Latent Model Research Articles

Exploring Reciprocal Associations Between Self-Reported Anxiety and Eating Disorder Symptoms Longitudinally: ABivariate Latent Change Score Approach.

Consistent data support an association between anxiety and eating disorders (EDs), and theoretical models of EDs suggest that anxiety may be involved in the etiology and maintenance of ED symptoms over time. However, the directionality of relations between these variables remains under-characterized, particularly within treatment settings. We used bivariate latent change score models to explore longitudinal associations between anxiety and ED symptoms in a sample of ED patients (N = 548, 93.2% female, Mage = 21.16, 78.8% White, 79.6% Non-Hispanic/Latinx) throughout intensive treatment and at 6- and 12-month follow-up. Best-fitting models exploring change in each variable independently (i.e., univariate models) suggested that changes in ED and anxiety symptoms decreased over time, but that change was influenced by an individual's symptom severity at the previous timepoint. Models exploring associations between changes in both variables over time (i.e., bivariate latent change score models) suggested the best fit for a model where both anxiety and ED symptoms at one timepoint were associated with later change in the other. Specifically, parameters within these models suggested that higher levels of anxiety were associated with increased subsequent reductions in ED symptoms, whereas elevations in ED symptoms were associated with decreased later reductions in anxiety. Our findings suggest that anxiety and ED symptoms are intricately related both within and outside of intensive treatment. Future multi-modal research exploring real-time links between anxiety and ED symptoms throughout treatment is critical to extend this work and inform improvements in targeted, mechanistic interventions for this population.

From imagination to activism: Cognitive alternatives motivate commitment to activism through identification with social movements and collective efficacy.

Having a vision and being able to imagine socially and ecologically just alternatives can motivate people for societal transformation. However, which psychological processes drive this link between the mental accessibility of societal alternatives and collective action? We hypothesized that collective efficacy beliefs and politicized identification form two pathways mediating the effects of cognitive alternatives on high-cost activist behaviour. Two studies and a pooled analysis tested these hypotheses longitudinally. Data were collected in two field settings: a climate camp and an online conference on socio-ecological visions. In line with our assumptions, and across three of the four analysed timeframes, latent change score modelling showed that changes in cognitive alternatives predicted changes in collective efficacy beliefs and social movement identification, which in turn, predicted changes in collective action intentions. We found clear evidence for our hypotheses in the short term and mixed evidence in the long term. Additional analyses including participative efficacy showed no relevant effects. We concluded that the ability to envision social change may foster a sense of agency as members of social movements. These processes linking imagination to activism are less about individual efficacy than about realizing the collective possibilities for change and identifying with the groups enacting it.

Understanding the transdiagnostic mechanisms underlying emerging psychopathology in adolescence: study protocol of a 1-year prospective epidemiological (EMERGE) study

IntroductionAdolescent mental health is a global public health challenge as most cases remain undetected and untreated, and consequently, have a high likelihood of persistence or recurrence. It is critical to...

Mental Health Risk and Protection Among First-Generation Latinx Immigrant Youth: A Latent Profile Analysis.

First-generation Latinx immigrant youth from the Northern Triangle (NT; El Salvador, Guatemala, and Honduras) face unique risks for experiencing stressors across the phases of migration, which could exacerbate their mental health. This study aimed to (a) identify and characterize unique latent profile groups based on response patterns to immigrant minority stress and psychosocial protective factor items and (b) examine the associations of latent profile membership with depression and anxiety symptoms among NT immigrant youth, controlling for study covariates (i.e., postmigration victimization and forced immigration-related family separation). Primary surveys assessing immigrant minority stress and psychosocial protective factors (i.e., ethnic identity importance, and family, peer, and school support) were administered (N = 172, age range = 14-21, 63% female). Latent profile analysis (Aim 1) and multiple linear regression (Aim 2) were conducted to examine the study aims. A three latent profile model was identified: (a) moderate immigrant minority stress and low psychosocial protection (weak resources), (b) moderate immigrant minority stress and moderate psychosocial protection (average resources), and (c) low immigrant minority stress and high psychosocial protection (strong resources) during postmigration. Multiple linear regression demonstrated that latent profile membership was significantly associated with mental health. Protective associations with mental health were consistently present among the strong resources group. Detrimental associations with mental health were consistently present among the weak resources group. The average resources group demonstrated both protective and detrimental associations with mental health. Postmigration victimization and forced immigration-related family separation covariates were significantly associated with mental health. Findings provide a foundation for further mental health prevention research with NT immigrant youth.

