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 .
A randomized controlled trial of continuous activity, short bouts, and a 10,000 step guideline in inactive adults.
OBJECTIVE Although several studies have examined the effect of accumulated bouts on health outcomes , the impact of recommending short bouts on activity-related behavior in health promotion efforts has received minimal investigation . METHOD During this 5-week study in 2007 - 2008 , 43 university employees ( 8 male , 35 female ) in the Southeastern United States were r and omly assigned to a group recommended to achieve ( a ) 10,000 steps ( 10 K ) , ( b ) 30-minutes ( 30 min ) of continuous physical activity , or ( c ) 30-minutes of activity in bouts of at least 10 minutes ( bouts ) . RESULTS AND CONCLUSIONS Repeated measures ANOVA revealed that the 10 K group showed the largest increase in step counts whereas the bouts group showed the smallest change over the intervention period , p=0.01 . Condition differences were most pronounced on days in which participants met their activity recommendation . Accelerometer results revealed that the 10 K ( d=1.1 ) and 30 min groups ( d=0.89 ) showed large increases in minutes of moderate to vigorous activity ( MVPA ) , whereas the bouts group showed minimal change ( d=0.11 ) . Although activity recommendations did not differentially affect self-efficacy , participants from all conditions showed decreased self-efficacy across the intervention ( p=0.02 ) , highlighting the need to develop strategies to increase self-efficacy in activity promotion efforts
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 .