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BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

Objective: Poor sleep quality is related to worse neurocognition in older adults and in people with HIV (PWH); however, many previous studies have relied only on self-report sleep questionnaires, which are inconsistently correlated with objective sleep measures. We examined relationships between objective and subjective sleep quality and neurocognition in persons with and without HIV, aged 50 and older. Method: Eighty-five adults (PWH n = 52, HIV-negative n = 32) completed comprehensive neuropsychological testing to assess global and domain-specific neurocognition. Objective sleep quality was assessed with wrist actigraphy (total sleep time, efficiency, sleep fragmentation) for five to 14 nights. Subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index. Results: Objective and subjective sleep measures were unrelated (p's > 0.30). Compared to HIV-negative participants, PWH had greater sleep efficiency (80% vs. 75%, p = 0.05) and were more likely to be using prescription and/or over the counter sleep medication (p = 0.04). In the whole sample, better sleep efficiency (p

ObjectivesWisdom is a personality trait comprising seven components: self-reflection, pro-social behaviors, emotional regulation, acceptance of diverse perspectives, decisiveness, social advising, and spirituality. Wisdom, a potentially modifiable trait, is strongly associated with well-being. We have published a validated 28-item San Diego Wisdom Scale, the SD-WISE-28. Brief scales are necessary for use in large population-based studies and in clinical practice. The present study aimed to create an abbreviated 7-item version of the SD-WISE.MethodParticipants included 2093 people, aged 20-82 years, recruited and surveyed through the online crowdsourcing platform Amazon Mechanical Turk. The participants' mean age was 46 years, with 55% women. Participants completed the SD-WISE-28 as well as validation scales for various positive and negative constructs. Psychometric analyses (factor analysis and item response theory) were used to select one item from each of the seven SD-WISE-28 subscales.ResultsWe selected a combination of items that produced acceptable unidimensional model fit and good reliability (ω = 0.74). Item statistics suggested that all seven items were strong indicators of wisdom, although the association was weakest for spirituality. Analyses indicated that the 28-item and 7-item SD-WISE are both very highly correlated (r = 0.92) and produce a nearly identical pattern of correlations with demographic and validity variables.ConclusionThe SD-WISE-7, and its derived Jeste-Thomas Wisdom Index (JTWI) score, balances reliability and brevity for research applications.

Persons with serious mental illnesses experience high rates of medical comorbidity, especially diabetes. This study examined initial implementation feasibility, acceptability, and appropriateness of a new 6-month Multicomponent Intervention for Diabetes risk reduction in Adults with Serious mental illnesses (MIDAS) among persons in residential care facilities (RCFs). We conducted a mixed-methods study using four types of quantitative and qualitative data sources (administrative data; structured facility-level observations; resident assessments including blood-based biomarkers, 24-h dietary recalls, and self-report physical activity; and focus groups/interviews with staff and participants), to assess evidence of and factors affecting intervention feasibility, acceptability, and appropriateness. It was feasible to provide a high percentage of MIDAS class sessions (mean 50 of 52 intended sessions delivered) and make nutrition-related RCF changes (substitutions for healthier food items and reduced portion sizes). Class attendance rates and positive feedback from residents and staff provided evidence of MIDAS acceptability and appropriateness for addressing identified health needs. The residents who attended ≥ 85% of the sessions had greater improvement in several desired outcomes compared to others. Implementing a fully integrated MIDAS model with more extensive changes to facilities and more fundamental health changes among residents was more challenging. While the study found evidence to support feasibility, acceptability, and appropriateness of individual MIDAS components, some challenges for full implementation and success in obtaining immediate health benefits were also apparent. The study results highlight the need for improving health among RCF populations and will inform MIDAS adaptations designed to improve intervention fit and effectiveness outcomes.

