International journal of geriatric psychiatry · 2026
Development of a Predictive Model for the Onset of Loneliness in Older Adults From the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes.
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Shimoda Takahiro, Katayama Osamu, Yamaguchi Ryo, Nakajima Chika, Kawakami Ayuka, Yamagiwa Daiki, Akaida Shoma, Shimada Hiroyuki
Аннотация
OBJECTIVES: Loneliness is a rising public health concern among older Japanese adults. We aimed to construct a predictive model for loneliness onset among older adults and evaluate its predictive performance.
METHODS: A total of 4050 participants responded to our survey (mean follow-up period: 3.1 [range, 2.8-3.3] years). Of these, 1806 older adults (age ≥ 65 years) who were not lonely at baseline were included. Loneliness was assessed using the UCLA Loneliness Scale (Version 3). A score of ≥ 44 indicated the presence of loneliness at follow-up. Predictive models for the onset of loneliness were developed using 12 machine-learning algorithms. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Sensitivity analyses examined the contribution of baseline UCLA-LS information, model stability, and class imbalance handling.
RESULTS: In total, 421 respondents (23.3%) reported loneliness at follow-up. The XGBoost model achieved the highest AUC of 0.740 (0.705-0.776), with an accuracy of 0.631, sensitivity of 0.806, and specificity of 0.577. SHAP analysis indicated that baseline UCLA-LS items were among the most influential predictors. When all baseline UCLA-LS items were excluded, the test AUC decreased to 0.635. Ten-fold cross-validation showed a mean AUC of 0.742 ± 0.065 for the full XGBoost model.
CONCLUSION: XGBoost showed moderate discrimination for predicting 3-year loneliness onset. However, baseline UCLA-LS information contributed substantially to model performance, and external predictors alone showed modest discriminative ability. Further refinement and external validation are needed before implementation.
METHODS: A total of 4050 participants responded to our survey (mean follow-up period: 3.1 [range, 2.8-3.3] years). Of these, 1806 older adults (age ≥ 65 years) who were not lonely at baseline were included. Loneliness was assessed using the UCLA Loneliness Scale (Version 3). A score of ≥ 44 indicated the presence of loneliness at follow-up. Predictive models for the onset of loneliness were developed using 12 machine-learning algorithms. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Sensitivity analyses examined the contribution of baseline UCLA-LS information, model stability, and class imbalance handling.
RESULTS: In total, 421 respondents (23.3%) reported loneliness at follow-up. The XGBoost model achieved the highest AUC of 0.740 (0.705-0.776), with an accuracy of 0.631, sensitivity of 0.806, and specificity of 0.577. SHAP analysis indicated that baseline UCLA-LS items were among the most influential predictors. When all baseline UCLA-LS items were excluded, the test AUC decreased to 0.635. Ten-fold cross-validation showed a mean AUC of 0.742 ± 0.065 for the full XGBoost model.
CONCLUSION: XGBoost showed moderate discrimination for predicting 3-year loneliness onset. However, baseline UCLA-LS information contributed substantially to model performance, and external predictors alone showed modest discriminative ability. Further refinement and external validation are needed before implementation.