Community profiles in northeastern and central Pennsylvania characterized by distinct social, natural, food, and physical activity environments and their relation to type 2 diabetes.

Background: Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities' multidimensional nature. Methods: In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status. Results: We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: "sparse rural," "developed rural," "inner suburb," and "deprived urban core." Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with "developed rural," T2D onset odds were higher in "deprived urban core" (1.24, CI = 1.16-1.33) and "inner suburb" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status. Conclusions: Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.

From Theory to Practice: A Transtheoretical Treatment and Training Model (4TM).

Background: In this paper, we present the conceptual background and clinical implications of a research-based transtheoretical treatment and training model (4TM). Method: The model implements findings from psychotherapy outcome, process, and feedback research into a clinical and training framework that is open to future research. Results: The framework is based on interventions targeting patient processes on a behavioral, cognitive, emotional, motivational, interpersonal, and systemic/socio-cultural level. The 4TM also includes a data-based decision support and feedback system called the Trier Treatment Navigator (TTN). Conclusion: We discuss important problems associated with clinical orientations solely based on one school of thought. We then contrast these concerns with a clinical and training framework that embraces ongoing research, serving as a guiding structure for process-based transtheoretical interventions. Such research-based psychological therapy can take both traditional and novel clinical developments as well as findings from psychotherapy research into account and be adaptively disseminated to a variety of patient populations.

Associations between chronic rhinosinusitis and the development of non-cystic fibrosis bronchiectasis.

BACKGROUND: Studies have shown an association between chronic rhinosinusitis (CRS) and non-cystic fibrosis (CF) bronchiectasis. OBJECTIVE: We aimed to determine if CRS increases the risk of developing non-CF bronchiectasis. METHODS: A retrospective analysis was conducted utilizing electronic medical records from an academic center. Patients with CRS without bronchiectasis, with at least one chest computed tomography (CT) performed after the diagnosis of CRS, were identified between January 2006 and December 2015. Charts were reviewed until May 2022. The control group was age, sex, and race matched, and included patients without CRS, asthma, or chronic obstructive pulmonary disease (COPD) who had at least one chest CT. Bronchiectasis was identified by chest CT radiology reports. The odds of developing bronchiectasis were analyzed in patients with CRS without asthma or COPD (Cohort 1) and patients with CRS with asthma or COPD (Cohort 2). RESULTS: The odds of developing bronchiectasis were significantly higher in patients with CRS (139/1594, 8.7%) compared to patients in the control group (443/7992 [5.5%], OR 1.63 [1.34-1.99]). Furthermore, the odds of developing bronchiectasis were higher in Cohort 1 (63/863 [7.3%], OR 1.34 [1.02-1.76]) and Cohort 2 (76/731 [10.4%], OR 1.98 [1.53-2.55]) versus the control group. After adjusting for confounding diseases, the association was attenuated in Cohort 1 (OR 1.22 [0.92-1.61]) but remained significant in Cohort 2 (OR 1.78 [1.37-2.31]). CONCLUSIONS: CRS is associated with the future development of non-CF bronchiectasis. Patients with CRS, especially those with asthma or COPD, have a higher likelihood of developing bronchiectasis than patients without CRS.

Love yourself as a therapist, doubt yourself as an institution? Therapist and institution effects on outcome, treatment length, and dropout.

OBJECTIVE: Research suggests that some therapists achieve better outcomes than others. However, an overlooked area of study is how institution differences impact patient outcomes independent of therapist variance. This study aimed to examine the role of institution and therapist differences in adult outpatient psychotherapy. METHOD: The study included 1428 patients who were treated by 196 therapists at 10 clinics. Two- and three-level hierarchical linear regression models were employed to investigate the effects of therapists and institutions on three dependent patient variables: (1) symptom change, (2) treatment duration, and (3) dropout. Level three explanatory variables were tested. RESULTS: The results showed that therapist effects (TE) were significant for all three types of treatment outcome (7.8%-18.2%). When a third level (institution) was added to the model, the differences between therapists decreased, and significant institution effects (IE) were found: 6.3% for symptom change, 10.6% for treatment duration, and 6.5% for dropout. The exploratory analyses found no predictors able to explain the systematic variation at the institution level. DISCUSSION: TE on psychotherapy outcomes remain a relevant factor but may have been overestimated in previous studies due to not properly distinguishing them from differences at the institution level.

