Joint trajectories of loneliness, depressive symptoms, and social anxiety from middle childhood to early adolescence: associations with suicidal ideation

The longitudinal impact of perceived school bullying on suicidal ideation in adolescents

Few empirical studies have explored the role of perceived school bullying (PSB) on suicidal ideation (SI) in adolescents, through the mediating mechanisms of psychotic-like experiences (PLEs) and depressive symptoms (DS). This study utilized a large sample of adolescents with three waves of data collection at 6-month intervals to examine the longitudinal effect of PSB on SI after 6 months and 1 year, as well as the mediation of PLEs and DS in the PSB-SI relationship. A total of 4,595 adolescents in the 7th grade (mean age = 13.03, 52% male) participated in the three-wave longitudinal study on adolescent mental health conducted between May 2021 and May 2022 in a city in South China. The measures included T1 PSB assessed using the Delaware School Climate Scale-Student (DSCS-S), T2 PLEs measured using the Positive Subscale of the Community Assessment of Psychic Experiences (CAPE-P8), T2 DS assessed using the Patient Health Questionnaire-2 (PHQ-2), and T3 SI measured using the Patient Health Questionnaire (PHQ-9). The prevalence rate of adolescents PLEs (T1 = 43.7%, T2 = 55.5%), DS (T1 = 11.1%, T2 = 12.0%), and SI (T1 = 17.8%, T2 = 16.8%, T3 = 17.7%) were reported. The prevalence rates of SI from T1 to T3 significantly increased with increases in the baseline levels of PSB, PLEs, and DS. Mediation analyses revealed that PLEs and DS at 6 months significantly mediated the association between PSB at baseline and SI 1 year later.

Developmental trajectories of loneliness in Chinese children: Environmental and personality predictors

BACKGROUND

This study aimed to identify the developmental trajectories of loneliness in Chinese children and examine the predictive roles of domain-specific environmental factors (i.e., family dysfunction and satisfaction of relatedness needs at school), personality factors (i.e., neuroticism and extraversion), and their interactions in these developmental trajectories.

METHODS

A total of 702 Chinese children (Mage = 8.95, SD = 0.76; 54.1 % boys) participated in assessments at six time points over three years at six-month intervals. Growth mixture modeling (GMM) was used to estimate trajectory classes for loneliness, followed by multivariate logistic regression analyses exploring associations between these classes and predictors.

RESULTS

GMM analyses identified three distinct trajectories of loneliness: "low-stable" (81.5 %), "moderate-increasing" (9.4 %), and "high-decreasing" (9.1 %). Multivariate logistic regression analyses revealed that family dysfunction and neuroticism served as risk factors for adverse loneliness trajectories, while satisfaction of relatedness needs at school and extraversion acted as protective factors. Furthermore, the interaction between family dysfunction and extraversion indicated that extraversion did not mitigate the adverse effects of high family dysfunction on children's loneliness, emphasizing the vital need to support positive family functioning among all children.

LIMITATIONS

This study did not incorporate biological variables (e.g., genetics), which are crucial in the evolutionary theory of loneliness.

CONCLUSIONS

The identification of three distinct trajectory groups of children's loneliness, along with key environmental and personality predictors, suggests that interventions should be tailored to each group's unique characteristics.

Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

BACKGROUND

Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definitions. The advancements in machine learning offer new opportunities for improving the measurement and assessment of loneliness through the development of risk assessment models.

METHODS

Data from the 2018 Chinese Longitudinal Healthy Longevity Survey, involving about 16,000 participants aged ≥ 65 years, were used. The study examined the relationships between loneliness and factors such as functional limitations, living conditions, environmental influences, age-related health issues, and health behaviors. Using R 4.4.1, seven assessment models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC.

RESULTS

Loneliness prevalence among elderly Chinese was 23.4%. Analysis identified 15 evaluative factors and evaluated seven models. Multi-layer perceptron stands out for its strong nonlinear mapping capability and adaptability to complex data, making it one of the most effective models for assessing loneliness risk.

CONCLUSION

The study found a 23.4% prevalence of loneliness among elderly individuals in China. SHAP values indicated that marital status has the strongest evaluative value across all forecasting periods. Specifically, elderly individuals who are never married, widowed, divorced, or separated are more likely to experience loneliness compared to their married counterparts.

Can Loneliness be Predicted? Development of a Risk Prediction Model for Loneliness among Elderly Chinese: A Study Based on CLHLS

Background

Loneliness is prevalent among the elderly, worsened by global aging trends. It impacts mental and physiological health. Traditional scales for measuring loneliness may be biased due to cognitive decline and varying definitions. Machine learning advancements offer potential improvements in risk prediction models.

Methods

Data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), involving over 16,000 participants aged ≥65 years, were used. The study examined the relationships between loneliness and factors such as cognitive function, functional limitations, living conditions, environmental influences, age-related health issues, and health behaviors. Using R 4.4.1, seven predictive models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC.

