Performance of growth mixture models in the presence of time-varying covariates

Trajectories and comorbid symptom networks of posttraumatic stress symptoms in frontline rescuers: A longitudinal study

BACKGROUND

Previous research has largely lacked studies that explore the trajectories of Posttraumatic stress symptoms (PTSS) and the structure of comorbid psychiatric symptom networks following traumatic event, while controlling for the severity of traumatic exposure. The present study aims to explore the characteristic trajectories of PTSS, in the context of ensuring controlled levels of traumatic exposure. Furthermore, the PTSS, depressive, and anxiety comorbid symptom networks of different PTSS trajectory subgroups are also investigated.

METHODS

A total of 296 frontline rescue personnel were enrolled into our study. In an effort to control for variations in traumatic exposure severity, this study ensured that all participants had same responsibilities and cumulative operational duration at the post-disaster rescue circumstance. Growth mixture models (GMMs) were employed to scrutinize the trajectories of PTSS. Additionally, network analysis was used to examine the comorbid symptom network of PTSS, depression, and anxiety.

RESULTS

Four distinct PTSS trajectories were identified, namely Persisting Symptom, Gradual Recovery, Gradual Aggravation, and Asymptomatic. Although both the Persisting Symptom and Gradual Aggravation groups belong to the high-risk subgroups for persistent PTSS, they exhibit differences in core symptoms within their respective networks. The core symptom for the Persisting Symptom Network is flashbacks, while for the Gradual Aggravation Network, it is sleep disturbances.

CONCLUSION

To the best of our knowledge, the present study represents the first research endeavor to integrate longitudinal trajectory analysis of PTSS with longitudinal symptom network analysis, clarifying the evolving features of PTSS but also offering valuable insights for early screening and intervention strategies.

Changes in mental health among Chinese university students before and during campus lockdowns due to the COVID-19 pandemic: a three-wave longitudinal study

The campus lockdown due to the COVID-19 pandemic has adversely affected mental health among university students. However, the heterogeneity in responses to campus lockdown is still poorly known. We collected three-wave prospective data on university students' mental health in Shanghai, China, in 2022: (i) in February before the pandemic; (ii) in April at the initial COVID-19 campus lockdown; and (iii) in May amidst the citywide lockdown. Overall, 205 university students completed sociodemographic questionnaires, the General Health Questionnaire-12 items (GHQ-12), and the Depression, Anxiety and Stress Scale-21 items (DASS-21). Generalized estimating equations were used to examine the longitudinal changes in mental health and symptoms of depression, anxiety, and stress. Latent class mixed models (LCMM) were constructed to identify distinct trajectories. Multinomial regression models were used to identify factors associated with status variation patterns. Mean GHQ-12 scores were 8.49, 9.66, and 11.26 at pre-pandemic and lockdown T1 and T2, respectively (p < 0.001). Mean scores for depression, anxiety, and stress were (5.96, 10.36, and 8.06, p < 0.001), (7.13, 6.67, and 7.16, p = 0.243), and (9.83, 7.28, and 11.43, p < 0.001), respectively. Changing trends of numbers of participants with clinical symptoms were consistent with those of mean scores. LCMM fitted three distinct trajectory classes, respectively, for GHQ-12, depression and anxiety symptoms, and four classes for stress symptoms. Participants with fair or poor peer relationships were more likely to belong to vulnerable trajectories concerning depression, anxiety, and stress symptoms. This study proves heterogeneity in mental health of university students in response to pandemic campus lockdown and highlights the necessity for identifying vulnerable groups to provide targeted support in future pandemics.

Trajectories and risk factors of fatigue following colorectal cancer diagnosis

AIM

This study sought to identify groups of colorectal cancer patients based upon trajectories of fatigue and examine how demographic, clinical and behavioural risk factors differentiate these groups.

METHOD

Patients were from six cancer centres in the United States and Germany. Fatigue was measured using the fatigue subscale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) at five time points (baseline/enrolment and 3, 6, 12 and 24 months after diagnosis). Piecewise growth mixture models identified latent trajectories of fatigue. Logistic regression models examined differences in demographic, clinical and behavioural characteristics between fatigue trajectory groups.

