Changes in shooting accuracy among basketball players under fatigue: a systematic review and meta-analysis

Objective

To investigate the influence of physical and mental fatigue of different intensities (mild, moderate or severe) on basketball shooting accuracy, with the aim of informing more effective training protocols and competition strategies.

Methods

Literature searches were conducted on Web of Science, PubMed, and EBSCO databases up to 25 June 2024. Inclusion and exclusion criteria were specified, and data extraction sheets were prepared. Study quality was assessed by using the Cochrane Risk of Bias Tool in Review Manager 5.4, and Stata18.0 software was used for heterogeneity analysis, subgroup analysis, forest plots, stratification analysis, and bias assessment.

Results

Moderate physical fatigue affected two-point shooting accuracy (P < 0.01),severe physical fatigue affected both two-point (P = 0.02) and three-point shooting accuracy (p < 0.01),with severe physical fatigue showing a greater detrimental impact on three-point shooting accuracy, while two-point shooting accuracy may vary under specific conditions. Additionally, adolescent athletes were less affected by severe physical fatigue compared to adult athletes or those with longer training experience. Moderate mental fatigue also significantly reduced free-throw accuracy (p < 0.01).

Conclusion

The shooting accuracy of basketball players was significantly affected by moderate and severe physical fatigue. Severe physical fatigue notably adversely affected the accuracy of three-point shooting relative to moderate fatigue; Additionally, moderate mental fatigue significantly reduced free-throw accuracy, which may be attributed to a decline in cognitive executive functions, highlighting the importance of fatigue management in sports training.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/#myprospero, identifier CRD42024539553.

The links between parenting, self-esteem, and depressive symptoms: a meta-analysis

INTRODUCTION

Framed by attachment and cognitive theories, the current meta-analysis tested the direct and indirect links among parenting dimensions (parental support, authoritative control, psychological control, and behavioral control), self-esteem, and depressive symptoms for children and adolescents.

METHODS

Based on 53 studies, and 74 independent samples, the present study used One-Stage Meta-analytic Structural Equation Modeling (OSMASEM) to investigate these links, including testing a mediation effect by self-esteem between parental support-, authoritative control-, psychological control-, behavioral control-, and depressive symptoms. It also tested for potential moderation effects by several variables.

RESULTS

The evidence indicated that parental support (β = .29), authoritative control (β = .23), and behavioral control (β = .07) are positive correlates of self-esteem, while psychological control (β = -.26) is a negative one. It also found that self-esteem was negatively associated with depressive symptoms (β range: -.49 to -.53). Furthermore, parental support (β = -0.14), authoritative control (β = -0.10), and behavioral control (β = -0.05) are negative correlates of depressive symptoms, while psychological control is a positive correlate (β = .11). OSMASEM also provided evidence that parental support, authoritative control, psychological control, and behavioral control are indirectly associated with depressive symptoms, via self-esteem.

CONCLUSION

Parenting dimensions are associated with depressive symptoms both directly and indirectly. That is, self-esteem partially mediates the relationships between parental support, authoritative control, psychological control, and behavioral control with depressive symptoms. Furthermore, OSMASEM moderator tests provided evidence of several significant moderators, including age, sex, parenting measurement, and self-esteem measurement.

Knowledge hiding and social exchange theory: a systematic review and meta-analysis

The literature on the antecedents and consequences of knowledge hiding remains fragmented, limiting its practical applications. Social exchange theory (SET), one of the most widely adopted sociological frameworks, offers unique insights into the dynamics of knowledge hiding. This study synthesizes the application of SET in analyzing the nomological framework of knowledge hiding through a systematic literature review and meta-analysis. A meta-analysis was conducted based on the random-effects model and the meta-analytic structural equation modeling method, incorporating 66 primary studies with a total of 20,603 participants. Additionally, we examined the mediating role of knowledge hiding by linking key antecedents and consequences. Moreover, an exploratory analysis was conducted to investigate the moderating effects of national culture and research methodology, providing evidence to justify the true heterogeneity in the pairwise relationships between knowledge hiding and its antecedents. The research results generally support most pairwise relationships between knowledge hiding and its correlates, which were theoretically developed based on SET. This study is the first attempt to explore the explanatory power of SET in analyzing the knowledge-hiding phenomenon, and whether the establishment of a knowledge exchange loop contributes to a deeper understanding of this dyadic construct.

Mechanisms of change in digital interventions for depression: A systematic review and meta-analysis of six mediator domains

BACKGROUND

While the efficacy of digital interventions for the treatment of depression is well established, comprehensive knowledge on how therapeutic changes come about is still limited. This systematic review aimed to provide an overview of research on change mechanisms in digital interventions for depression and meta-analytically evaluate indirect effects of potential mediators.

METHODS

The databases CENTRAL, Embase, MEDLINE, and PsycINFO were systematically searched for randomized controlled trials investigating mediators of digital interventions for adults with depression. Two reviewers independently screened studies for inclusion, assessed study quality and categorized potential mediators. Indirect effects were synthesized with a two-stage structural equation modeling approach (TSSEM).

RESULTS

Overall, 25 trials (8110 participants) investigating 84 potential mediators were identified, of which attentional (8 %), self-related (6 %), biophysiological (6 %), affective (5 %), socio-cultural (2 %) and motivational (1 %) variables were the scope of this study. TSSEM revealed significant mediation effects for combined self-related variables (ab = -0.098; 95 %-CI: [-0.150, -0.051]), combined biophysiological variables (ab = -0.073; 95 %-CI: [-0.119, -0.025]) and mindfulness (ab = -0.042; 95 %-CI: [-0.080, -0.015]). Meta-analytical evaluations of the other three domains were not feasible.

LIMITATIONS

Methodological shortcomings of the included studies, the considerable heterogeneity and the small number of investigated variables within domains limit the generalizability of the results.

