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 .
Safe To Walk? Neighborhood Safety and Physical Activity Among Public Housing Residents
Background Despite its health benefits , physical inactivity is pervasive , particularly among those living in lower-income urban communities . In such setting s , neighborhood safety may impact willingness to be regularly physically active . We examined the association of perceived neighborhood safety with pedometer-determined physical activity and physical activity self-efficacy . Methods and Findings Participants were 1,180 predominantly racial/ethnic minority adults recruited from 12 urban low-income housing complexes in metropolitan Boston . Participants completed a 5-d pedometer data - collection protocol and self-reported their perceptions of neighborhood safety and self-efficacy ( i.e. , confidence in the ability to be physically active ) . Gender-stratified bivariate and multivariable r and om effects models were estimated to account for within-site clustering . Most participants reported feeling safe during the day , while just over one-third ( 36 % ) felt safe at night . We found no association between daytime safety reports and physical activity among both men and women . There was also no association between night-time safety reports and physical activity among men ( p = 0.23 ) but women who reported feeling unsafe ( versus safe ) at night showed significantly fewer steps per day ( 4,302 versus 5,178 , p = 0.01 ) . Perceiving one 's neighborhood as unsafe during the day was associated with significantly lower odds of having high physical activity self-efficacy among both men ( OR 0.40 , p = 0.01 ) and women ( OR 0.68 , p = 0.02 ) . Conclusions Residing in a neighborhood that is perceived to be unsafe at night is a barrier to regular physical activity among individuals , especially women , living in urban low-income housing . Feeling unsafe may also diminish confidence in the ability to be more physically active . Both of these factors may limit the effectiveness of physical activity promotion strategies delivered in similar setting
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 .