ArticleJournal of pain research2025
Real-World Effectiveness of a Novel AI-Software Dependent Neuromodulation (ASDN) with Remote Monitoring Capability Field Stimulation Device for Chronic Pain: A 24-Month Analysis of Over 2000 Patients.
Article in Journal of pain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.BMC anesthesiology · 2026Article
- Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care.Frontiers in computational neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Chronic pain is a leading cause of disability worldwide, and conventional pharmacologic treatments are often limited by side effects, inadequate efficacy, and risk of dependency. Non-invasive neuromodulation therapies such as TENS and EMS offer alternatives but are traditionally constrained by fixed stimulation protocols and low user engagement. Objective: To evaluate the 24-month real-world effectiveness of EcoAI™, an AI-driven wearable system delivering adaptive TENS and EMS for chronic pain management in community settings. Design Setting and participants: This retrospective observational cohort study analyzed de-identified data from 2135 adult users across the United States between January 2023 and March 2025. All users completed at least one therapy session and submitted symptom data via a mobile application. Intervention: EcoAI delivers transcutaneous electrical nerve stimulation (TENS) to modulate afferent pain signaling and electrical muscle stimulation (EMS) to improve local circulation and neuromuscular function. An embedded AI engine dynamically adjusts stimulation intensity, waveform, and duration based on user-reported outcomes and physiological markers. Main Outcomes and Measures: Primary outcome: change in self-reported pain score (0-10 numeric scale). Secondary outcomes: mood, physical function, social engagement, work activity, and overall well-being. Session adherence and device usage patterns were also analyzed. Results: Across 187,930 recorded sessions, median pain scores declined from 6.0 at baseline to 4.0 at 6 months and 3.0 at 24 months. Statistically significant improvements (p < 0.001) were also observed in secondary domains. Optimal outcomes were achieved with 2-4 sessions per day lasting 20-59 minutes. Older adults (≥60 years) demonstrated greater engagement and pain relief. No serious adverse events were reported. Conclusions and Relevance: In this retrospective, decentralized study, the EcoAI platform demonstrated sustained, multidimensional benefit in adults with chronic pain. These findings support the potential of AI-driven TENS/EMS as a safe, scalable, and personalized adjunct to pharmacologic care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.