Evidence map›Paper›PMID 34705114›Full record

ArticleJournal of neurology2022

Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease.

F Elizabeth Godkin, Erin Turner, Youness Demnati, Adam Vert, Angela Roberts, Richard H Swartz, Paula M McLaughlin, Kyle S Weber, Vanessa Thai, Kit B Beyer and 16 more

Open access · bronzeAbstract read
In one paragraph

Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
3.6field-weighted citation impact, top 6% 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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
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  5. Acoustic stimulation as a promising technique to enhance slow-wave sleep in Alzheimer's disease: results of a pilot study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023
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  16. Mayo Clinic proceedings. Digital health · 2023
    Review
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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

26 authors at 7 institutions in 2 countries.

F Elizabeth GodkinDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Erin TurnerDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Youness DemnatiDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Adam VertDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Angela RobertsSchool of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.
Richard H SwartzDr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Paula M McLaughlinNova Scotia Health Authority, Halifax, NS, Canada.
Kyle S WeberDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Vanessa ThaiDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Kit B BeyerDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Benjamin CornishDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Agessandro AbrahaoDr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Sandra E BlackDr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Mario MasellisDr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Lorne ZinmanDr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Derek BeatonRotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.
Malcolm A BinnsRotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.
Vivian ChauRotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.
Donna KwanCentre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.
Andrew LimDr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Douglas P MunozCentre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.
Stephen C StrotherRotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.
Kelly M SunderlandRotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.
Brian TanRotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.
William E McIlroyDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Karen Van OoteghemDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada. kvanooteghem@uwaterloo.ca.ORCID http://orcid.org/0000-0002-6743-3332
University of Waterloo · CASunnybrook Health Science Centre · CABaycrest Hospital · CAQueen's University · CAUniversity of Toronto · CANorthwestern University · USNova Scotia Health Authority · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRemote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD).

methodsThirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance.

resultsAdherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners.

conclusionA continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.

Indexed as

Cardiovascular DiseasesNeurodegenerative DiseasesParkinson DiseaseWearable Electronic DevicesAgedFeasibility StudiesFemaleHumansMaleAdherenceCerebrovascular diseaseNeurodegenerative diseaseRemote monitoringUser acceptanceWearable sensors

Identifiers

PMID34705114
PMCPMC8548705
OpenAlexW3210068955

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.