Mobile Apps for Drug–Drug Interaction Checks in Chinese App Stores: Systematic Review and Content Analysis

Abstract

Background As a computerized drug–drug interaction (DDI) alert system has not been widely implemented in China, health care providers are relying on mobile health (mHealth) apps as references for checking drug information, including DDIs. Objective The main objective of this study was to evaluate the quality and content of mHealth apps supporting DDI checking in Chinese app stores. Methods A systematic review was carried out in November 2020 to identify mHealth apps providing DDI checking in both Chinese iOS and Android platforms. We extracted the apps’ general information (including the developer, operating system, costs, release date, size, number of downloads, and average rating), scientific or clinical basis, and accountability, based on a multidimensional framework for evaluation of apps. The quality of mHealth apps was evaluated by using the Mobile App Rating Scale (MARS). Descriptive statistics, including numbers and percentages, were calculated to describe the characteristics of the apps. For each app selected for evaluation, the section-specific MARS scores were calculated by taking the arithmetic mean, while the overall MARS score was described as the arithmetic mean of the section scores. In addition, the Cohen kappa (κ) statistic was used to evaluate the interrater agreement. Results A total of 7 apps met the selection criteria, and only 3 included citations. The average rating score for Android apps was 3.5, with a minimum of 1.0 and a maximum of 4.9, while the average rating score for iOS apps was 4.7, with a minimum of 4.2 and a maximum of 4.9. The mean MARS score was 3.69 out of 5 (95% CI 3.34-4.04), with the lowest score of 1.96 for Medication Guidelines and the highest score of 4.27 for MCDEX mobile. The greatest variation was observed in the information section, which ranged from 1.41 to 4.60. The functionality section showed the highest mean score of 4.05 (95% CI 3.71-4.40), whereas the engagement section resulted in the lowest average score of 3.16 (95% CI 2.81-3.51). For the information quality section, which was the focus of this analysis, the average score was 3.42, with the MCDEX mobile app having the highest score of 4.6 and the Medication Guidelines app having the lowest score of 1.9. For the overall MARS score, the Cohen interrater κ was 0.354 (95% CI 0.236-0.473), the Fleiss κ was 0.353 (95% CI, 0.234-0.472), and the Krippendorff α was 0.356 (95% CI 0.237-0.475). Conclusions This study systematically reviewed the mHealth apps in China with a DDI check feature. The majority of investigated apps demonstrated high quality with accurate and comprehensive information on DDIs. However, a few of the apps that had a massive number of downloads in the Chinese market provided incorrect information. Given these apps might be used by health care providers for checking potential DDIs, this creates a substantial threat to patient safety.

Multipurpose Mobile Apps for Mental Health in Chinese App Stores: Content Analysis and Quality Evaluation

“…To evaluated the quality of the apps, we used the Mobile App Rating Scale (MARS), a validated scoring tool for assessing the quality of mHealth apps [ 23 ]. MARS has been used to evaluate the quality of different apps, such as apps for mental disorders [ 24 , 25 ], nutrition [ 26 ], drug-drug interaction checks [ 27 , 28 ], and chronic disease management [ 29 - 31 ]. MARS contains 23 items, including 4 objective quality subscales of engagement, functionality, aesthetics, and information quality and 1 subjective quality subscale.…”

Mobile Health Apps Providing Information on Drugs for Adult Emergency Care: Systematic Search on App Stores and Content Analysis

“…In addition, apps for treatment adherence have been the subject of intensive research [ 30 - 35 ]. Some papers have also been published on apps about drug-drug interactions [ 36 , 37 ]. This is an issue traditionally addressed by health care professionals, although nowadays many apps for checking interactions are intended to be used by patients rather than health care professionals.…”

“…Interaction with other medicinal products was found in only 21% (10/47) of the apps, despite being a major problem in patient safety. Drug-drug interaction checks are one of the most frequent functional categories within the current medication-related app landscape [ 27 , 56 ], but relevant quality and accuracy problems have been detected in apps, including this feature [ 36 , 37 ]. In addition, other relevant information on drug safety, such as contraindications (26/47, 55%) and adverse reactions (24/47, 51%), was found in approximately half of the apps analyzed in our study.…”

“…A highly specific quality assessment tool was developed to assess the quality of apps targeting medication-related problems, including those with medication information features [ 40 ]. Nevertheless, the most commonly used methodology to assess the quality of medical apps is the Mobile Application Rating Scale [ 53 - 55 ], as well as in studies on drug apps [ 30 , 36 , 37 , 41 ]. The total number of features has been associated with the total Mobile Application Rating Scale score in a study on apps for potential drug-drug interaction decision support [ 36 ].…”

Prevalence and factors predisposing to potential drug–drug interactions in a Polish community-dwelling geriatric population: An observational, cross-sectional study

“…The use of an automatic interaction analysis system led to low specificity. 46,47 Furthermore, unlike the START/STOPP criteria, the Beers criteria or the FORTA list, we were not able to fully address the clinical context of the detected drug interactions. This is particularly important in older patients with multiple morbidities who require multidrug regimens to treat chronic diseases in accordance with the guidelines of evidence-based medicine.…”