BACKGROUND: Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.
METHODS: Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.
RESULTS: Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.
CONCLUSIONS: This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.

OBJECTIVE: Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.
METHODS: We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.
RESULTS: Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.
CONCLUSIONS: The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Adverse childhood experiences (ACEs) studies reveal the profound impacts of experiencing trauma and hardships in childhood. However, the cumulative risk approach of treating ACEs obscures the heterogeneity of ACEs and their consequences, making actionable interventions impossible. latent class analysis (LCA) has increasingly been used to address these concerns by identifying underlying subgroups of people who experience distinctive patterns of co-occurring ACEs. Though LCA has its strengths, the existing research produces few comparable findings because LCA results are dependent on ACEs measures and indicators, which vary widely by study. Therefore, a scoping review of ACEs studies using LCA that focuses on ACEs measures, indicators, and findings is needed to inform the field. Following Arksey and O\'Malley\'s five-stage scoping review methodological framework, we first identified 211 articles from databases of EBSCOhost, PubMed, and Scopus using \"adverse childhood experiences\" for title search and \"latent class analysis\" for abstract search. Based on the inclusion criteria of peer-reviewed articles written in English published from 2012 to 2022 and the exclusion criteria of nonempirical studies and the LCA not analyzing ACEs, we finally selected 58 articles in this scoping review. Results showed LCA has been increasingly endorsed in the ACEs research community to examine the associations between ACEs and human health and well-being across culturally diverse populations. LCA overcame the limitations of the traditional methods by revealing specific ACEs clusters that exert potent effects on certain outcomes. However, the arbitrary nature of selecting ACEs indicators, measures, and the limited use of theory impedes the field from moving forward.

Person-centered approaches, such as latent class analysis (LCA) and latent profile analysis (LPA), aid the identification of subgroups within sample populations. These methods can identify the patterns of co-occurrence between different forms of intimate partner violence (IPV), providing valuable information for prevention and intervention efforts. The aim of this systematic review was to yield a summary and conduct a critical evaluation of the current research that utilizes LCA/LPA to investigate IPV victimization profiles. We provide an outline of 14 relevant studies, retrieved from searches conducted on PsycInfo, Scopus, and Eric databases. There was a large amount of variability in relation to the forms of IPV assessed, measures utilized, number of classes identified, and the sample populations recruited. However, broad similarities were revealed as there were some commonly identified classes, including the no/low violence class, the physical and psychological victimization class, and the multiple victimization class, yet the labels assigned to those classes differed across studies. A range of external criteria (risk factors and consequences) were also identified as being associated with class membership. We highlight the methodological features which may have impacted data collection and class enumeration, including the differences in sample population, the range of IPV indicators assessed, the time period from which IPV data were recorded, and whether data were collected regarding participants\' current or previous relationships. Marginalized populations were underrepresented, and psychological abuse was most inconsistently operationalized. Recommendations for future research are provided, including recommendations with regard to labeling the classes for greater consistency across studies.

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.

The group-based trajectory modeling (GBTM) method is increasingly used in pharmacoepidemiologic studies to describe medication adherence trajectories over time. However, assessing the associations between these medication adherence trajectories and health-related outcomes remains challenging. The purpose of this review is to identify and systematically review the methods used to assess the association between medication adherence trajectories, estimated from the GBTM method, and health-related outcomes.
We will conduct a systematic review according to the recommendations of the Cochrane handbook for systematic reviews of interventions 6.2. Results will be reported following PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-analyses) recommendations. We will search in the following databases: PubMed, Embase, PsycINFO, Web of Science, CINAHL, and Cochrane Library. Two reviewers will independently select articles and extract data. Discrepancies at every step will be resolved through discussion, and consensus will be reached for all disagreed articles. A third reviewer will act as a referee if needed. We will produce tables to synthesize the modalities used to estimate medication adherence trajectories with GBTM. We will also synthesize the modalities used to assess the association between these medication adherence trajectories and health-related outcomes by identifying the types of health-related outcomes studied and how they are defined, the statistical models used, and how the medication adherence trajectories were used in these models, and the effect measure yield. We will also review the limitations and biases reported by the authors and their attempts to mitigate them. We will provide a narrative synthesis.
This review will provide a thorough exploration of the strategies and methods used in medication adherence research to estimate the associations between medication adherence trajectories, estimated with GBTM, and the different health-related outcomes. It will represent the first crucial steps toward optimizing these methods in adherence studies.
Prospero CRD42021213503 .

