TY - JOUR AU - Li, Sophia Xueying AU - Halabi, Ramzi AU - Selvarajan, Rahavi AU - Woerner, Molly AU - filippo, Isabell Griffith AU - Banerjee, Sreya AU - Mosser, Brittany AU - Jain, Felipe AU - Areán, Patricia AU - Pratap, Abhishek PY - 2022 DA - 2022/11/14 TI -远程研究的招聘和保留:知识从一个大型的、分散的实际研究乔- Res JMIR形式SP - e40765六世- 6 - 11千瓦——参与者招募KW -参与者保留KW -分散研究KW -主动和被动数据收集KW -保留KW -坚持KW -合规KW -接触KW -智能手机KW -移动健康KW - mHealth KW -传感器数据KW -临床研究KW -数据共享KW -招聘KW -手机AB -背景:智能手机越来越多地用于卫生研究。它们为参与者和研究人员之间提供了持续的联系，以监测大量人口的长期健康轨迹，而成本只是传统研究的一小部分。然而，尽管在远程研究中使用智能手机具有潜力，但迫切需要制定有效的战略，以具有代表性和公平的方式接触、招募和留住目标人群。目的:探讨远程研究中不同的招聘和激励分配方式对队列特征和长期保留的影响。还评估了影响主动和被动数据收集的现实因素。方法:我们使用一项大型远程观察研究的数据对参与者招募和保留进行了二次数据分析，旨在了解与感冒、流感和创伤性脑损伤对日常功能的影响相关的现实因素。我们在2020年3月15日至2022年1月4日分两个阶段进行招聘。在美国招募了超过10,000名智能手机用户，提供为期12周的每日调查和基于智能手机的被动感知数据。利用多元统计，我们调查了不同的招聘和激励分配方法对队列特征的潜在影响。 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. SN - 2561-326X UR - https://formative.www.mybigtv.com/2022/11/e40765 UR - https://doi.org/10.2196/40765 UR - http://www.ncbi.nlm.nih.gov/pubmed/36374539 DO - 10.2196/40765 ID - info:doi/10.2196/40765 ER -