Applications and Recruitment Performance of Web-Based Respondent-Driven Sampling: Scoping Review

Abstract

Background
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.

Objective
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.

Methods
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.

Results
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.

Conclusions
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.

Full text

Review
Applications and Recruitment Performance of Web-Based
Respondent-Driven Sampling: Scoping Review
Yannick B Helms1,2, MSc; Nora Hamdiui1,3, MSc; Mirjam E E Kretzschmar2, Prof Dr; Luis E C Rocha4, Prof Dr; Jim
E van Steenbergen1,5, MD, PhD; Linus Bengtsson6, MD, PhD; Anna Thorson7, MD, Prof Dr; Aura Timen1,8, MD,
Prof Dr; Mart L Stein1, PhD
1National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the
Environment, Bilthoven, Netherlands
2Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
3Department of Primary and Community Care, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands
4Department of Economics & Department of Physics and Astronomy, Ghent University, Ghent, Belgium
5Centre for Infectious Diseases, Leiden University Medical Centre, Leiden, Netherlands
6Flowminder Foundation, Stockholm, Sweden
7Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
8Athena Institute for Research on Innovation and Communication in Health and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Corresponding Author:
Yannick B Helms, MSc
National Coordination Centre for Communicable Disease Control
Centre for Infectious Disease Control
National Institute for Public Health and the Environment
Antonie van Leeuwenhoeklaan 9
Bilthoven
Netherlands
Phone: 31 30 274 70 00
Email: yannick.helms@rivm.nl
Abstract
Background: 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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusions: 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
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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.
(J Med Internet Res 2021;23(1):e17564) doi: 10.2196/17564
KEYWORDS
respondent-driven sampling; webRDS; online sampling; public health; interventions; research methodology; hard-to-reach
populations; probabilistic sampling
Introduction
Respondent-driven sampling (RDS) is a sampling method that
leverages social networks for recruiting individuals from
populations that lack a sampling frame. The method has often
been used to sample hard-to-reach populations, such as men
who have sex with men, people who use intravenous drugs, and
individuals with a migration background [1,2].
RDS starts with a convenience sample of members of a target
population. The initially recruited participants (known as seeds)
then recruit individuals from their social network (known as
peers). These recruitees, in turn, invite their own peers and so
on, resulting in a series of waves of recruitment [2,3]. Usually,
RDS utilizes a coupon system to track who recruits whom, and
requires that participants self-report the size of their social
network within the target population [3]. These data can be used
in a statistical model to account for the nonrandom data
collection. As such, under certain assumptions, RDS qualifies
as a probability sampling method that can generate unbiased
population estimates [4].
RDS has several applications besides generating population
estimates. For example, data on links between individuals
(obtained through tracking the recruitment process) allow for
studying interactions within and between participants’ social
networks. Among other things, this allows studying the spread
of diseases in populations [5]. Furthermore, RDS can be used
for recruitment of individuals for the delivery of health
interventions [6,7].
Recruitment through RDS traditionally requires physical
face-to-face interactions between individuals. However, over
the past decade a novel online variant of RDS, so-called
web-based RDS, was introduced. This potentially brings several
benefits over offline RDS [8]. In particular, internet-based
recruitment may (1) provide easy access and anonymity for
participants; (2) overcome time- and location-related barriers
to recruitment; and (3) provide an efficient, less laborious, and
logistically demanding medium for recruitment from the
researcher’s perspective [8-12]. However, web-based RDS also
introduces challenges, such as selection bias resulting from
differential access to the internet and problems with the
credibility of online research [10].
Nevertheless, the application of web-based RDS, its potential
benefits, and its drawbacks for recruiting individuals have not
yet been studied. Therefore, we aimed to provide an overview
of web-based RDS applications and to investigate factors related
to its recruitment performance, by means of a scoping review.
We are aware that the main purpose of typical RDS is to
generate population estimations. However, since we focus on
recruitment through web-based RDS, in this study, we were
equally interested in reported experiences with using web-based
RDS for the recruitment of individuals for the characterization
of social networks and the delivery of interventions. We also
highlight potential areas for future research on web-based RDS
and formulate general recommendations for researchers
interested in its application.
