Evidence map›Paper›PMID 29618233›Full record

ArticleHealth promotion practice2019

Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies.

Gabrielle Turner-McGrievy, Danielle E Jake-Schoffman, Camelia Singletary, Marquivieus Wright, Anthony Crimarco, Michael D Wirth, Nitin Shivappa, Trisha Mandes, Delia Smith West, Sara Wilcox and 3 more

Abstract read
In one paragraph

Article in Health promotion practice, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Observational
  2. Article
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

13 authors.

Gabrielle Turner-McGrievy1 University of South Carolina, Columbia, SC, USA.
Danielle E Jake-Schoffman1 University of South Carolina, Columbia, SC, USA.ORCID 0000-0001-6381-7323
Camelia Singletary1 University of South Carolina, Columbia, SC, USA.
Marquivieus Wright1 University of South Carolina, Columbia, SC, USA.
Anthony Crimarco1 University of South Carolina, Columbia, SC, USA.
Michael D Wirth1 University of South Carolina, Columbia, SC, USA.
Nitin Shivappa1 University of South Carolina, Columbia, SC, USA.
Trisha Mandes1 University of South Carolina, Columbia, SC, USA.
Delia Smith West1 University of South Carolina, Columbia, SC, USA.
Sara Wilcox1 University of South Carolina, Columbia, SC, USA.
Clemens Drenowatz1 University of South Carolina, Columbia, SC, USA.
Andrew Hester1 University of South Carolina, Columbia, SC, USA.
Matthew J McGrievy1 University of South Carolina, Columbia, SC, USA.

Funding

Developing the Dietary Inflammatory Index for Clinical ApplicationR44DK103377 · NIDDK · CONNECTING HEALTH INNOVATIONS, LLC · PI SHIVAPPA, NITIN, WIRTH, MICHAEL DAVID · 2014 to 2016
$1.8M
Increasing dietary self-monitoring and weight loss in an mHealth interventionR21CA187929 · NCI · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI TURNER-MCGRIEVY, GABRIELLE MICHELLE · 2015 to 2016
$353k
NCI NIH HHS R21 CA187929NIDDK NIH HHS R44 DK103377
6 · The paper itself

Abstract

backgroundWearable physical activity (PA) trackers are becoming increasingly popular for intervention and assessment in health promotion research and practice. The purpose of this article is to present lessons learned from four studies that used commercial PA tracking devices for PA intervention or assessment, present issues encountered with their use, and provide guidelines for determining which tools to use.

methodFour case studies are presented that used PA tracking devices (iBitz, Zamzee, FitBit Flex and Zip, Omron Digital Pedometer, Sensewear Armband, and MisFit Flash) in the field-two used the tools for intervention and two used the tools as assessment methods.

resultsThe four studies presented had varying levels of success with using PA devices and experienced several issues that impacted their studies, such as companies that went out of business, missing data, and lost devices. Percentage ranges for devices that were lost were 0% to 29% and was 0% to 87% for those devices that malfunctioned or lost data.

conclusionsThere is a need for low-cost, easy-to-use, accurate PA tracking devices to use as both intervention and assessment tools in health promotion research related to PA.

Indexed as

Attitude to HealthActigraphyData CollectionEquipment DesignExerciseFemaleFitness TrackersHealth PromotionHumansMaleassessmentexerciseinterventionmobile healthphysical activity trackerswearables

Identifiers

PMID29618233
PMCPMC6854681

What OpenQuestion holds

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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.