gms | German Medical Science

63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

02. - 06.09.2018, Osnabrück

Citizens’ use of wearable running technology – comparison of two marathon event field studies

Meeting Abstract

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  • Monika Pobiruchin - Hochschule Heilbronn - GECKO Institut, Heilbronn, Deutschland
  • Martin Wiesner - Hochschule Heilbronn - Medizinische Informatik, Heilbronn, Deutschland
  • Richard Zowalla - Hochschule Heilbronn - Medizinische Informatik, Heilbronn, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Osnabrück, 02.-06.09.2018. Düsseldorf: German Medical Science GMS Publishing House; 2018. DocAbstr. 134

doi: 10.3205/18gmds006, urn:nbn:de:0183-18gmds0067

Published: August 27, 2018

© 2018 Pobiruchin et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Introduction: Runners can choose among a great variety of wearable devices [1]. Several studies investigated on accuracy of commercially available devices [2]. However, these studies mainly focus on technical aspects and are conducted in laboratory settings [3], i.e., small sample sizes and homogeneous cohorts [4]. Research on device/app adoption seems to be underrepresented for real-world settings [5]. In 2016, a study including two questionnaires was conducted at a public running event in Heilbronn (approx. 6,500 runners) [6]. In 2017, we conducted a follow-up study to (a) validate the chosen methodology and (b) identify changes of mHealth-related preferences within the running community.

Methods: The questionnaires contained items on (i) used tracking devices, (ii) demographic data, (iii) running course (full/half/relay marathon, walking). To compare adoption rates in different age groups with data from 2016 [6], the same items were used in the 2017 study. Moreover, data on motivational aspects, openness to share data and privacy concerns was collected.

Interviewer staff selected runners randomly while they picked up their number bibs at the registration desks. Binary logistic regression was applied to investigate on determinants (sex, age, course type) for device use.

Results: In total, n=1,160 (2016) and n=845 (2017) questionnaires were collected. The comparison of the study sample with official starter lists revealed similar age and sex distributions for the marathon course. More than 130 distinct devices (n=136; 2017, n=156; 2016), sold by 23 manufacturers (2016: 36) were identified. Technology use was associated with younger age groups (OR=2.357 for 30-39yrs, P=.002). Females were less likely to use wearables (OR=0.745, P=.09). Similar results applied for 2016.

Three out of four runners stated to use a device (73.0% vs. 73.9% in 2016); most runners preferred GPS-enabled sports watches (GSW) (60.0% vs. 58.8%), smartphone/apps were chosen by 181 (24.2% vs. 24.4%). Wristband activity trackers and smartwatches were favored by 8.5% (2016: 5.5%). Adoption rates (2017) differed for different course types: 74.0% (marathon) used a GSW and only 14.2% a smartphone/app, whereas 58.2% of the half-marathon runners preferred a GSW, likewise 25.5% smartphone/app. Smartphone use is even higher for the (Nordic) walking group. However, our sample comprised merely n=22 (2017) walking participants.

Preferred running apps were comparable in the 2017/2016 study cohorts: The most popular app was Runtastic/Pro (63.9% vs. 69.5%) followed by Nike+ Run Club and Strava. Seventeen (2016: 23) different apps could be identified.

Discussion: These two field studies shed light on the actual adoption rates of wearables among runners in Germany. The study included >2,000 runners, all interviewed on-site. The authors consider this an advantage compared to online or telephone surveys. However, selection biases might have been introduced, e.g., for course type.

In comparison of 2016 and 2017, the adoption rate of wearable technology remained stable. The share of wristband activity trackers and smartwatches increased slightly. Still, their proportion is comparatively small. Publication of other aspects of the 2017 study, i.e., motivational factors, voluntary data sharing, privacy concern, is pending.

Supplemental Material: The full list of devices, manufacturers can be downloaded at: https://github.com/rzo1/trolli-study

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


References

1.
Swan M. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0. JSAN. 2012 Nov:217-53. DOI: 10.3390/jsan1030217 External link
2.
Hermsen S, Moons J, Kerkhof P, Wiekens C, De Groot M. Determinants for Sustained Use of an Activity Tracker: Observational Study. JMIR Mhealth Uhealth. 2017;5(10):e164. DOI: 10.2196/mhealth.7311 External link
3.
Kooiman TJM, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity trackers. BMC Sports Science, Medicine and Rehabilitation. 2015 Oct. DOI: 10.1186/s13102-015-0018-5 External link
4.
Schaefer SE, Ching CC, Breen H, German BJ. Wearing, Thinking, and Moving: Testing the Feasibility of Fitness Tracking with Urban Youth. American Journal of Health Education. 2016 Jan:8-16. DOI: 10.1080/19325037.2015.1111174 External link
5.
Evenson K, Goto M, Furberg D. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015 Dec:1-22.
6.
Pobiruchin M, Suleder J, Zowalla R, Wiesner M. Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study. JMIR Mhealth Uhealth. 2017 Feb 28:e24. DOI: 10.2196/mhealth.6395 External link