Implementation of a fully remote randomized clinical trial with cardiac monitoring

Mayfield, Jacob J.; Chatterjee, Neal A.; Noseworthy, Peter A.; Poole, Jeanne E.; Ackerman, Michael J.; Stewart, Jenell; Kissinger, Patricia; Dwyer, John; Hosek, Sybil; Oyedele, Temitope; Paolino, Kristopher; Friedman, Paul A.; Waters, Chloe; Moreno, José Joaquín Rodríguez; Leingang, Hannah; Heller, Kate B.; Morrison, Susan; Krows, Meighan; Barnabas, Ruanne V.; Baeten, Jared M.; Johnston, Christine; Bole, Medhavi; Braun, Alyssa; Drummond, Mark; Hauge, Kirsten; Humphreys, Madelaine; Hussein, Abir; Kuntz, Steve; Mathur, Anya; McClellan, Lindsey; Pholsena, Thepthara; Seymour, Matthew; Stankiewicz‐Karita, Helen C.; Taub, Jina; Thuesmunn, Zoe; Valinetz, Ethan; Varon, Dana; Wald, Anna; Wood, Brian; Dematteis, Maianna; Wicklander, Katie; Letterer, Rebecca A.; Sridhar, Arun Raghav Mahankali; Purcell, Jeff; Kirk, Mary Ellen; Baugh, Jennifer; Brown, Cait; Celum, Connie; Hamilton, Daphne; Haugen, Harald S.; Johnson, Rachel; Knauer, Jack; Liou, Caroline H.; Quame-Amaglo, Justice; Azaad,; Stalter, Randy M; Thomas, Katherine; Vazquez, Vianey; Young, Grant E.; Zia, Yasaman; Zimmermann, Azaad; Huang, Meei‐Li; Greninger, Alexander L.; Jerome, Keith R.; Wener, Mark H.; Brown, Deborah; Davenport, Nathaniel; Gambito, Omar; Arroyave, Luisa; Bereznicka, Agata; Berz, Jonathan P. B.; Buitron, P; Camuso, Michael; Cardoso, L de S; Cruz, Ricardo; Dedier, Julien; Dennaoui, Husam; Goldman, Anna L.; Henault, Lori; Johnson, Terrell K.; Koberna, Sarah; Martinez, Carlie; Martínez, E. García; Ng, Crystal; Rogers, Margot; Salerno, Kathleen; Streed, Carl G.; Truong, Ve

doi:10.1038/s43856-021-00052-w

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…The adoption of digital health tools to develop and deploy digitally augmented trials has been rising steadily since the first fully remote decentralized trial in 2011 [ 6 - 8 ]. Recent studies have shown the benefits of remote monitoring using smartphones for assessing real-world behavior [ 9 , 10 ], for managing chronic pain [ 11 ], cancer care [ 12 ], diabetes [ 13 ], Parkinson symptom severity [ 14 ], and cardiovascular health [ 15 ] and for the delivery of remote interventions [ 16 ]. The COVID-19 pandemic has further accelerated this growth, enabling over 220 digitally augmented trials in 2021 alone [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study

Li¹,

Halabi²,

Selvarajan³

et al. 2022

JMIR Form Res

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Background Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner. Objective We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated. Methods We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression. Results We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants’ sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence. Conclusions Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.

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Section: Introductionmentioning

confidence: 99%

Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study

Li¹,

Halabi²,

Selvarajan³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

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“…A strong agreement appeared between manually evaluated and AI-predicted QTc values (−1.76 ± 23.14 ms). Mayfield et al ( 34 ) described implementing a fully randomized clinical trial with cardiac monitoring. ECG collection was performed with the KardiaMobile 6L device.…”

Section: Resultsmentioning

confidence: 99%

Smart devices to measure and monitor QT intervals

Hoek,

Brouwer,

Voors

et al. 2023

Front. Cardiovasc. Med.

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Careful observation of the QT interval is important to monitor patients with long QT syndrome and during treatment with potentially QT-prolonging medication. It is also crucial in the development of novel drugs, in particular in case of a potential side effect of QT prolongation and in patients with increased risk of QT prolongation. The 12-lead electrocardiogram (ECG) is the gold standard to evaluate cardiac conduction and repolarization times. Smartwatches and smart devices offer possibilities for ambulatory ECG recording and therefore measuring and monitoring the QT interval. We performed a systematic review of studies on smartwatches and smart devices for QTc analysis. We reviewed PubMed for smartwatches and smart devices that can measure and monitor the QT interval. A total of 31 studies were included. The most frequent devices were (1) KardiaMobile 6L, a Food and Drug Administration-approved device for QTc analyses that provides a 6-lead ECG, (2) an Apple Watch, a smartwatch with an integrated ECG tool that allows recording of a single-lead ECG, and (3) the Withings Move ECG ScanWatch, an analog watch with a built-in single-lead ECG. The KardiaMobile 6L device and the Apple Watch provide accurate measurements of the QT interval, although the Apple Watch is studied in standard and non-standard positions, and the accuracy of QT measurements increased when the smartwatch was moved to alternative positions. Most studies were performed on patients, and limited results were available from healthy volunteers.

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