2.3. Statistical Analyses

Data were analysed using descriptive statistics with frequencies (n) and percentages (%) to describe the results. Chi-square tests were used to investigate differences between status regarding lifestyle habits, health-promotive interventions and record system, as well as the child’s BMI. Spearman’s rank-order correlations were used to investigate correlations between documented interventions and the record system and correlations between documented interventions and the child’s BMI. For correlation coefficients, the value 1.00 stood for perfect positive correlation, 0 stood for no correlation and −1.00 for perfect negative correlation. Values close to 1.00 or, on the other side of the range, close to −1.00 could be interpreted as strong correlations and values close to 0 were interpreted as weak correlations (19). The BMI classifications were designed according to established limit values for underweight, normal weight, overweight and obesity of the respective sex at age four [12]. When few records included BMI classification obesity, the overweight and obese subgroups were merged into a new subgroup. In 19 records (3.9%), calculated BMI was missing. These records were excluded from the analyses where the child’s BMI was a variable. The significance level was set to p < 0.05. Statistical analysis was performed using IBM SPSS version 24 (IBM, Armonk, NY, USA).