Background(S)-ketamine is a glutamatergic drug with potent and rapid acting effects for the treatment of depression. Little is known about the effectiveness of intranasal (S)-ketamine for treating patients with comorbid depression and post-traumatic stress disorder (PTSD).MethodsWe performed a retrospective case series analysis of clinical outcomes in 35 Veterans with co-morbid depression and PTSD who were treated with intranasal (S)-ketamine treatments at the VA San Diego Neuromodulation Clinic between Jan 2020 and March 2021. Veterans were not randomized or blinded to treatment. The primary outcome measured was a change in patient health questionnaire-9 (PHQ-9) and PTSD Checklist for DSM-5 (PCL-5) scores across the first 8 treatments (induction period) using a repeated measures analysis of variance (ANOVA). In a smaller sub-group (n = 19) of Veterans who received at least 8 additional treatments, we analyzed whether intranasal (S)-ketamine continued to show treatment effects. Finally, we performed a sub-group and correlation analyses to understand how changes in PHQ-9 and PCL-5 scores were related across treatments.FindingsDuring the induction phase of treatment there was an absolute reduction of 5.1 (SEM 0.7) on the patient health questionnaire-9 (PHQ-9) rating scale for depression, from 19.8 (SEM 0.7) at treatment 1 to 14.7 (SEM 0.8) at treatment 8 (week 4) (F(7238) = 8.3, p = 1e-6, partial η2 = 0.2). Five Veterans (14%) showed a clinically meaningful response (50% reduction in PHQ-9 score) at treatment 8. There was an absolute reduction of 15.5 +/- 2.4 on the patient checklist 5 (PCL-5) rating scale for PTSD, from 54.8 (SEM 2) at treatment 1 down to 39.3 (SEM 2.5) at treatment 8 (F(7238) = 15.5, p = 2e-7, partial η2 = 0.31). Sixteen Veterans (46%) showed a clinically meaningful response (reduction in PCL-5 of > 30%) in PTSD. Change in PHQ-9 correlated with change in PCL-5 at treatment 8 (r = 0.47, p = 0.005), but a decrease in PTSD symptoms were observable in some individuals with minimal anti-depressant response.InterpretationsWhile this is an open-label retrospective analysis, our results indicate that both depression and PTSD symptoms in Veterans with dual-diagnoses may improve with repeated intranasal (S)-ketamine treatment. The effects of (S)-ketamine on PTSD symptoms were temporally and individually distinct from those on depression, suggesting potentially different modes of action on the two disorders. This work may warrant formal randomized controlled studies on the effects of intranasal (S)-ketamine for individuals with co-morbid MDD and PTSD.FundingVA Center of Excellence in Stress and Mental Health, VA ORD (Career Development Award to DSR), Burroughs-Wellcome Fund Award (DSR), NIMH (EL).

We investigated subjective cognitive complaints (SCCs), as well as physical and mental health factors, in adults and older adults. U.S. residents (N = 2,962) were recruited via the Amazon Mechanical Turk platform and completed a 90-item survey. Overall, 493/1930 (25.5%) of younger adults and 278/1032 (26.9%) of older adults endorsed SCCs. Analyses revealed worse physical and mental health characteristics in the SCC+ compared to the SCC- group, with primarily medium (Cohen's d = 0.50) to large (0.80) effect sizes. Age did not moderate relationships between SCCs and physical/mental health. Results suggest that SCCs are associated with a diverse set of negative health characteristics such as poor sleep and high body mass index, and lower levels of positive factors, including happiness and wisdom. Effect sizes of psychological correlates were at least as large as those of physical correlates, indicating that mental health is critical to consider when evaluating SCCs.

BackgroundCognitive dysfunction in schizophrenia is the key predictor of functional disability and drives economic burden. Inflammation has been increasingly implicated in the pathogenesis of schizophrenia, yet its role in cognitive decline has not been evaluated. This study explores the association between inflammation and cognitive functioning in persons with schizophrenia.MethodsParticipants included 143 persons with schizophrenia (PwS) and 139 non-psychiatric comparison subjects (NCs) from an ongoing study of aging. Cognitive assessments included validated measures for executive functioning, processing speed, and visuospatial skills. Plasma levels of nine biomarkers associated with inflammation (high sensitivity C-reactive protein, intercellular adhesion molecule 1, serum amyloid A, interleukin-6, interleukin-8, interferon gamma-induced protein-10, monocyte chemotactic protein-1, fractalkine, and brain-derived neurotrophic factor) were quantified using commercially available, enzyme-linked immunosorbent assays. Partial least squares regression was used to develop a composite "inflammatory profile" to maximize correlations with the cognitive outcomes. We then constructed a best-fit model using these composites and their interactions with diagnosis and sex as the predictors, controlling for covariates.ResultsThe biomarker composite, which best correlated with scores on cognitive testing, included high sensitivity C-reactive protein, intercellular adhesion molecule 1, serum amyloid A, interleukin-6, and brain-derived neurotrophic factor, for a 5-biomarker "inflammatory profile." The best-fit model showed a significant biomarker composite by diagnosis by sex three-way interaction, for executive function and processing speed, but not visuospatial skill.ConclusionsThis approach to building an "inflammatory profile" may provide insight into inflammatory pathways affecting brain function and potential targets for anti-inflammatory interventions to improve cognition in schizophrenia.

