Evidence map›Paper›PMID 42236571›Full record

Observational studyRheumatology international2026

Step count recovery patterns in the first six weeks after knee replacement in individuals with knee osteoarthritis: a secondary analysis of a prospective observational cohort study using wrist-worn accelerometry.

Ayobami E Olanrewaju, Emma Pritchard, Shuai Shao, Andrew J Price, Aiden Doherty, Sabine N van der Veer, David C Wong, Scott R Small, Stephanie R Filbay, William G Dixon

Abstract readObservational Study
In one paragraph

Observational study in Rheumatology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ayobami E OlanrewajuSchool of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PT, UK. ayobami.olanrewaju@postgrad.manchester.ac.uk.ORCID http://orcid.org/0000-0002-4520-7019
Emma PritchardSchool of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PT, UK.ORCID http://orcid.org/0000-0002-0963-9260
Shuai ShaoSchool of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PT, UK.ORCID http://orcid.org/0009-0002-7028-0944
Andrew J PriceNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-4258-5866
Aiden DohertyNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-1840-0451
Sabine N van der VeerSchool of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PT, UK.ORCID http://orcid.org/0000-0003-0929-436X
David C WongUniversity of Leeds, Leeds Institute of Health Sciences, Leeds, UK.ORCID http://orcid.org/0000-0001-8117-9193
Scott R SmallNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-3603-8062
Stephanie R FilbayCentre for Health, Exercise and Sports Medicine, Department of Physiotherapy, University of Melbourne, Parkville, Melbourne, VIC, 3000, Australia.ORCID http://orcid.org/0000-0002-9624-0791
William G DixonSchool of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PT, UK.ORCID http://orcid.org/0000-0001-5881-4857

Funding

Joint-PhD award program scholarship from the University of Manchester and the University of Melbourne Joint-PhD award program scholarship from the University of Manchester and the University of MelbourneManchester Biomedical Research Centre NIHR203308National Health and Medical Research Council 1194428University of Oxford Clarendon Scholarship Fund University of Oxford Clarendon Scholarship FundWellcome TrustWellcome Trust 223100/Z/21/Z
6 · The paper itself

Abstract

Effective patient education and activity expectation-setting following knee replacement requires understanding postoperative recovery patterns. We investigated step-count recovery patterns, identified recovery trajectory clusters, and examined differences in postoperative outcomes and preoperative factors associated with those clusters. Using wrist-worn accelerometer, step-counts were measured for one week preoperatively and six weeks postoperatively in patients with knee osteoarthritis undergoing total or unicompartmental knee replacement (UKR). Patient-reported outcomes (OKS, EQ-5D-3L, EQ-VAS) were collected preoperatively and at six postoperative weeks. The proportion of participants exceeding their preoperative step-count was calculated. Latent class growth analysis identified trajectories of absolute (postoperative) and relative (postoperative-to-preoperative ratio) step-counts. Kruskal-Wallis with Dunn's test assessed differences in postoperative outcomes across trajectories, and multinomial logistic regression assessed associations between trajectories and preoperative factors. Eighty-two participants (aged 42-89 years; 52% women) were included. Within six weeks, 32% exceeded preoperative step-counts. Three trajectories (high, moderate and low recovery) were identified for both absolute and relative step-counts. For absolute steps, the "high recovery" group (n = 30, 37%) began with a median of 991 steps [IQR 1,335] at Week1 and achieved a median of 6,606 steps [IQR 2,940] by Week6. This compares to the "moderate" group (n = 24, 29%) who improved from 360 steps [IQR 481] at Week1 to 3,739 steps [IQR 1,918] at Week6, and the "low" group (n = 28, 34%) who increased from 29 steps [IQR 176] at Week1 to 1,452 steps [IQR 1,504] by Week6. For relative steps, the "high recovery" group (n = 39, 47%) reached 92% [IQR 29%] of their preoperative step-count at Week6, compared with 64% [IQR 25%] in the "moderate" group (n = 26, 32%) and 31% [IQR 7%] in the "low" group (n = 17, 21%). Patients in the high recovery trajectories reported less pain, better knee function (OKS) and better health status at six weeks versus low recovery trajectories. Membership of the high trajectories was more likely in those with higher preoperative step-count and for those managed with UKR. Patients with lower preoperative step-count were unlikely to achieve high absolute step-count and less likely to achieve high- or moderate-relative recovery trajectories versus those with a higher preoperative step-count. One in three individuals regained preoperative step-count by six weeks, with heterogeneity in recovery patterns. Greater recovery was associated with higher preoperative step-count and UKR procedures.

Indexed as

Arthroplasty, Replacement, KneeKnee JointOsteoarthritis, KneeAccelerometryAdultAgedAged, 80 and overCohort StudiesFemaleHumansMaleMiddle AgedPatient Reported Outcome MeasuresProspective StudiesRecovery of FunctionTime FactorsKnee arthroplastyKnee osteoarthritisPhysical activityPostoperative period

Identifiers

PMID42236571
PMCPMC13233972

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.