Eric Granholm’s research while affiliated with United States Department of Veterans Affairs and other places

Background and Hypothesis People with serious mental illness (SMI) have an increased risk of suicide ideation (SI) and suicide behavior (SB). Longitudinal studies on factors contributing to SI/SB in SMI are lacking. Interpersonal biases (ie, perceived burdensomeness and thwarted belongingness) are cross-sectionally related to SI/SB, but do they relate to longitudinal suicide risk or other illness factors? Ecological momentary assessment (EMA) offers a powerful approach to a deeper understanding of these complex relationships. Study Design Participants with SMI (N = 180) completed 3 in-lab visits (baseline, 6-month, and 12-month) and 10 days of EMA (3×/day) following the baseline visit. At all timepoints, participants were assessed for SI/SB and were classified as persistent, intermittent, or no SI or any reports of SB over the 12-month follow-up. Multinomial logistic regression models examined whether EMA burdensomeness, belongingness, social motivations, and psychotic symptoms predicted SI persistence or SB over 12 months. Time-series network analysis compared participants’ EMA data by baseline SI. Study Results Burdensomeness and belongingness related to persistent SI 12 months, as did voices, suspiciousness, and social motivations. Only burdensomeness and belongingness related to increased risk of SB over 12 months. Network analyses revealed unique lagged relationships in the baseline SI group: of suspiciousness to belongingness and social avoidance motivation to burdensomeness when compared to the baseline group without SI. Conclusions These findings indicate the importance of interpersonal risk factors and suspiciousness to trajectories of SI and SB over 12 months in SMI. Pending replication, these constructs may be potential suicide prevention treatment targets in SMI.

UNSTRUCTURED Subjective experiences of well-being, emotions, pain, and loneliness are a primary concern of persons living with Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) and their caregivers. Reliable and valid measurement approaches are critical to quantifying these inner processes in treatment studies, but AD/ADRD produce substantial roadblocks to self-report measures of subjective experiences. Technological approaches to gathering passive sensing data about subjective experiences have recently been developed. A promise of passive sensors is their capacity to gather continuous information that could be used to understand within-person dynamics of subjective experiences, and ultimately to better personalize interventions to unique timings and contexts. While there is much enthusiasm about the potential for such measurement tools, there remain a number of challenges (and opportunities for innovation) to generate useful passive measurement tools in AD/ADRD. We briefly summarize some of these challenges and provide suggestions for future innovators in this area.

Background and Hypothesis In accordance with the Cognitive Model of Negative Symptoms, defeatist performance beliefs (DPBs) are an important psychosocial mechanism of negative symptoms in schizophrenia-spectrum groups. DPBs are also mediators of negative symptom improvement in clinical trials. Despite the clinical significance of DPBs and their inclusion as a mechanism of change measure in clinical trials, the psychometric properties of the DPB scale have not been examined in any schizophrenia-spectrum group. Study Design This study evaluated the factor structure, reliability, and validity of the DPB scale in 943 schizophrenia and 250 clinical high-risk for psychosis (CHR) participants from multiple US sites. Confirmatory factor analyses tested competing factor structures: a unidimensional model—consistent with how DPBs are currently assessed—and multifactorial models with up to 4 factors identified with exploratory factor analyses. Study Results Models with 3 and 4 factors provided superior fit compared to the unidimensional model, with an advantage for the 3-factor model. The 3-factor model, consisting of Overvaluing Success, Overvaluing Failure, and Overvaluing Social Evaluation factors, demonstrated good replicability, temporal stability, and measurement invariance in schizophrenia and CHR samples. Convergent validity was demonstrated via significant correlations with negative symptoms and functioning, but limited associations were present with neurocognition. Discriminant validity was supported by low correlations with positive symptoms. Conclusions Findings support the validity and reliability of the 3-factor structure of the DPB scale across phases of psychosis. Use of a 3-factor structure may clarify the most critical DPB targets for negative symptom treatment and early prevention and intervention.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.

