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European Colloquium on Quantitative and Theoretical Human Geography

Nik Lomax
September 08, 2017

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.

Nik Lomax

September 08, 2017
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  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

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  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.

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  3. Bounts: lifestyle app data

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  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

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  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.

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  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.

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  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.

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  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.

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  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?

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  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.

    View Slide

  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

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  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

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  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.

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  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.

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  15. Assessing activity levels
    of individuals in a large,
    self-selecting dataset
    Nik Lomax and Michelle Morris
    University of Leeds
    [email protected]
    ECQTG | York | 8 September 2017

    View Slide