Measuring Multidimensional Aspects of Health in the Oldest Old Using the NIH Toolbox: Results From the ARMADA Study.

OBJECTIVE: The percentage of older adults living into their 80s and beyond is expanding rapidly. Characterization of typical cognitive performance in this population is complicated by a dearth of normative data for the oldest old. Additionally, little attention has been paid to other aspects of health, such as motor, sensory, and emotional functioning, that may interact with cognitive changes to predict quality of life and well-being. The current study used the NIH Toolbox (NIHTB) to determine age group differences between persons aged 65-84 and 85+ with normal cognition. METHOD: Participants were recruited in two age bands (i.e., 65-84 and 85+). All participants completed the NIHTB Cognition, Motor, Sensation, and Emotion modules. Independent-samples t-tests determined age group differences with post-hoc adjustments using Bonferroni corrections. All subtest and composite scores were then regressed on age and other demographic covariates. RESULTS: The 65-84 group obtained significantly higher scores than the 85+ group across all cognitive measures except oral reading, all motor measures except gait speed, and all sensation measures except pain interference. Age remained a significant predictor after controlling for covariates. Age was not significantly associated with differences in emotion scores. CONCLUSIONS: Results support the use of the NIHTB in persons over 85 with normal cognition. As expected, fluid reasoning abilities and certain motor and sensory functions decreased with age in the oldest old. Inclusion of motor and sensation batteries is warranted when studying trajectories of aging in the oldest old to allow for multidimensional characterization of health.

Evaluation of the NIH Toolbox Odor Identification Test across normal cognition, amnestic mild cognitive impairment, and dementia due to Alzheimer's disease.

INTRODUCTION: Olfactory decline is associated with cognitive decline in aging, amnestic mild cognitive impairment (aMCI), and amnestic dementia associated with Alzheimer's disease neuropathology (ADd). The National Institutes of Health Toolbox Odor Identification Test (NIHTB-OIT) may distinguish between these clinical categories. METHODS: We compared NIHTB-OIT scores across normal cognition (NC), aMCI, and ADd participants (N = 389, ≥65 years) and between participants positive versus negative for AD biomarkers and the APOE ε4 allele. RESULTS: NIHTB-OIT scores decreased with age (p < 0.001) and were lower for aMCI (p < 0.001) and ADd (p < 0.001) compared to NC participants, correcting for age and sex. The NIHTB-OIT detects aMCI (ADd) versus NC participants with 49.4% (56.5%) sensitivity and 88.8% (89.5%) specificity. NIHTB-OIT scores were lower for participants with positive AD biomarkers (p < 0.005), but did not differ based on the APOE ε4 allele (p > 0.05). DISCUSSION: The NIHTB-OIT distinguishes clinically aMCI and ADd participants from NC participants. HIGHLIGHTS: National Institutes of Health Toolbox Odor Identification Test (NIHTB-OIT) discriminated normal controls from mild cognitive impairment. NIHTB-OIT discriminated normal controls from Alzheimer's disease dementia. Rate of olfactory decline with age was similar across all diagnostic categories. NIHTB-OIT scores were lower in participants with positive Alzheimer's biomarker tests. NIHTB-OIT scores did not differ based on APOE genotype.

The wisdom of many in few: Finding individuals who are as wise as the crowd.

Is forecasting ability a stable trait? While domain knowledge and reasoning abilities are necessary for making accurate forecasts, research shows that knowing how accurate forecasters have been in the past is the best predictor of future accuracy. However, unlike the measurement of other traits, evaluating forecasting skill requires substantial time investment. Forecasters must make predictions about events that may not resolve for many days, weeks, months, or even years into the future before their accuracy can be estimated. Our work builds upon methods such as cultural consensus theory and proxy scoring rules to show talented forecasters can be discriminated in real time, without requiring any event resolutions. We define a peer similarity-based intersubjective evaluation method and test its utility in a unique longitudinal forecasting experiment. Because forecasters predicted all events at the same points in time, many of the confounds common to forecasting tournaments or observational data were eliminated. This allowed us to demonstrate the effectiveness of our method in real time, as time progressed and more information about forecasters became available. Intersubjective accuracy scores, which can be obtained immediately after the forecasts are made, were both valid and reliable estimators of forecasting talent. We also found that asking forecasters to make meta-predictions about what they expect others to believe can serve as an incentive-compatible method of intersubjective evaluation. Our results indicate that selecting small groups of, or even single forecasters, based on intersubjective accuracy can yield subsequent forecasts that approximate the actual accuracy of much larger crowd aggregates. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Measuring individual true change with PROMIS using IRT-based plausible values.

AIMS: A primary advantage of IRT-based patient-reported outcome measures such as PROMIS short forms and computer-adaptive tests is that each estimate of the latent trait comes with a standard error. Such measurement error needs to be acknowledged, in particular when monitoring individual patients over time. In this study, we use plausible values to account for measurement error and analyze the probability of true within-individual change. METHODS: We use a longitudinal, observational study of stable and exacerbated COPD patients (N = 185), providing PROMIS Physical Function and Fatigue T-scores over 3 months. At each measurement, we imputed 1000 plausible values from the scores' posterior distribution. These were then used to calculate probability of true change using a pre-specified threshold such as minimally important difference supported by the literature, or [Formula: see text] > 0. We demonstrate assessment of change in individuals and in groups, across different measures (Short Forms and CATs), and at various levels of confidence. RESULTS: Using plausible value imputation and with 95% certainty, 47.5% of participants in the exacerbated group reported less fatigue, compared with 26.5% of participants in the stable group. Comparison of Short Forms and CATs suggests that CATs have better ability to detect change compared to short forms. We also illustrate this method using an individual's probability of change at different time points. CONCLUSION: Plausible values offer a flexible way to include measurement error in analysis of individuals and on sample level. Assessment of probability of true change can complement existing distribution-based approaches and facilitates interpretation of improvement or decline.