Personalized Medicine in Psychiatry最新文献

{"title":"The depression and anxiety status and influencing factors survey of international students during COVID-19 outbreak period in Hunan, China: A cross-sectional study","authors":"Ishmail Conteh ,&nbsp;Bashiru Turay ,&nbsp;Kodzovi Sylvain Dovi ,&nbsp;Sheku Gbetuwa","doi":"10.1016/j.pmip.2024.100133","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100133","url":null,"abstract":"<div><p>International students are susceptible to several mental health disorders, including anxiety and depression amid the COVID-19 pandemic due to the implementation of epidemic control measures such as lockdown, quarantine, etc. The study aimed to investigate the depression and anxiety status as well as influencing factors of international students in Hunan, China during the COVID-19 outbreak lockdown. An online cross-sectional survey was conducted between May and June 2020. A snowball sampling technique was used to recruit participants. Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) were used to assess depression and anxiety status, respectively. 450 participants completed the online survey. The overall prevalence of depression and anxiety was 28.7 % and 36.9 %, respectively. Multivariate logistic regression analyses showed that having suspected symptoms of COVID-19 (aOR = 3.54, 95 %CI = 1.30–9.64) and having family members or friends infected with COVID-19 (aOR = 1.51, 95 %CI = 1.31–3.84) were positively associated with depression. Being single was positively associated with anxiety (aOR = 3.10, 95 %CI = 1.46–6.57). Also, obtaining news or updates about the COVID-19 daily (aOR = 1.76, 95 %CI = 1.13–3.75), and using electronic devices for 5–6 h every day (aOR = 2.21, 95 %CI = 1.17–4.17) showed a positive association with anxiety. Furthermore, having a high coping strategy was negatively associated with anxiety (aOR = 0.33, 95 %CI = 0.12–0.92). Although implementing lockdown was an excellent measure, it resulted in a high prevalence of depression and anxiety symptoms. The findings support previous studies that reported an increased in depression and anxiety status due to the COVID-19 pandemic. Therefore, mental health education and psychological support should be provided to this population during the pandemic or future epidemic diseases.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"47 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectory modeling and response prediction in transcranial magnetic stimulation for depression","authors":"Aaron N. McInnes ,&nbsp;Sarah T. Olsen ,&nbsp;Christi R.P. Sullivan,&nbsp;Dawson C. Cooper,&nbsp;Saydra Wilson,&nbsp;Ayse Irem Sonmez,&nbsp;C. Sophia Albott,&nbsp;Stephen C. Olson,&nbsp;Carol B. Peterson,&nbsp;Barry R. Rittberg,&nbsp;Alexander Herman,&nbsp;Matej Bajzer,&nbsp;Ziad Nahas,&nbsp;Alik S. Widge","doi":"10.1016/j.pmip.2024.100135","DOIUrl":"10.1016/j.pmip.2024.100135","url":null,"abstract":"<div><h3>Background</h3><p>Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores.</p></div><div><h3>Methods</h3><p>We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models.</p></div><div><h3>Results</h3><p>LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC=0.70, 95 % CI=[0.52–0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC=0.76, 95 % CI=[0.58–0.94], but likewise, not before.</p></div><div><h3>Conclusions</h3><p>In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"47 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive control circuit function predicts antidepressant outcomes: A signal detection approach to actionable clinical decisions","authors":"Leanne M. Williams ,&nbsp;Jerome Yesavage","doi":"10.1016/j.pmip.2024.100126","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100126","url":null,"abstract":"<div><h3>Background</h3><p>We previously identified a cognitive biotype of depression characterized by dysfunction of the brain’s cognitive control circuit, comprising the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), derived from functional magnetic resonance imaging (fMRI). We evaluate these circuit metrics as personalized predictors of antidepressant remission.</p></div><div><h3>Methods</h3><p>We undertook a secondary analysis of data from the international Study to Predict Optimized Treatment in Depression (iSPOT-D) for 159 patients who completed fMRI during a GoNoGo task, 8 weeks treatment with one of three study antidepressants and who were assessed for remission status (Hamilton Depression Rating Scale score of ≤ 7). Circuit predictors of remission were dLPFC and dACC activity and connectivity quantified in standard deviations. Using established software implementing receiver operating analysis (ROC) we calculated the sensitivity and specificity of these predictors at every cut-point for every circuit measure. We calculated number needed to treat (NNT) metrics for the ROC model identifying optimal cut-point values.</p></div><div><h3>Results</h3><p>ROC models identified maximum separation of remitters (62.5%) from non-remitters (21.2%) at an initial cut-point of −0.75 standard deviations for dLPFC activity and averaged circuit metrics at secondary cutpoints. The NNT was 3.72, implying that if 4 patients (rounding of 3.72) were randomly selected, one would be likely to remit, but if circuit metrics informed treatment, two would be likely to remit.