Login / Signup

Differences in awareness of positive and negative age-related changes accounting for variability in health outcomes.

Serena SabatiniObioha C UkoumunneAllyson BrothersManfred DiehlHans-Werner WahlClive G BallardRachel CollinsAnne CorbettHelen M BookerLinda Clare
Published in: European journal of ageing (2022)
Higher awareness of positive age-related changes (AARC gains) is related to better mental health, whereas higher awareness of negative age-related changes (AARC losses) is related to poorer mental and physical health. So far perceived gains and losses have been explored separately, but people report gains and losses concurrently in varying degrees, and different profiles of gains and losses may be differentially associated with health. We identified profiles of gains and losses and explored whether different profiles differed in physical, mental, and cognitive health. We used cross-sectional data from the PROTECT study (N = 6192; mean (SD) age = 66.1 (7.0)). Using latent profile analysis, a four-class solution showed the best model fit. We found that 45% of people perceived many gains and few losses (Class 1); 24% perceived moderate gains and few losses (Class 2); 24% perceived many gains and moderate losses (Class 3); 7% perceived many gains and many losses (Class 4). Analysis of variance and Chi-squared tests showed that Class 1 had relatively better physical, mental, and cognitive health, followed by Classes 2, 3, and 4. Experiencing one's ageing to a high degree as gain may be related to better health only when individuals interpret ageing as involving low levels of loss across several life domains. Risk in terms of poorer health emerged in those who perceived high losses. Considering gains and losses in parallel, rather than separately, may lead to a more fine-tuned understanding of relations with health.
Keyphrases
  • mental health
  • healthcare
  • public health
  • physical activity
  • mental illness
  • social support
  • depressive symptoms
  • cross sectional
  • health information
  • air pollution
  • machine learning
  • human health