Pivoting during a pandemic: lessons learned from transitioning a multisite randomized controlled trial to a remote protocol in response to COVID-19

Inverso, Hailey; Abadula, Fayo; Morrow, Troy; LeStourgeon, Lauren M.; Parmar, Angelee M; Streisand, Randi; Jaser, Sarah S.

doi:10.1093/tbm/ibab103

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…First, large-scale, fully remote data collection started close to the declaration of the COVID-19 pandemic in the United States, which is known to have changed our behavior and interaction with technology and devices [ 74 ]. Indeed, Inverso et al [ 75 ] showed higher engagement rates during the COVID-19 pandemic because of an increased reliance on technology during the lockdown. The WASH study began recruitment on March 15, 2020, shortly after the World Health Organization declared COVID-19 a pandemic on March 11, 2020.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…That said, to date baseline and follow-up surveys are completed by participants 87% of the time on average, which is equivalent or better than anticipated for a fully remote trial. 4 , 43 – 45 Second, it is unclear the degree to which using the Bluetooth scale for weight measurements will impact our outcomes, because regular weighing may improve weight loss or weight loss maintenance. 39 We will need to consider this in our post hoc analyses.…”

Section: Discussionmentioning

confidence: 99%

“… 1 – 3 Most behavioral health intervention trials in the U.S. have had to pivot to fully remote intervention and evaluation protocols as a result of restrictions on in-person research activities. 4 – 6 Moving these trials into a fully remote format, however, was not without precedence. Prior to the COVID-19 pandemic, research had shown the promise and challenges with these pivots across all stages of research, including participant recruitment, intervention delivery, and data collection.…”

Section: Introductionmentioning

confidence: 99%

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From hybrid to fully remote clinical trial amidst the COVID-19 pandemic: Strategies to promote recruitment, retention, and engagement in a randomized mHealth trial

et al. 2022

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Clinical trials worldwide were disrupted when the COVID-19 pandemic began in early 2020. Most intervention trials moved to some form of remote implementation due to restrictions on in-person research activities. Although the proportion of remote trials is growing, they remain the vast minority of studies in part due to few successful examples. Our team transitioned Goals for Reaching Optimal Wellness ( GROWell), an NIH-funded (R01NR017659) randomized control trial (RCT; ClinicalTrials.gov identifier NCT04449432) originally designed as a hybrid intervention, into a fully remote clinical trial. GROWell is a digital dietary intervention for people who enter pregnancy with overweight or obesity. Primary outcomes include gestational weight gain and six-month postpartum weight retention. Strategies that we have tested, refined, and deployed include: (a) use of a HIPAA-compliant, web-based participant recruitment and engagement platform; (b) use of a HIPAA-compliant digital health platform to disseminate GROWell and conduct study visits (c) interconnectivity of these two platforms for seamless recruitment, consent, enrollment, intervention delivery, follow-up, and study team blinding; (d) detailed SMS messages to address initial challenges with protocol adherence; (e) email notifications alerting the study team about missed participant surveys so they can follow-up; (f) remuneration using email gift cards with recipient choice of vendor; and (g) geotargeting social media campaigns to improve participation of Black Indigenous and People of Color Communities. These strategies have resulted in screen failure rates improving by 7%, study task adherence improving by an average of 20–30% across study visits, and study completion rates of 82%. Researchers may consider some or all of these approaches in future remote mHealth trials.

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“…Similar to other ongoing behavioral interventions [ 82 ], research focused on young children and T1D has adapted over the last few years to accommodate reliance on telehealth diabetes visits [ 83 ]. The necessity for remote delivery of interventions across this time period may prove advantageous for the future of intervention delivery, maximizing family convenience and reducing barriers that are commonly reported regarding participation in research [ 84 , 85 ].…”

Section: Current Issuesmentioning

confidence: 99%

Young Children with Type 1 Diabetes: Recent Advances in Behavioral Research

et al. 2022

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Purpose of Review This review provides a recent update of behavioral research pertinent to young children with T1D and addresses current priorities and future directions. Recent Findings Rates of type 1 diabetes (T1D) in young children (ages 1–7) are continuing to rise. Since 2014, changes to diabetes care and management have impacted young children and reinforced the need for increased attention and interventions to support diabetes management, especially in caregivers who are primarily responsible for their young child’s diabetes management. Summary T1D is associated with unique physiologic challenges in young children, with constant management demands elevating parental diabetes-related stress and fear of hypoglycemia. Diabetes technology use has significantly increased in young children, contributing to improvements in glycemic levels and parent and child psychosocial functioning. Yet despite the positive outcomes demonstrated in select clinical behavioral interventions, research with this young child age group remains limited in scope and quantity.

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Pivoting during a pandemic: lessons learned from transitioning a multisite randomized controlled trial to a remote protocol in response to COVID-19

Cited by 7 publications

References 8 publications

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

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

From hybrid to fully remote clinical trial amidst the COVID-19 pandemic: Strategies to promote recruitment, retention, and engagement in a randomized mHealth trial

Young Children with Type 1 Diabetes: Recent Advances in Behavioral Research

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