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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.

References

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

Being A Data Science Intern

Photo of Rosalind Martin outdoors wearing a blue coat and a scarf

Being A Data Science Intern – insights, challenges and benefits

Rosalind is one of the Leeds Institute for Data Analytics’s (LIDA) current Data Scientist Interns, with a background in Geography (BSc) and Geographical Information Systems (GIS MSc).

I’ve always been a fan of physical geography, but as module choices expanded throughout my degrees I was increasingly drawn to (spatial) data modules. I love using GIS and coding to solve big data challenges.

My internship has been made up of two six-month projects, both funded by the Consumer Data Research Centre (CDRC). My first project was titled ‘Isolation and Exclusion in a Social Distancing Covid World’. Here, I worked under the supervision of academics from the Universities of Newcastle and Leeds, aiming to identify people and households at risk of isolation and exclusion as a result of Covid lockdown rules.

Photo of Rosalind Martin outdoors wearing a blue coat and a scarf

My second project is in the world of nutrition where I’m working closely with Leeds academics, Dr Michelle Morris and Vicki Jenneson, and a retail partner. I am designing an open access tool which will assist retailers in implementing new policy restricting the promotion of foods that are high in fat, salt and sugar – a crucial part of reducing obesity in the UK.

What has been my experience of the LIDA Internship Programme?

Aerial view of desk with hands over a laptop keyboard, pot plant, glasses and pen

As I’m sure many people would echo, the Covid pandemic has placed our jobs in unfamiliar situations. The reality of this internship being my first full-time post means that I’ve not been comparing my days to ways I have worked in the past. Instead, my experience has been shaped by remote team working with virtual training, coffee breaks and meetings. Although working from home (WFH) comes with its own challenges and complexities, I believe this has given me the capacity to be thankful to work on engaging projects rather than pining for something I used to have!

Due to the pandemic, many interns have been able to experience otherwise inaccessible conferences and workshops as they’ve transitioned online. I’ve been to events held by The Alan Turing Institute, the Royal Society, CDRC and more! Working as a remote cohort, the interns have set up coffee breaks and a weekly “pub” session to replicate those water-cooler conversations, lost due to WFH. This space allows us to talk about our projects, seek help from others who have different skillsets and to simply get to know each other.

What have I been proud to have accomplished so far on the internship?

Coding while WFH has been a true test of my perseverance. In the absence of spinning my chair around to ask for a fresh pair of eyes, I’ve really had to learn how to use documentation and online forums to navigate my coding challenges. I’ve also learnt how best to send questions (with reproducible examples) to other interns or my supervisors. I’ve seen a visible increase in my confidence and ability between my first and second projects, and I know this skill will continue to serve me in future careers. 

What are my quick hacks for getting the most out of the internship?

  • Obtaining data always takes longer than you think: be proactive in learning methods, using dummy data and reading around the subject while you wait
  • Talk to the interns: each intern has a different background and therefore their own unique combination of skills. Ask questions and be ready to offer your own experiences if asked
  • Write detailed descriptions of your GitHub commits: your future self will thank you when you return from Annual Leave to find you have a detailed record of what you were working on before you left for your holiday

How has working with the Consumer Data Research Centre (CDRC) helped with the delivery of my first project?

My first intern project aimed to identify those at risk of isolation and exclusion under Covid lockdown rules. In order to make detailed predictions of impacted individuals and households, I worked with a micro-simulated synthetic population called SPENSER. This CDRC and Alan Turing Institute funded project was essential for me to make predications at the household level. I also used other datasets to support my work including CDRC’s Access to Healthy Assets and Hazards dataset. The availability of these datasets enabled me to explore the Covid restrictions that were thought to negatively impact an individual’s risk of isolation.

How will this Internship help me progress my career in data science?

I have learnt more of the mechanics of data access throughout both of my projects – ranging from obtaining freely-available through to applying for safeguarded datasets (including how long the process can sometimes take!). In my projects, I have had the opportunity to talk to the City Council, UK and international universities, not-for-profit organisations and retailers. Speaking to people in a wide range of data roles has helped me to better understand the opportunities available in data science, and how roles interact with non-data scientists. 

Why would I recommend the LIDA Data Science Internship?

The LIDA Data Science Internship has given me the opportunity to own the delivery of two data science projects situated in very different subject areas. This has really expanded my understanding of how data can be used to solve very complex but nationally topical challenges. Owning the delivery of the projects as someone straight out of their Master’s has been a challenge, but I have been well supported by experienced supervisors and the extended LIDA network. With the breadth of internship projects and collaborators available across and in partnership with LIDA, the internship is the place to be!

LIDA is currently recruiting for its next cohort of Data Scientist Interns, due to start at the end of September 2021, with several projects taking place within the CDRC. Click here for more information and to apply.

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

Programme

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?