# Understanding Sleep Health in Judo Referees: The Influence of Demographic and Athletic Factors
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Variables** & **n** & \(x\) & **SD** & **Min** & **Max** \\ \hline
**Age (year)** & & & 29.79 & 11.0 & 18 & 61 \\ \hline
**Referee Experience (year)** & & & 6.58 & 8.12 & 1 & 40 \\
**Athlete Background (year)** & & & 12.60 & 6.18 & 0 & 33 \\ \hline
**PSQI Total Score** & M & 42 & 6.50 & 3.37 & 1 & 240 \\  & F & 31 & 8.16 & 3.57 & 2 & 360 \\  & T & 73 & 7.21 & 3.53 & 1 & 19 \\
**PSQI Category** & **n** & \(x\) & **SD** & **Min** & **Max** & **N** \\ \cline{2-6}  & M & 15 & 3.00 & 1.13 & 1 & 5 \\
**Good Sleep (PSQI \(\leq\) 5)** & F & 6 & 3.50 & 1.22 & 2 & 5 \\
**Poor Sleep (5 \(>\) PSQI)** & M & 27 & 8.44 & 2.49 & 6 & 14 \\  & F & 25 & 9.28 & 2.98 & 6 & 19 \\
**Total Sleep Time (min)** & M & 42 & 451 & 89.9 & 240 & 660 \\  & F & 31 & 479 & 83.2 & 360 & 660 \\  & T & 73 & 463 & 87.6 & 240 & 660 \\ \hline \hline \end{tabular}  
* \(x\): Mean; SD: standard deviation; M: male; F: female; T: total.  
\end{table} 
Table 2: The descriptive statistical data on the participants’ age, referral experience, and athletic background. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Gender** & **n** & \(x\) & **SD** & **t** & \(p\) & **D** \\ \hline \multirow{2}{*}{**Total PSQI**} & Male & 42 & 6.50 & 3.37 & \multirow{2}{*}{\(-\)2.03} & \multirow{2}{*}{**0.046 \({}^{*}\)**} & \multirow{2}{*}{**0.23**} \\  & Female & 31 & 8.16 & 3.57 & & \\ \hline \multirow{4}{*}{} & \(x\): Mean; SD: standard deviation; t: independent samples test value; \(p\): significance level; D: Cohen’s d value:*: \(p\) \(>\) 0.05.  
\end{table} 
Table 3: The independent samples \(t\)-test comparing total PSQI scores between male and female participants. (N = 42) was 7.64 (SD = 3.72), and the average score for graduate graduates (N = 11) was 6.73 (SD = 3.52). The total difference between the groups was 19.16, and the mean square was 6.39. The total variance within groups (within groups) was 876.76, and the mean square was 12.71. According to the correlation analysis presented in Table 6, significant negative correlations were found between the PSQI total score and age (r = \(-\)0.322, \(p\) < 0.05) as well as refereeing experience (r = \(-\)0.277, \(p\) < 0.05). In contrast, significant positive correlations were observed between age and refereeing experience (r = 0.870, \(p\) < 0.001) and between age and sports background (r = 0.236, \(p\) < 0.05). In Table 7, three models were used to analyse the effects of the independent variables on the target variable. In the first model, gender had a significant effect (b = 1.66, SE = 0.818, t = 2.03, \(p\) = 0.046), and the R\({}^{2}\) value of the model was 0.0549. The second model additionally included age as a predictor. The overall model was improved, explaining 13.8% of the variance, R\({}^{2}\) = 0.138, R = 0.371. However, gender was no longer a significant predictor, b = 1.322, t = 1.66, \(p\) = 0.102. In contrast, age had a significant negative effect on the outcome, b = \(-\)0.094, SE = 0.036, t = \(-\)2.59, \(p\) = 0.012, indicating that as age increased, the outcome score decreased. The third and final model included athletic background. This model accounted for 19.4% of the variance in the outcome, R\({}^{2}\) = 0.194, R = 0.441. All predictors were statistically significant: (1) gender: b = 1.711, SE = 0.796, t = 2.15, \(p\) = 0.035; (2) athletic background: b = 0.143, SE = 0.065, t = 2.20, \(p\) = 0.031; (3) Age: b = \(-\)0.110, SE = 0.036, t = \(-\)3.06, \(p\) = 0.003. Results indicate that being male, having a longer athletic background, and being younger are associated with higher (worse) PSQI score values (i.e., poorer sleep quality) in the outcome variable.