Think Fast, Feel Fine, Live Long: A 29-Year Study of Cognition, Health, and Survival in Middle-Aged and Older Adults

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.

Subjective Impairments in Olfaction and Cognition Predict Dissociated Behavioral Outcomes

BACKGROUND

Self-rated subjective cognitive decline (SCD) and subjective olfactory impairment (SOI) are associated with objective cognitive decline and dementia. However, their relationship and co-occurrence is unknown. We aimed to (a) describe the occurrence of SOI, SCD and their overlap in the general population; (b) compare SOI and SCD in terms of longitudinal associations with corresponding objective olfactory and cognitive measures; and (c) describe how SOI and SCD may lead to distinct sensory and cognitive outcomes.

METHODS

Cognitively unimpaired individuals from the third wave of the Swedish population-based Betula study (n = 784, aged 35-90 years; 51% females) were split into self-rated SOI, SCD, overlapping SCD + SOI, and controls. Between-subject and within-subject repeated-measures MANCOVA were used to compare the groups regarding odor identification, cognition, age, sex, and education. Spearman correlation was used to assess the different patterns of association between olfaction and cognition across groups.

RESULTS

SOI was present in 21.1%, whereas SCD was present in 9.9% of participants. According to a chi-square analysis, the SCD + SOI overlap (2.7%) is on a level that could be expected if the phenomena were independent. Odor identification in SOI showed decline at the 10-year follow-up (n = 284) and was positively associated with cognition. The SOI and SCD groups showed distinct cognitive-olfactory profiles at follow-up.

CONCLUSIONS

SOI occur independently of SCD in the population, and these risk factors are associated with different cognitive and olfactory outcomes. The biological causes underlying SOI and SCD, as well as the risk for future cognitive impairment, need further investigation.

Behavioral Lifestyles and Survival: A Meta-Analysis

The aim of the study is to determine the association between Behavioral Lifestyles (regular physical activity, healthy diet, sleeping, and weight control) and longevity in the elderly. A search strategy was conducted in the PsycInfo, Medline, PubMed, Web of Science (WoS), and Scopus databases. The primary outcome was mortality/survival. Four variables (mean of participant's age at the baseline of the study, follow-up years of the study, gender, and year of publication) were analyzed to evaluate the role of potential moderators. Ninety-three articles, totaling more than 2,800,000 people, were included in the meta-analysis. We found that the lifestyles analyzed predict greater survival. Specifically, doing regular physical activity, engaging in leisure activities, sleeping 7-8 h a day, and staying outside the BMI ranges considered as underweight or obesity are habits that each separately has a greater probability associated with survival after a period of several years.

Cognition-Mortality Associations Are More Pronounced When Estimated Jointly in Longitudinal and Time-to-Event Models

With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults (N = 5,954; ages 50–87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology.

Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior

BACKGROUND

Coupling digital technology with traditional neuropsychological test performance allows collection of high-precision metrics that can clarify and/or define underlying constructs related to brain and cognition.

OBJECTIVE

To identify graphomotor and information processing trajectories using a digitally administered version of the Digit Symbol Substitution Test (DSST).

METHODS

A subset of Long Life Family Study participants (n = 1,594) completed the DSST. Total time to draw each symbol was divided into 'writing' and non-writing or 'thinking' time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90 s test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores.

RESULTS

Clustering revealed four 'thinking' time trajectories, with two clusters showing significant changes within the test. Participants in these clusters obtained lower episodic memory scores but were similar in other health and functional characteristics. Clustering of 'writing' time also revealed four performance trajectories where one cluster of participants showed progressively slower writing time. These participants had weaker grip strength, slower gait speed, and greater perceived physical fatigability, but no differences in cognitive test scores.

CONCLUSION

Digital data identified previously unrecognized patterns of 'writing' and 'thinking' time that cannot be detected without digital technology. These patterns of performance were differentially associated with measures of cognitive and physical function and may constitute specific neurocognitive biomarkers signaling the presence of subtle to mild dysfunction. Such information could inform the selection and timing of in-depth neuropsychological assessments and help target interventions.

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach

BACKGROUND

Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking.

OBJECTIVE

We used a combined methodology of machine learning and semi-parametric survival analysis to estimate the relative importance of 52 predictors in forecasting cognitive impairment and dementia in a large, population-representative sample of older adults.

