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 .
Contextual factors related to physical activity during daily middle school physical education.
OBJECTIVES Given the importance of optimizing physical activity in adolescents , the purpose of this study was to examine the effect of activity mode , environment , and semester on step counts/minute and MVPA during daily middle-school physical education ( PE ) . DESIGN A prospect i ve and observational research design . METHODS Participants included 232 students ( Mean age=13.3±0.4 years ) recruited from the seventh and eighth grade s from one public middle-school in the U.S. Activity modes were employed across the school year including motor skills , games , and fitness activities located in indoor and outdoor environments . Step counts/minute and MVPA  were monitored across 132 PE lessons during Fall and Spring semesters using NL-1000 piezoelectric pedometers . A three-way Multivariate Analysis of Covariance ( MANCOVA ) was employed to examine the effect of activity mode ( skill games vs. fitness ) , environment ( indoors vs. outdoors ) , and semester ( Fall vs. Spring ) on student step counts/minute and MVPA . MANCOVA was followed by separate ANCOVA tests . RESULTS MANCOVA yielded a statistically significant three-way interaction ( Wilks ' Λ=0.98 F(2 , 1153)=8.9 , P<0.001 ) . Follow-up tests supported that physical activity was higher during outdoor fitness activities in the Fall compared to indoor motor skills in the Spring for step counts/minute ( Mean difference=27.0 steps/minute , P<0.001 , Cohen 's d=1.6 ) and MVPA ( Mean difference=7.8min , P<0.001 , Cohen 's d=2.0 ) . CONCLUSIONS Daily middle-school physical activity was the highest during outdoor fitness activities in the Fall and the lowest during indoor motor skill games in the Spring
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 .