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谁能通过筛选流程?人工智能就绪数字语音库招募完成的社会人口学驱动因素

Who Makes It Through the Funnel? Sociodemographic Drivers of Recruitment Completion into an AI-Ready Digital Speech Bank.

作者信息

Nejat Peyman, Bachman Ashley D, Stubbs Vicki M, Duffy Joseph R, Stricker John L, Herasevich Vitaly, Jones David T, Utianski Rene L, Botha Hugo

机构信息

Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Office of Digital Innovation, Center for Clinical And Translational Science, Mayo Clinic, Rochester, Minnesota, USA.

出版信息

medRxiv. 2025 Aug 21:2025.08.18.25333909. doi: 10.1101/2025.08.18.25333909.

Abstract

BACKGROUND

Digital recruitment methods offer promising opportunities to address persistent challenges in clinical research participation, particularly in specialized fields like neurology. However, the impact of digital approaches across different socioeconomic and demographic groups remains inadequately understood. This study analyzed participant recruitment pathways in a digital neurology research study to identify sociodemographic factors associated with participation outcomes.

METHODS

We conducted a longitudinal analysis of 5,846 patients invited to participate in a remote speech capture study for neurological disease research between March and July 2024. Using data from Qualtrics, PTrax, and our recording platform, we tracked participant progression through multiple recruitment checkpoints. Socioeconomic status was assessed using the Housing-based Socioeconomic Status (HOUSES) index and Area Deprivation Index (ADI). We examined associations between participation pathways and demographic factors including age, sex, geographic location, and socioeconomic indices using Kruskal-Wallis and Wilcoxon rank-sum tests.

RESULTS

Only 415 participants (7.1%) completed all study requirements. Participants from neighborhoods with higher socioeconomic disadvantage (higher ADI national ranks) were significantly less likely to express interest in initial invitations (median ADI 45.0 vs. 42.0 for responders, <0.001). Urban participants completed enrollment faster than those from rural areas or urban clusters (median 32.0 days vs. 41.0 and 40.0 days, =0.011). Contrary to expectations, younger participants were more likely to drop out at multiple recruitment stages, with the median age increasing from 63 years in the invited cohort to 66.3 years among completers. Female participants required more time to complete enrollment compared to males (median 38.5 days vs. 32.0 days, =0.010). While neighborhood-level socioeconomic status significantly influenced participation, individual housing circumstances showed no significant association across recruitment stages.

CONCLUSIONS

Digital recruitment methods in neurological research do not automatically overcome traditional barriers to participation and may introduce new disparities related to the digital divide. The significant associations between participation outcomes and sociodemographic factors-particularly neighborhood socioeconomic status, geographic location, age, and sex-highlight the need for targeted recruitment strategies. Researchers should implement multi-channel approaches, design age-specific engagement strategies, address geographic disparities, and consider socioeconomic factors to enhance the inclusivity and effectiveness of digital recruitment in neurological research.

摘要

背景

数字招募方法为应对临床研究参与方面持续存在的挑战提供了有前景的机会,尤其是在神经病学等专业领域。然而,不同社会经济和人口群体对数字方法的影响仍未得到充分理解。本研究分析了一项数字神经病学研究中的参与者招募途径,以确定与参与结果相关的社会人口学因素。

方法

我们对2024年3月至7月期间受邀参加一项用于神经病学疾病研究的远程语音采集研究的5846名患者进行了纵向分析。利用Qualtrics、PTrax和我们的录音平台的数据,我们跟踪了参与者在多个招募关卡的进展情况。使用基于住房的社会经济地位(HOUSES)指数和地区贫困指数(ADI)评估社会经济地位。我们使用Kruskal-Wallis检验和Wilcoxon秩和检验研究了参与途径与年龄、性别、地理位置和社会经济指数等人口学因素之间的关联。

结果

只有415名参与者(7.1%)完成了所有研究要求。来自社会经济劣势较高社区(ADI全国排名较高)的参与者对初始邀请表达兴趣的可能性显著较低(应答者的ADI中位数为45.0,而完成者为42.0,<0.0且1)。城市参与者完成注册的速度比农村地区或城市集群的参与者更快(中位数分别为32.0天、41.0天和40.0天,P=0.011)。与预期相反,年轻参与者在多个招募阶段退出的可能性更大,完成者的年龄中位数从受邀队列中的63岁增加到66.3岁。与男性相比,女性参与者完成注册需要更多时间(中位数分别为38.5天和32.0天,P=0.010)。虽然社区层面的社会经济地位显著影响参与情况,但个体住房情况在各个招募阶段均未显示出显著关联。

结论

神经病学研究中的数字招募方法并不能自动克服传统的参与障碍,并且可能会引入与数字鸿沟相关的新差距。参与结果与社会人口学因素之间的显著关联——尤其是社区社会经济地位、地理位置、年龄和性别——凸显了制定有针对性的招募策略的必要性。研究人员应采用多渠道方法,设计针对特定年龄的参与策略,解决地理差异问题,并考虑社会经济因素,以提高神经病学研究中数字招募的包容性和有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9cd/12393637/c9523ce7498b/nihpp-2025.08.18.25333909v1-f0001.jpg

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