METHODS

Participants from the Health and Retirement Study (N = 9,979; aged 50-98 years) were followed for up to 10 years (M = 6.85 for cognitive impairment; M = 7.67 for dementia). Using a split-sample methodology, we first estimated the relative importance of predictors using machine learning (random forest survival analysis), and we then used semi-parametric survival analysis (Cox proportional hazards) to estimate effect sizes for the most important variables.

RESULTS

African Americans and individuals who scored high on emotional distress were at relatively highest risk for developing cognitive impairment and dementia. Sociodemographic (lower education, Hispanic ethnicity) and health variables (worse subjective health, increasing BMI) were comparatively strong predictors for cognitive impairment. Cardiovascular factors (e.g., smoking, physical inactivity) and polygenic scores (with and without APOEɛ4) appeared less important than expected. Post-hoc sensitivity analyses underscored the robustness of these results.

CONCLUSIONS

Higher-order factors (e.g., emotional distress, subjective health), which reflect complex interactions between various aspects of an individual, were more important than narrowly defined factors (e.g., clinical and behavioral indicators) when evaluated concurrently to predict cognitive impairment and dementia.

Slower Decline in Processing Speed Is Associated with Familial Longevity

INTRODUCTION

Cross-sectional analyses have associated familial longevity with better cognitive function and lower risk of cognitive impairment in comparison with individuals without familial longevity. The extent to which long-lived families also demonstrate slower rates of cognitive aging (i.e., change in cognition over time) is unknown. This study examined longitudinally collected data among 2 generations of the Long Life Family Study (LLFS) to compare rates of cognitive change across relatives and spouse controls.

METHODS

We analyzed change in 6 neuropsychological test scores collected approximately 8 years apart among LLFS family members (n = 3,972) versus spouse controls (n = 1,092) using a Bayesian hierarchical model that included age, years of follow-up, sex, education, generation, and field center and all possible pairwise interactions.

RESULTS

At a mean age of 88 years at enrollment in the older generation and 60 years in the younger generation, LLFS family members performed better than their spouses on the Digit Symbol Substitution Test (DSST) and the Logical Memory test. At follow-up, family members in the younger generation also showed slower decline than spouses on the DSST, whereas rates of change of Digit Span, fluency, and memory were similar between the 2 groups.

DISCUSSION/CONCLUSION

Individuals in families with longevity appear to have better cognitive performance than their spouses for cognitive processes including psychomotor processing, episodic memory, and retrieval. Additionally, they demonstrate longer cognitive health spans with a slower decline on a multifactorial test of processing speed, a task requiring the integration of processes including organized visual search, working and incidental memory, and graphomotor ability. Long-lived families may be a valuable cohort for studying resilience to cognitive aging.

Subcortical Ischemic Vascular Cognitive Impairment: Insights from Reaction Time Measures

In this study, reaction time (RT), intraindividual variability (IIV), and errors, and the effects of practice and processing load upon such function, were compared in patients with subcortical ischemic vascular cognitive impairment (SIVCI) [n = 27] and cognitively healthy older adults (CH) [n = 26]. Compared to CH aging, SIVCI was characterized by a profile of significantly slowed RT, raised IIV, and higher error levels, particularly in the presence of distracting stimuli, indicating that the integrity and/or accessibility of the additional functions required to support high processing load, serial search strategies, are reduced in SIVCI. Furthermore, although practice speeded RT in SIVCI, unlike CH, practice did not lead to an improvement in IIV. This indicates that improvement in RT in SIVCI can in fact mask an abnormally high degree of IIV. Because IIV appears more related to disease, function, and health than RT, its status and potential for change may represent a particularly meaningful, and relevant, disease characteristic of SIVCI. Finally, a high level of within-group variation in the above measures was another characteristic of SIVCI, with such processing heterogeneity in patients with ostensibly the same diagnosis, possibly related to individual variation in pathological load. Detailed measurement of RT, IIV, errors, and practice effects therefore reveal a degree of functional impairment in brain processing not apparent by measuring RT in isolation.

Neighborhood deprivation, prefrontal morphology and neurocognition in late childhood to early adolescence

Background:

Neighborhood deprivation adversely effects neurodevelopment and cognitive function; however, mechanisms remain unexplored. Neighborhood deprivation could be particularly impactful in late childhood/early adolescence, in neural regions with protracted developmental trajectories, e.g., prefrontal cortex (PFC).

Methods:

The Adolescent Brain Cognitive Development (ABCD) study recruited 10,205 youth. Geocoded residential history was used to extract individual neighborhood characteristics. A general cognitive ability index and MRI scans were completed. Associations with neurocognition were examined. The relation of PFC surface area and cortical thickness to neighborhood deprivation was tested. PFC subregions and asymmetry, with putative differential environmental susceptibility during key developmental periods, were explored. Analyses tested PFC area as a possible mediating mechanism.

