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Measuring Mental Effort for Creating Mobile Data Collection Applications
To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mit...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084515/ https://www.ncbi.nlm.nih.gov/pubmed/32138381 http://dx.doi.org/10.3390/ijerph17051649 |
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author | Schobel, Johannes Probst, Thomas Reichert, Manfred Schlee, Winfried Schickler, Marc Kestler, Hans A. Pryss, Rüdiger |
author_facet | Schobel, Johannes Probst, Thomas Reichert, Manfred Schlee, Winfried Schickler, Marc Kestler, Hans A. Pryss, Rüdiger |
author_sort | Schobel, Johannes |
collection | PubMed |
description | To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with [Formula: see text] participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials. |
format | Online Article Text |
id | pubmed-7084515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70845152020-03-24 Measuring Mental Effort for Creating Mobile Data Collection Applications Schobel, Johannes Probst, Thomas Reichert, Manfred Schlee, Winfried Schickler, Marc Kestler, Hans A. Pryss, Rüdiger Int J Environ Res Public Health Article To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with [Formula: see text] participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials. MDPI 2020-03-03 2020-03 /pmc/articles/PMC7084515/ /pubmed/32138381 http://dx.doi.org/10.3390/ijerph17051649 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Schobel, Johannes Probst, Thomas Reichert, Manfred Schlee, Winfried Schickler, Marc Kestler, Hans A. Pryss, Rüdiger Measuring Mental Effort for Creating Mobile Data Collection Applications |
title | Measuring Mental Effort for Creating Mobile Data Collection Applications |
title_full | Measuring Mental Effort for Creating Mobile Data Collection Applications |
title_fullStr | Measuring Mental Effort for Creating Mobile Data Collection Applications |
title_full_unstemmed | Measuring Mental Effort for Creating Mobile Data Collection Applications |
title_short | Measuring Mental Effort for Creating Mobile Data Collection Applications |
title_sort | measuring mental effort for creating mobile data collection applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084515/ https://www.ncbi.nlm.nih.gov/pubmed/32138381 http://dx.doi.org/10.3390/ijerph17051649 |
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