ArticleFrontiers in psychology2025
Physical health and cognitive ability factors in predicting retirement adjustment based on machine learning approach: results from the China Health and Retirement Longitudinal Study.
Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Association of baseline and cumulative CHG index with risk of new-onset sarcopenia in middle-aged and older adults.The journal of nutrition, health & aging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Retirement is one of the most significant status changes in an individual's later life. Physical health and cognitive ability are key predictors of retirement adjustment. However, studies have yet to investigate the role of different physical health and cognitive ability indicators simultaneously, and their non-linear association in relation to retirement adjustment. Methods: This study used machine learning methods to explore the predictive role of both physical and cognitive ability variables in retirement adjustment. Using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) database, a total of 1,314 participants met the retirement criteria, and the increase in life satisfaction and decrease in depression scores were extracted as the indicators of successful retirement adjustment. Various physical health and cognitive ability-related variables measured before retirement, alongside key demographic and lifestyle variables, were used as predictive variables to predict retirement adjustment 2 or 3 years later. Random forest (RF) and XGBoost classification models were used as predictors, and SHAP (SHapley Additive explanation) value analysis was used to explain the model prediction results. Results: The results indicated that the accuracy of the RF and XGBoost models outperformed regularized logistic regression. Self-rated hearing, income, attention and calculation ability, self-rated health, and time orientation ability were identified as the most influential predictors of retirement adjustment. Self-rated memory and sleep duration exhibited a non-linear relationship with retirement adjustment. Discussion: The present research extends current understanding of factors that promote adjustment to retirement and provides essential insights for preventing poor adjustment and intervening in retirement adjustment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.