Evidence map›Paper›PMID 28251357›Full record

SynthesisJournal of neurology2017

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review.

Ana Lígia Silva de Lima, Luc J W Evers, Tim Hahn, Lauren Bataille, Jamie L Hamilton, Max A Little, Yasuyuki Okuma, Bastiaan R Bloem, Marjan J Faber

Registry-linked trialOpen access · hybridAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05693207 (EMG to Detect and Monitor Freezing of Gait Among People With Parkinson's Disease), which is not on this map. Cited by 92 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 13 pooled it
11.8field-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.

NCT05693207 naunknown statusnot on this mapstarted 2021, after this paper: background citation

EMG to Detect and Monitor Freezing of Gait Among People With Parkinson's Disease

TypeinterventionalSponsorTel-Aviv Sourasky Medical CenterRan2021 to 2023Enrolled30ConditionsEvaluation of Muscle Contraction in People With PDArmsEMG
3 · Its place in the literature

Who cites it

92 citing papers in PubMed, 13 syntheses or guidelines pooled it, 188 citations in OpenAlex.

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32 more citing papers are in PubMed but not listed here.

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

9 authors at 5 institutions in 4 countries.

Ana Lígia Silva de LimaRadboud university medical center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands. ana.silvadelima@radboudumc.nl.
Luc J W EversDepartment of Neurology, Radboud university medical center, Nijmegen, The Netherlands.
Tim HahnDepartment of Neurology, Radboud university medical center, Nijmegen, The Netherlands.
Lauren BatailleMichael J Fox Foundation for Parkinson's Research, New York, USA.
Jamie L HamiltonMichael J Fox Foundation for Parkinson's Research, New York, USA.
Max A LittleAston University, Birmingham, UK.
Yasuyuki OkumaDepartment of Neurology, Juntendo University Shizuoka Hospital, Izunokuni, Shizuoka, Japan.
Bastiaan R BloemRadboud university medical center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Marjan J FaberDepartment of Neurology, Radboud university medical center, Nijmegen, The Netherlands.
Radboud University Nijmegen · NLMichael J. Fox Foundation · USRadboud University Medical Center · NLAston University · GBJuntendo University Shizuoka Hospital · JP

Funding

Parkinson's UK J-0901
6 · The paper itself

Abstract

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

Indexed as

Accidental FallsWearable Electronic DevicesGait Disorders, NeurologicHumansMonitoring, AmbulatoryParkinson DiseaseAmbulatory monitoringParkinson’s diseaseValidation studiesWearable sensors

Identifiers

PMID28251357
PMCPMC5533840
OpenAlexW2592073184

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.