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 .
Does physical activity counselling enhance the effects of a pedometer-based intervention over the long-term: 12-month findings from the Walking for Wellbeing in the west study
Background Pedometers provide a simple , cost effective means of motivating individuals to increase walking yet few studies have considered if short term changes in walking behaviour can be maintained in the long-term . The role of physical activity consultations in such interventions is unclear . The purpose of this study was to assess the sustainability of pedometer-based interventions and empirically examine the role of physical activity consultations using long-term results of a community-based walking study . Methods 79 low active Scottish men and women ( 63 women and 16 men ) from the Walking for Wellbeing in the West intervention study were r and omly assigned to receive either : Group 1 ; pedometer-based walking programme plus physical activity consultations or Group 2 ; pedometer-based walking programme and minimal advice .  Step counts ( Omron HJ-109E Step-O-Meter pedometer ) , 7 day recall of physical activity ( IPAQ long ) , mood ( PANAS ) and quality of life ( EuroQol EQ-5D ) were assessed pre-intervention and 12 , 24 and 48 weeks after receiving the intervention . Body mass , body mass index and waist and hip circumference were assessed pre-intervention and 12 and 24 weeks after receiving the intervention . Analyses were performed on an intention to treat basis ( baseline value carried forward for missing data ) using mixed-factorial ANOVAs and follow-up t-tests . Results A significant main effect of time ( p < 0.001 ) was found for step-counts attributable to significant increases in steps/day between : pre-intervention ( M = 6941 , SD = 3047 ) and 12 weeks ( M = 9327 , SD = 4136 ) , t(78 ) = - 6.52 , p < 0.001 , d = 0.66 ; pre-intervention and 24 weeks ( M = 8804 , SD = 4145 ) , t(78 ) = - 4.82 , p < 0.001 , d = 0.52 ; and pre-intervention and 48 weeks ( M = 8450 , SD = 3855 ) , t(78 ) = - 4.15 , p < 0.001 , d = 0.44 . Significant effects were found for several variables of self-reported physical activity , mood and quality of life and are discussed . No other significant effects in health related outcomes were found . Conclusion Both interventions successfully increased and maintained step counts over 12 months . Physical activity consultations may encourage individuals to be active in other ways beyond walking and to reduce sitting time . Trial Registration NumberCurrent Controlled Trials Ltd IS RCT
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 .