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 .
Comparative Effectiveness of Two Walking Interventions on Participation, Step Counts, and Health
Purpose : To ( 1 ) compare the effects of two worksite-based walking interventions on  employee participation  rates ; ( 2 ) compare average daily step counts between conditions , and ; ( 3 ) examine the effects of increases in average daily step counts on biometric and psychologic outcomes . Design : We conducted a cluster-r and omized trial in which six employer groups were r and omly selected and r and omly assigned to condition . Setting : Four manufacturing worksites and two office-based worksite served as the setting . Subjects : A total of 474 employees from six employer groups were included . Intervention : A st and ard walking program was compared to an enhanced program that included incentives , feedback , competitive challenges , and monthly wellness workshops . Measures : Walking was measured by self-reported daily step counts . Survey measures and biometric screenings were administered at baseline and 3 , 6 , and 9 months after baseline . Analysis : Analysis used linear mixed models with repeated measures . Results : During 9 months , participants in the enhanced condition averaged 726 more steps per day compared with those in the st and ard condition ( p < .001 ) . A 1000-step increase in average daily steps was associated with significant weight loss for both men ( −3.8 lbs . ) and women ( −2.1 lbs . ) , and reductions in body mass index ( −0.41 men , −0.31 women ) . Higher step counts were also associated with improvements in mood , having more energy , and higher ratings of overall health . Conclusions : An enhanced walking program significantly increases participation rates and daily step counts , which were associated with weight loss and reductions in body mass index
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 .