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The accurate assessment of instrumental activities of daily living (iADL) is essential for those with known or suspected Alzheimer's disease or related disorders (ADRD). This information guides diagnosis, staging, and treatment planning, and serves as a critical patient-centered outcome. Despite its importance, many iADL measures used in ADRD research and practice have not been sufficiently updated in the last 40-50 years to reflect how technology has changed daily life. For example, digital technologies are routinely used by many older adults and those with ADRD to perform iADLs (e.g., online financial management, using smartphone reminders for medications.) The purpose of the current study was to a) asses the applicability of technology-related iADL items in a clinical sample; b) evaluate whether technology-based iADLs are more difficult for those living with ADRD than their traditional counterparts; and c) test if adding technology-based iADL items changes the sensitivity and specificity of iADL measures to ADRD.
Participants and Methods:
135 clinically referred older adults (mean age 75.5 years) undergoing neuropsychological evaluation at a comprehensive multidisciplinary memory clinic were included in this study [37% with mild cognitive impairment (MCI) and 51.5% with dementia]. Collateral informants completed the Functional Activities Questionnaire (FAQ; Pfeffer, 1982) as well as 11 items created to parallel the FAQ wording that assessed technology-related iADLs such as digital financial management (i.e. online bill pay), everyday technology skills (i.e. using a smartphone; remembering a password), and other technology mediated activities (i.e. visiting internet sites; online shopping).
Results:
Care partners rated tech iADLs items as applicable for the majority of items. For example, technology skill items were applicable to 90.4% of the sample and online financial management questions were applicable for 76.4% of participants. Applicability ratings were similar across patients in their 60's and 70's, and lower in those over age 80. Care partners indicated less overall impairment on technology-related iADLs (M =1.22, SD =.88) than traditional FAQ iADLs (M =1.36, SD = .86), t(129) = 3.529, p =.001). A composite of original FAQ paperwork and bill pay items (M = 1.62, SD = 1.1) was rated as more impaired than digital financial management tasks (M = 1.30, SD = 1.09), t(122) = 4.77, p <.001). In terms of diagnostic accuracy, tech iADL items (AUC= .815, 95% CI [.731, -.890]) appeared to perform comparably to slightly better than the traditional FAQ (AUC =.788, 95% CI [.705, .874]) at separating MCI and dementia, though the difference between the two was not statistically significant in this small pilot sample.
Conclusions:
Technology is rapidly changing how older adults and those with ADRD perform a host of iADLs. This pilot study suggests broad applicability of tech iADL to the lives of those with ADRD and highlights how measurement of these skills may help identify trends in iADL habits that may help to mitigate the impact of ADRD on daily functions. Further, this data suggests the need to refine and improve upon existing iADL measures to validly capture the evolving technological landscape of those living with ADRD.
This commentary draws connections between technological culture emergence and recent trends in using assistive technology to reduce the burden of Alzheimer's disease. By the technical-reasoning hypothesis, cognitively-impaired individuals will lack the cognitive ability to employ technologies. By the technological reserve hypothesis, social-motivational and cultural transmissibility factors can provide foundations for using technology as cognitive prosthetics even during neurodegenerative illnesses.
Aging is marked by cognitive decline, which in the case of Alzheimer’s disease is associated with tremendous global economic burden. Identifying modifiable risk factors for cognitive decline is therefore of paramount importance. In this chapter, we describe how aging compromises sleep quality and sleep architecture at a rate that parallels normal age-related cognitive decline. We argue that understanding the neurocognitive functions of sleep – frontal lobe restoration, memory consolidation, and metabolite clearance – and how such functions change in later life will be key to informing why some older individuals maintain healthy cognitive functioning and other older individuals do not. Critically, by investigating how sleep, cognition, and aging interact, researchers and clinicians can develop sleep-related treatments that target preventing, or at least ameliorating, pathologies such as Alzheimer’s disease.
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