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Cited by 17Mobile Crowdsensing and CrowdsourcingMobile Health and mHealth ApplicationsHearing, Cochlea, Tinnitus, Genetics

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

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Pryss, Rudiger, Schlee, Winfried, Reichert, Manfred, Kurthen, Ira, Giroud, Nathalie, Jagoda, Laura, Neuschwander, Pia, Meyer, Martin +6 more·2019·2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI 10.1109/embc.2019.8857854
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Abstract

mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more in-depth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform.

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Authors
Pryss, R., Schlee, W., Reichert, M., Kurthen, I., Giroud, N., Jagoda, L., Neuschwander, P., Meyer, M., Neff, P., Schobel, J., Hoppenstedt, B., Spiliopoulou, M., Langguth, B., & Probst, T.
Affiliations
  • University Hospital Regensburg (DE)
  • Universität Ulm (DE)
  • University of Zurich (CH)
  • Concordia University (CA)
  • Otto-von-Guericke-Universität Magdeburg (DE)
  • Universität für Weiterbildung Krems (AT)
ORCID
Venue
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
IEEE
Published
2019-07
Volume
2019
Pages
3951-3955
ISSN
2375-7477 (ISSN-L)
DOI
10.1109/embc.2019.8857854
OpenAlex
W2955527179
Cited by
17
References
32
Citations by year
2025: 2 · 2024: 5 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 1
FWCI
2.61
Type
conference paper
Language
en
Open access
Licence
Locations
  • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
  • Zurich Open Repository and Archive (University of Zurich)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Life Sciences › Neuroscience › Sensory Systems › Hearing, Cochlea, Tinnitus, Genetics
Keywords
ecological momentary assessment, mobile crowdsensing, mobile health, Android, iOS, mobile operating systems, hearing loss, tinnitus, daily fluctuations, confounding variables, chronic disease
MeSH terms
Smartphone, Ecological Momentary Assessment, Humans, Surveys and Questionnaires, Research Design, Tinnitus, Telemedicine, Remote Sensing Technology, Mobile Applications

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