Results:

Neighborhood deprivation predicted neurocognitive performance (β = −0.11), even after accounting for parental education and household income (β = −0.07). Higher neighborhood deprivation related to greater overall PFC surface area (η²_p = 0.003), and differences in leftward asymmetry were observed for area (η²_p = 0.001), and thickness (η²_p = 0.003). Subregion analyses highlighted differences among critical areas that are actively developing in late childhood/early adolescence and are essential to modulating high order cognitive function. These included orbitofrontal, superior frontal, rostral middle frontal, and frontal pole regions (Cohen’s d = 0.03–0.09). PFC surface area partially mediated the relation between neighborhood deprivation and neurocognition.

Discussion:

Neighborhood deprivation related to cognitive function (a foundational skill tied to a range of lifetime outcomes) and PFC morphology, with evidence found for partial mediation of PFC on neurocognitive function. Results inform public health conceptualizations of development and environmental vulnerability.

Predicting mortality from 57 economic, behavioral, social, and psychological factors

In our prospective study using nationally representative data from 13,611 adults in the US Health and Retirement Study, we used traditional and machine-learning statistical approaches to reveal the most important factors across the behavioral and social sciences that predict mortality in older adults. In the study, we found that top predictors of mortality spanned all investigated domains, opening up opportunities for future hypothesis generation in observational and clinical studies and the identification of potential new targets for screening and policy.

Behavioral and social scientists have identified many nonbiological predictors of mortality. An important limitation of much of this research, however, is that risk factors are not studied in comparison with one another or from across different fields of research. It therefore remains unclear which factors should be prioritized for interventions and policy to reduce mortality risk. In the current investigation, we compare 57 factors within a multidisciplinary framework. These include (i) adverse socioeconomic and psychosocial experiences during childhood and (ii) socioeconomic conditions, (iii) health behaviors, (iv) social connections, (v) psychological characteristics, and (vi) adverse experiences during adulthood. The current prospective cohort investigation with 13,611 adults from 52 to 104 y of age (mean age 69.3 y) from the nationally representative Health and Retirement Study used weighted traditional (i.e., multivariate Cox regressions) and machine-learning (i.e., lasso, random forest analysis) statistical approaches to identify the leading predictors of mortality over 6 y of follow-up time. We demonstrate that, in addition to the well-established behavioral risk factors of smoking, alcohol abuse, and lack of physical activity, economic (e.g., recent financial difficulties, unemployment history), social (e.g., childhood adversity, divorce history), and psychological (e.g., negative affectivity) factors were also among the strongest predictors of mortality among older American adults. The strength of these predictors should be used to guide future transdisciplinary investigations and intervention studies across the fields of epidemiology, psychology, sociology, economics, and medicine to understand how changes in these factors alter individual mortality risk.

Lacking Pace but Not Precision: Age-Related Information Processing Changes in Response to a Dynamic Attentional Control Task

Age-related decline in information processing can have a substantial impact on activities such as driving. However, the assessment of these changes is often carried out using cognitive tasks that do not adequately represent the dynamic process of updating environmental stimuli. Equally, traditional tests are often static in their approach to task complexity, and do not assess difficulty within the bounds of an individual’s capability. To address these limitations, we used a more ecologically valid measure, the Swansea Test of Attentional Control (STAC), in which a threshold for information processing speed is established at a given level of accuracy. We aimed to delineate how older, compared to younger, adults varied in their performance of the task, while also assessing relationships between the task outcome and gender, general cognition (MoCA), perceived memory function (MFQ), cognitive reserve (NART), and aspects of mood (PHQ-9, GAD-7). The results indicate that older adults were significantly slower than younger adults but no less precise, irrespective of gender. Age was negatively correlated with the speed of task performance. Our measure of general cognition was positively correlated with the task speed threshold but not with age per se. Perceived memory function, cognitive reserve, and mood were not related to task performance. The findings indicate that while attentional control is less efficient in older adulthood, age alone is not a defining factor in relation to accuracy. In a real-life context, general cognitive function, in conjunction with dynamic measures such as STAC, may represent a far more effective strategy for assessing the complex executive functions underlying driving ability.

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.

Illness and intelligence are comparatively strong predictors of individual differences in depressive symptoms following middle age

OBJECTIVE

We compared the importance of socio-demographic, lifestyle, health, and multiple cognitive measures for predicting individual differences in depressive symptoms in later adulthood.

