Introduction
The research question for the study is whether nighttime media usage (NMU) is associated with disturbed sleep patterns and poor sleep quality in college students, which in turn may contribute to obesity factors. The purpose of the study seeks to evaluate the association between nighttime media use with sleep behaviors and variation in weight status for first-semester college students. The researchers hypothesize that first-semester college students with suboptimal sleep patterns demonstrate sleep disturbances caused by NMU and will be associated with weight gain for the semester (Whipps et al., 2018).
Methods
The study was a quantitative one, with the primary method of data collection being a survey. Participants were aged 18-24 and were recruited through first-year seminar courses, with the instructors and the study’s main investigator explaining the study and inviting students to participate. The sample of 128 students was recruited, all signing a consent form. Data collection was done through a comprehensive survey consisting of two parts, one evaluating sleep quality and duration while the other measuring NMU. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) that measures 7 variables related to sleep behavior, combining answers to formulate a score that determines the subject global sleep quality score (GSQ). Scores range from 0 to 21, with <5 being good sleepers and anything above resulting in poor sleep (Whipps et al., 2018).
NMU was measured through 7 questions, with 6 of the questions using a 5-point Likert scale and the last question determining the number of nights per week that NMU is present for statistical frequency purposes. Participants were also taken to a separate room, where anthropometric measures were taken in privacy with a trained professional. Measures of height, weight, body mass index (BMI), and waist circumference were recorded, with a follow-up date of approximately 10-11 weeks later. Researchers used a combination of data analysis and statistical tests. Descriptive statistics were computed for physical traits and sample t-tests were used to determine differences between genders. Frequency distributions were used for categorical scores for the PSQI evaluation and the NMU questionnaire. Pearson correlations helped to determine the extent of electronic devices used in bed and correlate NMU to categories on the PSQI such as sleep latency, duration, quality, and effectiveness. The type-I error rate with.05 variance was used for the determination of statistical significance. All statistical analyses were performed on the SPSS software (Whipps et al., 2018).
Results
The mean age of the participants was 18.8 ±0.5, and 93% were of the Caucasian ethnicity, 55% being female. Only a third of the participants slept the recommended 8 hours per night, while 25.4% slept 6.5 hours or less. Based on the PSQI scale, only 59.6% of the participants were optimal sleepers, while 40.4% were borderline or poor sleepers. On average, the change in weight of the participants increased 0.6 kg ±1.92 and a BMI increase of 0.1 kg/m2 ± 5.74. The NMU questionnaire indicates that 92.1% have electronic devices in the room when sleeping, and 94.7% use them as alarms. An analysis of anthropometric measures and NMU responses suggests that those playing games in bed would have higher initial and post-study weight. Other factors did not offer statistically significant changes. However texting, social media uses, and other device interruptions were moderately correlated with PSQI scores and bed hours or sleep interruptions (Whipps et al., 2018).
Discussion
Overall, the study efficacy can be considered of above-average validity but some elements can be improved upon in future research to increase validity of the results. Some strengths of the study include using established tools such as the PSQI index and BMI for anthropometric measures. It was interesting that the researchers chose to add the anthropometric measure as an additional variable to sleep quality which is commonly the primary element of these types of studies regarding electronic device usage. As described at the beginning of the study, obesity is a critical social issue in the United States, becoming prominent in young people as well, and affected partially by sleep quality and associated behaviors. Although the researchers note that physical activity and diets were not accounted for, the study showed stability in terms of weight over the study period. The directionality of the positive relationship between NMU and weight status could not be established due to the cross-sectional study design (Whipps et al., 2018). The study could have potentially included a chi-square test, which is one of the most common statistical tests for qualitative data, determining the frequency distribution of events to test the null hypothesis. Potentially the researchers could have also done a thematic analysis based on type of activity in a clearer manner coding clear data sets on which additional tests could be performed. The correspondence analysis technique is useful to explore relationships between categorical variables such as sleep quality and BMI in this study.
As reported by Whipps et al. (2018) in the study itself, a strong limitation of this study was the element of self-reported behaviors for both sleep and NMU which can create recall or response bias. Sleep questionnaires and diaries are considered subjective forms of sleep assessment which are the least accurate methods of tracking sleep. Subjective methods have a sensitivity of 73-97.7% and specificity range interval of 50-96% in comparison to objective methods such actigraphy or polysomnography have higher than 90% sensibility (Ibáñez et al., 2018). Perhaps this could be a direction for future research, where the association between NMU and sleep quality can be more carefully observed. It may require a quantitative study, but NMU can be observed through applications tracking device usage, while sleep quality can be tracked in domestic settings through contact devices (i.e. fitness bands or watches that track sleep accurately). As mentioned earlier, physical activity can be tracked using those same devices with self-reporting of dietary habits. Including a wider age range, potentially older college students as well maybe viable since the focus of the study applies to the majority of young people in college.
Conclusion
The study focused on determining how nighttime media usage affected quality of sleep and whether it had an effect on BMI. The research was done through a self-reported questionnaire on first-semester college students. Some positive correlation was found between NMU and sleep quality, with minor but not statistically relevant increases in BMI. Therefore, the causal effect between the variables could not be fully proven due to some limitations of the study being qualitative and using self-reported data. However, researchers took all steps to ensure validity and this study lays the groundwork for future research. One statistical test used was the Pearson correlation coefficient used to measure strength of association between two variables. It is a popular statistical test used in practically every industry and can be helpful in social sciences by statistically establishing relationships between the studied variables. This can be key in proving a hypothesis and ensuring validity of the research. The ability to analyze and critique studies as was done in this paper is important for any professional and research. This skill is necessary to distinguish valid and competent studies from those that are inaccurate or demonstrate qualitative fallacies or quantitative statistical errors. Since research is used to drive decision-making in many industries, the ability to critique quantitative research is vital in establishing a foundation of facts and data to support one’s actions.
References
Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep assessment methods. PeerJ, 6, e4849. Web.
Whipps, J., Byra, M., Gerow, K. G., & Guseman, E. H. (2018). Evaluation of nighttime media use and sleep patterns in first-semester college students. American Journal of Health Behavior, 42(3), 47-55.