Methods
Study Design
A scoping literature review [13] was conducted to gain insights
into the application and performance of web-based RDS. We
chose to conduct a scoping review because our aims were
primarily exploratory, in the sense that we intended to provide
an overview of the work done with web-based RDS so far and
to identify factors related to the recruitment performance of
web-based RDS. Table 1 provides an overview of the web-based
RDS terminology used in this review (partially adapted from
[14]).
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Table 1. Meaning of important web-based RDS terminology.
MeaningTerms
An individual participating in a study or intervention.Participant
A participant’s social contacts, such as friends or family members.Peers
An invitation (eg, in the form of a URL) that a participant can send to his/her peers, from the same target population,
to invite peers in the study/intervention. Coupons use unique identifier codes to link recruiters with their recruitees.
Coupon
The process of participants recruiting their peers.Peer recruitment
A member of the target population who is recruited by a researcher to initiate peer recruitment.Seed
A participant who recruits a peer by sending a coupon.Recruiter
An individual who receives a coupon from a recruiter and agrees to enroll in the study/intervention.Recruitee
A visualization of the peer recruitment process, in which all recruiters and their recruitees are linked in chains.Recruitment tree
The distance (the number of chain-links) between seeds and their recruitees, in which seeds are in wave 0, their recruitees
in wave 1, and so on.
Wave
Equilibrium is reached when the sample composition of selected key indicators (eg, age and gender) remains stable
over successive waves. Equilibrium indicates that the sample has become independent of the initially selected seeds.
Equilibrium
The options that participants have to forward their coupons to their peers.Recruitment options
The stimuli provided to an individual for participation (primary incentive) or for each individual recruited (secondary
incentive) to stimulate peer recruitment. An incentive can be material (tangible, eg, a gift card) or nonmaterial (intangible,
eg, anonymous survey results).
Incentive
If only a primary or secondary incentive is offered, this is referred to as a single incentive structure; if both are offered,
this is referred to as a double incentive structure.
Incentive structure
Measures for recruitment performance (eg, number of individuals recruited) used in this research.Recruitment performance
measures
Search Strategy and Article Selection
We searched PubMed, Web of Science, and Scopus for articles.
First, a preliminary search was conducted in PubMed to gauge
the quality and quantity of web-based RDS related articles and
to identify keywords to formulate the search syntaxes.
The term web-based RDS was introduced in 2008. In order to
ensure that potentially relevant articles from before the term
was introduced were included, we set our search range as 2000
to 2019. The following search terms were included in the final
search syntaxes (see Multimedia Appendix 1 for the full
syntaxes used):
1. Study type: implementation, development, testing, adoption,
pilot
2. Online: online, web-based, internet, internet-based
3. Recruitment strategy: respondent-driven, peer-driven,
participant-driven, snowball, chain-referral
4. Study purpose: intervention, sampling, recruitment, referral
Two researchers (YH and MS) independently screened the titles
and abstracts of all unique records identified. The full text of
selected records were then screened by one researcher (YH), to
apply the below eligibility criteria. After this, the remaining
articles were critically reviewed by YH, MS, and NH, before
being included.
Eligibility Criteria
We included peer-reviewed articles that described the use of
web-based RDS for the recruitment of participants for research
purposes (ie, for making population estimates or for studying
social networks) or for health intervention delivery. Articles
that at least reported the numbers of seeds, subsequent recruitees,
and discussed barriers and facilitators to the application of
web-based RDS were included. We excluded studies that
combined online and offline RDS without reporting on both
approaches separately; if a study reported online and offline
RDS separately, the online component was included. As we
meant to provide an overview of the applications and
performance of web-based RDS, our study was not limited to
any particular target population or geographical area. We
excluded studies that were not available in English or Dutch.
Data Extraction and Analysis
A data extraction table was developed to collect and organize
data. The table’s topics were iteratively identified and selected
based on (1) STROBE (Strengthening the Reporting of
Observational Studies in Epidemiology) RDS guidelines [15],
(2) topics discussed in a literature review with a similar purpose
and context (offline RDS) to this study [1], and (3) discussion
between YH, MS, and NH. Additional topics included study
design, main findings, recommendations for further research,
and limitations.
We used the process evaluation of complex interventions
framework [16] to analyze the application and recruitment
performance of web-based RDS. This framework explains the
outcomes of an intervention as a function of implementation
characteristics, mechanisms of impact, and contextual factors.
