This study examines the relationship between recreational screen time and physical activity in 281 children between the ages of five and eleven. Data was gathered over the course of a week and included demographic information, moderate aerobic activity, and self-reported and parental screen use. The goal of the study is to provide evidence for evidence-biased pediatric health promotion programs.
Task 1: Dataset opening
Figure 1: Dataset opened in SPSS
The dataset of EDEVICE.sav has been opened in SPSS. All the variables have been checked and no error is found. The output file has been saved under the of the student ID also.
Task 2: Descriptive stat
Figure 2: Descriptive Analysis, histogram and Normal Q plot of Difftime
There is a median difference of 5.9025 hours (SE = 0.25133) in the recreational screen time reported by parents and children, as shown by the variable DIFFTIME. The mean difference’s 95% confidence interval (CI) falls between 5.4078 and 6.3972. The distribution of the data shows a modest kurtosis (kurtosis = -0.083) and skewness of 0.113, which is somewhat positive.
Shapiro-Wilk and Kolmogorov-Smirnov tests of normality yield non-significant results (p > 0.05), while Kolmogorov-Smirnov test has a lower limit significance (p = 0.200*) (Abu, 2021). The Lilliefors Significance Correction, however, is in favor of normalcy.
Task 3: Calculation of New variables
Figure 3: DAILYACT and ACTIVEACT variable created and Recoded
According the NHS it has been developed a new categorical variable called ACTIVECAT to represent moderate aerobic activity levels for kids ages 5 to 18. ACTIVECAT differentiates between those who meet and those who fall short of the prescribed 60 minutes of moderate aerobic exercise per day by using a higher numeric code for lower activity levels. For the studies that follow, this categorical variable is essential because it provides information on possible relationships between physical activity and other research factors (Babbie et al., 2022). The resave of the dataset permits the incorporation of ACTIVECAT for an all-encompassing investigation of the influence of moderate aerobic exercise on the parameters under investigation.
Note: ACTIVECAT is the categorical variable of moderate aerobic activity status, where 1=above low level and 2=low level. GENDER is coded as 0=Female and 1=Male. CSCNTIME and PSCNTIME are the categorical variables of recreational screen time reported by child and parent, where 0=Normal usage and 1=High usage.
According to the levels of moderate aerobic activity status (ACTIVECAT), the table displays the descriptive statistics for each variable. There are no appreciable variations between the two groups of children’s mean age, BMI centile, or screen time (as reported by the parents as well as the children) (Elsaghair and Atilla, 2023). Within each group, the standard deviations show that these factors vary moderately. The range of these variables throughout the sample is indicated by the minimum and maximum values. With around half of the youngsters in each group being female and the other half being male, the gender distribution is also balanced. In each category, the percentage of kids and parents who reported high screen time (CSCNTIME and PSCNTIME) is also quite near to 50%.
Figure 5: One sample T test presented in publishable way
A one-simple t-test was performed on the variable DIFFTIME, which represents the reported difference, to investigate the possibility that children underreport their recreational screen time when compared to their parents (Jalolov, 2023). According to the null hypothesis (H0), there is no underreporting when the mean difference is equal to zero. The severe underreporting is suggested by the alternative hypothesis (H1). A t-statistic of 23.485 (do = 280, p < 0.001) was obtained from the test, which is strong evidence against the null hypothesis. The 5.90249 hours (95% CI: [5.4078, 6.3972]) mean difference suggests that youngsters significantly underreport when compared to parents. This is consistent with research that shows kids have a propensity to underreport how much time they spend on screens by at least half a day.
Task 6: Scatterplot and Histogram
Figure 6: ACTIVITY, PARTIME scatter plot
The scatter plot displays the children's activity time and screen time over the course of a week. Children who spend more time being active also tend to spend less time on screens, according to the plot, which indicates a modest negative association between the two factors (Pallant, 2020). There is a lot of variance in the data, though, and the link is not very strong. Some youngsters spend a lot of time on one activity and relatively little on the other, making them outliers in the data.
Figure 6: ACTIVECAT, PSCNTIME histogram
According to the NHS standards, the histogram plot illustrates the association between the children's screen usage over the course of a week and their active state. The largest tile in the plot, low active status and high screen usage, is where the bulk of the kids reside, according to the plan. This suggests that the majority of kids do not engage in the required amount of physical exercise and use screens for longer than is advised.
