scholarly journals Applications and recruitment performance of web-based respondent-driven sampling: a scoping review (Preprint)

2019 ◽  
Author(s):  
Yannick B. Helms ◽  
Nora Hamdiui ◽  
Mirjam E.E. Kretzschmar ◽  
Luis E.C. Rocha ◽  
Jim E. van Steenbergen ◽  
...  
Keyword(s):  
Social Network ◽  
Health Interventions ◽  
Population Estimates ◽  
Respondent Driven Sampling ◽  
Material Incentive ◽  
Web Based ◽  
Selection Procedures ◽  
Network Characteristics ◽  
Target Populations ◽  
Recruitment Performance

BACKGROUND Web-based respondent-driven sampling (webRDS) 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 webRDS have not yet been systematically investigated. OBJECTIVE Our objectives were to provide an overview of published research using webRDS, and to investigate factors related to webRDS recruitment performance. METHODS We conducted a scoping review on webRDS studies published between 2000 and 2019. We used Moore et al.’s (2015) ‘process evaluation of complex interventions framework’ to gain insights into how webRDS was implemented, what mechanisms of impact drove recruitment, what the role of context was, and how these components together influenced webRDS recruitment performance. RESULTS We included 18 studies from 8 countries, including both high and low-middle income countries, where 12 studies used webRDS for making population estimates, 3 for studying social network characteristics, and 3 for delivering health-related interventions. Studies used webRDS to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the numbers of seeds recruited, the proportion of successfully recruiting participants, the numbers of recruitment waves, the types 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 WebRDS 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 in regards to target populations’ online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing webRDS. 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 system cheaters through repeat participation. CLINICALTRIAL

scholarly journals Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

10.2196/24690 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e24690
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Web Based ◽  
Network Characteristics ◽  
Using Data

Background Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media–based weight loss intervention. Methods We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period and linked participants’ network characteristics (eg, in-degree, out-degree, network constraint) to participants’ changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss (β=–.94, P=.04); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009), and the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss (β=–.89, P=.02). Conclusions Our analyses using data from this pilot study linked participants’ network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Applications and recruitment performance of web-based respondent-driven sampling: a scoping review (Preprint)

10.2196/17564 ◽  
2019 ◽  
Author(s):  
Yannick B. Helms ◽  
Nora Hamdiui ◽  
Mirjam E.E. Kretzschmar ◽  
Luis E.C. Rocha ◽  
Jim E. van Steenbergen ◽  
...  
Keyword(s):  
Scoping Review ◽  
Respondent Driven Sampling ◽  
Web Based ◽  
Recruitment Performance

Social network characteristics of injection drug users in Montreal, Canada

2005 ◽  
Author(s):  
Prithwish De ◽  
Joseph Cox ◽  
Carole Morissette ◽  
Ann Jolly ◽  
Jean-Francois Boivin
Keyword(s):  
Social Network ◽  
Injection Drug Users ◽  
Drug Users ◽  
Injection Drug ◽  
Network Characteristics

scholarly journals Effectiveness of web-based and mobile health interventions designed to enhance adherence to physical activity for people with inflammatory arthritis: a systematic review

2021 ◽  
Author(s):  
Mandeep Sekhon ◽  
Claire White ◽  
Emma Godfrey ◽  
Aliya Amirova ◽  
Åsa Revenäs ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Inflammatory Arthritis ◽  
Risk Of Bias ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Post Intervention ◽  
Web Based ◽  
Intervention Content ◽  
Secondary Outcomes

Abstract Objective The aim of this systematic review was to assess the evidence from randomised controlled trials (RCT) and cohort studies for the effectiveness of digital interventions designed to enhance adherence to physical activity (PA) for people with inflammatory arthritis (IA) and describe the intervention content using established coding criteria. Methods Six electronic databases were searched for published and unpublished studies. Independent data extraction and quality assessment (Cochrane risk of bias II or ROBIN I) were conducted by two reviewers. The primary outcome was self-reported adherence to PA post-intervention. Secondary outcomes included self-reported adherence to PA at other timepoints, level of PA or engagement with intervention at any follow-up timepoint. Intervention content was assessed using the Consensus on Exercise Reporting Template and the Behaviour Change Techniques taxonomy version 1. Results From 11,136 reports, four moderate risk of bias studies (three RCTs, one cohort study) including 1,160 participants with rheumatoid arthritis or juvenile inflammatory arthritis were identified. Due to heterogeneity of outcomes, a narrative synthesis was conducted. Only one RCT reported a small between group difference in adherence to PA [mean difference (95% confidence intervals) -0.46 (-0.82. -0.09)] in favour of the intervention. There were no between group differences in any secondary outcomes. Interventions included between 3–11 behaviour change techniques but provided minimal exercise prescription information. Conclusion There is currently limited moderate quality evidence available to confidently evaluate the effect of web-based and mobile health interventions on adherence to PA or level of PA post intervention in people with IA.

scholarly journals Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

2021 ◽  
Author(s):  
Derk Bransen ◽  
Marjan J. B. Govaerts ◽  
Dominique M. A. Sluijsmans ◽  
Jeroen Donkers ◽  
Piet G. C. Van den Bossche ◽  
...  
Keyword(s):  
Social Network ◽  
Medical Students ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Learning Opportunities ◽  
Network Size ◽  
Tie Strength ◽  
Network Characteristics ◽  
Self Regulated Learning ◽  
Regulated Learning

Abstract Introduction Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students’ interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students’ co-regulatory networks, perceived learning opportunities, and self-regulated learning. Methods The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students’ perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning. Results Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = −0.474, p < 0.001). Students’ perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students’ perceptions of learning opportunities. Discussion The present study reinforces the importance of co-regulatory networks for medical students’ self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students’ self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.