Results

Loneliness prevalence among elderly Chinese was 23.4%. Analysis identified 16 predictive factors and evaluated seven models. Logistic regression was the most effective model for predicting loneliness risk due to its economic and operational advantages.

Conclusion

The study found a 23.4% prevalence of loneliness among elderly individuals in China. SHAP values indicated that higher MMSE scores correlate with lower loneliness levels. Logistic regression was the superior model for predicting loneliness risk in this population.

Identify adolescents' help-seeking intention on suicide through self- and caregiver's assessments of psychobehavioral problems: deep clustering of the Tokyo TEEN Cohort study

Background

Psychopathological and behavioral problems in adolescence are highly comorbid, making their developmental trajectories complex and unclear partly due to technical limitations. We aimed to classify these trajectories using deep learning and identify predictors of cluster membership.

Methods

We conducted a population-based cohort study on 3171 adolescents from three Tokyo municipalities, with 2344 pairs of adolescents and caregivers participating at all four timepoints (ages 10, 12, 14, and 16) from 2012 to 2021. Adolescent psychopathological and behavioral problems were assessed by using self-report questionnaires. Both adolescents and caregivers assessed depression/anxiety and psychotic-like experiences. Caregivers assessed obsession/compulsion, dissociation, sociality problem, hyperactivity/inattention, conduct problem, somatic symptom, and withdrawal. Adolescents assessed desire for slimness, self-harm, and suicidal ideation. These trajectories were clustered with variational deep embedding with recurrence, and predictors were explored using multinomial logistic regression.

Findings

Five clusters were identified: unaffected (60.5%), minimal problems; internalizing (16.2%), persistent or worsening internalizing problems; discrepant (9.9%), subjective problems overlooked by caregivers; externalizing (9.6%), persistent externalizing problems; and severe (3.9%), chronic severe problems across symptoms. Stronger autistic traits and experience of bullying victimization commonly predicted the four "affected" clusters. The discrepant cluster, showing the highest risks for self-harm and suicidal ideation, was predicted by avoiding help-seeking for depression. The severe cluster predictors included maternal smoking during pregnancy, not bullying others, caregiver's psychological distress, and adolescent's dissatisfaction with family.

Interpretation

Approximately 40% of adolescents were classified as "affected" clusters. Proactive societal attention is warranted toward adolescents in the discrepant cluster whose suicidality is overlooked and who have difficulty seeking help.

Funding

Japan Ministry of Health, Labor and Welfare, Japan Agency for Medical Research and Development, and Japan Science and Technology Agency.

Profiles and Transitions of Loneliness and Depressive Symptoms among Migrant Children: Predictive Role of Bullying Victimization

Although loneliness and depressive symptoms are particularly prominent among migrant children and often occur simultaneously, little is known about the co-occurring and transitional nature of loneliness and depressive symptoms among migrant children, and the role of bullying victimization on their profiles and transitions. This study examined the profiles and transitions of loneliness and depressive symptoms among migrant children using latent profile analysis and latent transition analysis, as well as how bullying victimization predicted their profile memberships and transitions. A total of 692 migrant children (55.3% males, Mage = 9.41, SD = 0.55, range = 8 to 12 years old at T1) participated in both two waves of the study over six months. The results indicated that low profile (59.2%), moderate profile (22.0%), moderately high profile (14.3%), and high profile (4.5%) were identified at Time 1; low profile (69.4%), predominantly loneliness profile (16.8%), predominantly depressive symptoms profile (6.5%), and high profile (7.3%) were identified at Time 2. Migrant children in at-risk profiles displayed varying degrees of transition. Migrant children experiencing more bullying victimization were more likely to belong or transition to at-risk profiles. The findings highlight the importance of subgroup differences considerations in understanding the co-occurring and transitional nature of loneliness and depressive symptoms, as well as the predictive role of bullying victimization, informing effective strategies for prevention and intervention.

Loneliness and mood disorders: consequence, cause and/or unholy alliance?

PURPOSE OF REVIEW

People with persistent depressive disorders and with bipolar disorder are more likely to feel lonely than people in the general population. This evidence update focused on studies in the last 2 years, characterized by the COVID-19 pandemic and consequent social distancing directives.

RECENT FINDINGS

Longitudinal studies identified that people who feel lonely are more likely to become depressed or to experience relapse of mood disorders. There is emerging evidence that feelings of loneliness or mandatory social isolation can precede manic episodes. Hence the relationship between loneliness and mood disorders is complex and bidirectional. Interventions were developed to reduce loneliness in people with mental health problems, including depressive disorders, through cognitive modification and/or supported socialisation. No loneliness-focused interventions have been specifically tailored to people with bipolar disorder.

SUMMARY

Studies carried out before and during the COVID-19 pandemic found that feelings of loneliness can be both consequences and precursors of persistent depression and bipolar disorder. Mood symptoms and loneliness have a cumulative negative effect on physical and mental health outcomes. Conceptual overlaps and relations between loneliness and mood symptoms should be clarified in qualitative studies. Theory-driven intervention models should be developed and tested in methodologically robust studies.