CONCLUSION

The findings further the understanding of potential change mechanisms in digital interventions for depression and highlight recommendations for future process research, such as the consideration of temporal precedence and experimental manipulation of potential mediators, as well as the application of network approaches.

Checking the inventory: Illustrating different methods for individual participant data meta-analytic structural equation modeling

Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that directly analyze the raw data, such as multilevel and multigroup SEM, and techniques based on summary statistics, such as correlation-based meta-analytical structural equation modeling (MASEM), discussing differences in procedures, capabilities, and outcomes. This is done by analyzing a previously published collection of datasets using open source software. A path model reflecting the theory of planned behavior is fitted to these datasets using different techniques involving SEM. Apart from differences in handling of missing data, the ability to include study-level moderators, and conceptualization of heterogeneity, results show differences in parameter estimates and standard errors across methods. Further research is needed to properly formulate guidelines for applied researchers looking to conduct individual participant data MASEM.

Handling dependent samples in meta-analytic structural equation models: A Wishart-based approach

We present an approach to meta-analytic structural equation models that relies on hierarchical modeling of sample covariance matrices under the assumption that the matrices are Wishart. The approach handles the commonplace fixed- and random-effects meta-analytic SEMs, and solves the problem of dependent covariance matrices where more than one covariance matrix is obtained from a single study or study author. The ability of the approach to adequately recover parameters is examined via a simulation study. The approach is implemented in the bayesianmasem R package and a demonstration shows applications of the model.

Exploring the adverse events of Oxford-AstraZeneca, Pfizer-BioNTech, Moderna, and Johnson and Johnson COVID-19 vaccination on Guillain-Barré Syndrome

The vaccination against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is an important public health strategy to prevent people from the pandemic. Vaccines are a game-changing tool, it is essential to understand the adverse events after COVID-19 vaccination. This study explored the adverse events of COVID-19 Vaccination Oxford-AstraZeneca, Pfizer-BioNTech, Moderna, Johnson and Johnson on Guillain-Barré Syndrome (GBS). In this study, initially 128 documents were identified from the databases, including Pub-Med, Web of Science-Clarivate Analytics, Scopus, and Google Scholar. The articles on COVID-19 vaccination and GBs were searched using the keywords "SARS-CoV-2, COVID-19, Vaccination, and Guillain Barré Syndrome, GBS", finally, 16 documents were included in the analysis and synthesis. After administering 1,680,042,214 doses of COVID-19 vaccines, 6177 cases were identified with 10.5 cases per million vaccine doses. A significant positive risk was found between COVID-19 vaccine administration and GBS with a risk rate of RR 1.97 (95% CI 1.26-3.08, p = 0.01). The mRNA vaccines were associated with 2076 cases, and 1,237,638,401 vaccine doses were linked with 4.47 GBS events per million vaccine doses. The first dose of the m-RNA vaccine was associated with 8.83 events per million doses compared to the second dose with 02 events per million doses. The viral-vector vaccine doses 193,535,249 were linked to 1630 GBS cases with 11.01 cases per million doses. The incidence of GBS after the first dose was 17.43 compared to 1.47 cases per million in the second dose of the viral-vector vaccine. The adverse events of the Oxford-AstraZeneca vaccine were linked to 1339 cases of GBS following 167,786,902 vaccine doses, with 14.2 cases per million doses. The Oxford-AstraZeneca vaccine significantly increased the risk of GBS RR: 2.96 (95% CI 2.51-3.48, p = 0.01). For the Pfizer-BioNTech vaccine, there were 7.20 cases per million doses of the vaccine, and no significant association was identified between the Pfizer-BioNTech vaccine and GBS incidence RR: 0.99 (95% CI 0.75-1.32, p = 0.96). Moderna vaccine was related with 419 cases of GBS after administering 420,420,909 doses, with 2.26 cases per million doses. However, Johnson and Johnson's vaccination was linked to 235 GBS after 60,256,913 doses of the vaccine with 8.80 cases per million doses. A significant association was seen between the risk of GBS and Ad.26.COV2. S vaccine, RR: 2.47 (95% CI 1.30-4.69, p < 0.01). Overall, a significant association was seen between the COVID-19 vaccines and the risk of GBS. The incidence of GBS was higher after the first dose compared to GBS cases per million in the second dose.

Inflammation as a mediator between adverse childhood experiences and adult depression: A meta-analytic structural equation model

Exposure to adverse childhood experiences (ACEs) confers a higher risk of developing depression in adulthood, yet the mediation of inflammation remains under debate. To test this model, we conducted a systematic review and two-stage structural equation modelling meta-analysis of studies reporting correlations between ACEs before age 18, inflammatory markers and depression severity in adulthood. Scopus, Pubmed, Medline, PsycInfo, and CINAHL were searched up to 2 October 2023. Twenty-two studies reporting data on C-reactive protein (CRP, n = 12,935), interleukin-6 (IL-6, n = 4108), tumour necrosis factor-α (TNF-α, n = 2256) and composite measures of inflammation (n = 1674) were included. Unadjusted models revealed that CRP (β = 0.003, 95 % LBCI 0.0002 to 0.0068), IL-6 (β = 0.003, 95 % LBCI 0.001 to 0.006), and composite inflammation (β = 0.009, 95 % LBCI 0.004 to 0.018) significantly mediated the association between ACEs and adult depression. The mediation effects no longer survived after adjusting for BMI; however, a serial mediation model revealed that BMI and IL-6 sequentially mediated the association between ACEs and depression (β = 0.002, 95 % LBCI 0.0005 to 0.0046), accounting for 14.59 % and 9.94 % of the variance of IL-6 and depressive symptoms, respectively. Due to the cross-sectional nature of assessment of inflammation and depression findings should be approached with caution; however, results suggest that complex interactions of psychoneuroimmunological and metabolic factors underlie the association between ACEs and adulthood depression.