Social network characteristics moderate associations between cortical thickness and cognitive functioning in older adults

2021 ◽  
Author(s):  
Neika Sharifian ◽  
Afsara B. Zaheed ◽  
Emily P. Morris ◽  
Ketlyne Sol ◽  
Jennifer J. Manly ◽  
...  
Keyword(s):  
Older Adults ◽  
Social Network ◽  
Cognitive Functioning ◽  
Cortical Thickness ◽  
Network Characteristics

scholarly journals Social Networks and Loneliness During the COVID-19 Pandemic

2021 ◽  
Vol 7 ◽  
pp. 237802312098525
Author(s):  
Balazs Kovacs ◽  
Nicholas Caplan ◽  
Samuel Grob ◽  
Marissa King
Keyword(s):  
Social Network ◽  
Social Isolation ◽  
Close Relationships ◽  
Network Size ◽  
Global Network ◽  
Network Data ◽  
Network Characteristics ◽  
Face To Face ◽  
Social Network Data ◽  
Social Network Size

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

scholarly journals B-Graph Sampling to Estimate the Size of a Hidden Population

2015 ◽  
Vol 31 (4) ◽  
pp. 723-736 ◽  
Author(s):  
Marinus Spreen ◽  
Stefan Bogaerts
Keyword(s):  
Social Network ◽  
Sampling Design ◽  
Respondent Driven Sampling ◽  
Sampling Frame ◽  
Hidden Populations ◽  
Hidden Population ◽  
Graph Sampling ◽  
Graph Design ◽  
Applied Design ◽  
Incomplete Sampling

Abstract Link-tracing designs are often used to estimate the size of hidden populations by utilizing the relational links between their members. A major problem in studies of hidden populations is the lack of a convenient sampling frame. The most frequently applied design in studies of hidden populations is respondent-driven sampling in which no sampling frame is used. However, in some studies multiple but incomplete sampling frames are available. In this article, we introduce the B-graph design that can be used in such situations. In this design, all available incomplete sampling frames are joined and turned into one sampling frame, from which a random sample is drawn and selected respondents are asked to mention their contacts. By considering the population as a bipartite graph of a two-mode network (those from the sampling frame and those who are not on the frame), the number of respondents who are directly linked to the sampling frame members can be estimated using Chao’s and Zelterman’s estimators for sparse data. The B-graph sampling design is illustrated using the data of a social network study from Utrecht, the Netherlands.

scholarly journals A randomized wait-list controlled trial of a social support intervention for caregivers of patients with primary malignant brain tumor

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maija Reblin ◽  
Dana Ketcher ◽  
Rachael McCormick ◽  
Veronica Barrios-Monroy ◽  
Steven K. Sutton ◽  
...  
Keyword(s):  
Social Support ◽  
Social Network ◽  
Controlled Trial ◽  
Well Being ◽  
Wait List ◽  
Web Based ◽  
Caregiver Support ◽  
Support Intervention ◽  
Link Type ◽  
Prospective Longitudinal

Abstract Background Informal family caregivers constitute an important and increasingly demanding role in the cancer healthcare system. This is especially true for caregivers of patients with primary malignant brain tumors based on the rapid progression of disease, including physical and cognitive debilitation. Informal social network resources such as friends and family can provide social support to caregivers, which lowers caregiver burden and improves overall quality of life. However, barriers to obtaining needed social support exist for caregivers. To address this need, our team developed and is assessing a multi-component caregiver support intervention that uses a blend of technology and personal contact to improve caregiver social support. Methods We are currently conducting a prospective, longitudinal 2-group randomized controlled trial which compares caregivers who receive the intervention to a wait-list control group. Only caregivers directly receive the intervention, but the patient-caregiver dyads are enrolled so we can assess outcomes in both. The 8-week intervention consists of two components: (1) The electronic Social Network Assessment Program, a web-based tool to visualize existing social support resources and provide a tailored list of additional resources; and (2) Caregiver Navigation, including weekly phone sessions with a Caregiver Navigator to address caregiver social support needs. Outcomes are assessed by questionnaires completed by the caregiver (baseline, 4-week, 8-week) and the cancer patient (baseline, and 8-week). At 8 weeks, caregivers in the wait-list condition may opt into the intervention. Our primary outcome is caregiver well-being; we also explore patient well-being and caregiver and patient health care utilization. Discussion This protocol describes a study testing a novel social support intervention that pairs a web-based social network visualization tool and resource list (eSNAP) with personalized caregiver navigation. This intervention is responsive to a family-centered model of care and calls for clinical and research priorities focused on informal caregiving research. Trial registration clinicaltrials.gov, Registration number: NCT04268979; Date of registration: February 10, 2020, retrospectively registered.

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