BackgroundPhysical activity reduces the risk of noncommunicable diseases and is therefore anessential component of a healthy lifestyle. Regular engagement in physical activity canproduce immediate and long term health benefits. However, physical activity levels arenot as high as might be expected. For example, according to the global World HealthOrganization (WHO) 2017 statistics, more than 80% of the world's adolescents areinsufficiently physically active. In response to this problem, physical activity programshave become popular, with step counts commonly used to measure programperformance. Analysing step count data and the statistical modeling of this data istherefore important for evaluating individual and program performance.This studyreviews the statistical methods that used to model and evaluate physical activityprograms, using step counts.MethodsAdhering to PRISMA guidelines,this review systematically searched for relevant journalarticles which were published between January 2000 and August 2017 in any of threedatabases (PubMed, PsycINFO and Web of Science). Only the journal articles whichused a statistical model in analysing step counts for a healthy sample of participants,enrolled in an intervention involving physical exercise or a physical activity program,were included in this study. In these programs the activities considered were naturalelements of everyday life rather than special activity interventions.ResultsThis systematic review was able to identify 78 unique articles describing statisticalmodels for analysing step counts obtained through physical activity programs. Generallinear models and generalized linear models were the most popular methods usedfollowed by multilevel models, while structural equation modeling was only used formeasuring the personal and psychological factors related to step counts. Surprisingly nouse was made of time series analysis for analysing step count data. The review alsosuggested several strategies for the personalisation of physical activity programs.ConclusionsOverall, it appears that the physical activity levels of people involved in such programsvary across individuals depending on psychosocial, demographic, weather and climaticfactors. Statistical models can provide a better understanding of the impact of thesefactors, allowing for the provision of more personalised physical activity programs,which are expected to produce better immediate and long-term outcomes forparticipants. It is hoped that this review will identify the statistical methods which aremost suitable for this purpose.