Task 7: Correlations and Regressions
Figure 7: Pearson and Spearman correlation between ACTIVITY and PARTIME
In contrast to the mean recreational screen time (PARTIME) of 24.82 hours (SD = 8.80) reported by parents, the mean moderate aerobic exercise (exercise) of 281 children is 7.54 hours (SD = 9.29). A poor link between children’s physical activity and screen time reported by parents is shown by a significant negative correlation (-0.122, p = 0.041). The results call for more research but also raise possible implications for therapies including screen time and physical exercise .
Figure 8: Regression between ACTIVITY and PARTIME
In a linear regression model, linear trends and variability may be captured by using the continuous version (ACTIVITY and PARTIME). Non-linear interactions or patterns, nevertheless, could be missed. Group comparisons are made easier by the categorical version (ACTIVECAT and PSCNTIME), however grouping may compromise accuracy. For an ANOVA, it is necessary to verify assumptions such as homogeneity and normalcy. ACTIVITY accounts for a little 1.5% variation in PARTIME in the linear regression model (R2 = 0.015). A little negative link between more activity and somewhat less reported screen time is showing by the negative standardized coefficient (-0.122, p = 0.041).
Figure 8: Univariate Regression
A significant 77.7% of the variation in PARTIME can be explained by the combined effects of CHDTIME, AGE, GENDER, and BMICTILE in the multiple regression model (R2 = 0.777). CHDTIME has a noteworthy positive correlation (B = 0.968, p < 0.001), suggesting a favorable relationship between increasing recreational screen time and higher reported screen time.
Figure 8: Multivariate Regression
The covariates in the multiple regression model, which includes ACTIVITY, AGE, GENDER, and BMICTILE, account for 55% of the variation in PARTIME (R2 = 0.550). There is significance in the model (p < 0.001). ACTIVITY has a significant negative correlation (B = -3.850, p < 0.001), suggesting a decrease in screen time reported by parents and an increase in moderate aerobic activity (Watkins, 2021). The residuals show a 5.91 standard deviation and a mean that is almost equal to zero, indicating a good match.
Task 8: Presentation of Multivariate Regression
Figure 8: Presentation of Multivariate Regression
The effect of recreational screen time (CHDTIME) was significant in the unadjusted model (B = 0.968, p < 0.001). ACTIVITY had a negative impact on screen time (B = -3.850, p < 0.001) in the adjusted model, highlighting its significant contribution to a decrease in reported screen time (Purwanto, 2021). Five quarters of the variance in the dependent variable in the multiple regression model may be explained by the predictors combined. There is statistical significance in the model (p < 0.001).
Task 9: Recommendations
The majority of the 281 youngsters in the Portsmouth research were found to be less active and to use screens more frequently than was advised. Active kids used screens less frequently. There was a marginally significant correlation found between screen time and physical activity. Reduced parent-reported screen time was linked to increased physical activity when age and BMI of the child were taken into consideration (Rahman and Muktadir, 2021). Children’s activity levels and other factors might account for more than half of the variation in screen time. The utilization of parent reports for accuracy and the particular focus on Portsmouth youngsters are strengths. The limited age range and dependence on self and parent-reports are limitations. There has to be more objective measurement (Sadriddinovich, 2023). To further investigate relationships in this area, it is suggested using device monitoring and a larger, representative sample in future study. Programs and policies that support active lives and responsible technology usage may benefit from the findings.
Conclusion
This study discovered alarming patterns in Portsmouth children's excessive screen use and lack of physical activity. To further elucidate the link between these parameters, more study employing objective metrics is necessary. In the end, the results may influence policies and initiatives that encourage young people in the community to lead active lifestyles and to use technology responsibly.
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References
Journals
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Babbie, E., Wagner III, W.E. and Zaino, J., 2022. Adventures in social research: Data analysis using IBM SPSS statistics. Sage Publications.
Elsaghair, W.S. and Atilla, D.C., 2023. Analys Using SPSS 23 Software for Monitoring Internet Technology Over Mobile Communications.
Jalolov, T.S., 2023. PEDAGOGICAL-PSYCHOLOGICAL FOUNDATIONS OF DATA PROCESSING USING THE SPSS PROGRAM. INNOVATIVE DEVELOPMENTS AND RESEARCH IN EDUCATION, 2(23), pp.220-223.
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