Journal of Clinical Psychiatry最新文献

{"title":"Physical Exercise and Health, 3: The Health Care Professional and Patient's Guide to Understanding What to Do, How, and Why-Part 1.","authors":"Chittaranjan Andrade","doi":"10.4088/JCP.23f15183","DOIUrl":"10.4088/JCP.23f15183","url":null,"abstract":"<p><p>Physical activity and exercise are associated with important short- and long-term health benefits. It does not take much effort to reap these benefits; every little bit of activity counts, including activities that are performed as part of daily life. Everybody can exercise, even those with existing medical conditions, and even those with existing cardiac or orthopedic conditions; all that is necessary is to tailor the exercise to individual capacity with appropriate dos and don'ts. This article, addressed to health care professionals, their patients, and the general public, provides practical guidance on exercise, mostly in the form of points and short paragraphs, so that the reader can easily understand and assimilate what to do, how, and why. The article explains what the health benefits of exercise are; how much exercise one ideally needs to perform for optimal health outcomes; what targets to set for exercise; what effects exercise has on muscles, bones, and the heart; and how these effects are beneficial. The article offers suggestions on how to create time and opportunity for exercise, how to motivate oneself for exercise, and how to avoid boredom and discouragement. Most important of all, this article provides a detailed discussion on exercise-related risks, especially orthopedic risks, and how to avoid these risks. Protecting the knee joint from injury receives particular attention. Practical guidance on what to actually do appears in the next article in the series.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138812542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Media Images Can Predict Suicide Risk Using Interpretable Large Language-Vision Models.","authors":"Yael Badian, Yaakov Ophir, Refael Tikochinski, Nitay Calderon, Anat Brunstein Klomek, Eyal Fruchter, Roi Reichart","doi":"10.4088/JCP.23m14962","DOIUrl":"https://doi.org/10.4088/JCP.23m14962","url":null,"abstract":"<p><p><b><i>Background:</i></b> Suicide, a leading cause of death and a major public health concern, became an even more pressing matter since the emergence of social media two decades ago and, more recently, following the hardships that characterized the COVID-19 crisis. Contemporary studies therefore aim to predict signs of suicide risk from social media using highly advanced artificial intelligence (AI) methods. Indeed, these new AI-based studies managed to break a longstanding prediction ceiling in suicidology; however, they still have principal limitations that prevent their implementation in real-life settings. These include \"black box\" methodologies, inadequate outcome measures, and scarce research on non-verbal inputs, such as images (despite their popularity today).</p><p><p><b><i>Objective:</i></b> This study aims to address these limitations and present an interpretable prediction model of clinically valid suicide risk from images.</p><p><p><b><i>Methods:</i></b> The data were extracted from a larger dataset from May through June 2018 that was used to predict suicide risk from textual postings. Specifically, the extracted data included a total of 177,220 images that were uploaded by 841 Facebook users who completed a gold-standard suicide scale. The images were represented with CLIP (Contrastive Language-Image Pre-training), a state-of-the-art deep-learning algorithm, which was utilized, unconventionally, to extract predefined interpretable features (eg, \"photo of sad people\") that served as inputs to a simple logistic regression model.</p><p><p><b><i>Results:</i></b> The results of this hybrid model that integrated theory-driven features with bottom-up methods indicated high prediction performance that surpassed common deep learning algorithms (area under the receiver operating characteristic curve [AUC] = 0.720, Cohen <i>d</i> = 0.82). Further analyses supported a theory-driven hypothesis that at-risk users would have images with increased negative emotions and decreased belongingness.</p><p><p><b><i>Conclusions:</i></b> This study provides a first proof that publicly available images can be leveraged to predict validated suicide risk. It also provides simple and flexible strategies that could enhance the development of real-life monitoring tools for suicide.