Background

The ultimate goal of artificial intelligence (AI) is to develop technologies that are best able to serve humanity. This will require advancements that go beyond the basic components of general intelligence. The term "intelligence" does not best represent the technological needs of advancing society, because it is "wisdom", rather than intelligence, that is associated with greater well-being, happiness, health, and perhaps even longevity of the individual and the society. Thus, the future need in technology is for artificial wisdom (AW).

Methods

We examine the constructs of human intelligence and human wisdom in terms of their basic components, neurobiology, and relationship to aging, based on published empirical literature. We review the development of AI as inspired and driven by the model of human intelligence, and consider possible governing principles for AW that would enable humans to develop computers which can operationally utilize wise principles and result in wise acts. We review relevant examples of current efforts to develop such wise technologies.

Results

AW systems will be based on developmental models of the neurobiology of human wisdom. These AW systems need to be able to a) learn from experience and self-correct; b) exhibit compassionate, unbiased, and ethical behaviors; and c) discern human emotions and help the human users to regulate their emotions and make wise decisions.

Conclusions

A close collaboration among computer scientists, neuroscientists, mental health experts, and ethicists is necessary for developing AW technologies, which will emulate the qualities of wise humans and thus serve the greatest benefit to humanity. Just as human intelligence and AI have helped further the understanding and usefulness of each other, human wisdom and AW can aid in promoting each other's growth.

INTRODUCTION: The U.S. Military Veterans aged 65 and older comprise an estimated 43% of the 22 million living Veterans in the United States. Veterans have high rates of physical, psychiatric, and social challenges, but it is not known whether Veteran status confers additional risk for cognitive or functional impairments in later life. Thus, this investigation specifically compared older Veterans with their non-Veteran peers in cognitive functioning and performance-based functional capacity. MATERIALS AND METHODS: Participants (N = 110; 29 Veterans and 81 non-Veterans) were part of a larger longitudinal study on biopsychosocial functioning in independently living older adult residents of a Continuing Care Senior Housing Community. The University of California San Diego Institutional Review Board approved the study and all participants provided written informed consent. Participants provided demographic and mental health information and were administered a comprehensive neuropsychological battery. Functional capacity was assessed using the UCSD Performance-Based Skills Assessment-Brief (UPSA-B), which uses financial and communication role-plays to assess everyday functioning skills. Neuropsychological scores were appropriately normed prior to analysis. Multivariate Analyses of Variances with post hoc t-tests and an Analysis of Covariance were used to examine neuropsychological and functional capacity differences, respectively, between Veterans and non-Veterans. RESULTS: Veterans did not differ from non-Veterans in educational attainment (16.4 years versus 15.5 years, P = 0.110), but they were significantly older (mean age 86.9 years ± 5.7, versus 81.74 years ± 6.53; P < 0.001) and were more likely to be male (X2 [1, N = 110] = 62.39, P < 0.001). Thus, though neuropsychological norms already accounted for demographic differences in our participants, age and sex were controlled in the Analysis of Covariance predicting UPSA-B score from Veteran status. Results suggested that, compared to non-Veterans, Veterans had significantly worse performance in the list learning portion of a test of verbal memory (Hopkins Verbal Learning Test-Revised, Total Recall; t = 2.56, P = 0.012, d = 0.56). Veterans and non-Veterans did not significantly differ in performance on the delayed recall portion of the verbal learning test and did not differ on a cognitive screening test (Montreal Cognitive Assessment) or on measures of premorbid intellectual functioning (Wide Range Achievement Test-4 Reading), language (Boston Naming Test, Verbal Fluency), visual memory (Brief Visuospatial Memory Test-Revised), attention/working memory (WAIS-IV Digit Span), processing speed (WAIS-IV Digit Symbol Coding), executive function (Delis-Kaplan Executive Function System Trails and Color-Word Test), or functional capacity (UPSA-B). Because our examination of multiple outcomes might have inflated Type I error, we performed a post hoc adjustment of P values using Benjamini-Hochberg procedures and the group difference in verbal learning remained significant. CONCLUSIONS: Despite largely similar function in most domains, Veterans performed significantly more poorly in verbal list learning than their non-Veteran peers. Additional attention should be given to the understanding, assessment, and possible treatment of learning and memory differences in older Veterans, as this may be an area in which Veteran status confers additional risk or vulnerability to decline. This is the first study to compare objective neuropsychological and functional performance between older (age 65+) US Veterans and non-Veterans.

Objective

The COVID-19 pandemic has had potentially severe psychological implications for older adults, including those in retirement communities, due to restricted social interactions, but the day-to-day experience of loneliness has received limited study. We sought to investigate sequential association, if any, between loneliness, activity, and affect.

Methods

We used ecological momentary assessment (EMA) with dynamic network analysis to investigate the affective and behavioral concomitants of loneliness in 22 residents of an independent living sector of a continuing care retirement community (mean age 80.2; range 68-93 years).

Results

Participants completed mean 83.9% of EMA surveys (SD = 16.1%). EMA ratings of loneliness were moderately correlated with UCLA loneliness scale scores. Network models showed that loneliness was contemporaneously associated with negative affect (worried, anxious, restless, irritable). Negative (but not happy or positive) mood tended to be followed by loneliness and then by exercise or outdoor physical activity. Negative affect had significant and high inertia (stability).

Conclusions

The data suggest that EMA is feasible and acceptable to older adults. EMA-assessed loneliness was moderately associated with scale-assessed loneliness. Network models in these independent living older adults indicated strong links between negative affect and loneliness, but feelings of loneliness were followed by outdoor activity, suggesting adaptive behavior among relatively healthy adults.

