Introduction
In their article, Johansson et al. (2016) attempt to explore the impact of adolescents’ widespread use of technology before bedtime on their daily functioning. The title chosen by the authors is “Adolescent Sleep and the Impact of Technology Use before Sleep on Daytime Function”, which accurately reflects the essence of the article. The abstract contains a clear structure, containing a purpose, design, and method, results, conclusion, and practice implications sections.
The readers understand that the key problem discussed in the given research paper is to examine the role of technology use before sleeping on the daytime function of teenagers. The first sentence of the abstract situates the article within the context of the problem, stating that the rapid development of devices may pose previously undiscovered threats. While the main advantages of the abstract include its appropriate structure, important details on the study design, and practical implications, its drawbacks refer to a lack of a graphical abstract and a substantial interpretation of the results obtained.
Purpose Statement and Literature Analysis
The opening part of the article begins with the introduction of the context, including alarming statistics that adolescents need 8-10 hours of night sleep, while they tend to have seven hours or even less. Johansson et al. (2016) note a range of factors that may affect sleep patterns, such as biological processes, the desire to remain connected with their peers via the Internet, playing games, and psychological issues. Accordingly, one may definitely claim that the problem statement is clear and relevant, while the niche to be explored is also identified. In particular, the authors mention that the quality of daytime function that follows bedtime screen use is not yet clear.
The culmination of the introduction contains some information on the purpose of the article. The authors state that they will research the outcomes of using devices by adolescents for one hour before sleep, focusing on subsequent daytime function and comparing it with that of those who report adequate or inadequate sleep. Therefore, they aim to investigate how the use of computers, smartphones, reading devices, and television affects refreshment, wake time, and daytime sleepiness.
The article’s conceptual model seeks to establish a link between technology before bedtime, sleep quality, and daytime function in teenagers. By understanding these relationships, the authors aim to provide evidence-based recommendations for promoting healthy sleep habits and mitigating the adverse effects of technology on adolescents’ overall well-being. To support their literature review, Johansson et al. (2016) draw on substantial evidence from other research articles. Readers can easily access more than 20 articles published in scholarly journals within the last decade, as all relevant information is provided in the references section.
The in-text citations correctly follow the American Psychological Association (APA) guidelines on referencing. However, speaking about the gaps, one should note that it would be better to present more statistics, so that the readers may verify their statements. Also, the authors fail to present the key variables and concepts used in their study, while the readers find no hypotheses to be tested. For example, it is not clear what is meant by ‘adequate’ and ‘inadequate’ sleep. Nevertheless, the critical review of this part identified no controversies or inconsistencies.
Methodology
This study by Johansson et al. (2016) is a secondary analysis of data collected from the 2011 National Sleep Foundation’s Sleep in America Poll. In other words, the sample for the study was fully selected from the mentioned source, which ensures substantial data from respondents, but limits the authors’ ability to verify the collected information and minimize errors. To gather the necessary data, the mentioned poll used the Internet surveys and telephone calls.
The sample consists of 259 respondents (52.2% males and 46.3% females) aged between 13 and 21 years, which represents the population of interest. The majority of the participants are Caucasian students, while American Asians, Hispanics, and Africans comprise the minority groups. Among the interviewed persons, there are employed, students, or employed students. The discussed study design appears to be appropriate and consistent with the study’s purpose.
Validity implies the accuracy and effectiveness of the measurements and findings to evaluate the intended phenomenon (Sürücü & Maslakci, 2020). Since it is a cross-sectional research, it offers a snapshot of technology usage and sleep habits at a particular point in time. However, it does not establish causalities between technology use and sleep quality, which reduces its internal validity.
Several experimental studies are needed to establish the chronological links and strengthen the internal validity of the findings. As for external validity, the method of data collection from the nationally representative survey increases the generalizability of the findings. Instrument validity is appropriate since a panel of sleep experts has worked on the survey instruments, which helps to ensure that their content properly measures the intended constructs related to sleep patterns and technology use in the bedroom by adolescents.
The concept of reliability implies the consistency and solidity of the measurements or findings (Sürücü & Maslakci, 2020). In the given case, the reliability of the study can be evaluated by examining the methodology and data collection strategies used. The study utilized random digit dialing for the telephone surveys, while the Internet surveys were distributed to members of an e-rewards panel. These approaches help confirm a diverse and representative sample of participants. Additionally, the use of the nationally representative sample of 255 respondents aged 13-21 years enhances the reliability of the findings. The survey information was collected through structured questionnaires developed by sleep experts, which further supports the reliability of the study.
When discussing the data analysis plan, it is crucial to highlight that the study’s extraneous variables include participants’ age, socioeconomic status, physical activity, and mental health, as all of these factors may impact the results. The authors also note that a sampling error for their subsample is ± 7.5% points, which means that the confidence interval is 95% (Johansson et al., 2016). In other words, they tried to minimize the role of extraneous factors by providing an adequate confidence interval. The independent variable chosen by the authors is technology use before sleep, which is measured through self-reported data on the duration and frequency of using devices, as well as the types of activities engaged in.
