Digital Media Use And Sleep Variability Among University Students

Methods

The article “Digital media use in the 2 h before bedtime is associated with sleep variables in university students” was published on August 28 2015. It is authored by Kathryn M. Orzech, Michael A. Grandner, Brandy M. Roane and Mary A. Carskadon. The aim of this study is to investigate the relationship between self-reported sleep patterns and digital media use in first-year university students, (Orzech, 2016). The researchers’ motivation in writing this article it to agree with the findings of many studies on the negative effects of the use of digital media on sleep outcomes and by becoming more accurate in the media activities students engage in 2 hours before sleeping.

The research is important as it will help mobile phone designers come up with devices that have the ability to lower the adverse effects digital media on sleep. The findings of this study will be helpful to university health bodies that seek to help students practice healthy lives by avoiding the use of digital media devices near bedtime, therefore, getting better quality and quantity of sleep, In addition, this research will bring to light the effects of poor quality and quantity of sleep thereby challenging university students to change. The study, therefore, makes a huge contribution in the field of Psychology, Education and Technology, (Cain, 2010). The dependent variable used by the researchers is sleep outcomes whereas the independent variable is digital media use.  The usage of digital media is what has an effect on sleep outcomes and not vice versa, (Jacobsen, 2010). The researcher’s measure by which sleep outcomes, the quantity, and quality, are affected by digital media uses such as computer work, texting, surfing the internet, visiting social networking sites, emailing and listening to music.

The research question seeks to establish how sleep might be modified by use of digital media 2 hours before sleeping time of university students. The hypothesis is that high rates of media usage, increased diversification of media usage, and use of particular digital media would be linked to bad sleep results. The research question and hypothesis have been placed under the last part of section two. I think that it would have been better to outline the question and hypothesis at the introduction of section two so as to give readers a finer logical flow from chapter one, the introduction. The argument posed by the researchers is adequate since they have broadly discussed previous studies and depicted the repercussions of digital media use on the quality and quantity of sleep.

Results

There are no gaps in the argument presented by the researchers. They have given a good argument after a proper and detailed account of the gaps they identified from other researches. For example the failure of Human-Computer-Empirical (HCI) practical studies to use sleep as a named variable, (Hershner, 2014). Kathryn M. Orzech, Michael A. Grandner, Brandy M. Roane and Mary A. Carskadon dispute the findings of experimental studies that are conducted in laboratories, as there is a high likelihood of the findings not being a correct representation of the real-world media use by a university student, (Orzech, 2016). This validates the need for this research. The call for further research by, (Tavernier, 2014) on university-aged students as contrasted with younger children and adolescents might be said to be amongst the motivation of the researchers to carry out this study. However, based on the fact that it has already been widely agreed that digital media use has adverse effects on the quality and quantity of sleep, one can easily pre-empt what the findings and conclusion of the research are likely to be (Banister, 2011).

For nine weeks, online sleep diaries were offered to 261 first year university students. The diaries questioned sleep patterns of the students. Around 57 diaries were filled per participant for the nine weeks. A voluntary digital media survey was also offered. However, this was only on nine occasions, that is, each Monday of the week. A total of two hundred and fifty-four students, 52% being female, managed to fill one survey at the minimum and a related sleep diary. Students aged 18 years and above who had filled a short survey on mood and sleep after being accepted to the University in spring were re-contacted and requested to take part in the study. This was before the commencement of classes in fall 2011. They were given instructions and taught how to access an online portal where they were expected to fill their sleep diaries daily as from September 7, 2011, to November 15, 2011, (Orzech, 2016).

This method had the limitation that participants would not consistently complete their diaries. The researchers have overcome this limitation by giving students $1 for every diary they completed and bonuses for consistently filling three and seven diaries. This served as a motivation to the participants and ensured that there was a maximum collection of data. This method used the researchers is feasible in the collection of data. The research was conducted ethically as the researchers did not coerce students in participating in the study, (Ali, 2014). By inviting students to a debriefing session at the conclusion of the study whereby students got the chance to ask questions and hold discussions, the research proved to be more ethical, (Diener, 2008). Additionally, the Lifespan Institutional Review Board assented to this study. The dependent variable used by the researchers is sleep outcomes whereas the independent variable is digital media use.

Conclusion

The dependent variable that is sleep outcomes was measured through the 15-min blocks. The researchers worked out the number of blocks in the last hour prior sleep where university students were engaged in each activity. Digital media use that was the independent variable was made up of activities in Table 1 that included; computer work, email or instant messaging, internet or game on phone, listening to audiobook, listening to music, not on list, playing video games, reading e-books, reading printed material, social networking site, surfing the internet, talking on the phone, texting on phone, watching online videos, watching TV/DVD and chatting via webcam. On completion of a sleep diary, automatically generated block times popped up for each participant. They chose either primary or secondary activity for each 15-min block and filled with the activities they were engaged in.

