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Recent technology has enabled researchers to collect ecological momentary assessments (EMA) to examine within-person correlates of suicidal thoughts. Prior studies examined generalized temporal dynamics of emotions and suicidal thinking over brief periods, but it is not yet known how variable these processes are across people. We use data EMA data delivered over two weeks with youth/young adults (N = 60) who reported past year self-injurious thoughts/behaviors. We used group iterative multiple model estimation (GIMME) to model group- and person-specific associations of negative emotions (i.e., fear, sadness, shame, guilt, and anger) and suicidal thoughts. 29 participants (48.33%) reported at least one instance of a suicidal thought and were included in GIMME models. In group level models, we consistently observed autoregressive effects for suicidal thoughts (e.g., earlier thoughts predicting later thoughts), although the magnitude and direction of this link varied from person-to-person. Among emotions, sadness was most frequently associated with contemporaneous suicidal thoughts, but this was evident for less than half of the sample, while other emotional correlates of suicidal thoughts broadly differed across people. No emotion variable was linked to future suicidal thoughts in >14% of the sample, Emotion-based correlates of suicidal thoughts are heterogeneous across people. Better understanding of the individual-level pathways maintaining suicidal thoughts/behaviors may lead to more effective, personalized interventions. •Suicide is a leading cause of death.•Mental health providers are limited in their ability to predict suicide risk.•We used intensive longitudinal data and idiographic networks.•These methods tested person-specific links in affect and suicidal thoughts (STBs).•Although links of emotions and STBs varied; there were unique subgroups.

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. Summary 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.

Cognitive tasks delivered during ecological momentary assessment (EMA) may elucidate the short-term dynamics and contextual influences on cognition and judgements of performance. This paper provides initial validation of a smartphone task of facial emotion recognition in serious mental illness. A total of 86 participants with psychotic disorders (non-affective and affective psychosis), aged 19-65, were administered in-lab 'gold standard' affect recognition, neurocognition, and symptom assessments. They subsequently completed 10 days of the mobile facial emotion recognition task, assessing both accuracy and self-assessed performance, along with concurrent EMA of psychotic symptoms and mood. Validation focused on task adherence and predictors of adherence, gold standard convergent validity, and symptom and diagnostic group variation. The mean rate of adherence to the task was 79%; no demographic or clinical variables predicted adherence. Convergent validity was observed with in-lab measures of facial emotion recognition, and no practice effects were observed on the mobile facial emotion recognition task. EMA reports of more severe voices, sadness, and paranoia were associated with worse performance, whereas mood more strongly associated with self-assessed performance. The mobile facial emotion recognition task was tolerated and demonstrated convergent validity with in-lab measures of the same construct. Social cognitive performance, and biased judgements previously shown to predict function, can be evaluated in real-time in naturalistic environments.

There is no consensual definition of “successful aging.” Our aim was to review the literature on proportions of subjects meeting criteria and individual components of definitions of successful aging as well as correlates of these definitions. We conducted a literature search for published English-language peer-reviewed reports of data-based studies of adults over age 60 that included an operationalized definition of successful aging. The authors categorized the components of these definitions and independent variables examined in relation to successful aging (e.g., gender, education, and social contacts). The authors identified 28 studies with 29 different definitions that met our criteria. Most investigations used large samples of community-dwelling older adults. The mean reported proportion of successful agers was 35.8% (standard deviation: 19.8) but varied widely (interquartile range: 31%). Multiple components of these definitions were identified, although 26 of 29 included disability/physical functioning. The most frequent significant correlates of the various definitions of successful aging were age (young-old), nonsmoking, and absence of disability, arthritis, and diabetes. Moderate support was found for greater physical activity, more social contacts, better self-rated health, absence of depression and cognitive impairment, and fewer medical conditions. Gender, income, education, and marital status generally did not relate to successful aging. Despite variability among definitions, approximately one-third of elderly individuals were classified as aging successfully. The majority of these definitions were based on the absence of disability with lesser inclusion of psychosocial variables. Predictors of successful aging varied yet point to several potentially modifiable targets for increasing the likelihood of successful aging.

