Virtual friendly visitor program: combatting loneliness in community dwelling older adults - Gordon, Barbara A.; Miceli, Chelsea B.; Yankeelov, Pamela A.; Cotton, Samantha G.; Faul, Anna C. (Frontiers in Public Health, 2024)
IntroductionLoneliness is a critical public health issue affecting older adults, with significant impacts on their mental and physical health, including increased risks of depression, cognitive decline, and higher mortality rates, necessitating distinct approaches for each condition given their unique implications and the exacerbation of these issues during the COVID-19 pandemic. We examine the implementation and outcomes of a Friendly Visitor Program (FVP) designed to mitigate loneliness among older adults. The program involved social work student interns providing virtual visits to older adults using computers and tablets, with the goal of enhancing social interaction and support.MethodsThe study utilized a qualitative narrative design for process evaluation and a longitudinal non-experimental, prospective research design for outcome evaluation, employing a three-level cross-classified longitudinal growth model to assess changes in loneliness among VFVP participants while also testing potential predictors of these changes.ResultsFindings indicated that the program was associated with reduced loneliness over time. Younger and White participants performed better in the program than older participants from other races and ethnicity. Satisfaction with visits and willingness to recommend the program were significant predictors of reduced loneliness. Unexpectedly, greater comfort with technology correlated with increased loneliness, suggesting overreliance on digital interactions may not substitute for in-person contact. Furthermore, improved social networks was associated with reduced loneliness, highlighting the importance of strong social networks.DiscussionThe study underscores the potential of friendly visitor interventions in addressing the challenges of lonely older adults and provides insights for optimizing such programs in the future.