Distinct trajectories of perinatal depression in Chinese women: application of latent growth mixture modelling

Background Current research on perinatal depression rarely pays attention to the continuity and volatility of depression symptoms over time, which is very important for the early prediction and prognostic evaluation of perinatal depression. This study investigated the trajectories of perinatal depression symptoms and aimed to explore the factors related to these trajectories. Methods The study recruited 550 women during late pregnancy (32 ± 4 weeks of gestation) and followed them up 1 and 6 weeks postpartum. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS). Latent growth mixture modelling (LGMM) was used to identify trajectories of depressive symptoms during pregnancy. Results Two trajectories of perinatal depressive symptoms were identified: “decreasing” (n = 524, 95.3%) and “increasing” (n = 26, 4.7%). History of smoking, alcohol use and gestational hypertension increased the chance of belonging to the increasing trajectories, and a high level of social support was a protective factor for maintaining a decreasing trajectory. Conclusions This study identified two trajectories of perinatal depression and the factors associated with each trajectory. Paying attention to these factors and providing necessary psychological support services during pregnancy would effectively reduce the incidence of perinatal depression and improve patient prognosis.

Trajectories of depression and anxiety symptoms during psychological therapy for common mental health problems

Background: There is substantial variation in patient symptoms following psychological therapy for depression and anxiety. However, reliance on endpoint outcomes ignores additional interindividual variation during therapy. Knowing a patient’s likely symptom trajectories could guide clinical decisions.Aim: Identify latent classes of patients with similar symptom trajectories over the course of psychological therapy and explore associations between baseline variables and trajectory class. Method: Patients received high-intensity psychological treatment for depression or anxiety at NHS Improving Access to Psychological Therapies (IAPT) services in South London (N = 16,258). To identify trajectories, we performed growth mixture modelling of depression and anxiety symptoms over 11 sessions. We then ran multinomial regressions to identify baseline variables associated with trajectory class membership.Results: Trajectories of depression and anxiety symptoms were highly similar and best modelled by four classes. Three classes started with moderate-severe symptoms and showed (1) minimal change, (2) gradual improvement, and (3) fast improvement. A final class (4) started with mild symptoms and demonstrated small improvement. Within the trajectory classes with moderate-severe baseline symptoms, patients in the two classes showing improvement as opposed to minimal change tended not to be prescribed psychotropic medication or report a disability and were in employment. Those showing fast improvement additionally reported lower baseline functional impairment on average. Conclusion: Multiple trajectory classes of depression and anxiety symptoms were associated with baseline characteristics. Identifying the most likely trajectory for a patient at the start of treatment could inform decisions about the suitability and continuation of therapy, ultimately improving patient outcomes.

Developmental trajectories of autistic social traits in the general population

Background Autistic people show diverse trajectories of autistic traits over time, a phenomenon labelled ‘chronogeneity’. For example, some show a decrease in symptoms, whilst others experience an intensification of difficulties. Autism spectrum disorder (ASD) is a dimensional condition, representing one end of a trait continuum that extends throughout the population. To date, no studies have investigated chronogeneity across the full range of autistic traits. We investigated the nature and clinical significance of autism trait chronogeneity in a large, general population sample. Methods Autistic social/communication traits (ASTs) were measured in the Avon Longitudinal Study of Parents and Children using the Social and Communication Disorders Checklist (SCDC) at ages 7, 10, 13 and 16 (N = 9744). We used Growth Mixture Modelling (GMM) to identify groups defined by their AST trajectories. Measures of ASD diagnosis, sex, IQ and mental health (internalising and externalising) were used to investigate external validity of the derived trajectory groups. Results The selected GMM model identified four AST trajectory groups: (i) Persistent High (2.3% of sample), (ii) Persistent Low (83.5%), (iii) Increasing (7.3%) and (iv) Decreasing (6.9%) trajectories. The Increasing group, in which females were a slight majority (53.2%), showed dramatic increases in SCDC scores during adolescence, accompanied by escalating internalising and externalising difficulties. Two-thirds (63.6%) of the Decreasing group were male. Conclusions Clinicians should note that for some young people autism-trait-like social difficulties first emerge during adolescence accompanied by problems with mood, anxiety, conduct and attention. A converse, majority-male group shows decreasing social difficulties during adolescence.