A comparison of univariate and meta-analytic structural equation modeling approaches to reliability generalization applied to the Maslach Burnout Inventory

Introduction

Reliability is a property of tests scores that varies from sample to sample. One way of generalizing reliability of a test is to perform a meta-analysis on some reliability estimator. In 2011, a reliability generalization meta-analysis on the Maslach Burnout Inventory (MBI) was conducted, concluding that average alpha values for the MBI dimensions ranged from 0.71 to 0.88. In the present study, we aimed to update the average reliability values of the MBI by conducting a literature search from 2010 until now and comparing to statistical procedures of meta-analysis: the Univariate approach, that were used in the previous study, and a novel meta-analytic approach based on structural equation modeling.

Method

An estimation of average reliability was done based on 69 independent primary reliability coefficients for the Univariate approach. The average reliability was based on 9 independent studies in the case of the Meta-analytic Structural Equation Modeling (MASEM) approach. Given that MASEM has the additional capability of testing the internal structure of a test, we also fitted several models.

Results

The data was well-suited to the bifactor model, revealing the dominance of the general factor over the domain-specific ones. Acceptable overall alpha and omega coefficients were achieved for the two of the MBI dimensions, having depersonalization reliability estimates below recommendations.

Discussion

In general, the MBI can be viewed as a highly interconnected three-factor scale, being its appropriate for research purposes.

Relationship between social participation and life satisfaction in community-dwelling older adults: Multiple mediating roles of depression and cognitive function

BACKGROUND

Numerous studies have explored the relationship between social participation, depression, cognitive function, and life satisfaction in community-dwelling older adults. However, the results of these studies were not consistent. Therefore, the in-depth excavation of the complex relationship mechanism between these four variables is limited. This study aims to explore whether depression and cognitive function act as the multiple mediators between social participation and life satisfaction in community-dwelling older adults.

METHODS

This study used the meta-analytic structural equation modeling (MASEM) approach. Six databases were comprehensively searched to investigate the relationship between social participation, depression, cognitive function, and life satisfaction in community-dwelling older adults from inception to March 1, 2023.

RESULTS

The analysis included 50 articles that reported 66 effect sizes (N = 161,854). The MASEM found the relationship between social participation and life satisfaction was mediated by depression [β = 0.085, 95 % CI (0.053, 0.126)] and by cognitive function [β = 0.015, 95 % CI (0.002, 0.033)], respectively. Depression and cognitive function also played multiple mediating roles between social participation and life satisfaction in community-dwelling older adults [β = 0.005, 95 % CI (0.001, 0.012)].

CONCLUSIONS

There was not only a direct association between social participation and life satisfaction, but also an association through multiple mediating roles of depression and cognitive function in community-dwelling older adults. In the process of using diverse interventions to promote social participation in community-dwelling older adults, more attention should be paid to their psychological status and cognitive function to eliminate intrinsic barriers so as to improve life satisfaction effectively.

Gut microbiota composition in depressive disorder: a systematic review, meta-analysis, and meta-regression

Studies investigating gut microbiota composition in depressive disorder have yielded mixed results. The aim of our study was to compare gut microbiome between people with depressive disorder and healthy controls. We did a meta-analysis and meta-regression of studies by searching PubMed, Web of Science, Embase, Scopus, Ovid, Cochrane Library, ProQuest, and PsycINFO for articles published from database inception to March 07, 2022. Search strategies were then re-run on 12 March 2023 for an update. We undertook meta-analyses whenever values of alpha diversity and Firmicutes, Bacteroidetes (relative abundance) were available in two or more studies. A random-effects model with restricted maximum-likelihood estimator was used to synthesize the effect size (assessed by standardized mean difference [SMD]) across studies. We identified 44 studies representing 2091 patients and 2792 controls. Our study found that there were no significant differences in patients with depressive disorder on alpha diversity indices, Firmicutes and Bacteroidetes compared with healthy controls. In subgroup analyses with regional variations(east/west) as a predictor, patients who were in the West had a lower Chao1 level (SMD -0.42[-0.74 to -0.10]). Subgroup meta-analysis showed Firmicutes level was decreased in patients with depressive disorder who were medication-free (SMD -1.54[-2.36 to -0.72]), but Bacteroidetes level was increased (SMD -0.90[0.07 to 1.72]). In the meta-regression analysis, six variables cannot explain the 100% heterogeneity of the studies assessing by Chao1, Shannon index, Firmicutes, and Bacteroidetes. Depleted levels of Butyricicoccus, Coprococcus, Faecalibacterium, Fusicatenibacter, Romboutsia, and enriched levels of Eggerthella, Enterococcus, Flavonifractor, Holdemania, Streptococcus were consistently shared in depressive disorder. This systematic review and meta-analysis found that psychotropic medication and dietary habit may influence microbiota. There is reliable evidence for differences in the phylogenetic relationship in depressive disorder compared with controls, however, method of measurement and method of patient classification (symptom vs diagnosis based) may affect findings. Depressive disorder is characterized by an increase of pro-inflammatory bacteria, while anti-inflammatory butyrate-producing genera are depleted.

Sleep quality as a mediator of the relation between depression and chronic pain: a systematic review and meta-analysis

BACKGROUND

Chronic pain and depression represent two global health problems with considerable economic consequences. Although existing literature reports on the relation between depression and pain conditions, meta-analytic evidence backing the mediating role of sleep disturbance as one of the main symptoms of depression is scarce. To examine the extent to which sleep disturbance mediates the depression-chronic pain association, we conducted a systematic review and meta-analysis of the associations of chronic pain, depression, and sleep quality.

