The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model–Based Content Analysis

S, Shanmuga Nathan; Arulchelvan, S.

doi:10.2196/15719

Cited by 9 publications

(6 citation statements)

References 30 publications

(41 reference statements)

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“…Despite a wealth of literature on mHealth apps in general and user-generated app reviews in particular, research on the relationship between prescription and reimbursement and user experience is still in its infancy. A significant body of research focuses on a qualitative, clinical evaluation of mHealth apps in Germany, centering around physician's/healthcare practitioners' perspectives of mHealth apps [9][10][11][12] , whereas international publications have focused predominantly on app development issues 20,21 , and feature evaluation [22][23][24] to make mHealth apps more accessible and user-friendly. While certification has been a theme researched in the context of mHealth apps 8 , there simply has been no real-world data comparing the user experience with regulated mHealth apps to that with their consumer counterparts, as such apps did not exist.…”

Section: Discussionmentioning

confidence: 99%

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany

et al. 2023

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Germany introduced prescription-based mobile health (mHealth) apps in October 2020, becoming the first country to offer them fully reimbursed by health insurance. These regulated apps, known as DiGAs, undergo a rigorous approval process similar to pharmaceuticals, including data protection measures and sometimes clinical trials. This study compares the user experience of DiGAs with non-prescription mHealth apps in Germany, analyzing both average app store ratings and written reviews. Our study pioneers the use of BERTopic for sentiment analysis and topic modeling in the mHealth research domain. The dataset comprises 15 DiGAs and 50 comparable apps, totaling 17,588 German-language reviews. Results reveal that DiGAs receive higher contemporary ratings than non-regulated apps (Android: 3.82 vs. 3.77; iOS: 3.78 vs. 3.53; p < 0.01; non-parametric Mann–Whitney–Wilcoxon test). Key factors contributing to positive user experience with DiGAs are customer service and personalization (15%) and ease of use (13%). However, challenges for DiGAs include software bugs (24%) and a cumbersome registration process (20%). Negative user reviews highlight concerns about therapy effectiveness (11%). Excessive pricing is the main concern for the non-regulated group (27%). Data privacy and security receive limited attention from users (DiGAs: 0.5%; comparators: 2%). In conclusion, DiGAs are generally perceived positively based on ratings and sentiment analysis of reviews. However, addressing pricing concerns in the non-regulated mHealth sector is crucial. Integrating user experience evaluation into the review process could improve adherence and health outcomes.

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Section: Discussionmentioning

confidence: 99%

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany

et al. 2023

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“…The two reviewers (PMS and LK) discussed uncertainties regarding the reviewed applications, and if they could not agree, the third reviewer (SAMM) was consulted to reach a consensus. The assessment was methodically adapted from similar studies ( 28 , 29 , 30 ).…”

Section: Methodsmentioning

confidence: 99%

Assessing the accessibility and quality of mobile health applications for the treatment of obesity in the German healthcare market

Stapelfeldt,

Müller,

Kerkemeyer

2024

Front. Health Serv.

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IntroductionOverweight and obesity are among the most prevalent health problems worldwide leading to various diseases and having a significant impact on the healthcare system. In Germany, the prevalence of obesity among adults is 19%. Mobile health applications offer a new approach to treatment and prevention and have been proven effective in previous studies. However, it is essential to investigate the availability and quality of these digital applications. The aim of this systematic assessment is to evaluate the accessibility and quality of digital health applications in German language designed to treat obesity.MethodsIn January 2024, a systematic search for mobile health applications was conducted on both the Google Play Store and Apple App Store. Just those apps available in German for both iOS and Android were considered acceptable. The German Mobile Application Rating Scale (MARS-G) was used to assess the quality of the apps. The content of mobile health applications was evaluated using the guideline from the German Obesity Society for the treatment of obesity. The characteristics of the apps were summarized and presented, and the results were analyzed using descriptive statistics and presented in tables.ResultsAfter screening, ten apps were included in the review. The apps varied in terms of calorie tracking, individual workout plans, educational aspects, nutritional plans, and exercises for behavioral change. On average, 6.4 out of 12 items of the German Obesity guideline recommendations were fulfilled. The MARS score (possible range from 1–5) reached a mean of 3.39 (SD = 0.39). The section “Engagement” had the lowest quality score with a mean of 3.14 (SD = 0.57), while the section “Aesthetics” achieved the highest mean of 3.57 (SD = 0.52).DiscussionMost German mobile health applications for managing obesity meet some guideline recommendations. They demonstrate adequate to good quality according to the MARS score. Assessing the quality of mobile health applications can be challenging for patients, despite being easily accessible and low-threshold. However, such digital health applications, reimbursed by the German SHI, offer evidence-based information, even if access can be associated with higher hurdles.

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“…recorded number of downloads, sample size, median and interquartile range (IQR) for each score (ORCHA, UX, PCA and DP) when separated by assigned download level (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). The sample size for download levels varied from 0 to 177 health apps.…”

Section: Plos Onementioning

confidence: 99%

“…In line with this, previous studies have shown that app quality ratings are often not significantly positively associated with user ratings. For instance, user ratings were found not to be significantly correlated with Mobile Application Ratings Scale (MARS) scores [ 16 , 17 ] or ‘PsyberGuide credibility ratings scale’ (PGCRS) scores [ 18 ]. A study from 2022 [ 19 ], found a weak but significant negative correlation between their criteria and scores and user ratings for women with anxiety during pregnancy.…”

Section: Introductionmentioning

confidence: 99%

Don’t judge a book or health app by its cover: User ratings and downloads are not linked to quality

Hyzy,

Bond,

Mulvenna

et al. 2024

PLoS ONE

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Objective To analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps. Materials and methods Utilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models. Results For user experience, professional/clinical assurance and data privacy scores, all models had very low adjusted R squared values (< .02). Suggesting that there is no meaningful link between subjective user ratings or the number of health app downloads and objective quality measures. Spearman correlations suggested that prior downloads only had a very weak positive correlation with user experience scores (Spearman = .084, p = .012) and data privacy scores (Spearman = .088, p = .009). There was a very weak negative correlation between downloads and professional/clinical assurance score (Spearman = -.081, p = .016). Additionally, user ratings demonstrated a very weak correlation with no statistically significant correlations observed between user ratings and the scores (all p > 0.05). For ORCHA scores multiple linear regression had adjusted R-squared = -.002. Conclusion This study highlights that widely available proxies which users may perceive to signify the quality of health apps, namely user ratings and downloads, are inaccurate predictors for estimating quality. This indicates the need for wider use of quality assurance methodologies which can accurately determine the quality, safety, and compliance of health apps. Findings suggest more should be done to enable users to recognise high-quality health apps, including digital health literacy training and the provision of nationally endorsed “libraries”.

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The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model–Based Content Analysis

Cited by 9 publications

References 30 publications

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany

Assessing the accessibility and quality of mobile health applications for the treatment of obesity in the German healthcare market

Don’t judge a book or health app by its cover: User ratings and downloads are not linked to quality

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