The dependent variable is daytime function, focusing on cognitive performance, academic achievement, mood, and overall well-being, which is measured based on the Epworth Sleepiness Scale (ESS). The use of the above analysis method, along with follow-up questions, seems to ensure the appropriateness of understanding the links between the collected data volumes. Statistical tests such as t-tests, Mann-Whitney U, and Fisher’s exact test were conducted to analyze the data. The authors clearly explain their decision to select these statistical techniques, citing that they have been confirmed as useful in various academic studies and are relevant to the study’s aim.
It is crucial to consider the study’s limitations when evaluating its methodology. The study relies on self-reported data, which may be subject to a social desirability bias or recall bias. Additionally, the article’s findings are based on a single survey conducted in 2011; both technology use and sleep patterns may have changed in the years since then. Therefore, the generalizability and relevance of the findings to the modern technological setting should be considered.
Therefore, while the study demonstrates reasonable reliability and validity through its representative sampling and rigorous methodology, it is essential to identify further research needs to strengthen the validity of the findings in the context of contemporary device use. It would be beneficial to conduct longitudinal studies to examine the long-term impact of technology use on sleep patterns and daytime function in adolescents.
The survey instrument encompassed a combination of questions regarding demographics, sleep patterns, and the use of technology in the hour before bedtime. However, there is no description of the materials and equipment used; perhaps because the participants did not require anything. The study replication is available since the authors thoroughly present the procedures they conducted.
Regarding the protection of human subjects, the study notes that the University of Pittsburgh’s Institutional Review Board has sanctioned this secondary research (Johansson et al., 2016). No information regarding the informed consent is provided, but it was probably mentioned in the initial study. Correspondingly, the readers have no awareness of any additional protection of the respondents’ ethical rights.
Results and Their Presentation
The results reveal that adolescents had an average sleep duration of 7.3 ± 1.3 hours. The authors also report that the use of technology increases with the age of the participants. Almost all respondents (97%) reported using one or more forms of technology before going to sleep. The study discovered a significant association between increased technology use and the frequency of being woken up in the middle of the night by a cell phone, waking too early, feeling unrefreshed upon waking, and daytime sleepiness (p < 0.05). Moreover, adolescents who reported inadequate sleep had a higher frequency of technology use before bedtime, shorter sleep duration, and greater daytime sleepiness compared to those who reported adequate sleep (p < 0.05).
Johansson et al. (2016) accurately structure their results, presenting detailed information in tables. The first table compares the demographic and clinical characteristics of participants, while the second table provides a review of daytime function issues. The use of headings and statistical data not only enhances the clarity of the results but also increases their reliability.
For instance, the authors provide the p-value, which points to the fact that the findings are significant, and the null hypothesis can be rejected. Additionally, the authors mention the r-value correlation, which indicates the relationship between two variables (Kent, 2020). For example, it was identified that the daytime sleepiness of the respondents decreases with age (r = −0.16, p < 0.05). Nevertheless, the authors did not provide an F-value to present their findings.
Authors’ Claims
The results properly support the discussion section and present potential practical implications. The research question posed is fully addressed, as the authors found a link between bedtime device use and subsequent daytime function issues. It is worth noting that this article aligns with the existing literature, highlighting the detrimental effects of technology use on adolescent sleep (Kent, 2020). Moreover, this research moves the scientific knowledge further by claiming practical steps to address the target problem. Healthcare professionals, educators, and parents should limit technology use before bedtime and encourage a conducive sleep environment.
Among the limitations clarified by the authors are a recall bias, the cross-sectional nature of the study, and a lack of device evaluation. It is essential to note that the findings of this study were based on data from 2011, and since then, technology use patterns and behaviors among adolescents may have evolved. Thus, future research should continue to monitor and explore the impact of emerging technologies on adolescent sleep and daytime function.
Furthermore, the article does not delve deeply into the reasons behind the association between pre-sleep technology use and its negative consequences on sleep and daytime function. Future research could investigate the specific mechanisms through which technology affects sleep, such as the influence of blue light exposure or psychological factors related to technology use.
Conclusion
The authors of the study are well-known figures in the given area of research, as Eileen R. Chasens holds a PhD degree, while Ann E.E. Johansson and Maria A. Petrisko hold a BSN degree. The article’s impact factor is 43, which indicates its moderate importance within the field. As for practical use of knowledge obtained from this study, healthcare professionals who interact with adolescents should be aware of the impact of technology on sleep and consider providing education and interventions to promote healthy sleep habits.
References
Johansson, A. E., Petrisko, M. A., & Chasens, E. R. (2016). Adolescent sleep and the impact of technology use before sleep on daytime function. Journal of Pediatric Nursing, 31(5), 498–504.
Kent, R. (2020). Data construction and data analysis for survey research. Bloomsbury Publishing.
Sürücü, L., & Maslakci, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), 2694–2726.