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 .
Do low step count goals inhibit walking behavior: a randomized controlled study
Objective : Confirmation and quantification of observed differences in goal -directed walking behavior . Design : Single-blind , split-half r and omized trial . Setting : Small rural university , Pennsylvania , United States . Subjects : A total of 94 able-bodied subjects ( self-selected volunteer students , faculty and staff of a small university ) were r and omly assigned walking goals , and 53 completed the study . Interventions : Incentivized pedometer-monitored program requiring recording the step-count  for 56-days into a custom-made website providing daily feedback . Main outcome measures : Steps logged per day . Results : During the first half of the study , the 5000 and 10,000 step group logged significantly different steps 7500 and 9000 , respectively ( P > 0.05 ) . During the second half of the study , the 5000 and 10,000 step groups logged 7000 and 8600 steps , respectively ( significance P > 0.05 ) . The group switched from 5000 to →10,000 steps logged , 7900 steps for the first half and 9500 steps for the second half ( significance P > 0.05 ) . The group switched from 10,000 to 5000 steps logged 9700 steps for the first half and 9000 steps for the second half , which was significant ( p > 0.05 ) . Conclusions : Levels of walking behavior are influenced by the goals assigned . Subjects with high goals walk more than those with low goals , even if they do not meet the assigned goal . Reducing goals from a high to low level can reduce walking behavior
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 .