ArticlePain management2025
Wearable sensor technologies for individuals with back pain: a scoping review.
Article in Pain management, 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.
- AI-Supported Functional Pain Phenotyping in Low Back Pain Using Sensor-Based Gait and Neuromuscular Biomarkers.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence in postural management: a critical review of detection, correction, and clinical applicability.Journal of orthopaedic surgery and research · 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
10 authors.
Funding
Abstract
Recent advancements in wearable data measurement technologies have allowed for real-time collection of biosignals related to spinal function and back pain. These data also have the potential to completely transform back pain treatment paradigms, to improve diagnostic movement phenotyping and to track treatment effectiveness longitudinally. The primary objective of the present scoping review was to investigate the status of development and trends in the use of wearable sensor technologies employed to measure biosignals related to spinal function and back pain, to identify the major developments and future trends for this field.Until recently, much of the wearable sensor data related to spinal function and back pain have come from a relatively small number of technologies, were sampled by a judiciously placed single device, and were analyzed using traditional statistical modeling techniques. However, based on the state of the literature, the field of wearable sensors for spine appears to have reached an inflection point where the previous limiting factors are no longer significant barriers. The growing number of wearable sensor types, combined with real-time interpretation using machine-learning algorithms, is paving the way for objective and comprehensive evaluations of spinal movements that can guide both research and clinical practice.
Indexed as
Identifiers
What Socratic 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 Socratic graph.