BackgroundRacemic (R,S)-ketamine is a glutamatergic drug with potent and rapid acting antidepressant effects. An intranasal formulation of (S)-ketamine was recently approved by the US Food and Drug Administration (FDA) to be used in individuals with treatment-resistant depression (TRD). There are no data directly comparing outcomes on depression or other comorbidities between these two formulations of ketamine. However, recent meta-analyses have suggested that IV racemic ketamine may be more potent than IN-(S)-ketamine.MethodsWe retrospectively analyzed clinical outcomes in 15 Veterans with comorbid TRD and post-traumatic stress disorder (PTSD) who underwent ketamine treatment at the VA San Diego Neuromodulation Clinic. All Veterans included in this analysis were given at least 6 intranasal (IN)-(S)-ketamine treatments prior to switching to treatment with IV racemic ketamine.ResultsVeterans receiving ketamine treatment ( across both IN-(S)-ketamine and IV-(R,S)-ketamine) showed significant reductions in both the Patient Health Questionnaire-9 (PHQ-9), a self-report scale measuring depression symptoms (rm ANOVA F(14,42) = 12.6, p

Wisdom is a multi-component trait that is important for mental health and well-being. In this study, we sought to understand gender differences in relative strengths in wisdom. A total of 659 individuals aged 27-103 years completed surveys including the 3-Dimensional Wisdom Scale (3D-WS) and the San Diego Wisdom Scale (SD-WISE). Analyses assessed gender differences in wisdom and gender's moderating effect on the relationship between wisdom and associated constructs including depression, loneliness, well-being, optimism, and resilience. Women scored higher on average on the 3D-WS but not on the SD-WISE. Women scored higher on compassion-related domains and on SD-WISE Self-Reflection. Men scored higher on cognitive-related domains and on SD-WISE Emotion Regulation. There was no impact of gender on the relationships between wisdom and associated constructs. Women and men have different relative strengths in wisdom, likely driven by sociocultural and biological factors. Tailoring wisdom interventions to individuals based on their profiles is an important next step.

BackgroundWith the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.ObjectiveThe aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations.MethodsWe collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses.ResultsWe found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P

ObjectiveThe 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.MethodsWe 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).ResultsParticipants 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).ConclusionsThe 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.

ObjectivesThere has been growing research interest in loneliness and wisdom in recent decades, but no cross-cultural comparisons of these constructs using standardized rating measures in older adults, especially the oldest-old. This was a cross-sectional study of loneliness and wisdom comparing middle-aged and oldest-old adults in Cilento, Italy and San Diego, United States.MethodWe examined loneliness and wisdom, using the UCLA Loneliness Scale Version 3 (UCLA-3) and San Diego Wisdom Scale (SD-WISE), respectively, in four subject groups: adults aged 50-65 and those ≥90 years from Cilento, Italy (N = 212 and 47, respectively) and San Diego, California, USA (N = 138 and 85, respectively).ResultsAfter controlling for education, there were no significant group differences in levels of loneliness, while on SD-WISE the Cilento ≥90 group had lower scores compared to the other three groups. There was a strong inverse correlation between loneliness and wisdom in each of the four subject groups. Loneliness was negatively associated while wisdom was positively associated with general health, sleep quality, and happiness in most groups, with varying levels of significance.ConclusionThese results largely support cross-cultural validity of the constructs of loneliness and wisdom, and extend previous findings of strong inverse correlations between these two entities. Loneliness has become a growing public health problem, and the results of our study suggest that wisdom could be a protective factor against loneliness, although alternative explanations are also possible. Research on interventions to reduce loneliness by enhancing wisdom in older adults is needed.

Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.

BackgroundWrist-worn actigraphy can objectively measure sleep, and has advantages over self-report, particularly for people with Bipolar Disorder (BD) for whom self-reports might be influenced by affect. Clinically useful data reduction approaches are needed to explore these complex data.MethodsWe created a composite score of sleep metrics in BD based on 51 BD and 80 healthy comparison (HC) participants. Subjects wore an actigraph for up to 14 consecutive 24-h periods, and we assessed total sleep time (TST), wake after sleep onset (WASO), percent sleep (PS), and number of awakenings (NA). We focused on participants who had at least 5 nights of actigraphy data. We computed z-scores for within-person means of sleep measures for BD subjects versus HCs, which were averaged to create a composite measure. We correlated this composite with participant characteristics, and used LASSO regression to identify sleep measures best explaining variability in identified correlates.ResultsSleep measures and the composite did not differ between BDs and HCs; however, there was considerable variability in z-scores among those with BD. In BDs, the composite score was higher in women (t(49) = 2.28, p = 0.027) and those who were employed (t(34) = 2.34, p = 0.025), and positively correlated with medication load (r = 0.41, p = 0.003) while negatively correlated with Young Mania Rating Scale (YMRS; r = -0.35, p = 0.030). In LASSO regression, TST and NA best explained medication load while PS best explained employment and YMRS.ConclusionWhile a composite score of sleep metrics may provide useful information about sleep quality globally, our findings suggest that selection of theory-driven sleep measures may be more clinically meaningful.