We adapted this framework to fit web-based RDS specifications.
In this study, we viewed web-based RDS as an intervention
with the purpose of recruiting individuals. We defined outcomes
as web-based RDS recruitment performance, implementation
characteristics as the seed selection and recruitment process,
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mechanisms of impact as mechanisms to stimulate peer
recruitment, and context as the setting in which web-based RDS
was conducted.
Figure 1 (adapted from [16]) shows the analytical framework
integrated with a schematic representation of the web-based
RDS recruitment process. Table 2 shows the extraction table’s
topics, and operationalized measures thereof, grouped by
components of the process evaluation framework.
Analyses focused on uncovering factors that influenced
recruitment performance, based on comparing implementation
characteristics, peer recruitment and recruitment performance
measures between included studies. Data were presented in a
narrative fashion.
Figure 1. Analytical framework for web-based RDS recruitment performance (adapted from [16]).
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Table 2. Topics for data extraction and associated measures.
MeasuresTopics
Study setting (context)
Country •Country
Target population •Target population (specify)
Purpose •Study purpose (eg, population estimates, social networks, intervention)
Seed recruitment and selection (implementation characteristics)
Seed recruitment •Recruitment platform (specify)
Seed selection •Selection procedure (specify)
Number of seeds recruited •Number of seeds recruited that participated in the study
Peer recruitment (mechanisms of impact)
Incentives •Material or nonmaterial (specify)
•Single or double (specify)
•Maximum value of incentive
Recruitment options •Recruitment options (specify)
Coupons •Number of coupons allowed (ie, that a participant can forward)
Reminders •Reminders sent to enhance recruitment (yes/no)
Recruitment performance (outcomes)
Total participants recruited •Sample sizea
Successfully recruiting participantsb,c •Proportion of seeds who successfully initiated recruitment
•Proportion of all participants who successfully recruited peers to the study
Waves •Maximum number of waves observed
Equilibrium •Equilibrium reached (yes/no, and after how many waves)
Duration of data collection •Duration of data collection
Barriers and facilitators •Barriers and facilitators indicated to have influenced recruitment performance
aSample size was calculated as sample size minus duplicate or fraudulent entries, if reported.
bWe defined a successfully recruiting participant as a participant who invites at least one other person who participates in the study, regardless of the
eligibility (the latter says more about how strict or elaborate researchers set eligibility criteria rather than about participants’ability to peer recruit). This
excludes participants who merely sent out invitations with no response. If reported, duplicate or fraudulent entries were excluded.
cIf not otherwise reported, this metric was manually counted and calculated from the reported recruitment tree.
Ethical Considerations
No ethical issues were foreseen in this study. Results
Included Studies
We identified 393 unique records. The final number of articles
included in this review was 18. See Figure 2 for a detailed
account of the study inclusion procedure.
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Figure 2. PRISMA flowchart.
Study Settings
Six studies took place in the United States [8,17-21], 6 studies
took place in Western Europe [22-27], 4 studies took place in
southeast Asia [23,28-31], and 2 studies took place in Oceania
[32,33]. Studies were published between 2008 and 2019, with
the majority [17,18,22-24,26,27,30,32,33] from 2015 onward
(see Table 3).
In 12 studies, participants were recruited with the aim of
generating population estimates [8,19-23,26,28,29,31-33]; 3
studies aimed to study social networks and contact patterns
relevant to the spread of infectious diseases [24,25,28], and 3
studies recruited participants for delivering interventions
[17,18,27].
Studies focused on a diverse set of target populations: the
general population [19,24,25,28,30,32], university students
[8,20], men who have sex with men [23,29,31], individuals who
smoke [17], individuals using wheelchairs [33], parents of
children aged 10 to 14 [18], people with precarious employment
[22], young adults at risk of chlamydia infection [27],
individuals who have migrated from Syria [26], and individuals
who use marijuana [21].
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Table 3. Characteristics of the articles included in review.
