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.

This study examines the relationship between language use and psychological characteristics of the communicator. The aim of the study was to find models predicting the depressivity of the writer based on the computational linguistic markers of his/her written text. Respondents’ linguistic fingerprints were traced in four texts of different genres. Depressivity was measured using the Depression, Anxiety and Stress Scale (DASS-21). The research sample (N = 172, 83 men, 89 women) was created by quota sampling an adult Czech population. Morphological variables of the texts showing differences (M-W test) between the non-depressive and depressive groups were incorporated into predictive models. Results: Across all participants, the data best fit predictive models of depressivity using morphological characteristics from the informal text “letter from holidays” (Nagelkerke r2 = 0.526 for men and 0.670 for women). For men, models for the formal texts “cover letter” and “complaint” showed moderate fit with the data (r2 = 0.479 and 0.435). The constructed models show weak to substantial recall (0.235 – 0.800) and moderate to substantial precision (0.571 – 0.889). Morphological variables appearing in the final models vary. There are no key morphological characteristics suitable for all models or for all genres. The resulting models’ properties demonstrate that they should be suitable for screening individuals at risk of depression and the most suitable genre is informal text (“letter from holidays”).

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School-aged children spend 31-60 % of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30 % of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing. In this study, we propose three novel advanced handwriting parametrization techniques based on modulation spectra, fractional order derivatives, and tunable Q-factor wavelet transform to improve the identification of GD using online handwriting. For this purpose, we analyzed signals acquired from 7 basic graphomotor tasks performed by 53 children attending 3rd and 4th grade at several primary schools around the Czech Republic. Combining the newly proposed features with the conventionally used ones, we were able to identify GD with 84 % accuracy. In this study, we showed that using advanced parametrization of basic graphomotor movements can be potentially used to improve our capabilities of quantifying problems with the development of legible, fast-paced handwriting, and help with the early diagnosis of handwriting difficulties frequently manifested in developmental dysgraphia.

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