ObjectiveThe growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.DesignParticipants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared.SettingIndependent living sector of a senior housing community in San Diego County.ParticipantsEighty English-speaking older adults with age range 66-94 (mean 83 years).MeasurementsInterviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions.ResultsLonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89).ConclusionsAI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs.

There is growing interest in the role of compassion in promoting health and well-being, with cross-sectional data showing an inverse correlation with loneliness. This is the first longitudinal study examining both compassion toward others (CTO) and compassion toward self (CTS) as predictors of mental and physical health outcomes including loneliness, across adult lifespan. We followed 552 women and 538 men in San Diego County for up to 7.5 (mean 4.8 and SD 2.2) years, using validated rating scales for CTO, CTS, and loneliness. Linear mixed-effects models were employed to examine age- and sex-related trajectories of CTO and CTS over time. Linear regression models were used to evaluate baseline and longitudinal relationships of CTO and CTS with mental well-being, physical well-being, and loneliness. CTS and CTO were weakly intercorrelated. Women had higher baseline CTO than men. While CTO was stable over time and across the lifespan, CTS scores had an inverse U-shaped relationship with age, peaking around age 77. There were significant baseline × slope interactions of both CTO and CTS predicting improvements in physical well-being in adults

We investigated subjective cognitive complaints (SCCs), as well as physical and mental health factors, in adults and older adults. U.S. residents (N = 2,962) were recruited via the Amazon Mechanical Turk platform and completed a 90-item survey. Overall, 493/1930 (25.5%) of younger adults and 278/1032 (26.9%) of older adults endorsed SCCs. Analyses revealed worse physical and mental health characteristics in the SCC+ compared to the SCC- group, with primarily medium (Cohen's d = 0.50) to large (0.80) effect sizes. Age did not moderate relationships between SCCs and physical/mental health. Results suggest that SCCs are associated with a diverse set of negative health characteristics such as poor sleep and high body mass index, and lower levels of positive factors, including happiness and wisdom. Effect sizes of psychological correlates were at least as large as those of physical correlates, indicating that mental health is critical to consider when evaluating SCCs.

Loneliness and wisdom have opposing impacts on health and well-being, yet their neuro-cognitive bases have never been simultaneously investigated. In this study of 147 healthy human subjects sampled across the adult lifespan, we simultaneously studied the cognitive and neural correlates of loneliness and wisdom in the context of an emotion bias task. Aligned with the social threat framework of loneliness, we found that loneliness was associated with reduced speed of processing when angry emotional stimuli were presented to bias cognition. In contrast, we found that wisdom was associated with greater speed of processing when happy emotions biased cognition. Source models of electroencephalographic data showed that loneliness was specifically associated with enhanced angry stimulus-driven theta activity in the left transverse temporal region of interest, which is located in the area of the temporoparietal junction (TPJ), while wisdom was specifically related to increased TPJ theta activity during happy stimulus processing. Additionally, enhanced attentiveness to threatening stimuli for lonelier individuals was observed as greater beta activity in left superior parietal cortex, while wisdom significantly related to enhanced happy stimulus-evoked alpha activity in the left insula. Our results demonstrate emotion-context driven modulations in cognitive neural circuits by loneliness versus wisdom.

ObjectiveThis study assessed the impact of a Leadership Fellowship, sponsored by the American Psychiatric Association (APA) and APA Foundation, on the careers of psychiatry residents and examined the influence of gender and year of Fellowship completion. This 2-year program for residents offered multiple opportunities to interact with professional leaders at various levels.MethodsA retrospective online survey of alumni of the APA Leadership Fellowship, who had completed this Fellowship between 2003 and 2019, was conducted.ResultsOut of the 93 psychiatrists who were sent the survey, 59 alumni responded (60.8% response rate). Most respondents had remained involved with organized psychiatry groups and 80% held leadership positions. Respondents reported high levels of satisfaction with the APA Fellowship experience, noting the importance of peer networking. Overall, male and female respondents were similar in their subsequent leadership positions and satisfaction with the survey. Similarly, Fellows who completed the Fellowship in 2003-2015 had responses about the experience that were largely similar to those in the 2016-2019 cohort that had not yet completed their residency.ConclusionA vast majority of alumni of the APA Leadership Fellowship had become leaders in their workplace or organized psychiatry groups. The leadership-focused career development programs for psychiatry trainees are important avenues to develop a diverse cohort of future leaders in psychiatry.

