This was a cross-sectional study using the MAUQ. The original English version was developed to assess the usability of mHealth apps among patients and health care providers with a strong internal consistency [12]. The MAUQ for interactive apps for health care providers was used in this study. The MAUQ contains a total of 21 items and 3 dimensions: usability and satisfaction (8 items), system information arrangement (6 items), and efficiency (7 items) scored on a 7-point Likert scale ranging from 1 (“extremely strongly agree”) to 7 (“extremely strongly disagree”). The usability of the app is the average score of all items (total score divided by the number of items). The closer the average score is to 1, the higher the app’s usability is. A sample size of 382 participants was determined to ensure a robust participant-to-item ratio, which exceeds the minimum requirement of 10:1 for exploratory factor analysis (EFA), thereby ensuring the stability of the factor structure.

The inclusion criteria were (1) health care providers (physicians or nurses) currently working in the participating hospitals, (2) individuals who had used an mHealth app for at least 1 month to provide medical services, and (3) those who voluntarily consented to participate. Exclusion criteria were (1) individuals with less than 1 year of clinical experience and (2) those who were unable to complete the online questionnaire for technical or personal reasons.

This cross-sectional descriptive study was conducted in 1 third-class hospital. The convenience sampling method was adopted to select health care providers from a hospital who used an mHealth app (UHealth) to provide medical services. All participants gave informed consent and voluntarily took part in the study. UHealth is an app designed for the population with chronic diseases, providing comprehensive management such as medication reminders, online consultation, and disease knowledge [18]. The inclusion criteria were physicians or nurses who had ever used the UHealth app. The research tools included the MAUQ-C and health care workers’ general information, including their age, sex, marital status, educational level, and professional title.

This study was conducted in accordance with the Declaration of Helsinki and was approved by the ethics committee of Changhai Hospital (approval CHE2023-061). All participants provided informed consent before taking part in the study. On the first page of the online survey, we provided a detailed introduction to the study and an informed consent form. Participants were required to click a box stating, “I agree to participate in this research of my own volition” to proceed. The Wenjuanxing platform automatically logged their consent, and participants were free to opt out at any time by closing the survey. To ensure privacy and confidentiality, all collected data were deidentified and stored in a secure, password-protected database accessible only to the research team. No personally identifiable information was collected or disclosed, and no compensation was provided.

A total of 382 questionnaires were forwarded to health care workers in the hospital who used an mHealth app to provide medical care services for patients. Of these 382 health care workers, 248 (64.9%) were male, and 134 (35.1%) were female. Most users (n=363, 95%) were under 50 years of age. Table 1 provides further details.

The results (Table 2) indicated a significant difference between the high- and low-score groups (t₂₁₁=−14.497; P<.001). Items with no statistically significant difference (critical ratio<3 or P<.05) between the high- and low-score groups would be deleted [20]. However, the results showed that there were significant differences (critical ratio=8.594-16.613; P<.001) in each item between the high- and low-score groups; thus, no item was deleted.

The Pearson correlation coefficient was used to assess the correlation among 21 items. Items with a correlation coefficient below 0.4 or a P value below .05 would be deleted. As shown in Table 3, all the items were significant, and a positive correlation was found among the values of the assessment phase. The correlation coefficient between the score of each item and the total score of the questionnaire was calculated (0.505-0.743; P<.001); thus, all items were retained.

As shown in Table 4, the score range of the CVI in the questionnaire was 0.833 to 1 (minimum value≥0.78) [20], indicating that the items were comprehensible, and the CVI of the total score of the questionnaire was 0.992.

mHealth apps that provide health information and comprehensive management may be helpful both for patients and health care workers, and their use should be encouraged. The evaluations of health care providers as the main users of mHealth apps are also critical. Thus, we translated the original MAUQ (interactive apps for health care providers) into Chinese and validated the modified questionnaire in our study. This study represents the first cross-cultural adaptation and validation of the MAUQ for interactive apps for health care providers into Chinese, addressing a critical gap in usability assessment tools for medical professionals in China’s mHealth landscape.

The Chinese adaptation process of the health care provider version of the MAUQ was standardized. Before the Chinese adaptation of this scale, authorization was obtained, and the preparatory work was standardized. The translation model by Brislin [17] was strictly followed. The translators and back translators all had a master’s degree or above. For the disputed items in each version, decisions on modification were made through collective discussions by the research group, so the translation process was standardized and accurate. After the initial Chinese-adapted scale was developed, experts in the fields of medicine and English were invited to conduct cross-cultural adjustments from different professional perspectives. Revisions were made while ensuring the equivalence of the Chinese-adapted scale and the original scale to the greatest extent so as to ensure accuracy, comprehensibility of the item content, and its consistency with the local culture and further improve the adaptability of the scale. Then, a preliminary validation was carried out among medical staff. Methods such as item analysis and reliability and validity tests were used to test the internal consistency and effectiveness. The results of the presurvey study showed that the items of this Chinese-adapted scale were clear, which was convenient for the research participants to understand and answer. Therefore, the Chinese adaptation process of this scale was scientific and standardized, and the results were reliable.