Importance Primary open-angle glaucoma (POAG) polygenic risk scores (PRSs) continue to be evaluated in primarily European-ancestry populations despite higher prevalence and worse outcomes in African-ancestry populations. Objective To evaluate how established POAG PRSs perform in African-ancestry samples from the Genetics in Glaucoma Patients of African Descent (GIGA), Genetics of Glaucoma in Individuals of African Descent (GGLAD), and Million Veteran Program (MVP) datasets and compare these with European-ancestry samples. Design, Setting, and Participants This was a multicenter, cross-sectional study of POAG cases and controls from Tanzania, South Africa, Nigeria, Ghana, and the US. Included were individuals of African descent from South Africa and Tanzania from the GIGA dataset; individuals of African descent from Ghana, Nigeria, and the US from the GGLAD dataset; and individuals of African or European descent from the US in the MVP dataset. Data were analyzed from January 2022 to July 2023. Exposures Three PRSs derived from large meta-analyses of European and Asian populations, namely Gharahkhani et al (Gharahkhani PRS), Han et al (Han PRS), and Craig et al (Craig PRS). Main Outcomes and Measures Odds ratios (ORs) for POAG risk stratification comparing the highest and lowest quintiles; area under the receiver operating characteristic curve (AUROC), and liability coefficient of determination ( R ² ) for the addition of PRS to a baseline of age, sex, and first 5 principal components. Results A total of 11 673 cases and 66 432 controls were included in this study across 7 ancestral groups. Mean (SD) age of the total participants was 76.9 (8.7) years, with 74 304 males (95.1%). The following were included in each dataset: GIGA (663 cases, 476 controls), GGLAD (1471 cases, 1482 controls), and MVP (9559 cases, 64 474 controls). Increases in ORs were found for the highest POAG risk quintile ranging from an OR of 1.68 (95% CI, 1.17-2.43) in Ghanaians to 7.05 (95% CI, 2.73-19.6) in the South African multiple ancestry group (which derives from at least 5 distinct ancestral groups: Khoisan, Bantus, Europeans, Indians, and Southeast Asians) with the Gharahkhani PRS. The Han PRS showed OR increases for the highest POAG risk quintile ranging from 2.27 (95% CI, 1.49-3.47) in African American individuals in the GGLAD dataset to 7.24 (95% CI, 6.47-8.12) in Europeans. The Craig PRS predicted OR increases in the highest quintile for all groups ranging from 1.51 (95% CI, 1.05-2.18) in Ghanaians to 6.31 (95% CI, 5.67-7.04) in Europeans. However, AUROC and R ² increases above baseline were lower for all African-ancestry compared with European-ancestry groups in the 3 tested PRSs. Conclusions and Relevance In this cross-sectional study, despite some improvements in OR-based risk stratification using the Gharahkhani PRSs, Han PRSs, and Craig PRSs, consistently lower improvements in AUROC and R ² for African-ancestry compared with European-ancestry groups highlight the need for risk prediction models tailored to diverse populations.

Objectives Currently, there are no effective treatments for functional outcomes (i.e., role and social) and negative symptoms for youth at clinical high-risk (CHR) for psychosis. Investigations into possible mechanisms that may contribute to the improvement of functioning and negative symptoms are needed in CHR research to help inform psychosocial treatments. The present study examined whether functioning and negative symptoms were mediated by asocial beliefs, defeatist beliefs, self-efficacy, maladaptive schemas, anxiety, depression, social cognition, or attenuated psychotic symptoms (APS) in a large clinical trial. Methods CHR participants ( n = 203; 104 females; 99 males) were recruited as part of a three-site randomized control trial comparing group cognitive-behavioural social skills training (CBSST) versus a supportive therapy group. Mediation analyses were conducted to test the relationships between treatment group, mediators (asocial beliefs, defeatist beliefs, self-efficacy, maladaptive schemas, anxiety, depression, social cognition, and APS), and outcome (social and role functioning, and negative symptoms). The mediation analyses employed conditional process path analysis via ordinary least squares regression. Results At the end of treatment, but not 12-month follow-up, more severe APS were found to mediate the impact of treatment on negative symptoms, and social and role functioning. The greater the severity of APS, the less likely that CBSST would result in improvement in negative symptoms and social and role functioning. Many of the other variables showed significant associations with social (less for role) functioning and negative symptoms but did not mediate the effect of treatment on these outcomes at the end of treatment or 12-month follow-up. Conclusions There were no significant mediators except for APS at the end of treatment. Since more severe APS may result in participants being unable to fully participate in therapy and thus limit their gains, clinical implications may include offering some individual therapy to prepare these young people to benefit from the group treatment.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.

Background Patient participation in treatment decision making is a pillar of recovery-oriented care and is associated with improvements in empowerment and well-being. Although demand for increased involvement in treatment decision-making is high among veterans with serious mental illness, rates of involvement are low. Collaborative decision skills training (CDST) is a recovery-oriented, skills-based intervention designed to support meaningful patient participation in treatment decision making. An open trial among veterans with psychosis supported CDST’s feasibility and demonstrated preliminary indications of effectiveness. A randomized control trial (RCT) is needed to test CDST’s effectiveness in comparison with an active control and further evaluate implementation feasibility. Methods The planned RCT is a hybrid type 1 trial, which will use mixed methods to systematically evaluate the effectiveness and implementation feasibility of CDST among veterans participating in a VA Psychosocial Rehabilitation and Recovery Center (PRRC) in Southern California. The first aim is to assess the effectiveness of CDST in comparison with the active control via the primary outcome, collaborative decision-making behavior during usual care appointments between veterans and their VA mental health clinicians, and secondary outcomes (i.e., treatment engagement, satisfaction, and outcome). The second aim is to characterize the implementation feasibility of CDST within the VA PRRC using the Practical Robust Implementation and Sustainability Model framework, including barriers and facilitators within the PRRC context to support future implementation. Discussion If CDST is found to be effective and feasible, implementation determinants gathered throughout the study can be used to ensure sustained and successful implementation at this PRRC and other PRRCs and similar settings nationally. Trial registration ClinicalTrials.gov NCT04324944. Registered on March 27, 2020. Trial registration data can be found in Appendix 1.