Evidence map›Paper›PMID 27388359›Full record

ArticleBMJ open2016

Interest and preferences for using advanced physical activity tracking devices: results of a national cross-sectional survey.

Stephanie Alley, Stephanie Schoeppe, Diana Guertler, Cally Jennings, Mitch J Duncan, Corneel Vandelanotte

Abstract read
In one paragraph

Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 2 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

47 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  12. Accuracy of consumer-based activity trackers as measuring tool and coaching device in breast and colorectal cancer survivors.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023
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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

6 authors.

Stephanie AlleyPhysical Activity Research Group, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, Queensland, Australia.
Stephanie SchoeppePhysical Activity Research Group, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, Queensland, Australia.
Diana GuertlerInstitute of Social Medicine and Prevention, University of Greifswald, Greifswald, Germany.
Cally JenningsFaculty of Physical Education and Recreation, University of Alberta, Edmonton, Alberta, Canada.
Mitch J DuncanFaculty of Health and Medicine, School of Medicine & Public Health; Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, New South Wales, Australia.ORCID 0000-0002-9166-6195
Corneel VandelanottePhysical Activity Research Group, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, Queensland, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPedometers are an effective self-monitoring tool to increase users' physical activity. However, a range of advanced trackers that measure physical activity 24 hours per day have emerged (eg, Fitbit). The current study aims to determine people's current use, interest and preferences for advanced trackers. DESIGN AND

participantsA cross-sectional national telephone survey was conducted in Australia with 1349 respondents. OUTCOME MEASURES: Regression analyses were used to determine whether tracker interest and use, and use of advanced trackers over pedometers is a function of demographics. Preferences for tracker features and reasons for not wanting to wear a tracker are also presented.

resultsOver one-third of participants (35%) had used a tracker, and 16% are interested in using one. Multinomial regression (n=1257) revealed that the use of trackers was lower in males (OR=0.48, 95% CI 0.36 to 0.65), non-working participants (OR=0.43, 95% CI 0.30 to 0.61), participants with lower education (OR=0.52, 95% CI 0.38 to 0.72) and inactive participants (OR=0.52, 95% CI 0.39 to 0.70). Interest in using a tracker was higher in younger participants (OR=1.73, 95% CI 1.15 to 2.58). The most frequently used tracker was a pedometer (59%). Logistic regression (n=445) revealed that use of advanced trackers compared with pedometers was higher in males (OR=1.67, 95% CI 1.01 to 2.79) and younger participants (OR=2.96, 95% CI 1.71 to 5.13), and lower in inactive participants (OR=0.35, 95% CI 0.19 to 0.63). Over half of current or interested tracker users (53%) prefer to wear it on their wrist, 31% considered counting steps the most important function and 30% regarded accuracy as the most important characteristic. The main reasons for not wanting to use a tracker were, 'I don't think it would help me' (39%), and 'I don't want to increase my activity' (47%).

conclusionsActivity trackers are a promising tool to engage people in self-monitoring a physical activity. Trackers used in physical activity interventions should align with the preferences of target groups, and should be able to be worn on the wrist, measure steps and be accurate.

Indexed as

Attitude to HealthConsumer BehaviorExerciseActigraphyAdultAgedAge FactorsAustraliaCross-Sectional StudiesEducational StatusFemaleFitness TrackersHumansLogistic ModelsMaleMiddle AgedHealth promotionPedometerPhysical activitySelf-monitoringTrackerWearables

Identifiers

PMID27388359
PMCPMC4947799

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.