Literature DB >> 30836010

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

Myriam V Thoma1,2, Jan Höltge1,2, Shauna L McGee1,2, Andreas Maercker1,2, Mareike Augsburger1.   

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.

Entities:  

Keywords:  Successful aging; conditional interference; longitudinal k-means; random forest; trajectories

Mesh:

Year:  2019        PMID: 30836010     DOI: 10.1080/13607863.2019.1584787

Source DB:  PubMed          Journal:  Aging Ment Health        ISSN: 1360-7863            Impact factor:   3.658


  2 in total

1.  Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

Authors:  Mareike Augsburger; Isaac R Galatzer-Levy
Journal:  BMC Psychiatry       Date:  2020-06-23       Impact factor: 3.630

2.  Resilience and Stress in Later Life: A Network Analysis Approach Depicting Complex Interactions of Resilience Resources and Stress-Related Risk Factors in Older Adults.

Authors:  Myriam V Thoma; Jan Höltge; Carla M Eising; Viviane Pfluger; Shauna L Rohner
Journal:  Front Behav Neurosci       Date:  2020-11-17       Impact factor: 3.558

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.