This study reports on the development of a scale (MAUS-EC) to measure the usability of mHealth apps among older adults with chronic diseases and also assessed its psychometric proprieties among a sample of older Chinese adults. This scale has 23 items in six domains to operationalize the usability, providing researchers and stakeholders with a reliable and valid instrument to improve the age-appropriateness of mHealth.
Compared with previous usability testing tools, such as SUS, PSSUQ, and MAUQ [19–21], we adopted a series of organized and standardized empirical studies focused on elderly patients with chronic diseases. The development of scale items is based on a systematic literature review and the existing framework, showing a solid theoretical foundation. In-depth interviews extended information relating to the interactions between the patients and mHealth. Our experienced research team members promoted the accuracy and readability of the items through discussion. The Delphi method was used to further modify the scale items, conducted among experts from the central, northern, and eastern regions as well as the southern coastal areas of China. Their expertise included chronic disease care, aging care, and mHealth. These observations reflect the geographical and professional representation of the experts. The Delphi survey also gave them sufficient time and energy to consult relevant materials, enrich background knowledge, think more thoroughly, and express their views fully about the scale through "back-to-back" communication [49]. A pilot survey using a small sample strengthened the feasibility of measurement.
In this study, we analyzed the differentiation, representativeness, independence, and internal consistency of all items. We found that the items performed well in terms of differentiation, which may be related to systematic development progress and multiple revisions. The initial version of MAUS-EC formulated after two rounds of expert consultation, consisted of 32 items, each meeting the selection criteria for stability [52]. We referred to relevant indicators and combined expertise during the item screening process. CFA was conducted to examine the factor structure, resulting in six dimensions (perceived usefulness, perceived ease of use, access empowerment, technology anxiety, perceived security, and professional trust) forming the final version of MAUS-EC. These dimensions encompass various aspects of the use of mHealth for elderly patients with chronic diseases, including cognitive, behavioral, physiological, and psychological factors. The comprehensiveness of the scale dimensions ensures that what is measured under each dimension is different. Employing a one-size-fits-all tool, such as the System Usability Scale (SUS) [19], for assessing usability is problematic. This is especially evident when considering the unique challenges faced by aging individuals living with chronic conditions since they have their own set of needs and experiences when interacting with mHealth apps. While there may be some overlap with dimensions such as ease of use, ease of learning, simplicity, effectiveness, information, and user interface in PSSUQ [20], these characteristics alone do not capture the diverse complexities involved. The MAUQ [21] faces even more significant challenges, as it excludes older adults without a high school or higher education and those aged outside the range of 18 to 65 years in its development. However, our tool effectively addresses these gaps, ensuring inclusivity and relevance.
The first subdimension (items 1 to 6), which is perceived usefulness, refers to the extent to which an older patient believes that using mHealth would enhance their chronic disease management. A system characterized by high perceived usefulness is one where users perceive a positive relationship between its use and their performance outcomes [70]. Higher scores in this dimension indicate that users perceive the application as providing valuable support in their healthcare management, thereby enhancing overall usability. Perceived ease of use, the second subdimension across items 7 to 10, refers to the degree to which an individual believes that using the mHealth application would require minimal effort. Effort is a finite resource that individuals allocate to chronic disease management. Consequently, all else being equal, an application perceived to be easier to use than another is more likely to be accepted by users [70]. Higher scores in this dimension indicate that users find the application easy to use and navigate, leading to improved usability. Access empowerment is the third subdimension (items 11 to 14). pertains to the technical support mHealth provides for the population. It evaluates the extent to which users feel empowered to access and utilize the application to manage their health independently. Higher scores in this dimension indicate that users feel confident and empowered in utilizing the application.
The fourth subdimension (items 15 to 17), technology anxiety [71], addresses the negative emotions that may arise when using mHealth. This dimension addresses the negative emotions that may arise when using mHealth applications, such as anxiety or discomfort related to technology use. Lower scores in this dimension indicate lower levels of technology anxiety, leading to improved usability. Perceived Security - the fifth subdimension (items 18, 19, and 20), concerns the degree to which users believe their personal information can be secured when using mHealth applications. It evaluates users' perceptions of data confidentiality, integrity, and protection from unauthorized access or misuse, which is similar to the subjective judgment on perceived security risk among healthcare practitioners [72]. Higher scores in this dimension indicate greater trust and confidence in the security measures of the application, thereby enhancing usability by fostering user trust and engagement.
Items 20 to 23 are professional trust that measures the degree of trust in the professionalism of mHealth applications. It assesses users' perceptions of the accuracy, relevance, and credibility of health information provided through the application. Higher scores in this dimension indicate greater trust in the credibility and reliability of the application, enhancing usability by promoting user satisfaction and adherence to health recommendations.
