Evidence map›Paper›PMID 28724509›Full record

ArticleJournal of medical Internet research2017

Youth Oriented Activity Trackers: Comprehensive Laboratory- and Field-Based Validation.

John R Sirard, Brittany Masteller, Patty S Freedson, Albert Mendoza, Amanda Hickey

Abstract readValidation Study
In one paragraph

Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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.

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

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

  1. Pooled it
  2. Pooled it
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  4. Use of consumer monitors for estimating energy expenditure in youth.Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme · 2020
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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

5 authors.

John R SirardDepartment of Kinesiology, University of Massachusetts Amherst, Amherst, MA, United States.ORCID 0000-0003-4613-5683
Brittany MastellerDepartment of Kinesiology, University of Massachusetts Amherst, Amherst, MA, United States.ORCID 0000-0002-7763-4445
Patty S FreedsonDepartment of Kinesiology, University of Massachusetts Amherst, Amherst, MA, United States.ORCID 0000-0003-1758-915X
Albert MendozaDepartment of Kinesiology, University of Massachusetts Amherst, Amherst, MA, United States.ORCID 0000-0001-6666-2400
Amanda HickeyDepartment of Health Science, Keene State University, Keene, NH, United States.ORCID 0000-0003-1755-1047

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCommercial activity trackers are growing in popularity among adults and some are beginning to be marketed to children. There is, however, a paucity of independent research examining the validity of these devices to detect physical activity of different intensity levels.

objectivesThe purpose of this study was to determine the validity of the output from 3 commercial youth-oriented activity trackers in 3 phases: (1) orbital shaker, (2) structured indoor activities, and (3) 4 days of free-living activity.

methodsFour units of each activity tracker (Movband [MB], Sqord [SQ], and Zamzee [ZZ]) were tested in an orbital shaker for 5-minutes at three frequencies (1.3, 1.9, and 2.5 Hz). Participants for Phase 2 (N=14) and Phase 3 (N=16) were 6-12 year old children (50% male). For Phase 2, participants completed 9 structured activities while wearing each tracker, the ActiGraph GT3X+ (AG) research accelerometer, and a portable indirect calorimetry system to assess energy expenditure (EE). For Phase 3, participants wore all 4 devices for 4 consecutive days. Correlation coefficients, linear models, and non-parametric statistics evaluated the criterion and construct validity of the activity tracker output.

resultsOutput from all devices was significantly associated with oscillation frequency (r=.92-.99). During Phase 2, MB and ZZ only differentiated sedentary from light intensity (P<.01), whereas the SQ significantly differentiated among all intensity categories (all comparisons P<.01), similar to AG and EE. During Phase 3, AG counts were significantly associated with activity tracker output (r=.76, .86, and .59 for the MB, SQ, and ZZ, respectively).

conclusionsAcross study phases, the SQ demonstrated stronger validity than the MB and ZZ. The validity of youth-oriented activity trackers may directly impact their effectiveness as behavior modification tools, demonstrating a need for more research on such devices.

Indexed as

AccelerometryAdolescentBehavior TherapyCalorimetry, IndirectChildEnergy MetabolismExerciseFemaleFitness TrackersHumansLaboratoriesLinear ModelsMaleMotor ActivityReproducibility of Resultschildfitness trackersmovement

Identifiers

PMID28724509
PMCPMC5541239

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Registered trials

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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.