Feasibility and acceptability of a mobile intervention for patients with psychosis following psychiatric hospitalization: A pilot randomized controlled trial.

This pilot randomized control trial examines the feasibility and acceptability of a novel mHealth intervention for patients with schizophrenia spectrum disorders following discharge from inpatient hospitalization. Using cognitive behavior therapy for psychosis strategies, the app provides just-in-time assessment and intervention for individuals to promote healthy coping skills and treatment adherence. We assessed the mHealth intervention relative to a comparison app that included mobile assessment plus psychoeducation alone. Patients were assessed at hospital discharge, as well as 1-, 2-, and 4-months postdischarge. Forty-two adults with schizophrenia spectrum disorders discharging from inpatient care participated in the study. Our a priori-defined feasibility and acceptability goals were mostly achieved during the study, in terms of the proposed recruitment and retention rates, mHealth app engagement, app satisfaction ratings, clinical improvement observed over time, and absence of adverse events related to the study. The participants were significantly more engaged in the mHealth intervention (74%) versus the comparison app (43%). Over the course of the study, dysfunctional coping and psychiatric symptoms significantly declined in both groups. Future larger trials are needed to confirm the efficacy of the mHealth intervention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring trainee perspectives on barriers and facilitators in serious mental illness doctoral training.

Although there have been momentous and critical advancements in serious mental illness (SMI) psychology doctoral training models and competencies, there is still much systemic change needed to increase access to evidence-based SMI training for psychologists. In the last decade, there has been little to no growth or expansion of SMI training opportunities in clinical psychology doctoral programs in the United States, and psychologists are underrepresented in serving clients with SMI. As trainees and trainers committed to careers in SMI, the contributors aimed to identify barriers and facilitators throughout each stage of the SMI doctoral training pathway, including pregraduate school, graduate school, and internship to reflect critically on systemic issues that have impeded trainees from accessing the appropriate tools for SMI training. This conceptual article reviews the existing literature on barriers and facilitators to accessing SMI training. In centering trainee perspectives, the contributors also reflect on their own experiences through brief narratives illustrating the barriers and facilitators they have faced across training stages with existing empirical research. Recommendations made by the contributors include increasing access to financial support and mechanisms, diversifying departments and practicum sites to include SMI-focused training, and people in positions of power advocating for and centering trainee voices in departmental training decisions and development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Why We Need to Stop Labeling Behaviors Influencing a Person's Weight Ideal or Healthy.

This commentary argues that financial incentives for employees who meet body mass index requirements reinforce healthism, a false and oppressive ideology. Healthism is the view that personal health is the vehicle of well-being and that health is achieved by taking personal responsibility for habit modification. Healthist views about body shape and body weight enforce oppressive norms and can lead to pernicious harms, especially to members of vulnerable groups. Overall, this article argues that persons and organizations ought not to label behaviors that influence body shape and weight in normative terms, such as "ideal" or "healthy."

Computationally efficient parameter estimation for spatial individual-level models of infectious disease transmission.

Individual-level models incorporate individual-specific covariate information, such as spatial location, to model infectious disease transmission. However, fitting these models with traditional Bayesian methods becomes cumbersome as model complexity or population size increases. We consider a spatial individual-level model with a binary susceptibility covariate. A method for fitting this model to aggregate-level data using traditional Metropolis-Hastings MCMC and then disaggregating the results to obtain individual-level estimates for epidemic metrics is proposed. This so-called "Cluster-Aggregate-Disaggregate" (CAD) method is compared to two approximate Bayesian computation (ABC) algorithms in a simulation study. The methods are also applied to a data set from the 2001 U.K. foot and mouth disease epidemic. While the CAD and ABC methods both performed reasonably well at capturing epidemic metrics, the CAD method was found to be much easier to implement and reduced computation time (relative to the traditional model-fitting method) more consistently than the ABC methods.

Treatment engagement of depressed patients with and without psychosis in a partial hospital program: Dropout, symptom reduction, and satisfaction.

BACKGROUND: The ways patients with psychosis and depression engage in therapeutic treatment is not well understood. To determine if an intensive outpatient psychotherapy program could benefit patients experiencing psychotic symptoms, it is important to know how these individuals engage with psychotherapeutic treatment. METHODS: The present study from the Rhode Island Hospital Methods to Improve Diagnostic Assessment and Services (MIDAS) project compared dropout rates, treatment response, and satisfaction among 219 individuals with psychosis and major depressive disorder (MDD) to 2,545 individuals with MDD at a general, Acceptance and Commitment Therapy-based partial hospital program (PHP). RESULTS: Those with psychosis were significantly less likely to complete treatment. Approximately one-fifth of all patients experienced at least a 50% reduction in depressive and anxiety symptoms. The vast majority of patients with psychosis were highly satisfied with treatment. CONCLUSIONS: Findings suggest patients with psychosis have a higher risk of premature dropout. Patients with psychosis demonstrated a reduction in symptoms during PHP treatment and self-reported high satisfaction with treatment. This study calls for the implementation of practices to reduce premature dropout for patients with psychosis, and for future research on the effectiveness of general psychiatric treatment for those with psychotic symptoms.

Telehealth Treatment of Patients With Borderline Personality Disorder in a Partial Hospital Setting During the COVID-19 Pandemic: Comparative Safety, Patient Satisfaction, and Effectiveness of In-Person Treatment.

There are no studies of the safety and effectiveness of telehealth psychiatric treatment of partial hospital level of care, in general, and for borderline personality disorder (BPD) in particular. In the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project, the authors compared the effectiveness of their partial hospital treatment program in treating patients with BPD. For both the in-person and telehealth partial hospital level of care, patients with BPD were highly satisfied with treatment and reported a significant reduction in symptoms from admission to discharge. Both groups reported a significant improvement in functioning, coping ability, positive mental health, and general well-being. A large effect size of treatment was found in both treatment groups. No patients attempted suicide. Telehealth partial hospital treatment was as effective as in-person treatment in terms of patient satisfaction, symptom reduction, and improved functioning and well-being for patients with BPD.