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Significant Decrease in Alcohol Use Disorder Symptoms Secondary to Semaglutide Therapy for Weight Loss: A Case Series.","authors":"Jesse R Richards, Madisen Fae Dorand, Kyleigh Royal, Lana Mnajjed, Maria Paszkowiak, W Kyle Simmons","doi":"10.4088/JCP.23m15068","DOIUrl":"https://doi.org/10.4088/JCP.23m15068","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Despite being a major cause of preventable death worldwide, alcohol use disorder (AUD) currently has only 3 FDA-approved pharmacotherapies. The glucagon-like peptide-1 receptor agonist (GLP-1RA) semaglutide has shown promise in preclinical studies for reducing alcohol consumption, but there are currently no randomized clinical trials that associate a decline in AUD symptoms with semaglutide use. This case series presents 6 patients with positive AUD screenings who were treated with semaglutide for weight loss. All subsequently exhibited significant improvement in AUD symptoms.</p><p><p><b><i>Methods:</i></b> Retrospective chart review was utilized to identify patients treated with semaglutide for weight loss who also had positive screenings for AUD on the Alcohol Use Disorder Identification Test (AUDIT; score > 8 considered positive) prior to initiation of semaglutide therapy. Six patients were identified who met these criteria. A paired <i>t</i> test was utilized to compare initial AUDIT scores with AUDIT scores after initiation of semaglutide therapy.</p><p><p><b><i>Results:</i></b> All 6 identified patients (100%) had significant reduction in AUD symptomatology based on AUDIT score improvement following treatment with semaglutide (mean decrease of 9.5 points, <i>P</i> < .001).</p><p><p><b><i>Conclusions:</i></b> This case series is consistent with preclinical data and suggests that GLP-1RAs have strong potential in the treatment of AUD. Additional randomized, placebo-controlled clinical studies are needed to fully assess the efficacy of semaglutide in treating AUD.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceptance and Mindfulness-Based Exposure Therapy for PTSD After Cardiac Arrest: An Open Feasibility Trial.","authors":"Maja Bergman, John C Markowitz, Ian M Kronish, Sachin Agarwal, Chana T Fisch, Elizabeth Eder-Moreau, Yuval Neria","doi":"10.4088/JCP.23m14883","DOIUrl":"https://doi.org/10.4088/JCP.23m14883","url":null,"abstract":"<p><p><b><i>Background:</i></b> Posttraumatic stress disorder (PTSD) is prevalent after surviving sudden cardiac arrest (SCA). SCA-induced PTSD is associated with increased mortality and cardiovascular risk, yet no psychotherapeutic treatment has been developed and tested for this population. Exposure therapy is standard treatment for PTSD, but its safety and efficacy remain unconfirmed for SCA survivors: current protocols do not address their specific disease course and have high attrition. Mindfulness-based interventions are typically well-tolerated and have shown promise in reducing PTSD symptoms from other traumas.</p><p><p><b><i>Objective:</i></b> This study sought to determine feasibility, safety, and preliminary efficacy of acceptance and mindfulness-based exposure therapy (AMBET), a novel SCA-specific psychotherapy protocol combining mindfulness and exposure-based interventions with cardiac focused psychoeducation to reduce symptoms and improve health behaviors in patients with post-SCA PTSD.</p><p><p><b><i>Methods:</i></b> We conducted an open feasibility pilot study from January 2021 to April 2022 with a small sample (N = 11) of SCA survivors meeting <i>DSM-5</i> PTSD criteria. AMBET comprised eight 90-minute remotely delivered individual sessions. Clinical evaluators assessed PTSD symptoms using the Clinician-Administered PTSD Scale for <i>DSM-5</i> (CAPS-5) at baseline, midpoint, posttreatment, and 3-month follow-up.</p><p><p><b><i>Results:</i></b> Ten (91%) of 11 enrolled patients completed treatment. Satisfaction was high and patients reported no adverse events. PTSD symptoms significantly improved statistically (<i>P < </i>.001) and clinically with large effect sizes (<i>g = </i>1.34-2.21) and treatment gains sustained at 3-month follow-up. Posttreatment, 80% of completers (n<i> </i>=<i> </i>8) showed significant treatment response, 70% (n<i> </i>=<i> </i>7) with PTSD diagnostic remission. No patient reported symptom increases.</p><p><p><b><i>Conclusions:</i></b> This initial trial found AMBET feasible, safe, and potentially efficacious in reducing PTSD following SCA. These encouraging pilot results warrant further research.</p><p><p><b><i>Trial Registration:</i></b> ClinicalTrials.gov identifier: NCT04596891.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Telemental Health Utilization in Commercial Health Insurance Plans in the United States From 2010 Through 2019.","authors":"Aziza Arifkhanova, Andrew Elhabr, Christian Murray, Jaya Khushalani, Antonio Neri, Jennifer Kaminski PhD, Richard W Puddy, Turgay Ayer","doi":"10.4088/JCP.23m14931","DOIUrl":"https://doi.org/10.4088/JCP.23m14931","url":null,"abstract":"<p><p><b><i>Objective:</i></b> We sought to characterize patterns of utilization of telemental health among commercially insured individuals over the decade preceding COVID-19.