Reliability evaluation can reflect the stability and consistency of the scale. In this study, the Cronbach α was used for consistency testing, and test-retest reliability was used for stability testing. The results showed that the Cronbach α of the MAUQ-C for health care providers was 0.919 (>0.8). The Cronbach α values of the 3 dimensions of the scale were 0.928, 0.877, and 0.886, all above 0.8, indicating that the Chinese-adapted scale had good internal consistency. The overall test-retest reliability of the scale was 0.881, and the test-retest reliability of each dimension was 0.759, 0.874, and 0.745, all above 0.7, indicating that this Chinese-adapted scale had good stability. Validity evaluation can show whether the scale truly and accurately reflects the content it surveys. In this study, methods such as content validity and factor analysis were used to test the validity of the Chinese-adapted scale. The content validity results showed that the I-CVI (0.833-1) of each item and the Scale-Content Validity Index/Average (0.992) of the scale met the standards, indicating that the experts had a high degree of recognition of the Chinese-adapted items and their corresponding measurement content and each item could effectively reflect the usability of the procedure, which indicated that the content validity of the MAUQ-C for health care providers was good. EFA extracted 3 common factors, which were consistent with the dimension division of the original scale, indicating that the item results were reasonable and the structural validity was good. The CFA results showed that the model fit met the standards (minimum discrepancy/df=1.810; root-mean-square error of approximation=0.058; incremental fit index=0.950; comparative fit index=0.950; goodness-of-fit index=0.887; normed fit index=0.895; relative fit index=0.881), indicating that the Chinese-adapted scale was consistent with the original scale in structure. These psychometric properties are consistent with previous validation studies of the MAUQ in other languages [8,11,15]. For instance, the 3-factor structure identified in our study aligns with the original development study by Zhou et al [12], confirming that the usability dimensions—usability and satisfaction, system information arrangement, and efficiency—are robust across different cultural and linguistic contexts. This cross-cultural stability suggests that the MAUQ-C is a reliable instrument for measuring usability among Chinese health care providers, offering a solid foundation for further research in this population.

Owing to the rapid development of the internet, mHealth apps have also developed rapidly. As the usability evaluation of apps can effectively identify and correct problems in and thereby optimize their operation, this method has become a common step in the app development process. In addition, the usability of apps also affects patients’ use experience and the final intervention effect. Therefore, scholars at home and abroad have paid more and more attention to the usability evaluation of mHealth apps in recent years [10]. Unlike widely used instruments such as the SUS, Software Usability Measurement Inventory, or Questionnaire for User Interaction Satisfaction—which focus on technical usability for general software or websites—the MAUQ is uniquely tailored to mHealth contexts. Its differentiation between patient and health care provider versions and emphasis on interactive app functionalities addresses the specialized needs of health care professionals, whose perspectives are critical for optimizing the clinical workflow integration of mHealth tools.

The robust psychometric properties of the MAUQ-C suggest that usability is not a monolithic construct but is highly context dependent. Unlike general usability scales (eg, the SUS), the MAUQ-C’s focus on system information arrangement and efficiency reflects the specific cognitive and operational demands placed on health care providers. The mechanism underlying this validity lies in the instrument’s alignment with the clinical workflow; when an app reduces the cognitive load of data entry or information retrieval, app users perceive higher usability. This study’s original contribution is that, first, it provides a psychometrically sound instrument tailored to the Chinese clinical environment and, second, it shifts the focus of mHealth evaluation from generic technical performance to professional-specific utility, which is a prerequisite for the successful integration of digital health into routine clinical practice.

Debates in mHealth usability research have highlighted the tension between subjective self-reported measures and objective behavioral metrics. While objective data (eg, task completion time and error rates) provide precise insights into technical performance, they often fail to capture the user’s cognitive load, satisfaction, and perceived workflow integration—dimensions that are critical for long-term app adoption in clinical settings. Our study contributes to this debate by demonstrating that the MAUQ-C provides a reliable subjective assessment that complements rather than replaces objective performance data. However, we acknowledge that relying solely on self-reported instruments may be subject to recall and social desirability bias. Future research should aim to triangulate MAUQ-C scores with objective use logs to provide a more holistic view of mHealth usability.

Unlike previous system usability evaluation tools, the MAUQ was not only developed for mHealth apps. Its 4 original versions are also carefully divided according to whether the app interacts with patients, and the evaluation objects are divided into 2 categories: patients and medical staff, which is more targeted. While the patient version of the MAUQ has been adapted into Chinese, this study fills the unmet need for a validated health care worker–specific tool. This is significant because health care providers serve as both end users and intermediaries in mHealth interventions, making their usability feedback essential for enhancing app adoption and effectiveness in clinical settings.

This study has several limitations. First, the evaluation was restricted to an app for populations with chronic diseases; future research should validate the MAUQ-C across diverse mHealth contexts to ensure broader generalizability. Second, the use of convenience sampling in 2 hospitals may introduce selection bias; thus, multicenter randomized studies are recommended to enhance representativeness. Third, the cross-sectional design precludes the assessment of long-term usability or the impact of app updates; longitudinal studies are needed to capture evolving user perceptions. In addition, conducting EFA and CFA on the same dataset may have inflated model fit indexes. Future validation should use a split-sample approach or independent datasets to provide a more rigorous confirmation of the factor structure. Finally, this study lacks an assessment of convergent and discriminant validity. Future research should compare the MAUQ-C with established usability instruments such as the SUS or the PSSUQ to further demonstrate its unique utility and construct validity in measuring mHealth usability for health care professionals.

The MAUQ-C for interactive apps for health care providers is a psychometrically sound and culturally adapted tool ideal for assessing mHealth app usability in Chinese clinical settings. Its validation advances mHealth research and practice by providing health care professionals, developers, and researchers with a reliable instrument to drive user-centered design and improve the quality of digital health interventions.