Correction: Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

[This corrects the article DOI: 10.2196/45407.].

Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

BACKGROUND: Advancements in mobile health technologies and machine learning approaches have expanded the framework of behavioral phenotypes in obesity treatment to explore the dynamics of temporal changes. OBJECTIVE: This study aimed to investigate the dynamics of behavioral changes during obesity intervention and identify behavioral phenotypes associated with weight change using a hybrid machine learning approach. METHODS: In total, 88 children and adolescents (ages 8-16 years; 62/88, 71% male) with age- and sex-specific BMI ≥85th percentile participated in the study. Behavioral phenotypes were identified using a hybrid 2-stage procedure based on the temporal dynamics of adherence to the 5 behavioral goals during the intervention. Functional principal component analysis was used to determine behavioral phenotypes by extracting principal component factors from the functional data of each participant. Elastic net regression was used to investigate the association between behavioral phenotypes and weight change. RESULTS: Functional principal component analysis identified 2 distinctive behavioral phenotypes, which were named the high or low adherence level and late or early behavior change. The first phenotype explained 47% to 69% of each factor, whereas the second phenotype explained 11% to 17% of the total behavioral dynamics. High or low adherence level was associated with weight change for adherence to screen time (ß=-.0766, 95% CI -.1245 to -.0312), fruit and vegetable intake (ß=.1770, 95% CI .0642-.2561), exercise (ß=-.0711, 95% CI -.0892 to -.0363), drinking water (ß=-.0203, 95% CI -.0218 to -.0123), and sleep duration. Late or early behavioral changes were significantly associated with weight loss for changes in screen time (ß=.0440, 95% CI .0186-.0550), fruit and vegetable intake (ß=-.1177, 95% CI -.1441 to -.0680), and sleep duration (ß=-.0991, 95% CI -.1254 to -.0597). CONCLUSIONS: Overall level of adherence, or the high or low adherence level, and a gradual improvement or deterioration in health-related behaviors, or the late or early behavior change, were differently associated with weight loss for distinctive obesity-related lifestyle behaviors. A large proportion of health-related behaviors remained stable throughout the intervention, which indicates that health care professionals should closely monitor changes made during the early stages of the intervention. TRIAL REGISTRATION: Clinical Research Information Science KCT0004137; https://tinyurl.com/ytxr83ay.

Characteristics of Lithium Ions and Superoxide Anions in EMI-TFSI and Dimethyl Sulfoxide.

To clarify the microscopic effects of solvents on the formation of the Li(+)-O2(­) process of a Li­O2 battery, we studied the kinetics and thermodynamics of these ions in dimethyl sulfoxide (DMSO) and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI-TFSI) using classical molecular dynamics simulation. The force field for ions­solvents interactions was parametrized by force matching first-principles calculations. Despite the solvation energies of the ions are similar in both solvents, their mobility is much higher in DMSO. The free-energy profiles also confirm that the formation and decomposition rates of Li(+)-O2(­) pairs are greater in DMSO than in EMI-TFSI. Our atomistic simulations point out that the strong structuring of EMI-TFSI around the ions is responsible for these differences, and it explains why the LiO2 clusters formed in DMSO during the battery discharge are larger than those in EMI-TFSI. Understanding the origin of such properties is crucial to aid the optimization of electrolytes for Li­O2 batteries.

Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation.

In covariance structure analysis, two-stage least-squares (2SLS) estimation has been recommended for use over maximum likelihood estimation when model misspecification is suspected. However, 2SLS often fails to provide stable and accurate solutions, particularly for structural equation models with small samples. To address this issue, a regularized extension of 2SLS is proposed that integrates a ridge type of regularization into 2SLS, thereby enabling the method to effectively handle the small-sample-size problem. Results are then reported of a Monte Carlo study conducted to evaluate the performance of the proposed method, as compared to its nonregularized counterpart. Finally, an application is presented that demonstrates the empirical usefulness of the proposed method.

Exploratory factor analysis for small samples.

Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature. However, little is known about the differential performance of these three approaches to exploratory factor analysis in a small sample size scenario. A simulation study and an empirical example demonstrate that regularized exploratory factor analysis may be recommended over the two traditional approaches, particularly when sample sizes are small (below 50) and the sample covariance matrix is near singular.

Regularized Exploratory Bifactor Analysis With Small Sample Sizes.

