Evidence map›Paper›PMID 38243008›Full record

ArticleScientific reports2024

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.

Cameron Kirk, Arne Küderle, M Encarna Micó-Amigo, Tecla Bonci, Anisoara Paraschiv-Ionescu, Martin Ullrich, Abolfazl Soltani, Eran Gazit, Francesca Salis, Lisa Alcock and 38 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 57 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 2 pooled it
68.7field-weighted citation impact, top 1% 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

57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.

  1. Pooled it
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  8. Real-world walking and activity patterns in community-dwelling older adults: a cross-sectional analysis of baseline data from the SMART-AGE trial.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2026
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  13. Observational
  14. Article
  15. Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

48 authors at 20 institutions in 15 countries.

Cameron Kirk *Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK.
Arne Küderle *Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
M Encarna Micó-Amigo *Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK.
Tecla BonciDepartment of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.
Anisoara Paraschiv-IonescuLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Martin UllrichMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Abolfazl SoltaniLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Eran GazitCenter for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Francesca SalisDepartment of Biomedical Sciences, University of Sassari, Sassari, Italy.
Lisa AlcockTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK.
Kamiar AminianLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Clemens BeckerRobert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany.
Stefano BertulettiDepartment of Biomedical Sciences, University of Sassari, Sassari, Italy.
Philip BrownThe Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.
Ellen BuckleyDepartment of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.
Alma CantuSchool of Computing, Newcastle University, Newcastle Upon Tyne, UK.
Anne-Elie CarsinBarcelona Institute for Global Health (ISGlobal), Barcelona, Spain.
Marco CarusoDepartment of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
Brian CaulfieldInsight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
Andrea CereattiDepartment of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
Lorenzo ChiariDepartment of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy.
Ilaria D'AscanioDepartment of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy.
Judith Garcia-AymerichBarcelona Institute for Global Health (ISGlobal), Barcelona, Spain.
Clint HansenDepartment of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany.
Jeffrey M HausdorffCenter for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Hugo HidenThe Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.
Emily HumeDepartment of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle Upon Tyne, UK.
Alison KeoghInsight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
Felix KlugeMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Sarah KochBarcelona Institute for Global Health (ISGlobal), Barcelona, Spain.
Walter MaetzlerDepartment of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany.
Dimitrios MegaritisDepartment of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle Upon Tyne, UK.
Arne MuellerNovartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland.
Martijn NiessenMcRoberts BV, The Hague, The Netherlands.
Luca PalmeriniDepartment of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy.
Lars SchwickertRobert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany.
Kirsty ScottDepartment of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.
Basil SharrackDepartment of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Henrik SillénDigital Health R&D, AstraZeneca, Sweden.
David SingletonInsight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
Beatrix VereijkenDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.
Ioannis VogiatzisDepartment of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle Upon Tyne, UK.
Alison J YarnallTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK.
Lynn RochesterTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK.
Claudia MazzàDepartment of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.
Bjoern M EskofierMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Silvia Del DinTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK. silvia.del-din@ncl.ac.uk.
Mobilise-D consortium
Novartis (Switzerland) · CHRobert Bosch Stiftung · DEUniversity College Dublin · IEUniversity of Sheffield · GBVita-Salute San Raffaele University · ITClarion University · USNewcastle University · GBAmgen (United States) · USNorwegian University of Science and Technology · NOCentre Hospitalier Universitaire de Montpellier · FRImperial College London · GBKU Leuven · BEUniversity of Sassari · ITPfizer (United Kingdom) · GBUniversity of Zurich · CHAstraZeneca (Sweden) · SENorthumbria University · GBChristian-Albrechts-Universität zu Kiel · DEFriedrich-Alexander-Universität Erlangen-Nürnberg · DEUniversitätsklinikum Erlangen · DE

Funding

Wellcome Trust
6 · The paper itself

Abstract

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

Indexed as

Walking SpeedWearable Electronic DevicesAgedGaitHumansResearch DesignWalking

Identifiers

PMID38243008
PMCPMC10799009
OpenAlexW4391014863

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.