The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms.

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

Substantial Increases in Healthcare Students' State Empathy Scores Owing to Participation in a Single Improvisation Session.

PURPOSE: To determine whether the 12-item state empathy scale could be modified reliably to measure empathy in healthcare professions students and to detect changes in their empathy owing to a single improvisation (improv) session. METHODS: Three cohorts of students from two healthcare professions programs (total = 165 students) participated in an improv session. During the session, one of the researchers (BS) tasked the students with several improv activities. Participants' self-reported state empathy scores were assessed at three time points (pre-improv, post-improv, and end of semester) using revised, in-class paper versions of the State Empathy Scale. RESULTS: The exploratory factor analysis revealed a single factor solution for the revised scale, justifying the creation of an overall state empathy score from the questionnaire. Cronbach's alpha reliability values averaged 0.87. Students' mean empathy scores were higher directly after the improv session than directly prior to the session (p < 0.0001; effect size = r = 0.67, 0.55, and 0.79 for cohorts 1, 2, and 3, respectively). CONCLUSIONS: These findings show that a single one- or two-hour improv session can foster substantial increases in healthcare professional students' state empathy for one another. Greater healthcare professional empathy and compassion foster better healthcare team cooperation and patient outcomes, so healthcare professionals and their students should engage in such empathy-enhancing activities at regular intervals throughout their training and careers.

Invasive Fungal Sinusitis in an Immunocompetent Patient After Sinus Surgery for Chronic Rhinosinusitis with Nasal Polyps and a Mycetoma.

Acute invasive fungal sinusitis (AIFS) classically presents as an aggressive fungal infection that can spread beyond its origin in the sinuses in immunocompromised patients. Although there have been reports of AIFS in immunocompetent, non-diabetic patients, it is extremely rare and the true mechanism behind it is unknown. A thirty-eight year old immunocompetent, non-diabetic woman underwent bilateral ESS for chronic rhinosinusitis with nasal polyps at a tertiary care center and post-operatively developed AIFS. Patient underwent uncomplicated ESS, was packed with foam containing triamcinolone and discharged on steroid rinses and a prednisone taper. Surgical pathology demonstrated left-sided colonization with non-invasive fungal elements consistent with a mycetoma. She presented on post-operative Day 11 with headache and left-sided retro-orbital pain. A culture of her left nasal cavity grew Rhizopus spp and MRI demonstrated evidence of invasive fungal infection of left sphenoid mucosa as well as inflammatory changes in the left orbit centered at the orbital apex. She was started on amphotericin and underwent a left-sided debridement with biopsies which demonstrated angioinvasive fungal disease. Her vision in her left eye worsened to 20/800 and she was treated with transcutaneous retrobulbar injection of amphotericin B. After stable interval imaging she was discharged on a long-term course of antifungals. Extensive immunologic work-up was unremarkable. We describe a case of an immunocompetent patient who developed AIFS after sinus surgery for CRS and a mycetoma likely as a result of local immune suppression and post-surgical trauma. Laryngoscope, 2024.

The Environment and Headache: a Narrative Review.

PURPOSE OF REVIEW: In this narrative review, we summarize the peer-reviewed literature published between 2017 and 2022 that evaluated ambient environmental risk factors for primary headache disorders, which affect more than half of the population globally. Primary headache disorders include migraine, tension-type headache (TTH), and trigeminal and autonomic cephalalgias (TAC). RECENT FINDINGS: We identified 17 articles that met the inclusion criteria via PubMed or Google Scholar. Seven studies (41%) relied on data from US populations. The remaining studies were conducted in China, Taiwan, Germany, Ghana, Japan, the Netherlands, South Korea, and Turkey. Air pollution was the most frequently assessed environmental risk factor. Most studies were cross-sectional and focused on all-cause or migraine headaches; one study included TTH, and none included TAC. Short-term exposure to fine particulate matter (PM2.5) was not consistently associated with headache endpoints, but long-term exposure to PM2.5 was associated with migraine headache prevalence and severity across multiple studies. Elevated ambient temperature, changes in weather, oil and gas well exposure, and less natural greenspace, but not noise pollution, were also associated with headache. No studies considered water pollution, metal exposure, ultrafine particulate matter, or wildfire smoke exposure. There is a need for ongoing research focused on headache and the environment. Study designs with the greatest explanatory power may include longitudinal studies that capture the episodic nature of headache and case-crossover analysis, which control for time-invariant individual-level confounders by design. There is also a clear need for research that considers comorbid psychiatric illness and socioeconomic position as powerful modifiers of the effect of the environment on headache.

