Exploring the Quality of Health Apps: The ISYS Ranking System
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Abstract
Background: Approximately 63 Billion apps were downloaded in 2012, of which, an estimated 44 million were health-related. Paradoxically, most downloaded health-apps are seldomly used within a month of their initial download date, mainly because it is difficult for users to identify the overall quality of the app before it is downloaded. In essence, when users begin interacting with the recently downloaded app, they realize that it is not exactly what they were looking for.
The variety options from which to download from have also been reported as overwhelming for patients. With hundreds, sometimes thousands, of sellers and app flavors that range from smoking cessation, to weight loss, to health (e.g., blood glucose and blood pressure), and even bowel movement-social apps (e.g., iPoop), patients need an user-friendly way to identify what will be good for them and what they want.
The quality of health-related information on the web is not a new topic of debate, as it was identified in the early nineties, as soon as the internet became accessible to the masses. More recently however, large policy-making bodies have begun to regulate health-related apps in an effort to improve the quality available to consumers. On September 2013, for example, the FDA issued the Mobile Medical Applications Guidance for Industry and Food and Drug Administration Staff Guidelines.
The FDA is not alone and although other “high-level†policy-making bodies and ranking initiatives have been proposed, few rating systems are scalable to the number of apps being released on a daily basis and even fewer follow an evidence based, patient-centric, quality ranking process.
Objective: We aimed to create a patient-centric, evidence-based, scalable, health-app ranking system.
Methods: This research was conducted using a three phase approach: 1) Literature Review; 2) Delphi of Experts; and 3) Ranking System Validation. A broad search string was used to query English- and Spanish-language articles published since 1993 on the PubMed and EMBASE databases. Supplemental (scoping queries) were also completed on Google Scholar. A total of 18 articles met our inclusion criteria and their relevant data was extracted using a standardized template. To prevent Hawthorne Bias, the evidence from the literature review was incorporated into the second round of the Delphi. Data is currently being synthesized into validated for health-app quality. Statistical analyses are being conducted in MS Excel 2013.
Results and Conclusion: We identified four axes which can be combined to appraise quality of health-related apps. Upon ranking system validation completion, we will undergo a patent-filing process. However, we are committed to presenting our latest results during the congress presentation.
The variety options from which to download from have also been reported as overwhelming for patients. With hundreds, sometimes thousands, of sellers and app flavors that range from smoking cessation, to weight loss, to health (e.g., blood glucose and blood pressure), and even bowel movement-social apps (e.g., iPoop), patients need an user-friendly way to identify what will be good for them and what they want.
The quality of health-related information on the web is not a new topic of debate, as it was identified in the early nineties, as soon as the internet became accessible to the masses. More recently however, large policy-making bodies have begun to regulate health-related apps in an effort to improve the quality available to consumers. On September 2013, for example, the FDA issued the Mobile Medical Applications Guidance for Industry and Food and Drug Administration Staff Guidelines.
The FDA is not alone and although other “high-level†policy-making bodies and ranking initiatives have been proposed, few rating systems are scalable to the number of apps being released on a daily basis and even fewer follow an evidence based, patient-centric, quality ranking process.
Objective: We aimed to create a patient-centric, evidence-based, scalable, health-app ranking system.
Methods: This research was conducted using a three phase approach: 1) Literature Review; 2) Delphi of Experts; and 3) Ranking System Validation. A broad search string was used to query English- and Spanish-language articles published since 1993 on the PubMed and EMBASE databases. Supplemental (scoping queries) were also completed on Google Scholar. A total of 18 articles met our inclusion criteria and their relevant data was extracted using a standardized template. To prevent Hawthorne Bias, the evidence from the literature review was incorporated into the second round of the Delphi. Data is currently being synthesized into validated for health-app quality. Statistical analyses are being conducted in MS Excel 2013.
Results and Conclusion: We identified four axes which can be combined to appraise quality of health-related apps. Upon ranking system validation completion, we will undergo a patent-filing process. However, we are committed to presenting our latest results during the congress presentation.
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