Search

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

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.

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.

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.

Increasing well-being, happiness, resilience, wisdom, and related positive psychiatry outcomes is possible via behavioral and psychological interventions. Convergence of psychiatry with computer science, engineering, and related disciplines will allow for the impact of these interventions to be personalized, maintained, and augmented in a number of ways. To illustrate such potential impacts, the authors use an example of a recent trial of a novel intervention to improve resilience within senior housing communities. Specialized technologies have the potential to improve fidelity and dissemination of positive psychiatry interventions. With the shortage of specialized mental health clinicians and the growing need for mental health services, technology can be used to extend the in-clinic services and provide greater support in the free-living environment. Development of such technologies to improve well-being will require mental health specialists to work directly with computer scientists, engineers, and people with mental illnesses. These collaborations will require infrastructure and support from research and academic organizations that partner with industry and community-based agencies and organizations.

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.

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.

Loneliness 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. Correlates 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. Age 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. Our 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.

Late-onset schizophrenia remains an understudied subtype of schizophrenia, despite growing recognition of its impact and distinction from early-onset schizophrenia. This chapter reviews the existing literature on late-onset schizophrenia including beginning with the nomenclature and epidemiology. Then we provide a review of key risk factors and correlates—including genetic risk, sex differences, comorbid sensory loss and physical illness, cognitive and psychiatric symptoms, sociodemographic factors, adverse life events, neuropathology, and inflammation. The chapter ends with clinical issues, including symptoms, differential diagnosis, treatments, and prognosis. Recent studies have examined the role of oestrogen treatments and a new therapy for tardive dyskinesia therapy as well as inflammatory mechanisms in schizophrenia.

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.

The World Health Organization (WHO) estimates older adults aged 60+ will double by 2050 with 80% living in low to moderate income countries. As remote research studies supported by digital devices increase separation be- tween researchers and participants, it is important to maintain participant trust. Research participants have expressed an interest in accessing both group and in- dividual level results, which are not easily accessible. To bridge this gap, we en- gaged residents of a local continuing care senior housing community (CCSHC) to co-design documents used to convey information about study results. The pro- cess informed the refinement of informational materials for communicating sci- entific research that the CCSHC community considers accessible and meaning- ful.

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.

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.

Individuals with schizophrenia have higher mortality rates than the population at large. General mortality rates have declined in developed countries since the early 1970s, extending average lifespan by nearly a decade. This review of eight longitudinal studies of mortality in schizophrenia found that the mean standardized mortality ratio (SMR, a measure of mortality rate in schizophrenia compared to the general population) increased 37%, from 2.2 in the pre-1970s studies to 3.0 in the post-1970s reports. Major changes in societal stigma, healthcare, and economic policy are urgently warranted to ensure that this vulnerable segment of the population participates in the longevity revolution.

ObjectiveInflammation may play a role in the accelerated physical aging reported in schizophrenia, though biomarker findings and associations with demographic and clinical factors are inconsistent.MethodsIn a cross-sectional, case-control design, 95 outpatients with schizophrenia (mean age ± SD: 48.1 ± 10.2 years) and 95 demographically comparable healthy comparison subjects (HCs) (mean age ± SD: 48.1 ± 12.1 years) were studied. Sociodemographic and clinical data were collected, and plasma levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interferon-γ (IFN-γ) were assayed. The authors compared cytokine levels, examined demographic and clinical associations, and adjusted for relevant variables with linear models.ResultsIndividuals with schizophrenia had higher levels of TNF-α and IL-6 but not IFN-γ than HCs. Age was not related to cytokine levels, and age relationships did not differ between diagnostic groups. Women had higher levels of IL-6. TNF-α and IL-6 levels were significantly correlated with depressive symptoms, and adjustment for depression reduced the group effect for both. Within the HCs, TNF-α levels were associated with physical comorbidity and body mass index. IL-6 levels were significantly correlated with body mass index and within schizophrenia patients, with worse mental and physical well-being. Accounting for physical morbidity and mental well-being reduced group differences in TNF-α and IL-6 levels, respectively. Worse positive symptoms were associated with higher IL-6 levels.ConclusionHigher TNF-α and IL-6 levels in schizophrenia patients were associated with depression, physical comorbidity, and mental well-being. Further longitudinal studies are warranted to assess inflammation as a potential treatment target for a subgroup of schizophrenia.