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 .
Effects of integrating pedometers, parental materials, and E-mail support within an extracurricular school sport intervention.
PURPOSE The objective of this study was to evaluate the impact of a school-based intervention ( Program X ) incorporating pedometers and e-mail support on physical activity , sedentary behavior , and healthy eating  in adolescents . METHODS A r and omized control trial was used to evaluate the impact of the Program X intervention .  Six schools ( N = 124 participants ; mean age 14.1 + /- .8 years ) were r and omized to intervention or control conditions for the 6-month study period . Objective ly recorded physical activity ( mean steps/day ) , self-reported sedentary behavior , and dietary habits were measured at baseline and at 6-month follow-up and intervention effects were assessed using repeated- measures analysis of variance and chi(2 ) tests . RESULTS Participants in the intervention group increased their step counts by 956 + /- 4107 steps/day ( boys ) and 999 + /- 1999 ( girls ) . Repeated- measures analysis of variance revealed significant group-by-time interactions for boys ( F = 7.4 , p = .01 , d = .80 ) and girls ( F = 29.6 , p < .001 , d = 1.27 ) for mean steps/day . The intervention significantly decreased the number of energy-dense/low-nutrient snacks consumed by boys ( chi(2 ) = 4.0 , p = .043 ) and increased the number of fruit serves among girls ( chi(2 ) = 4.8 , p = .028 ) . The intervention did not have a statistically significant effect on sedentary behavior . CONCLUSION A school-based intervention incorporating physical activity monitoring using pedometers and e-mail support was successful in promoting physical activity and selected healthy eating behaviors in adolescent boys and girls
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 .