Psychological distress across the deployment cycle: exploratory growth mixture model

Background Prior research has identified behavioural health outcomes as key sequelae to combat deployment. However, relatively little is known about differential patterns of change in depression or generalised anxiety linked to deployment to a combat zone. In this paper, we add to the existing trajectory literature and examine key predictive factors of behavioural health risk. Aims The primary aim is to leverage growth mixture modelling to ascertain trajectories of psychological distress, operationalised as a coherent construct combining depression and generalised anxiety, and to identify factors that differentiate adaptive and maladaptive patterns of change. Method Data were collected from a brigade combat team prior to a combat deployment to Afghanistan, during deployment, at immediate re-integration and approximately 2–3 months thereafter. The main outcome was measured using the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS). Results Three latent trajectories were identified: a low–stable trajectory, a declining trajectory and a rising trajectory. Most individuals aligned with the low–stable trajectory. A conditional model using covariates measured during deployment showed that the low–stable trajectory differed consistently from the remaining trajectories on self-reported loneliness and non-combat deployment stressors. Conclusions The examination of differential patterns of adaptation, to identify individuals at higher risk, is critical for the efficient targeting of resources. Our findings further indicate that loneliness may be a useful leverage point for clinical and organisational intervention.

Growth mixture models: a case example of the longitudinal analysis of patient‐reported outcomes data captured by a clinical registry

Background An assumption in many analyses of longitudinal patient-reported outcome (PRO) data is that there is a single population following a single health trajectory. One approach that may help researchers move beyond this traditional assumption, with its inherent limitations, is growth mixture modelling (GMM), which can identify and assess multiple unobserved trajectories of patients’ health outcomes. We describe the process that was undertaken for a GMM analysis of longitudinal PRO data captured by a clinical registry for outpatients with atrial fibrillation (AF). Methods This expository paper describes the modelling approach and some methodological issues that require particular attention, including (a) determining the metric of time, (b) specifying the GMMs, and (c) including predictors of membership in the identified latent classes (groups or subtypes of patients with distinct trajectories). An example is provided of a longitudinal analysis of PRO data (patients’ responses to the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire) collected between 2008 and 2016 for a population-based cardiac registry and deterministically linked with administrative health data. Results In determining the metric of time, multiple processes were required to ensure that “time” accounted for both the frequency and timing of the measurement occurrences in light of the variability in both the number of measures taken and the intervals between those measures. In specifying the GMM, convergence issues, a common problem that results in unreliable model estimates, required constrained parameter exploration techniques. For the identification of predictors of the latent classes, the 3-step (stepwise) approach was selected such that the addition of predictor variables did not change class membership itself. Conclusions GMM can be a valuable tool for classifying multiple unique PRO trajectories that have previously been unobserved in real-world applications; however, their use requires substantial transparency regarding the processes underlying model building as they can directly affect the results and therefore their interpretation.

Association between physical-activity trajectories and cognitive decline in adults 50 years of age or older

