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 .
Predicting Change in Physical Activity: a Longitudinal Investigation Among Weight-Concerned College Women
Background Engaging in regular physical activity ( PA ) is critical for health , and adopting a consistent PA routine early in life is associated with greater PA over the lifespan . College women with weight concerns are at risk for weight gain , which may be prevented with regular PA . However , little is known about changes in PA engagement in this at-risk group . Purpose Using an outcome expectancy framework , this study used a prospect i ve longitudinal design to examine changes in PA during the first 2 years of college . We tested for concurrent and prospect i ve within-person relations between body satisfaction/perceived eating behavior and  PA  to determine when weight-concerned college women may increase or decrease PA . Methods Women who reported weight concerns at the start of college ( n = 294 ) completed five assessment s over 2 years , including measured weight , body/eating experiences , and 4 days of pedometer steps ( per assessment ) . Multilevel models addressed the result ing nested data structure ( days within assessment s within participants ) . Results Over 2 years , within-person change accounted for 65 % of PA variability ( ICC = 0.35 ) . PA  was greatest at ( and subsequent to ) times when body satisfaction was lower , and when disinhibited eating and hedonic hunger were higher , than an individual ’s average ( ps < 0.05 ) . These changes were associated with 1–3 % of the recommended daily step totals . Conclusions Weight-conscious college women  show greater PA after negative eating and weight experiences . As these experiences change over time , health promotion efforts should help college women identify alternative , positive motivators for PA , which could facilitate consistent PA engagement
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 .