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 .
Psychosocial Determinants of Physical Activity in Children Attending Afterschool Programs: A Path Analysis
Background Physical activity ( PA ) is important for controlling childhood obesity , but a comprehensive PA model for school-aged children is lacking . Objectives Guided by the youth PA promotion ( YPAP ) model , this study estimated the direct and indirect effects of self-efficacy , enjoyment , parental influence , and environment on self-reported PA  and pedometer steps . A secondary purpose was to explore the association between self-reported PA and pedometer steps . Methods An observational and prospect i ve study was conducted among 133 children , aged 8–11 years old , from 10 elementary schools with afterschool programs in a Midwestern U.S. school district from August through October 2013 .  PA was assessed by a 7-day recall scale and pedometers . Other variables were assessed by vali date d question naires . Results Approximately 65 ( 49 % ) children were overweight or obese — only 17 ( 13 % ) met national PA recommendations — and body mass index z-score was negatively correlated with pedometer steps ( r = −.18 p = .04 ) . A path analysis showed that self-efficacy had a direct effect on self-reported PA and pedometer steps , enjoyment had only a direct effect on self-reported PA , and parental influence had a direct effect on pedometer steps and an indirect effect on self-reported PA through self-efficacy and enjoyment . The association between self-reported PA and pedometer steps was not significant . Discussion Because this study only partially supports the YPAP model , studies with a larger sample size and longitudinal design are essential to further examine this model . The nonsignificant relationship of self-reported PA with pedometer steps may be due to the systematic error result ing from a common method artifact of self-report . Given the importance of parental influence , enjoyment , and self-efficacy , targeting these three determinants in future interventions to increase PA among children is recommended
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 .