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 .
The effect of a pedometer-based community walking intervention "Walking for Wellbeing in the West" on physical activity levels and health outcomes: a 12-week randomized controlled trial
Background Recent systematic review s have suggested that pedometers may be effective motivational tools to promote walking . However , studies tend to be of a relatively short duration , with small clinical based sample s. Further research is required to demonstrate their effectiveness in adequately powered , community based studies . Objective Using a r and omized controlled trial design , this study assessed the impact of a 12-week graduated pedometer-based walking intervention on daily step-counts , self-reported physical activity and health outcomes in a Scottish community sample not meeting current physical activity recommendations . MethodS ixty-three women and 16 men ( 49.2 years ± 8.8 ) were r and omly assigned to either an intervention ( physical activity consultation and 12-week pedometer-based walking program ) or control ( no action ) group . Measures for step-counts , 7-day physical activity recall , affect , quality of life ( n = 79 ) , body mass , BMI , % body fat , waist and hip circumference ( n = 76 ) , systolic/diastolic blood pressure , total cholesterol and HDL cholesterol ( n = 66 ) were taken at baseline and week 12 . Analyses were performed on an intention to treat basis using 2-way mixed factorial analyses of variance for parametric data and Mann Whitney and Wilcoxon tests for non-parametric data . Results Significant increases were found in the intervention group for step-counts ( p < .001 ) , time spent in leisure walking ( p = .02 ) and positive affect ( p = .027 ) . Significant decreases were found in this group for time spent in weekday ( p = .003 ) , weekend ( p = .001 ) and total sitting ( p = .001 ) with no corresponding changes in the control group . No significant changes in any other health outcomes were found in either group . In comparison with the control group at week 12 , the intervention group reported a significantly greater number of minutes spent in leisure time ( p = .008 ) , occupational ( p = .045 ) and total walking ( p = .03 ) , and significantly fewer minutes in time spent in weekend ( p = .003 ) and total sitting ( p = .022 ) . Conclusion A pedometer-based walking program , incorporating a physical activity consultation , is effective in promoting walking and improving positive affect over 12 weeks in community based individuals . The discussion examines possible explanations for the lack of significant changes in health outcomes . Continued follow-up of this study will examine adherence to the intervention and possible result ing effects on health outcomes
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 .