METHOD

Data came from 6203 community-dwelling older adults (age 41-93 years at study entry) from the United Kingdom. Predictors (36 in total) were assessed up to four times across a period of approximately 12 years. Depressive symptoms were measured with the Geriatric Depression Scale. Statistical methods included multiple imputation (for missing data), random forest analysis (a machine learning approach), and multivariate regression.

RESULTS

On average, depressive symptoms increased gradually following middle age and appeared to accelerate in later life. Individual differences in depressive symptoms were most strongly associated with differences in combined symptoms of physical illness (positive relation) and fluid intelligence (negative relation). The strength of association between depressive symptoms and fluid intelligence was unaffected by differences in health status within a subsample of chronically depressed individuals.

CONCLUSION

Joint consideration of general health status and fluid intelligence may facilitate prediction of depressive symptoms severity during later life and may also serve to identify sub-populations of community-dwelling elders at risk for chronic depression.

Cardiovascular symptoms and longitudinal declines in processing speed differentially predict cerebral white matter lesions in older adults

It is well established that cerebral white matter lesions (WML), present in the majority of older adults, are associated with cardiovascular and cerebrovascular diseases and also with cognitive decline. However, much less is known about how WML are related to other important individual characteristics and about the generality vs. brain region-specificity of WML. In a longitudinal study of 112 community-dwelling adults (age 50-71 years at study entry), we used a machine learning approach to evaluate the relative strength of 52 variables in association with WML burden. Variables included socio-demographic, lifestyle, and health indices-as well as multiple cognitive abilities (modeled as latent constructs using factor analysis)-repeatedly measured at three- to six-year intervals. Greater chronological age, symptoms of cardiovascular disease, and processing speed declines were most strongly linked to elevated WML burden (accounting for ∼49% of variability in WML). Whereas frontal lobe WML burden was associated both with elevated cardiovascular symptoms and declines in processing speed, temporal lobe WML burden was only significantly associated with declines in processing speed. These latter outcomes suggest that age-related WML-cognition associations may be etiologically heterogeneous across fronto-temporal cerebral regions.

A prospective study of associations among helping, health, and longevity

How does helping behavior contribute to the health and the longevity of older helpers? From an evolutionary perspective, the ultimate cause may be rooted in ancestral parenting and grandparenting. These activities may have generalized to a neural and hormonal caregiving system that also enabled prosocial behavior beyond the family. From a psychological perspective, helping others may be associated with healthy aging, which, in turn, contributes to longevity as a proximate cause. Yet little is known about the extent to which mediating factors such as the health benefits of helping behaviors translate into enhanced longevity, particularly in regard to grandparenting. To fill this gap, we conducted mediation analyses (structural equation models) to examine whether grandparenting and supporting others in the social network contributed directly or indirectly (through better health 5-6 years later) to the longevity of older helpers. We drew on longitudinal data from the Berlin Aging Study (N = 516), in which older adults in Berlin, Germany, were interviewed at baseline (1990-1993, mean age at entry = 85 years) and continuously followed up until 2009. Results suggest that the associations of both grandparenting and supporting others with enhanced longevity are mediated by better prospective health (indirect effect). The effect of helping was not fully mediated, however-helping was also directly associated with increased longevity independently of the health indicators measured. The results were robust against effects of the helper's preexisting health status and sociodemographic characteristics of participants, their children, and grandchildren. We conclude that better prospective health contributes to the link between helping and longevity, but does not fully account for it. Other potential contributing mechanisms remain to be identified. As populations age across the globe, identifying mechanisms that foster health in old age can help to highlight potential targets for public health interventions.

Self-Rated Health and Sick Leave among Nurses and Physicians: The Role of Regret and Coping Strategies in Difficult Care-Related Situations

Moral distress - such as feeling strong regret over difficult patient situations - is common among nurses and physicians. Regret intensity, as well as the coping strategies used to manage regrets, may also influence the health and sickness absence of healthcare professionals. The objective of this study was to determine if the experience of regret related to difficult care-related situations is associated with poor health and sick leave and if coping strategies mediate these associations. Two cross-sectional surveys were conducted in Switzerland (Geneva, 2011 and Zurich, 2014). Outcomes were self-rated health (SRH) and sick leave in the last 6 months. We examined the associations of regret intensity with the most important care-related regret, number of recent care-related regrets, and coping strategies, using regressions models. Among 775 respondents, most reported very good SRH and 9.7% indicated absence from work during four working days or more. Intensity of the most important regret was associated with poor SRH among nurses and physicians, and with higher sick leave among nurses. Maladaptive emotion-focused strategies were associated with poor SRH among nurses, whereas adaptive emotion-focused strategies were positively associated with higher SRH and lower sick leave among physicians. Because care-related regret is an integral part of clinical practice in acute care hospitals, helping physicians and, especially, nurses to learn how to deal with negative events may yield beneficial consequences at the individual, patient care, and institutional level.