RESULTS

Among 1615 participants (57% men, 86% non-Hispanic White, mean age 61 ± 13 years at diagnosis), three distinct groups were identified. In the high fatigue group (36%), fatigue significantly increased in the first 6 months after diagnosis and then showed statistically and clinically significant improvement from 6 to 24 months (P values < 0.01). Throughout the study period, average fatigue met or exceeded cutoffs for clinical significance. In the moderate (34%) and low (30%) fatigue groups, fatigue levels remained below or near population norms across the study period. Patients who were diagnosed with Stage II-IV disease and/or current smokers were more likely to be in the high fatigue than in the moderate fatigue group (P values < 0.05).

CONCLUSION

A large proportion of colorectal cancer patients experienced sustained fatigue after initiation of cancer treatment. Patients with high fatigue at the time of diagnosis may benefit from early supportive care.

Successful ageing at work: The role of job characteristics in growth trajectories of work ability and motivation to work amongst older workers

In order to age successfully at work, people need to maintain or improve their work ability and motivation to work. This implies a process that develops over time and can differ substantially between individuals. This study investigated whether different trajectories of perceived work ability and motivation to work can be distinguished between older employees over time and to what extent job demands and job resources are predictive of these different trajectories. We applied growth mixture modelling amongst 5799 employees of 45 years and older at four time points. We found five distinct groups of older workers that differed in their trajectories of perceived work ability and four types of groups of older workers that differed in their trajectories of their motivation to work. Higher levels of physical demands, mental demands, autonomy, supervisor support, and colleague support were less common in unfavourable trajectories. This study gives Human Resource Management practitioners insight into how jobs should be designed to stimulate successful ageing at work.

Analysis of heterogeneous growth changes in longitudinal height of children

BACKGROUND

There have been methodologies developed for a wide range of longitudinal data types; nevertheless, the conventional growth study is restricted if individuals in the sample have heterogeneous growth trajectories across time. Using growth mixture modeling approaches, we aimed to investigate group-level heterogeneities in the growth trajectories of children aged 1 to 15 years.

METHOD

This longitudinal study examined group-level growth heterogeneities in a sample of 3401 males and 3200 females. Data were analyzed using growth mixture modeling approaches.

RESULTS

We examined different trajectories of growth change in children across four low- and middle-income countries using a data-driven growth mixture modeling technique. The study identified two-group trajectories: the most male samples group (n = 4260, 69.7%) and the most female samples group (n = 2341, 81.6%). The findings show that the two groups had different growth trajectories. Gender and country differences were shown to be related to growth factors; however, the association varied depending on the trajectory group. In both latent groups, females tended to have lower growth factors (initial height and rate of growth) than their male counterparts. Compared with children from Ethiopia, children from Peru and Vietnam tended to exhibit faster growth in height over time: In contrast, children from India showed a lower rate of change in both latent groups than that of children from Ethiopia.

CONCLUSIONS

The height of children in four low- and middle-income countries showed heterogeneous changes over time with two different groups of growth trajectories.

Clustering of the causes of death in Northeast Iran: a mixed growth modeling

BACKGROUND

Processing and analyzing data related to the causes of mortality can help to clarify and monitor the health status, determine priorities, needs, deficiencies, and developments in the health sector in research and implementation areas. In some cases, the statistical population consists of invisible sub-communities, each with a pattern of different trends over time. In such cases, Latent Growth Mixture Models (LGMM) can be used. This article clusters the causes of individual deaths between 2015 and 2019 in Northeast Iran based on LGMM.

METHOD

This ecological longitudinal study examined all five-year mortality in Northeast Iran from 2015 to 2019. Causes of mortality were extracted from the national death registration system based on the ICD-10 classification. Individuals' causes of death were categorized based on LGMM, and similar patterns were placed in one category.

RESULTS

Out of the total 146,100 deaths, ischemic heart disease (21,328), malignant neoplasms (17,613), cerebrovascular diseases (11,924), and hypertension (10,671) were the four leading causes of death. According to statistical indicators, the model with three classes was the best-fit model, which also had an appropriate interpretation. In the first class, which was also the largest class, the pattern of changes in mortality due to diseases was constant (n = 98, 87.50%). Second-class diseases had a slightly upward trend (n = 10, 8.92%), and third-class diseases had a completely upward trend (n = 4, 3.57%).