Final sample size (excluding
fraudulent/duplicate entries,
as reported)
Target populationStudy purposeSettingReference
595 (551)People with precarious em-
ployment
Generating population esti-
mates
Stockholm county, SwedenJonsson et al [22]
195 (—a)
Individuals who have migrat-
ed from Syria
Generating population esti-
mates
Munich, GermanyWeinmann et al [26]
235 (—)Parents of children in grades
6-8 (ie, aged 10-14 years)
Intervention deliveryWashington and Colorado,
United States
Oesterle et al [18]
366 (356)General population (youth
and young adults)
Generating population esti-
mates
VietnamTran et al [30]
759 (—)Individuals who smokeIntervention deliveryUnited StatesSadasivam et al [17]
19 (19)Individuals using
wheelchairs
Generating population esti-
mates
New ZealandBourke et al [33]
780 (—)General population (youth)Generating population esti-
mates
Perth metropolitan area,
Australia
Hildebrand et al [32]
1448 (1429)General populationStudying social networks
and contact patterns relevant
to the spread of infectious
diseases
NetherlandsStein et al [24]
148 (130)Men who have sex with menGenerating population esti-
mates
SwedenStromdahl et al [23]
68 (68)Young adults at risk of a
Chlamydia trachomatis in-
fection
Intervention deliverySouth Limburg, NetherlandsTheunissen et al [27]
982 (870)Men who have sex with menGenerating population esti-
mates
VietnamBengtsson et al [31]
72 (—)Individuals who use marijua-
na
Generating population esti-
mates
Oregon, United StatesCrawford [21]
358 (—)General populationStudying social networks &
contact patterns relevant to
the spread of infectious dis-
eases
Netherlands and ThailandStein et al [25]
257 (245)General populationStudying social networks &
contact patterns relevant to
the spread of infectious dis-
eases
ThailandStein et al [28]
3448 (—)General population (young
adults)
Generating population esti-
mates
United StatesBauermeister et al [19]
676 (591)Men who have sex with menGenerating population esti-
mates
VietnamBengtsson et al [29]
378 (—)University studentsGenerating population esti-
mates
Cornell University, United
States
Wejnert [20]
159 (—)University studentsGenerating population esti-
mates
Cornell University, United
States
Heckathorn and Wejnert [8]
aNot specified.
Recruitment-Related Results
Seed Recruitment and Selection
Five studies recruited seeds through online platforms in the
form of targeted Facebook advertisements [17-19,32] or online
participatory research panels [24] (see Multimedia Appendix
2), while 6 studies combined online (eg, online advertisements)
with offline platforms, such as interest groups [23,26,28],
researchers’ social networks [22,25,26,28], or social venues
[22,26,29,31] and 2 studies only recruited seeds offline—1 at
a sexual health clinic [27] and 1 through a previous research
project [21].
In most studies, researchers established contact with potential
seeds as part of the seed selection procedure. This was done to
confirm potential seeds’identity or eligibility [22,27,32,33], to
select seeds with specific characteristics [19,22,26,30], or to
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confirm potential seeds’ motivation and ability to invite and
recruit peers [21-23,30]. Contact between researchers and seeds
consisted of phone calls [19,22], emails [8,18,25,28], or
in-person or group meetings [25,27,28]. In 3 studies, seed
selection consisted only of an online eligibility screener and
consent form [17,24,30].
The number of recruited seeds ranged between 1 [21] and 1015
[24].
Peer Recruitment
Most studies allowed participants to recruit their peers (eg, by
sharing a URL) through preferred means of communication,
such as WhatsApp or Facebook [17,19,21,22,29-32] or email
[8,20,33]. Some studies additionally provided participants with
the opportunity to provide their peers’ contact details to the
researchers, after which they contacted participants’ peers via
email (see Multimedia Appendix 2) [22,24,25,28,29,31].
In 4 studies, there was no limit for the number of coupons that
participants could forward [17,18,27,30]. In other studies, the
limit was 3 coupons [8,20,26,32,33], 4 coupons
[22-25,28,29,31], 5 coupons [21], or 10 coupons [19].
Most studies had a double-incentive structure
[8,17-20,22,23,26,29,31-33]. The majority of studies used
material incentives as opposed to [8,19,20,22,23,26,30,32,33]
or in combination with [17,18,24,29,31] nonmaterial incentives,
and 4 studies only used nonmaterial incentives [21,25,27,28].
Material incentives were electronic gift cards [19,22,23,26,32],
phone credits [29-31], or lottery tickets [24,29,31,33].
