ArticleNPJ digital medicine2023
Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07147140 (Evaluation of Physiological Parameters in Patients With Complex Regional Pain Syndrome of the Lower Limbs Treated With Dorsal Root Ganglion Stimulation.), which is not on this map. Cited by 19 papers, 3 of them syntheses that pooled 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.
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.
Evaluation of Physiological Parameters in Patients With Complex Regional Pain Syndrome of the Lower Limbs Treated With Dorsal Root Ganglion Stimulation.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Wearable Technology and Machine Learning for Prediction of Performance-Based and Patient-Reported Outcome Measures: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Objective, long-term mobility data in patients with chronic pain after lumbar spine surgery treated with spinal cord stimulation-a prospective, multicenter trial.Regional anesthesia and pain medicine · 2026Trial
- Smartwatch-derived digital biomarkers distinguish episodic pain phenotypes in chronic low back pain.medRxiv : the preprint server for health sciences · 2026Article
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- Remote Therapeutic Monitoring in Musculoskeletal Pain Medicine: A Systematic Review and Comparison with Remote Physiologic Monitoring.Journal of pain research · 2026Review
- From behavioral signals to therapy management: a real-world pilot implementation of human-in-the-loop AI workflow in proactive patient care.Frontiers in digital health · 2026Article
- Pain assessment and determination methods with wearable sensors: a scoping review.Medical & biological engineering & computing · 2026Article
- The Role of Pharmacogenetic Biomarkers in Pain.Biomedicines · 2025Article
- Mobile Health and Gamification of Chronic Pain Management: A Narrative Review.Current pain and headache reports · 2025Review
- Reviewing the Advances in Remote Therapeutic and Physiological Monitoring and Applicability for Chronic Pain Management.Journal of pain research · 2025Review
- Clinical pain management: Current practice and recent innovations in research.Cell reports. Medicine · 2024Review
- Article
- Effect of neuromodulation for chronic pain on the autonomic nervous system: a systematic review.BJA open · 2024Review
- Benchmark findings from a veteran electronic patient-reported outcomes evaluation from a chronic pain management telehealth program.BMC health services research · 2024Article
- Cardiac sensing at a spinal cord stimulation lead: a promising on-device potential biomarker for pain and wellbeing.Frontiers in physiology · 2024Article
- Machine Learning in Pain Neuromodulation.Advances in experimental medicine and biology · 2024Review
- Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain.Bioelectronic medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.
Identifiers
What OpenQuestion holds
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.