Rodent age-related impairments in discriminating perceptually similar objects parallel those observed in humans

The ability to accurately remember distinct episodes is supported by high-level sensory discrimination. Performance on mnemonic similarity tasks, which test high-level discrimination, declines with advancing age in humans and these deficits have been linked to altered activity in hippocampal CA3 and dentate gyrus. Lesion studies in animal models, however, point to the perirhinal cortex as a brain region critical for sensory discriminations that serve memory. Reconciliation of the contributions of different regions within the cortical-hippocampal circuit requires the development of a discrimination paradigm comparable to the human mnemonic similarity task that can be used in rodents. In the present experiments, young and aged rats were cross-characterized on a spatial water maze task and two variants of an object discrimination task: one in which rats incrementally learned which object of a pair was rewarded and different pairs varied in their similarity (Experiment 1), and a second in which rats were tested on their ability to discriminate a learned target object from multiple lure objects with an increasing degree of feature overlap (Experiment 2). In Experiment 1, aged rats required more training than young to correctly discriminate between similar objects. Comparably, in Experiment 2, aged rats were impaired in discriminating a target object from lures when the pair shared more features. Discrimination deficits across experiments were correlated within individual aged rats, though, for the cohort tested, aged rats were not impaired overall in spatial learning and memory. This could suggest discrimination deficits emerging with age precede declines in spatial or episodic memory, an observation that has been made in humans. Findings of robust impairments in object discrimination abilities in the aged rats parallel results from human studies, supporting use of the developed tasks for mechanistic investigation of cortical-hippocampal circuit dysfunction in aging and disease.

Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools

BACKGROUND

A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model.

OBJECTIVES

To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults.

INCLUSION CRITERIA POPULATION

Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals.

INDEX TEST

Available frailty measures in older adults.

REFERENCE TEST

Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests.

DIAGNOSIS OF INTEREST

Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes.

TYPES OF STUDIES

Quantitative systematic reviews.

SEARCH STRATEGY

A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015.

METHODOLOGICAL QUALITY

Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis.

DATA EXTRACTION

Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews.

DATA SYNTHESIS

Data were only presented in a narrative form due to the heterogeneity of included reviews.

RESULTS

Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty indicators were analyzed, most of which were applied to community-dwelling older people. The Frailty Index was examined in almost all these dimensions, with the exception of reliability, and its diagnostic and predictive characteristics were shown to be satisfactory. Gait speed showed high sensitivity, but only moderate specificity, and excellent predictive ability for future disability in activities of daily living. The Tilburg Frailty Indicator was shown to be a reliable and valid measure for frailty screening, but its diagnostic accuracy was not evaluated. Screening Letter, Timed-up-and-go test and PRISMA 7 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) demonstrated high sensitivity and moderate specificity for identifying frailty. In general, low physical activity, variously measured, was one of the most powerful predictors of future decline in activities of daily living.

CONCLUSION

Only a few frailty measures seem to be demonstrably valid, reliable and diagnostically accurate, and have good predictive ability. Among them, the Frailty Index and gait speed emerged as the most useful in routine care and community settings. However, none of the included systematic reviews provided responses that met all of our research questions on their own and there is a need for studies that could fill this gap, covering all these issues within the same study. Nevertheless, it was clear that no suitable tool for assessing frailty appropriately in emergency departments was identified.

Speed of Visual Search in Old Age: 1950 to 2016

OBJECTIVES

To review work on the effects of old age on speed of visual search and of discriminations between signals and choices of responses between 1950 and 2016.

METHODS

Literature review and Brain Google.

RESULTS

Mild existential despondency.

DISCUSSION

Models for age changes in discrimination between signals and choices of responses have been based on comparisons of speed. The concept of speed has been used in four distinct ways: as a directly measured task performance index, as a hypothetical functional system performance characteristic, as a factor in psychometric models computing mutual variance in calendar age and as a neurophysiological performance characteristic. Since 1950, these measures have, in turn, determined models for speed of perceptual discriminations. Since the 1990s, advances in neuroimaging have not only transformed the data available, but also the nature of the questions that we ask.