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 .
A classroom-based physical activity intervention for urban kindergarten and first-grade students: a feasibility study.
BACKGROUND Urban elementary schools in minority communities with high obesity prevalence may have limited re sources for physical education ( PE ) to achieve daily activity recommendations . Little is known whether integrating physical activity ( PA ) into classrooms can increase activity levels of students attending such schools . METHODS We conducted a cluster r and omized , controlled trial among kindergarten and first- grade students from four Bronx , New York , schools to determine feasibility and impact of a classroom-based intervention on  students ' PA levels  . Students in two intervention schools received the Children 's Hospital at Montefiore Joining Academics and Movement ( CHAM JAM ) , an audio CD consisting of 10-minute , education-focused aerobic activities led by teachers three times a day . PA was objective ly measured by pedometer . Each subject wore a sealed pedometer during the 6-hour school day for 5 consecutive days at baseline ( Time 1 ) and 8 weeks postintervention ( Time 2 ) . Hierarchical linear models were fit to evaluate differences in mean number of steps between the two groups . RESULTS A total of 988 students participated ( intervention group , n=500 ; control group , n=488 ) . There was no significant difference at baseline between the two groups on mean number of steps ( 2581 [ st and ard deviation ( SD ) , 1284 ] vs. 2476 [ SD , 1180 ] ; P=0.71 ) . Eight weeks post-CHAM JAM , intervention group students took significantly greater mean number of steps than controls ( 2839 [ SD , 1262 ] vs. 2545 [ SD , 1153 ] ; P=0.0048 ) after adjusting for baseline number of steps and other covariates ( grade , gender , recess , and PE class ) . CHAM JAM was equally effective in gender , grade level , and BMI subgroups . CONCLUSIONS CHAM JAM significantly increased  school-based PA among kindergarten and first- grade students in inner-city schools . This approach holds promise as a cost-effective means to integrate the physical and cognitive benefits of PA into high-risk schools
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 .