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 .
Association between physical activity (PA) guidelines and body composition variables in middle-aged and older women.
The aim of this study was to investigate the association between volume and frequency of moderate-intensity PA and step-based recommendations and individual body composition variables . Our cohort included 167 healthy ambulatory women ( mean age 62.8 ± 4.8 years ; body mass index [ BMI ] 27.3 ± 4.2 kg/m(2 ) ) who carried out daily activities while wearing the ActiGraph GT1 M accelerometer over a seven day period .  Measurements of BMI , body fat mass index ( BFMI ) , fat-free mass index ( FFMI ) , waist-hip ratio ( WHR ) and visceral fat area ( VFA ) were obtained by the InBody 720 multifrequency bioelectrical impedance analysis ( MFBIA ) device . The significant relationship ( r(s)=0.66 ; p<0.05 ) was found between moderate PA and steps per day . Moderate PA ( r(2)=0.03 - 0.06 ) and steps per day ( r(2)=0.05 - 0.20 ) were significantly associated with observed body composition parameters . Women spending > 300 min/week in moderate PA showed significantly lower values of BFMI ( p=0.02 ) than those who spent 150 - 300 min/week . Carrying out moderate PA for 30 min 5 days a week was significantly associated with lower BMI ( p=0.04 ; η(2)=0.02 ) , BFMI ( p=0.02 ; η(2)=0.03 ) and VFA ( p=0.03 ; η(2)=0.03 ) . In addition , higher amounts of daily steps were significantly associated with lower BMI ( p=0.00 ; η(2)=0.16 ) , BFMI ( p=0.00 ; η(2)=0.21 ) , VFA ( p=0.00 ; η(2)=0.20 ) and WHR ( p=0.00 ; η(2)=0.13 ) . A clear association was found between the generally recommended PA guidelines and body composition variables for the women examined in this study . However , the concept of 10,000 steps/day appears to be the strongest predictor of health-related body composition values
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 .