</p></div><div><h3>Conclusions</h3><p>Our findings contribute to identifying clinically actionable circuit measures for clinical trials and clinical practice. Future studies are needed to replicate these findings and expand the assessment of longer-term outcomes.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140815886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The narrative crisis model of suicide as a framework for suicide prevention","authors":"Megan L. Rogers ,&nbsp;Sarah Bloch-Elkouby ,&nbsp;Inna Goncearenco ,&nbsp;Lisa J. Cohen ,&nbsp;Charles B. Nemeroff ,&nbsp;Igor Galynker","doi":"10.1016/j.pmip.2024.100130","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100130","url":null,"abstract":"<div><p>The Narrative Crisis Model of Suicide (NCM) is an iterative, dynamic, diathesis-stress model that conceptualizes individuals’ progression to suicidal behavior through the incorporation of four distinct and successive stages. The NCM strives to reflect a paradigm change in the prevention of suicide: rather than assessing individuals’ suicide risk and categorizing them along different risk levels based on their self-reported suicidal ideation at a given time, the NCM proposes to assess psychological vulnerabilities and processes characteristic of critical milestones in the progression from chronic factors to acute suicidal mental states. As a comprehensive model, the NCM provides an empirically grounded conceptual framework for intervention at each stage of the psychological progression towards suicidal action. In this article, we propose stage-specific treatment modalities, moving progressively from acute to chronic risk. Such treatments would first target the acute Suicide Crisis Syndrome (Stage 4), the subacute suicidal narrative (Stage 3), deficits in stress management (Stage 2), and finally long-term risk factors/trait vulnerabilities (Stage 1). Although future research is needed to establish the optimal combination and sequence of empirically-supported interventions, the NCM may be a useful framework to guide innovations in clinical intervention and research.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141303080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Texas child mental health network: A child and adolescent research registry","authors":"Carol A. Tamminga ,&nbsp;Madhukar H. Trivedi ,&nbsp;Karen D. Wagner ,&nbsp;Sarah Wakefield ,&nbsp;D. Jeffrey Newport ,&nbsp;James Norcross ,&nbsp;David L. Lakey ,&nbsp;Charles B. Nemeroff","doi":"10.1016/j.pmip.2024.100124","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100124","url":null,"abstract":"<div><p>The Texas Child Mental Health Care Consortium (TCMHCC) includes all twelve Health-Related Institutions (HRI) in the State of Texas. At each HRI, teams of child health faculty and staff are reviewing needs of children in Texas and providing new primary care interventions for children (eg, CPAN, TCHATT, CPWE). In addition to the clinical care programs, two research registries (for Trauma and Depression) have been developed to inform future care pathways with new data. The registries collect similar kinds of descriptive, medical and psychosocial data reliably. Because advances have suggested that events and experiences in early years could be the origins of adult mental health challenges, these research registries could contribute to the development and application of treatments during youth to mitigate both youth and adult manifestations of associated mental health vulnerabilities. These registries are set up and the members are reliably gathering baseline data throughout the state, which was the initial goal. The registries now include treatments, Trauma-CBT for Trauma and Behavioral Activation for Depression, to test for efficacy and specificity. We look forward to testing and comparing additional treatments in the future studies.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140549691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of machine learning and deep learning models in detecting depression on social media: A systematic literature review","authors":"Wadzani Aduwamai Gadzama,&nbsp;Danlami Gabi,&nbsp;Musa Sule Argungu,&nbsp;Hassan Umar Suru","doi":"10.1016/j.pmip.2024.100125","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100125","url":null,"abstract":"<div><p>Depression is regarded as one of the world's primary concerns. Recent researchers use artificial intelligence techniques like machine learning and deep learning to identify depressive symptoms automatically. This literature review focuses on using machine learning and deep learning models in depression detection on social media. Advances in deep learning have improved methods for identifying depression, which is one of the illnesses that affect the health of individuals. Some researchers employ a variety of deep-learning approaches to improve the diagnosis, detection, and prediction of depression to support expert decision-making. The researchers identified the available prediction techniques and tools used to detect, forecast, compare, and classify depression in victims systematically. Twenty-eight (28) articles relevant to machine