Smartphone-Based Gait Assessment Captures Functional Recovery Following Total Knee Arthroplasty.

Celeste A Thai, Jakob R Marrone, Lauren C Tran, Britta Berg-Johansen

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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.

From One-Size-Fits-All to Data-Driven Recovery: A Narrative Review of Wearable Technologies Toward Personalized Rehabilitation After Total Hip and Knee Arthroplasty.Journal of personalized medicine · 2026
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

4 authors.

Celeste A ThaiPhysical Therapy Department, College of Health Professions, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60044, USA.

Jakob R MarroneBiomedical Engineering Department, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.

Lauren C TranBiomedical Engineering Department, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.

Britta Berg-JohansenBiomedical Engineering Department, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.ORCID 0009-0002-3595-6996

Funding

Graduate Education Scholarship, Cal Poly NAHannah Forbes Foundation, Cal Poly NA

6 · The paper itself

Abstract

Novel smartphone-based methods offer an accessible and promising alternative to traditional tools for performing clinical gait assessments in total knee arthroplasty (TKA) patients. The OneStep app uses the smartphone's sensors and proprietary machine learning algorithms to measure gait parameters of walking trials, including stride length, step length, step width, gait velocity, cadence, and double stance time. The objectives of this study were to (1) validate the reliability of the OneStep app against a traditional motion capture (MoCap) system and (2) use the OneStep app to measure functional recovery of TKA patients pre- and post-operatively. For Objective 1, walking trials using both OneStep and MoCap were conducted with N = 17 healthy adults (9M/8F, aged 22.29 ± 2.08 years). Results showed that of all gait variables, cadence (

Indexed as

Arthroplasty, Replacement, KneeGaitGait AnalysisRecovery of FunctionSmartphoneAdultAgedBiomechanical PhenomenaFemaleHumansMaleMobile ApplicationsMotion CaptureWalkingbiomechanicsgaitmotion capturesmartphonesTKA

Identifiers

PMID41600229

DOI10.3390/s26020432

PMCPMC12846111

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.