Evidence map›Paper›PMID 31449705›Full record

ArticleMovement disorders : official journal of the Movement Disorder Society2020

Home-based monitoring of falls using wearable sensors in Parkinson's disease.

Ana Lígia Silva de Lima, Tine Smits, Sirwan K L Darweesh, Giulio Valenti, Mladen Milosevic, Marten Pijl, Heribert Baldus, Nienke M de Vries, Marjan J Meinders, Bastiaan R Bloem

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In one paragraph

Article in Movement disorders : official journal of the Movement Disorder Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 6 pooled it
–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

52 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Interventions for preventing falls in Parkinson's disease.The Cochrane database of systematic reviews · 2022
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  12. Observational
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  17. Triboelectric Nanogenerators for Preventive Health Monitoring.Nanomaterials (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

10 authors.

Ana Lígia Silva de LimaDepartment of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Tine SmitsPhilips Research, Department Personal Health, Eindhoven, the Netherlands.
Sirwan K L DarweeshDepartment of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Giulio ValentiPhilips Research, Department Personal Health, Eindhoven, the Netherlands.
Mladen MilosevicPhilips Research North America, Acute Care Solutions Department, Cambridge, Massachusetts, USA.
Marten PijlPhilips Research, Department Personal Health, Eindhoven, the Netherlands.
Heribert BaldusPhilips Research, Department Personal Health, Eindhoven, the Netherlands.
Nienke M de VriesDepartment of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Marjan J MeindersDepartment of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Bastiaan R BloemDepartment of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFalling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life.

methodsWe matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling.

resultsThe incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0).

conclusionHaving PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Indexed as

Wearable Electronic DevicesAccidental FallsAgedFemaleHumansIncidenceMaleMiddle AgedParkinson DiseasePostural Balancefalls incidencehome-based monitoringParkinson's diseasewearable sensor

Identifiers

PMID31449705
PMCPMC7003816

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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.