Several methods of factor extraction have recently gained popularity as a procedure for dealing with estimation problems associated with small sample sizes, which can be found in the various behavioral science disciplines, such as comparative psychology and behavior genetics. Two popular approaches for particularly small samples (below 50) include unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA). However, it is unclear how well each of the approaches performs with small samples in the context of exploratory bifactor modeling. In the current study, a comprehensive simulation study was conducted to evaluate the small sample behavior of the two approaches in terms of bifactor structure recovery under different sample size, factor loading, number of variables per factor, number of factors, and factor correlation experimental conditions. The results show that REFA is recommended for use over ULS-FA, particularly in the conditions involving low factor loadings, few group factors, or a small number of variables per factor.

Consistent Partial Least Squares Path Modeling via Regularization.

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

A Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing (CAT) Using a Medical Licensing Examination.

Arithmetic mean, Harmonic mean, and Jensen equality were applied to marginalize observed standard errors (OSEs) to estimate CAT reliability. Based on different marginalization method, three empirical CAT reliabilities were compared with true reliabilities. Results showed that three empirical CAT reliabilities were underestimated compared to true reliability in short test length (<40), whereas the magnitude of CAT reliabilities was followed by Jensen equality, Harmonic mean, and Arithmetic mean when mean of ability population distribution is zero. Specifically, Jensen equality overestimated true reliability when the number of items is over 40 and mean ability population distribution is zero. However, Jensen equality was recommended for computing reliability estimates because it was closer to true reliability even if small numbers of items was administered regardless of the mean of ability population distribution, and it can be computed easily by using a single test information value at θ = 0. Although CAT is efficient and accurate compared to a fixed-form test, a small fixed number of items is not recommended as a CAT termination criterion for 2PLM, specifically for 3PLM, to maintain high reliability estimates.

Measuring theory of mind in schizophrenia research: Cross-cultural validation.

Theory of mind (ToM) is the ability to understand mental states of others and it is crucial for building sensitivity to other persons or events. Measuring ToM is important for understanding and rehabilitating social cognitive impairments in persons with schizophrenia. The Social Attribution Task-Multiple Choice (SAT-MC) has been successfully employed to measure ToM between individuals with schizophrenia (SZ) and healthy controls (HC) in North America. Given that the SAT-MC uses geometric shapes, is nonverbal and less culturally loaded than other social cognition measures, it may serve for measuring ToM in schizophrenia across cultures. A total of 120 participants (30 per group; Korean SZ; Korean HC; North American SZ; North American HC) were selected from existing databases to examine the reliability and validity of the SAT-MC. Internal consistency, factor structure, measurement invariance, discriminant validity, and convergent/divergent validity were examined. The SAT-MC had good internal consistency regardless of the clinical and cultural group as evidence by Cronbach's α ≥ 0.78 in all groups. Confirmatory factor analysis confirmed the one-factor model with a good model fit (χ2 = 188.122, TLI = 0.958, CFI = 0.963, RMSEA = 0.045). The SAT-MC was sensitive to detect individual differences in ToM of SZ and HC, regardless of culture (p < 0.001), and significantly correlated with other social cognition tasks (Hinting and Reading the Mind in the Eyes Test) among Korean and North American patients. The SAT-MC is a reliable measure for evaluating ToM in both Koreans and North Americans with or without schizophrenia, supporting its potential utility in diverse language and cultures for schizophrenia research.

Exploratory factor analysis with small sample sizes: a comparison of three approaches.

Exploratory factor analysis (EFA) has emerged in the field of animal behavior as a useful tool for determining and assessing latent behavioral constructs. Because the small sample size problem often occurs in this field, a traditional approach, unweighted least squares, has been considered the most feasible choice for EFA. Two new approaches were recently introduced in the statistical literature as viable alternatives to EFA when sample size is small: regularized exploratory factor analysis and generalized exploratory factor analysis. A simulation study is conducted to evaluate the relative performance of these three approaches in terms of factor recovery under various experimental conditions of sample size, degree of overdetermination, and level of communality. In this study, overdetermination and sample size are the meaningful conditions in differentiating the performance of the three approaches in factor recovery. Specifically, when there are a relatively large number of factors, regularized exploratory factor analysis tends to recover the correct factor structure better than the other two approaches. Conversely, when few factors are retained, unweighted least squares tends to recover the factor structure better. Finally, generalized exploratory factor analysis exhibits very poor performance in factor recovery compared to the other approaches. This tendency is particularly prominent as sample size increases. Thus, generalized exploratory factor analysis may not be a good alternative to EFA. Regularized exploratory factor analysis is recommended over unweighted least squares unless small expected number of factors is ensured.