The Clinical Characteristics of Patients With Borderline Personality Disorder in Different Treatment Settings.

The present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) Project compares patients with borderline personality disorder (BPD) in an outpatient practice (n = 390) and a partial hospital setting (n = 358) on diagnostic comorbidities, symptoms experienced, suicidality, and occupational impairment. The patients in the partial program were diagnosed with significantly more psychiatric disorders and were more frequently diagnosed with dysthymia, generalized anxiety disorder, alcohol and substance use disorders, adjustment disorders, and posttraumatic stress disorder. Those at the partial hospital had significantly higher levels of suicidal ideation than those in the outpatient practice. The samples did not differ on utilization of disability or suicide attempts. Treatment setting may have implications in the recognition of the disorder in clinical practice, the development and support of etiological theories, identification of core deficits, and evaluation of psychosocial morbidity associated with BPD.

The impact of state cannabis legislation, county-level socioeconomic and dog-level characteristics on reported cannabis poisonings of companion dogs in the USA (2009-2014).

With current trends in cannabis legalization, large efforts are being made to understand the effects of less restricted legislation on human consumption, health, and abuse of these products. Little is known about the effects of cannabis legalization and increased cannabis use on vulnerable populations, such as dogs. The objective of this study was to examine the effects of different state-level cannabis legislation, county-level socioeconomic factors, and dog-level characteristics on dog cannabis poisoning reports to an animal poison control center (APCC). Data were obtained concerning reports of dog poisoning events, county characteristics, and state cannabis legislation from the American Society for the Prevention of Cruelty to Animals' (ASPCA) APCC, the US Census Bureau, and various public policy-oriented and government websites, respectively. A multilevel logistic regression model with random intercepts for county and state was fitted to investigate the associations between the odds of a call to the APCC being related to a dog being poisoned by a cannabis product and the following types of variables: dog characteristics, county-level socioeconomic characteristics, and the type of state-level cannabis legislation. There were significantly higher odds of a call being related to cannabis in states with lower penalties for cannabis use and possession. The odds of these calls were higher in counties with higher income variability, higher percentage of urban population, and among smaller, male, and intact dogs. These calls increased throughout the study period (2009-2014). Reporting of cannabis poisonings were more likely to come from veterinarians than dog owners. Reported dog poisonings due to cannabis appear to be influenced by dog-level and community-level factors. This study may increase awareness to the public, public health, and veterinary communities of the effects of recreational drug use on dog populations. This study highlights the need to educate dog owners about safeguarding cannabis products from vulnerable populations.

Conducting Outcomes Research in a Clinical Practice Setting: The Effectiveness and Acceptability of Acceptance and Commitment Therapy (ACT) in a Partial Hospital Program.

Multiple challenges exist integrating research into clinical practice, particularly in acute care settings where randomized controlled trials may be impractical or unethical. Partial or day hospitals are one such setting. As compared to outpatients and inpatients, relatively little research is conducted or reported in partial hospital program (PHP) patients, leaving providers in this setting without a solid empirical basis from which to draw. We report treatment outcomes and patient satisfaction from the first 750 patients enrolled in a clinical research PHP utilizing the acceptance and commitment therapy (ACT) treatment model. ACT is a well-established, transdiagnostic behavior therapy. However, to date no study has examined the use of ACT in acute day hospitals. We hypothesized that applying ACT with this patient group would result in significantly improved depression, anxiety, functioning, and quality of life from intake to discharge. We additionally expected that patients would report high satisfaction with ACT treatment groups. Patients (n = 750 full sample, n = 518 completed treatment) completed daily measures of symptoms, functioning, and ACT processes at intake and discharge, and postgroup satisfaction surveys. Results showed significant improvements in symptoms and impairment. ACT process variables also increased over the course of treatment. Importantly, patient satisfaction was high for all groups. Thus, ACT provides an appropriate, flexible, effective, and satisfactory model for this patient group. A comparison of our findings using ACT to the limited research using other models in PHP settings is discussed, as well as challenges in the overall process of integrating research into routine clinical care.

Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a).

Plasma lipoproteins are important carriers of cholesterol and have been linked strongly to cardiovascular disease (CVD). Our study aimed to achieve fine-grained measurements of lipoprotein subpopulations such as low-density lipoprotein (LDL), lipoprotein(a) (Lp(a), or remnant lipoproteins (RLP) using electron microscopy combined with machine learning tools from microliter samples of human plasma. In the reported method, lipoproteins were absorbed onto electron microscopy (EM) support films from diluted plasma and embedded in thin films of methyl cellulose (MC) containing mixed metal stains, providing intense edge contrast. The results show that LPs have a continuous frequency distribution of sizes, extending from LDL (> 15 nm) to intermediate density lipoprotein (IDL) and very low-density lipoproteins (VLDL). Furthermore, mixed metal staining produces striking "positive" contrast of specific antibodies attached to lipoproteins providing quantitative data on apolipoprotein(a)-positive Lp(a) or apolipoprotein B (ApoB)-positive particles. To enable automatic particle characterization, we also demonstrated efficient segmentation of lipoprotein particles using deep learning software characterized by a Mask Region-based Convolutional Neural Networks (R-CNN) architecture with transfer learning. In future, EM and machine learning could be combined with microarray deposition and automated imaging for higher throughput quantitation of lipoproteins associated with CVD risk.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.

BACKGROUND: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.