Using large administrative data for mining patients' trajectories for risk stratification: An example from urological diseases.

To identify latent clusters among urological patients by examining hospitalisation rate trajectories and their association with risk factors and outcome quality indicators. Victorian Admitted Episodes Dataset, containing information on all hospital admissions in Victoria from 2009 to 2019. The top twenty ICD-10 primary diagnosis codes in urology were used to select patients (n = 98,782) who were included in the study. Latent class trajectory modelling (LCTM) was used to cluster urological patient hospitalisation trajectories. Logistic regression was used to find baseline factors that influence cluster membership, the variables tested included comorbidities, baseline diagnosis codes, and socio-demographic factors. The analysis was further stratified into non-surgical procedures and surgical procedures. Five clusters of hospitalisation trajectories were identified based on clustering hospitalisation rates over time. Higher hospitalisation clusters were strongly associated with longer length of stay, higher readmission rates and higher complication rates. Higher-risk groups were strongly associated with comorbidities such as renal disease and diabetes. For surgical procedures, urological cancers (kidney, prostate and bladder cancer) and irradiation cystitis were associated with higher-risk groups. For non-surgical procedures, calculus of the bladder, urethral stricture and bladder neck obstruction were associated with higher-risk groups. For patients with two or more admissions, liver cardiovascular disease and being diagnosed with benign prostatic hyperplasia were also associated with higher risk groups. A novel statistical approach to cluster hospitalisation trajectories for urological patients was used to explore potential clusters of patient risks and their associations with outcome quality indicators. This study supports the observation that baseline comorbidities and diagnosis can be predictive of higher hospitalisation rates and, therefore, poorer health outcomes. This demonstrates that it is possible to identify patients at risk of developing complications, higher length of stay and readmissions by using baseline comorbidities and diagnosis from administrative data.

Corrigendum: Investigating the influencing factors of vaccination decisions for newly-developed and established vaccines: a comparative study based on latent class logit models in China

Corrigendum: Investigating the influencing factors of vaccination decisions for newly-developed and established vaccines: a comparative study based on latent class logit models in China

Abstract 4121890: Resting Heart Rate Trajectories over 25 Years: Patterns, Predictors, and Prognostic Impact in the Atherosclerosis Risk in Communities (ARIC) Study

Background: Abnormal resting heart rate (RHR) is associated with cardiovascular events and mortality. Longitudinal RHR trajectories may offer unique prognostic, but research exploring these trajectories over extended periods and their prognostic implications is limited. Methods: Using data from 5,794 participants in the Atherosclerosis Risk in Communities (ARIC) study, we first characterized RHR trajectories over 25 years (from visit 1 [1987-89] to visit 5 [2011-13]) with latent class growth mixture models. Then, we determined the predictors of those trajectories using multinomial logistic regression (n=5,432 with data on predictors of interest). We ran Cox models to quantify the associations between RHR trajectories with cardiovascular outcomes (coronary heart disease, heart failure, stroke) and all-cause mortality (n=4,329 free of cardiovascular disease at baseline). Results: We identified one typical pattern (stable/slight decline, 88.4%) and three atypical patterns (stable-slight increase, 9.2%; increase-decrease, 2.0%; stable-sharp increase, 0.4%) (Figure-Left panel). Obesity, ever smoking, a history of heart failure, and lower education levels predicted the atypical patterns. The stable-increase (combined for slight and sharp) trajectory was significantly and robustly associated with heart failure and all-cause mortality (Figure-Right panel). The increase-decrease trajectory showed a hazard ratio of ~2 for coronary heart disease, although statistical significance was restricted in the demographically adjusted model. Conclusions: We identified one typical (stable/slight decline) and three atypical (increase-decrease, stable-slight increase, and stable-sharp increase) RHR trajectories over 25 years. The atypical trajectories, particularly the stable-increase pattern, were associated with poorer prognosis. These findings highlight the value of monitoring long-term RHR trajectories for cardiovascular risk assessment.

Concurrent Mentions of Vaping and Alcohol on Twitter: Latent Dirichlet Analysis.