BackgroundAs 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.ObjectiveThis 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.MethodsThis 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.ResultsFour 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.ConclusionsOur 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.

ObjectiveOlder adults are at a high risk for loneliness, which impacts their health, well-being, and longevity. While related to social isolation, loneliness is a distinct, internally experienced, distressing feeling. The present qualitative study sought to identify characteristics of loneliness in older adults living independently within a senior housing community, which is typically designed to reduce social isolation.MethodSemi-structured qualitative interviews regarding the experience of loneliness, risk factors, and ways to combat it were conducted with 30 older adults, ages 65-92 years. The interviews were audiotaped, transcribed, and coded using a grounded theory analytic approach based on coding, consensus, co-occurrence, and comparison.ResultsThree main themes with multiple subthemes are described: (A) Risk and Protective factors for loneliness: age-associated losses, lack of social skills or abilities, and protective personality traits; (B) Experience of loneliness: Sadness and lack of meaning as well as Lack of motivation; and (C) Coping strategies to prevent or overcome loneliness: acceptance of aging, compassion, seeking companionship, and environment enables socialization.DiscussionDespite living within a communal setting designed to reduce social isolation, many older adults described feeling lonely in stark negative terms, attributing it to aging-associated losses or lack of social skills and abilities. However, interviewees also reported positive personal qualities and actions to prevent or cope with loneliness, several of which mirrored specific components of wisdom. The results support the reported inverse relationship between loneliness and wisdom and suggest a potential role for wisdom-enhancing interventions to reduce and prevent loneliness in older populations.

ObjectiveOlder adults are at a high risk for loneliness, which impacts their health, well-being, and longevity. While related to social isolation, loneliness is a distinct, internally experienced, distressing feeling. The present qualitative study sought to identify characteristics of loneliness in older adults living independently within a senior housing community, which is typically designed to reduce social isolation.MethodSemi-structured qualitative interviews regarding the experience of loneliness, risk factors, and ways to combat it were conducted with 30 older adults, ages 65-92 years. The interviews were audiotaped, transcribed, and coded using a grounded theory analytic approach based on coding, consensus, co-occurrence, and comparison.ResultsThree main themes with multiple subthemes are described: (A) Risk and Protective factors for loneliness: age-associated losses, lack of social skills or abilities, and protective personality traits; (B) Experience of loneliness: Sadness and lack of meaning as well as Lack of motivation; and (C) Coping strategies to prevent or overcome loneliness: acceptance of aging, compassion, seeking companionship, and environment enables socialization.DiscussionDespite living within a communal setting designed to reduce social isolation, many older adults described feeling lonely in stark negative terms, attributing it to aging-associated losses or lack of social skills and abilities. However, interviewees also reported positive personal qualities and actions to prevent or cope with loneliness, several of which mirrored specific components of wisdom. The results support the reported inverse relationship between loneliness and wisdom and suggest a potential role for wisdom-enhancing interventions to reduce and prevent loneliness in older populations.

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.

ObjectiveWisdom has gained increasing interest among researchers as a personality trait relevant to well-being and mental health. We previously reported development of a new 24-item San Diego Wisdom Scale (SD-WISE), with good to excellent psychometric properties, comprised of six subscales: pro-social behaviors, emotional regulation, self-reflection (insight), tolerance for divergent values (acceptance of uncertainty), decisiveness, and social advising. There is controversy about whether spirituality is a marker of wisdom. The present cross-sectional study sought to address that question by developing a new SD-WISE subscale of spirituality and examining its associations with various relevant measures.MethodsData were collected from a national-level sample of 1,786 community-dwelling adults age 20-82 years, as part of an Amazon M-Turk cohort. Participants completed the 24-item SD-WISE along with several subscales of a commonly used Brief Multidimensional Measure of Religiousness/Spirituality, along with validated scales for well-being, resilience, happiness, depression, anxiety, loneliness, and social network.ResultsUsing latent variable models, we developed a Spirituality subscale, which demonstrated acceptable psychometric properties including a unidimensional factor structure and good reliability. Spirituality correlated positively with age and was higher in women than in men. The expanded 28-item, 7-subscale SD-WISE total score (called the Jeste-Thomas Wisdom Index or JTWI) demonstrated acceptable psychometric properties. The Spirituality subscale was positively correlated with good mental health and well-being, and negatively correlated with poor mental health. However, compared to other components of wisdom, the Spirituality factor showed weaker (i.e., small-to-medium vs. medium-to-large) association with the SD-WISE higher-order Wisdom factor (JTWI).ConclusionSimilar to other components as well as overall wisdom, spirituality is significantly associated with better mental health and well-being, and may add to the predictive utility of the total wisdom score. Spirituality is, however, a weaker contributor to overall wisdom than components like pro-social behaviors and emotional regulation. Longitudinal studies of larger and more diverse samples are needed to explore mediation effects of these constructs on well-being and health.

