Evidence map›Paper›PMID 31455144›Full record

ArticleBehavioral sleep medicine

Field-based Measurement of Sleep: Agreement between Six Commercial Activity Monitors and a Validated Accelerometer.

Andrew G Kubala, Bethany Barone Gibbs, Daniel J Buysse, Sanjay R Patel, Martica H Hall, Christopher E Kline

Registry-linked trialAbstract read
In one paragraph

Article in Behavioral sleep medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04568408 (Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands), which is not on this 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
–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.

NCT04568408 completednot on this map

Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands

Typeobservational_patient_registrySponsorUniversidade da CoruñaRan2020 to 2021Enrolled43ConditionsSleep DisorderArmsXiaomi MiBand 5
3 · Its place in the literature

Who cites it

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

  1. Pooled it
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  5. Effect of wearables on sleep in healthy individuals: a randomized crossover trial and validation study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020
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  19. Study Protocol on the Validation of the Quality of Sleep Data from Xiaomi Domestic Wristbands.International journal of environmental research and public health · 2021
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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

6 authors.

Andrew G KubalaDepartment of Health and Physical Activity, University of Pittsburgh , Pittsburgh, Pennsylvania.ORCID 0000-0002-8937-9111
Bethany Barone GibbsDepartment of Health and Physical Activity, University of Pittsburgh , Pittsburgh, Pennsylvania.ORCID 0000-0002-0732-6148
Daniel J BuysseDepartment of Psychiatry, University of Pittsburgh , Pittsburgh, Pennsylvania.ORCID 0000-0002-3288-1864
Sanjay R PatelDepartment of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania.ORCID 0000-0002-9142-5172
Martica H HallDepartment of Psychiatry, University of Pittsburgh , Pittsburgh, Pennsylvania.ORCID 0000-0003-0642-2098
Christopher E KlineDepartment of Health and Physical Activity, University of Pittsburgh , Pittsburgh, Pennsylvania.ORCID 0000-0003-1025-9430

Funding

A Novel Risk Factor for Cardiovascular Disease: The Insomnia-Short Sleep PhenotypK23HL118318 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KLINE, CHRISTOPHER EDWARD · 2014 to 2018
$776k
NHLBI NIH HHS K23 HL118318
6 · The paper itself

Abstract

objectiveTo examine agreement between multiple commercial activity monitors (CAMs) and a validated actigraph to measure sleep.

methodsThirty adults without sleep disorders wore an Actiwatch Spectrum (AW) and alternated wearing 6 CAMs for one 24-h period each (Fitbit Alta, Jawbone Up3, Misfit Shine 2, Polar A360, Samsung Gear Fit2, Xiaomi Mi Band 2). Total sleep time (TST) and wake after sleep onset (WASO) were compared between edited AW and unedited CAM outputs. Comparisons between AW and CAM data were made via paired t-tests, mean absolute percent error (MAPE) calculations, and intra-class correlations (ICC). Intra-model reliability was performed in 10 participants who wore a pair of each AW and CAM model.

resultsFitbit, Jawbone, Misfit, and Xiaomi overestimated TST relative to AW (53.7-80.4 min, P ≤ .001). WASO was underestimated by Fitbit, Misfit, Samsung and Xiaomi devices (15.0-27.9 min; P ≤ .004) and overestimated by Polar (27.7 min, P ≤ .001). MAPEs ranged from 5.1% (Samsung) to 25.4% (Misfit) for TST and from 36.6% (Fitbit) to 165.1% (Polar) for WASO. TST ICCs ranged from .00 (Polar) to .92 (Samsung), while WASO ICCs ranged from .38 (Misfit) to .69 (Samsung). Differences were similar between poor sleepers (Pittsburgh Sleep Quality Index global score >5; n = 10) and good sleepers. Intra-model reliability analyses revealed minimal between-pair differences and high ICCs.

conclusionsAgreement between CAMs and AW varied by device, with greater agreement observed for TST than WASO. While reliable, variability in agreement across CAMs with traditional actigraphy may complicate the interpretation of CAM data obtained for clinical or research purposes.

Indexed as

AccelerometryActigraphyAdolescentAdultFemaleHumansMaleMiddle AgedReproducibility of ResultsSleepYoung Adult

Identifiers

PMID31455144
PMCPMC7044030

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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.