Evidence map›Paper›PMID 38450553›Full record

ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2024

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease.

Emer P Doheny, Klavs Renerts, Andreas Braun, Esther Werth, Christian Baumann, Philipp Baumgartner, Philippa Morgan-Jones, Monica Busse, Madeleine M Lowery, Hans H Jung

Open access · greenAbstract readComparative StudyEvaluation Study
In one paragraph

Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

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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 at 3 institutions in 3 countries.

Emer P DohenySchool of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.
Klavs RenertsDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
Andreas BraunDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
Esther WerthDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
Christian BaumannDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
Philipp BaumgartnerDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
Philippa Morgan-JonesCentre for Trials Research, Cardiff University, Cardiff, Wales, United Kingdom.
Monica BusseCentre for Trials Research, Cardiff University, Cardiff, Wales, United Kingdom.
Madeleine M LowerySchool of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland.
Hans H JungDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
University Hospital of Zurich · CHCardiff University · GBUniversity College Dublin · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

STUDY

objectivesWearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD.

methodsTen participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data.

resultsFB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79-0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5% sensitivity and 93.7 ± 2.5% specificity, rapid eye movement sleep with high sensitivity and specificity (78.7 ± 31.9%, 95.6 ± 2.3%), and deep sleep with lower sensitivity but high specificity (56.4 ± 28.8%, 95.0 ± 4.8%). FB heart rate was strongly correlated with PSG, and the mean absolute error between FB and PSG heart rate data was 1.16 ± 0.42 beats/min. At home, longer sleep and shorter wake times were observed compared with hospital data, whereas percentage sleep stage times were consistent with hospital data.

conclusionsResults suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD. CITATION: Doheny EP, Renerts K, Braun A, et al. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease.

Indexed as

Heart Rate DeterminationHuntington DiseasePolysomnographySelf-TestingSleep StagesWearable Electronic DevicesAdultFemaleHumansMaleReproducibility of ResultsSensitivity and SpecificityWristhome monitoringneurodegenerationPPGPSGwearable sensor

Identifiers

PMID38450553
PMCPMC11217637
OpenAlexW4392544763

What OpenQuestion holds

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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.