Evidence map›Paper›PMID 39178034›Full record

ArticleJMIR mHealth and uHealth2024

Establishing a Consensus-Based Framework for the Use of Wearable Activity Trackers in Health Care: Delphi Study.

Kimberley Szeto, John Arnold, Erin Marie Horsfall, Madeline Sarro, Anthony Hewitt, Carol Maher

Abstract readConsensus Statement
In one paragraph

Article in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Kimberley SzetoAlliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Perfomance, University of South Australia, Adelaide, Australia.ORCID 0000-0001-9469-9139
John ArnoldAlliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Perfomance, University of South Australia, Adelaide, Australia.ORCID 0000-0002-1158-8917
Erin Marie HorsfallAllied Health and Human Perfomance, University of South Australia, Adelaide, Australia.ORCID 0009-0005-2357-3498
Madeline SarroAllied Health and Human Perfomance, University of South Australia, Adelaide, Australia.ORCID 0009-0007-4809-6485
Anthony HewittSouthern Adelaide Local Health Network, South Australia Health, Adelaide, Australia.ORCID 0000-0003-4357-3418
Carol MaherAlliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Perfomance, University of South Australia, Adelaide, Australia.ORCID 0000-0002-8676-0224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings.

objectiveThis study aimed to develop a consensus-based framework for the optimal use of WATs in health care.

methodsA 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants' opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist's clarity, utility, and appropriateness.

resultsParticipation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients' data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4.

conclusionsThis study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector.

Indexed as

Delphi TechniqueFitness TrackersAdultExerciseFemaleHumansMaleMiddle AgedSurveys and QuestionnairesWearable Electronic Devicesexercisehealth caremanagementmonitorphysical activitypreventionpromotesedentary behaviorsupportsurveytrackerutilitywearablewearable activity trackerwearableswearable technologywearable tracker

Identifiers

PMID39178034
PMCPMC11380062

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.