This paper is in the following e-collection/theme issue:

mHealth for Data Collection and Research (981) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) Human Factors and Usability Case Studies (809) Usability of Apps and User Perceptions of mHealth (993)

Published on in Vol 6, No 6 (2018): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9826, first published .
Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Authors of this article:

Johannes Schobel1 Author Orcid Image ;   Rüdiger Pryss1 Author Orcid Image ;   Thomas Probst2 Author Orcid Image ;   Winfried Schlee3 Author Orcid Image ;   Marc Schickler1 Author Orcid Image ;   Manfred Reichert1 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Alwashmi M, Hawboldt J, Davis E, Fetters M. The Iterative Convergent Design for Mobile Health Usability Testing: Mixed Methods Approach. JMIR mHealth and uHealth 2019;7(4):e11656 View
  2. Shatte A, Teague S. schema: an open-source, distributed mobile platform for deploying mHealth research tools and interventions. BMC Medical Research Methodology 2020;20(1) View
  3. Kleinstäuber M, Gottschalk J, Ruckmann J, Probst T, Rief W. Acceptance and Cognitive Reappraisal as Regulation Strategies for Symptom Annoyance in Individuals with Medically Unexplained Physical Symptoms. Cognitive Therapy and Research 2019;43(3):570 View
  4. Schobel J, Probst T, Reichert M, Schickler M, Pryss R. Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications. IEEE Access 2019;7:61204 View
  5. Schobel J, Probst T, Reichert M, Schlee W, Schickler M, Kestler H, Pryss R. Measuring Mental Effort for Creating Mobile Data Collection Applications. International Journal of Environmental Research and Public Health 2020;17(5):1649 View
  6. Weierstall R, Crombach A, Nandi C, Bambonyé M, Probst T, Pryss R. Effective Adoption of Tablets for Psychodiagnostic Assessments in Rural Burundi: Evidence for the Usability and Validity of Mobile Technology in the Example of Differentiating Symptom Profiles in AMISOM Soldiers 1 Year After Deployment. Frontiers in Public Health 2021;9 View
  7. Ngadiman N, Sulaiman S, Idris N, Samingan M, Mohamed H. Systematic Review on Software Quality in Educational Applications. IEEE Access 2021;9:60187 View
  8. Schobel J, Volz M, Hörner K, Kuhn P, Jobst F, Schwab J, Ikonomi N, Werle S, Fürstberger A, Hoenig K, Kestler H. Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results. International Journal of Environmental Research and Public Health 2021;18(10):5092 View
  9. Ngadiman N, Sulaiman S, Idris N, Samingan M, Mohamed H. Checklist Approach for the Development of Educational Applications by Novice Software Developers. IEEE Access 2023;11:900 View
  10. Willms A, Rhodes R, Liu S. The Development of a Hypertension Prevention and Financial-Incentive mHealth Program Using a “No-Code” Mobile App Builder: Development and Usability Study. JMIR Formative Research 2023;7:e43823 View

Books/Policy Documents

  1. Hoppenstedt B, Witte T, Ruof J, Kammerer K, Tichy M, Reichert M, Pryss R. Augmented Reality, Virtual Reality, and Computer Graphics. View
  2. Karthan M, Hieber D, Dautovic A, Pryss R, Schobel J. Digitale Innovationen in der Pflege. View