Trends in the Explanatory or Pragmatic Nature of Cardiovascular Clinical Trials Over 2 Decades

Sepehrvand, Nariman; Alemayehu, Wendimagegn; Das, Debraj; Gupta, Arjun K.; Gouda, Pishoy; Ghimire, Anukul; Du, Amy; Hatami, Sanaz; Babadagli, Hazal E.; Verma, Shiv; Kashour, Zakariya

doi:10.1001/jamacardio.2019.3604

Cited by 34 publications

(39 citation statements)

References 20 publications

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“…Various platforms, including mailed questionnaires, Web-based forms, and research kits, are used in these types of trials [27]- [29]. Consequently, pragmatic trials are an emerging source of clinical evidence pertaining to issues ranging from pediatric asthma and cardiovascular diseases to monetary incentives for smoking cessation [37], [52], [53]. However, they are subject to several challenges, such as low patient recruitment, insufficient data collection, and treatment variability [26], [54].…”

Section: B Pragmatic Trials and Platformsmentioning

confidence: 99%

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Park

Kim

et al. 2020

IEEE Access

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The number of digital healthcare mobile applications in the market is exponentially increasing owing to the development of mobile networks and widespread usage of smartphones. However, only few of these applications have been adequately validated. Like many mobile applications, in general, the use of healthcare applications is considered safe; thus, developers and end users can easily exchange them in the marketplace. However, existing platforms are unsuitable for collecting reliable data for evaluating the effectiveness of the applications. Moreover, these platforms reflect only the perspectives of developers and experts, and not of end users. For instance, typical clinical trial data collection methods are not appropriate for participant-driven assessment of healthcare applications because of their complexity and high cost. Thus, we identified the need for a participant-driven data collection platform for end users that is interpretable, systematic, and sustainable, as a first step to validate the effectiveness of the applications. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. The interpretable data preparation consists of a protocol database system and semantic feature retrieval method that allow a person without professional knowledge to create a protocol. The systematic data storage stage includes calculation of the collected data reliability weight. For sustainable data collection, we integrated a weight method and a future reward distribution function. We validated the methods through statistical tests involving 718 human participants. The results of a validation experiment demonstrate that the compared methods differ significantly and prove that the choice of an appropriate method is essential for reliable data collection, to facilitate effectiveness validation of digital healthcare applications. Furthermore, we created a Web-based system for our pilot platform to collect reliable data in an integrated pipeline. We compared the platform features using existing clinical and pragmatic trial data collection platforms. INDEX TERMS Digital health, digital healthcare app data collection platform, crowdsourcing, participatory trial, biomedical informatics.

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Section: B Pragmatic Trials and Platformsmentioning

confidence: 99%

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Park

Kim

et al. 2020

IEEE Access

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“…These studies have been narrowed down to a defined number of cited articles, 8 to specific journals, 9 or to a limited number of years. 10 …”

Section: Introductionmentioning

confidence: 99%

Evolution of colorectal cancer screening research in the past 25 years: text-mining analysis of publication trends and topics

Soffer

Klang

Tau

et al. 2020

Therap Adv Gastroenterol

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Background: There is a growing research effort in the field of colorectal cancer (CRC) screening, with varying topics and shifting research foci over the years. The aim of this study was to apply a text-mining technique to evaluate trends in publications for CRC screening in the last 25 years. Methods: We retrieved MEDLINE/PubMed datasets from 1992–2017. We selected keywords from Medical Subject Headings to include CRC screening related publications. For each article, we extracted the following data: title, journal, publication date, abstract, article type, citation frequency, and country of origin. Articles were categorized into topics using word combination and title match technique. Results: In 1992–2017, 14,119 CRC screening related papers were published. The US had the highest number of papers ( n = 4824) and China had the highest growth rate in publications. Overall, the most researched topic was “screening and surveillance programs” (38%). The topics of “quality assurance” ( r = 0.87) and “racial disparities” ( r = 0.91) have gained increased research attention over the years. In total, 11 of the 20 most cited articles in the field were published in The New England Journal of Medicine. Conclusion: The number of publications devoted to CRC screening has grown, with high-quality research reaching top-tier journals. A surge in the number of publications has been increasing in countries previously less involved in research in the field. Screening programs remain the most researched topic, and quality indicators is attracting a growing attention. Text-mining analysis of CRC screening research contributes to an understanding of publication trends and topics and can point to the need for potential future investigations.

