Sequential Activity Patterns and Outcome-Specific, Real-Time, and Target Group-Specific Feedback: The SPORT Algorithm

Purpose : Physical activity (PA) is crucial for health, but there is insufficient evidence about PA patterns and their operationalization. The authors developed two algorithms (SPORT constant and SPORT linear ) to quantify PA patterns and check whether pattern information yields additional explained variance (compared with a compositional data approach [CoDA]). Methods : To measure PA, 397 (218 females) adolescents with a mean age of 12.4 ( SD  = 0.6) years wore an ActiGraph on their lower back for 1 week. The SPORT algorithms are based on a running value, each day starting with 0 and minutely adapting depending on the behavior being performed. The authors used linear regression models with a behavior-dependent constant (SPORT constant ) and a function of time-in-bout (SPORT linear ) as predictors and body mass index z scores (BMI z ) and fat mass percentages (%FM) as exemplary outcomes. For generalizability, the models were validated using five-fold cross-validation where data were split up in five groups, and each of them was a test data set in one of five iterations. Results : The CoDA and the SPORT constant models explained low variance in BMI z (2% and 1%) and low to moderate variance in %FM (both 5%). The variance being explained by the SPORT linear models was 6% (BMI z ) and 9% (%FM), which was significantly more than the CoDA models ( p  < .001) according to likelihood ratio tests. Conclusion : Among this group of adolescents, SPORT linear explained more variance of BMI z and %FM than CoDA. These results suggest a way to enable research about PA patterns. Future research should apply the SPORT linear algorithm in other target groups and with other health outcomes.