CONCLUSIONS

Identifying the rising trends of diseases leading to death using LGMM can be a suitable tool for the prevention and management of diseases by managers and health policy. Some chronic diseases are increasing up to 2019, which can serve as a warning for health policymakers in society.

Which method is optimal for estimating variance components and their variability in generalizability theory? evidence form a set of unified rules for bootstrap method

OBJECTIVE

The purpose of this study is to compare the performance of the four estimation methods (traditional method, jackknife method, bootstrap method, and MCMC method), find the optimal one, and make a set of unified rules for Bootstrap.

METHODS

Based on four types of simulated data (normal, dichotomous, polytomous, and skewed data), this study estimates and compares the estimated variance components and their variability of the four estimation methods when using a p×i design in generalizability theory. The estimated variance components are vc.p, vc.i and vc.pi and the variability of estimated variance components are their estimated standard errors (SE(vc.p), SE(vc.i) and SE(vc.pi)) and confidence intervals (CI(vc.p), CI(vc.i) and CI(vc.pi)).

RESULTS

For the normal data, all the four methods can accurately estimate the variance components and their variability. For the dichotomous data, the |RPB| of SE (vc.i) of traditional method is 128.5714, the |RPB| of SE (vc.i), SE (vc.pi) and CI (vc.i) of jackknife method are 42.8571, 43.6893 and 40.5000, which are larger than 25 and not accurate. For the polytomous data, the |RPB| of SE (vc.i) and CI (vc.i) of MCMC method are 59.6612 and 45.2500, which are larger than 25 and not accurate. For the skewed data, the |RPB| of SE (vc.p), SE (vc.i) and SE (vc. pi) of traditional method and MCMC method are over 25, which are not accurate. Only the bootstrap method can estimate variance components and their variability accurately across different data distribution. Nonetheless, the divide-and-conquer strategy must be used when adopting the bootstrap method.

CONCLUSIONS

The bootstrap method is optimal among the four methods and shows the cross-distribution superiority over the other three methods. However, a set of unified rules for the divide-and-conquer strategy need to be recommended for the bootstrap method, which is optimal when boot-p for p (person), boot-pi for i (item), and boot-i for pi (person × item).

Fostering practice-based evidence through routine outcome monitoring in a university psychotherapy service for common mental health problems: a protocol for a naturalistic, observational study

INTRODUCTION

Data-informed psychotherapy and routine outcome monitoring are growing as referents in psychotherapy research and practice. In Ecuador, standardised web-based routine outcome monitoring systems have not been used yet, precluding data-driven clinical decisions and service management. Hence, this project aims at fostering and disseminating practice-based evidence in psychotherapy in Ecuador by implementing a web-based routine outcome monitoring system in a university psychotherapy service.

METHODS AND ANALYSES

This is a protocol for an observational naturalistic longitudinal study. Progress and outcomes of treatment in the Centro de Psicología Aplicada of the Universidad de Las Américas in Quito, Ecuador will be examined. Participants will be adolescents and adults (≥11 years) seeking treatment, as well as therapists and trainees working at the centre between October 2022 and September 2025. Clients' progress will be monitored by a range of key variables: psychological distress, ambivalence to change, family functioning, therapeutic alliance and life satisfaction. Sociodemographic information and satisfaction with treatment data will be collected before and at the end of treatment, respectively. Also, semi-structured interviews to explore therapists' and trainees' perceptions, expectations and experiences will be conducted. We will analyse first contact data, psychometrics of the measures, reliable and clinically significant change, outcome predictors as well as trajectories of changes. Moreover, we will conduct a framework analysis for the interviews.

ETHICS AND DISSEMINATION

The protocol for this study was approved by the Human Research Ethics Committee of the Pontificia Universidad Católica del Ecuador (#PV-10-2022). The results will be disseminated in peer-reviewed scientific articles, at conferences and in workshops.

TRIAL REGISTRATION NUMBER

NCT05343741.

