How Feasible is Multiple Time Point Web-Based Data Collection with Individuals Experiencing Street Homelessness?

Abstract

Three barriers investigators often encounter when conducting longitudinal work with homeless or other marginalized populations are difficulty tracking participants, high rates of no-shows for follow-up interviews, and high rates of loss to follow-up. Recent research has shown that homeless populations have substantial access to information technologies, including mobile devices and computers. These technologies have the potential both to make longitudinal data collection with homeless populations easier and to minimize some of these methodological challenges. This pilot study's purpose was to test whether individuals who were homeless and sleeping on the streets-the Bstreet homeless^-would answer questions remotely through a web-based data collection system at regular Bfollowup^intervals. We attempted to simulate longitudinal data collection in a condensed time period. Participants (N = 21) completed an in-person baseline interview. Each participant was given a remotely reloadable gift card. Subsequently, weekly for 8 weeks, participants were sent an email with a link to a SurveyMonkey questionnaire. Participants were given 48 h to complete each questionnaire. Data were collected about life on the streets, service use, community inclusion, substance use, and high-risk sexual behaviors. Ten dollars was remotely loaded onto each participant's gift card when they completed the questionnaire within the completion window. A substantial number of participants (67% of the total sample and 86% of the adjusted sample) completed at least seven out of the eight follow-up questionnaires. Most questionnaires were completed at public libraries, but several were completed at other types of locations (social service agencies, places of employment, relative/friend/acquaintance's domiciles, or via mobile phone). Although some of the questions were quite sensitive, very few participants skipped any questions. The only variables associated with questionnaire completion were frequency of computer use and education-both positive associations. This pilot study suggests that collecting longitudinal data online may be feasible with a subpopulation of persons experiencing homelessness. We suspect that participant follow-up rates using web-based data collection methods have the potential to exceed follow-up rates using traditional in-person interviews. If this population of persons experiencing street homelessness can be successful with this method of data collection, perhaps other disenfranchised, difficult-to-track, or difficult-to-reach populations could be followed using web-based data collection methods. Local governments are striving to decrease the Bdigital divide,^providing free or greatly discounted wi-fi connectivity as well as mobile computer lab access to low-income geographic areas. These actions, in combination with increased smart phone ownership, may permit vulnerable populations to connect and communicate with investigators.

The power divide: Mobile communication in Los Angeles’ Skid Row

“…Previous studies have identified numerous challenges to random sampling in survey research with homeless populations, including difficulties with enumeration, the transient nature of the population, and the very fact that homelessness is a fluid problem, with individuals often moving in and out of homelessness in a relatively short time span (Eyrich-Garg & Moss, 2017; Fitzgerald et al., 2001). Following standard practice in homelessness research (e.g., Eyrich-Garg, 2011; Guadgano et al., 2013; Rice & Barman-Adhikari, 2014), this study is based on a convenience sample of individuals recruited through the Los Angeles Public Library.…”

Perspectives of researchers with lived experience in implementation science research: Opportunities to close the research-to-practice gap in substance use systems of care

“…RLE may also improve upon the data collection instruments through which other implementation constructs are measured, such as reach ( Proctor et al, 2011 ). For example, if a harm-reduction intervention is designed for implementation with people who are unhoused (a notoriously difficult population to track; Eyrich-Garg & Moss, 2017 ), RLE may provide insight into how best to measure the overall reach of the intervention and/or follow enrolled subjects over time. Further, RLE can help identify practical outcome measures (positive or negative) beyond the typical abstinence measures in common practice ( Paquette et al, 2022 ).…”