Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Jan 12, 2022)
Date Submitted: Jun 12, 2021
Open Peer Review Period: Jun 12, 2021 - Jun 21, 2021
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Assessing Quality Factors and User Preference of Drug Reference Apps in Taiwan: Systematic Search and Evaluation
ABSTRACT
Background:
Drug reference apps play various distinct and vital roles through the medication use process. A drug reference app that carried out comprehensive localized drug information would greatly improve the efficiency and quality of work for physicians, nurses, pharmacists, and patients.
Objective:
This current study aimed to describe a systematic and stepwise process to identify drug reference apps with localized drug information in Taiwan. Moreover, we assessed the quality of these apps by using a reliable quality assessment tool and further analyzing the influential factors for user ratings.
Methods:
A two-step algorithm (KESS) consisting of keyword growing and systematic search was proposed. Apps were divided into two groups: higher user ratings and lower user ratings. Seven independent reviewers were trained to evaluate these apps using Mobile App Rating Scale (MARS). A logistic regression model was fitted, and average marginal effects (AME) were calculated to identify the effects of factors for higher user ratings. A p-value< 0.05 was considered statistically significant.
Results:
A total of 23 drug reference apps in Taiwan had been identified and analyzed. Ten apps had higher user star ratings (>=4 stars), and 13 apps had lower user star ratings ( < 4 stars). These drug reference apps had acceptable quality with an average MARS score of 3.23. Apps with higher user star ratings had higher MARS scores than the lowers (engagement (2.70 v.s. 2.50, P= .005), functionality (3.85 v.s. 3.49, P= .003), aesthetics (3.39 v.s. 2.98, P < .001), and information (3.55 v.s. 3.25, P= .005)). The regression model showed five influential factors for higher user ratings (navigation, AME, 13.15%; performance, AME, 11.03%; visual appeal, AME, 10.87%; credibility, AME, 10.67%; quantity of information, AME, 10.42%).
Conclusions:
The proposed KESS algorithm could be a valuable and unbiased framework for systematic search for app. While the higher engagement, more functionality, better aesthetics, and more information associated with higher user ratings, there are five most influential factors, navigation, performance, visual appeal, credibility, and quantity of information among the four elements.
Citation
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