Comparative Discrimination of Life's Simple 7 and Life's Essential 8 to Stratify Cardiovascular Risk: Is the Added Complexity Worth It?

BACKGROUND: Life's Simple 7 (LS7) is an easily calculated and interpreted metric of cardiovascular health based on 7 domains: smoking, diet, physical activity, body mass index, blood pressure, cholesterol, and fasting glucose. The Life's Essential 8 (LE8) metric was subsequently introduced, adding sleep metrics and revisions of the previous 7 domains. Although calculating LE8 requires additional information, we hypothesized that it would be a more reliable index of cardiovascular health. METHODS: Both the LS7 and LE8 metrics yield scores with higher values indicating lower risk. These were calculated among 11 609 Black and White participants free of baseline cardiovascular disease (CVD) in the Reasons for Geographic and Racial Differences in Stroke study, enrolled in 2003 to 2007, and followed for a median of 13 years. Differences in 10-year risk of incident CVD (coronary heart disease or stroke) were calculated as a function LS7, and LE8 scores were calculated using Kaplan-Meier and proportional hazards analyses. Differences in incident CVD discrimination were quantified by difference in the c-statistic. RESULTS: For both LS7 and LE8, the 10-year risk was approximately 5% for participants around the 99th percentile of scores, and a 4× higher 20% risk for participants around the first percentile. Comparing LS7 to LE8, 10-year risk was nearly identical for individuals at the same relative position in score distribution. For example, the "cluster" of 2013 participants with an LS7 score of 7 was at the 35.8th percentile in distribution of LS7 scores, and had an estimated 10-year CVD risk of 8.4% (95% CI, 7.2%-9.8%). In a similar location in the LE8 distribution, the 1457 participants with an LE8 score of 60±2.5 at the 39.4th percentile of LE8 scores had a 10-year risk of CVD of 8.5% (95% CI, 7.1%-10.1%), similar to the cluster defined by LS7. The age-race-sex adjusted c-statistic of the LS7 model was 0.691 (95% CI, 0.667-0.705), and 0.695 for LE8 (95% CI, 0.681-0.709) (P for difference, 0.12). CONCLUSIONS: Both LS7 and LE8 were associated with incident CVD, with discrimination of the 2 indices practically indistinguishable. As a simpler metric, LS7 may be favored for use by the general population and clinicians.

Unreliable Continuous Treatment Indicators in Propensity Score Analysis.

Propensity score analyses (PSA) of continuous treatments often operationalize the treatment as a multi-indicator composite, and its composite reliability is unreported. Latent variables or factor scores accounting for this unreliability are seldom used as alternatives to composites. This study examines the effects of the unreliability of indicators of a latent treatment in PSA using the generalized propensity score (GPS). A Monte Carlo simulation study was conducted varying composite reliability, continuous treatment representation, variability of factor loadings, sample size, and number of treatment indicators to assess whether Average Treatment Effect (ATE) estimates differed in their relative bias, Root Mean Squared Error, and coverage rates. Results indicate that low composite reliability leads to underestimation of the ATE of latent continuous treatments, while the number of treatment indicators and variability of factor loadings show little effect on ATE estimates, after controlling for overall composite reliability. The results also show that, in correctly specified GPS models, the effects of low composite reliability can be somewhat ameliorated by using factor scores that were estimated including covariates. An illustrative example is provided using survey data to estimate the effect of teacher adoption of a workbook related to a virtual learning environment in the classroom.

Estimating power in complex nonlinear structural equation modeling including moderation effects: The powerNLSEM R-package.

The model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the R package powerNLSEM. After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the powerNLSEM package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates.

Evaluating natural and anthropogenic inputs on the distribution of potentially toxic elements in urban soil of Valdivia, Chile.