In summary, MAUS-EC was developed through a theory-driven and evidence-based approach while following the principles of the development process, reflecting good reliability and validity. The qualitative and quantitative evaluation and the screening of the items met the requirements of psychometrics. This feasible scale helps evaluate the usability of mHealth apps from the perspective of older adults with chronic diseases. The instrument is also easy to understand, without redundant items, and it has a suitable number of items for older adults with chronic diseases to respond to. Future research should focus more on improving the usability of mHealth and enhancing the age-appropriateness of the healthcare delivery model in the evolving digital environment through this tool. Particular attention should be paid to individuals with low scores to reduce digital health inequalities.
Implications
The development of the mHealth application usability scale for older adults with chronic diseases has profound implications beyond design and implementation. It catalyzes ongoing research and innovation in mobile health, fostering collaboration across the healthcare ecosystem to improve the relevance, trustworthiness, security, and accessibility of mobile health solutions. By translating research findings into actionable strategies, stakeholders can collectively enhance the quality of care and life for elderly patients with chronic conditions.
Participatory design, involving end users throughout the iterative design process [73], will form the foundation for prototype features and functions tailored to address the management needs and priorities of older adults. Multidisciplinary teams, including clinician-scientists, user experience researchers, app designers, and developers, collaborate systematically to develop applications that adhere to established principles. Leveraging collective expertise, these teams address usability barriers identified through the scale, enhancing user engagement and adherence to digital self-care solutions. Simplified interfaces, enhanced navigation aids, and consistent wearable devices aim to reduce physical and cognitive load for older adults. Education on privacy and data security features addresses concerns about personal information, fostering trust in the application. Additionally, gamification elements and reward systems make applications more engaging and less anxious, incentivizing users to achieve their health goals. The effective translation of new knowledge, social technologies, and engagement techniques holds the promise of yielding novel approaches for empowering, engaging, and educating older adults with chronic diseases [74].
Continuous evaluation and refinement of mHealth apps, facilitated through usability scales, ensure their feasibility and effectiveness in meeting the evolving needs of older adults with chronic diseases. Insights gleaned from usability assessments play a crucial role in shaping healthcare policy and guiding the development of guidelines and standards for mobile health application design. Policymakers are thus empowered to advocate for user-centered design principles and robust testing protocols, thereby enhancing the quality and safety of mobile health interventions and fostering trust among users and healthcare providers. Moreover, perceived value, acting as a mediator in mobile app usage intention and promotion strategies [75], can be further bolstered through appropriate propaganda. In this context, APP operators may employ fear-based advertising strategies to incentivize users to adopt security protection measures and related software services [76], thereby reinforcing the overall efficacy and acceptance of mobile health applications.
A comprehensive onboarding process is vital in ensuring that older adults can effectively utilize mHealth apps. This process not only guides users through the initial setup and use of the application but also provides personalized training resources and accessible help channels, which are essential for overcoming any technological challenges they may face. However, the role of social workers, support communities, and caregivers cannot be overstated in this context [77]. They play a crucial role in empowering older adults to embrace and navigate these digital health solutions with confidence. Through their assistance, guidance, and education efforts, they not only enhance access empowerment but also alleviate the technology anxiety that users may experience. This fosters a supportive and inclusive environment where older adults feel encouraged to embrace digital health solutions, leading to improved engagement and ultimately better health outcomes.
Further research directions could include testing the scale's applicability in longitudinal studies to assess its sensitivity to changes over time among older adults with chronic diseases. Moreover, exploring the scale's effectiveness in different cultural contexts would offer valuable insights into its cross-cultural validity and usability. These future research endeavors would contribute to refining the scale's utility and ensuring its effectiveness across diverse populations and settings, ultimately enhancing its impact on the design and implementation of mobile health solutions for elderly patients with chronic conditions.
Limitations
The limitations of this study should be acknowledged for a comprehensive understanding of its findings. Firstly, the elderly patients with chronic diseases surveyed were exclusively from Guangzhou City, Guangdong Province in China, representing an urban setting. To ensure the scale's applicability to rural older adults, future research should include participants from rural areas. Moreover, China's culture of collectivism and family-centeredness may influence older adults' perceptions of mobile health applications, potentially impacting usability. Cultural factors should be considered when interpreting the study's results and adapting the usability scale for use in diverse cultural contexts. Additionally, the study's cross-sectional design limits its ability to assess the long-term usability of mHealth applications. Longitudinal studies would be beneficial to evaluate the sustained usability and effectiveness of these applications among older adults with chronic diseases. Furthermore, the mHealth apps examined in this study represent only a subset of those available on the market. Including a wider range of samples and considering additional usability factors process would enhance its utility for designing and developing mHealth apps tailored to the intended user base.