Introduction: Cognitive deficits in people with schizophrenia (PWS) are a major predictor of disability and functioning, yet the underlying pathophysiology remains unclear. A possible role of amyloid and tau biomarkers (hallmarks of Alzheimer's disease) is still speculative in schizophrenia. Exosomes or extracellular vesicles, involved with cell-to-cell communication and waste removal, can be used to assay brain-based proteins from peripheral blood. To our knowledge, this is the first study of exosomal amyloid and tau protein levels in PWS. Methods: This cross-sectional study included 60 PWS and 60 age- and sex-comparable non-psychiatric comparison subjects (NCs), age range 26-65 years. Assessments of global cognitive screening, executive functioning, psychopathology, and physical measures were conducted. Exosomes were extracted and precipitated from fasting plasma and identified as neuron-derived exosomes (NDEs) or astrocyte-derived exosomes (ADEs). Human-specific ELISAs were used to assay levels of amyloid-beta 1-42 (Aβ42), amyloid-beta 1-40 (Aβ40), and phosphorylated T181 tau (P-T181-tau). Plasma assays for aging biomarkers (C-reactive protein and F2-isoprostanes) were also performed. Results: ADE-Aβ42 levels were higher in PWS compared to NCs, though the other exosomal markers were similar between the two groups. Higher ADE-P-T181-tau levels were associated with worse executive functioning. Among PWS, higher ADE-P-T181-tau levels were associated with less severe negative symptoms and increased F2-isoprostane levels. Astrocyte-derived Aβ marker levels were sensitive and specific in differentiating between diagnostic groups. Among PWS, Aβ40 levels differed most by exosomal origin. Discussion: Exosomal markers may provide novel insights into brain-based processes (e.g., aging, oxidative stress) from peripheral blood samples.

Wisdom is a unique human personality trait with cognitive, affective or compassionate, and reflective dimensions. We evaluated relationships of three specific dimensions of wisdom with cognitive function and physical and mental well-being in people with HIV (PWH) and HIV-negative (HIV-) participants. Subjects included 138 adults (61 PWH, 77 HIV-) from the San Diego community. Validated measures were used to assess wisdom and well-being. Cognitive function was assessed via the Montreal Cognitive Assessment. We conducted multivariate linear regressions to evaluate the associations of wisdom dimensions with cognitive function and physical and mental well-being. Compared to the HIV- group, PWH had lower mean scores on cognitive function, and physical and mental well-being, and cognitive and reflective dimensions of wisdom, but similar scores on affective or compassionate wisdom. Among PWH, higher total wisdom scores were associated with older age, lower likelihood of substance dependence, greater mental well-being, better cognitive function, higher resilience, social support, and optimism scores, as well as lower levels of perceived stress and nadir CD4 count. Our findings of an association of different dimensions of wisdom with physical and/or mental well-being in PWH would point to a possibility that enhancing these dimensions of wisdom might improve health outcomes in PWH.

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.

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.

ObjectivePoor sleep quality is related to worse neurocognition in older adults and in people with HIV (PWH); however, many previous studies have relied only on self-report sleep questionnaires, which are inconsistently correlated with objective sleep measures. We examined relationships between objective and subjective sleep quality and neurocognition in persons with and without HIV, aged 50 and older. Method: Eighty-five adults (PWH n = 52, HIV-negative n = 32) completed comprehensive neuropsychological testing to assess global and domain-specific neurocognition. Objective sleep quality was assessed with wrist actigraphy (total sleep time, efficiency, sleep fragmentation) for five to 14 nights. Subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index. Results: Objective and subjective sleep measures were unrelated (p's > 0.30). Compared to HIV-negative participants, PWH had greater sleep efficiency (80% vs. 75%, p = 0.05) and were more likely to be using prescription and/or over the counter sleep medication (p = 0.04). In the whole sample, better sleep efficiency (p

