BACKGROUND: Thailand\'s HIV epidemic is heavily concentrated among men who have sex with men (MSM), and surveillance efforts are mostly based on case surveillance and local biobehavioral surveys.
OBJECTIVE: We piloted Kai Noi, a web-based respondent-driven sampling (RDS) survey among MSM.
METHODS: We developed an application coded in PHP that facilitated all procedures and events typically used in an RDS office for use on the web, including e-coupon validation, eligibility screening, consent, interview, peer recruitment, e-coupon issuance, and compensation. All procedures were automated and e-coupon ID numbers were randomly generated. Participants\' phone numbers were the principal means to detect and prevent duplicate enrollment. Sampling took place across Thailand; residents of Bangkok were also invited to attend 1 of 10 clinics for an HIV-related blood draw with additional compensation.
RESULTS: Sampling took place from February to June 2022; seeds (21 at the start, 14 added later) were identified through banner ads, micromessaging, and in online chat rooms. Sampling reached all 6 regions and almost all provinces. Fraudulent (duplicate) enrollment using \"borrowed\" phone numbers was identified and led to the detection and invalidation of 318 survey records. A further 106 participants did not pass an attention filter question (asking recruits to select a specific categorical response) and were excluded from data analysis, leading to a final data set of 1643 valid participants. Only one record showed signs of straightlining (identical adjacent responses). None of the Bangkok respondents presented for a blood draw.
CONCLUSIONS: We successfully developed an application to implement web-based RDS among MSM across Thailand. Measures to minimize, detect, and eliminate fraudulent survey enrollment are imperative in web-based surveys offering compensation. Efforts to improve biomarker uptake are needed to fully tap the potential of web-based sampling and data collection.

Respondent-driven sampling (RDS) is a peer chain-recruitment method for populations without a sampling frame or that are hard-to-reach. Although RDS is usually done face-to-face, the online version (WebRDS) has drawn a lot of attention as it has many potential benefits, despite this, to date there is no clear framework for its implementation. This article aims to provide guidance for researchers who want to recruit through a WebRDS.
Description of the development phase: guidance is provided addressing aspects related to the formative research, the design of the questionnaire, the implementation of the coupon system using a free software and the diffusion plan, using as an example a web-based cross-sectional study conducted in Spain between April and June 2022 describing the working conditions and health status of homecare workers for dependent people.
The application of the survey: we discuss about the monitoring strategies throughout the recruitment process and potential problems along with proposed solutions.
Under certain conditions, it is possible to obtain a sample with recruitment performance similar to that of other RDS without the need for monetary incentives and using a free access software, considerably reducing costs and allowing its use to be extended to other research groups.

BACKGROUND: The ongoing war in Yemen has created a severe and protracted crisis that has left nearly three-quarters of the population in need of urgent humanitarian assistance. Despite eight years of conflict there exist few robust estimates of how the conflict (and the conflict combined with the COVID-19 pandemic) have affected mortality in Yemen. As the security situation has limited access to affected populations we have designed a novel alternative to local mortality surveys.
METHODS: We used a web-based, respondent-driven sampling method to disseminate a mortality survey amongst the global Yemeni diaspora. We used Cox proportional hazards survival models to estimate the association between the exposure (i.e. between the pre-conflict, conflict, and conflict/pandemic periods) and mortality risk, adjusted for gender and birth cohort.
RESULTS: Eighty-nine eligible respondents completed the survey. Respondents provided data on the status of 1704 individuals of whom 85 (5%) had died; of these 65 (3.8%) were reported to have died in Yemen. An analysis of survivorship of respondents\' parents after their 50th birthday (adjusted for gender and birth cohort) provided weak evidence that the war and pandemic periods were associated with higher mortality when compared to the pre-war period. Analysis of the subset of individuals who died in Yemen also suggested an increased, but non-significant hazard of dying during the war/pandemic period: this association tended towards significance when allowing for varying degrees of out-migration from Yemen across the cohort. The number of deaths amongst respondents\' siblings and children under five in Yemen were too low to allow meaningful analysis.
CONCLUSIONS: Our data suggest increased mortality during the war/pandemic period, compared to the pre-war period, among older Yemeni adults. However, our findings require careful interpretation as our study design cannot establish causation, and as our small and non-representative sample appeared skewed towards higher-income, urban communities. Surveys of diaspora populations offer a promising means of describing mortality patterns in crisis-affected populations; though, large numbers of respondents are likely required to achieve accurate mortality estimates and to adjust for selection bias.

Respondent-driven sampling (RDS) refers both to a chain-referral sampling method and an analytical model for analysing sampled data. Web-based respondent-driven sampling (webRDS) uses internet-based recruitment coupled with an electronic survey to carry out RDS studies; there is currently no commercially available webRDS solution. We designed and developed a webRDS solution to support a research study aimed at estimating conflict-attributable mortality in Yemen. Our webRDS solution is composed of an existing survey platform (i.e. ODK) and a bespoke RDS system. The RDS system is designed to administer and manage an RDS survey cascade and includes: (1) an application programming interface, (2) a study participant client, and (3) an administrator interface. We report here on the design of the webRDS solution and its implementation.
We consulted members of the Yemeni diaspora throughout the development of the solution. Technical obstacles were largely the result of: WhatsApp\'s policies on bulk messaging and automated messaging behaviour, the inherent constraints of SMS messaging, and SMS filtering behaviour. Language support was straight-forward yet time consuming. Survey uptake was lower than expected. Factors which may have impacted uptake include: our use of consumable survey links, low interest amongst the diaspora population, lack of material incentives, and the length and subject matter of the survey itself. The SMS/WhatsApp messaging integration was relatively complex and limited the information we could send potential participants.
Despite lower-than expected survey uptake we believe our webRDS solution provides efficient and flexible means to survey a globally diverse population.

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.

BACKGROUND: Respondent-driven sampling (RDS) is a method successfully used to research hard-to-access populations. Few studies have explored the use of the Internet and social media with RDS, known as Web-based RDS (WebRDS). This study explored the use of combining both \"traditional\" RDS and WebRDS to examine the influences on adolescent alcohol use.
OBJECTIVE: This paper reports on the recruitment processes and the challenges and enablers of both RDS and WebRDS. It details comparative recruitment data and provides a summary of the utility of both methods for recruiting adolescents to participate in an online survey investigating youth alcohol norms.
METHODS: Process evaluation data collected from research staff throughout the study were used to assess the challenges and solutions of RDS and WebRDS. Pearson chi-square test (Fisher\'s exact test if applicable) was used to compare the differences in sociodemographics and drinking behavior between data collected by RDS and WebRDS.
RESULTS: Of the total sample (N=1012), 232 adolescents were recruited by RDS and 780 by WebRDS. A significantly larger proportion of Aboriginal or Torres Strait Islander (P<.001) participants who spoke English as their main language at home (P=.03), and of middle and lower socioeconomic status (P<.001) was found in the RDS sample. The RDS sample was also found to have a higher occurrence of past 7-day drinking (P<.001) and past 7-day risky drinking (P=.004). No significant differences in gender, age, past month alcohol use, and lifetime alcohol use were observed between the RDS and WebRDS samples. This study revealed RDS and WebRDS used similar lengths of chains for recruiting participants; however, WebRDS conducted a faster rate of recruitment at a lower average cost per participant compared to RDS.
CONCLUSIONS: Using WebRDS resulted in significant improvements in the recruitment rate and was a more effective and efficient use of resources than the traditional RDS method. However, WebRDS resulted in partially different sample characteristics to traditional RDS. This potential effect should be considered when selecting the most appropriate data collection method.