Psychological characteristics and stress differentiate between high from low health trajectories in later life: a machine learning analysis.

Abstract:

:Objective: This study set out to empirically identify joint health trajectories in individuals of advanced age. Predictors of subgroup allocation were investigated to identify the impact of psychological characteristics, stress, and socio-demographic variables on more favorable aging trajectories.Method: The sample consisted of N = 334 older adults (MAGE=68.31 years; SD = 9.71). Clustered health trajectories were identified using a longitudinal variant of k-means and were based on health and satisfaction with life. Random forests with conditional interference were computed to examine predictive capabilities. Key predictors included psychological resilience resources, exposure to childhood adversities, and chronic stress. Data was collected via a survey, at two different time points one year apart.Results: Two different clustered health trajectories were identified: A 'constant high health' (low number of health-related symptoms, 65.6%) and a 'maintaining low health' profile (high number of symptoms, 34.4%). Over the one-year study period, both symptom profiles remained stable. Random forest analyses showed chronic stress to be the most important predictor in the interaction with other risk and also buffering factors.Conclusion: This study provides empirical evidence for two stable health trajectories in later life over one year. These results highlight the importance of chronic stress, but also psychological resilience resources in predicting aging trajectories.

journal_name

Aging Ment Health

journal_title

Aging & mental health

authors

Thoma MV,Höltge J,McGee SL,Maercker A,Augsburger M

doi

10.1080/13607863.2019.1584787

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

1098-1107

issue

7

eissn

1360-7863

issn

1364-6915

journal_volume

24

pub_type

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