</p><p><p><b><i>Methods:</i></b> We developed telemental health service groups from the US PharMetrics Plus database, using diagnostic codes to identify those diagnosed with mental health conditions and procedure codes to capture mental health visits delivered via telehealth sessions. We analyzed 2 indicators of utilization between January 1, 2010, and December 31, 2019: (1) the percentage of patients with mental health needs who used telemental health services and (2) the percentage of all mental health services provided via telehealth. We stratified our analyses by year, patient gender, patient age, and geographic region.</p><p><p><b><i>Results:</i></b> The proportion of mental health visits delivered via telemental health increased from 0.002% to 0.162% between 2010 and 2019. A larger proportion of males received telemental health services as compared to females; however, the proportion of mental health visits delivered via telehealth was higher for females than for males. Patients aged 18 to 34 years and those in the western US had the highest utilization compared to other age groups and geographic regions.</p><p><p><b><i>Conclusions:</i></b> Telemental health utilization comprised a small fraction of overall mental health services and beneficiaries in the IQVIA PharMetrics Plus claims data, but increased over time, with differences documented in utilization based on patient gender, patient age, geographic region, and type of telemental health claim. Evidence from this study may serve as a pre-pandemic baseline for comparison against future evaluations of telehealth expansion policies.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report.","authors":"Tejas Phaterpekar, John-Jose Nunez, Emma Morton, Yang S Liu, Bo Cao, Benicio N Frey, Roumen V Milev, Daniel J Müller, Susan Rotzinger, Claudio N Soares, Valerie H Taylor, Rudolf Uher, Sidney H Kennedy, Raymond W Lam","doi":"10.4088/JCP.23m14864","DOIUrl":"10.4088/JCP.23m14864","url":null,"abstract":"<p><p><b><i>Background:</i></b> Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment.</p><p><p><b><i>Methods:</i></b> Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP).</p><p><p><b><i>Results:</i></b> Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains.</p><p><p><b><i>Conclusions:</i></b> Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.</p><p><p><b><i>Trial Registration:</i></b> ClinicalTrials.gov identifiers NCT00021528 and NCT01655706.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134650302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors and Risk Factors of Treatment-Resistant Depression: A Systematic Review.","authors":"Shane J O'Connor, Nilay Hewitt, Joanna Kuc, Lucinda S Orsini","doi":"10.4088/JCP.23r14885","DOIUrl":"10.4088/JCP.23r14885","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To systematically review the literature to identify and categorize the predictors and risk factors for treatment-resistant depression (TRD).</p><p><p><b><i>Data Sources:</i></b> Online databases (PubMed, MEDLINE, Embase, and APA PsycNet) and relevant conference sources were searched from inception up to January 24, 2022. The following keywords were used: <i>treatment-resistant depression</i>, <i>depressive disorder</i>, <i>predictors</i>, <i>risk</i>, and <i>biomarkers</i>.</p><p><p><b><i>Study Selection:</i></b> All studies that included a definition of TRD were included. A total of 1,686 abstracts were screened, and 57 studies were included in the final data synthesis.</p><p><p><b><i>Data Extraction:</i></b> Data were extracted using a data extraction form developed for this study.</p><p><p><b><i>Results:</i></b> The most frequently reported mental predictors/risk factors were greater symptom severity (9 studies), suicidality (8 studies), and recurrent depression (6 studies). Cardiovascular disease (4 studies), pain (3 studies), and thyroid dysfunction (3 studies) were the most common physical predictors/risk factors, while younger age (7 studies) and female gender (6 studies) were the most common demographic predictors/risk factors. Higher levels of neuroticism appeared twice in the literature. Several articles reported on genetic, biological, and imaging variables, but results were too heterogenous to identify common predictors/risk factors.</p><p><p><b><i>Conclusions:</i></b> TRD is a complex disorder with many contributing factors that need to be identified and addressed earlier in the disease course to prevent its development or facilitate better treatment outcomes. Future work should focus on replicating the key predictors/risk factors identified in this review.