Nonmaterial incentives included showing participants
aggregated study results [21,29,31] or visual insights into the
study’s recruitment process (eg, anonymized recruitment trees)
[17,21,24,25,28], allowing participants to donate material
incentives [29,31], and delivering political or social messages
to prospective participants [18,21].
The maximum value of material incentives that participants
could earn ranged between US $12.45 in Vietnam [29,31] and
US $115 in the United States [17], but 2 studies had no
maximum incentive value, since their number of incentivized
coupons was unlimited [18,30].
Nine studies reported having sent reminders
[17,22,24,27-29,31-33].
Recruitment Performance
The final sample sizes ranged between 19 [33] and 3448 [19].
Three studies recruited less than 100 participants [21,27,33],
and 6 studies recruited more than 600 participants
[17,19,24,29,31,32] (see Multimedia Appendix 2).
The proportion of seeds and the proportion of all participants
who successfully recruited ranged between 7.5% [18] and 100%
[21], and between 9.2% [27] and 55% [8], respectively. The
maximum number of waves ranged between 1 [33] to 29 [31],
and 8 studies reported fewer than 10 waves
[17,21,23-25,27,28,33]. RDS sample distribution reached
equilibrium in 5 studies, after 1 to 11 waves [8,20,22,26,29,31].
Data collection took between 72 hours [8] and 7 months [25].
In most studies, data collection took between 2 months and 3
months [18-21,26,28,30].
Factors Influencing Recruitment Performance
Overall, studies that recruited more seeds relatively often used
online platforms (such as Facebook or other web communities)
for seed recruitment, selected seeds less rigorously (eg, with
less or no contact between potential seeds and researchers), and
recruited seeds from larger geographical areas (eg, the entire
country as opposed to a municipal area). Studies that recruited
fewer seeds relatively often did so through a combination of
both online and offline, or only offline platforms, with more
elaborate seed selection procedures, and in smaller geographical
areas.
Studies that recruited more seeds, through online platforms, and
with less rigorous selection procedures, reported relatively lower
percentages of successfully recruiting seeds. For example, the
3 studies that recruited the most seeds reported between 7.6%
and 24.7% successfully recruiting seeds [17,18,24], compared
to 67.4% to 100% in the 3 studies with the fewest recruited
seeds [8,20,21].
Studies that did not offer at least one guaranteed material
incentive (ie, not lottery-based compensation) reached no more
than 6 waves and reported between 9.2% and 38.9% successfully
recruiting participants [21,25,27,28].
Authors of included studies suggested a lack of monetary
incentives [21,25,28,33], a lack of different recruitment options
[19,22], and cheating (in order to retrieve multiple incentives)
by participants [8,19,29,32] as potential factors related to
web-based RDS protocols that influenced recruitment
performance. The incompatibility of questionnaires or
recruitment options with mobile platforms [18,19,23,30], and
recruitment emails being identified as spam [8,19,28] were
suggested as technical difficulties influencing recruitment
performance. Seeds’ motivation (or a lack thereof) to initiate
recruitment [22,28,32,33], stigma regarding the study subject
[27,33], online connectedness of the target population
[8,17,22,26,33], and differential access to the Internet
[8,18,19,28-32], were suggested as psychological and structural
characteristics of the target population influencing recruitment
performance.
Discussion
Overview
This is the first review investigating the application and
recruitment performance of web-based RDS; a novel online
sampling method. We identified 18 articles that described the
use of web-based RDS. Out of all studies, 12 recruited
participants for making population estimates, 3 recruited
participants to study social network characteristics (contact
patterns relevant to the spread of infectious diseases), and 3
recruited participants to deliver interventions. Studies were
conducted in 8 countries, including both high- and low-middle
income countries, over 4 continents. Between 19 and 3448
participants were recruited from various populations, including
some without a sampling frame, such as men who have sex with
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men. The heterogenous nature of the included studies (with
respect to their aims and setup) made it difficult to compare
their recruitment processes and to draw generalizable inferences
regarding recruitment performance.
Principal Findings
We found that studies that recruited relatively more seeds,
through online platforms, and with less rigorous selection
procedures reported lower percentages of successfully recruiting
seeds. The exact reasons for this observation remain unclear.
However, we suggest that recruiting more seeds relatively limits
the time and resources available to thoroughly prepare (ie,
motivate and inform) and select seeds. In turn, this may limit
seeds’ motivation to initiate peer recruitment, or lead to less
suited (eg, less socially connected) seeds being selected. Both
of these factors are known to be important for inducing and
sustaining seed and peer recruitment [34,35].
