Using gamification to enhance clinical trial start-up activities.
Using gamification to enhance clinical trial start-up activities.
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DOI:
10.1017/cts.2022.405
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发表时间:
2022
影响因子:
2.6
通讯作者:
Hanley, Daniel
中科院分区:
文献类型:
--
作者:
Lane, Karen;Majkowski, Ryan;Gruber, Joshua;Amirault, Daniel;Hillery, Shannon;Wieber, Cortney;Thompson, Dixie D.;Huvane, Jacqueline;Bridges, Jordan;Ryu, E. Paul;Eyzaguirre, Lindsay M.;Gildea, Marianne;Thompson, Richard E.;Ford, Daniel E.;Hanley, Daniel
The Trial Innovation Network (TIN) is a collaborative initiative within the National Center for Advancing Translational Science (NCATS) Clinical and Translational Science Awards (CTSA) Program. To improve and innovate the conduct of clinical trials, it is exploring the uses of gamification to better engage the trial workforce and improve the efficiencies of trial activities. The gamification structures described in this article are part of a TIN website gamification toolkit, available online to the clinical trial scientific community. The game designers used existing electronic trial platforms to gamify the tasks required to meet trial start-up timelines to create friendly competitions. Key indicators and familiar metrics were mapped to scoreboards. Webinars were organized to share and applaud trial and game performance. Game scores were significantly associated with an increase in achieving start-up milestones in activation, institutional review board (IRB) submission, and IRB approval times, indicating the probability of completing site activation faster by using games. Overall game enjoyment and feelings that the game did not apply too much pressure appeared to be an important moderator of performance in one trial but had little effect on performance in a second. This retrospective examination of available data from gaming experiences may be a first-of-kind use in clinical trials. There are signals that gaming may accelerate performance and increase enjoyment during the start-up phase of a trial. Isolating the effect of gamification on trial outcomes will depend on a larger sampling from future trials, using well-defined, hypothesis-driven statistical analysis plans.
影响因子:
1.5
作者:
Goodlett, Dana;Hung, Anna;Mullins, C. Daniel
通讯作者:
Mullins, C. Daniel
DOI:
10.1016/j.elerap.2013.01.004
发表时间:
2013-07-01
影响因子:
6
作者:
Hamari, Juho
通讯作者:
Hamari, Juho
影响因子:
1.5
作者:
Lamberti, Mary Jo;Brothers, Cindy;Getz, Ken
通讯作者:
Getz, Ken