Celebrating collaboration: the CDRC Masters Dissertation Scheme

Celebrating collaboration: the CDRC Masters Dissertation Scheme. Thursday 29th April 2021, 10:30-15:00.

The CDRC Masters Dissertation Scheme, now in its tenth year, has been successfully run by the Consumer Data Research Centre for the last seven years. The event celebrated the success of the scheme, and explored the changing nature of academic-industry collaboration. Masters students who had gone through the scheme presented project case studies, and a selection of alumni spoke of the positive impact the scheme had had on their data science careers. A panel session rounded off the event with a discussion of the possibilities and ambitions for the next seven years of the Masters Dissertation Scheme. The event was attended by industry partners, MDS alumni, and the CDRC team including Paul Longley, Alex Singleton, and Jonathan Reynolds.

Speaker biographies


1030-1130: The Business of Engagement. Session recording (Longley 0:06, Dugmore 7:05, Reynolds 28:27, Squires 41:21)

  • Introduction & welcome: Professor Paul Longley, Director, CDRC
  • The evolution of academic-industry collaboration: Keith Dugmore, Demographic Decisions. Slides
  • CDRC: Where are they now? MDS 7 years on: Dr Jonathan Reynolds, Deputy Director (Oxford), CDRC. Slides
  • The business of engagement: the firm’s perspective: Martin Squires, Director of Advanced Analytics, Pets at Home. Slides

1145-1245: Alumni presentations. Session recording (Murage 2:16, Davies 25:10, Tonge & Montt 45:53)

  • Nombuyiselo Murage, Tamoco. Dissertation at Tamoco. MSc Geographic Data Science, University of Liverpool. Slides
  • Alec Davies, Pets at Home. Dissertation at Sainsbury’s. MSc Geographic Data Science, University of Liverpool, PhD Geographic Data Science. Slides
  • Christian Tonge, Movement Strategies. MSc Geographic Data Science, University of Liverpool, and Cristobal Montt, Movement Strategies. MSc Data Science, City, University of London. Dissertations at Movement Strategies. Slides

1400-1505: Alumni presentations (continued) and panel discussion. Session recording (Ushakova 1:48, Samson 21:29, Panel 37:26)

  • Alumni presentation: Dr Anastasia Ushakova, Senior Research Associate, University of Lancaster. Dissertation at British Gas.
    MSc Public Policy, UCL; PhD Computational Social Science. Slides
  • Alumni presentation: Nick Samson, Associate Director, CBRE. Dissertation at British Gas. MSc Geographic Information Science, UCL. Slides
  • Panel Discussion. The next 7 years. Achievements and ambitions: Alex Singleton, Deputy Director (Liverpool), CDRC;
    Samantha Hughes, Analytics Innovation Manager, Avon; Martin Squires, Director of Advanced Analytics, Pets at Home.
  • Thanks & conclusion: Professor Paul Longley, Director, CDRC

Nick Samson, 2014 MDS alumnus. Dissertation at British Gas. Project title: Can smart meters save consumers and British Gas money and carbon by pinpointing which consumers are most likely and best placed to install insulation in their homes?

Analysing student eating habits

Analysing student eating habits

Scientists have for the first time used anonymous data from pre-payment food cards to get a unique insight into the eating habits of first year university students.

Data scientists from the Consumer Data Research Centre at the University of Leeds have been able to build a detailed picture of what 835 students ate, and when, by analysing the data linked to their pre-payment food cards.

The cards revealed what they were buying in the campus refectory and associated food outlets.

The analysis gives the most accurate picture to date of first year student diets. Many previous studies have used food diaries, but their accuracy can be variable because they rely on the student remembering exactly – and being honest about – what they have eaten.

Dr Michelle Morris, a University Academic Fellow in Health Data Analytics based at Leeds Institute for Data Analytics, said understanding student diet had public health implications.

Previous studies in the UK and the US have shown that “fresher” students are at risk of weight gain, probably as a result of the lifestyle changes that come with starting university.

In the US, they talk of the “Freshmen 15”, the 15lbs (6.8kg) that students put on. In the UK, research indicates the average student gains 7.7lbs (3.5kg).

The findings, Assessing diet in a university student population: A longitudinal food card transaction data approach, have been published in the British Journal of Nutrition.

The study, which pre-dated the coronavirus outbreak and followed the students aged 18 to 24 over their first semester (12 teaching weeks), revealed student eating habits which clustered around seven dietary behaviours:

  • Vegetarian: with popular purchases being salads, breakfast cereals, yoghurt and fromage frais and a notable absence of meat products
  • Omnivores: which included the most average amounts of all products purchased, with above average amounts of ice cream, desserts and cakes, breakfast cereals and fish.
  • Dieters: with above average purchases of soups, pasta, noodles and salad.
  • Dish of the Day: which included above average purchases of meat and meat products.
  • Grab and Go: which included above average purchases of sandwiches, crisps, nuts and eggs.
  • Carb Lovers: with bread, cheese, egg products and pasta being among the top picks.
  • Snackers: with confectionery, crisps, nuts being above average choices.

Dr Morris, said the dietary patterns were ranked on the basis of “healthfulness”, with vegetarian the most healthful and snackers being the least.

She added: “Our analysis shows that although some students followed one dietary pattern throughout the semester many switched between them.

“Some students moved from a more healthy to a less healthy pattern; for example,  some vegetarians switched to an omnivore diet; and vice versa with some of the students who started off as snackers – the least healthful diet – did move to the Dish of the Day which offered a more balanced range of food options.

“Worryingly perhaps, the most popular move was from a dieter pattern, to the snacking pattern.”

Females were found to be heavily represented among the vegetarians (88%) and dieters (80%) while the men dominated the dish of the day (84%) and grab and go (62%) diet patterns.

This information could be used to target information about healthier eating to students.
Dr Michelle Morris, Leeds Institute for Data Analytics

Dr Morris said the most popular dietary pattern amongst the slightly older students, those aged between 20 and 24, was the omnivore pattern of eating – that could be due to the fact that they may already have lived away from home and settled into a more varied dietary pattern.

She said: “The information from this analysis reveals the pattern of the students’ eating habits, and how that changes over time. That is information that could be used to target information about healthier eating to students.

“Research has shown that adult eating habits take root early in adulthood. So, time spent at University is a great time to encourage healthy eating behaviours that could remain with them for life.”

The research was funded by the Economic and Social Research Council through a Strategic Network for Obesity grant. Maintaining the anonymity of the students was of utmost importance at all stages of the research.

Notes to editor

For further information or interview requests, please contact University of Leeds Media Relations and Communications Officer David Lewis via d.lewis@leeds.ac.uk