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 .
The effect of a pedometer-based physical activity intervention on sitting time.
OBJECTIVE To assess whether the " 10,000 Steps Ghent " intervention had any effect on self-reported sitting time . METHODS A multi- strategy community-based intervention was implemented in 2005 to promote physical activity ( PA ) to adults living in Ghent , Belgium . In 2005 , 648 r and omly selected participants ( aged 25 to 75 ) from the intervention community Ghent and 592 from a comparison community , completed the International Physical Activity Question naire ( IPAQ ) and a pedometer log .  Of these , 440 intervention participants and 426 comparison participants completed the follow-up measurements in 2006 . RESULTS A decrease of 12 min in total daily sitting time was found in the intervention community , compared with an increase of 18 min/day in the comparison community ( F=9.5 , p=0.002 ) . The effect was seen for both weekday ( p=0.044 ) and weekend day ( p<0.001 ) sitting times . In the intervention community , total daily sitting time decreased more in the participants who increased their step counts ( -18 min/day ; t=2.5 ; p=0.012 ) , than in those who did not ( no change ; t=0.8 , ns ) . CONCLUSIONS After 1 year of intervention , total , weekday , and weekend day sitting times were reduced in the intervention community , while sitting time increased in the comparison community
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 .