ObjectiveOf the many existing health models, models of health behavior are considered optimal for research and application as they focus on concrete forms of behavior that support, maintain, or undermine one’s health, and they accentuate the individual as the initiator of this behavior. Research in this area follows a broad range of concrete partial manifestations of health behavior. Is it necessary to differentiate between various types of health behavior or could these partial manifestations be combined under one common scale?MethodsData acquisition tool: Health-Related Behavior Scale (HRBS, 42 items). Data processing methods: principal component analysis (the internal structure of HRBS), confirmatory factor analysis (the latent factor structure of four tested models). Sample: N=1,664 adult respondents.ResultsThe HRBS described ten areas of health-related behavior (ten extracted factors). All tested models of latent structure showed almost identical mathematical and statistical values of the model.ConclusionHealth-related behavior includes a set of partial behaviors (behavior related to nutrition, addictive substances, movement, and physical exercises). An unambiguous latent factor structure has not been revealed. An open question remains whether there is one latent factor behind all health-related behaviors or whether there are multiple latent factors. The use of one or the other model should be deduced from the underlying theory and research objectives. To find a reliable model of health behavior, it is necessary to include moderators and mediators such as personality, attitude, or economic status.

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The Brunel Mood States is a 24-items long questionnaire (formerly referred to as the Profile of Mood States for Adolescents POMS-A) used to capture emotional profile of an individual. It has been used in various settings including sport psychology, where it is considered a valid indicator for overtraining syndrome. The aim of this study was to develop the Czech adaptation of BRUMS and verify its psychometric properties in adolescent athletes. The data were collected from a sample of 246 participant (50.8% females; age range 14-19 years). Confirmatory factor analysis was used to evaluate original six-dimensional structure (with factors of Depression, Tension, Confusion, Anger, Fatigue, and Vigor). Even though this model showed acceptable fit to the data, Depression and Tension factors were empirically indistinguishable. Therefore, we proposed and verified alternative five-factor model with these two factors collapsed. Measurement invariance across gender was assessed using the Multiple Indicators Multiple Causes (MIMIC) model. Although three items showed signs of differential item functioning, the Czech adaptation of the instrument can in general be considered a measurement invariant.

Dispositional optimism is a psychological trait associated with both physical and mental health in adults. Less is known about the associations between optimism and health among adolescents. This study aimed to investigate the relationship between dispositional optimism, health-related behavior and mental health among Czech adolescents. Further on, the effect of gender and age (early vs. late adolescence) on mental health levels was explored. Instruments: short form of the Mental Health Continuum (MHC-SF); Health Behaviours Scale; Life Orientation Test -Revised (LOT-R). Sample: 1376 Czech adolescents aged 11-19 (M = 15.9; SD = 2.3), 56.8% females. Statistical analysis: Hierarchical multiple regression analysis with three sets of predictors. The first set of predictors (age and gender) explained 7.3% of variance in MHC-SF scores; the second set of predictors -LOT-R optimism and pessimism scores -explained another 25% of variance; and the third set of predictors (health-related behaviors) explained additional 16.3% of variance. The final model explained 48.6% of variance in mental health among adolescents. Dimensions of dispositional optimism turned out to be the strongest set of predictors of positive mental health in adolescents, while health behavior components predicted the mental health levels to a lesser extent. Gender and age were also significant predictors of mental health, with boys and younger adolescents scoring higher.

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Background One of the most widely used instruments to measure depression in childhood and adolescence is Kovacs’s Children’s Depression Inventory (CDI). Even though this particular measure sparked massive interest among researchers, there is no clear consensus about its factorial structure. It has been suggested that inconsistencies in findings can be partly ascribed to the cultural context. The aim of this study was a) to examine and verify the factor structure of CDI in the Czech population and b) to assess gender-related psychometric differences using the mean and covariance structure (MACS) approach and differential item functioning (DIF) analysis. Methods The research sample consisted of 1,515 adolescents (ages 12 to 16 years, 53.7% female) from a non-clinical general population. Based on exploratory factor analysis (EFA) on a random subsample (N = 500), we proposed a model that was subsequently tested on the rest of the sample (N = 1,015) using confirmatory factor analysis (CFA). Following the MACS procedure, we assessed measurement invariance in boys and girls. The between-group comparison was further supplemented by a DIF analysis. Results The proposed hierarchical four-factor model (General Symptoms, Negative Self-Concept, Inefficiency, and Social Anhedonia) with a second-order factor of depression fitted the data reasonably well (χ2 = 1281.355; df = 320; RMSEA = 0.054, CFI = 0.925). Regarding gender differences, we found no substantial signs of measurement invariance using the MACS approach. Boys and girls differed in first-order latent means (girls scored higher on General Symptoms with a standardized mean difference of 0.52 and on Negative Self-Concept with a standardized mean difference of 0.31). DIF analysis identified three items with differential functioning. However, the levels of differential functioning were only marginal (in two items) or marginal/moderate and the presence of DIF does not substantially influence scoring of CDI. Conclusion In the general adolescent population in the Czech Republic, the CDI can be considered a reliable instrument for screening purposes in clinical settings and for use in research practice. Instead of the originally proposed five-factor model, we recommend using the newly established four-factor structure. The measure seems to show only marginal psychometric differences with respect to gender, and overall measurement invariance in boys and girls seems to be a tenable assumption.