Background

As of March 2021, in the USA, the COVID-19 pandemic has resulted in over 500,000 deaths, with a majority being people over 65 years of age. Since the start of the pandemic in March 2020, preventive measures, including lockdowns, social isolation, quarantine, and social distancing, have been implemented to reduce viral spread. These measures, while effective for risk prevention, may contribute to increased social isolation and loneliness among older adults and negatively impact their mental and physical health.

Objective

This study aimed to assess the impact of the COVID-19 pandemic and the resulting "Stay-at-Home" order on the mental and physical health of older adults and to explore ways to safely increase social connectedness among them.

Methods

This qualitative study involved older adults living in a Continued Care Senior Housing Community (CCSHC) in southern California, USA. Four 90-minute focus groups were convened using the Zoom Video Communications platform during May 2020, involving 21 CCSHC residents. Participants were asked to describe how they were managing during the "stay-at-home" mandate that was implemented in March 2020, including its impact on their physical and mental health. Transcripts of each focus group were analyzed using qualitative methods.

Results

Four themes emerged from the qualitative data: (1) impact of the quarantine on health and well-being, (2) communication innovation and technology use, (3) effective ways of coping with the quarantine, and (4) improving access to technology and training. Participants reported a threat to their mental and physical health directly tied to the quarantine and exacerbated by social isolation and decreased physical activity. Technology was identified as a lifeline for many who are socially isolated from their friends and family.

Conclusions

Our study findings suggest that technology access, connectivity, and literacy are potential game-changers to supporting the mental and physical health of older adults and must be prioritized for future research.

Objective

The current cross-sectional study examined cognition and performance-based functional abilities in a continuing care senior housing community (CCSHC) that is comparable to other CCSHCs in the US with respect to residents' demographic characteristics.

Method

Participants were 110 older adult residents of the independent living unit. We assessed sociodemographics, mental health, neurocognitive functioning, and functional capacity.

Results

Compared to normative samples, participants performed at or above expectations in terms of premorbid functioning, attention span and working memory, processing speed, timed set-shifting, inhibitory control, and confrontation naming. They performed below expectation in verbal fluency and verbal and visual learning and memory, with impairment rates [31.4% (>1 SD below the mean) and 18.49% (>1.5 SD below the mean)] well above the general population (16% and 7%, respectively). Within the cognitive test battery, two tests of delayed memory were most predictive of a global deficit score. Most cognitive test scores correlated with performance-based functional capacity.

Conclusions

Overall, results suggest that a subset of older adults in the independent living sector of CCSHCs are cognitively and functionally impaired and are at risk for future dementia. Results also argue for the inclusion of memory tests in abbreviated screening batteries in this population. We suggest that CCSHCs implement regular cognitive screening procedures to identify and triage those older adults who could benefit from interventions and, potentially, a transition to a higher level of care.

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

Physical, emotional, and cognitive changes are well documented in aging populations. We administered a comprehensive battery of mental and physical health measures and the Montreal Cognitive Assessment (MoCA; a cognitive screening tool) to 93 independently living older adults (OAs) residing in a Continuing Care Senior Housing Community. Performance on the Timed Up-and-Go (TUG) test (a measure of functional mobility) correlated more strongly with the MoCA total score than did measures of aging, psychiatric symptoms, sleep, and both self-report and objective physical health. Furthermore, it was associated with MoCA Attention, Language, Memory, and Visuospatial/Executive subscales. The MoCA-TUG relationship remained significant after controlling for demographic and physical/mental health measures. Given that the TUG explained significantly more variance in broad cognitive performance than a comprehensive battery of additional physical and mental health tests, it may function as a multimodal measure of health in OAs, capturing physical changes and correlating with cognitive measures.

BACKGROUND

As of March 2021, in the USA, the COVID-19 pandemic has resulted in over 500,000 deaths, with a majority being people over 65 years of age. Since the start of the pandemic in March 2020, preventive measures, including lockdowns, social isolation, quarantine, and social distancing, have been implemented to reduce viral spread. These measures, while effective for risk prevention, may contribute to increased social isolation and loneliness among older adults and negatively impact their mental and physical health.

OBJECTIVE

This study aimed to assess the impact of the COVID-19 pandemic and the resulting “Stay-at-Home” order on the mental and physical health of older adults and to explore ways to safely increase social connectedness among them.

METHODS

This qualitative study involved older adults living in a Continued Care Senior Housing Community (CCSHC) in southern California, USA. Four 90-minute focus groups were convened using the Zoom Video Communications platform during May 2020, involving 21 CCSHC residents. Participants were asked to describe how they were managing during the “stay-at-home” mandate that was implemented in March 2020, including its impact on their physical and mental health. Transcripts of each focus group were analyzed using qualitative methods.

RESULTS

Four themes emerged from the qualitative data: (1) impact of the quarantine on health and well-being, (2) communication innovation and technology use, (3) effective ways of coping with the quarantine, and (4) improving access to technology and training. Participants reported a threat to their mental and physical health directly tied to the quarantine and exacerbated by social isolation and decreased physical activity. Technology was identified as a lifeline for many who are socially isolated from their friends and family.

CONCLUSIONS

Our study findings suggest that technology access, connectivity, and literacy are potential game-changers to supporting the mental and physical health of older adults and must be prioritized for future research.

PURPOSE OF REVIEW:Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. RECENT FINDINGS:We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI's potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance. As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual's unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.