AI-guided EMS for chronic musculoskeletal pain: subgroup-level outcomes from a digital health platform
Maja Green1,2, Adam Cabble1, Maria Kappell1
1Department of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.
Objective:
Chronic musculoskeletal pain is common and lacks effective long-term therapies. Transcutaneous electrical nerve stimulation (TENS) and electrical muscle stimulation (EMS) offer noninvasive alternatives, but conventional devices are limited by static protocols and poor adherence. The NXTSTIM EcoAI platform integrates TENS/EMS with artificial intelligence to deliver personalized, adaptive therapy. This study examined 24-month real-world outcomes of EcoAI, focusing on usage and subgroup-level efficacy.
Methods:
A retrospective analysis was conducted using de-identified data from 2,050 adults using EcoAI at home. The primary endpoint was change in pain intensity (0-10 numeric rating scale). Secondary endpoints included functional status, session engagement, and qualitative pain self-efficacy.
Results:
Across ~185,000 sessions, users reported significant pain reduction. Mean pain decreased by 2.4 points (p < 0.001), exceeding 30% improvement, with benefits sustained at 12 and 24 months. Functional interference and mood improved significantly (p < 0.01). Older adults (≥60 years) achieved comparable or greater relief despite slightly lower usage. No serious adverse events occurred. Findings aligned with prior EcoAI analyses showing optimal outcomes with 2-4 daily sessions of 20-59 minutes.
Conclusion:
EcoAI provided clinically meaningful, durable pain relief with improved function and mood, supporting personalized home-based neuromodulation as a safe, effective adjunct for chronic musculoskeletal pain.