ImportanceWisdom is a neurobiological personality trait made up of specific components, including prosocial behaviors, emotional regulation, and spirituality. It is associated with greater well-being and happiness.ObjectiveTo evaluate the effectiveness of interventions to enhance individual components of wisdom.Data sourcesMEDLINE and PsycINFO databases were searched for articles published through December 31, 2018.Study eligibility criteriaRandomized clinical trials that sought to enhance a component of wisdom, used published measures to assess that component, were published in English, had a minimum sample size of 40 participants, and presented data that enabled computation of effect sizes were included in this meta-analysis.Data extraction and synthesisRandom-effect models were used to calculate pooled standardized mean differences (SMDs) for each wisdom component and random-effects meta-regression to assess heterogeneity of studies.Main outcomes and measuresImprovement in wisdom component using published measures.ResultsFifty-seven studies (N = 7096 participants) met review criteria: 29 for prosocial behaviors, 13 for emotional regulation, and 15 for spirituality. Study samples included people with psychiatric or physical illnesses and from the community. Of the studies, 27 (47%) reported significant improvement with medium to large effect sizes. Meta-analysis revealed significant pooled SMDs for prosocial behaviors (23 studies; pooled SMD, 0.43 [95% CI, 0.22-0.3]; P = .02), emotional regulation (12 studies; pooled SMD, 0.67 [95% CI, 0.21-1.12]; P = .004), and spirituality (12 studies; pooled SMD, 1.00 [95% CI, 0.41-1.60]; P = .001). Heterogeneity of studies was considerable for all wisdom components. Publication bias was present for prosocial behavior and emotional regulation studies; after adjusting for it, the pooled SMD for prosocial behavior remained significant (SMD, 0.4 [95% CI, 0.16-0.78]; P = .003). Meta-regression analysis found that effect sizes did not vary by wisdom component, although for trials on prosocial behaviors, large effect sizes were associated with older mean participant age (β, 0.08 [SE, 0.04]), and the reverse was true for spirituality trials (β, -0.13 [SE, 0.04]). For spirituality interventions, higher-quality trials had larger effect sizes (β, 4.17 [SE, 1.07]), although the reverse was true for prosocial behavior trials (β, -0.91 [SE 0.44]).Conclusions and relevanceInterventions to enhance spirituality, emotional regulation, and prosocial behaviors are effective in a proportion of people with mental or physical illnesses and from the community. The modern behavioral epidemics of loneliness, suicide, and opioid abuse point to a growing need for wisdom-enhancing interventions to promote individual and societal well-being.

BackgroundThe 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).MethodsWe 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.ResultsAW 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.ConclusionsA 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.

AbstractSubjective cognitive decline (SCD), or internal feelings of reduced mental capacity, is of increasing interest in the scientific, clinical, and lay community. Much of the extant literature is focused on SCD as a risk factor for Alzheimer’s disease in older adults, while less attention has been paid to non-cognitive health correlates of SCD across adulthood. Consequently, we investigated physical and mental health correlates of SCD in younger, middle-aged, and older adults. We recruited 3,407 U.S. residents through Amazon’s Mechanical Turk, an online labor market. Participants completed a 90-item self-report survey questionnaire assessing sociodemographic characteristics, physical health, sleep, depression, anxiety, loneliness, wisdom, self-efficacy, and happiness. Overall, 493/1930 (25.5%) of younger adults (18-49) and 278/1032 (26.9%) of older adults (50 or older) endorsed the SCD item. Multivariate analysis of variance and follow-upt-tests revealed worse physical and mental health characteristics in people endorsing SCD compared to those who did not, with effect sizes primarily in the medium to large range. Additionally, age did not moderate relationships between SCD and physical and mental health. Results suggest that SCD is associated with a diverse set of negative health characteristics such as poor sleep and high body mass index, and lower levels of positive factors including happiness and wisdom. Effect sizes of psychological correlates of SCD were as large as (or larger than) those of physical correlates, indicating that mental health and affective symptoms may be critical to consider when evaluating SCD. Overall, findings from this large, national U.S. sample suggest the presence of relationships between SCD and multiple psychological and perceived health factors; our results also show that SCD may be highly prevalent in both younger and older adults, suggesting that it be assessed across the adult lifespan.

