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Relationships between Sleep, Screen Time, and Academic Performance in Australian University Students Using Demographic questionnaires and Pittsburgh Sleep Quality Index (Lab Report Sample)


Relationships between Sleep, Screen Time, and Academic Performance in Australian University Students Using Demographic questionnaires and Pittsburgh Sleep Quality Index. Instructions were to prepare a report using the provided results


Relationships between Sleep, Screen Time, and Academic Performance in Australian University Students Using Demographic questionnaires and Pittsburgh Sleep Quality Index
Word count: 1829
Excessive screen time is associated with various psychological implications such as obesity as well as deterioration of academic performance. The current study examines the correlation between sleep, screen time and academic performance among Australian university students. The hypothesis being researched is, in addition to the three variables, economic empowerment also contributed to differences in the time dedicated to sleep and screen time among the university students who participated in the study. The author proposes that participants who had more screen time had broad access to screen media thus increasing their susceptibility to poor sleep quality. The researcher deploys a demographic questionnaire and the Pittsburgh Sleep Quality Index (PISQ) to answer the formulated hypothesis. The participants included Australian university students who were recruited via several techniques ranging from social media, emails among others to be elaborated in the methodology section. The obtained data was analyzed using IBM SPSS version 23 to get correlations among the three variables.
Sleep, Screen time, Academic performance, SPSS, PISQ, Demographic questionnaire
In reference to Magee, Lee, and Vella (2014), the duration of sleep and time allocated to using computers or television viewing has been associated with major impacts on health as well as the well-being of children. For instance, the researchers identified bidirectional relationships when shorter sleep patterns were coupled with excessive screen time. Sleep duration and screen time were predictive factors for poor academic performance, behavioral problems and health concerns such as obesity.
Increased exposure to screens elevated sleep problems through which children were left to deal with shorter sleep patterns in addition to night walks as well as disturbed sleep. According to the displacement hypothesis presented by Magee, Lee, and Vella (2014) increased screen time limits the duration set for sleep thus interfering with the circadian rhythms while promoting physiological arousal. Mizuno et al. (2011), on the other hand, argued that the lack of motivation amongst adolescents to involve in physical activities during day time may result from the absence of adequate sleep leading to inactivity and tiredness.
In a survey carried out by Adachi-Mejia et al. (2007), it was reported that children who had Television sets in their bedrooms had a greater risk of being overweight. In the study, 22.3% were overweight children who had TV sets in their room watching a movie an hour daily before sleep. Besides, this also increased the inability of these kids to indulge in physical activities or participation in sports. The use of screen media, socio-demographic factors, and sleep, had interrelations with the academic performance of adolescents, but this association remains under-researched.
With regards to Bowers and Berland (2013), teens who had higher grades were associated fewer times with watching sedentary Television media and had more sleep time. Use of screen media has intensified in this generation both in the lifestyles of adolescents and children and is associated with potential health consequences. Peiro-Velert et al. (2014) reported that screen-related behaviors were responsible for energy imbalances that increase the risk of obesity among other non-communicable diseases such as heart conditions.
According to Bowers and Berland (2013), Sleep had adverse effects on the level of academic performance. Besides, age, gender, as well as socioeconomic status, were related to the degree of academic performance. Screen media use has been linked to insufficient sleep time due to shorter sleep times or delayed bedtime. In conclusion, Dworak et al. (2007) reported that sedentary screen media affected children’s sleep as well as their memory thus affecting learning. Television watching and computer games were also associated with deterioration of verbal cognitive performance.
The objective of this study is to include Australian university students and to examine the correlation between screen time, sleep, and level of academic performance. A total of 183 participants were involved. There were 145 female participants and 38 males with 160 single and 23 married. The mean age was 21.7 years with the minimum age at 18 years and a maximum age of 48 years. A total of 127 were employed whereas the unemployed were 51. The participants were given a consent form to understand the ethical issues surrounding the study. The Ethic approval was obtained from the RMIT University for the data collected from the field with regards to the three variables being analyzed.
