Analysis of article using Artificial Intelligence tools
|Author||Fancourt, D.; Steptoe, A.; Cadar, D.|
|Title||Cultural engagement and cognitive reserve: museum attendance and dementia incidence over a 10-year period|
Fancourt, D., Steptoe, A., & Cadar, D. (2018). Cultural engagement and cognitive reserve: Museum attendance and dementia incidence over a 10-year period. The British Journal of Psychiatry, 213(5), 661-663.
|Keywords||dementia; cultural engagement; museums; social engagement; cognitive reserve
|Link to article|| https://doi.org/10.1192/bjp.2018.129
|Abstract||Theories of cognitive reserve, disuse syndrome and stress have suggested that activities that are mentally engaging, enjoyable and socially interactive could be protective against the development of dementia. Using data from the English Longitudinal Study of Ageing, this study shows that for adults aged 50 and older visiting museums every few months or more was associated with a lower incidence rate of dementia over a 10-year follow-up period compared with less-frequent visiting. This association was independent of demographics, socioeconomic status, health-related variables including sensory impairment, depression, vascular conditions and other forms of community engagement. Visiting museums may be a promising psychosocial activity to support the prevention of dementia.
|Metodology||Visiting museums, art galleries and exhibitions was measured using a self-report scale asking about frequency of engagement (‘never’, ‘less than once a year’, ‘about once or twice a year’, ‘every few months’, ‘about once a month’ or ‘twice a month or more’). We collapsed the final three categories together to provide an overall four-point scale. Incidence rates (IR) of dementia were computed per 1000 personyears in relation to museum visits frequency. We used Poisson regression analyses to calculate the incidence rate ratio (IRR) of dementia incidence and 95% CIs. Model 1 was unadjusted, model 2 adjusted for demographics, model 3 additionally adjusted for health-related factors, and model 4 was additionally adjusted for other indicators of community engagement. All analyses were weighted using baseline cross-sectional weights derived from ELSA to ensure the sample was representative of the English population. We applied three types of sensitivity analyses. To explore in more detail how age might affect the relationship observed, sensitivity analyses first included age as an interaction term, and then split participants into those above (51.6%) and below (48.4%) the age of 65. To confirm that analyses were not biased by the inclusion of participants already experiencing preclinical symptoms of dementia, we excluded all participants who developed dementia in the 2 years following baseline and re-rananalyses. Finally, in order to account for non-response on cultural participation (15.4%), we imputed missing data on cultural participation using chained equations, which included all health-related variables in the prediction model to generate 100 imputed data-sets (each had a final n = 4607). The missing-at-random assumption was strengthened by the fact that some of the same variables used to predict cultural engagement are also known to predict non-response in ELSA (including age, education and wealth)
||Technique||Poisson regression analyses; self-report scales; cross-sectional weights; chained equations|