A marginal structural model analysis for loneliness: implications for intervention trials and clinical practice.

Objective: Clinical scientists, policymakers, and individuals must make decisions concerning effective interventions that address health-related issues. We use longitudinal data on loneliness and depressive symptoms and a new class of causal models to illustrate how empirical evidence can be used to inform intervention trial design and clinical practice.

Method: Data were obtained from a population-based study of non-Hispanic Caucasians, African Americans, and Latino Americans (N = 229) born between 1935 and 1952. Loneliness and depressive symptoms were measured with the UCLA Loneliness Scale-Revised and Center for Epidemiologic Studies Depression Scale, respectively. Marginal structural causal models were employed to evaluate the extent to which depressive symptoms depend not only on loneliness measured at a single point in time (as in prior studies of the effect of loneliness) but also on an individual's entire loneliness history.

Results: Our results indicate that if interventions to reduce loneliness by 1 standard deviation were made 1 and 2 years prior to assessing depressive symptoms, both would have an effect; together, they would result in an average reduction in depressive symptoms of 0.33 standard deviations, 95% CI [0.21, 0.44], p < .0001.

Conclusions: The magnitude and persistence of these effects suggest that greater effort should be devoted to developing practical interventions on alleviating loneliness and that doing so could be useful in the treatment and prevention of depressive symptoms. In light of the persistence of the effects of loneliness, our results also suggest that, in the evaluation of interventions on loneliness, it may be important to allow for a considerable follow-up period in assessing outcomes.