National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands.
J Med Internet Res. 2021 Jan 15;23(1):e17564. doi: 10.2196/17564.
Web-based respondent-driven sampling is a novel sampling method for the recruitment of participants for generating population estimates, studying social network characteristics, and delivering health interventions. However, the application, barriers and facilitators, and recruitment performance of web-based respondent-driven sampling have not yet been systematically investigated.
Our objectives were to provide an overview of published research using web-based respondent-driven sampling and to investigate factors related to the recruitment performance of web-based respondent-driven sampling.
We conducted a scoping review on web-based respondent-driven sampling studies published between 2000 and 2019. We used the process evaluation of complex interventions framework to gain insights into how web-based respondent-driven sampling was implemented, what mechanisms of impact drove recruitment, what the role of context was in the study, and how these components together influenced the recruitment performance of web-based respondent-driven sampling.
We included 18 studies from 8 countries (high- and low-middle income countries), in which web-based respondent-driven sampling was used for making population estimates (n=12), studying social network characteristics (n=3), and delivering health-related interventions (n=3). Studies used web-based respondent-driven sampling to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the number of seeds recruited, the proportion of successfully recruiting participants, the number of recruitment waves, the type of incentives offered to participants, and the duration of data collection. Studies that recruited relatively more seeds, through online platforms, and with less rigorous selection procedures reported relatively low percentages of successfully recruiting seeds. Studies that did not offer at least one guaranteed material incentive reported relatively fewer waves and lower percentages of successfully recruiting participants. The time of data collection was shortest in studies with university students.
Web-based respondent-driven sampling can be successfully applied to recruit individuals for making population estimates, studying social network characteristics, and delivering health interventions. In general, seed and peer recruitment may be enhanced by rigorously selecting and motivating seeds, offering at least one guaranteed material incentive, and facilitating adequate recruitment options regarding the target population's online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing web-based respondent-driven sampling, such as having less opportunities to implement rigorous seed selection procedures when recruiting many seeds, as well as issues around online rather than physical participation, such as the risk of cheaters participating repeatedly.
基于网络的应答者驱动抽样是一种新颖的抽样方法，用于招募参与者以生成人口估计数、研究社会网络特征和提供健康干预措施。然而，基于网络的应答者驱动抽样的应用、障碍和促进因素以及招募表现尚未得到系统研究。
我们的目的是提供一个关于使用基于网络的应答者驱动抽样的已发表研究的概述，并调查与基于网络的应答者驱动抽样的招募表现相关的因素。
我们对 2000 年至 2019 年期间发表的基于网络的应答者驱动抽样研究进行了范围综述。我们使用复杂干预措施的过程评估框架来深入了解基于网络的应答者驱动抽样是如何实施的，哪些影响机制驱动了招募，背景在研究中扮演了什么角色，以及这些组成部分如何共同影响基于网络的应答者驱动抽样的招募表现。
我们纳入了来自 8 个国家（高、中低收入国家）的 18 项研究，其中基于网络的应答者驱动抽样用于进行人口估计（n=12）、研究社会网络特征（n=3）和提供与健康相关的干预措施（n=3）。这些研究使用基于网络的应答者驱动抽样从各种目标人群中招募了 19 到 3448 名参与者。研究在招募的种子数量、成功招募参与者的比例、招募波数、提供给参与者的激励类型以及数据收集的持续时间等方面存在很大差异。招募相对较多的种子、通过在线平台进行且采用相对宽松的选择程序的研究报告的成功招募种子的百分比相对较低。不提供至少一种有保证的物质激励的研究报告的招募波数相对较少，成功招募参与者的百分比也较低。在有大学生参与的研究中，数据收集的时间最短。
基于网络的应答者驱动抽样可成功应用于招募个体进行人口估计、研究社会网络特征和提供健康干预措施。一般来说，可以通过严格选择和激励种子、提供至少一种有保证的物质激励以及促进与目标人群的在线联系和沟通行为相关的充足招募选择来增强种子和同伴招募。在实施基于网络的应答者驱动抽样时，应考虑潜在的权衡，例如，当招募大量种子时，可能会减少实施严格的种子选择程序的机会，以及与在线而不是物理参与相关的问题，例如作弊者重复参与的风险。
