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• W2996071438 abstract "A better understanding of differential cognitive aging trajectories is essential to advise policy and the public about dementia risk reduction that enables cognitive healthspan to better match human lifespan. A major impediment to most studies of brain and cognitive aging is the ability to achieve sample sizes that are large, representative of the heterogeneous national population, and longitudinal. Internet and smartphone usage continues to rise dramatically in many countries, and this fact holds across a range of socioeconomic and demographic characteristics. Recent work demonstrates that internet or smartphone app-based approaches can be used to recruit large research cohorts to assess cognitive performance. We propose research approaches that merge electronic-based cohorts and face-to-face research to power the longitudinal study of cognitive aging in the evolving digital age. The worldwide average human lifespan has increased over the past century. These changing demographics demand a reinvention of experimental approaches to study the brain and aging, with the aim of better matching cognitive healthspan with human lifespan. Past studies of cognitive aging included sample sizes that tended to be underpowered, were not sufficiently representative of national population characteristics, and often lacked longitudinal assessments. As a step to address these shortcomings, we propose a framework that encourages interaction between electronic-based and face-to-face study designs. We argue that this will achieve the necessary synergy to accelerate progress in the discovery and application of personalized interventions to optimize brain and cognitive health. The worldwide average human lifespan has increased over the past century. These changing demographics demand a reinvention of experimental approaches to study the brain and aging, with the aim of better matching cognitive healthspan with human lifespan. Past studies of cognitive aging included sample sizes that tended to be underpowered, were not sufficiently representative of national population characteristics, and often lacked longitudinal assessments. As a step to address these shortcomings, we propose a framework that encourages interaction between electronic-based and face-to-face study designs. We argue that this will achieve the necessary synergy to accelerate progress in the discovery and application of personalized interventions to optimize brain and cognitive health. a measurable biological phenotype or molecule that can serve as an indicator of a normal or abnormal biological process, the presence of disease risk or resistance, or response to a therapeutic intervention. a decline in cognitive function, such as reasoning, memory, and processing speed, that occurs as people age. change in the rate of a condition according to year of birth, regardless of age and calendar time. starting in the 1970s with the introduction of the personal computer and subsequent technology that widely provided the ability to transfer information freely and quickly. a variable that changes at a constant rate across individuals, or is constant in an individual over time, for example, age, sex, or ethnicity. the concept that depending on each person’s genetics, experiences, and exposures, rates of brain and cognitive aging will differ across time. a situation in which the effect of one variable on an outcome measure depends on the state of a second variable, for example, the impact of smoking on cognition depends on the sex of an individual. a study design in which data is gathered from the same participants repeatedly over time. refers to the effects of aging on the brain that result in cognitive changes across the lifespan. age-related changes in brain structure and function that occurs in all individuals outside the boundaries of disorder or disease. optimized management of the aging process – especially from the aspect of brain and cognitive aging – using the principles of precision medicine which affords individualized intervention recommendations. optimized medical care and therapeutic interventions for an individual based on genomic or molecular profiling in addition to health, lifestyle, and demographic factors. a variable that is random across individuals used to estimate some unknown value in the population; for example, cognitive decline and learning trajectories. the probability that a study will detect an effect if an effect exists. findings that erroneously suggest a statistical association between an outcome and a factor that does not exist. systematic inflation of a finding in a study, leading to unreplicable results; typically occurring in an underpowered study evaluating novel hypotheses." @default.
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• W2996071438 date "2020-01-01" @default.
• W2996071438 modified "2023-09-30" @default.
• W2996071438 title "Reinventing Neuroaging Research in the Digital Age" @default.
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• W2996071438 doi "https://doi.org/10.1016/j.tins.2019.11.004" @default.
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