Evidence map›Paper›PMID 40392580›Full record

Observational studyJMIR formative research2025

Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study.

Martin A Berge, Anisoara Paraschiv-Ionescu, Cameron Kirk, Arne Küderle, Encarna Micó-Amigo, Clemens Becker, Andrea Cereatti, Silvia Del Din, Monika Engdal, Judith Garcia-Aymerich and 15 more

Abstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Observational
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

25 authors.

Martin A BergeDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0001-8741-0739
Anisoara Paraschiv-IonescuLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.ORCID 0000-0002-2341-4383
Cameron KirkTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.ORCID 0000-0003-2508-5816
Arne KüderleMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0002-5686-281X
Encarna Micó-AmigoTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.ORCID 0000-0002-8968-6373
Clemens BeckerGeriatric Center, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.ORCID 0000-0003-1624-8353
Andrea CereattiDepartment of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.ORCID 0000-0002-7276-5382
Silvia Del DinTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.ORCID 0000-0003-1154-4751
Monika EngdalDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0009-0007-4854-2561
Judith Garcia-AymerichBarcelona Institute for Global Health, Barcelona, Spain.ORCID 0000-0002-7097-4586
Karoline B GrønvikDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0009-0008-4594-0000
Clint HansenDepartment of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany.ORCID 0000-0003-4813-3868
Jeffrey M HausdorffCenter for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.ORCID 0000-0002-1608-0776
Jorunn L HelbostadDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0003-0214-9290
Carl-Philipp JansenGeriatric Center, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.ORCID 0000-0002-2779-7019
Lars Gunnar JohnsenDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0001-5500-498X
Jochen KlenkDepartment of Geriatrics and Rehabilitation, Robert Bosch Hospital, Stuttgart, Germany.ORCID 0000-0002-5987-447X
Sarah KochBarcelona Institute for Global Health, Barcelona, Spain.ORCID 0000-0001-9461-8407
Walter MaetzlerDepartment of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany.ORCID 0000-0002-5945-4694
Dimitrios MegaritisTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.ORCID 0000-0001-6786-4346
Arne MüllerNovartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland.ORCID 0000-0001-6551-2283
Lynn RochesterTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.ORCID 0000-0001-5774-9272
Lars SchwickertDepartment of Geriatrics and Rehabilitation, Robert Bosch Hospital, Stuttgart, Germany.ORCID 0009-0000-9220-4841
Kristin TaraldsenDepartment of Rehabilitation Science and Health Technology, OsloMet, Oslo, Norway.ORCID 0000-0002-8466-5450
Beatrix VereijkenDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0002-2231-8138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlgorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip fracture, who often walk slowly for short durations, is underexplored.

objectiveThis study examined DMO accuracy and reliability in a hip fracture cohort considering walking bout (WB) duration, physical function, days since surgery, and walking aid use.

methodsIn total, 19 community-dwelling participants were real-world monitored for 2.5 hours using a lower back wearable device and a reference system combining inertial modules, distance sensors, and pressure insoles. A total of 6 DMO estimates from 164 WBs from 58% (11/19) of the participants (aged 71-90 years; assessed 32-390 days after surgery; Short Physical Performance Battery [SPPB] scores of 3-12; gait speed range 0.39-1.34 m/s) were assessed against the reference system at the WB and participant level. We stratified by WB duration (all WBs, WBs of >10 seconds, WBs of 10-30 seconds, and WBs of >30 seconds) and lower versus higher SPPB scores and observed whether days since surgery and walking aid use affected DMO accuracy and reliability.

resultsAcross WBs, walking speed and distance ranged from 0.25 to 1.29 m/s and from 1.7 to 436.5 m, respectively. Estimation of walking speed, cadence, stride duration, number of steps, and distance stratified by WB duration showed intraclass correlation coefficients (ICCs) ranging from 0.50 to 0.99 and mean relative errors (MREs) from -6.9% to 12.8%. Stride length estimation showed poor reliability, with ICCs ranging from 0.30 to 0.49 and MREs from 6.1% to 13.2%. Walking speed and distance ICCs in the higher-SPPB score group ranged from 0.85 to 0.99, and MREs ranged from -10.1% to -1.7%. In the lower-SPPB score group, walking speed and distance ICCs ranged from 0.17 to 0.99, and MREs ranged from 13.5% to 32.6%. There was no discernible effect of time since surgery or walking aid use.

conclusionsIn total, 5 accurate and reliable real-world DMOs were identified in older adults after hip fracture: walking speed, cadence, stride duration, number of steps, and distance. Accuracy and reliability of most DMOs improved when excluding WBs of <10 seconds and were higher for WBs of >30 seconds than for WBs of 10 to 30 seconds and for participants with higher physical function. DMOs capture daily gait as early as 1 month after surgery also in people using walking aids. However, as most WBs in this cohort were short, there was a trade-off between improving accuracy and reliability by excluding short WBs and losing a substantial amount of data. These results have important implications for establishing the clinical validity of DMOs and evaluating the effects of interventions on daily-life gait, thereby facilitating the design of optimal care pathways.

Indexed as

Hip FracturesWalkingAgedAged, 80 and overCross-Sectional StudiesFemaleHumansIndependent LivingMaleReproducibility of ResultsWalking SpeedWearable Electronic Devicesactive agingcadencedigital mobility assessmentdistancefree livinggaitwalking speedwearable device

Identifiers

PMID40392580
PMCPMC12134698

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.