Evidence map›Paper›PMID 31774406›Full record

ArticleJMIR mHealth and uHealth2019

Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial.

Arne Mueller, Holger Alfons Hoefling, Amir Muaremi, Jens Praestgaard, Lorcan C Walsh, Ola Bunte, Roland Martin Huber, Julian Fürmetz, Alexander Martin Keppler, Matthias Schieker and 5 more

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 pooled it
15.0field-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

40 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.

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  13. GPS Suitability for Physical Frailty Assessment.Sensors (Basel, Switzerland) · 2024
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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

15 authors at 5 institutions in 4 countries.

Arne MuellerNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0001-6551-2283
Holger Alfons HoeflingNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0003-1209-8634
Amir MuaremiNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0002-8227-4149
Jens PraestgaardBiostatistics and Pharmacometrics, Novartis Pharmaceuticals Corporation, East Hannover, NJ, United States.ORCID 0000-0001-5122-7190
Lorcan C WalshNovartis Business Services, Novartis Ireland Ltd, Dublin, Ireland.ORCID 0000-0001-8787-6889
Ola BunteNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0002-0718-8910
Roland Martin HuberNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0002-8346-3654
Julian FürmetzUniversity Hospital, Ludwigs-Maximillians Universität, Munich, Germany.ORCID 0000-0001-5529-1632
Alexander Martin KepplerUniversity Hospital, Ludwigs-Maximillians Universität, Munich, Germany.ORCID 0000-0002-1800-5278
Matthias SchiekerNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0002-1230-3653
Wolfgang BöckerUniversity Hospital, Ludwigs-Maximillians Universität, Munich, Germany.ORCID 0000-0003-0554-0201
Ronenn RoubenoffNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0002-3959-3179
Sophie BrachatNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0002-3398-8225
Daniel S RooksNovartis Institutes for BioMedical Research, Cambridge, MA, United States.ORCID 0000-0002-1121-3517
Ieuan ClayNovartis Institutes for BioMedical Research, Basel, Switzerland.ORCID 0000-0001-9722-8834
Novartis (Switzerland) · CHLudwig-Maximilians-Universität München · DENovartis (Ireland) · IENovartis (United States) · USTris Pharma (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital technologies and advanced analytics have drastically improved our ability to capture and interpret health-relevant data from patients. However, only limited data and results have been published that demonstrate accuracy in target indications, real-world feasibility, or the validity and value of these novel approaches.

objectiveThis study aimed to establish accuracy, feasibility, and validity of continuous digital monitoring of walking speed in frail, elderly patients with sarcopenia and to create an open source repository of raw, derived, and reference data as a resource for the community.

methodsData described here were collected as a part of 2 clinical studies: an independent, noninterventional validation study and a phase 2b interventional clinical trial in older adults with sarcopenia. In both studies, participants were monitored by using a waist-worn inertial sensor. The cross-sectional, independent validation study collected data at a single site from 26 naturally slow-walking elderly subjects during a parcours course through the clinic, designed to simulate a real-world environment. In the phase 2b interventional clinical trial, 217 patients with sarcopenia were recruited across 32 sites globally, where patients were monitored over 25 weeks, both during and between visits.

resultsWe have demonstrated that our approach can capture in-clinic gait speed in frail slow-walking adults with a residual standard error of 0.08 m per second in the independent validation study and 0.08, 0.09, and 0.07 m per second for the 4 m walk test (4mWT), 6-min walk test (6MWT), and 400 m walk test (400mWT) standard gait speed assessments, respectively, in the interventional clinical trial. We demonstrated the feasibility of our approach by capturing 9668 patient-days of real-world data from 192 patients and 32 sites, as part of the interventional clinical trial. We derived inferred contextual information describing the length of a given walking bout and uncovered positive associations between the short 4mWT gait speed assessment and gait speed in bouts between 5 and 20 steps (correlation of 0.23) and longer 6MWT and 400mWT assessments with bouts of 80 to 640 steps (correlations of 0.48 and 0.59, respectively).

conclusionsThis study showed, for the first time, accurate capture of real-world gait speed in slow-walking older adults with sarcopenia. We demonstrated the feasibility of long-term digital monitoring of mobility in geriatric populations, establishing that sufficient data can be collected to allow robust monitoring of gait behaviors outside the clinic, even in the absence of feedback or incentives. Using inferred context, we demonstrated the ecological validity of in-clinic gait assessments, describing positive associations between in-clinic performance and real-world walking behavior. We make all data available as an open source resource for the community, providing a basis for further study of the relationship between standardized physical performance assessment and real-world behavior and independence.

Indexed as

AgedAged, 80 and overCross-Sectional StudiesFemaleFitness TrackersFrailtyHumansLongitudinal StudiesMaleMiddle AgedMonitoring, PhysiologicValidation Studies as TopicWalking Speedaccelerometryalgorithmsclinical trialsdata collectiondatasetfrailtygaitmobility limitationopen source datawalking speedwearable electronic devices

Identifiers

PMID31774406
PMCPMC6906618
OpenAlexW2990315536

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.