What data are smartphone users willing to share with researchers?

Beierle, Felix; Tran, Vinh Thuy; Allemand, Mathias; Neff, Patrick; Schlee, Winfried; Probst, Thomas; Zimmermann, Johannes; Pryss, Rüdiger

doi:10.1007/s12652-019-01355-6

Cited by 49 publications

(36 citation statements)

References 48 publications

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“…Mobile apps and their data collection capabilities are only one direction that has garnered a lot of attention in the last years [1]. Especially in healthcare and medicine, mobile apps are a basis for new data sources and medical insights [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

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Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Stach

Probst

Meßner

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

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The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth.

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Section: Introductionmentioning

confidence: 99%

“…Technically, MHAD consists of a powerful rating platform to manage MARS ratings as well as an information platform 1 . The work at hand delineates the important requirements of MHAD, its technical features, as well as its use in practice.…”

Section: Introductionmentioning

confidence: 99%

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Stach

Probst

Meßner

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

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“…Health risks are outlined on the website. However, since safety, security, and privacy requirements are constantly evolving, a more transparent informed consent, additional security measures and a privacypreserving design would be desirable for the future (e.g., Beierle et al, 2019).…”

Section: Lessons Learned From the Trackyourtinnitus Projectmentioning

confidence: 99%

“…Optionally, in a privacy-preserving design, this data should be encrypted in a way that it can only be decrypted by each 6 https://www.docker.com/ 7 https://kubernetes.io/ respective user himself. In the best case, a dedicated privacy model is incorporated or developed (e.g., Beierle et al, 2019).…”

Section: R53 Social Featuresmentioning

confidence: 99%

Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain

et al. 2020

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The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.

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“…3) An architecture must be conceived that enables sophisticated crowdsensing collection procedures [9]. 4) Mobile native apps must be implemented that enable (1) an user experience that is welcomed by the crowd users [10], [11] as well as (2) enables reliable sensor measurements.…”

Section: Introductionmentioning

confidence: 99%