ABSTRACTObjectivesThis study of loneliness across adult lifespan examined its associations with sociodemographics, mental health (positive and negative psychological states and traits), subjective cognitive complaints, and physical functioning. DesignAnalysis of cross-sectional data Participants340 community-dwelling adults in San Diego, California, mean age 62 (SD = 18) years, range 27–101 years, who participated in three community-based studies. MeasurementsLoneliness measures included UCLA Loneliness Scale Version 3 (UCLA-3), 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Social Isolation Scale, and a single-item measure from the Center for Epidemiologic Studies Depression (CESD) scale. Other measures included the San Diego Wisdom Scale (SD-WISE) and Medical Outcomes Survey- Short form 36. ResultsSeventy-six percent of subjects had moderate-high levels of loneliness on UCLA-3, using standardized cut-points. Loneliness was correlated with worse mental health and inversely with positive psychological states/traits. Even moderate severity of loneliness was associated with worse mental and physical functioning. Loneliness severity and age had a complex relationship, with increased loneliness in the late-20s, mid-50s, and late-80s. There were no sex differences in loneliness prevalence, severity, and age relationships. The best-fit multiple regression model accounted for 45% of the variance in UCLA-3 scores, and three factors emerged with small-medium effect sizes: wisdom, living alone and mental well-being. ConclusionsThe alarmingly high prevalence of loneliness and its association with worse health-related measures underscore major challenges for society. The non-linear age-loneliness severity relationship deserves further study. The strong negative association of wisdom with loneliness highlights the potentially critical role of wisdom as a target for psychosocial/behavioral interventions to reduce loneliness. Building a wiser society may help us develop a more connected, less lonely, and happier society.

Lay perceptions of “successful aging” are important for understanding this multifaceted construct and developing ways to assist older adults to age well. The purpose of this qualitative study was to obtain older adults' individual perspectives on what constitutes successful aging along with their views regarding activities and interventions to enhance its likelihood. Qualitative interviews were conducted with 22 community-dwelling adults older than 60 years. Participants were recruited from retirement communities, a low-income senior housing complex, and a continued learning center in San Diego County, CA. Interview transcripts were analyzed using a “Coding Consensus, Co-occurrence, and Comparison” grounded theory framework. The mean age of participants was 80 years (range: 64–96 years), with 59% being women. Two primary themes were identified as key to successful aging, i.e., self-acceptance/self-contentment (with subthemes of realistic self-appraisal, a review of one's life, and focusing on the present) and engagement with life/self-growth (with subthemes of novel pursuits, giving to others, social interactions, and positive attitude). A balance between these two constructs seemed critical. A need for interventions that address support systems and personally tailored information to make informed decisions and enhance coping strategies were also emphasized. Older adults viewed successful aging as a balance between self-acceptance and self-contentedness on one hand and engagement with life and self-growth in later life on the other. This perspective supports the concept of wisdom as a major contributor to successful aging. Interventions to enhance successful aging may include those that promote productive and social engagement along with effective coping strategies.

Psychosocial interventions for serious mental illness are resource intensive and poorly accessible. Brief interventions (eg, single session) that are augmented by follow-on automated mobile health intervention may expand treatment access. This was a randomized single-blind controlled trial with 255 individuals diagnosed with schizophrenia or bipolar disorder. Participants were randomized to one of three conditions: CBT2go, which combined one individual session of cognitive behavioral therapy with automated thought challenging/adaptive behavior delivered through mobile devices; Self-Monitoring (SM), which combined single-session illness psychoeducation with self-monitoring of symptoms; and treatment-as-usual (TAU). Participants were assessed at baseline, 6 weeks (midpoint), 12 weeks (posttreatment), and 24 weeks (follow-up) with our primary outcome global psychopathology (Brief Psychiatric Rating Scale-expanded version [BPRS-24]), and secondary outcomes community functioning (Specific Level of Function; SLOF) and defeatist performance beliefs (DPBs). We also collected data on adverse events. Outcome analyses on the primary outcome, BPRS Total score, indicated a significant time (0-24 wk) by group interaction with significant but modest improvement comparing two active conditions (CBT2go and SM) relative to TAU. Effects of CBT2go were not different from SM. There was a significant time × group interaction with better SLOF scores in CBT2go across 24 weeks, but not in SM. There were no time-by-group effects on DPBs. DPBs decreased in the CBT2go condition but not in SM. These results indicated that single intervention augmented by mobile intervention was feasible and associated with small yet sustained effects on global psychopathology and, when inclusive of CBT, community function compared with usual care.