Mental and physical health among the French population before and during the first and second COVID-19 lockdowns: Latent class trajectory analyses using longitudinal data

BACKGROUND

The French government issued national COVID-19-related confinement and stay-at-home orders depending on different epidemic levels in a bid to stem the coronavirus pandemic and its resurgence. The long-term impact of lockdown measures on the general population may vary. We aimed to identify and characterize self-reported mental and physical health trajectories in the French population from pre-lockdown to the first and second COVID-19 lockdowns and to identify factors associated with health status variation patterns.

METHODS

We did a secondary analysis of the MAVIE cohort in France. Volunteers of this national cohort were recruited between November 2014 and December 2019, and information was collected at recruitment (pre-lockdown), April-May 2020 (the first lockdown), and October-December 2020 (the second lockdown). Latent class mixed models were built to identify distinct anxiety (as measured by GAD-7) and depressive (as measured by PHQ-9) symptoms, and self-perceived mental and physical health trajectories. Factors associated with status variation were identified by logistic or multinomial regression.

RESULTS

A total of 613 participants with data in all three data collection waves were included. Respondents spent almost half as much time on traditional media, websites and social media during the second lockdown as during the first. Mean anxiety scores were 1.96, 2.37 and 2.82 at pre-lockdown, and the first and second lockdowns, respectively. Mean depressive scores were 3.12, 3.36 and 3.95, respectively. Latent class mixed models fitted two and three distinct trajectory classes respectively for anxiety symptoms ('no pre-pandemic anxiety, slightly increase', 58.9%; 'consistently fair', 41.1%) and depressive symptoms ('consistently very low', 34.6%; 'consistently low', 56.1%; 'increasing and clinically significant at the second lockdown', 9.3%), and four classes for self-perceived mental and physical health. Females were more likely to belong to trajectories of the most vulnerable one as regard to the symptoms of anxiety and depression, and self-perceived mental and physical health. The younger participants were also more vulnerable to anxiety symptoms and those with a clinical diagnosis or a positive COVID-19 test for the participant or relatives were more likely to belong to vulnerable trajectories for depressive symptoms and self-perceived mental health.

CONCLUSION

A continuing increase in the mean scores of anxiety and depression symptoms was observed throughout the two lockdown periods in France. Further analyses revealed distinct patterns with a small fraction of volunteers experiencing worsening mental and physical health symptoms. This vulnerable small part of the population requires targeted support.

Impact of a School-Based Multicomponent Positive Psychology Intervention on Adolescents' Time Attitudes: A Latent Transition Analysis

Time attitudes, which refer to positive and negative feelings towards the past, present, and future, are a salient phenomenon in the developmental stage of adolescence and have been related to better well-being. Positive feelings towards time can be promoted in the school setting through empirically validated positive psychology interventions. However, the extent to which these interventions impact the time attitudes of adolescents remains unknown. The current study investigated the influence of a multicomponent positive psychology intervention on adolescents’ transitions between time attitude profiles and how these transitions are related to their emotional, social, and psychological well-being. Participants consisted of 220 (M = 14.98; 47.3% female) adolescents from two Spanish high schools who participated in the six-week Get to Know Me+ program. Adolescents’ time attitudes and well-being were measured via the Adolescents and Adult Time Inventory–Time Attitudes and the Mental Health Continuum–Short Form, respectively, at pre- and postintervention. Participants were clustered in different profiles through a latent profile analysis, and the transitions were analyzed using a latent transition analysis. Five profiles were identified (negative, present/future negative, past negative, optimistic, and positive), and results indicated that adolescents who participated in the intervention were more likely to transition to positive profiles (optimistic and positive) and generally reported higher well-being, especially those in the negative, present/future negative, and optimistic profiles. Preliminary evidence showed that school-based multicomponent positive psychology interventions can have a positive impact on adolescents’ feelings towards time and well-being.