Studies that did not offer at least one guaranteed material
incentive reached relatively lower percentages of successfully
recruiting participant, and fewer waves. We thus suggest that
such incentives are particularly important to sustain recruitment,
as sampling waves increase (ie, monetary incentives appear to
carry further than nonmonetary incentives). This is in agreement
with wider offline RDS literature [34] and indicates that benefits
of online recruitment for participants (eg, easy access for
participants) and nonmonetary incentives do not render material
incentives redundant if the primary aim is to generate
recruitment waves.
However, some studies that recruited participants for
interventions reported relatively low percentages of successfully
recruiting participants, despite offering substantial monetary
incentives. This indicates that online peer recruitment for
interventions benefits (or suffers) from factors other than peer
recruitment for research purposes. Potentially, peer recruitment
for interventions depends more on participants’ affinity toward
an intervention (eg, related to intervention framing, packaging),
or its anticipated or experienced outcomes. Note, however, that
these findings are based only on few studies and require further
research to substantiate.
The majority of studies took between 2 and 3 months to collect
data. However, online recruitment was relatively faster in some
studies. For example, one study [8] achieved their targeted
sample size (N=150) in 72 hours, and another [19] recruited
3448 participants in 6 weeks. Both these study populations were
composed of university students who may be particularly
digitally literate and have extensive well-connected online
networks. This finding indicates the importance of these factors
when considering applying this online method.
Strengths and Limitations
One strength of this literature review was the wide search
strategy. It provides an extensive overview of peer reviewed
literature relevant for investigating web-based RDS
peer-recruitment processes. Another strength was the application
of the process evaluation framework, which offered a practical
structure for investigating different factors influencing
web-based RDS recruitment performance.
One limitation is that we excluded all articles not reporting on
the recruitment process in sufficient detail and all studies not
exclusively using online peer recruitment or reporting on online
and offline peer recruitment separately. Some valuable
contextual and comparative information between online and
offline recruitment might therefore have been missed. For some
crucial recruitment performance measures (eg, the percentage
of successfully recruiting participants), we had to rely on a
manual count of recruitment trees, since the original data sets
were unavailable.
Practical Implications and Opportunities for Future
Research Using Web-Based RDS
Based on this review, it remains difficult to assess how
successful web-based RDS is at achieving the aims for which
it is employed (ie, generating population estimates, studying
social networks, delivering interventions). For example, only
5 out of 12 studies aiming to generate population estimates
reported that the sample composition reached equilibrium.
Several studies likely achieved equilibrium (estimated from
reported sample size and observed number of waves), but did
not report this as such. Studies that used web-based RDS for
studying social networks or delivering interventions were mostly
feasibility or implementation studies, making it difficult to
assess how successful the online method is at reaching the
endpoints. At this point in time, we believe that there are not
enough studies to draw meaningful conclusions regarding the
overall success of web-based RDS for generating population
estimates, studying social networks, or delivering interventions.
Nevertheless, web-based RDS may be a particularly suited
recruitment method when random sampling techniques are not
feasible, the target population is geographically dispersed or
hidden (which is a challenge for offline sampling), and the target
population is extensively connected online [8]. Therefore,
despite the heterogenous nature of the studies included in this
review, which limits the generalizability of the studies’
recruitment processes and performance, we outline several
recommendations for future research into, or using web-based
RDS.
First, consistent with offline RDS literature, the results suggest
that recruiting a relatively small and thoroughly selected group
of seeds (to whom a significant amount of resources can be
dedicated for motivational and informing purposes) and
providing at least one guaranteed material incentive is the most
successful strategy for generating a substantial number of waves
[1,34]. As such, this is the preferred setup for studies aiming to
reach equilibrium for population estimates. If this is not the
primary objective, for example when recruiting individuals for
studying network characteristics or delivering interventions,
recruiting a larger number of seeds through less rigorous means
and providing lower or nonmaterial incentives may be preferred.
