Evidence map›Paper›PMID 36658136›Full record

ArticleScientific data2023

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization.

Luca Palmerini, Luca Reggi, Tecla Bonci, Silvia Del Din, M Encarna Micó-Amigo, Francesca Salis, Stefano Bertuletti, Marco Caruso, Andrea Cereatti, Eran Gazit and 12 more

Open access · goldAbstract read
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
5.7field-weighted citation impact, top 3% 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

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 citations in OpenAlex.

  1. Pooled it
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  16. Developing digital health technologies for frontotemporal degeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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  19. Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
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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

22 authors at 11 institutions in 5 countries.

Luca PalmeriniUniversity of Bologna, Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', Bologna, Italy. luca.palmerini@unibo.it.ORCID 0000-0003-4758-662X
Luca ReggiUniversity of Bologna, Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy.
Tecla BonciThe University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK.ORCID 0000-0002-8255-4730
Silvia Del DinNewcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
M Encarna Micó-AmigoNewcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
Francesca SalisUniversity of Sassari, Department of Biomedical Sciences, Sassari, Italy.
Stefano BertulettiUniversity of Sassari, Department of Biomedical Sciences, Sassari, Italy.
Marco CarusoPolitecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy.ORCID 0000-0002-1529-8095
Andrea CereattiPolitecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy.
Eran GazitTel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel.
Anisoara Paraschiv-IonescuLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Abolfazl SoltaniLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Felix KlugeMachine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0003-4921-6104
Arne KüderleMachine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
Martin UllrichMachine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
Cameron KirkNewcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
Hugo HidenNewcastle University, School of Computing, Newcastle, UK.
Ilaria D'AscanioUniversity of Bologna, Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', Bologna, Italy.
Clint HansenNeurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany.ORCID 0000-0003-4813-3868
Lynn RochesterNewcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
Claudia MazzàThe University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK.
Lorenzo ChiariUniversity of Bologna, Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', Bologna, Italy.
Newcastle University · GBFriedrich-Alexander-Universität Erlangen-Nürnberg · DELaboratori Guglielmo Marconi (Italy) · ITÉcole Polytechnique Fédérale de Lausanne · CHInsigneo · GBPolitecnico di Torino · ITUniversity of Sassari · ITNewcastle upon Tyne Hospitals NHS Foundation Trust · GBTel Aviv Sourasky Medical Center · ILUniversity Hospital Schleswig-Holstein · DEUniversity of Bologna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.

Identifiers

PMID36658136
PMCPMC9852581
OpenAlexW4317567286

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.