BackgroundAging is associated with numerous stressors that negatively impact older adults' well-being. Resilience improves ability to cope with stressors and can be enhanced in older adults. Senior housing communities are promising settings to deliver positive psychiatry interventions due to rising resident populations and potential impact of delivering interventions directly in the community. However, few intervention studies have been conducted in these communities. We present a pragmatic stepped-wedge trial of a novel psychological group intervention intended to improve resilience among older adults in senior housing communities.DesignA pragmatic modified stepped-wedge trial design.SettingFive senior housing communities in three states in the US.ParticipantsEighty-nine adults over age 60 years residing in independent living sector of senior housing communities.InterventionRaise Your Resilience, a manualized 1-month group intervention that incorporated savoring, gratitude, and engagement in value-based activities, administered by unlicensed residential staff trained by researchers. There was a 1-month control period and a 3-month post-intervention follow-up.MeasurementsValidated self-report measures of resilience, perceived stress, well-being, and wisdom collected at months 0 (baseline), 1 (pre-intervention), 2 (post-intervention), and 5 (follow-up).ResultsTreatment adherence and satisfaction were high. Compared to the control period, perceived stress and wisdom improved from pre-intervention to post-intervention, while resilience improved from pre-intervention to follow-up. Effect sizes were small in this sample, which had relatively high baseline resilience. Physical and mental well-being did not improve significantly, and no significant moderators of change in resilience were identified.ConclusionThis study demonstrates feasibility of conducting pragmatic intervention trials in senior housing communities. The intervention resulted in significant improvement in several measures despite ceiling effects. The study included several features that suggest high potential for its implementation and dissemination across similar communities nationally. Future studies are warranted, particularly in samples with lower baseline resilience or in assisted living facilities.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.

ObjectiveLoneliness is a prevalent and serious public health problem due to its effects on health, well-being, and longevity. Understanding correlates of loneliness is critical for guiding efforts toward the development of evidence-based strategies for prevention and intervention. Considering that patterns of association between age and loneliness vary, the present study sought to examine age-related differences in risk and protective factors for loneliness.MethodsCorrelates of loneliness were examined through a large web-based survey of 2,843 participants (aged 20-69 years) from across the United States from April 10, 2019, through May 10, 2019. Participants completed the 4-item UCLA Loneliness Scale, San Diego Wisdom Scale (with the following subscales measuring components of wisdom: Prosocial Behaviors, Emotional Regulation, Self-Reflection, Acceptance of Divergent Values, Decisiveness, and Social Advising), and other scales measuring psychosocial variables. Multivariate regression analyses were conducted to identify the best model of loneliness and examine potential age-related differences.ResultsAge demonstrated a nonlinear quadratic relationship with loneliness (Wald statistic = 5.48, P = .019); levels were highest in the 20s and lowest in the 60s with another peak in the mid-40s. Across all decades, loneliness was associated with not having a spouse or partner (P < .001), sleep disturbance (P < .02), lower prosocial behaviors (P < .001), and smaller social network (P < .001). Lower social self-efficacy (P < .001) and higher anxiety (P < .005) were associated with worse loneliness in all age decades, except the 60s. Loneliness was uniquely associated with decisiveness in the 50s (P = .012) and with education (P = .046) and memory complaints (P = .013) in the 60s.ConclusionsOur findings identify several potentially modifiable targets related to loneliness, including several aspects of wisdom and social self-efficacy. Differential predictors at different decades suggest a need for a personalized and nuanced prioritizing of prevention and intervention targets.

Introduction: Cognitive deficits in people with schizophrenia (PWS) are a major predictor of disability and functioning, yet the underlying pathophysiology remains unclear. A possible role of amyloid and tau biomarkers (hallmarks of Alzheimer's disease) is still speculative in schizophrenia. Exosomes or extracellular vesicles, involved with cell-to-cell communication and waste removal, can be used to assay brain-based proteins from peripheral blood. To our knowledge, this is the first study of exosomal amyloid and tau protein levels in PWS. Methods: This cross-sectional study included 60 PWS and 60 age- and sex-comparable non-psychiatric comparison subjects (NCs), age range 26-65 years. Assessments of global cognitive screening, executive functioning, psychopathology, and physical measures were conducted. Exosomes were extracted and precipitated from fasting plasma and identified as neuron-derived exosomes (NDEs) or astrocyte-derived exosomes (ADEs). Human-specific ELISAs were used to assay levels of amyloid-beta 1-42 (Aβ42), amyloid-beta 1-40 (Aβ40), and phosphorylated T181 tau (P-T181-tau). Plasma assays for aging biomarkers (C-reactive protein and F2-isoprostanes) were also performed. Results: ADE-Aβ42 levels were higher in PWS compared to NCs, though the other exosomal markers were similar between the two groups. Higher ADE-P-T181-tau levels were associated with worse executive functioning. Among PWS, higher ADE-P-T181-tau levels were associated with less severe negative symptoms and increased F2-isoprostane levels. Astrocyte-derived Aβ marker levels were sensitive and specific in differentiating between diagnostic groups. Among PWS, Aβ40 levels differed most by exosomal origin. Discussion: Exosomal markers may provide novel insights into brain-based processes (e.g., aging, oxidative stress) from peripheral blood samples.

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.