Second, despite the limited number of studies that recruited
participants for interventions in this review, some implications
in this regard stand out. For example, one study [27] found that
through web-based RDS, individuals could be reached for
sexually transmitted disease testing who were not reached before
through traditional sexual health services. Another study [17]
similarly noted that with each successive wave, the proportion
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of not-ready-to-quit smokers in the sample increased. These
findings indicate that web-based RDS recruitment is particularly
interesting for interventions if the aim is to reach more reluctant,
or previously unreached individuals. The challenge here is to
adequately incentivize peer recruitment (as discussed under
principal findings).
Third, the results indicate that providing multiple recruitment
options and facilitating the use of mobile platforms for
participation and recruitment may enhance web-based RDS
recruitment performance. However, it remains largely unclear
how these factors influence peer recruitment across different
settings, or even within certain target populations. For example,
as indicated also by several studies included in this review,
differential access to mobile communications, or the internet
in general, may impose barriers to peer recruitment to readily
excluded members of a given population. In addition, online
communication behavior and the types of digital communication
platforms used may differ between different networks, which
could affect even relatively well-connected individuals. This is
exemplified by one study [32] included in this review that
compared web-based RDS to traditional offline RDS and found
that individuals with lower socioeconomic status were less likely
to be recruited through web-based RDS. Besides socioeconomic
status, other factors known from literature that influence access
to or use of the internet (and may therefore also influence online
peer recruitment) include sociodemographic (eg, age, gender),
socioeconomic (eg, household income, educational attainment),
social (eg, degree of isolation, political context), and personal
(eg, self-efficacy, mental health) factors [36]. To account for
these potential sources of bias, we suggest thorough exploration
of the target population’s online networks and communication
behaviors, in a formative research stage.
Finally, we recommend that researchers using web-based RDS
follow STROBE-RDS guidelines when reporting their studies
[15]. A number of studies did not consistently report the
numbers of total distributed and returned coupons, the numbers
of recruitment waves, the numbers of individuals collecting
their incentives, and the numbers of recruitees by seeds. Similar
gaps in reporting on offline RDS data have been found in a
previous review [37]. In addition, we encourage researchers to
report relevant recruitment performance measures, such as the
percentages of successful recruiters or the average numbers of
recruitees per participant. This information is crucial for
studying how to optimize peer recruitment in the future.
Conclusions
We have given a comprehensive overview of web-based RDS,
what it is used for, how it is applied, and what factors influence
its recruitment performance. Web-based RDS can be
successfully applied to recruit individuals for making population
estimates, studying social networks, and delivering health
interventions. Peer recruitment may be enhanced by rigorously
selecting and motivating seeds, offering at least one guaranteed
material incentive, and facilitating adequate recruitment options
regarding target populations’ online connectedness and
communication behavior. Potential trade-offs should be taken
into account when implementing web-based RDS. Examples
are recruiting many seeds and less opportunities for rigorous
seed selection procedures, as well as issues around online rather
than physical participation, such as risks of cheaters through
repeated participation. Furthermore, we have demonstrated the
feasibility of—and described outcome measures for—research
into web-based RDS recruitment using a process evaluation
approach. The main points discussed in this literature review
provide researchers with guidelines on key aspects and
technicalities to consider when planning their web-based RDS
research.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Search syntaxes.
[PDF File (Adobe PDF File), 61 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Recruitment results.
[PDF File (Adobe PDF File), 144 KB-Multimedia Appendix 2]
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Abbreviations
RDS: respondent-driven sampling
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology
Edited by G Eysenbach; submitted 20.12.19; peer-reviewed by J Duggan, T Lam; comments to author 12.06.20; revised version
received 26.06.20; accepted 19.07.20; published 15.01.21
Please cite as:
Helms YB, Hamdiui N, Kretzschmar MEE, Rocha LEC, van Steenbergen JE, Bengtsson L, Thorson A, Timen A, Stein ML
Applications and Recruitment Performance of Web-Based Respondent-Driven Sampling: Scoping Review
J Med Internet Res 2021;23(1):e17564
URL: http://www.jmir.org/2021/1/e17564/
doi: 10.2196/17564
PMID:
©Yannick B Helms, Nora Hamdiui, Mirjam E E Kretzschmar, Luis E C Rocha, Jim E van Steenbergen, Linus Bengtsson, Anna
Thorson, Aura Timen, Mart L Stein. Originally published in the Journal of Medical Internet Research (http://www.jmir.org),
15.01.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic
information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be
included.
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