Mobile cognitive testing is growing in popularity, with numerous advantages over traditional cognitive testing; however, the field lacks studies that deeply examine mobile cognitive test data from general adult samples. This study characterized performance for a suite of 8 mobile cognitive tests from the NeuroUX platform in a sample of US adults across the adult lifespan. Overall, 393 participants completed 8 NeuroUX cognitive tests and a brief ecological momentary assessment survey once per day on their smartphones for 10 consecutive days; each test was administered 5 times over the testing period. The tests tapped the domains of executive function, processing speed, reaction time, recognition memory, and working memory. Participants also completed a poststudy usability feedback survey. We examined alternate form test-retest reliability; practice effects; and associations between scores (averages and intraindividual variability) and demographics as well as test-taking context (ie, smartphone type, being at home vs not at home, and being alone vs not alone). Our final sample consisted of 393 English-speaking US residents (aged 20-79 y; female: n=198, 50.4%). Of the 367 participants who provided responses about their race and ethnicity, 258 (70.3%) were White. Of the 393 participants, 181 (46.1%) were iOS users, and 212 (53.9%) were Android users. Of 12 test scores derived from the 8 tests, 9 (75%) showed good to excellent test-retest reliability (intraclass correlation coefficients >0.76). Practice effects (ie, improvements in performance) were observed for 4 (33%) of the 12 scores. Older age was associated with worse performance on most of the test scores (9/12, 75%) and greater within-person variability for nearly all reaction time scores (3/4, 75%). Relationships with smartphone type showed better performance among iOS users and those with newer Android software versions compared to those with older software. Being at home (vs not at home) was associated with better performance on tests of processing speed. Being alone (vs not alone) was associated with better performance on tests of recognition and working memory. Poststudy feedback indicated that participants found NeuroUX easy to learn and use, an enjoyable experience, and an app that would be helpful in understanding their thinking skills. Only 4.2% (16/379) endorsed privacy concerns, and 77.3% (293/379) reported that they would be willing to share their results with their health care provider. Older age-but not other demographics-was associated with finding the tests more challenging. In a sample of adults across a wide age range, this study characterized features that are particularly important for the interpretation of remote, repeated mobile cognitive testing performance, including test-retest reliability, practice effects, smartphone type, and test-taking context. These data enhance the understanding and application of mobile cognitive testing, paving the way for improved clinical decision-making, personalized interventions, and advancements in cognitive research.

Abstract Smartphone-based ecological mobile cognitive tests (EMCTs) can measure cognitive abilities in the real world, complementing traditional neuropsychological assessments. We evaluated the validity of an EMCT of recognition memory designed for use with people with serious mental illness, as well as relevant contextual influences on performance. Participants with schizophrenia (SZ), schizoaffective disorder, and bipolar disorder (BD) completed in-lab assessments of memory (Hopkins Verbal Learning Test, HVLT), other cognitive abilities, functional capacity, and symptoms, followed by 30 days of EMCTs during which they completed our Mobile Variable Difficulty List Memory Test (VLMT) once every other day (3 trials per session). List length on the VLMT altered between 6, 12, and 18 items. On average, participants completed 75.3% of EMCTs. Overall performance on VLMT 12 and 18 items was positively correlated with HVLT (ρ = 0.52, P < .001). People with BD performed better on the VLMT than people with SZ. Intraindividual variability on the VLMT was more specifically associated with HVLT than nonmemory tests and not associated with symptoms. Performance during experienced distraction, low effort, and out of the home location was reduced yet still correlated with the in-lab HVLT. The VLMT converged with in-lab memory assessment, demonstrating variability within person and by different contexts. Ambulatory cognitive testing on participants’ personal mobile devices offers more a cost-effective and “ecologically valid” measurement of real-world cognitive performance.