Effectiveness Of An AI-Based Home Exercise App For Rehabilitation In Rotator Cuff-Related Shoulder Pain: A Randomized Controlled Trial

Abstract

Background Rotator cuff–related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. Objectives To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. Design Single-center, assessor-blinded, randomized controlled trial with two parallel groups. Method Forty-six adults (mean age 59 years) with rotator cuff–related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. Results Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD −0.7; 95% CI −1.13 to −0.14 and MD −1.01; 95% CI −1.8 to −0.2, respectively). Upper limb function improved more at Week 4 (MD −7.3; 95% CI −12.3 to −2.2). FABQ scores decreased more at Week 12 (MD −7.6; 95% CI −14 to −0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). Conclusion Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff–related shoulder pain.