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| Home > Publications database > Predictors of health-related quality of life in older adults over a course of twelve years - Results from a large population-based study using a machine learning approach. |
| Journal Article | DKFZ-2025-01843 |
; ; ; ; ;
2025
Cambridge Univ. Press
Cambridge
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Please use a persistent id in citations: doi:10.1016/j.inpsyc.2025.100141
Abstract: The proportion of older people is growing dramatically, implying that predictors of health-related quality of life (HRQoL) in older adults are of major interest within public health research.Analyses were based on the ESTHER study, a German population-based cohort study conducted in the federal state of Saarland, Germany. The study was initiated in 2000-2002 and included 9940 community-dwelling older adults recruited via general practioners. At the 8-year follow-up (2008-2010), 6071 active participants were offered additional home visits, of whom 3124 agreed to participate. These 3124 participants (mean age (SD) 69.6 (6.3) years; 52.6 % female) served as baseline sample for our analysis. Predictions were made at 3-year intervals up to 12 years (20-year follow-up; 2020-2021, n = 1438). Physical and mental HRQoL was assessed using the Short Form Health Survey (SF-12). 47 features were investigated. Random forest regression was used to identify the most important predictors.Physical HRQoL was predictable up to 6 years, with top 5 predictors being: somatic symptom burden, bio-psycho-social (BPS) health care needs, frailty, age, and BMI class. For mental HRQoL, predictors consistently ranging among the top 5 across all time intervals were: somatic symptom burden, BPS health care needs, symptoms of depression, and symptoms of anxiety. There appeared to be a time-dependent shift in key predictors of mental HRQoL, with symptoms of depression and anxiety being most important in short-term, while somatic symtom burden and BPS health care needs were most important in long-term.Somatic symptom burden and bio-psycho-social health care needs emerged as key predictors of both, physical and mental HRQoL in older adults. These variables may be important to consider when developing future interventions aimed to improve HRQoL in older adults, and could also be relevant for policies concerned with successful aging.
Keyword(s): Bio-psycho-social health care needs ; Health-related quality of life ; Longitudinal study ; Machine learning ; Older Adults ; Random forest
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