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 .
Canadian children's and youth's pedometer-determined steps/day, parent-reported TV watching time, and overweight/obesity: The CANPLAY Surveillance Study
Background This study examines associations between pedometer-determined steps/day and parent-reported child 's Body Mass Index ( BMI )  and time typically spent watching television between school and dinner . Methods Young people ( aged 5 - 19 years ) were recruited through their parents by r and om digit dialling and mailed a data collection package . Information on height and weight and time spent watching television between school and dinner on a typical school day was collected from parents . In total , 5949 boys and 5709 girls reported daily steps .  BMI was categorized as overweight or obese using Cole 's cut points . Participants wore pedometers for 7 days and logged daily steps . The odds of being overweight and obese by steps/day and parent-reported time spent television watching were estimated using logistic regression for complex sample s. Results Girls had a lower median steps/day ( 10682 versus 11059 for boys ) and also a narrower variation in steps/day ( interquartile range , 4410 versus 5309 for boys ) . 11 % of children aged 5 - 19 years were classified as obese ; 17 % of boys and girls were overweight . Both boys and girls watched , on average , < 40 minutes of television between school and dinner on school days . Adjusting for child 's age and sex and parental education , the odds of a child being obese decreased by 20 % for every extra 3000 steps/day and increased by 21 % for every 30 minutes of television watching . There was no association of being overweight with steps/day , however the odds of being overweight increased by 8 % for every 30 minutes of additional time spent watching television between school and dinner on a typical school day . Discussion Television viewing is the more prominent factor in terms of predicting overweight , and it contributes to obesity , but steps/day attenuates the association between television viewing and obesity , and therefore can be considered protective against obesity . In addition to replacing opportunities for active alternative behaviours , exposure to television might also impact body weight by promoting excess energy intake . Conclusions In this large nationally representative sample , pedometer-determined steps/day was associated with reduced odds of being obese ( but not overweight ) whereas each parent-reported hour spent watching television between school and dinner increased the odds of both overweight and obesity
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 .