Generational differences in loneliness and its psychological and sociodemographic predictors: An exploratory and confirmatory machine learning study

Research output: Contribution to journalArticlepeer-review

Abstract

Background
Loneliness is a growing public health issue in the developed world. Among older adults, loneliness is a particular challenge, as the older segment of the population is growing and loneliness is comorbid with many mental as well as physical health issues. Comorbidity and common cause factors make identifying the antecedents of loneliness difficult, however, contemporary machine learning techniques are positioned to tackle this problem.
Methods
This study analyzed four cohorts of older individuals, split into two age groups – 45 to 69 and 70 to 79 – to examine which common psychological and sociodemographic are associated with loneliness at different ages. Gradient boosted modeling, a machine learning technique, and regression models were used to identify and replicate associations with loneliness. 
Results
In all cohorts, higher emotional stability was associated with lower loneliness. In the older group, social circumstances such as living alone were also associated with higher loneliness. In the younger group, extraversion’s association with lower loneliness was the only other confirmed relationship. 
Conclusions
Different individual and social factors might underlie loneliness differences in distinct age groups. Machine learning methods have the potential to unveil novel associations between psychological and social variables, particularly interactions, and mental health outcomes.
Original languageEnglish
Pages (from-to)1-10
JournalPsychological Medicine
VolumeN/A
Early online date9 Mar 2020
DOIs
Publication statusE-pub ahead of print - 9 Mar 2020

Keywords

  • aging
  • geriatric psychiatry
  • loneliness
  • machine learning; personality

Fingerprint

Dive into the research topics of 'Generational differences in loneliness and its psychological and sociodemographic predictors: An exploratory and confirmatory machine learning study'. Together they form a unique fingerprint.

Cite this