METHODS

We systematically searched for literature in MEDLINE and other relevant databases and identified cohort and case-control studies on depression, sleep disturbance, and chronic pain. Forty-nine studies were eligible, with a total population of 120 489 individuals. We obtained direct and indirect path coefficients via two-stage meta-analytic structural equation modelling, examined heterogeneity via subgroup analyses, and evaluated primary studies quality.

RESULTS

We found a significant, partial mediation effect of sleep disturbance on the relation between depression and chronic pain. The pooled path coefficient (coef.) of the indirect effect was 0.03 (95% confidence interval [CI]: 0.01-0.05) and accounted for 12.5% of the total effect of depression on chronic pain. This indirect effect also existed for cohort studies (coef. 0.02; 95% CI: 0.002-0.04), European studies (coef. 0.03; 95% CI: 0.004-0.05), and studies that adjusted for confounders (coef. 0.04; 95% CI: 0.01-0.09).

CONCLUSIONS

Sleep disturbance partially mediates the association between depression and pain. Although plausible mechanisms could explain this mediation effect, other explanations, including reverse causation, must be further explored.

SYSTEMATIC REVIEW PROTOCOL

PROSPERO CRD42022338201.

Factor Structure and Criterion-Related Validity of the Air Force Officer Qualifying Test Form T for Pilot Applicants

The Air Force Officer Qualifying Test (AFOQT) is the primary selection tool for officer applicants in the U.S. Air Force (USAF) for nearly seven decades. The AFOQT is revised and modified periodically, with rigorous equating and linking effort to ensure comparability and connectivity across forms. The most recent version of AFOQT is Form T that includes 10 cognitive ability and knowledge subtests. Despite the continuing validation effort of the AFOQT across forms, it was mostly directed to the general population of officer applicants, but not to any specific subpopulation. The current investigation reported three studies in an attempt to provide evidence for factor structure and criterion-related validity of AFOQT Form T for pilot applicants via four analytical approaches: meta-analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The results suggested that AFOQT Form T data are best represented by a bifactor model with a general ability and four specific abilities, and that each latent construct has a distinct predictive utility for pilot performance criteria.

Mediating effect of post-stroke depression between activities of daily living and health-related quality of life: meta-analytic structural equation modeling

BACKGROUND

Stroke survivors face various problems that affect their health-related quality of life (HRQoL). Reduced activities of daily living (ADL) may contribute to post-stroke depression (PSD) and low HRQoL, and depression might be associated with low HRQoL. However, these relationships are not well known. This study aimed to analyze correlations among ADL, PSD, and HRQoL in stroke survivors and further explore the mediating role of PSD between ADL and HRQoL.

METHODS

This study utilized meta-analytic structural equation modeling (MASEM) on systematically searched articles from six electronic databases, namely PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure (China), Wanfang database (China), and SinoMed (China), from inception up to July 31, 2021. Two researchers independently assessed study eligibility, and data from the eligible studies were encoded and assessed for quality. MASEM was utilized to examine correlations among ADL, PSD, and HRQoL, with an estimation of a pooled correlation matrix under a random-effects model. The matrix was directly fitted to a structural equation model using webMASEM.

RESULTS

In total, 8580 articles were screened, and data from 27 studies involving 33 effect sizes were used in the MASEM analysis. Correlations among the three variables were significant (both P < 0.01). Furthermore, PSD partially mediated the correlation between ADL and HRQoL (β = 0.24, 95% confidence interval 0.15-0.30).

CONCLUSIONS

The findings suggest that both decreased ADL and PSD may reduce HRQoL, while a decrease in ADL tends to cause depression after stroke. Therefore, ADL and PSD reductions should be improved to achieve better HRQoL of stroke survivors. Future studies should continue to discuss other factors affecting HRQoL to achieve optimal recovery in stroke survivors.

How to Evaluate Theory-Based Hypotheses in Meta-Analysis Using an AIC-Type Criterion

Meta-analysis techniques allow researchers to aggregate effect sizes-like standardized mean difference(s), correlation(s), or odds ratio(s)-of different studies. This leads to overall effect-size estimates and their confidence intervals. Additionally, researchers can aim for theory development or theory evaluation. That is, researchers may not only be interested in these overall estimates but also in a specific ordering or size of them, which then reflects a theory. Researchers may have expectations regarding the ordering of standardized mean differences or about the (ranges of) sizes of an odds ratio or Hedges' g. Such theory-based hypotheses most probably contain inequality constraints and can be evaluated with the Akaike's information criterion type (i.e., AIC-type) confirmatory model selection criterion called generalized order-restricted information criterion (GORICA). This paper introduces and illustrates how the GORICA can be applied to meta-analyzed estimates. Additionally, it compares the use of the GORICA to that of classical null hypothesis testing and the AIC, that is, the use of theory-based hypotheses versus null hypotheses. By using the GORICA, researchers from all types of fields (e.g., psychology, sociology, political science, biomedical science, and medicine) can quantify the support for theory-based hypotheses specified a priori. This leads to increased statistical power, because of (i) the use of theory-based hypotheses (cf. one-sided vs. two-sided testing) and (ii) the use of meta-analyzed results (that are based on multiple studies which increase the combined sample size). The quantification of support and the power increase aid in, for instance, evaluating and developing theories and, therewith, developing evidence-based treatments and policy.