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134650303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal Description and Prediction of Smoking Among Borderline Patients: An 18-Year Follow-Up Study.","authors":"Marcelo J A A Brañas, Frances R Frankenburg, Christina M Temes, Garrett M Fitzmaurice, Mary C Zanarini","doi":"10.4088/JCP.22m14756","DOIUrl":"10.4088/JCP.22m14756","url":null,"abstract":"<p><p><b><i>Objective:</i></b> The objectives of this study were (1) to compare smoking between recovered and non-recovered patients with borderline personality disorder (BPD) over the course of 18 years and (2) to assess baseline predictors of tobacco use in patients with BPD.</p><p><p><b><i>Methods:</i></b> A total of 264 borderline patients were interviewed concerning their smoking history beginning at the 6-year follow-up wave in a longitudinal study of the course of BPD (McLean Study of Adult Development) and re-interviewed at 2-year intervals over the next 18 years. Initial data collection of the larger study happened between June 1992 and December 1995, and the <i>DSM-III-R</i> and the Revised Diagnostic Interview for Borderlines (DIB-R) were used as the diagnostic instruments for BPD.</p><p><p><b><i>Results:</i></b> Recovered patients had a 48% lower prevalence of smoking than non-recovered patients at 6-year follow-up (a significant difference; <i>P</i> = .01). Also, the rate of decline in smoking for the recovered group was 68% and was significantly faster (<i>P</i> = .008) than for the non-recovered group over the subsequent 18 years. Alcohol abuse or dependence (relative risk [RR] = 1.22; 95% CI, 1.06-1.40; <i>P</i> = .005), lower levels of education (RR = 1.28; 95% CI, 1.15-1.42; <i>P</i> < .001), and higher levels of the defense mechanism of denial (RR = 1.08; 95% CI, 1.03-1.13; <i>P</i> = .002) were significant predictors of smoking in borderline patients in multivariate analyses.</p><p><p><b><i>Conclusions:</i></b> Taken together, the results of this study suggest that recovery status was an important element in the prevalence of smoking among borderline patients over time. They also suggest that smoking was predicted by 3 factors: prior psychopathology, demographics, and psychological maturity.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pimavanserin 34 mg at Bedtime for the Treatment of Insomnia in 6 Veterans With Posttraumatic Stress Disorder.","authors":"Melissa B Jones, Ritwick Agrawal, Amir Sharafkhaneh, Gursimrat Bhatti, Ruosha Li, Laura Marsh, Ricardo E Jorge","doi":"10.4088/JCP.23br14992","DOIUrl":"10.4088/JCP.23br14992","url":null,"abstract":"","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11414078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medication Adherence in a Transdiagnostic First-Episode Psychosis Sample.","authors":"Stephanie M London, Philip B Cawkwell, Ann K Shinn","doi":"10.4088/JCP.23m14947","DOIUrl":"10.4088/JCP.23m14947","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Medication adherence is an important component of treatment and has the potential to influence illness trajectory in individuals with first-episode psychosis (FEP). We sought to examine time to medication non-adherence as well as factors related to non-adherence in a real-world FEP clinic.</p><p><p><b><i>Methods:</i></b> We conducted a survival analysis to examine time to medication non-adherence using data extracted from medical records of patients admitted to a FEP clinic at an academic psychiatric hospital between May 2012 and October 2017 (n = 219). The risk pool included patients who were adherent during the first 6 months in the clinic (n = 122). Data were extracted for the entire length of participants' time in the clinic, up to 66 months. Pre-selected clinical and demographic variables of interest were extracted and entered into a Cox proportional hazards model.</p><p><p><b><i>Results:</i></b> Of the risk pool of 122 patients, 37 (30%) had documented non-adherence events. The risk of non-adherence was 0.35 (95% CI, 0.25-0.46) and 0.49 (95% CI, 0.37-0.63) at the 24- and 36-month time points, respectively, and plateaued after 36 months. Non-White race (adjusted HR = 3.69; <i>P</i> = .003; 95% CI, 1.57-8.70), lack of insight in the prior 6 months (adjusted HR = 3.24; <i>P</i> = .005; 95% CI, 1.43-7.35), and substance use in the prior 6 months (adjusted HR = 2.58; <i>P</i> = .022; 95% CI, 1.15-5.81) were significant predictors of non-adherence.</p><p><p><b><i>Conclusions:</i></b> Clinicians should consider efforts to strengthen therapeutic alliance with non-White patients, improve insight, and help patients reduce or cease substance use when supporting medication adherence in the FEP population.</p>","PeriodicalId":50234,"journal":{"name":"Journal of Clinical Psychiatry","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71428625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}