Latent class growth analyses reveal overrepresentation of dysfunctional fear conditioning trajectories in patients with anxiety-related disorders compared to controls

Recent meta-analyses indicated differences in fear acquisition and extinction between patients with anxiety-related disorders and comparison subjects. However, these effects are small and may hold for only a subsample of patients. To investigate individual trajectories in fear acquisition and extinction across patients with anxiety-related disorders (N = 104; before treatment) and comparison subjects (N = 93), data from a previous study (Duits et al., 2017) were re-analyzed using data-driven latent class growth analyses. In this explorative study, subjective fear ratings, shock expectancy ratings and startle responses were used as outcome measures. Fear and expectancy ratings, but not startle data, yielded distinct fear conditioning trajectories across participants. Patients were, compared to controls, overrepresented in two distinct dysfunctional fear conditioning trajectories: impaired safety learning and poor fear extinction to danger cues. The profiling of individual patterns allowed to determine that whereas a subset of patients showed trajectories of dysfunctional fear conditioning, a significant proportion of patients (≥50 %) did not. The strength of trajectory analyses as opposed to group analyses is that it allows the identification of individuals with dysfunctional fear conditioning. Results suggested that dysfunctional fear learning may also be associated with poor treatment outcome, but further research in larger samples is needed to address this question.

On the motivational nature of authentic leadership practices: a latent profile analysis based on self-determination theory

Purpose

Although one of the central premises of authentic leadership theory is that authentic leaders mobilize their followers, the underlying motivational mechanisms of this process remain poorly understood. Drawing on self-determination theory, this study aims to fill that gap by examining authentic leadership practices (ALP) as theoretical antecedents of employees' motivation profiles.

Design/methodology/approach

Latent profile analyses conducted on a sample of 501 employees revealed four profiles: self-determined, unmotivated, highly motivated and moderately motivated.

Findings

ALP were associated with a higher likelihood of membership into the most adaptive motivation profiles. Employees in these profiles displayed more optimal job functioning: higher organizational commitment and performance, and lower intentions to leave their organization.

Originality/value

These findings underscore the predictive power of autonomous motivation for employee functioning and provide new insights into how ALP can improve work motivation, and hence job functioning. Our results account not only for how ALP affects the complete range of behavioral regulations at work but also the different patterns in which these regulations combine within employees.

Latent Class and Transition Analysis of Alzheimer's Disease Data

This study uses independent latent class analysis (LCA) and latent transition analysis (LTA) to explore accurate diagnosis and disease status change of a big Alzheimer's disease Neuroimaging Initiative (ADNI) data of 2,132 individuals over a 3-year period. The data includes clinical and neural measures of controls (CN), individuals with subjective memory complains (SMC), early-onset mild cognitive impairment (EMCI), late-onset mild cognitive impairment (LMCI), and Alzheimer's disease (AD). LCA at each time point yielded 3 classes: Class 1 is mostly composed of individuals from CN, SMC, and EMCI groups; Class 2 represents individuals from LMCI and AD groups with improved scores on memory, clinical, and neural measures; in contrast, Class 3 represents LMCI and from AD individuals with deteriorated scores on memory, clinical, and neural measures. However, 63 individuals from Class 1 were diagnosed as AD patients. This could be misdiagnosis, as their conditional probability of belonging to Class 1 (0.65) was higher than that of Class 2 (0.27) and Class 3 (0.08). LTA results showed that individuals had a higher probability of staying in the same class over time with probability &gt;0.90 for Class 1 and 3 and probability &gt;0.85 for Class 2. Individuals from Class 2, however, transitioned to Class 1 from time 2 to time 3 with a probability of 0.10. Other transition probabilities were not significant. Lastly, further analysis showed that individuals in Class 2 who moved to Class 1 have different memory, clinical, and neural measures to other individuals in the same class. We acknowledge that the proposed framework is sophisticated and time-consuming. However, given the severe neurodegenerative nature of AD, we argue that clinicians should prioritize an accurate diagnosis. Our findings show that LCA can provide a more accurate prediction for classifying and identifying the progression of AD compared to traditional clinical cut-off measures on neuropsychological assessments.

An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software

The use of finite mixture modelling (FMM) is becoming increasingly popular for the analysis of longitudinal repeated measures data. FMMs assist in identifying latent classes following similar paths of temporal development. This paper aims to address the confusion experienced by practitioners new to these methods by introducing the various available techniques, which includes an overview of their interrelatedness and applicability. Our focus will be on the commonly used model-based approaches which comprise latent class growth analysis (LCGA), group-based trajectory models (GBTM), and growth mixture modelling (GMM). We discuss criteria for model selection, highlight often encountered challenges and unresolved issues in model fitting, showcase model availability in software, and illustrate a model selection strategy using an applied example.