The increasing population in urban areas in the last decades requires an effort to understand the geochemistry of contaminant elements in urban soil. Topsoil plays a crucial role in the exposure of Potentially Toxic Elements (PTEs) to humans through ingestion, dermal contact, and inhalation. In Chile, the last census revealed that 88.6% of people live in cities or towns and only 11.4% in rural areas. This study presents the first systematic geochemical survey of urban soil in the city of Valdivia, in the South of Chile. Topsoil samples (0-10 cm depth) were collected in less disturbed locations within the city at 130 sampling sites using a grid of 0.25 km2 squares covering a total area of approximately 30 km2. The concentrations of Al, Fe, Na, Ca, Mg, K, Ti, Be, V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Sn, Cd, Se, Pb and Hg were measured. The results showed that high concentrations of Cu, V, Zn and Pb are located mainly in the city's northern area and exceed international soil quality legislation for agricultural use. Data processing comprised plotting of individual spatial distribution maps and the use of a combination of multivariate statistical methods. Hierarchical cluster analysis and principal component analysis identified three element associations. The two element groups V-Al-Ti-Fe-Cr-Co-Mn-Be-Ni and Ca-Na-K-As-Mg are interpreted as a dominant lithological origin related to the most pristine soil conditions in less populated areas. By contrast, the Sn-Pb-Zn-Mo-(Cu-Hg) association presents a significant correlation with urbanization indicators, including vehicular traffic and industrial activities developed since the end of the nineteenth century in Valdivia.

Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted composites.

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite scores is preferred for prediction and diagnosis of individuals. While path analysis with composite scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally weighted composites. However, path analysis with Bartlett-factor scores and the partial least-squares approach to SEM perform the best with respect to effect size and power.

New indicator for discordance between patient-reported and traditional disease activity outcomes in patients with early rheumatoid arthritis.

OBJECTIVE: To unravel disease impact in early RA by separately quantifying patient-reported (PRF), clinical (CF) and laboratory (LF) factors. We propose a new indicator, the discordance score (DS), for early identification and prediction of patient's unmet needs and of future achievement of sustained remission (SR) and RA-related quality of life (QoL). METHODS: Factor-scores obtained by factor analysis in the CareRA trial, allowed to compute DS, reflecting the difference between PRF and the mean of CF and LF. Improvement from baseline to week 104 (%) and area-under-the-curve (AUC) across time points per factor-score were calculated and compared between patients achieving/not achieving sustained (week 16-104) remission (DAS28CRP < 2.6) with ANOVA. Logistic and linear regressions were used to predict SR based on previous factor and discordance scores, and QoL at year 1 and 2 based on DS at week 16. RESULTS: PRF, CF and LF scores improved rapidly within 8 weeks. PRF improved 57%, CF 90% and LF 27%, in those achieving SR, compared with 32% (PRF: P = 0.13), 77% (CF: P < 0.001) and 9% (LF: P = 0.36) in patients not achieving SR. Patients achieving SR had an AUC of 15.7, 3.4 and 4.8 for PRF, CF and LF, respectively, compared with 33.2, 10.1 and 7.2 in participants not achieving SR (P < 0.001 for all). Early discordance was associated with later factor scores, QoL and self-efficacy. CONCLUSIONS: All factor scores improved rapidly, especially in patients achieving sustained remission. Patient-reported burden improved less. Discordance scores could help predicting the need for additional non-pharmacological interventions to achieve sustained remission and decrease disease impact.

Age and sex-related variability in the presentation of generalized anxiety and depression symptoms.

BACKGROUND: Generalized anxiety and depression are extremely prevalent and debilitating. There is evidence for age and sex variability in symptoms of depression, but despite comorbidity it is unclear whether this extends to anxiety symptomatology. Studies using questionnaire sum scores typically fail to address this phenotypic complexity. METHOD: We conducted exploratory and confirmatory factor analyses on Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) items to identify latent factors of anxiety and depression in participants from the Genetic Links to Anxiety and Depression Study (N = 35,637; 16-93 years). We assessed age- and sex-related variability in latent factors and individual symptoms using multiple logistic regression. RESULTS: Four factors of mood, worry, motor, and somatic symptoms were identified (comparative fit index [CFI] = 0.99, Tucker-Lewis Index [TLI] = 0.99, root mean square error of approximation [RMSEA] = 0.07, standardized root mean square residuals [SRMR] = 0.04). Symptoms of irritability (odds ratio [OR] = 0.81) were most strongly associated with younger age, and sleep change (OR = 1.14) with older age. Males were more likely to report mood and motor symptoms (p < .001) and females to report somatic symptoms (p < .001). CONCLUSION: Significant age and sex variability suggest that classic diagnostic criteria reflect the presentation most commonly seen in younger males. This study provides avenues for diagnostic adaptation and factor-specific interventions.

