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 .
Walking towards health in a university community: a feasibility study.
OBJECTIVE Using a r and omised control trial design , this study assessed the impact of two walking interventions , on the work day step counts and health of UK academic and administrative , university employees . METHOD A convenience sample of 58 women ( age 42+/-10 years ) and 6 men ( age 40+/-11 years ) completed baseline and intervention measures for step counts , % body fat , waist circumference and systolic/diastolic blood pressure , during a ten-week period ( October to December , 2005 ) . Before intervention , baseline step counts ( five working days ) were used to r and omly allocate participants to a control ( maintain normal behaviour , n=22 ) and two treatment groups ( " walking routes " , n=21 ; " walking in tasks " , n=21 ) . Intervention effects were evaluated by calculating differences between pre-intervention and intervention data . A one-way ANOVA analysed significant differences between groups . RESULTS A significant intervention effect ( p<0.002 ) was found for step counts , with mean differences indicating a decrease in steps for the control group ( -767 steps/day ) and increases in the " walking routes " ( + 926 steps/day ) and " walking in tasks " ( + 997 steps/day ) groups . Small , non-significant changes were found in % body fat , waist circumference and blood pressure . CONCLUSIONS Findings have implication s for work-based physical activity promotion and the development of walking interventions within the completion of work-based tasks
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 .