Critical Evaluation of a Quantitative Research Study
Introduction
Whipps et al. (2018) investigated the specific causes of poor quality and duration of sleep, noting that the presence of media devices in the bedroom is worth discussing. The research question was to describe the effect of cell phone presence in the bedroom on sleep in young adults or college students. Besides, the researchers explored the direct link between nighttime media habits and sleep hygiene. The purpose of the study was to determine the relationship between media device presence and use at nighttime and sleep patterns of first-semester college students, and if the behaviors were associated with weight gain. They hypothesized that young adults exhibiting suboptimal sleep patterns would show sleep disturbance via nighttime media usage and would be related to weight gain over the one-semester duration.
Methods
The study participants were 18-24-year-old first-semester students at the University of Wyoming, with 128 consenting. First-year seminar (FYS) instructors recruited the subjects from their classrooms. The main investigator assisted in the survey completion during the class period. The investigators undertook the anthropometric measures in an adjacent room divided by a privacy screen. Whipps et al. used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality and duration, a device that measures 7 variables associated with sleep. The combination of scores determines the global sleep quality (GSQ), with PSQI scores ranging from 0 to 21, the highest indicating lower sleep quality. Hence, a GSQ greater than 5 indicates poor sleepers while those less than 5 show good sleepers. The researchers also quantified survey responses for statistical analysis using PSQI-specific scoring procedures. Besides, the study assessed nighttime media usage (NMU) with 7 questions adapted from Adachi-Mejia et al. The survey entailed scoring items using the 5-point Likert scale responses, with 1 signifying never and 5 meaning all the time. Hence, the information was used to determine the frequency with which nighttime media disturbs sleep and the rate of media device access before bed. Moreover, the study measured anthropometric factors like height, weight, and body mass index (BMI), and waist circumference at baseline and follow-up.
The researchers computed descriptive statistics for physical characteristics and applied independent sample t-tests to identify potential gender differences. Since there were no statistically significant variances between men and women, the main variable outcome analyses were not done separately for each sex. The researchers also calculated frequency distributions for categorical scores from the PSQUI and responses to the NMU questionnaire. They used Pearson correlations to determine the extent of correlation of electronic devices’ presence in the bedroom and NMU with sleep latency, duration, quality, and efficiency. Besides, the study assessed statistical significance with a type-I error 0.5 rate. Version 23.0 of the IBM Statistical Package for Social Sciences (SPSS) helped in the statistical analyses.
Results
The results showed that between the initial and final measurements, there was an increase in the mean weight and BMI of the participants. However, the mean waist circumference reduced at a rate approximately equal to the tolerable error limit of 1cm identified in the study protocol. The sleep quantity and quality variables showed the mode time for going to bed for the students was 11:00 PM, and the most common rise time was 8:00 AM. Besides, the average self-reported duration of sleep was 7.26 +- 0.93 hours, and the mean time spent in bed was 8.12 +- 0.93 hours. The students also spent an average of 19.6 +- 16.9 minutes in bed before sleeping. Furthermore, the study showed only 33.33 percent of the participants reported meeting the sleep recommendations of at least 8 hours per night. Those who slept less than 8 hours were 25.4 percent of the subjects and spent 6.5 hours or less. The PSQI results also indicated those who scored between 1 and 5, categorized as optimal sleepers were 68, while the borderline category sleepers that scored between 6 and 7 were 26. The poor sleepers, with a score of 8 or more, were 20. Hence, 59.6 percent were optimal sleepers while 40.4 percent were either borderline or poor sleepers. The NMU outcomes indicated 105 participants had their smartphones or tablets in their rooms as they sleep frequently or all the time, while 108 used their devices as their alarm.
Besides, the participants who reported playing games in bed frequently were more likely to have a higher initial weight, post-weight, initial waist circumference, baseline BMI, and post-BMI. However, no other correlations were significant. There was also no association between NMU and any anthropometric variable change over the 8-week duration. Bivariate Pearson correlation models also showed a moderate relationship between texting after bed and the Global PSQI score (r=0.199, p= 0.04). Bed hours moderately correlated with texting in bed and device interruptions. However, social media usage and playing games in bed were weakly to moderately-associated with interruptions, and playing games had a moderate relationship to interruptions.
Discussion
The strength of the study is that it assesses the specific age group of young adult college students, an area that has not been well investigated in other studies. In other words, the results address a research gap and can be generalized to other colleges. Besides, the study assessed 7 variables associated with sleep, which presents a comprehensive analysis of the correlation between NMU and sleep behaviors. However, since the survey applies self-reported measures, the results may be inaccurate and could confound the outcomes. There is the likelihood of recall or response bias regarding the NMU and self-reported sleep. The study also did not evaluate any potential underlying clinical conditions like sleep apnea. Future research should consider assessing the directionality of the association of NMU and sleep behaviors. Therefore, a one-tailed statistical test will be appropriate to evaluate the possibility of media use in bed negatively affecting sleep patterns.
Conclusion
In conclusion, Whipps et al. described the effect of cell phone presence in the bedroom on sleep in college students. The study found that most of the participants did not meet the sleep recommendations due to NMU. Thus, the critique of the article shows that the t-test could be applied to assess the difference in the attitude of family members in their choice of body disposal. The aim is to educate family members and help them make a decision that considers cost and land conservation factors.
Reference
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.