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 .
Change in work day step counts, wellbeing and job performance in Catalan university employees: a randomised controlled trial
Using a r and omised controlled trial design , this feasibility study assessed the impact of two walking interventions on quality of life ( QoL ) and job performance of Catalan university employees . A convenience sample of 70 employees completed baseline and intervention measures of step counts ( Yamax SW 200 pedometer ) , wellbeing ( SF-12 question naire ) and work performance ( Work Limitations Question naire ) over 9 weeks . Before intervention , baseline step counts ( five working days ) were used to r and omly allocate participants to a control ( n = 26 ) , “ walking routes ” ( n = 19 ) and “ walking while working ” ( n = 25 ) groups . Intervention effects were evaluated by calculating differences between pre-intervention and intervention data . One-way ANOVA was used to examine differences between groups . No significant group differences were found for changes in work-day step counts , QoL or work performance . When data from the two intervention groups were pooled ( n = 44 ) there was a significant increase in step counts ( + 659 steps/day ; n = 12 ; p < 0.01 ) among participants classified as ` Sedentary — Low active ' ( 0—7499 steps/day ) at baseline . In contrast there was a significant decrease ( —637 steps/day ; p < 0.05 ) in those initially categorised as ` Active ' ( > 10,000 steps/day ; n = 21 ) and no change in those categorised as ` Moderately Active ' ( 7500—9999 , n = 11 ) . The ` Sedentary — Low activity ' group showed consistently greater improvements in QoL and work performance scores than the Moderate and Active groups . Initially low active participants showed the greatest increase in step counts and improved QoL and work productivity profiles . These data indicate the potential for improving QoL and job productivity through workplace walking in inactive Catalan employees
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 .