Evidence map›Paper›PMID 34032154›Full record

SynthesisHand (New York, N.Y.)2023

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review.

Francisco R Avila, Rickey E Carter, Christopher J McLeod, Charles J Bruce, Davide Giardi, Gunel Guliyeva, Antonio J Forte

Abstract readSystematic Review
In one paragraph

Synthesis in Hand (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Francisco R AvilaDivision of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.ORCID 0000-0001-7856-1367
Rickey E CarterDepartment of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA.
Christopher J McLeodDepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
Charles J BruceDepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
Davide GiardiDepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.ORCID 0000-0001-9780-6403
Gunel GuliyevaDivision of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.
Antonio J ForteDivision of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.ORCID 0000-0003-2004-7538

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one.

methodsWe performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry.

resultsOf the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices.

conclusionGloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera-based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

Indexed as

Wearable Electronic DevicesHandHumansRange of Motion, Articularbend sensorgoniometer accuracyhand goniometryhand range of motionhand rehabilitationinertial sensormanual goniometrysensor glovesensor technologywearable sensor technology

Identifiers

PMID34032154
PMCPMC10035090

What OpenQuestion 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 OpenQuestion graph.