Evidence map›Paper›PMID 33646135›Full record

ArticleJMIR mHealth and uHealth2021

Using Fitbit as an mHealth Intervention Tool to Promote Physical Activity: Potential Challenges and Solutions.

Guilherme M Balbim, Isabela G Marques, David X Marquez, Darshilmukesh Patel, Lisa K Sharp, Spyros Kitsiou, Sharmilee M Nyenhuis

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

42 citing papers in PubMed.

  1. Trial
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  3. A multidomain trial for cognition in women with cardiovascular disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Trial
  4. Trial
  5. Randomized Controlled Trial of Aerobic Exercise for Smoking Cessation Among Individuals With Elevated Depressive Symptoms.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024
    Trial
  6. Trial
  7. Trial
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  9. Observational
  10. Article
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  15. Review
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  20. Personalized Meta-Federated Learning for IoT-Enabled Health Monitoring.IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society · 2024
    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

7 authors.

Guilherme M Balbim *Department of Kinesiology and Nutrition, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-9059-0299
Isabela G MarquesRheumatology Division, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil.ORCID 0000-0002-4174-0705
David X MarquezDepartment of Kinesiology and Nutrition, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0001-8903-4941
Darshilmukesh PatelDepartment of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-0775-8047
Lisa K SharpCollege of Pharmacy, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-7809-9042
Spyros Kitsiou *Department of Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-5281-321X
Sharmilee M Nyenhuis *Division of Pulmonary, Critical Care, Sleep and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-7381-9733

Funding

Strengthening Stakeholder Engagement in Human Research Protections.UL1TR002003 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KARNIK, NIRANJAN SUBHASH, MERMELSTEIN, ROBIN J. · 2016 to 2024
$33.7M
Pilot CoreP30AG022849 · NIA · UNIVERSITY OF ILLINOIS AT CHICAGO · PI Jun Ma · 2003 to 2026
$14.7M
Pilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6M
Targeting of UnControlled Hypertension in Emergency Department (TOUCHED)R33HL139454 · NHLBI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI PRENDERGAST, HEATHER MARIE · 2019 to 2022
$2.1M
Testing Adaptive Interventions to Improve Physical Activity for Sedentary WomenR01NR017635 · NINR · RUSH UNIVERSITY MEDICAL CENTER · PI BUCHHOLZ, SUSAN WEBER, DANIEL, MANJU NARESH · 2018 to 2021
$1.9M
Promoting Activity After COPD Exacerbations (PACE)K23HL130524 · NHLBI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI PRIETO-CENTURION, VALENTIN · 2016 to 2019
$827k
ACTION Intervention: Physical Activity in Minority Women with AsthmaK01HL133370 · NHLBI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI NYENHUIS, SHARMILEE MARIA · 2016 to 2019
$741k
An innovative mobile health intervention to improve self-care in patients with heart failureR21NR018281 · NINR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KITSIOU, SPYROS · 2019 to 2020
$455k
NCATS NIH HHS UL1 TR002003NHLBI NIH HHS K01 HL133370NHLBI NIH HHS K23 HL130524NHLBI NIH HHS R33 HL139454NIA NIH HHS P30 AG022849NIEHS NIH HHS P30 ES027792NINR NIH HHS R01 NR017635NINR NIH HHS R21 NR018281
6 · The paper itself

Abstract

Consumer-based physical activity (PA) trackers, also known as wearables, are increasingly being used in research studies as intervention or measurement tools. One of the most popular and widely used brands of PA trackers is Fitbit. Since the release of the first Fitbit in 2009, hundreds of experimental studies have used Fitbit devices to facilitate PA self-monitoring and behavior change via goal setting and feedback tools. Fitbit's ability to capture large volumes of PA and physiological data in real time creates enormous opportunities for researchers. At the same time, however, it introduces a number of challenges (eg, technological, operational, logistical), most of which are not sufficiently described in study publications. Currently, there are no technical reports, guidelines, nor other types of publications discussing some of these challenges and offering guidance to researchers on how to best incorporate Fitbit devices in their study design and intervention to achieve their research goals. As a result, researchers are often left alone to discover and address some of these issues during the study through "trial and error." This paper aims to address this gap. Drawing on our cumulative experience of conducting multiple studies with various Fitbit PA trackers over the years, we present and discuss various key challenges associated with the use of Fitbit PA trackers in research studies. Difficulties with the use of Fitbit PA trackers are encountered throughout the entire research process. Challenges and solutions are categorized in 4 main categories: study preparation, intervention delivery, data collection and analysis, and study closeout. Subsequently, we describe a number of empirically tested strategies used in 4 of our interventional studies involving participants from a broad range of demographic characteristics, racial/ethnic backgrounds, and literacy levels. Researchers should be prepared to address challenges and issues in a timely fashion to ensure that the Fitbit effectively assists participants and researchers in achieving research and outcome goals.

Indexed as

Fitness TrackersTelemedicineData CollectionExerciseHumansadultsFitbitfitness trackersinterventioninterventional studiesolder adultsphysical activitysmartphoneswearable

Identifiers

PMID33646135
PMCPMC7961407

What OpenQuestion holds

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LicenceCC BY
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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.