Objectives This study aims to address the knowledge gap and summarise the measurement for intrinsic capacity for the five WHO domains across different populations. It specifically aims to identify measurement tools, methods used for computation of a composite intrinsic capacity index and factors associated with intrinsic capacity among older adults. Methods We performed literature review in Medline, including search terms “aged” or “elderly” and “intrinsic capacity” for articles published from 2000–2020 in English. Studies which assessed intrinsic capacity in the five WHO domains were included. Information pertaining to study setting, methods used for measuring the domains of intrinsic capacity, computation methods for composite intrinsic capacity index, and details on tool validation were extracted. Results Seven articles fulfilling the inclusion criteria were included in the review. Of these, the majority were conducted in community settings (n=5) and were retrospective studies (n=6). The most commonly used tools for assessing intrinsic capacity were gait speed test and chair stand test (locomotion); handgrip-strength and mini-nutritional assessment (vitality); Mini-Mental State Examination (cognition); Geriatric Depression Scale (GDS) and Center for Epidemiological Studies Depression Scale (CES-D) (psychological), and self-reported vision and health questionnaires (sensory). Among the tools used to operationalise the domains, we found variations and non-concordance, especially in the vitality and psychological domains, which make inter-study comparison difficult. Validated scales were less commonly used for vitality and sensory domains. Biomarkers were used for locomotion, vitality, and sensory domains. Self-reported measures were mostly used in the psychological and sensory domains. Three studies operationalised a global score for intrinsic capacity, whereby scores from the individual domains were used to create a composite intrinsic capacity index, using two approaches: a) Structural equation modelling, and b) Sub-scores for each domain which were combined either by arithmetic sum or average. Conclusion We identified considerable variations in measurement instruments and processes which are used to assess intrinsic capacity, especially among the vitality and psychological domains. A standardized intrinsic capacity composite score for clinical or community settings has not been operationalised yet. Further validation via prospective studies of the intrinsic capacity concept and computation of composite score using validated scales are needed.
Early screening for Alzheimer’s disease (AD) is crucial for disease management, intervention, and healthcare resource accessibility. Medical assessments of AD diagnosis include the utilisation of biological markers (biomarkers), positron emission tomography (PET) scans, magnetic resonance imaging (MRI) images, and cerebrospinal fluid (CSF). These methods are resource intensive as well as physically invasive, whereas neuropsychological tests are fast, cost effective, and simple to administer for providing early AD diagnosis. However, neuropsychological assessments contain elements related to executive functions, memory, orientation, learning, judgment, and perceptual motor function (among others) that overlap, making it difficult to identify the key elements that trigger the progression of dementia or mild cognitive impairment (MCI). This research investigates the elements of the Functional Activities Questionnaire (FAQ) an early screening method using a data driven approach based on feature selection and classification. The aim is to determine the key items in the FAQ that may trigger AD advancement. To achieve the aim, real data observations of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project have been processed using the proposed data driven approach. The results derived by the machine learning techniques in the proposed approach on data subsets of the FAQ items with demographics show models with accuracy, sensitivity, and specificity all exceeding 90%. In addition, FAQ elements including Administration and Shopping related activities showed correlations with the progression class; these elements cover four out of the six Diagnostic and Statistical Manual’s (DSM-5’s) neurocognitive domains.
Prognosis of Alzheimer’s disease (AD) progression has been recognized as a challenging problem due to the massive numbers of cognitive, and pathological features recorded for patients and controls. While there have been many studies investigated the diagnosis of dementia using pathological characteristics, predicting the advancement of the disease using cognitive elements has not been heavily studied particularly using technologies like artificial intelligence and machine learning. This research aims at evaluating items of the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13) test to determine key cognitive items that influence the progression of AD. A methodology that consists of machine learning and feature selection (FS) techniques was designed, implemented, and then tested against real data observations (cases and controls) of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository with a narrow scope on cognitive items of the ADAS-Cog-13 test. Results obtained by ten-fold cross validation and using dissimilar classification and FS techniques revealed that the decision tree algorithm produced classification models with the best performing results from the cognitive items. For ADAS-Cog-13 test, memory and learning features including word recall, delayed word recall and word recognition were the key items pinpointing to AD advancement. When these three cognitive items are processed excluding demographics by C4.5 algorithm the models derived showed 82.90% accuracy, 87.60% sensitivity and 78.20% specificity.
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