The sample of this research is 261 first year students for 2011. These students were over the age of 18. I fail to understand why the researchers decided to pick 1^st-year students and not 2^nd, 3^rd or 4^th years. I believe to get a better representation of university students, it would have been better to ensure the sample was made up of students from every year, for example at least 65 from each year. The study design employed by the researchers was a survey and later on an analytic study, (Denscombe, 2014). This design was suitable for answering the research question and hypothesis that were outlined in Chapter 2 of the article, (Hasson, 2010).

Main results of the study included:

1. Reading of printed material and computer work is the most regular primary activities whereas the most regular secondary activities were texting and listening to music.
2. Texting, computer work, and reading rose as the sleeping time approached.
3. University students use digital media on average, for more than an hour of the two hours before sleeping time.
4. Total Sleep Time (TST) and Bedtime (BT) were notably related to the amount of media use as well as the diversity whilst Sleep Onset Latency (SOL – how many minutes’ participants estimated it took them to fall asleep) and Wake After Onset (WASO – how many minutes participants estimated they were awake during the night) were not. Total Time in Bed (TIB) was notably linked to the variation of media use but not the amount use.

The hypothesis that high rates of media usage, increased diversification of media usage, and use of particular digital media would be linked to bad sleep results was supported by the findings of the research. However, since the total time in bed was not notably linked to the variation of media use, I believe the researchers should not have fully attributed the diversity of media use to have a negative effect on sleep outcomes. There are five tables used by the researchers in showing the results and no figures because of the nature of this study. Table 1 shows the media activity choices; Table 2 gives mean sleep values on variables of interest while Table 3 show most common primary and secondary media activities. Tables four and five in the research discussion are not so easy to understand. However, if one reads through the explanations of the tables keenly, they get a grip of what each table represents, the relationship of factors represented by the tables and how important they are.

Conclusion

The findings of this research disagree with the findings of (Tavernier, 2014) who conclude the use of media does not result to sleep problems, but instead, young adults with sleep difficulties end up spending extra time on digital media. It is difficult to compare digital media use findings of this research to others that have been published, (Mark, 2014). This is because of lack of a standard method of data collection. It is just one research involving the study of Japanese University students keeping a time diary. Nonetheless, the Japanese study used wider and different categories compared to this one, (Asaoka, 2007). In as much as the researchers agree on the negative effects of digital media use on sleep outcomes, they do not agree on the standard method of data collection, (Zimmerman, 2008). In spite of this challenge it has been established that according to the mean sleep values from the findings of this research, it indicates that this university’s students had a tendency of going to bed later (1:13 AM on average) than those from other universities – (Galambos, 2009) – (12:37 AM on average). This has been attributed to the fact that class at the University the research was conducted was not earlier than 9:00 AM. The connection established by this study between sleep outcomes and video gaming is no different compared to the findings of other studies, (Smyth, 2007).

The main strength of this research is its methodological meticulousness in the collection of long-term digital media uses and sleep outcomes as opposed to the shallow data collection methods applied by previous researchers. The research employs a good measurement of sleep outcomes and use of the media in a real-world setting. The detailed data collected has helped the researchers analyze detailed connections between the uses of digital media and sleep patterns. One of the constraints of the study is that participants who rigorously gathered the information needed might not be proper representatives of university students. Another limitation involved is the self-report kind of data researched and the likelihood that some participants in the sample were involved in the troublesome use of the internet. The study has external and internal validity though not in full. Results of the research cannot be easily generalized to other people and situations. Internal validity is achieved by the researchers, as there is a very minimal chance of confounding in the study, (Malterud, 2011). Be that as it may, I don’t think that the research is entirely reliable because, in missing to get a sample that fully represents your population under study, the risk of making an uninformed conclusion is high, (Bush, 2013).

The authors have failed to address the limitations adequately. The limitation of their choice of the 1^st year university students’ sample has only been addressed by stating that a similar study in the UK got similar findings. I believe that the researchers should have at least explained why and how they picked first-year university students and not 2^nd, 3^rd or 4^th-year students as their sample, (Maxwell, 2007). This research was not wholly appropriate for testing of the hypothesis because of the major limitation listed.  The theoretical implication is in support of the assertion that digital media use has adverse effects on sleep outcomes. The practical implication is in support of digital media designs that improve heat by alerting users to adopt a culture of not using their gadgets near sleeping time. Another practical implication is in support of university bodies that promote the health of students by helping them get better quality and quantity of sleep.

1. A collection of more details on particular digital media activities engaged in by students in the evening to determine, for example, the effects of sleep in completing sleep diaries at night.
2. Investigation of particular psychological constructs like fatigue, goal-oriented behavior and motivation
3. A design research that will determine responses of individuals to prototype gadgets that queried or recognized approaching sleeping time and modified their operation appropriately.

The study is a good one. The researchers have skilfully avoided putting a lot of focus on their overall arguments about effects of computers on human behavior. However, the researchers should be keen when typing their paper to avoid typing errors. For example, the research question on section 2.3, page 45: “How might sleep be modified by digital media use near bedtime in a University student population?” (Orzech, 2016). The repetition of the word “might” confuses a reader thereby forcing them to either continue reading without understanding the vital research question or pause for a moment and figure out the mistake made and what the researchers were trying to put across.

References

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