Evidence map›Paper›PMID 35653180›Full record

ReviewJMIR rehabilitation and assistive technologies2022

Inertial Measurement Units and Application for Remote Health Care in Hip and Knee Osteoarthritis: Narrative Review.

Michael J Rose, Kerry E Costello, Samantha Eigenbrot, Kaveh Torabian, Deepak Kumar

Open access · goldAbstract readReview
In one paragraph

Review in JMIR rehabilitation and assistive technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 9% of its field
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Michael J Rose *Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5400-2461
Kerry E Costello *Department of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5928-1471
Samantha EigenbrotDepartment of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States.ORCID https://orcid.org/0000-0002-4968-0639
Kaveh TorabianDepartment of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States.ORCID https://orcid.org/0000-0003-4265-7345
Deepak KumarDepartment of Physical Therapy & Athletic Training, Boston University College of Health & Rehabilitation Sciences: Sargent College, Boston, MA, United States.ORCID https://orcid.org/0000-0003-1728-484X
Boston University · US

Funding

RHEUMATOLOGY TRAINING GRANTT32AR007598 · NIAMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI FELSON, DAVID TOBIN · 1997 to 2018
$2.3M
Mind Your Walk Intervention for Community-Based Management of Knee OA: A Feasibility StudyK01AR069720 · NIAMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KUMAR, DEEPAK · 2017 to 2021
$676k
Machine learning for analysis of walking patterns and physical activity in knee osteoarthritisF32AR076907 · NIAMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI COSTELLO, KERRY ELIZABETH · 2020 to 2021
$82k
NIAMS NIH HHS F32 AR076907NIAMS NIH HHS K01 AR069720NIAMS NIH HHS T32 AR007598
6 · The paper itself

Abstract

backgroundMeasuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients' natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.

objectiveThis narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.

methodsA literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.

resultsFrom the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.

conclusionsInertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.

Indexed as

accelerometerbiofeedbackgyroscopemagnetometerremote monitoringwearable technology

Identifiers

PMID35653180
PMCPMC9204569
OpenAlexW4282929190

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.