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测量创建移动数据采集应用程序的心理努力。

Measuring Mental Effort for Creating Mobile Data Collection Applications.

机构信息

Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany.

Institute of Databases and Information Systems, Ulm University, 89069 Ulm, Germany.

出版信息

Int J Environ Res Public Health. 2020 Mar 3;17(5):1649. doi: 10.3390/ijerph17051649.

Abstract

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 N = 80 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.

摘要

为了解决基于纸质的数据收集程序的缺点,QuestionSys 方法使具有很少或没有编程知识的研究人员能够灵活地按需配置移动数据收集应用程序。QuestionSys 的移动应用程序方法主要旨在减轻 mHealth 场景中基于纸质的收集程序的现有缺点。重要的是,研究人员应该能够以有效的方式收集数据。为了评估 QuestionSys 的适用性,已经进行了几项研究来衡量在实践中使用该框架的努力。在这项工作中,呈现了一项研究的结果,该研究调查了配置移动应用程序所需的心理努力的心理洞察力。具体来说,在一项涉及 N = 80 名参与者的研究中验证了创建数据收集工具的心理努力,该研究分为两个阶段。通过这种方式,参与者根据过程建模方面的先验知识被分为新手和专家,这是所开发方法的基本支柱。在研究过程中,每个参与者都对 10 个工具进行建模,同时评估了几个性能指标(例如,所需时间或错误)。然后,将这些措施的结果与与必须建模的任务相关的自我报告的心理努力进行比较。一方面,获得的结果显示心理努力和性能指标之间存在很强的相关性。另一方面,自我报告的心理努力在研究过程中显著下降,因此对测量的性能指标产生了积极的影响。总的来说,这项研究表明,没有先验知识的新手在短时间内获得了足够的经验,可以成功地自行建模数据收集工具。因此,QuestionSys 是处理临床试验等大规模数据收集场景的有用工具。

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