The Effects of Switching to Video Therapy on In-Session Processes in Psychotherapy During the COVID-19 Pandemic.

OBJECTIVE AND AIM: This study aimed to assess the impact of switching from face-to-face (f2f) psychotherapy to video therapy (VT) due to the COVID-19 pandemic on in-session processes, i.e., the therapeutic alliance, coping skills, and emotional involvement, as rated by both patients and therapists. METHODS: A total of N = 454 patients with mood or anxiety disorders were examined. The intervention group (IG) consisted of n = 227 patient-therapist dyads, who switched from f2f to VT, while the control group (CG) consisted of n = 227 patient-therapist dyads, who were treated f2f before the pandemic. To evaluate the effects of switching to VT on in-session processes, three longitudinal piecewise multilevel models, one per process variable, were fitted. Each process variable was regressed on the session number with a slope for the three sessions before switching to VT and a second slope for up to six VT sessions afterwards. RESULTS: The therapeutic alliance significantly increased after switching from f2f to VT across the two groups (IG and CG) and raters (patients and therapists) with no differences between IG and CG. On average, patients rated the therapeutic alliance better than therapists. Coping skills significantly increased after switching from f2f to VT across the two groups and raters, but the CG rated coping skills higher than the IG after the switch. Overall, therapists rated coping skills higher than patients. Emotional involvement did not significantly increase after switching to VT across the two groups and raters and there was no significant difference between patient and therapist ratings. DISCUSSION: In conclusion, the switch to VT had no negative impact on the therapeutic alliance and emotional involvement. However, more coping skills were reported in the CG than in the IG after the switch to VT, which was mainly due to a stagnation in patient-rated coping skills in the IG.

Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling.

We aim to use topic modeling, an approach for discovering clusters of related words ("topics"), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics ( r =0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics ( r =0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

Symptom Reduction and Engagement in a Cognitive-Behavioral Mobile Phone App: A Study of User Profiling to Determine Prognostic Indicators.

OBJECTIVE: We investigated the presence of latent transition profiles in a sample of users of a cognitive-behavioral mental health app for the general population. Users' baseline characteristics were used as predictors of the profiles. The role of engagement with the app in the transition profiles was examined. METHOD: A total of 541 users completed the Patient Health Questionnaire-9 and the General Anxiety Disorder-7 when started using the app and 30 days after. Random-Intercept Latent Transition Analysis was implemented to identify users' profiles and transition patterns as classes. The age of the users and the Emotional Self-Awareness Scale-Revised (ESAS-R) were used as predictors of class membership at baseline. The Homework Rating Scale-Mobile Application (HRS-MA; as a measure of engagement) was used as a predictor of class membership at 30 days of app use. RESULTS: A 3-class solution was obtained according to the severity of symptoms (from mild to moderately severe). Age and ESAS-R predicted class membership initially; the higher the age and ESAS-R, the higher the probability of starting using the app with lower distress levels. The HRS-MA predicted class membership at 30 days of app use; the higher the engagement for more symptomatic and younger users, the higher the probability of improvement. However, older users tended to engage less. CONCLUSION: Our findings underpin the relevance of easily accessible digital interventions for young adults with mild to moderate mental health problems. Further studies and developments are required to enhance these apps for older cohorts.

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

OBJECTIVE: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD: We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS: The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION: Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Where it all begins: Predicting initial therapeutic skills before clinical training in cognitive behavior therapy.