learning and thirty-two (32) articles linked to deep learning were chosen and considered using boolean keyword searches in different publishing databases and filters. A significant number of the studies, according to the conclusions of the analysis, used machine learning techniques such as decision trees, K-nearest neighbours, naive bayes, random forests, and support vector machines. The deep learning models that are most frequently utilised include convolutional neural networks, long short-term memory, and recurrent neural networks with different datasets to detect subjects suffering from depression using social media data. The datasets used in these studies include Twitter, Facebook, Reddit, tweets from the Kaggle website, and clinic patients’ records. These datasets can include posts, comments, audio, video, images, and interviews. The results of this study revealed that, recently, several approaches have focused on using deep learning for depression detection. The paper highlighted that most research focuses on the detection and identification of depression. Prospects for cutting-edge studies in the detection of depression and other illnesses that are related to health were also suggested.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140350895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The narrative crisis model of suicide: A review of empirical evidence for an innovative dynamic model of suicide and a comparison with other theoretical frameworks","authors":"Sarah Bloch-Elkouby ,&nbsp;Megan L. Rogers ,&nbsp;Inna Goncearenco ,&nbsp;Nadia Yanez ,&nbsp;Charles Nemeroff ,&nbsp;Lakshmi Chennapragada ,&nbsp;Lisa J. Cohen ,&nbsp;Igor Galynker","doi":"10.1016/j.pmip.2024.100131","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100131","url":null,"abstract":"<div><p>The Narrative Crisis Model of Suicide (NCM) is a dynamic multi-stage model that incorporates well-documented long- and short-term risk factors to conceptualize individuals’ progression to suicidal behavior. Comprising chronic long-term suicide vulnerabilities, subacute mental state of Suicidal Narrative, and acute Suicide Crisis Syndrome, the NCM emerged out of the urgent need to better understand, assess, and treat mental processes resulting in suicide with the ultimate goal of creating a comprehensive framework for suicide prevention. This paper conducts the first peer-reviewed critical review of the NCM. In the first section, we provide a comprehensive presentation of the NCM with emphasis on its innovative approach to address common pitfalls in clinical conceptualizations and assessment of suicidal mental states and suicide risk. Second, we present a narrative review of the empirical support for each NCM component and the emerging evidence for the whole model. Last, we compare the NCM to alternative models of suicide in order to highlight their shared features as well as the NCM’s unique contribution to risk assessment and prevention. By clarifying the tenets of the NCM, its emerging empirical support, and its relationships to the other models of suicide in the extant literature, this critical review paper sets the stage for future studies on the model.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Free testosterone is associated with perceived stress in women","authors":"Mónica Flores-Ramos ,&nbsp;Lucía Martínez-Mota ,&nbsp;Roberto Silvestri-Tommasoni ,&nbsp;Griselda Jiménez Domínguez ,&nbsp;Denisse Linares Silva","doi":"10.1016/j.pmip.2024.100129","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100129","url":null,"abstract":"<div><h3>Background</h3><p>The role of testosterone in mood regulation has been studied by different authors with inconsistent results. It seems that low testosterone could be related to depressed mood in hypogonadal men; however, in women, both high and low testosterone levels have been implicated in depression. In this study, we evaluated androgen levels in depressed patients, comparing between sexes, and testing anxiety levels.</p></div><div><h3>Methods</h3><p>We included unmedicated women and men diagnosed with major depressive disorder, according to DSM-5 criteria. Patients with drug and alcohol abuse, hormonal medication, uncontrolled medical illness, or psychiatric disorders other than depression or anxiety were excluded. We applied the HDRS, State and Trait Anxiety Inventory (STAI), and Perceived Stress Scale (PSS) to all participants and measured cortisol, total testosterone, free testosterone, and dehydroepiandrosterone.</p></div><div><h3>Results</h3><p>A total of 71 participants (21 male) completed all evaluations. In the overall sample, we detected correlations between cortisol levels and STAI scores, as well as between perceived stress and cortisol. In our correlation analysis of testosterone and psychological evaluations stratified by sex, we found that in women, higher levels of free testosterone were associated with increased perceived stress, as indicated by the PSS.</p></div><div><h3>Limitations</h3><p>A small sample was recruited for the study. Furthermore, although validated anxiety scales were used, all data were self-reported.