How do psychologically based interventions for chronic musculoskeletal pain work? A systematic review and meta-analysis of specific moderators and mediators of treatment

Psychologically based interventions aim to improve pain-related functioning by targeting pain-related fears, cognitions and behaviors. Mediation and moderation analyses permit further examination of the effect of treatment on an outcome. This systematic review and meta-analysis aims to synthetize the evidence of specific mediators and moderators (i.e., treatment targets) of psychologically based treatment effects on pain and disability. A total of 28 mediation and 11 moderation analyses were included. Thirteen mediation studies were included in a meta-analysis, and the rest was narratively synthetized. Reductions in pain-related fear (indirect effect [IE]: -0.07; 95% confidence interval [CI]: -0.11, -0.04) and catastrophizing (IE: -0.07; 95%CI: -0.14, -0.00), as well as increases in self-efficacy (IE: -0.07; 95%CI: -0.11, -0.04), mediated effects of cognitive behavioral therapy on disability but not on pain intensity, when compared to control treatments. Enhancing pain acceptance (IE: -0.17; 95%CI: -0.31, -0.03) and psychological flexibility (IE: -0.30; 95%CI: -0.41, -0.18) mediated acceptance and commitment therapy effects on disability. The narrative synthesis showed conflicting evidence, which did not support a robust moderated effect for any of the examined constructs. Overall, the methodological quality regarding mediation was low, and some key pitfalls are highlighted alongside recommendations to provide a platform for future research.

Challenges in Systematic Reviews and Meta-Analyses of Mediation Analyses

Systematic reviews and meta-analyses of mediation studies are increasingly being implemented in practice. Nonetheless, the methodology for conducting such review and analysis is still in a development phase, with much room for improvement. In this paper, we highlight and discuss challenges that investigators face in systematic reviews and meta-analyses of mediation studies and propose ways of accommodating these in practice.

A Primer on Meta-Analytic Structural Equation Modeling: the Case of Depression

In this paper, we show how the methods of systematic reviewing and meta-analysis can be used in conjunction with structural equation modeling to summarize the results of studies in a way that will facilitate the theory development and testing needed to advance prevention science. We begin with a high-level overview of the considerations that researchers need to address when using meta-analytic structural equation modeling (MASEM) and then discuss a research project that brings together theoretically important cognitive constructs related to depression to (a) show how these constructs are related, (b) test the direct and indirect effects of dysfunctional attitudes on depression, and (c) test the effects of study-level moderating variables. Our results suggest that the indirect effect of dysfunctional attitudes (via negative automatic thinking) on depression is two and a half times larger than the direct effect of dysfunctional attitudes on depression. Of the three study-level moderators tested, only sample recruitment method (clinical vs general vs mixed) yielded different patterns of results. The primary difference observed was that the dysfunctional attitudes → automatic thoughts path was less strong for clinical samples than it was for general and mixed samples. These results illustrate how MASEM can be used to compare theoretically derived models and predictions resulting in a richer understanding of both the empirical results and the theories underlying them.

Exploring Perceived and Objective Measures of the Neighborhood Environment and Associations with Physical Activity among Adults: A Review and a Meta-Analytic Structural Equation Model

There is an urgent need to understand factors that promote physical activities (PA) because it is one of the modifiable risk factors for global mortality. None of the previous reviews considered both perceived and objective characteristics of the same environment. The first aim was to review the literature on studies investigating the relationship between PA behavior of adults and perceived and objective physical environment measures. The second aim was to verify the potential mediational role of the perceived measure in the relationship between the objective measure of the environment and PA using meta-analytic SEM. Only 15 studies reported a relationship between PA and both environmental measures. One of the most studied characteristics of the physical environment is the accessibility to recreational/PA facilities. Both objective and subjective measures of accessibility to PA facilities are associated with PA. Meta-SEM results suggest a significant effect of the objective accessibility to facilities on PA behavior (β = 0.15) and on the perceived measure (β = 0.10), but the indirect effect was not significant. No significant effect was found for the perceived measure on PA, suggesting that individuals' level of awareness about their environments may have played a role. This prompts a need to create awareness campaigns.

Meta-analytic structural equation modeling made easy: A tutorial and web application for one-stage MASEM

Meta-analytic structural equation modeling (MASEM) refers to fitting structural equation models (SEMs) (such as path models or factor models) to meta-analytic data. Currently, fitting MASEMs may be challenging for researchers that are not accustomed to working with R software and packages. Therefore, we developed webMASEM; a web application for MASEM. This app implements the one-stage MASEM approach, and allows users to apply MASEM in a user-friendly way. The aim of this article is to provide a tutorial on one-stage MASEM and a practical guide to webMASEM. We will pay specific attention to how the data should be structured and prepared for webMASEM, because mistakes in this step may lead to faulty results without receiving an error message. The use of webMASEM is illustrated with an analysis of a meta-analytic path model in which the path coefficients are moderated by a study-level variable, a meta-analytic factor model in which the factor loadings are moderated by a study-level variable, and a meta-analytic panel model in which the effects are moderated by a study-level variable. All used datafiles and R scripts are available online.

Synthesizing Indirect Effects in Mediation Models With Meta-Analytic Methods

AIMS

A mediator is a variable that explains the underlying mechanism between an independent variable and a dependent variable. The indirect effect indicates the effect from the predictor to the outcome variable via the mediator. In contrast, the direct effect represents the predictor's effort on the outcome variable after controlling for the mediator.

METHODS

A single study rarely provides enough evidence to answer research questions in a particular domain. Replications are generally recommended as the gold standard to conduct scientific research. When a sufficient number of studies have been conducted addressing similar research questions, a meta-analysis can be used to synthesize those studies' findings.

RESULTS

The main objective of this paper is to introduce two frameworks to integrating studies using mediation analysis. The first framework involves calculating standardized indirect effects and direct effects and conducting a multivariate meta-analysis on those effect sizes. The second one uses meta-analytic structural equation modeling to synthesize correlation matrices and fit mediation models on the average correlation matrix. We illustrate these procedures on a real dataset using the R statistical platform.

CONCLUSION

This paper closes with some further directions for future studies.