To train novice students adequately, it is crucial to understand where they start and how they develop their skills. This study examined the impact of novice students' characteristics on their initial clinical micro-skills when treating simulated patients with cognitive behavior therapy. The sample consisted of 44 graduate psychology students treating seven simulated patients. Clinical micro-skills were measured both using video-based ratings in reaction to short video clips of simulated patients (via the Facilitative Interpersonal Skills (FIS) performance task) and by using video-based ratings within a session with a simulated patient (using the Inventory of Therapeutic Interventions and Skills; ITIS). Two separate LASSO regressions were performed using machine learning to select potential predictors for both skills assessments. Subsequently, a bootstrapping algorithm with 10,000 iterations was used to examine the variability of regression coefficients. Using LASSO regression, we identified two predictors for clinical micro-skills in standardized scenarios: extraversion (b = 0.10) and resilience (b = 0.09), both were not significantly associated with clinical micro-skills. Together, they explained 15% of the skill variation. Bootstrapping confirmed the stability of these predictors. For clinical micro-skills in sessions, only competitiveness was excluded by LASSO regression, and all predictors showed significant instability. The results provide initial evidence that trainees' resilience and extraversion should be promoted in the clinical training of cognitive behavior therapy. More studies on clinical micro-skills and training with larger sample sizes are needed to fully understand clinical development.

Treatment credibility as a mechanism of change in cognitive behavioral therapy: Effects on depression and anxiety.

OBJECTIVE: This study aimed to provide evidence for treatment credibility (TC) as a potential mechanism of change in cognitive behavioral therapy (CBT). Therefore, it focused on within-person effects that are free of the influence of stable characteristics and thus allow to exclude certain alternative explanations for the association under study. METHOD: The sample included 1,423 patients receiving outpatient CBT, who presented a wide variety of psychiatric diagnoses (mostly affective and anxiety disorders). TC, depression, and anxiety were measured every fifth session from Session 5 to 25 using the Credibility Expectancy Questionnaire (CEQ), the Patient Health Questionnaire-9 (PHQ-9), and the General Anxiety Disorder-7 (GAD-7), respectively. Symptom severity was assessed every session using the Hopkins Symptom Checklist-11. Within- and between-person effects of TC, depression, and anxiety were analyzed using the latent curve model with structured residuals (LCM-SRs). In exploratory analyses, within-person effects of TC on next-session symptom severity were assessed using a modification of the LCM-SR. RESULTS: LCM-SRs exhibited excellent fit in main analyses. There were significant negative correlations of both intercepts and slopes (between-person level) of CEQ and PHQ-9 as well GAD-7. No significant cross-lagged effects (within-person level) were found over the five-session interval. However, session-wise analyses revealed significant cross-lagged effects of CEQ on Hopkins Symptom Checklist-11. CONCLUSIONS: This study is the first to find significant within-person effects of TC in session-wise analyses. This lends preliminary support to the notion of TC as a mechanism of change. The lack of significant findings at the five-session interval is discussed considering the specific design used in this study. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension.

Hypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)-dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysine N-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E). KMT2E-AS1 stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α-dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within a KMT2E intron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)-specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo, KMT2E-AS1 deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, the KMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.

Emotion dynamics of clients with test anxiety before and after an imagery-based treatment.

BACKGROUND AND OBJECTIVES: Imagery-based techniques have become a promising means in the treatment of test anxiety (TA). Although previous studies have demonstrated the effectiveness of imagery-based treatment, not all clients seem to benefit from it. The present study compares clients' pre- as well as post-treatment emotion dynamics between responders and non-responders. Furthermore, it examines treatment-related changes in emotion dynamics in both subgroups. METHODS: The results are based on 44 clients suffering from TA who underwent a six-session imagery-based treatment and include Ecological Momentary Assessment (EMA). Emotions were assessed with the Profile of Mood States four times a day over the course of two weeks before and after the treatment. Temporal networks were computed to index emotion dynamics. RESULTS: Pre-treatment emotion dynamics differed between responders and non-responders. Similarly, post-treatment emotion dynamics differed as well between both groups. Some changes were also observed between pre-treatment and post-treatment networks: for responders, fatigue no longer predicted anger, and depression predicted itself; for non-responders, calmness predicted fatigue, anger, depression, contentment, and anxiety. In addition, fatigue no longer predicted itself and anxiety predicted vigor. LIMITATIONS: The investigation is marked by several limitations: a liberal inclusion threshold of at least a 50% response to EMA prompts, and a relatively homogenous sample. CONCLUSION: These results provide first evidence for the idea that emotion dynamics may be associated with response to treatment for TA. Furthermore, effective imagery-based treatments may be tied to changes within these dynamics.

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example.

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient's response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.