The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability.

Propensity score (PS) methods are implemented by researchers to balance the differences between participants in control and treatment groups that exist in observational studies using a set of baseline covariates. Propensity scores are most commonly calculated using baseline covariates in a logistic regression model to predict the binary grouping variable (control versus treatment). Low reliability associated with the covariates can adversely impact the calculation of treatment effects in propensity score models. The incorporation of latent variables when calculating propensity scores has been suggested to offset the negative impact of covariate unreliability. Simulation studies were conducted to compare the performance of latent variable methods with traditional propensity score methods when estimating the treatment effect under conditions of covariate unreliability. The results indicated that using factor scores or composite variables to compute propensity scores resulted in biased estimates and inflated Type I error rates as compared to using latent factors to compute propensity scores in certain conditions. This was largely dependent upon the number of infallible covariates also included in the PS model and the outcome analysis model analyzed. Implications of the findings are discussed.

Multilevel Factor Score Regression.

Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE.

Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies.

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.

Refining scores based on patient reported outcomes - statistical and medical perspectives.

BACKGROUND: Patient Reported Outcomes (PRO) are gaining more and more importance in the context of clinical trials. The assessment of PRO is frequently performed by questionnaires where the multiple items of a questionnaire are usually pooled within summarizing scores. These scores are used as variables to measure subjective aspects of treatments and diseases. In clinical research, the calculation of these scores is mostly kept very simple, e.g. by a simple summation of item values. In the medical literature, there is hardly any guidance for performing a refinements of questionnaires and for deducing adequate scores. In contrast, in psychometric literature, there are plenty of more sophisticated methods, which overcome typical assumptions made in traditional (sum) scores, however to the prize of more complicated algorithms, which might be difficult to communicate. When faced with the practical task to refine an existing questionnaire, there exist a clear gap of guidance for applied medical researchers. By this article we try to fill this important gap between psychometric theory and medical application by illustrating our methodological choices on the example of a clinical PRO questionnaire. METHODS: Based on our experiences with the refinement of the BCTOS, a PRO questionnaire to assess aesthetic and function after breast conserving therapy in breast cancer patients, we present the following general steps that we performed by refining the BCTOS questionnaire and its scores: 1. Refinement of the length of the questionnaire and the (item-factor) structure. 2. Selection of the factor score estimation method. 3. Validation of the refined questionnaire and scores with respect to validity, reliability and structure based on a validation cohort. RESULTS: Our step-step-step procedure helped us to shorten the current form of the BCTOS and to redefine the factor structure. By this, the compliance of patients can be increased and the interpretation of the results becomes more coherent. CONCLUSIONS: We present a step-by-step procedure to refine an existing medical questionnaire along with its scores illustrated and discussed by the refinement of the BCTOS. TRIAL REGISTRATION: Due to the character of the study (no intervention study), no registration was performed.

Birmingham vasculitis activity score at diagnosis is a significant predictor of relapse of polyarteritis nodosa.

The objective of this study was to investigate whether clinical and laboratory data, Birmingham vasculitis activity score (BVAS) and five factor scores (FFS) at diagnosis could predict relapse in 30 patients with polyarteritis nodosa (PAN) having the follow-up duration for over 12 months. We reviewed the medical charts of 30 patients with PAN. We obtained clinical and laboratory data at diagnosis, and we compared them between the two groups based on relapse. The optimal cut-off values of BVAS and FFS (1996) at diagnosis to predict relapse were extrapolated. The mean age of patients (15 men) was 50.8 years, and the mean follow-up duration was 64.1 months. Nine patients (30.0%) had experience relapse after remission. Patients having relapse showed the higher frequency of weight loss and ocular symptoms and the less frequency of diastolic hypertension than those having not (p < 0.005 for all). On multivariate logistic regression analysis, weight loss was the only independent predictor of relapse, but on Cox Hazard model analysis, its statistical significance disappeared. The mean initial BVAS and FFS (1996) of patients in relapse group were higher than those of patients in no relapse group (p < 0.005 for all). Patients having initial BVAS over 13.5 and FFS (1996) over 1 exhibited significantly higher risk of relapse than those having not (RR 40.0 and RR 7.0, respectively). However, initial BVAS over 13.5 only remained significant in Kaplan-Meier survival analysis. In conclusion, BVAS over 13.5 at diagnosis was the only independent predictor of relapse of PAN.

Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances?

The covariances of observed variables reproduced from conventional factor score predictors are generally not the same as the covariances reproduced from the common factors. We sought to find a factor score predictor that optimally reproduces the common part of the observed covariances. It was found algebraically that-under some conditions-the single observed variable with highest loading on a factor reproduces the non-diagonal elements of the observed covariance matrix more exactly than the conventional factor score predictors. This finding is linked to Spearman's and Wilson's 1929 debate on the use of single variables as factor score predictors. A population-based and a sample-based simulation study confirmed the algebraic result that taking a single variable can outperform conventional factor score predictors in reproducing the non-diagonal covariances when the nonzero loading size and the number of nonzero loadings per factor are small. The results indicated that a weighted aggregation of variables does not necessarily lead to an improvement of the score over the variable with the highest loading.

Thinking About Sum Scores Yet Again, Maybe the Last Time, We Don't Know, Oh No . . .: A Comment on.

The relative advantages and disadvantages of sum scores and estimated factor scores are issues of concern for substantive research in psychology. Recently, while championing estimated factor scores over sum scores, McNeish offered a trenchant rejoinder to an article by Widaman and Revelle, which had critiqued an earlier paper by McNeish and Wolf. In the recent contribution, McNeish misrepresented a number of claims by Widaman and Revelle, rendering moot his criticisms of Widaman and Revelle. Notably, McNeish chose to avoid confronting a key strength of sum scores stressed by Widaman and Revelle-the greater comparability of results across studies if sum scores are used. Instead, McNeish pivoted to present a host of simulation studies to identify relative strengths of estimated factor scores. Here, we review our prior claims and, in the process, deflect purported criticisms by McNeish. We discuss briefly issues related to simulated data and empirical data that provide evidence of strengths of each type of score. In doing so, we identified a second strength of sum scores: superior cross-validation of results across independent samples of empirical data, at least for samples of moderate size. We close with consideration of four general issues concerning sum scores and estimated factor scores that highlight the contrasts between positions offered by McNeish and by us, issues of importance when pursuing applied research in our field.

Comparative effectiveness of two different doses of botulinum toxin A for the treatment of mild to moderate depression.

OBJECTIVE: Botulinum toxin A has been shown to be effective in managing depression. This study aimed to evaluate the antidepressant and antianxiety effects of two different doses of botulinum toxin A in patients with mild to moderate depression. METHODS: A total of 140 patients diagnosed with mild to moderate depression at the Department of Neurology of the Second Affiliated Hospital of Soochow University from September 2020 to September 2021 were enrolled for the study. The patients were allocated into two groups and treated with two different doses of botulinum toxin A (50 units or 100 units). Depression scores (HAMD, HAMA, SDS, and SAS) were evaluated at baseline and 1, 2, 4, 8, and 12 weeks after treatment. RESULTS: There was a significant improvement in the depressive and anxiety symptoms following treatment with the botulinum toxin A after 12 weeks compared to the baseline. However, there were no significant differences between the two groups. Further, the factor scores of anxiety/somatization, blocking, sleep disorder, and cognitive disorder were significantly decreased after 12 weeks of treatment with 50 units of botulinum toxin A compared to the baseline (P < 0.05). Further, the factor scores of somatic and mental anxiety were significantly decreased at different time points after treatment with 50 units of botulinum toxin A compared to the baseline (P < 0.05). CONCLUSION: Local injections of 50 units and 100 units of botulinum toxin A shows equal efficacy. Therefore, 50 units of botulinum toxin A could be used clinically to manage mild to moderate depression.

Non-parametric Regression Among Factor Scores: Motivation and Diagnostics for Nonlinear Structural Equation Models.

