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


ABSTRACT: INTRODUCTION:Falling 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. METHODS:We 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. RESULTS:The incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P?

SUBMITTER: Silva de Lima AL 

PROVIDER: S-EPMC7003816 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Silva de Lima Ana Lígia AL   Smits Tine T   Darweesh Sirwan K L SKL   Valenti Giulio G   Milosevic Mladen M   Pijl Marten M   Baldus Heribert H   de Vries Nienke M NM   Meinders Marjan J MJ   Bloem Bastiaan R BR  

Movement disorders : official journal of the Movement Disorder Society 20190826 1


<h4>Introduction</h4>Falling 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.<h4>Methods</h4>We 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 eit  ...[more]

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