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 Influence of the Local Neighbourhood Environment on Walking Levels during the Walking for Wellbeing in the West Pedometer-Based Community Intervention
We investigated the relationship between  walking levels  and the local neighbourhood physical environment during the Walking for Wellbeing in the West ( WWW ) r and omised pedometer-based community intervention .  Walking activity was recorded as step counts at baseline ( n = 76 ) , and at 3 months ( n = 57 ) , 6 months ( n = 54 ) , and 12 months ( n = 45 ) post-intervention . Objective physical environment data were obtained from GIS data sets and street surveys conducted using the SWAT audit tool . Sixty-nine environment variables were reduced to eight environment factors using principal axis factoring , and the relationship between environment factors and ( i ) step counts , and ( ii ) the change in step counts relative to baseline , was examined using hierarchical multiple linear regression , controlling for age , gender , income , and deprivation . Five environment factors were significant predictors of step counts , but none were significant predictors of the change in step counts relative to baseline . None of the demographic variables included in the analysis were significant predictors at any stage of the study . Total variance explained by the environment ranged from 6 % ( P < 0.05 ) to 34 % ( P < 0.01 ) , with lowest levels during the initial stages of the study . The physical environment appears to have influenced walking levels during the WWW intervention , and to have contributed to the maintenance of walking levels post-intervention
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 .