Latent class analysis of loneliness and connectedness in US young adults during COVID-19

The coronavirus disease 2019 (COVID-19) pandemic in the United States has exacerbated a number of mental health conditions and problems related to prolonged social isolation. While COVID-19 has led to greater loneliness and a lack of social connectedness, little is known about who are the most affected and how they are impacted. Therefore, we performed a Latent Class Analysis using items from two scales - the UCLA Loneliness Scale and the Social Connectedness Scale - to characterize different experiences of loneliness and connectedness, examine their relationship with mental health and substance use symptoms, including depression, anxiety, drinking, and drug use.

Data were drawn from an anonymous one-time online survey examining the mental health of 1008 young adults (18-35 years old) during COVID-19. A latent class analysis (LCA) was conducted to observe and identify classes based on responses to loneliness and connectedness scale items, and to examine the existence of subgroups among this young adult population.

We identified a 4-class model of loneliness and connectedness: (1) Lonely and Disconnected - highest probabilities in items of loneliness and disconnectedness, (2) Moderately Lonely and Disconnected - adaptive levels of some isolation and disconnection during COVID-19, (3) Ambivalent Feelings - displaying negative responses in particular to negatively-worded items while simultaneously affirming positively worded items, and (4) Connected and Not Lonely - lowest probabilities in items of loneliness and disconnectedness.

Key findings include (1) the delineation of classes by levels of loneliness and connectedness showcasing differential mental health and substance use symptoms, (2) the utility of item-level evaluation with LCA in determining specific classes of people in need of outreach and intervention, and (3) the promise of social connection to bolster resilience in young adults.