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 .
The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions.
OBJECTIVE : To test the feasibility of creating a valid and reliable checklist with the following features : appropriate for assessing both r and omised and non-r and omised studies ; provision of both an overall score for study quality and a profile of scores not only for the quality of reporting , internal validity ( bias and confounding ) and power , but also for external validity . DESIGN : A pilot version was first developed , based on epidemiological principles , review s , and existing checklists for r and omised studies . Face and content validity were assessed by three experienced review ers and reliability was determined using two raters assessing 10 r and omised and 10 non-r and omised studies . Using different raters , the checklist was revised and tested for internal consistency ( Kuder-Richardson 20 ) , test-retest and inter-rater reliability ( Spearman correlation coefficient and sign rank test ; kappa statistics ) , criterion validity , and respondent burden . MAIN RESULTS : The performance of the checklist improved considerably after revision of a pilot version . The Quality Index had high internal consistency ( KR-20 : 0.89 ) as did the subscales apart from external validity ( KR-20 : 0.54 ) . Test-retest ( r 0.88 ) and inter-rater ( r 0.75 ) reliability of the Quality Index were good . Reliability of the subscales varied from good ( bias ) to poor ( external validity ) . The Quality Index correlated highly with an existing , established instrument for assessing r and omised studies ( r 0.90 ) . There was little difference between its performance with non-r and omised and with r and omised studies . Raters took about 20 minutes to assess each paper ( range 10 to 45 minutes ) . CONCLUSIONS : This study has shown that it is feasible to develop a checklist that can be used to assess the method ological quality not only of r and omised controlled trials but also non-r and omised studies . It has also shown that it is possible to produce a checklist that provides a profile of the paper , alerting review ers to its particular method ological strengths and weaknesses . Further work is required to improve the checklist and the training of raters in the assessment of external validity
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 .