Participant retention strategies that minimise attrition in longitudinal cohort studies have evolved considerably in recent years. This study aimed to assess, via systematic review and meta-analysis, the effectiveness of both traditional strategies and contemporary innovations for retention adopted by longitudinal cohort studies in the past decade. Health research databases were searched for retention strategies used within longitudinal cohort studies published in the 10-years prior, with 143 eligible longitudinal cohort studies identified (141 articles; sample size range: 30 to 61,895). Details on retention strategies and rates, research designs, and participant demographics were extracted. Meta-analyses of retained proportions were performed to examine the association between cohort retention rate and individual and thematically grouped retention strategies. Results identified 95 retention strategies, broadly classed as either: barrier-reduction, community-building, follow-up/reminder, or tracing strategies. Forty-four of these strategies had not been identified in previous reviews. Meta-regressions indicated that studies using barrier-reduction strategies retained 10% more of their sample (95%CI [0.13 to 1.08]; p = .01); however, studies using follow-up/reminder strategies lost an additional 10% of their sample (95%CI [− 1.19 to − 0.21]; p = .02). The overall number of strategies employed was not associated with retention. Employing a larger number of retention strategies may not be associated with improved retention in longitudinal cohort studies, contrary to earlier narrative reviews. Results suggest that strategies that aim to reduce participant burden (e.g., flexibility in data collection methods) might be most effective in maximising cohort retention.

Background: Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional methods, penalised regression models improve prediction in new data by shrinking the size of coefficients and retaining those with coefficients greater than zero. However, the performance and selection of indicators depends on the specific algorithm implemented. The purpose of this study was to examine the predictive performance and feature (i.e., indicator) selection capability of common penalised logistic regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods.Design: Data were drawn from the Australian Temperament Project, a multigenerational longitudinal study established in 1983. The analytic sample consisted of 1,292 (707 women) participants. A total of 102 adolescent psychosocial and contextual indicators were available to predict young adult daily smoking.Findings: Penalised logistic regression methods showed small improvements in predictive performance over logistic regression and forward selection. However, no single penalised logistic regression model outperformed the others. Elastic-net models selected more indicators than either LASSO or adaptive LASSO. Additionally, more regularised models included fewer indicators, yet had comparable predictive performance. Forward selection methods dismissed many indicators identified as important in the penalised logistic regression models.Conclusions: Although overall predictive accuracy was only marginally better with penalised logistic regression methods, benefits were most clear in their capacity to select a manageable subset of indicators. Preference to competing penalised logistic regression methods may therefore be guided by feature selection capability, and thus interpretative considerations, rather than predictive performance alone.

Background and Objectives Brain lesions are a well-recognized etiology of dystonia. These cases are especially valuable because they offer causal insight into the neuroanatomical substrates of dystonia. To date, knowledge of lesion-induced dystonia comes mainly from isolated case reports or small case series, restricting broader description and analysis. Methods Cases of lesion-induced dystonia were first identified from a systematic review of published literature. Latent class analysis then investigated whether patients could be classified into subgroups based on lesion location and body regions affected by dystonia. Regression analyses subsequently investigated whether subgroup membership predicted clinical characteristics of dystonia. Results Three hundred fifty-nine published cases were included. Lesions causing dystonia occurred in heterogeneous locations, most commonly in the basal ganglia (46.2%), followed by the thalamus (28.1%), brainstem (22.6%), and white matter (21.2%). The most common form of lesion-induced dystonia was focal dystonia (53.2%), with the hand (49.9%) and arm (44.3%) most commonly affected. Of all cases, 86.6% reported co-occurring neurologic manifestations and 26.1% reported other movement disorders. Latent class analysis identified 3 distinct subgroups of patients: those with predominantly limb dystonias, which were associated with basal ganglia lesions; those with hand dystonia, associated with thalamic lesions; and those with predominantly cervical dystonia, associated with brainstem and cerebellar lesions. Regression demonstrated significant differences between these subgroups on a range of dystonia symptoms, including dystonic tremor, symptom latency, other movement disorders, and dystonia variability. Discussion Although dystonia can be induced by lesions to numerous brain regions, there are distinct relationships between lesion locations and dystonic body parts. This suggests that the affected brain networks are different between types of dystonia.

