Financial Incentives to Increase Cardiac Rehabilitation Participation Among Low-Socioeconomic Status Patients

Gaalema, Diann E.; Elliott, Rebecca J.; Savage, Patrick D.; Rengo, Jason L.; Cutler, Alex Y.; Pericot‐Valverde, Irene; Priest, Jeffrey S.; Shepard, Donald S.; Higgins, Stephen T.; Ades, Philip A.

doi:10.1016/j.jchf.2018.12.008

Cited by 56 publications

(55 citation statements)

References 44 publications

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“…It is intuitive that a reward can drive active participation. Recent scientific findings support the fact that rewards affect data acquisition [41], [73]. In our method, we provide financial future rewards in the form of exchangeable cryptocurrency to achieve sustainable data collection [74], [75].…”

Section: B Systematic Data Storagementioning

confidence: 85%

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 Systematic Data Storagementioning

confidence: 85%

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Park

Kim

et al. 2020

IEEE Access

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“…More research is needed to understand how to improve rates of CR completion. A recently published trial suggests that financial incentives may be effective,10 including in patients of low socioeconomic status, who are under-represented in programmes despite great need. Results of some other ongoing trials are also eagerly anticipated 11 12.…”

Section: Discussionmentioning

confidence: 99%

Cochrane corner: increasing patient utilisation of cardiac rehabilitation

Pio

Chaves

Davies

et al. 2019

Heart

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“…A reward can drive active participation, which is an intuitive fact. Recent scientific 253 findings support the fact that a reward has an impact on data acquisition [34,54]. We provided financial rewards for sustainable data collection as exchangeable 255 cryptocurrency [55,85].…”

mentioning

confidence: 81%

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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Financial Incentives to Increase Cardiac Rehabilitation Participation Among Low-Socioeconomic Status Patients

Cited by 56 publications

References 44 publications

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Cochrane corner: increasing patient utilisation of cardiac rehabilitation

Reliable data collection in participatory trials to assess digital healthcare apps

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