Abstract
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.
Highlights
Physical inactivity is the fourth leading global risk factor for noncommunicable disease in the world (World Health Organization et al, 2010), responsible for an estimated one in six deaths in the UK (Public Health England, 2019)
We identify the demographic and socioeconomic characteristics of the user population from active app user profiles. We characterise both patterns in usage of the Bounts application and patterns in physical activity behaviour, including likelihood to meet physical activity guidelines, by examining how these usage and physical activity behaviours vary by the socio-demographic characteristics of users
As this is one of the first studies to utilise a large volume of secondary physical activity app data to coinvestigate app usage alongside activity behaviour captured, we evaluate our findings in the context of this novel data source
Summary
Physical inactivity is the fourth leading global risk factor for noncommunicable disease in the world (World Health Organization et al, 2010), responsible for an estimated one in six deaths in the UK (Public Health England, 2019). Capturing physical activity is difficult, with traditional methods such as interviews and surveys relying on participant recall. These selfreported methods have issues relating to memory and social desir ability bias (Sylvia et al, 2014), often leading to an overestimation of physical activity (Janevic et al, 2012). These self-report methods are advantageous in their ability to survey a representative sample. Physical activity incorporates a wide range of activities, from traditional sports and walking, to any activity that requires increased energy expenditure, such as gardening and cleaning
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