Evidence map›Paper›PMID 32282332›Full record

ArticleJMIR mHealth and uHealth2020

Low-Cost Consumer-Based Trackers to Measure Physical Activity and Sleep Duration Among Adults in Free-Living Conditions: Validation Study.

Laurent Degroote, Gilles Hamerlinck, Karolien Poels, Carol Maher, Geert Crombez, Ilse De Bourdeaudhuij, Ann Vandendriessche, Rachel G Curtis, Ann DeSmet

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06231420 (Daily Step Count Using Pedometer for Sarcopenic Management in Patient With Cirrhosis), which is not on this map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
5.1field-weighted citation impact, top 4% 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.

NCT06231420 naunknown statusnot on this mapstarted 2024, after this paper: background citation

Daily Step Count Using Pedometer for Sarcopenic Management in Patient With Cirrhosis: Randomized Controlled Trial

TypeinterventionalSponsorMahidol UniversityRan2024 to 2025Enrolled70ConditionsSarcopenia, Cirrhosis, Liver, Daily Step CountArmsEncourage using pedometer, pedometer
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 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

9 authors at 5 institutions in 2 countries.

Laurent DegrooteDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.ORCID 0000-0002-3064-1437
Gilles HamerlinckDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.ORCID 0000-0002-6921-4846
Karolien PoelsDepartment of Communication Studies, University of Antwerp, Antwerp, Belgium.ORCID 0000-0002-5276-0293
Carol MaherSchool of Health Sciences, University of South Australia, Adelaide, Australia.ORCID 0000-0002-8676-0224
Geert CrombezDepartment of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium.ORCID 0000-0002-4744-8561
Ilse De BourdeaudhuijDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.ORCID 0000-0001-9969-7597
Ann VandendriesscheDepartment of Public Health and Primary Care, Ghent University, Ghent, Belgium.ORCID 0000-0002-9619-430X
Rachel G CurtisSchool of Health Sciences, University of South Australia, Adelaide, Australia.ORCID 0000-0002-1341-7804
Ann DeSmetDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.ORCID 0000-0002-7473-140X
Ghent University · BEGhent University Hospital · BEUniversity of South Australia · AUUniversité Libre de Bruxelles · BEUniversity of Antwerp · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption.

objectiveThis study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2.

methodsA free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0.

resultsLarge variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA.

conclusionsThis study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

Indexed as

AccelerometryMonitoring, AmbulatoryAdultExerciseHumansReproducibility of ResultsSleepaccelerometryfitness trackersmobile phonephysical activitysleep

Identifiers

PMID32282332
PMCPMC7268004
OpenAlexW3007476422

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.