Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness.
Objective: Recruitment of a sufficiently large and representative patient sample and its retention during central nervous system (CNS) trials presents major challenges for study sponsors. Technological advances are reshaping clinical trial operations to meet these challenges, and the COVID-19 pandem...
| Publicado en: | Innovations in Clinical Neuroscience Vol. 20; no. 7-9; pp. 40 - 47 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Matrix Medical Communications, LLC
Jul-Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950565&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950565 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21588333 DAD5 jtl: Innovations in Clinical Neuroscience issn: 21588333 maglogo: N pubinfo: dt: Jul-Sep2023 vid: 20 iid: 7-9 pid: 33670 pub: Matrix Medical Communications, LLC place: West Chester, Pennsylvania artinfo: ui: 171950565 171950565 171950565 171950565 ppf: 40 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness. aug: au: LUTZ, JACQUELINE PRATAP, ABHISHEK LENZE, ERIC J. BESTHA, DURGA LIPSCHITZ, JESSICA M. KARANTZOULIS, STELLA VAIDYANATHAN, UMA ROBIN, JESSICA HORAN, WILLIAM BRANNAN, STEPHEN MITTOUX, AURELIA DAVIS, MICHAEL C. LAKHAN, SHAHEEN E. KEEFE, RICHARD affil: Medical Office, Click Therapeutics, Inc. in New York, New York sug: subj: Clinical Trials Neurosciences Technology, Medical Diffusion of Innovation Research Subject Recruitment Methods Research Subject Retention Methods Human Communication Central Nervous System Advertising Social Media ab: Objective: Recruitment of a sufficiently large and representative patient sample and its retention during central nervous system (CNS) trials presents major challenges for study sponsors. Technological advances are reshaping clinical trial operations to meet these challenges, and the COVID-19 pandemic further accelerated this development. Method of Research: The International Society for CNS Clinical Trials and Methodology (ISCTM; www.isctm.org) Innovative Technologies for CNS Trials Working Group surveyed the state of technological innovations for improved recruitment and retention and assessed their promises and pitfalls. Results: Online advertisement and electronic patient registries can enhance recruitment, but challenges with sample representativeness, conversion rates from eligible prescreening to enrolled patients, data privacy and security, and patient identification remain hurdles for optimal use of these technologies. Electronic medical records (EMR) mining with artificial intelligence (AI)/machine learning (ML) methods is promising but awaits translation into trials. During the study treatment phase, technological innovations increasingly support participant retention, including adherence with the investigational treatment. Digital tools for adherence and retention support take many forms, including patient-centric communication channels between researchers and participants, real-time study reminders, and digital behavioral interventions to increase study compliance. However, such tools add technical complexities to trials, and their impact on the generalizability of results are largely unknown. Conclusion: Overall, the group found a scarcity of systematic data directly assessing the impact of technological innovations on study recruitment and retention in CNS trials, even for strategies with already high adoption, such as online recruitment. Given the added complexity and costs associated with most technological innovations, such data is needed to fully harness technologies for CNS trials and drive further adoption. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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