Co-use of alcohol and e-cigarettes (often called vaping) has been linked with long-term health outcomes, including increased risk for substance use disorder. Co-use may have been exacerbated by the COVID-19 pandemic. Social networking sites may offer insights into current perspectives on polysubstance use. The aims of this study were to investigate concurrent mentions of vaping and alcohol on Twitter (subsequently rebranded X) during a time of changing vaping regulations in the United States and the emergence of the COVID-19 pandemic. Tweets including both vape- and alcohol-related terms posted between October 2019 and September 2020 were analyzed using latent Dirichlet allocation modeling. Distinct topics were identified and described. Three topics were identified across 6437 tweets: (1) flavors and flavor ban (n=3334, 51.8% of tweets), (2) co-use discourse (n=1119, 17.4%), and (3) availability and access regulation (n=1984, 30.8%). Co-use discussions often portrayed co-use as positive and prosocial. Tweets focused on regulation often used alcohol regulations for comparison. Some focused on the perceived overregulation of vaping (compared to alcohol), while others supported limiting youth access but not at the expense of adult access (eg, stronger age verification over product bans). Across topics, vaping was typically portrayed as less harmful than alcohol use. The benefits of flavors for adult smoking cessation were also discussed. The distribution of topics across time varied across both pre- and post-regulatory change and pre- and post-COVID-19 pandemic declaration periods, suggesting shifts in topic focus salience across time. Co-use discussions on social media during this time of regulatory change and social upheaval typically portrayed both vaping and alcohol use in a positive light. It also included debates surrounding the differences in regulation of the 2 substances-particularly as it related to limiting youth access. Emergent themes from the analysis suggest that alcohol was perceived as more harmful but less regulated and more accessible to underage youth than vaping products. Frequent discussions and comparisons of the 2 substances as it relates to their regulation emphasize the still-evolving vaping policy landscape. Social media content analyses during times of change may help regulators and policy makers to better understand and respond to common concerns and potential misconceptions surrounding drug-related policies and accessibility.

Abstract 4127413: High-normal Blood Pressure (130-139/85-89mmHg) is Associated with an Increased Risk of Mortality

Background: The relationship between high-normal blood pressure (BP) and mortality lacks high-quality evidence based on large population cohort. The aim of this study was to comprehensively investigate the association of high-normal BP and its trajectory with all-cause and cause-specific mortality. Methods: In a community-based population cohort from the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project, 3,598,940 participants aged 35-75 years with baseline BP were included. High-normal BP was defined as a systolic blood pressure (SBP) of 130-139 mmHg and/or a diastolic blood pressure (DBP) of 85-89 mmHg at baseline. Four trajectories of BP change were identified in 105,894 participants included in the study, who had more than 2 follow-up BP measurements, using latent mixture modeling. Among them, high-normal-increasing was defined as an increase in SBP from 130-139 at baseline to over 140 at the last follow-up. Findings: Compared with the optimal BP group (SBP < 120 mmHg and DBP < 80 mmHg, [18.1%]), participants with high-normal BP (18.7%) had a 4% increase in all-cause mortality risk (hazard ratio [HR] 1.04, 95% confidence interval [CI]: 1.01-1.07) and a 28% increase in CVD mortality risk (HR 1.28, 95% CI: 1.21-1.34), with the greatest increase in mortality risk observed from hemorrhagic stroke (HR 1.75, 95% CI: 1.55-1.98). Among BP trajectories, compared with participants with optimal-stable BP (25.3%), those with high-normal-increasing BP (3.5%) had a 25% increase in all-cause mortality risk (HR 1.25, 95% CI: 1.00-1.54) and a 67% increase in CVD mortality risk (HR 1.66, 95% CI: 1.21-2.30), with the greatest increase in mortality risk also observed from hemorrhagic stroke (HR 3.15, 95% CI: 1.38-7.20). In our study cohort, approximately 0.7% and 1.3% of all-cause mortality was attributable to the high-normal BP at baseline and high-normal-increasing BP trajectory, respectively. Interpretation: Individuals presenting high-normal BP at baseline or high-normal-increasing BP trajectory afterward exhibited a significantly elevated mortality risk, which is most pronounced for hemorrhagic stroke. Early management of BP in patients with high-normal BP is crucial for the prevention and treatment of hypertension.

Detection and Correction of Likert Scale Multiplicative Response-Style Bias

Individuals may differ in their tendency to fully use the range of a Likert scale. Such individual response styles may have an effect on estimates of correlations. It is shown with simulated data that a relatively simple nonlinear latent model can be used to detect and correct (to some extent) for the bias introduced by differences in multiplicative response styles. A real-world dataset is examined to show that the effect may occur in reality and may lead to biased conclusions.

Excess Mortality and its Determinants During the COVID-19 Pandemic in 21 Countries: An Ecological Study from the C-MOR Project, 2020 and 2021.

