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 school-based physical activity interventions increase or reduce inequalities in health?
Little is known about the effectiveness of school-based health promotion on physical activity inequalities among children from low-income areas . This study compared the two-year change in physical activity among 10 - 11 year-old children attending schools with and without health promotion programs by activity level , body weight status , and socioeconomic background s to assess whether health promotion programs reduce or exacerbate health inequalities . This was a quasi-experimental trial of a Comprehensive School Health ( CSH ) program implemented in schools located in socioeconomically disadvantaged neighbourhoods in Edmonton , Alberta , Canada . In the spring of 2009 and 2011 , pedometer ( 7 full days ) and demographic data were collected from cross-sectional sample s of grade five children from 10 intervention and 20 comparison schools . Socioeconomic status was determined from parent self-report . Low-active , active , and high-active children were defined according to step-count tertiles . Multilevel linear regression methods adjusted for potential confounders were used to assess the relative inequity in physical activity and were compared between groups and over-time . In 2009 , a greater proportion of students in the intervention schools were overweight ( 38 % vs. 31 % p = 0.03 ) and were less active ( 10,827 vs. 12,265 steps/day p < 0.001 ) . Two years later , the relative difference in step-counts between intervention and comparison schools reduced from -15.5 % to 0 % among low-active students , from -13.4 % to 0 % among active students , and from -15.1 % to -2.7 % among high-active students . The relative difference between intervention and comparison schools reduced from -11.1 % to -1.6 % among normal weight students , from -16.8 % to -1.4 % among overweight students , and was balanced across socioeconomic subgroups . These findings demonstrate that CSH programs implemented in socioeconomically disadvantaged neighbourhoods reduced inequalities in physical activity . Investments in school-based health promotion are a viable , promising , and important approach to improve physical activity and prevent childhood obesity , and may also reduce inequalities in health
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 .