Surprisingly no use was made of time series analysis for analysing step count data .
The review also suggested several strategies for the personalisation of physical activity programs .
Conclusions Overall , it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial , demographic , weather and climatic factors .
Statistical models can provide a better underst and ing of the impact of these factors , allowing for the provision of more personalised physical activity programs , which are expected to produce better immediate and long-term outcomes for participants .
Background Physical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle .
Regular engagement in physical activity can produce immediate and long term health benefits .
However , physical activity levels are not as high as might be expected .
For example , according to the global World Health Organization ( WHO ) 2017 statistics , more than 80 % of the world ’s adolescents are insufficiently physically active .
In response to this problem , physical activity programs have become popular , with step counts commonly used to measure program performance .
Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance .
This study review s the statistical methods that are used to model and evaluate physical activity programs , using step counts .
Reduction in pedometer-determined physical activity in the adult Danish population from 2007 to 2012
Aims : To examine the development in pedometer-determined physical activity from 2007–2008 to 2011–2012 in the adult Danish population . Methods : The study population comprised two r and om sample s of 18–75-year-old individuals who took part in cross-sectional studies in 2007–2008 ( n=224 ) and 2011–2012 ( n=1515 ) .  Pedometer data ( sealed Yamax SW 200 ) were obtained for seven consecutive days . Data for 1624 participants ( 48.2 % men ) were included in the analysis . An overall step-defined activity level was examined based on a graduated step index ( sedentary , low active , somewhat active , active , highly active ) . The pedometer-determined outcomes were analysed using regression models . Results : A borderline significant decline ( p=0.077 ) from 8788 to 8341 steps/day ( −446 ( 95 % confidence intervals −50 , 943 ) ) was found between 2007–2008 and 2011–2012 . Furthermore , a 23.7 % ( 95 % confidence intervals −41.7 % , −0.1 % ) lower overall step-defined activity level was observed in 2011–2012 compared to 2007–2008 . These changes were primarily due to a reduced level of activity among women . The proportion of individuals taking ⩾10,000 steps/day decreased non-significantly from 34.8 % to 29.3 % , whereas the proportion taking < 5000 steps/day did not differ between survey periods . Conclusions : This nationally representative survey suggests an overall reduction in the physical activity level among Danish adults . The reduction was due to a shift in the population distribution from higher to lower levels of activity . If this shift is true , it is worrying from a public health perspective . Our study result needs , however , to be confirmed by other population studies
Surprisingly no use was made of time series analysis for analysing step count data .
The review also suggested several strategies for the personalisation of physical activity programs .
Conclusions Overall , it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial , demographic , weather and climatic factors .
Statistical models can provide a better underst and ing of the impact of these factors , allowing for the provision of more personalised physical activity programs , which are expected to produce better immediate and long-term outcomes for participants .
Background Physical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle .
Regular engagement in physical activity can produce immediate and long term health benefits .
However , physical activity levels are not as high as might be expected .
For example , according to the global World Health Organization ( WHO ) 2017 statistics , more than 80 % of the world ’s adolescents are insufficiently physically active .
In response to this problem , physical activity programs have become popular , with step counts commonly used to measure program performance .
Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance .
This study review s the statistical methods that are used to model and evaluate physical activity programs , using step counts .