Although loneliness is typically associated with adolescence and old age, research has revealed that it is prevalent across the life span. The present study contributes to the loneliness literature by investigating a broad range of risk factors in a Dutch sample ( N = 52,341) ranging from late adolescence to old age using a cross-sectional survey administered by the regional public health services in the province of Limburg in the Netherlands. Risk factors associated with higher levels of self-reported loneliness across the life span were being male, lower education levels, inadequacy of financial resources, mental health, informal caregiving that is experienced as burdensome, and limited social contact or network type. In addition, in early adulthood, having a non-western migration background and having a physical disability were associated with higher levels of loneliness, whereas living alone, having a non-western migration background, and not having a paid job were risk factors of loneliness in middle adulthood. In late adulthood, living alone and having a physical disability were associated with loneliness. The present study demonstrates that different stages of life are associated with different vulnerability factors of loneliness. Hence, the prevention of loneliness might require different interventions in different age groups.

The present study examined the relationship between developmental patterns of loneliness and psychosocial functioning among adolescents (9–21 years; N = 110, 52% male). Four-wave longitudinal data were obtained from the Nijmegen Longitudinal Study (NLS) on Infant and Child Development. Loneliness was measured at 9, 13, 16, and 21 years of age and anxiety, depression and self-esteem at 9 and 21 years of age. Using k-means cluster analysis, three trajectories of loneliness were identified as “stable low” (56% of the subjects), “high decreasing” (22% of the subjects), and “low increasing” (22% of the subjects). Importantly, trajectories of loneliness across adolescence significantly predicted psychosocial functioning in young adulthood. Both the “high-decreasing” and “low-increasing” loneliness clusters were associated with higher risk of depression and lower self-esteem compared to the “stable low” loneliness cluster. The “low-increasing” loneliness cluster was associated with higher risk of anxiety compared to the “stable low” loneliness cluster. These results indicate that loneliness in adolescence is a vulnerability that manifests itself in higher levels of anxiety and depression and lower self-esteem in young adulthood.

Social connectedness is a fundamental human need. The Evolutionary Theory of Loneliness (ETL) predicts that a lack of social connectedness has long-term mental and physical health consequences. Social support is a potential mechanism through which loneliness influences health. The present cross-sectional study examined the relationship between loneliness and mental health, and the mediating effects of social support in a Dutch adult sample (N = 187, age 20 to 70). The health variables included in the study are anxiety, depression, somatic symptoms as measured by the SCL-90, and the DSM-5 diagnosis somatic symptom disorder. The results indicated that social support partially mediated the relationship between loneliness and anxiety, depression, and somatic symptoms. These results indicate that social support partially explains the relationship between loneliness and physical and mental health issues. The relationship between loneliness and being diagnosed with somatic symptom disorder was not mediated by social support. This suggests that the mechanisms through which loneliness relates to either somatic symptoms or somatic symptom disorder are different.

Introduction In this study, we compare three different longitudinal clustering methods. As a case study, the comparison of the methods is conducted for the development of loneliness from middle childhood to young adulthood. The aim is to explore how two nonparametric longitudinal cluster methods compare with a model‐based latent class mixture model approach. Methods The trajectories of loneliness of 130 young people between 9 and 21 years of age, were analyzed to find a set clusters within these trajectories. The data for this study were obtained from the Nijmegen Longitudinal Study on Infant and Child Development (The Netherlands). Loneliness was measured at four waves at the age of 9, 13, 16, and 21 years. The nonparametric methods are in the R‐packages kml and traj, and the model‐based in the lcmm package. Results All methods indicated that the optimal number of clusters to describe the heterogeneity across the trajectories was three. The kml and lcmm methods showed the most similarity in shape of all clusters and fitted the data relatively well, while the traj method yielded somewhat different shapes and didn't fit the data well. Conclusions All three methods corroborate the literature in this field by finding that the largest portion of subjects experience stable and low levels of loneliness. However, the clustering methods also reveal that there is a portion of subjects that experience changes in loneliness during adolescence. By comparing the results of nonparametric clustering methods to the latent class mixture model, this study equips researchers with an example of how to implement these models and thereby contributes to the literature on longitudinal clustering in the social sciences. Altogether the analyses show that it might be useful to investigate different algorithms to identify the most robust solution.

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