Evidence map›Paper›PMID 36474469›Full record

ArticleOsteoarthritis and cartilage open2022

Feasibility of personalised hip load modification using real-time biofeedback in hip osteoarthritis: A pilot study.

Laura E Diamond, Daniel Devaprakash, Bradley Cornish, Melanie L Plinsinga, Andrea Hams, Michelle Hall, Rana S Hinman, Claudio Pizzolato, David J Saxby

Open access · goldAbstract read
In one paragraph

Article in Osteoarthritis and cartilage open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 28% 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

8 citing papers in PubMed, 16 citations in OpenAlex.

  1. Hip Joint Loading During Walking Is Associated With Cartilage Defect Severity in Young Adult Football Players With Hip/Groin Pain.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026
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  8. Rehabilitation Phases, Precautions, and Mobility Goals Following Total Hip Arthroplasty.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2023
    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

9 authors at 2 institutions in 1 country.

Laura E DiamondGriffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
Daniel DevaprakashGriffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
Bradley CornishSchool of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
Melanie L PlinsingaMenzies Health Institute Queensland, Griffith University, Nathan, Australia.
Andrea HamsGriffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
Michelle HallCentre for Health, Exercise and Sports Medicine, The University of Melbourne, Melbourne, Australia.
Rana S HinmanCentre for Health, Exercise and Sports Medicine, The University of Melbourne, Melbourne, Australia.
Claudio PizzolatoGriffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
David J SaxbyGriffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
Griffith University · AUThe University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: (i) Compare the feasibility of three load modification strategies to immediately increase hip contact force in people with hip osteoarthritis (OA) using real-time visual biofeedback during walking, and (ii) prospectively evaluate changes in pain and physical function following 6-weeks of walking using a prescribed personalised load modification strategy. Design: Twenty participants with symptomatic mild-to-moderate hip OA walked on an instrumented treadmill while motion capture and electromyographic data were recorded (normal walk), then under three conditions: (i)neutral trunk lean; (ii)neutral pelvic obliquity; (iii)increased step length. The biomechanical parameter of interest and corresponding target value were displayed in real-time. Hip contact forces were subsequently computed using a calibrated electromyography-informed neuromusculoskeletal model. A decision tree was used to prescribe a personalised load modification strategy to each participant for integration into walking over 6-weeks. Results: Only the step length modification significantly increased peak hip contact force compared to normal walking when performed by all participants (11.34 [95%CI 4.54,18.13]%, P ​< ​0.01). After participants were prescribed a personalised load modification strategy, both neutral pelvis (n ​= ​5, 11.88[95%CI -0.49,24.24]%) and step length (n ​= ​10, 12.79[95%CI 0.49,25.09]%) subgroups increased peak hip contact force >10%. After 6-weeks, 77% and 46% of participants reported a clinically important improvement in hip pain during walking and physical function, respectively. Conclusion: Most participants with hip OA could immediately increase hip contact force through personalised movement retraining by a magnitude estimated to promote cartilage heath and reported an improvement in symptoms after 6-weeks. Findings provide preliminary support for a personalised load modification-based intervention for hip OA.

Indexed as

BiomechanicsContact forceElectromyographyGait retrainingMovement strategyNeuromusculoskeletal modelling

Identifiers

PMID36474469
PMCPMC9718151
OpenAlexW4200032123

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.