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 .
Do walking strategies to increase physical activity reduce reported sitting in workplaces: a randomized control trial
Background Interventions design ed to increase workplace physical activity may not automatically reduce high volumes of sitting , a behaviour independently linked to chronic diseases such as obesity and type II diabetes . This study compared the impact two different walking strategies had on step counts and reported sitting times . Methods Participants were white-collar university employees ( n = 179 ; age 41.3 ± 10.1 years ; 141 women ) , who volunteered and undertook a st and ardised ten-week intervention at three sites .  Pre-intervention step counts  ( Yamax SW-200 ) and  self-reported sitting times  were measured over five consecutive workdays . Using pre-intervention step counts , employees at each site were r and omly allocated to a control group ( n = 60 ; maintain normal behaviour ) , a route-based walking group ( n = 60 ; at least 10 minutes sustained walking each workday ) or an incidental walking group ( n = 59 ; walking in workday tasks ) . Workday step counts and reported sitting times were re-assessed at the beginning , mid- and endpoint of intervention and group mean± SD steps/day and reported sitting times for pre-intervention and intervention measurement points compared using a mixed factorial ANOVA ; paired sample -t-tests were used for follow-up , simple effect analyses . Results A significant interactive effect ( F = 3.5 ; p < 0.003 ) was found between group and step counts . Daily steps for controls decreased over the intervention period ( -391 steps/day ) and increased for route ( 968 steps/day ; t = 3.9 , p < 0.000 ) and incidental ( 699 steps/day ; t = 2.5 , p < 0.014 ) groups . There were no significant changes for reported sitting times , but average values did decrease relative to the control ( routes group = 7 minutes/day ; incidental group = 15 minutes/day ) . Reductions were most evident for the incidental group in the first week of intervention , where reported sitting decreased by an average of 21 minutes/day ( t = 1.9 ; p < 0.057 ) . Conclusion Compared to controls , both route and incidental walking increased physical activity  in white-collar employees . Our data suggests that workplace walking , particularly through incidental movement , also has the potential to decrease employee sitting times , but there is a need for on-going research using concurrent and objective measures of sitting , st and ing and walking
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 .