Evidence map›Paper›PMID 28246070›Full record

ArticleJMIR mHealth and uHealth2017

Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study.

Monika Pobiruchin, Julian Suleder, Richard Zowalla, Martin Wiesner

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Data-driven approach to quantify trust in medical devices using Bayesian networks.Experimental biology and medicine (Maywood, N.J.) · 2023
    Review
  11. Wearable Devices in Diving: Scoping Review.JMIR mHealth and uHealth · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Monika PobiruchinGECKO Institute for Medicine, Informatics & Economics, Heilbronn University, Heilbronn, Germany.ORCID http://orcid.org/0000-0002-9925-2173
Julian SulederDepartment of Medical Informatics, Heilbronn University, Heilbronn, Germany.ORCID http://orcid.org/0000-0002-0652-111X
Richard ZowallaDepartment of Medical Informatics, Heilbronn University, Heilbronn, Germany.ORCID http://orcid.org/0000-0003-1236-7398
Martin WiesnerConsumer Health Informatics SIG, German Association for Medical Informatics, Biometry & Epidemiology (GMDS e.V.), Cologne, Germany.ORCID http://orcid.org/0000-0003-3346-9633

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundToday, runners use wearable technology such as global positioning system (GPS)-enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors.

objectiveThe aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially "smart" devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices.

methodsA pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other.

resultsA total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event.

conclusionsA great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances.

Indexed as

activity monitoringathletemobile phonesphysical activitywearables

Identifiers

PMID28246070
PMCPMC5350460

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.