</p></div><div><h3>Conclusions</h3><p>The role of testosterone in anxiety differs between male and female depressed patients. Perceived stress could be an important measurement in women, as it was related to FT levels in this sample.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468171724000152/pdfft?md5=7333ef0413ed2096ed9ed27fa422b1d2&pid=1-s2.0-S2468171724000152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141242358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidation of molecular processes and biomarkers linked to the genetic variations driving the etiology of mood disorders","authors":"Hai Duc Nguyen ,&nbsp;Giang Huong Vu ,&nbsp;Woong-Ki Kim","doi":"10.1016/j.pmip.2024.100128","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100128","url":null,"abstract":"<div><h3>Objectives</h3><p>We aimed to identify the significant genetic variations and molecular mechanisms associated with the pathogenesis of mood disorders associated with genetic variants.</p></div><div><h3>Methods</h3><p>Genome-wide association studies (GWAS, ID: EFO_0003761), ClueGO plug-in, and GEO database were the main approaches.</p></div><div><h3>Results</h3><p>Ten biomarkers that are frequently related to mood disorders were identified across all studies. In terms of mood disorders, a collection of biomarkers has been found, with specific attention given to seven fundamental genetic variations. A comprehensive analysis revealed a total of five central biomarkers that exhibited a significant association with unipolar depression, with an additional five central biomarkers that demonstrated a significant association with mixed traits. Biomarkers that have been linked to an increased vulnerability to mood disorders have been found to disrupt many cellular processes in neuronal development, including cell morphogenesis, cell projection organization, and cell adhesion. The main signaling pathways associated with biomarkers that contribute to a reduced susceptibility to mood disorders encompass the activation of neuro projection morphogenesis, O-linked glycosylation, modulation of chemical synaptic transmission, and G-protein-coupled glutamate receptor activity. hsa-miR-3937, hsa-miR-4499, and hsa-miR-7106-5p exhibited the most significant downregulation in the GSE182194 dataset. A microRNA (hsa-miR-26b-5p) and transcription factors (zinc finger proteins) were found to have a pivotal role in the elucidation of genetic variations linked to mood disorders across different datasets (EFO_0003761, GSE101521, and GSE217811).</p></div><div><h3>Conclusions</h3><p>Our findings provide a foundational framework for prospective therapeutic approaches aimed at addressing mood disorders, specifically focusing on the genetic variants and pathways linked to this condition.</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468171724000140/pdfft?md5=923a3140905b2bc33887a321137d1e24&pid=1-s2.0-S2468171724000140-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The temporal dynamics of daily stress, affect, and several affect regulation processes, in patients with chronic mood and anxiety disorders before and after a mindful yoga intervention","authors":"Nina K. Vollbehr ,&nbsp;Sanne H. Booij ,&nbsp;H.J. Rogier Hoenders ,&nbsp;Brian D. Ostafin ,&nbsp;Agna A. Bartels-Velthuis","doi":"10.1016/j.pmip.2024.100123","DOIUrl":"https://doi.org/10.1016/j.pmip.2024.100123","url":null,"abstract":"<div><h3>Background and Objectives</h3><p>Patients with chronic mood and anxiety disorders experience many life stressors and are more reactive to these stressors. Although mindful yoga might reduce stress reactivity, little is known about the affect regulation mechanisms involved, such as repetitive negative thinking, fear of emotion, acting with awareness and body awareness.</p></div><div><h3>Design and Methods</h3><p>Using experience sampling methodology, 12 patients with chronic mood and anxiety disorders completed five daily assessments for 15 days before and after a 9-week mindful yoga intervention. Interrupted time-series analyses were used to assess mean-level change from pre-to-post intervention and vector autoregressive models to assess change in the temporal associations.</p></div><div><h3>Results</h3><p>Most individuals experienced positive changes in affect and the proposed affect regulation processes. Fear of emotion showed changes from pre-to-post intervention for most individuals (67%), followed by acting with awareness (58%), body awareness (58%) and repetitive negative thinking (50%). In the dynamic relationships between stressors, the four affect regulation processes and affect, there were individual differences in which pathways changed and how they changed.</p></div><div><h3>Conclusions</h3><p>After a mindful yoga intervention, affect and several affect regulation processes improve in most individuals. Achieving this in the context of daily life stress, seems to be more complicated.</p></div><div><h3>Trial registration</h3><p>ISRCTN register (study ID ISRCTN13612864).</p></div>","PeriodicalId":19837,"journal":{"name":"Personalized Medicine in Psychiatry","volume":"45 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468171724000097/pdfft?md5=fb4c1bc27d688a2f20c3b5f1843121c0&pid=1-s2.0-S2468171724000097-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140163803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}