The COVID-19 pandemic overwhelmed health systems, resulting in a surge in excess deaths. This study clustered countries based on excess mortality to understand their response to the pandemic and the influence of various factors on excess mortality within each cluster. This ecological study is part of the COVID-19 MORtality (C-MOR) Consortium. Mortality data were gathered from 21 countries and were previously used to calculate weekly all-cause excess mortality. Thirty exposure variables were considered in five categories as factors potentially associated with excess mortality: population factors, health care resources, socioeconomic factors, air pollution, and COVID-19 policy. Estimation of Latent Class Linear Mixed Model (LCMM) was used to cluster countries based on response trajectory and Generalized Linear Mixture Model (GLMM) for each cluster was run separately. Using LCMM, two clusters were reached. Among 21 countries, Brazil, the USA, Georgia, and Poland were assigned to a separate cluster, with the mean of excess mortality z-score in 2020 and 2021 around 4.4, compared to 1.5 for all other countries assigned to the second cluster. In both clusters the population incidence of COVID-19 had the greatest positive relationship with excess mortality while interactions between the incidence of COVID-19, fully vaccinated people, and stringency index were negatively associated with excess mortality. Moreover, governmental variables (government revenue and government effectiveness) were the most protective against excess mortality. This study highlighted that clustering countries based on excess mortality can provide insights to gain a broader understanding of countries' responses to the pandemic and their effectiveness.

Association between body mass index trajectories and type 2 diabetes incidence over an 18-year follow-up in the Tehran Lipid and Glucose Study.

Body mass index (BMI) is a well-known risk factor for type 2 diabetes mellitus (T2DM). We aimed to identify BMI trajectory patterns and evaluate their association with T2DM during 18 years of follow-up. We included 6026 participants aged 20 to 65 from the prospective population-based Tehran Lipid and Glucose Study (TLGS). The BMI trajectory patterns were identified using latent growth mixture modeling (LGMM) during the measurement period. The association between the BMI trajectory patterns and future T2DM was evaluated using the Cox proportional hazard regression models. Three BMI trajectory patterns of low-increasing (38.6%), medium-increasing (47.1%), and high-increasing (14.3%) were detected. The participants in the medium-increasing and high-increasing BMI trajectory groups had HRs of 1.87 (95% CI 1.53-2.28) and 3.41(95% CI 2.71-4.29) for T2DM incidence, respectively. Within the normoglycemic subpopulation, the high-increasing BMI group had an HR of 3.82 (95% CI 2.79-5.24) compared to the low-increasing BMI group, while in the prediabetic subpopulation, the high-increasing BMI group had an HR of 2.93 (95% CI 2.04-4.19) compared to the low-increasing BMI group. Body weight varies in a relatively stable pattern in adulthood over the long-term period. Medium and high-increasing BMI trajectory patterns have a significantly increased risk for future T2DM in both normoglycemic and prediabetic individuals. Identifying BMI trajectory patterns can help healthcare providers in early prediction, risk assessment, and monitoring strategies development for the prevention of T2DM.

Long-term characterization of behavior phenotypes in children with seizures: Analytic approach matters.

Behavioral problems in children with new onset epilepsies have been well established in the literature. More recently, the literature indicates the presence of unique behavioral patterns or phenotypes in youth with epilepsy that vary significantly in vulnerability and resilience to behavioral problems. This study contrasts the interpretation of behavioral risk as inferred from cross-sectional versus latent group analytic perspectives, as well as the presence, consistency, stability, and progression of behavioral phenotypes in youth with new onset epilepsy and sibling controls over 3 years. Three hundred twelve participants (6-16 years old) were recruited within 6 weeks of their first recognized seizure along with 223 unaffected siblings. Each child's behavior was recorded by parents and teachers frequently over 36 months using the Child Behavior Checklist (CBCL), and each child completed self-report measures of depression symptoms over 36 months. Measures were evaluated cross-sectionally and longitudinally to identify clusters with prototypical behavioral trajectories. Cross-sectional analyses exhibited a pattern of generalized and undifferentiated behavioral problems compared to sibling controls at baseline and prospectively. In contrast, latent trajectory modeling identified three distinct behavior phenotype clusters across all raters (parents, teachers, and youth) over baseline and longitudinal assessments. CBCL Cluster 1 (~30% of youth with epilepsy) exhibited behavior similar to/better than controls, Cluster 2 (~50%) exhibited moderate behavior issues, and Cluster 3 (~20%) exhibited the most pronounced/problematic behavior, falling into Achenbach's clinically relevant behavior range. Behavior within clusters remained stable and consistent. Teachers' and children's behavior assessments corresponded to these cluster groupings consistently over 36 months. Predictors of cluster membership include seizure syndrome type and social determinants of health. This study demonstrates the varying public health perspectives of behavioral risk in youth with epilepsy that result as a function of analytic approach as well as the presence of distinct latent behavioral trajectory phenotypes over time in youth with new onset epilepsy.

