Abstract
There is a lack of studies of alternative techniques differing from the straight leg raise test (SLR) and the passive knee extension test (PKE) to diagnose short hamstring syndrome (SHS). We built a predictive model with simple parameters to diagnose SHS and implemented it in a mobile app. This cross-sectional study analyzed 85 Spanish boys aged 10–16 years who played soccer in 2012. Outcomes: SHS (SLR<70° and/or PKE>15°), and grade II SHS (SLR<60° and/or PKE≥35°). Secondary variables: toe-touch test (TT), body mass index (BMI), age, laterality and number of years registered as part of a federation. A risk table implemented in a mobile app was built to estimate the probability of SHS and grade II SHS according to secondary variables. The area under the ROC curve (AUC) was calculated and we constructed risk groups. Scoring factors for SHS: low TT, younger age and lower BMI. AUC: 0.89 (95% CI: 0.82–0.96, p<0.001). Scoring factors for grade II SHS: younger age, higher BMI, left footed and lower TT. AUC: 0.78 (95% CI: 0.68–0.88, p<0.001). We provide a tool with minimum material but with a high discriminatory power to quickly calculate whether a boy who plays soccer has SHS. The models need validation studies.
Key words
musculoskeletal diseases - early diagnosis - sports medicine - soccer - athletic injuries - mobile applications