Background: Topic modeling approaches allow researchers to analyze and represent written texts. One of the commonly used approaches in psychology is latent Dirichlet allocation (LDA), which is used for rapidly synthesizing patterns of text within “big data,” but outputs can be sensitive to decisions made during the analytic pipeline and may not be suitable for certain scenarios such as short texts, and we highlight resources for alternative approaches. This review focuses on the complex analytical practices specific to LDA, which existing practical guides for training LDA models have not addressed. Objective: This scoping review used key analytical steps (data selection, data preprocessing, and data analysis) as a framework to understand the methodological approaches being used in psychology research using LDA. Methods: A total of 4 psychology and health databases were searched. Studies were included if they used LDA to analyze written words and focused on a psychological construct or issue. The data charting processes were constructed and employed based on common data selection, preprocessing, and data analysis steps. Results: A total of 68 studies were included. These studies explored a range of research areas and mostly sourced their data from social media platforms. Although some studies reported on preprocessing and data analysis steps taken, most studies did not provide sufficient detail for reproducibility. Furthermore, the debate surrounding the necessity of certain preprocessing and data analysis steps is revealed. Conclusions: Our findings highlight the growing use of LDA in psychological science. However, there is a need to improve analytical reporting standards and identify comprehensive and evidence-based best practice recommendations. To work toward this, we developed an LDA Preferred Reporting Checklist that will allow for consistent documentation of LDA analytic decisions and reproducible research outcomes.

The COVID-19 pandemic presents significant risks to the mental health and wellbeing of families. This study aimed to examine: (1) patterns of parent and child (0–18 years) mental health, parent substance use, couple conflict, parenting practices, and family functioning during COVID-19 compared to pre-pandemic data; and (2) associations between parent, child, and family outcomes during the pandemic and both pre-existing risk factors and COVID-19 stressors. Participants were Australian mothers (81%) and fathers aged 18 years and over who were parents of a child 0–18 years (N=2,365). Parents completed an online self-report survey assessing mental health, substance use, couple conflict, parenting, and family functioning during ‘stage three’ COVID-19 restrictions in April 2020. Data were compared to pre-pandemic data from four Australian population-based cohorts. Compared to pre-pandemic estimates, during the pandemic period parents reported higher rates of parent mental health symptoms (Cohen’s d=0.26-.81, all p<.001), higher parenting irritability (d=0.17-.46, all p<.001), lower family positive expressiveness (d=-0.18, p<.001), and higher alcohol consumption (22% vs 12% drinking four or more days per week, p<.001). In multivariable analyses, pre-existing financial deprivation and COVID-19 stressors were associated with greater severity in parent and child mental health symptoms, parent emotion dysregulation, parenting irritability, couple conflict and family positive/negative expressiveness. Parents and children with pre-existing mental health conditions had elevated difficulties during the pandemic across most domains. Our data suggest wide-ranging, detrimental family impacts associated with the COVID-19 pandemic; and support policy actions to assist families with financial supports, leave entitlements, and social housing.

Purpose The Australian Temperament Project Generation 3 Study (ATPG3) was established to examine the extent to which offspring social and emotional development is shaped in the decades prior to conception, in parent and grandparent histories of psychosocial adjustment (eg, emotional regulation, relationship quality and prosociality) and maladjustment (eg, depressive symptoms, substance use and antisociality). Participants The Australian Temperament Project (ATP) commenced in 1983 as a population representative survey of the social and emotional health of 2443 young Australians (Generation 2: 4–8 months old) and their parents (Generation 1). Since then, families have been followed from infancy to young adulthood (16 waves). Between 2012 and 2018, the cohort was screened biannually for pregnancies (Generation 3), with assessments conducted in the third trimester of pregnancy, and at 8 weeks and 1 year postpartum. Findings to date A total of 1167 offspring (607 female) born to 703 Generation 2 parents (400 mothers) were recruited into the ATPG3 Study. Findings to date highlight: (1) strong continuities in depressive symptoms and substance use from adolescence through to becoming a parent; (2) a role for persistent preconception mental health problems in risk for parent–child bonding difficulties, as well as infant emotional reactivity and behaviour problems; (3) the importance of secure attachments in adolescence in reducing long-term risk for postpartum mental health problems; and (4) the protective nature of perceived social support, both preconception and postpartum, in strengthening relationship quality and social support during the COVID-19 pandemic. Future plans Assessments of ATPG3 families in preschool and middle childhood are currently funded and underway. We intend to maintain the offspring cohort through childhood, adolescence, young adulthood and into parenthood. Data will be used to map preconception determinants of emotional health, and enhance approaches to population monitoring and targeted intervention over the life course and across generations.