European Colloquium on Quantitative and Theoretical Human Geography

This presentation outlines initial results from an exciting and novel data set which lets us assess people's activity levels.

Transcript

1. Assessing activity levels of individuals in a large, self-selecting dataset Nik Lomax and Michelle Morris University of Leeds ECQTG | York | 8 September 2017
2. Context: sedentary populations • Obesity is a global health concern, adult obesity levels increasing almost universally. • Net-cost of healthcare and welfare for overweight and obese people in the UK is £2.47 billion per annum. • The use of activity tracking devices, as an intervention to decrease sedentary behaviour is well researched. • Many previous studies use relatively small samples.
3. Bounts: lifestyle app data
4. Bounts: lifestyle app data • App built by Active Inspiration Technologies • >500k unique users • 13 months activity data • Continuously updated • Daily user activity including step count • GPS trace data
5. Gender differences • What does the literature tell us? • Women are more likely to use a fitness tracker than men • In our sample? • True – in the Bounts data 75% users are female.
6. Steps by gender • What does the literature tell us? • On average, men take slightly more steps than women each day. • In our sample? • True – but fairy large difference. Women = ~6k. Men = ~7.5k.
7. Enough steps? • 10,000 steps per day often used as a target (despite criticism, e.g. Tudor- Locke and Bassett 2004)1. • Large variation in Bounts data but many not achieving ‘enough’ steps. 1Tudor-Locke, C. and Bassett, D.R., 2004. How many steps/day are enough?. Sports medicine, 34(1), pp.1-8.
8. Steps by age • What does the literature tell us? • Number of steps taken declines as age increases. • In our sample? • False. We see a surprising positive correlation between age and number of steps.
9. Seasonality and marketing • What does the literature tell us? • Levels of physical activity peak in summer; energy expenditure decreases in winter. • In our sample? • Some evidence of seasonal difference and a ‘holiday period’ effect. Clocks go back Clocks go forward Holiday lull?
10. Seasonality and marketing • What does the literature tell us? • Levels of physical activity peak in summer; energy expenditure decreases in winter. • In our sample? • Some evidence of seasonal difference and a ‘holiday period’ effect.
11. Seasonality and marketing • But patterns better explained by changes in marketing and terms/ conditions of app use? TV campaign Ts&Cs/ user experience change
12. Geographical variation • Many of the areas averaging over 7,000 steps per day are peripheral or coastal areas. • The most active areas can be seen in Outer London, e.g. Sutton and Enfield. Partnership with Liverpool Council
13. Key findings and message • Activity patterns and levels in these data are different from the general population (i.e. findings reported in previous studies). • Bounts participants are self-selecting and likely motivated to be more active. • Encouraging people to use a rewards scheme/ track their activity levels can help increase activity levels and decrease risk.
14. Further work • Investigation of other activity types (gyms, tennis clubs, etc.) in relation to user demographics and seasonal activity. • Monitor effect of partnerships and marketing (e.g. links with Liverpool City Council). • Investigation of activity spaces and obesogenic environments using GPS trace data.
15. Assessing activity levels of individuals in a large, self-selecting dataset Nik Lomax and Michelle Morris University of Leeds n.m.lomax@leeds.ac.uk ECQTG | York | 8 September 2017