Eskinder Eshetu Ali1, Amanda Kai Sin Teo2, Sherlyn Xue Lin Goh3, Lita Chew4, Kevin Yi-Lwern Yap5. 1. Department of Pharmacy, Faculty of Science, National University of Singapore, Republic of Singapore. Electronic address: eskinder@u.nus.edu. 2. Department of Biological Sciences, Faculty of Science, National University of Singapore, Republic of Singapore. Electronic address: amandateokaisin@u.nus.edu. 3. Department of Economics, Faculty of Arts and Social Sciences, National University of Singapore, Republic of Singapore. Electronic address: sherlyn.goh.xue.lin@yale-nus.edu.sg. 4. Department of Pharmacy, Faculty of Science, National University of Singapore, Republic of Singapore; Department of Pharmacy, National Cancer Centre Singapore, Republic of Singapore. Electronic address: lita.chew@nccs.com.sg. 5. National Pharmacy Programme Management Office, Office of Director of Medical Services, Ministry of Health, Republic of Singapore. Electronic address: kevin_yap@moh.gov.sg.
Abstract
BACKGROUND: With the recent proliferation of smartphone medication adherence applications (apps), it is increasingly more difficult for patients and clinicians to identify the most useful app. OBJECTIVE: To develop a quality assessment tool for medication adherence apps, and evaluate the quality of such apps from the major app stores. METHODS: In this study, a Medication Adherence App Quality assessment tool (MedAd-AppQ) was developed and two evaluators independently assessed apps that fulfilled the following criteria: availability in English, had at least a medication reminder feature, non-specific to certain disease conditions (generic apps), free of technical malfunctions and availability on both the iPhone Operating System (iOS) and Android platforms. Descriptive statistics, Mann-Whitney U test, Pearson product moment correlation and Spearman rank-order correlation were used for statistical analysis. RESULTS: MedAd-AppQ was designed to have 24 items (total 43 points) categorized under three sections: content reliability (11 points), feature usefulness (29 points) and feature convenience (3 points). The three sections of MedAd-AppQ were found to have inter-rater correlation coefficients of 0.801 (p-value < .001) or higher. Based on analysis of 52 apps (27 iOS and 25 Android), quality scores ranged between 7/43 (16.3%) and 28/43 (65.1%). There was no significant difference between the quality scores of the Android and iOS versions. None of the apps had features for self-management of side effects. Only two apps in each platform provided disease-related and/or medication information. CONCLUSIONS: MedAd-AppQ can be used to reliably assess the quality of adherence apps. Clinicians can use the tool in selecting apps for use by patients. Developers of adherence apps should consider features that provide therapy-related information and help patients in medications and side-effects management.
BACKGROUND: With the recent proliferation of smartphone medication adherence applications (apps), it is increasingly more difficult for patients and clinicians to identify the most useful app. OBJECTIVE: To develop a quality assessment tool for medication adherence apps, and evaluate the quality of such apps from the major app stores. METHODS: In this study, a Medication Adherence App Quality assessment tool (MedAd-AppQ) was developed and two evaluators independently assessed apps that fulfilled the following criteria: availability in English, had at least a medication reminder feature, non-specific to certain disease conditions (generic apps), free of technical malfunctions and availability on both the iPhone Operating System (iOS) and Android platforms. Descriptive statistics, Mann-Whitney U test, Pearson product moment correlation and Spearman rank-order correlation were used for statistical analysis. RESULTS:MedAd-AppQ was designed to have 24 items (total 43 points) categorized under three sections: content reliability (11 points), feature usefulness (29 points) and feature convenience (3 points). The three sections of MedAd-AppQ were found to have inter-rater correlation coefficients of 0.801 (p-value < .001) or higher. Based on analysis of 52 apps (27 iOS and 25 Android), quality scores ranged between 7/43 (16.3%) and 28/43 (65.1%). There was no significant difference between the quality scores of the Android and iOS versions. None of the apps had features for self-management of side effects. Only two apps in each platform provided disease-related and/or medication information. CONCLUSIONS:MedAd-AppQ can be used to reliably assess the quality of adherence apps. Clinicians can use the tool in selecting apps for use by patients. Developers of adherence apps should consider features that provide therapy-related information and help patients in medications and side-effects management.
Authors: Martin Hensher; Paul Cooper; Sithara Wanni Arachchige Dona; Mary Rose Angeles; Dieu Nguyen; Natalie Heynsbergh; Mary Lou Chatterton; Anna Peeters Journal: J Am Med Inform Assoc Date: 2021-06-12 Impact factor: 4.497