Evidence map›Paper›PMID 41134054›Full record

ArticlePain management2025

AI-guided EMS for chronic musculoskeletal pain: subgroup-level outcomes from a digital health platform.

Maja Green, Adam Cabble, Maria Kappell, Shari Kappell, Varun Chakravarti, Krishnan Chakravarthy

Abstract read
In one paragraph

Article in Pain management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maja GreenDepartment of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.ORCID 0000-0002-7982-2503
Adam CabbleDepartment of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.
Maria KappellDepartment of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.
Shari KappellDepartment of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.
Varun ChakravartiDepartment of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.
Krishnan ChakravarthyDepartment of Pain Medicine, NXTSTIM Inc., San Diego, CA, USA.ORCID 0000-0002-7994-3786

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveChronic 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.

methodsA 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.

resultsAcross ~185,000 sessions, users reported significant pain reduction. Mean pain decreased by 2.4 points (

conclusionEcoAI 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.

Indexed as

Artificial IntelligenceChronic PainMusculoskeletal PainPain ManagementTranscutaneous Electric Nerve StimulationAdultAgedDigital HealthFemaleHumansMaleMiddle AgedPain MeasurementRetrospective StudiesTreatment OutcomeYoung Adultartificial intelligenceChronic musculoskeletal paindigital healthelectrical muscle stimulationneuromodulationself-management

Identifiers

PMID41134054
PMCPMC12674414

What OpenQuestion holds

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Registered trials

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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.