Objective Mobile health (mHealth) systems are becoming more common for chronic disease management, but usability studies are still needed on patients' perspectives and mHealth interaction performance. This deficiency is addressed by our quantitative usability study of a mHealth diabetes system evaluating patients' task performance, satisfaction, and the relationship of these measures to user characteristics. Materials and Methods We used metrics in the International Organization for Standardization (ISO) 9241-11 standard. After standardized training, 10 patients performed representative tasks and were assessed on individual task success, errors, efficiency (time on task), satisfaction (System Usability Scale [SUS]) and user characteristics. Results Tasks of exporting and correcting values proved the most difficult, had the most errors, the lowest task success rates, and consumed the longest times on task. The average SUS satisfaction score was 80.5, indicating good but not excellent system usability. Data trends showed males were more successful in task completion, and younger participants had higher performance scores. Educational level did not influence performance, but a more recent diabetes diagnosis did. Patients with more experience in information technology (IT) also had higher performance rates. Discussion Difficult task performance indicated areas for redesign. Our methods can assist others in identifying areas in need of improvement. Data about user background and IT skills also showed how user characteristics influence performance and can provide future considerations for targeted mHealth designs. Conclusion Using the ISO 9241-11 usability standard, the SUS instrument for satisfaction and measuring user characteristics provided objective measures of patients' experienced usability. These could serve as an exemplar for standardized, quantitative methods for usability studies on mHealth systems.

Mobile health platforms offer significant opportunities for improving diabetic self-care, but only if adequate usability exists. Expert evaluations such as heuristic evaluation can provide distinct usability information about systems. The purpose of this study was to complete a usability evaluation of a mobile health system for diabetes patients using a modified heuristic evaluation technique of (1) dual-domain experts (healthcare professionals, usability experts), (2) validated scenarios and user tasks related to patients' self-care, and (3) in-depth severity factor ratings. Experts identified 129 usability problems with 274 heuristic violations for the system. The categories Consistency and Standards dominated at 24.1% (n = 66), followed by Match Between System and Real World at 22.3% (n = 61). Average severity ratings across system views were 2.8 (of 4), with 9.3% (n = 12) rated as catastrophic and 53.5% (n = 69) as major. The large volume of violations with severe ratings indicated clear priorities for redesign. The modified heuristic approach allowed evaluators to identify unique and important issues, including ones related to self-management and patient safety. This article provides a template for one type of expert evaluation adding to the informaticists' toolbox when needing to conduct a fast, resource-efficient and user-oriented heuristic evaluation. 

Introduction: Self-management of chronic diseases using mobile health (mHealth) systems and applications is becoming common. Current evaluation methods such as formal usability testing can be very costly and time-consuming; others may be more efficient but lack a user focus. We propose an enhanced cognitive walkthrough (CW) method, the user-centered CW (UC-CW), to address identified deficiencies in the original technique and perform a beginning validation with think aloud protocol (TA) to assess its effectiveness, efficiency and user acceptance in a case study with diabetes patient users on a mHealth self-management application. Materials and methods: A total of 12 diabetes patients at University of Utah Health, USA, were divided into UC-CW and think aloud (TA) groups. The UC-CW method included: making the user the main evaluator for detecting usability problems, having a dual domain facilitator, and using three other improved processes: validated task development, higher level tasks and a streamlined evaluation process. Users interacted with the same mHealth application for both methods. Post-evaluation assessments included the NASA RTLX instrument and a set of brief interview questions. Results: Participants had similar demographic characteristics. A total of 26 usability problems were identified with the UC-CW and 20 with TA. Both methods produced similar ratings: severity across all views (UC-CW = 2.7 and TA = 2.6), numbers of problems in the same views (Main View [UC-CW = 11, TA = 10], Carbohydrate Entry View [UC-CW = 4, TA = 3] and List View [UC-CW = 3, TA = 3]) with similar heuristic violations (Match Between the System and Real World [UC-CW = 19, TA = 16], Consistency and Standards [UC-CW = 17, TA = 15], and Recognition Rather than Recall [UC-CW = 13, TA = 10]). Both methods converged on eight usability problems, but the UC-CW group detected five critical issues while the TA group identified two. The UC-CW group identified needed personalized features for patients’ disease needs not identified with TA. UC-CW was more efficient on average time per identified usability problem and on the total evaluation process with patients. NASA RTLX scores indicated that participants experienced the UC-CW half as cognitively demanding. Common themes from interviews indicated the UC-CW as enjoyable and easy to perform while TA was considered somewhat awkward and more cognitively challenging. Conclusions: UC-CW was effective for finding severe, recurring usability problems and it highlighted the need for personalized user features. The method was also efficient and had high user acceptance. These results indicate UC-CW's utility and user acceptance in evaluating a mHealth self-management application. It provides an additional usability evaluation technique for researchers. © 2019