We provide a framework for motivating and diagnosing the functional form in the structural part of nonlinear or linear structural equation models when the measurement model is a correctly specified linear confirmatory factor model. A mathematical population-based analysis provides asymptotic identification results for conditional expectations of a coordinate of an endogenous latent variable given exogenous and possibly other endogenous latent variables, and theoretically well-founded estimates of this conditional expectation are suggested. Simulation studies show that these estimators behave well compared to presently available alternatives. Practically, we recommend the estimator using Bartlett factor scores as input to classical non-parametric regression methods.

Children's perception of food parenting practices: adaptation and validation of the comprehensive feeding practices questionnaire in Chilean adolescents.

Introduction: Assessment of the Comprehensive Feeding Practices Questionnaire in adolescents (CFPQ-Teen) is still limited, with no evaluation of the measurement invariance. The participants comprised 473 Chilean adolescents of both sexes from dual-income nuclear families. The aims of this study were: (1) to adapt to Spanish and validate a model of five-factor version the CFPQ-Teen; (2) to examine the psychometric properties, (3) to evaluate the measurement invariance according to the adolescents' gender; and (4) to compare the scores of each factor between female and male adolescents. Methods: The instrument was translated, back-translated, and adapted from the CFPQ-Teen, confirming the equivalence, conceptual, and face validity in a pilot sample of 40 adolescents. An exploratory factor analysis was performed on the five-factor model of the CFPQ-Teen: Monitoring, Adolescent Control, Restriction for weight control, Parental Modeling, and Environment. The Environment factor was eliminated as a result. Results: The confirmatory factor analysis presented good reliability, convergent, discriminant, and concurrent validity values. In addition, medium to high goodness-of-fit levels were obtained by eliminating an item from the Adolescent Control factor. These results confirm a final 20-item model representing four factors. The multigroup invariance analysis of the measurement model verified configural, metric, scalar, and partial strict invariance. No significant differences were found between females and males in the scores on the four factors. Discussion: These results enable comparisons by sex on the perceptions of Food Parenting Practices from the analyzed factors, primarily within the context of the Chilean sample.

Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial.

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through implementation of moderated nonlinear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex modeling building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.

Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors.

INTRODUCTION: We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores. METHOD: We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval. RESULTS: NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction. CONCLUSIONS: Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.

Game Addiction Scale for Adolescents-Psychometric Analyses of Gaming Behavior, Gender Differences and ADHD.

Background: Internet gaming disorder (IGD) was recently added in the Diagnostic and Statistical Manual of Mental Disorder as a "condition for further studies." There is no consensus regarding which rating scales should be used but many scholars suggest the GASA (Game Addiction Scale for Adolescents) and a ranking of the criteria, "the core approach" to avoid over-diagnosing of disordered gaming. Male gender and ADHD are commonly listed as risk factors for disordered gaming but little is known about sex differences in gaming and gender specific health correlates. Purpose: The present study aims to evaluate the core approach and the specific indicators of gaming behavior in GASA from a multifactorial perspective and explore the gender differences in a clinical setting, focusing on ADHD. Patients and Methods: Children and adolescents aged 8-18 years (n = 144) from Child and adolescent psychiatry (CAP) in Skane were assessed with the GASA. Psychometric analyses including confirmatory factor analyses (CFA) and structural equation modeling (SEM) were used to identify well-defined constructs and gender differences. Refined factor scores for single constructs were the outcome of alignment, a procedure for assessing measurement equivalence across gender. New model-based gaming behavior variables were used for descriptive statistics and ANOVA testing of gender differences. Results: The results confirm that the core approach two-factor model is valid for the CAP sample, as well as a theory based psycho-social model for gaming behavior with over consumption and negative social and emotional consequences. Our findings suggest that negative consequences of over consumption take a social direction for boys and an emotional direction for girls. Also, ADHD was significantly associated with over consumption of video games and the negative consequences thereof for girls. Conclusion: Guided by psychometric analyses, the GASA could be strengthened by advancing the questionnaire design and by adding complementary items in order to illuminate the complexity of gaming behavior. Our findings suggest that additional research on potential gender related discrepancies of disordered gaming is needed.