A Treatment-Based Classification Algorithm to Treat Achilles Tendinopathy: An Exploratory Case Series

Context: Achilles tendinopathy is relatively common in both the general and athletic populations. The current gold standard for the treatment of Achilles tendinopathy is eccentric exercise, which can be painful and time consuming. While there is limited research on indirect treatment approaches, it has been proposed that tendinopathy patients do respond to indirect approaches in fewer treatments without provoking pain. Objective: To determine the effectiveness of using a treatment-based-classification (TBC) algorithm as a strategy for classifying and treating patients diagnosed with Achilles tendinopathy. Participants: 11 subjects (mean age 28.0 +/- 15.37 y) diagnosed with Achilles tendinopathy. Design: Case series. Setting: Participants were evaluated, diagnosed, and treated at multiple clinics. Main Outcome Measures: Numeric Rating Scale (NRS), Disablement in the Physically Active Scale (DPA Scale), Victorian Institute of Sport Assessment Achilles (VISA-A), Global Rating of Change (GRC), and Nirschl Phase Rating Scale were recorded to establish baseline scores and evaluate participant progress. Results: A repeated-measures ANOVA was conducted to analyze NRS scores from initial exam to discharge and at 1-mo follow-up. Paired t tests were analyzed to determine the effectiveness of using a TBC algorithm from initial exam to discharge on the DPA Scale and VISA-A. Descriptive statistics were evaluated to determine outcomes as reported on the GRC. Conclusion: The results of this case series provide evidence that using a TBC algorithm can improve function while decreasing pain and disability in Achilles tendinopathy participants.