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

Adolescent mental time travel predicting meaning in life: The potential mediating role of self-continuity.

Knowing who we are and what we are living for helps us to better adjust in everyday life and confront negative life events, especially for adolescents who are going through critical developmental periods when changes in life could bring both psychopathology risk yet opportunity to achieve a better self. The current study focused on mental time travel, the mental visit to the past or future, and examined the impact on adolescents' perceived meaning in life, with the potential mediating factor of self-continuity. A total of 1543 high school students aged 12 to 18 years old (Mage = 15.02, SDage = 1.58, 52% girls) from Jiangsu Province, China were recruited in a two-wave longitudinal survey that separated by an interval of 6 months. Participants reported their proneness to engage in nostalgia or future prospection at T1 and sense of self-continuity at T2, while the perceived meaning in life were reported at both time points. The latent structural equation models were established with items as indicators for all study variables. Mental time travel, including both nostalgia and future prospection, facilitated adolescent meaning in life via increased self-continuity, except that future prospection showed only positive indirect effect, while nostalgia demonstrated direct yet negative impact on meaning in life after accounting for the positive mediation effect. Findings highlighted the distinct effects of the past- and future-oriented mental time travel on adolescent meaning in life, and provided insights for promoting adolescent psychological adjustment.

Recidivism risk factors among Mississippi justice-involved youth: latent class analysis of the SAVRY

ABSTRACT The Structured Assessment of Violence Risk in Youth (SAVRY) instrument is a popular risk-need-responsivity tool for justice-involved youth. This study conducted a multi-group (based on sex) latent class analysis model of the SAVRY items to explore heterogeneity in risk/needs among youths involved in the juvenile court in Mississippi between 2017 and 2021. Results indicated a three-class solution best with the data, with Very Low, Low, and Bimodal classes. Generally, these groups were similar across sex, though the Bimodal subgroup was proportionally larger for females than males. Subsequent analyses indicated the Bimodal group was significantly younger and more involved in future one-year recidivism. These findings suggest subgroups of risk exist and should be examined further to assess future recidivism and other risks.

Profiles of Nonpharmacologic Treatment Receipt in the First 10 Weeks Following a New Back Pain Diagnosis Among Active Duty Soldiers: A Latent Class Analysis.

US service members experience high rates of back pain. Guidelines prioritize nonpharmacologic treatment (NPT) as first-line pain treatments; however, NPT utilization patterns research is limited. This study examined NPT patterns of care within the first 10 weeks following an index back pain diagnosis. Data were from the Substance Use and Psychological Injury Combat Study and included 222,427 active duty soldiers with an index back pain diagnosis following return from Afghanistan/Iraq deployment in 2008-2014. We fit a series of latent class analysis models to identify homogenous subgroups of soldiers with NPT utilization during the 10-week period and measured associations with soldier characteristics and clinical characteristics within 90 days before the back pain diagnosis, with the distinct NPT utilization classes. Approval for this study was granted by the Brandeis University Committee for Protection of Human Subjects (Institutional Review Board #14153) and the Uniformed Services University Institutional Review Board. Only half of the soldiers received any NPT within their 10-week early treatment window. Latent class analysis identified 4 classes over the 10-week early treatment window: Class 1 (None/Low NPT, 65%); Class 2 (High and Decreasing NPT, 15%); Class 3 (Low and Increasing NPT, 13%), and Class 4 (Sustained NPT, 7%). Soldier clinical characteristics from the 90-day preperiod window were most important in distinguishing class membership in relation to Class 1, particularly comorbid pain conditions, diagnosis of traumatic brain injury, receipt of prescription opioids, and receipt of invasive surgery. Patterns of weekly NPT utilization during an early treatment window following a new back pain diagnosis varied temporally, with approximately half of soldiers using NPT. Half of the soldiers did not receive any NPT within their 10-week early treatment window, which highlights opportunities for increasing use of NPT utilization among military members with a new back pain episode. Future research is needed in the Military Health System to examine the extent to which NPT patterns are associated with pain management outcomes.