ObjectivesTo investigate the associations of physical-activity trajectories with the level of cognitive performance and its decline in adults 50 years of age or older.MethodsWe studied 38729 individuals (63 ± 9 years; 57% women) enrolled in the Survey of Health, Ageing and Retirement in Europe (SHARE). Physical activity was self-reported and cognitive performance was assessed based on immediate recall, verbal fluency, and delayed recall. Physical-activity trajectories were estimated using growth mixture modelling and linear mixed effects models were used to investigate the associations between the trajectories and cognitive performance.ResultsThe models identified two physical-activity trajectories of physical activity: constantly-high physical activity (N=27634: 71%) and decreasing physical activity (N=11095; 29%). Results showed that participants in the decreasing physical-activity group exhibited a lower level of cognitive performance compared to the high physical-activity group (immediate recall: ß=0.94; 95% confidence interval [CI]=0.92 to 0.95; verbal fluency: ß=0.98; 95% CI=0.97 to 0.98; delayed recall: ß=0.95; 95% CI=0.94 to 0.97). Moreover, compared with participants in the constantly-high physical-activity group, participants in the decreasing physical-activity group showed a steeper decline in all cognitive measures (immediate recall: ß=-0.04; 95% CI=-0.05 to −0.04; verbal fluency: ß=-0.22; 95% CI=-0.24 to −0.21; delayed recall: ß=-0.04; 95% CI=-0.05 to −0.04).ConclusionsPhysical-activity trajectories are associated with the level and evolution of cognitive performance in adults over 50 years. Specifically, our findings suggest that a decline in physical activity over multiple years is associated with a lower level and a steeper decline in cognitive performance.

Understanding different trajectories of mental health across the general population during the COVID-19 pandemic

Background The COVID-19 pandemic and nationally mandated restrictions to control the virus have been associated with increased mental health issues. However, the differential impact of the pandemic and lockdown on groups of individuals, and the personal characteristics associated with poorer outcomes are unknown. Method Data from 21 938 adults in England who participated in a stratified cohort study were analysed. Trajectories of depression and anxiety symptoms were identified using growth mixture modelling. Multinomial and logistic regression models were constructed to identify sociodemographic and personality-related risk factors associated with trajectory class membership. Results Four trajectories of depression and five for anxiety were identified. The most common group presented with low symptom severity throughout, other classes were identified that showed: severe levels of symptoms which increased; moderate symptoms throughout; worsening mental health during lockdown but improvements after lockdown ended; and for anxiety only, severe initial anxiety that decreased quickly during lockdown. Age, gender, ethnicity, income, previous diagnoses, living situation, personality factors and sociability were associated with different trajectories. Conclusions Nearly 30% of participants experienced trajectories with symptoms in the clinical range during lockdown, and did not follow the average curve or majority group, highlighting the importance of differential trajectories. Young, female, outgoing and sociable people and essential workers experienced severe anxiety around the announcement of lockdown which rapidly decreased. Younger individuals with lower incomes and previous mental health diagnoses experienced higher and increasing levels of symptoms. Recognising the likely symptom trajectories for such groups may allow for targeted care or interventions.

Examining changes in parent-reported child and adolescent mental health throughout the UK’s first COVID-19 national lockdown.

Background The COVID-19 pandemic has significantly changed the lives of children and adolescents, forcing them into periods of prolonged social isolation and time away from school. Understanding the psychological consequences of the UK’s lockdown for children and adolescents, the associated risk factors, and how trajectories may vary for children and adolescents in different circumstances is essential so that the most vulnerable children and adolescents can be identified and appropriate support can be implemented. Methods Parents and carers (n = 2988) in the U.K. with children and adolescents aged between 4 and 16 years completed an online survey about their child’s mental health. Growth curve analysis was used to examine the changes in conduct problems, hyperactivity/inattention and emotional symptoms between the end of March/beginning of April and July using data from four monthly assessments. Additionally, growth mixture modelling identified mental health trajectories for conduct problems, hyperactivity/inattention and emotional symptoms separately and subsequent regression models were used to estimate predictors of mental health trajectory membership. Results Overall levels of hyperactivity and conduct problems increased over time whereas emotional symptoms remained relatively stable, though declined somewhat between June and July. Change over time varied according to child age, the presence of siblings, and with Special Educational Needs (SEN)/ Neurodevelopmental Disorders (ND). Subsequent growth mixture modelling identified three, four and five trajectories for hyperactivity/inattention, conduct problems and emotional symptoms, respectively. Though many children maintained “stable3low” symptoms, others experienced elevated symptoms by July. These children were more likely to have a parent/carer with higher levels of psychological distress, to have SEN/ND, or to be younger in age. Conclusions The findings support previous literature and highlight that certain risk factors were associated with poorer mental health trajectories for children and adolescents during the pandemic.

