RESUMO
Mobile Personal Health Records (mPHRs), which make it possible to track and manage users' health information, can be an important aid in improving people's health. Despite its potential benefits, poor usability of systems can hinder the adoption and use of mPHRs. This study aims to evaluate the usability of a mobile health application in terms of perceived cognitive workload and performance. The cognitive workload experienced by 30 volunteers (15 experienced and 15 inexperienced), was measured while performing the given tasks with the NASA-Task Load Index (NASA-RTLX) scale, and the duration of the fulfillment of the tasks by eye tracking device. While there was no significant difference between the two user groups in the completion time of the tasks, a significant difference was found in the perceived cognitive load. "Making an appointment", which could take much longer to complete than other tasks, resulted in the highest cognitive load for all users. Further usability research using think-aloud protocols and user interviews could provide insights into design improvements for reducing cognitive load and enhancing performance.
Assuntos
Registros de Saúde Pessoal , Aplicativos Móveis , Telemedicina , Humanos , Carga de Trabalho , CogniçãoRESUMO
OBJECTIVES: Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user. METHODS: In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model. RESULTS: All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method. CONCLUSIONS: The majority of the task execution times comprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation, the number of mental operators should be reduced.