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 .
Weekday-weekend patterns of physical activity and screen time in parents and their pre-schoolers
Background This study focuses on the comparison of weekday/weekend  parent-child behavioural patterns ( step count ( SC ) and screen time ( ST ) ) and answers the question of whether achieving the recommendations for daily SC ( 10,000 ) in parents also helps their preschool children achieve the recommended daily SC ( 11,500 ) . Methods The participants ( 278 parents aged 30–45 and their 194 children aged 4–7 ) were r and omly recruited from 10 Czech public kindergartens . The participants recorded SC ( pedometer Yamax Digiwalker SW-200 ) and ST duration ( proxy-report ) for seven consecutive days ( ≥8 h/day ) during September – October 2014 and April – May 2015 . Differences between weekdays/weekends SC or ST were tested using a paired t-test . The odds of achieving the recommended daily SC for children were estimated using general logistic regression separately for weekdays and weekends . Results Only the mothers were found to have a significantly lower SC at weekends than on weekdays . All of the participants showed significantly more ST at weekends than on weekdays ( daughters : 78.6 vs. 45.7 min/day , p < 0.001 ; sons : 78.8 vs. 55.8 min/day ; mothers : 93.0 vs. 68.3 min/day ; and fathers : 116.6 vs. 87.5 min/day ) . Daughters and sons were significantly more likely to achieve daily SC recommendation if a ) the SC on weekdays during the daily routine in kindergarten exceeded the median of kindergarten SC or b ) at weekends if their mother ( OR : 9.67 , 95 % CI : 3.57–26.23 ) exceeded 10,000 steps a day . Conclusions Especially at weekends , preschoolers have higher odds of meeting the recommended 11,500 steps per day when their mother reaches 10,000 steps per day and this is independent of the amount of parents ’ ST . Moreover , physical activity in kindergarten helps preschool children meet the 11,500 recommended steps per day on weekdays . Therefore , interventions to promote physical activity in preschoolers should focus on kindergartens and encourage involvement of their families
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 .