Advancements in Diffusion Models for Image Generation: A Comparative Analysis of DDPM, LDM, and DDIM

Abstract. This research provides a thorough exploration of diffusion models in image generation, comparing various methodologies to assess their efficacy and efficiency. The study begins with an introduction to foundational technologies and key concepts, progressing through an analysis of basic and advanced models, including Latent Diffusion Models (LDMs), Denoising Diffusion Implicit Models (DDIMs), and control models. The research evaluates these models based on their performance, computational efficiency, and future development potential. The review details the evolution of diffusion models from early stochastic processes to their current status as advanced generative models. Key principles, such as iterative noise addition and removal, are examined to understand the transformation from simple distributions to complex data representations. Innovations enhancing model efficiency, including advancements in score matching and neural network integration, are discussed. A thorough comparative analysis highlights the strengths and limitations of each model. The study identifies ongoing challenges such as interpretability and computational cost and proposes future research directions to address these issues. The findings aim to guide researchers and practitioners in advancing diffusion model technologies, offering insights into their impact on image generation and potential future developments.

Building Narratives Through Empathy: The Role of Empathy Mechanisms and Associative Thinking in Creative Writing

ABSTRACTEmpathy emerges as a pivotal skill in creative writing, yet previous studies lack an understanding of empathy's multidimensionality and specific impact of its facets on the capacity to generate narrative stories. This cross‐sectional study delved into the various cognitive and affective empathy facets—that is, perspective‐taking, online simulation, emotion contagion, proximal responsivity, and peripheral responsivity—and their contributions to creative writing. Further, it examined the mediating effects of associative thinking—conceptualized as a common empathy‐creativity resource—on the relationship between empathy facets and creative writing. Two‐hundred twenty participants (university students) completed performance‐based tasks and self‐report measures of creative writing, associative thinking, and empathy. A latent mediation model implemented in Structural Equation Modeling indicated two effects of empathy facets on creative writing, with perspective‐taking emerging as a positive predictor. Additionally, peripheral responsivity (i.e., affect sharing in indirect contexts) exhibited an indirect positive influence on creative writing, mediated by associative thinking. In summary, associative thinking appears to be an important ingredient in both empathy and creative writing, while cognitive empathy, specifically intuitive perspective‐taking, contributes significantly to creative writing skills. Future studies should further explore these connections and their causalities, possibly using experimental or longitudinal approaches.

Examining what works for whom and how in mindfulness-based cognitive therapy (MBCT) for recurrent depression: moderated-mediation analysis in the PREVENT trial.

Personalised management of recurrent depression, considering individual patient characteristics, is crucial. This study evaluates the potentially different mediating role of mindfulness skills in managing recurrent depression using mindfulness-based cognitive therapy (MBCT) among people with varying depression severity. Data from the Prevention of Depressive Relapse or Recurrence (PREVENT) trial, comparing MBCT (with antidepressant medication (ADM) tapering support, MBCT-tapering support) versus maintenance-ADM, were used. The study included pre, post, 9-, 12-, 18- and 24-month follow-ups. Adults with ≥3 previous major depressive episodes, in full/partial remission (below threshold for a current episode), on ADM, were assessed for eligibility in primary care practices in the UK. People were randomised (1:1) to MBCT-tapering support or maintenance-ADM. We used the Beck Depression Inventory-II to evaluate depressive symptom changes over the six time points. Pre-post treatment, we employed the Five Facets of Mindfulness Questionnaire to gauge mindfulness skills. Baseline symptom and history variables were used to identify individuals with varying severity profiles. We conducted Latent Profile Moderated-Mediation Growth Mixture Models. A total of 424 people (mean (s.d.) age = 49.44 (12.31) years; with 325 (76.7%) self-identified as female) were included. A mediating effect of mindfulness skills, between trial arm allocation and the linear rate of depressive symptoms change over 24 months, moderated by depression severity, was observed (moderated-mediation index = -0.27, 95% CI = -0.66, -0.03). Conditional indirect effects were -0.42 (95% CI = -0.78, -0.18) for higher severity (expected mean BDI-II reduction = 10 points), and -0.15 (95% CI = -0.35, -0.02) for lower severity (expected mean BDI-II reduction = 3.5 points). Mindfulness skills constitute a unique mechanism driving change in MBCT (versus maintenance-ADM). Individuals with higher depression severity may benefit most from MBCT-tapering support for residual symptoms. It is unclear if these effects apply to those with a current depressive episode. Future research should investigate individuals who are not on medication. This study provides preliminary evidence for personalised management of recurrent depression. ISRCTN26666654.