Evidence map›Paper›PMID 40965324›Full record

ArticlePain management2025

Wearable sensor technologies for individuals with back pain: a scoping review.

Jordan J Ryan, Emma Bowden, Matthew M Hancock, Kali Power, N Wesley Edwards, Emily White, David T Fullwood, Ulrike Mitchell, Jennifer A Bowden, Anton E Bowden

Abstract readScoping Review
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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

10 authors.

Jordan J RyanMechanical Engineering Department, Brigham Young University, Provo, UT, USA.ORCID 0000-0002-5947-0993
Emma BowdenMechanical Engineering Department, Brigham Young University, Provo, UT, USA.ORCID 0000-0002-3650-9824
Matthew M HancockMechanical Engineering Department, Brigham Young University, Provo, UT, USA.
Kali PowerMechanical Engineering Department, Brigham Young University, Provo, UT, USA.
N Wesley EdwardsMechanical Engineering Department, Brigham Young University, Provo, UT, USA.
Emily WhiteIndependent Researcher, Milwaukee, Wisconsin, USA.
David T FullwoodMechanical Engineering Department, Brigham Young University, Provo, UT, USA.ORCID 0000-0002-2298-8756
Ulrike MitchellExercises Sciences Department, Brigham Young University, Provo, UT, USA.ORCID 0000-0002-7486-3672
Jennifer A BowdenSummit Brain, Spine and Orthopedics, Lehi, UT, USA.ORCID 0000-0003-4959-675X
Anton E BowdenMechanical Engineering Department, Brigham Young University, Provo, UT, USA.ORCID 0000-0003-3124-7551

Funding

Wearable nanocomposite sensor system for diagnosing mechanical sources of low back pain and guiding rehabilitationUH3AR076723 · NIAMS · BRIGHAM YOUNG UNIVERSITY · PI BOWDEN, ANTON E. · 2021 to 2021
$1.2M
NIAMS NIH HHS UH3 AR076723
6 · The paper itself

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

Back PainWearable Electronic DevicesHumansBack painbiomarkersbiosignalsspinewearable sensor technologies

Identifiers

PMID40965324
PMCPMC12562684

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.