Spss 25.0 for windows

Statistical analyses were performed using IBM SPSS® 25.0 for Windows (IBM, Armonk, New York, USA). Continuous variables were tested for normality using the Shapiro–Wilk test. Continuous variables are presented as the median, minimum, and maximum because they were not normally distributed (P < 0.05). Binary variables are presented as numbers and percentages. Nominal variables are presented as numbers with percentages per category. To statistically compare variables between groups, the Mann–Whitney U test was used for continuous variables and the chi-square test was used for dichotomous and nominal variables. Pairwise correlation was additionally assessed using Spearman's rank correlation coefficients. The influence of age, gender, education, smoking status, diabetes, arterial hypertension, neurological pathology, autoimmune pathology, intraocular lens status, familial history of glaucoma, familial history of AMD, intake of vitamin preparations for the prevention of AMD, the presence of a corrected refractive error, and previous ophthalmologist visits on the need for referral to an ophthalmologist was studied. Statistical significance was accepted based on two-sided P values of <0.05.

Differences in the general characteristics (continuous and categorical variables) of both mothers and children according to maternal different status of GDM and HDP were tested using the univariate analysis of variance or chi-square test. We used the general linear models.
to compare children's Z scores for BMI and used logistic regression models to estimate odds ratios of childhood overweight according to maternal different status of GDM and HDP. All analyses were adjusted for maternal age, gestational age, education, smoking status, history of treatment of GDM, and HDP, children's sex, age, birth weight, and feeding status (Model 1), and then children's lifestyles including outdoor physical activity time, screen watching time, sleep time, daily energy intake, energy from fat and dietary fiber intake (Model 2), and further for maternal pre-pregnancy BMI and gestational weight gain (Model 3). All the statistical analyses were performed with SPSS 25.0 for windows software package (IBM SPSS statistics 25) and SAS Proprietary Software 9.4 (SAS Institute Inc., Cary, NC, USA). Two-sided P < 0.05 was considered statistically significant.

All values were expressed as mean ± standard deviation or median, and the Mann–Whitney U test was used for intergroup comparison. SPSS^® 25.0 for Windows (IBM, Chicago, U.S.A.) was used for statistical analysis, and p < 0.05 was defined as significantly different for all values.

Structural equations among latent constructs were examined to test the conceptual structural equation model (SEM). The SEM was used to analyze causal models and simultaneously estimate a series of interrelated dependence relationships. Thus, data analysis was carried out using structural estimation modeling. Before this study tested the research model, SPSS 25.0 for Windows (IBM Corp) was used to show the important descriptive information on demographic variables, including participant characteristics such as gender, age, and educational background. This information also included behaviors related to the use of wearable medical devices, such as the time spent on the internet, the preferred online medical platform provider, and the frequency of using wearable medical devices. Model evaluation involved a two-step analysis [22 (link)] using the software IBM Amos 21.0. For this purpose, the author first built a measurement model using confirmatory factor analysis for the model to check its fit and then built the SEM and examined the hypothesized causal paths among the constructs by performing a simultaneous test. This helped to observe whether the conceptual framework had provided an acceptable fit to the empirical data.

Although the criterion for breastfeeding duration to group participants vary widely in previous studies, many studies included “never,” “1 month,” “3 months,” “6 months,” and “12 months” as the duration criteria [6 (link)]. In our study, participants were grouped by the following breastfeeding durations according to previous studies [9 (link), 18 (link)]: “never,” “up to 1 month,” “1–3 months,” “3–6 months,” “6–12 months,” “12–18 months” and “over 18 months.” We used logistic regression to investigate the odds ratio for delayed development of the early period (T1, 5.5 months to T3, 26.2 months) assessed by Denver II and K-ASQ. To compare the outcomes of K-ASQ as continuous variables, language ability at T4 (38.7 months), and intelligence and academic function at T9 (99.2 months) among the groups of breastfeeding duration, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were utilized. In all analyses, the adjusted model included the children’s sex, age, gestational age, birth weight, parents’ education level, and household income as covariates. To adjust for multiple comparisons included in our analysis, we performed the Benjamini–Hochberg test with a false discovery rate threshold of 0.05 for the crude and adjusted models, respectively [19 (link)]. Statistical analyses were conducted using the software package SPSS 25.0 for Windows (IBM Co., Armonk, NY, USA).