Association between physical-activity trajectories and cognitive decline in adults 50 years of age or older

Aims To investigate the associations of physical-activity trajectories with the level of cognitive performance (CP) and its decline in adults 50 years of age or older. Methods We studied 38 729 individuals (63 ± 9 years; 57% women) enrolled in the Survey of Health, Ageing and Retirement in Europe (SHARE). Physical activity was self-reported and CP was assessed based on immediate recall, verbal fluency and delayed recall. Physical-activity trajectories were estimated using growth mixture modelling and linear mixed-effects models were used to investigate the associations between the trajectories and CP. Results The models identified two trajectories of physical activity: constantly high physical activity (N = 27 634: 71%) and decreasing physical activity (N = 11 095; 29%). Results showed that participants in the decreasing physical-activity group exhibited a lower level of CP compared to the high physical-activity group (immediate recall: ß = 0.94; 95% confidence interval [CI] = 0.92–0.95; verbal fluency: ß = 0.98; 95% CI = 0.97–0.98; delayed recall: ß = 0.95; 95% CI = 0.94–0.97). Moreover, compared with participants in the constantly high physical-activity group, participants in the decreasing physical-activity group showed a steeper decline in all cognitive measures (immediate recall: ß = −0.04; 95% CI = −0.05 to −0.04; verbal fluency: ß = −0.22; 95% CI = −0.24 to −0.21; delayed recall: ß = −0.04; 95% CI = −0.05 to −0.04). Conclusions Physical-activity trajectories are associated with the level and evolution of CP in adults over 50 years. Specifically, our findings suggest that a decline in physical activity over multiple years is associated with a lower level and a steeper decline in CP.

Effectiveness of a Theory-Driven mHealth Intervention on Patients’ Post-Surgery Rehabilitation Adherence: A Randomized Clinical Trial (Preprint)

BACKGROUND Low adherence to post-surgery rehabilitation programs among anterior cruciate ligament (ACL) reconstruction patients is frequently reported. It is important to develop effective interventions that promote adherence to treatment and rehabilitation in ACL ruptured patients. OBJECTIVE This study aimed to assess effects of a theory-based smartphone-delivered intervention on ACL ruptured patients’ psychological, behavioral, and clinical outcomes during post-surgery rehabilitation period. METHODS We recruited 96 eligible participants (Mage = 27.824, SD = 8.732, range = 18 to 53; female = 38.947%) who underwent ACL reconstruction surgery. Participants were randomly assigned to a treatment group (n=41), which received standard post-surgical treatment and smartphone application (“ACL-Well”) delivering the intervention, or a control group (n=55), which received standard post-surgical treatment only. The primary outcomes were the recovery outcomes from ACL surgery, measured by knee muscle strength and laxity, and subjective knee evaluation completed 4-month post-intervention. Secondary outcomes were the psychological and behavioral outcomes measured at baseline within 2 weeks of surgery, and at 2- and 4-month post-intervention follow-up. RESULTS ANCOVA suggested no significant difference between the intervention and the control group in the recovery outcomes. Growth mixture modelling revealed self-determined treatment motivation declined significantly over the intervention period in the control group, but not in the intervention group. Intention and rehabilitation adherence also revealed similar patterns among patients who had lower levels of motivational or behavioral factors of rehabilitation at baseline. CONCLUSIONS The smartphone application developed in this project shows promise as a means to promote orthopedic outpatients’ motivation and adherence to treatment, but fell short in promoting recovery. Research on long-term effects of interventions are needed. CLINICALTRIAL HKUCTR-2761