Unveiling the cognitive underpinnings of instrumental activities of daily living across age

Background Healthy ageing is accompanied by a slow but progressive decline of Instrumental Activities of Daily Living (IADL) (Jagger et al., 2001). In abnormal ageing trajectories, such IADL show a steeper decline. Yet, we lack reliable tools to precisely detect those changes signalling departure from healthy ageing (Nygard, 2003). This project investigates which specific cognitive impairments account for the normal age-related decline of IADL. We aim to gather evidence informing when such decline no longer signals normality late in life. Methods We asked 12 healthy young and 12 healthy older adults to perform a theory-driven multiple errand task (MET) and a brief neuropsychological test battery. The MET requested participants to memorise 6 everyday kitchen errands each compromising from 8 to 10 subtasks in a specific order. Kitchen items were real and typical of such an environment. During the MET, participants experienced controlled interferences to which they had to react (e.g., count the shelves when dogs bark). The experimenter scored accuracy, actions missed, order errors, intrusions, etc. Results Both groups required similar amount of time to study the MET [t(17) = 0.586; p= 0.57]. Healthy older adults took significantly longer to perform them [t(17) = -4.245; p< 0.001]. When the subtasks order was considered, younger adults outperformed older adults [t(22) = 2.745; p= 0.012]. Such differences disappeared when order was not considered [t(22) = 2.074; p= 0.055]. Healthy older adults made significantly more subtask order errors [t(22) = -2.473; p= 0.022], errand order errors [t(15.577) = -2.984; p= 0.009], and intrusions [t(12) = -2.448; p= 0.031]. Speed of processing accounted for accuracy with (r=0.653, p=0.001) and without order (r=0.476; p=0.019), order error (r=-0.634, p =0.001), and inhibition of interference (r=-0.502, p =0.012). Conclusions Speed of processing has been considered a biomarker of cognitive ageing (Deary et al., 2010). Our study indicates that it does account for the majority of challenges older adults encounter during IADL. Such knowledge should be considered by the new generation of IADL tests (e.g., relying on Informatics Technologies) as this theory incorporated to such tools will help distinguish normal and abnormal ageing trajectories.