A mounting body of evidence suggests that polysubstance use (PSU) is common among people who use opioids (PWUO). Measuring PSU, however, is statistically and methodologically challenging. Person-centered analytical approaches (e.g., latent class analysis) provide a holistic understanding of individuals\' substance use patterns and help understand PSU heterogeneities among PWUO and their specific needs in an inductive manner. We reviewed person-centered studies that characterized latent patterns of PSU among PWUO.
We searched MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Google Scholar from inception, through to June 15, 2020, for empirical peer-reviewed studies or gray literature that reported on latent classes of PSU among PWUO. Two independent reviewers completed the title, abstract, full-text screening, and data extraction. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale, and quality of reporting was evaluated using the Guidelines for Reporting on Latent Trajectory Studies checklist. Studies\' findings were summarized and presented in a narrative fashion.
Out of the 3372 initial unique studies identified, 30 were included. PSU operationalization varied substantially among the studies. We identified five distinct PSU latent classes frequently observed across the studies: Infrequent/low PSU, PSU primarily involving heroin use, PSU primarily involving heroin and stimulant use, PSU primarily involving stimulant use, and frequent PSU. Belonging to higher frequency or severity PSU classes were associated with frequent injection drug use, sharing needles and paraphernalia, high-risk sexual behaviours, as well as experiences of adversities, such as homelessness, incarceration, and poor mental health.
PSU patterns vary significantly across different subgroups of PWUO. The substantial heterogeneities among PWUO need to be acknowledged in substance use clinical practices and policy developments. Findings call for comprehensive interventions that recognize these within-group diversities and address the varying needs of PWUO.

This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is person-centered provides strategies that increase the possibilities for investigating ASR phenotypes. We first examine the utility of examining underlying phenotypes using latent profile/class analysis drawing on cross-sectional data. The use of trajectory analysis to investigate developmental change is then discussed, with attention to phenotypes using trajectories of individual behaviors as well as phenotypes based on multi-trajectory modeling. Data analysis strategies and measurement approaches from recent examples of these person-centered approaches to the conceptualization and investigation of appetite self-regulation and its development in childhood are examined. Where relevant, examples from older children as well as developmental, clinical and educational psychology are drawn on to discuss when and how person-centered approaches can be used. We argue that there is scope to incorporate recent advances in biological and psychoneurological knowledge about appetite self-regulation as well as fundamental processes in the development of general self-regulation to enhance the examination of phenotypes and their trajectories across childhood (and beyond). The discussion and conclusion suggest directions for future research and highlight the potential of person-centered approaches to progress knowledge about the development of appetite self-regulation in childhood.

Background: Many phenotypic studies have estimated the degree of comorbidity between Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), but few have examined the latent, or unobserved, structure of combined ASD and ADHD symptoms. This is an important perquisite toward better understanding the overlap between ASD and ADHD. Methods: We conducted a scoping review of studies that examined the factor or latent class structure of ASD and ADHD symptoms within the same clinical or general population sample. Results: Eight studies met final inclusion criteria. Four factor analysis studies found that ASD and ADHD domains loaded separately and one found that some ASD and ADHD domains loaded together. In the three latent class studies, there were evidence of profiles with high levels of co-occurring ASD and ADHD symptoms. Conclusions: Our scoping review provides some evidence of phenotypic overlap between ASD and ADHD at the latent, or unobserved, level, particularly when using a \"person-centered\" (latent class analysis) vs. a \"variable-centered\" (factor analysis) approach.

While the efficacy of Internet- and mobile-based interventions (IMIs) for treating anxiety disorders is well established, there is no comprehensive overview about the underlying therapeutic processes so far. Thus, this systematic review and meta-analysis evaluated research on mediators and mechanisms of change in IMIs for adult anxiety disorders (PROSPERO: CRD42020185545). A systematic literature search was performed in five databases (i.e., CENTRAL, Embase, MEDLINE, PsycINFO and ClinicalTrials.gov). Two reviewers independently screened studies for inclusion, assessed the risk of bias and adherence to quality criteria for process research. Overall, 26 studies (N = 6042) investigating 64 mediators were included. Samples consisted predominantly of participants with clinically relevant symptoms of generalized anxiety disorder and severe health anxiety, as well as of participants with non-clinically relevant anxiety symptoms. The largest group of examined mediators (45%) were cognitive variables, evincing also the second highest proportion of significance (19/29); followed in numbers by skills (examined: 22%; significant: 10/14) and a wide range of other (19%; 7/12), emotional/affective (11%; 2/7) and behavioral mediators (3%; 1/2). Meta-analytical synthesis of mediators, limited by a small number of eligible studies, was conducted by deploying a two-stage structural equation modeling approach, resulting in a significant indirect effect for negative thinking (k = 3 studies) and non-significant indirect effects for combined cognitive variables, both in clinical (k = 5) and non-clinical samples (k = 3). The findings of this review might further the understanding on presumed change mechanisms in IMIs for anxiety, informing intervention development and the concurrent optimization of outcomes. Furthermore, by reviewing eligible mediation studies, we discuss methodological implications and recommendations for future process research, striving for causally robust findings. Future studies should investigate a broader range of variables as potential mediators, as well as to develop and apply original (digital) process and engagement measures to gather qualitative and high-resolution data on therapeutic processes.