Improving the reporting quality of reliability generalization meta-analyses: The REGEMA checklist

Reliability generalization (RG) is a meta-analytic approach that aims to characterize how reliability estimates from the same test vary across different applications of the instrument. With this purpose RG meta-analyses typically focus on a particular test and intend to obtain an overall reliability of test scores and to investigate how the composition and variability of the samples affect reliability. Although several guidelines have been proposed in the meta-analytic literature to help authors improve the reporting quality of meta-analyses, none of them were devised for RG meta-analyses. The purpose of this investigation was to develop REGEMA (REliability GEneralization Meta-Analysis), a 30-item checklist (plus a flow chart) adapted to the specific issues that the reporting of an RG meta-analysis must take into account. Based on previous checklists and guidelines proposed in the meta-analytic arena, a first version was elaborated by applying the nominal group methodology. The resulting instrument was submitted to a list of independent meta-analysis experts and, after discussion, the final version of the REGEMA checklist was reached. In a pilot study, four pairs of coders applied REGEMA to a random sample of 40 RG meta-analyses in Psychology, and results showed satisfactory inter-coder reliability. REGEMA can be used by: (a) meta-analysts conducting or reporting an RG meta-analysis and aiming to improve its reporting quality; (b) consumers of RG meta-analyses who want to make informed critical appraisals of their reporting quality, and (c) reviewers and editors of journals who are considering submissions where an RG meta-analysis was reported for potential publication.

Is perfectionism a vulnerability factor for depressive symptoms, a complication of depressive symptoms, or both? A meta-analytic test of 67 longitudinal studies

Decades of research implicates perfectionism in depressive symptoms. Yet, inconsistent findings, underpowered studies, and the tendency for researchers to assume one direction of influence have clouded understanding of whether perfectionism is a vulnerability factor for depressive symptoms, a complication of depressive symptoms, or both. Our primary aim was to address this by using meta-analytic structural equation modeling to test cross-lagged reciprocal relations between depressive symptoms and two perfectionism factors: perfectionistic concerns and perfectionistic strivings. Our secondary aim was to catalyze a search for moderators that might explain heterogeneity by conducting a bivariate random-effects meta-analysis. The literature search yielded 67 longitudinal studies (N = 20,583) composed of undergraduates, community members, medical students, treatment-seeking adults, and patients with mental health problems. The relationship between perfectionistic concerns and depressive symptoms was reciprocal, with perfectionistic concerns predicting increased depressive symptoms and vice versa. In contrast, the relationship between perfectionistic strivings and depressive symptoms was unidirectional, with perfectionistic strivings conferring vulnerability for depressive symptoms, but not the reverse. Clinicians who overlook the reciprocal relationship between perfectionistic concerns and depressive symptoms might miss information vital to accurate conceptualization, assessment, and treatment. Perfectionistic strivings may be distinguishable from perfectionistic concerns by being more in line with classical models of personality vulnerability.

The effects of pre- and post-partum depression on child behavior and psychological development from birth to pre-school age: a protocol for a systematic review and meta-analysis

BACKGROUND

Pre- and post-partum depression is a common mood disorder with detrimental effects on both mother and child. The aim of the proposed review is to summarize evidence related to the effects of both pre- and post-partum depression on child behavior and development from birth to preschool age. In particular, our review will address mutual relations between pre- and post-partum depression in order to determine whether pre- and post-partum depression predict child psychological outcomes independently, whether there is an effect of timing of depression on child outcomes, whether pre- and post-partum depression interact to affect child outcomes, and whether the effect of pre-partum depression is mediated by depression after child's birth.

METHODS

We will include prospective longitudinal studies that report data about the effects of both pre- and post-partum depression on child psychological outcomes as published in peer-reviewed academic journals since January 1998. We will search EMBASE, MEDLINE, PsycARTICLES, PsycINFO, ISI Web of Science, Scopus, and Wiley Online databases to identify original research articles written in English. Two independent reviewers will screen search results in two stages: (i) titles and abstracts and (ii) full text. The first one will extract data into tables, while the latter will verify whether the data extracted are correct. We will assess the risk of bias in the selected studies using the Critical Appraisal Skills Programme (CASP), Cohort Study Checklist. The results of the review will be reported in a narrative form. If there are sufficient data available, a meta-analysis will be conducted using metaSEM package in R.

DISCUSSION

The proposed review will be the first systematic review summarizing the effects of both pre- and post-partum depression on child psychological development and behavior from birth to preschool age. The results of such a review may contribute to a better understanding of mutual relations between pre- and post-partum depression in their effects on child outcomes. They may also shed light on what periods in early human development are most vulnerable to the effects of maternal depression.

TRIAL REGISTRATION

PROSPERO CRD42018106269.

Association of Ketamine With Psychiatric Symptoms and Implications for Its Therapeutic Use and for Understanding Schizophrenia: A Systematic Review and Meta-analysis

Importance

Ketamine hydrochloride is increasingly used to treat depression and other psychiatric disorders but can induce schizophrenia-like or psychotomimetic symptoms. Despite this risk, the consistency and magnitude of symptoms induced by ketamine or what factors are associated with these symptoms remain unknown.

Objective

To conduct a meta-analysis of the psychopathological outcomes associated with ketamine in healthy volunteers and patients with schizophrenia and the experimental factors associated with these outcomes.

Data Sources

MEDLINE, Embase, and PsychINFO databases were searched for within-participant, placebo-controlled studies reporting symptoms using the Brief Psychiatric Rating Scale (BPRS) or the Positive and Negative Syndrome Scale (PANSS) in response to an acute ketamine challenge in healthy participants or patients with schizophrenia.

Study Selection

Of 8464 citations retrieved, 36 studies involving healthy participants were included. Inclusion criteria were studies (1) including healthy participants; (2) reporting symptoms occurring in response to acute administration of subanesthetic doses of ketamine (racemic ketamine, s-ketamine, r-ketamine) intravenously; (3) containing a placebo condition with a within-subject, crossover design; (4) measuring total positive or negative symptoms using BPRS or PANSS; and (5) providing data allowing the estimation of the mean difference and deviation between the ketamine and placebo condition.

