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Abstract. Increasingly large number of the applications installed on smartphones tends to harm the application lookup efficiency. In this paper, we introduce Nihao, a personalized intelligent app launcher system, which could help the users to find apps quickly. Nihao predicts which app the user will likely open next based on a Bayesian Network model leveraging the contextual information such as the time of day, the day of week, the user's location and the last used app with the hypothesis that the users' app usage pattern is context dependent. Through the field study with seven users over six weeks, we first validate the above hypothesis by comparing the prediction accuracy of Nihao with other predictors. We found that the larger UI change did not necessarily yield longer app lookup time as the app lookup time highly depended on the app icon position on screen, which suggested the prediction accuracy was the most important factor in designing such a system. At the end of the study, we conducted a user survey to evaluate Nihao qualitatively. The survey results show that five out of seven users were quite satisfied with the prediction of Nihao and thought it could help to save both app lookup and management time by ranking the app icons automatically while Nihao did not help the other two users much since they used their phones primarily for calling and texting (not for apps).

Usability is of significant importance for any interactive software. In the mobile domain, applications face more challenges to deliver good experiences to end users due to the characteristics and usage of mobile devices in ubiquitous computing contexts. The situation may be exacerbated for mobile health applications since the target population or domain may impose even stricter usability requirements.Heuristic Evaluation (HE) or guideline review has proven itself to be an e↵ective approach among many usability evaluation methods. Organizing heuristic evaluation by usability professionals, however, can be costly and time consuming, particularly for frequent prototype updates generated by fast iterations. Manual inspection by human experts also su↵ers from scalability issues as mobile applications often need to run on a diverse set of hardware platforms.To help find potential usability problems at an early stage and reduce the workload of human usability experts, we propose an inspection framework to conduct automated guideline reviews of mobile health applications. The inspection framework is based on the Health Information Management Systems Society (HIMSS) usability guidelines for mHealth applications. First, we translate the high level descriptions of usability guidelines into operationalized metrics that can be measured by software. Second, we demonstrate the translation is meaningful by providing detailed analysis of suggested metrics and real-world case studies. We hope this framework can be used to enforce a minimum bar for the usability of mobile health applications and further adapted when new products in the field are developed.