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 .
Predicting students' physical activity and health-related well-being: a prospective cross-domain investigation of motivation across school physical education and exercise settings.
A three-wave prospect i ve design was used to assess a model of motivation guided by self-determination theory ( Ryan & Deci , 2008 ) spanning the context s of  school physical education ( PE ) and exercise .  The outcome variables examined were health-related quality of life ( HRQoL ) , physical self-concept ( PSC ) , and 4 days of objective ly assessed estimates of activity . Secondary school students ( n = 494 ) completed question naires at three separate time points and were familiarized with how to use a sealed pedometer . Results of structural equation modeling supported a model in which perceptions of autonomy support from a PE teacher positively predicted PE-related need satisfaction ( autonomy , competence , and relatedness ) . Competence predicted PSC , whereas relatedness predicted HRQoL. Autonomy and competence positively predicted autonomous motivation toward PE , which in turn positively predicted autonomous motivation toward exercise ( i.e. , 4-day pedometer step count ) . Autonomous motivation toward exercise positively predicted step count , HRQoL , and PSC . Results of multi sample structural equation modeling supported gender invariance . Suggestions for future work are discussed
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 .