Center for Information Systems and Technology (CISAT), Claremont Graduate University, 130 E. Ninth St. ACB225, Claremont, CA, 91711, USA.
College of Pharmacy, Western University of Health Sciences, Pomona, CA, USA.
J Med Syst. 2023 Oct 20;47(1):108. doi: 10.1007/s10916-023-02006-2.
Medication non-adherence is a prevalent healthcare problem with poor health outcomes and added healthcare costs. MedScrab, a gamification-based mHealth app, is the first attempt to deliver crucial life-saving medication information to patients and increase their medication adherence. The paper presents the development of MedScrab and a two-phase mixed-method usability evaluation of MedScrab. Phase I qualitatively evaluated MedScrab using a think-aloud protocol for its usability. With 51 participants, qualitative data analysis of Phase I revealed two themes: positive functionality of the app and four areas of improvement. The improvement recommendations were incorporated into MedScrab's design. Phase I also validated a widely used mHealth App Usability Questionnaire (MAUQ). Quantitative data analysis of Phase I reduced the original 18-item MAUQ scale to a 15-item scale with two factors: ease of use (4 items) and usefulness and satisfaction (11 items). Phase II surveyed 83 participants from Amazon's Mechanical Turk using a modified MAUQ. The modified MAUQ scale showed strong internal consistency (Cronbach alpha = 0.959) and high factor loadings (between 0.623 and 0.987). The study design of the usability evaluation can serve as a methodological guide for designing, evaluating, and improving mHealth apps.The usability study showed that MedScrab was perceived as ease of use (6.24 out of 7) with high usefulness and satisfaction (5.72 out of 7). The quantitative data analysis results support the use of the modified MAUQ as a valid instrument to measure the usability of the MedScrab. However, the instrument should be used with adaptation based on the app's characteristics.
药物依从性差是一个普遍存在的医疗保健问题，会导致不良的健康结果和增加医疗保健成本。MedScrab 是一款基于游戏化的移动健康应用程序，是首次尝试向患者提供关键的救命药物信息并提高他们的药物依从性。本文介绍了 MedScrab 的开发过程以及 MedScrab 的两阶段混合方法可用性评估。第一阶段通过使用出声思维协议对其可用性进行了定性评估。通过 51 名参与者，第一阶段的定性数据分析揭示了两个主题：应用程序的积极功能和四个改进领域。改进建议被纳入了 MedScrab 的设计中。第一阶段还验证了一个广泛使用的移动健康应用程序可用性问卷 (MAUQ)。第一阶段的定量数据分析将原始的 18 项 MAUQ 量表减少到一个 15 项量表，分为两个因素：易用性（4 项）和有用性和满意度（11 项）。第二阶段使用修改后的 MAUQ 对来自 Amazon Mechanical Turk 的 83 名参与者进行了调查。修改后的 MAUQ 量表显示出很强的内部一致性（Cronbach alpha = 0.959）和高因子负荷（在 0.623 到 0.987 之间）。该可用性评估的研究设计可以作为设计、评估和改进移动健康应用程序的方法指南。可用性研究表明，MedScrab 被认为易于使用（7 分制中的 6.24 分），具有很高的有用性和满意度（7 分制中的 5.72 分）。定量数据分析结果支持使用修改后的 MAUQ 作为衡量 MedScrab 可用性的有效工具。然而，该工具应根据应用程序的特点进行适应性使用。
