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 .
Sociodemographic determinants of pedometer-determined physical activity among Japanese adults.
BACKGROUND Although previous studies have reported physical activity and its sociodemographic determinants using self-report measures , there have been few studies using pedometers . PURPOSE To ascertain pedometer-determined physical activity and its sociodemographic determinants among community residents living in four Japanese cities . METHODS A cross-sectional mail survey was conducted from February 2007 to January 2008 with a sample of 4000 residents ( aged 20 - 69 years and 50 % male ) who were r and omly selected from the registry of residential addresses . Complete responses for both question naire and pedometer were obtained from 790 residents ( 48.3±13.7 years , 46.7 % male ) . Associations of 11 sociodemographic variables with steps per day were examined using multiple logistic regression analyses . Data were analyzed in 2010 . RESULTS Men averaged 8763±3497 steps/day and women averaged 8242±3277 steps/day . Further , 29.0 % of men and 27.8 % of women walked ≥10,000 steps/day . City of residence , good self-rated health , low educational attainment , and not owning a car were associated with taking ≥10,000 steps/day in men , whereas employed status and dog ownership were associated with walking ≥10,000 steps/day in women . CONCLUSIONS The results contribute to underst and ing of step-defined physical activity and its sociodemographic determinants . A diversity of step counts by sociodemographic variables clarifies specific population s among Japanese who are in need of intervention to promote physical activity
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 .