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


Thoma, Myriam V; Höltge, Jan; McGee, Shauna L; Maercker, Andreas; Augsburger, Mareike (2019). Psychological characteristics and stress differentiate between high from low health trajectories in later life: a machine learning analysis. Aging & Mental Health:ePub ahead of print.

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 (M=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.

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 (M=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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
08 Research Priority Programs > Dynamics of Healthy Aging
Dewey Decimal Classification:150 Psychology
Uncontrolled Keywords:DoktoratPsych
Language:English
Date:5 March 2019
Deposited On:11 Mar 2019 15:50
Last Modified:11 Mar 2019 15:51
Publisher:Taylor & Francis
ISSN:1360-7863
OA Status:Closed
Publisher DOI:https://doi.org/10.1080/13607863.2019.1584787
PubMed ID:30836010

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