The materials used in the study include the demographic questionnaire and the Pittsburg Sleep Quality Index. In the latter instrument, the feedback was self-reported aimed at examining the time spent by Australian University students in screen time and the impact it had on their academic performance. The PSQI index used in the study comprises of 9 questions, 19 items for the participant to self-report sleep quality and disturbance over the study period. In reference to Benham (2010), the index has several components including sleep latency, sleep duration, use of medication, daytime dysfunction, sleep quality and sleep efficiency.
Screen time in this case involved computers, video games, and television. Besides, information regarding employment status was also collected to test the hypothesis. IBM SPSS version 23 was the instrument deployed in analyzing the feedback obtained from the data collection instruments used.
Data was collected from 4th April to 20th April 2016. The study participants, who are Australian University students were recruited through avenues such as emails, social media, snowballing techniques as well as through the blackboard. The online survey was hosted by a website, Qualtrics. The consent form was presented first after collecting the participant information which was followed by a demographic questionnaire and the PSQI. In this study, the researchers assumed that submission of the survey indicated consent. In conclusion, the data was analyzed using IBM SPSS version 23. The study results are tabulated in the section below.
Table  SEQ Table \* ARABIC 1
Data collected from the Australian University students
Information MSDRange Frequency (%) Males38 (20.80 Females145 (79.2) Age21.704.96 18-48 Single 160 (87.4)Married/De facto 23 (12.6)Full Time168 (91.8)Part Time11 (6.0)Employment127 (69.4)Unemployed51 (27.9)Grade Average76.487.9450-98Screen time hours/day6.942.92Screen time before bedtime1.961.57Times woken up to use other devices1.130.44Times woken up to check phone1.400.91Woken up by Phone1.270.87
Table  SEQ Table \* ARABIC 2
Night Screen TimeWoken to check mobileWoken to check other deviceWoken by Mobile GPASleep Quality (PSQI).18
.001 .22
.32Night Screen time before bedtime-.15
.025Woken to check mobile during the night-.15
.025Woken to check other device during the night -.11
.07Woken by mobile during the night-0.9
The mean age of the Australian university students was 21.7 years with a majority 91% of the participants having been enrolled as full-time students. 6.0% of the population was made up of part-time students. The employment rate among the participants was 69.4% and 27% without any jobs. The grade average was found to be 76.48 with a standard deviation of 7.94. The range of this category was 50-98. The average hours spent on screen time on a daily basis was 6.94 with a standard deviation of 2.92 hours.
The report also found the average screen time before bed was 1.96 hours with a standard deviation of 1.57 hours. The average times woken up to use other devices was identified to be 1.13 with a standard deviation of 0.44. The times woken up to check the phone had an average of 1.40, SD 0.91. The participants had an average 1.13 hours as time woken up to use other devices. On other occasions, an average 1.27 hours were spent as time woken up by phone.
There were correlations in the three variables being investigated in the study. Sleep quality was affected by the night screen time. Sleep quality had negative implications on the GPA (-0.4/.32) hence explaining the adverse effects resulting from inadequate sleep time. Night screen time before bedtime also had adverse consequences on the GPA (-.15/.025). The findings support the formulated hypothesis where the relationship between screen time, sleep, and academic performance is indicated through correlations identified after the analysis of the data.
In reference to Benham (2010), screen time and sleep duration had implications for the well-being of children. In the current study, the results also provide the hours of sleep interruption which have contributed to an enormous impact on sleep quality. According to Barber (2014), self-control is considered as a process through which individuals can adjust their feelings, thoughts and behaviors to attain a given objective. Sleep was thus a key channel through which individuals can restore self-control.
Magee, Lee, and Vella (2014) reported an increase in the average screen time from 1999 to 2009, 6.21 hours to 7.38 hours respectively. Children in 2011 were also estimated to spend fewer hours asleep while in the current study; it is evident there are alarming figures ...
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