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“…Various platforms, including mailed questionnaires, web-based forms, 152 and research kits, are used for these kinds of trials [10,15,18]. Consequently, 153 pragmatism is an emerging source of clinical evidence, from pediatric asthma and 154 cardiovascular diseases to monetary incentives for smoking cessation [6,42,95]. However, 155 it is subject to several challenges such as patient recruitment, insufficient data 156 collection, and treatment variability [33,101].…”

mentioning

confidence: 99%

“…We 748 calculated the DRW based on the average value of RT, LSA, IR, and SR. The average 749 DRW of the "greater than 95…”

mentioning

confidence: 99%

Reliable data collection in participatory trials to assess digital healthcare apps

Park¹,

Park

Kim

et al. 2020

Preprint

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Digital healthcare mobile apps are experiencing exponential growth due to the development of the mobile network and widespread usage of smartphones. Unlike high growth rates, sufficient validated apps are few. Because mobile health apps are low-risk technology, and it tends to reduce regulatory oversight. With this tendency, the apps have a direct trade characteristic between developers and end-users. The existing platforms are not suitable to collect reliable data for evaluating the effectiveness of the apps. Moreover, these platforms only reflect the perspectives of developers and experts, not end-users. For instance, the methods of data collection in clinical trials are not appropriate for participant-oriented assessment of healthcare apps since its complexity and high cost. Thus, we identified a need for a participant-oriented data collection platform for end-users as an interpretable, systematic, and sustainable tool-as a first step to validate the effectiveness of the apps. To collect reliable data in the participatory trial format, we have defined data preparation, storage, and sharing stage. Interpretable data preparation consists of a protocol database system and semantic feature retrieval method to create a protocol without professional knowledge. Collected data reliability weight calculation belongs to the systematic data storage stage. To sustainable data collection, we integrate the weight method and reward distribution function. We validate the methods with 718 of human participants to conduct statistical tests. The validation results showed that the methods have significant differences in the comparative experiment, and we convinced the methods are essential to reliable data collection. Furthermore, we created a web-based system for our pilot platform to collect reliable data in an integrated pipeline. We validate the platform features with existing clinical and pragmatic trial data collection platforms. In conclusion, we show that the method and platform support reliable data collection, forging a path to effectiveness validation of digital healthcare apps. 1/371 Digital healthcare has become one of the most promising fields in the healthcare 2 industry with the widespread popularity of wireless devices such as smartphones. 3Notably, approximately 200 mobile health apps are listed daily in mobile app stores, 4 and investments in digital health are booming [25,89]. While many of the apps are 5 general wellness applications that help users to manage their activities, some healthcare 6 apps claim to improve a symptom or disease directly. The treatments proposed by the 7 apps take the form of information or visual stimuli, which are effectively delivered to 8 users as digital therapy. reSET-O, an 84-day prescription digital therapeutic app for the 9 treatment of opioid use disorder, has been approved by the U.S. Food and Drug 10 Administration (FDA) [51]. Akili Interactive has clinically demonstrated that interactive 11 digital treatment through a video game, which is under review by the FDA, may 12 i...

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Trends in the Explanatory or Pragmatic Nature of Cardiovascular Clinical Trials Over 2 Decades

Cited by 34 publications

References 20 publications

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Evolution of colorectal cancer screening research in the past 25 years: text-mining analysis of publication trends and topics

Reliable data collection in participatory trials to assess digital healthcare apps

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