Data Extraction and Synthesis

Two independent investigators extracted study-level data for a random-effects meta-analysis. Total, positive, and negative BPRS and PANSS scores were extracted. Subgroup analyses were conducted examining the effects of blinding status, ketamine preparation, infusion method, and time between ketamine and placebo conditions. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed.

Main Outcomes and Measures

Standardized mean differences (SMDs) were used as effect sizes for individual studies. Standardized mean differences between ketamine and placebo conditions were calculated for total, positive, and negative BPRS and PANSS scores.

Results

The overall sample included 725 healthy volunteers (mean [SD] age, 28.3 [3.6] years; 533 [73.6%] male) exposed to the ketamine and placebo conditions. Racemic ketamine or S-ketamine was associated with a statistically significant increase in transient psychopathology in healthy participants for total (SMD = 1.50 [95% CI, 1.23-1.77]; P < .001), positive (SMD = 1.55 [95% CI, 1.29-1.81]; P < .001), and negative (SMD = 1.16 [95% CI, 0.96-1.35]; P < .001) symptom ratings relative to the placebo condition. The effect size for this association was significantly greater for positive than negative symptoms of psychosis (estimate, 0.36 [95% CI, 0.12-0.61]; P = .004). There was significant inconsistency in outcomes between studies (I2 range, 77%-83%). Bolus followed by constant infusion increased ketamine's association with positive symptoms relative to infusion alone (effect size, 1.63 [95% CI, 1.36-1.90] vs 0.84 [95% CI, 0.35-1.33]; P = .006). Single-day study design increased ketamine's ability to generate total symptoms (effect size, 2.29 [95% CI, 1.69-2.89] vs 1.39 [95% CI, 1.12-1.66]; P = .007), but age and sex did not moderate outcomes. Insufficient studies were available for meta-analysis of studies in schizophrenia. Of these studies, 2 found a statistically significant increase in symptoms with ketamine administration in total and positive symptoms. Only 1 study found an increase in negative symptom severity with ketamine.

Conclusions and Relevance

This study found that acute ketamine administration was associated with schizophrenia-like or psychotomimetic symptoms with large effect sizes, but there was a greater increase in positive than negative symptoms and when a bolus was used. These findings suggest that bolus doses should be avoided in the therapeutic use of ketamine to minimize the risk of inducing transient positive (psychotic) symptoms.

A Bayesian approach to discrete multiple outcome network meta-analysis

In this paper we suggest a new Bayesian approach to network meta-analysis for the case of discrete multiple outcomes. The joint distribution of the discrete outcomes is modeled through a Gaussian copula with binomial marginals. The remaining elements of the hierarchial random effects model are specified in a standard way, with the logit of the success probabilities given by the sum of a baseline log-odds and random effects comparing the log-odds of each treatment against the reference and having a Gaussian distribution centered at the vector of pooled effects. An adaptive Markov Chain Monte Carlo algorithm is devised for running posterior inference. The model is applied to two datasets from Cochrane reviews, already analysed in two papers so to assess and compare its performance. We implemented the model in a freely available R package called netcopula.

Diet quality during preconception or pregnancy and gestational weight gain: protocol for a systematic review and meta-analysis

INTRODUCTION

Inappropriate gestational weight gain (GWG), including inadequate and excessive GWG, has become pandemic across nations and continents. This review aims to synthesise the evidence on the correlation between diet quality and GWG. If this association is confirmed, improving diet quality could become an intervention target in the efforts to reduce inappropriate GWG.

METHODS AND ANALYSIS

We will conduct a systematic review of all prospective cohort studies on diet quality in preconception or pregnancy and GWG. Our secondary outcomes include gestational diabetes, pre-eclampsia and birth weight. A comprehensive search of all published articles in MEDLINE ALL (Ovid), Embase (Ovid), Food Science and Technology Abstracts (Ovid) and CINAHL (EBSCOHost), from database creation to 20 April 2019, will be conducted. Studies will be screened for eligibility by title, abstract and full text in duplicate by two independent reviewers. Study quality and risk of bias will be assessed using the adapted Newcastle-Ottawa Scale. Results will be reported following the meta-analysis of observational studies in epidemiology guidelines. If sufficient data are available, a meta-analysis will be conducted to synthesise the effect size reported as OR with 95% CI using both fixed-effect and random-effect models. I² statistics and visual inspection of the forest plots will be used to assess heterogeneity and identify the potential sources of heterogeneity. Publication bias will be assessed by visual inspections of funnel plots and Egger's test.

ETHICS AND DISSEMINATION

Formal ethical approval is not required as no primary data will be collected. We aim to publish the results of this study in a peer-reviewed journal and present them at conferences and scientific meetings to promote knowledge transfer.

PROSPERO REGISTRATION NUMBER

CRD42019128732.

Beyond Meta-Analysis: Secondary Uses of Meta-Analytic Data

Secondary uses of meta-analytic data (SUMAD) represent advanced analyses and applications of first-order meta-analytic results for theoretical (e.g., theory testing) and practical (e.g., evidence-based practice) purposes to produce novel knowledge that cannot be directly obtained from the input meta-analytic results. First-order meta-analytic results in the form of bivariate effect sizes have been used as input to such secondary analyses and applications. Given the increasing popularity of SUMAD in human resource management (HRM) and organizational behavior (OB), there is a need for a systematic review on this topic. This article has two primary goals. First, it reviews essential works regarding SUMAD in the fields of HRM/OB and provides taxonomies of SUMAD in theoretical and practical domains. Second, it introduces recent SUMAD and discusses future directions that encourage more innovative and rigorous research endeavors along this line.

