Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer's Disease (TRC-PAD)

阿尔茨海默病预防试验 (APT) 网络研究招募临床前和前驱期阿尔茨海默病 (TRC-PAD) 试验准备队列

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Abstract

BACKGROUND: The Alzheimer Prevention Trials (APT) Webstudy is the first stage in establishing a Trial-ready Cohort for Preclinical and Prodromal Alzheimer's disease (TRC-PAD). This paper describes recruitment approaches for the APT Webstudy. OBJECTIVES: To remotely enroll a cohort of individuals into a web-based longitudinal observational study. Participants are followed quarterly with brief cognitive and functional assessments, and referred to Sites for in-clinic testing and biomarker confirmation prior to enrolling in the Trial-ready Cohort (TRC). DESIGN: Participants are referred to the APT Webstudy from existing registries of individuals interested in brain health and Alzheimer's disease research, as well as through central and site recruitment efforts. The study team utilizes Urchin Tracking Modules (UTM) codes to better understand the impact of electronic recruitment methods. SETTING: A remotely enrolled online study. PARTICIPANTS: Volunteers who are at least 50 years old and interested in Alzheimer's research. MEASUREMENTS: Demographics and recruitment source of participant where measured by UTM. RESULTS: 30,650 participants consented to the APT Webstudy as of April 2020, with 69.7% resulting from referrals from online registries. Emails sent by the registry to participants were the most effective means of recruitment. Participants are distributed across the US, and the demographics of the APT Webstudy reflect the referral registries, with 73.1% female, 85.0% highly educated, and 92.5% Caucasian. CONCLUSIONS: We have demonstrated the feasibility of enrolling a remote web-based study utilizing existing registries as a primary referral source. The next priority of the study team is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population.

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