A meta-analysis of procedures to change implicit measures

Using a novel technique known as network meta-analysis, we synthesized evidence from 492 studies (87,418 participants) to investigate the effectiveness of procedures in changing implicit measures, which we define as response biases on implicit tasks. We also evaluated these procedures' effects on explicit and behavioral measures. We found that implicit measures can be changed, but effects are often relatively weak (|ds| < .30). Most studies focused on producing short-term changes with brief, single-session manipulations. Procedures that associate sets of concepts, invoke goals or motivations, or tax mental resources changed implicit measures the most, whereas procedures that induced threat, affirmation, or specific moods/emotions changed implicit measures the least. Bias tests suggested that implicit effects could be inflated relative to their true population values. Procedures changed explicit measures less consistently and to a smaller degree than implicit measures and generally produced trivial changes in behavior. Finally, changes in implicit measures did not mediate changes in explicit measures or behavior. Our findings suggest that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Introducing Continuous Time Meta-Analysis (CoTiMA)

Meta-analysis of panel data is uniquely suited to uncovering phenomena that develop over time, but extant approaches are limited. There is no straightforward means of aggregating findings of primary panel studies that use different time lags and different numbers of waves. We introduce continuous time meta-analysis (CoTiMA) as a parameter-based approach to meta-analysis of cross-lagged panel correlation matrices. CoTiMA enables aggregation of studies using two or more waves even if there are varying time lags within and between studies. CoTiMA thus provides meta-analytic estimates of cross-lagged effects for a given time lag regardless of the frequency with which that time lag is used in primary studies. We describe the continuous time underpinnings of CoTiMA, its advantages over discrete-time, correlation-based meta-analysis of structural equation models (MASEM), and how CoTiMA would be applied to meta-analysis of panel studies. An example is then used to illustrate the approach. We also conducted Monte Carlo simulations demonstrating that bias is larger for time category–based MASEM than for CoTiMA under various conditions. Finally, we discuss data requirements, open questions, and possible future extensions.

Testing moderator hypotheses in meta-analytic structural equation modeling using subgroup analysis

Meta-analytic structural equation modeling (MASEM) is a statistical technique to pool correlation matrices and test structural equation models on the pooled correlation matrix. In Stage 1 of MASEM, correlation matrices from independent studies are combined to obtain a pooled correlation matrix, using fixed- or random-effects analysis. In Stage 2, a structural model is fitted to the pooled correlation matrix. Researchers applying MASEM may have hypotheses about how certain model parameters will differ across subgroups of studies. These moderator hypotheses are often addressed using suboptimal methods. The aim of the current article is to provide guidance and examples on how to test hypotheses about group differences in specific model parameters in MASEM. We illustrate the procedure using both fixed- and random-effects subgroup analysis with two real datasets. In addition, we present a small simulation study to evaluate the effect of the number of studies per subgroup on convergence problems. All data and the R-scripts for the examples are provided online.

Some reflections on combining meta-analysis and structural equation modeling

Meta-analysis and structural equation modeling (SEM) are 2 of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta-analytic SEM are highlighted. Another goal of the paper is to illustrate how meta-analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta-analysis and SEM.

Correlation-Based Meta-Analytic Structural Equation Modeling: Effects of Parameter Covariance on Point and Interval Estimates

More and more researchers use meta-analysis to conduct multivariate analysis to summarize previous findings. In the correlation-based meta-analytic structural equation modeling (cMASEM), the average sample correlation matrix is used to estimate the average population model. Using a simple mediation model, we illustrated that random effects covariation in population parameters can theoretically bias the path coefficient estimates and lead to nonnormal random effects distribution of the correlations. We developed an R function for researchers to examine by simulation the impact of random effects in other models. We then reanalyzed two real data sets and conducted a simulation study to examine the magnitude of the impact on realistic situations. Simulation results suggest parameter bias is typically negligible (less than .02), parameter bias and root mean square error do not differ across methods, 95% confident intervals are sometimes more accurate for the two-stage structural equation modeling approach with a diagonal random effects model, and power is sometimes higher for the traditional Viswesvaran-Ones approach. Given the increasing popularity of cMASEM in organizational research, these simulation results form the basis for us to make several recommendations on its application.

Perceived problem-solving deficits and suicidal ideation: Evidence for the explanatory roles of thwarted belongingness and perceived burdensomeness in five samples

Perceived social problem-solving deficits are associated with suicide risk; however, little research has examined the mechanisms underlying this relationship. The interpersonal theory of suicide proposes 2 mechanisms in the pathogenesis of suicidal desire: intractable feelings of thwarted belongingness (TB) and perceived burdensomeness (PB). This study tested whether TB and PB serve as explanatory links in the relationship between perceived social problem-solving (SPS) deficits and suicidal thoughts and behaviors cross-sectionally and longitudinally. The specificity of TB and PB was evaluated by testing depression as a rival mediator. Self-report measures of perceived SPS deficits, TB, PB, suicidal ideation, and depression were administered in 5 adult samples: 336 and 105 undergraduates from 2 universities, 53 homeless individuals, 222 primary care patients, and 329 military members. Bias-corrected bootstrap mediation and meta-analyses were conducted to examine the magnitude of the direct and indirect effects, and the proposed mediation paths were tested using zero-inflated negative binomial regressions. Cross-sectionally, TB and PB were significant parallel mediators of the relationship between perceived SPS deficits and ideation, beyond depression. Longitudinally and beyond depression, in 1 study, both TB and PB emerged as significant explanatory factors, and in the other, only PB was a significant mediator. Findings supported the specificity of TB and PB: Depression and SPS deficits were not significant mediators. The relationship between perceived SPS deficits and ideation was explained by interpersonal theory variables, particularly PB. Findings support a novel application of the interpersonal theory, and bolster a growing compendium of literature implicating perceived SPS deficits in suicide risk. (PsycINFO Database Record

Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists—and probably the most crucial one—is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.