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Phone Use and the Quality of Sleep in Young Adults


Sleep is one of the fundamental prerequisites for meeting the biological and psychological needs of a person. The quality of sleep is largely contingent on a person’s lifestyle habits. For many people, the rise of the digital era means increased use of gadgets such as smartphones in everyday life and a surge in daily screen time. The healthiness of this new habit is raising concerns: potentially, it is disruptive to good night sleep and has other negative effects on mental and physical health.

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Fossum et al. (2014) show that the use of electronic media before bed has a negative effect on sleep quality. The mean screen time reported by participants between the ages of 18 and 49 was 46.6 minutes. Both phone and computer use for gaming, surfing, and reading has been found to be predictors of insomnia. Moreover, the use of electronic media has the potential of changing a person’s circadian rhythms and chronotype. According to Fossum et al. (2014), those using gadgets before bed were less prone to morningness (being active in the first half of the day).

At present, there are studies that are more concentrated on the effects of phone use as opposed to all other types of gadgets. Akca, Senturk, and Alsac (2019) describe the quality of sleep in 1040 randomly selected undergraduate students in Turkey and conclude that the majority of them (roughly 70%) had sleeping problems. Frequent phone use before bedtime was found to be a significant contributing factor to the decreased quality of sleep, as it explained 7% of the variance in the dependent variable.

Nathan and Zeitzer (2017) provide evidence for a positive relationship between mobile phone use and daytime sleepiness in California high school students. According to Nathan and Zeitzer (2017), the rising popularity and ubiquity of phone use in teenagers are parallel to a rapid decline in the amount of sleep that they are getting during the night. Interestingly enough, the number of texts and calls during the day has not been found to be associated with daytime sleepiness. Multivariate regression analysis has demonstrated that being female, feeling a need to be accessible by phone at all times, and past attempts to reduce mobile phone use were predictors of high scores on the Epworth Sleepiness Scale (ESS). Apart from that, Nathan and Zeitzer (2017) theorize that staying up late because of the phone as well as being awakened during the night by calls and texts contribute to daytime sleepiness.

However, it is not only adolescents that should be wary of the dangers of excessive phone use before bedtime. Exelmans and Bulck (2016) show that phone use before sleep is associated with lower scores on the Pittsburgh Sleep Quality Index (PSQI) that as follows from the title, assesses sleep quality. The activities that have been found to be the most disruptive to goodnight sleep were making calls and sending messages after the lights had been out (Exelmans & Bulck, 2016). Despite the generally negative effect of phone use on sleep quality, different age groups experienced different issues. Exelmans and Bulck (2016) write that the participants under the age of forty suffered from fatigue and later rises while older participants (< 94 years old) were waking up earlier than usual.

Now that it has been established that phone use has a negative impact on sleep quality, it is compelling to pinpoint what implications for key life outcomes this relationship has. A study conducted in South Korea, a country with a particularly large smartphone penetration (88% of the population are owners), by Choi (2017) linked phone use to school performance. Just like Akca, Senturk, and Alsac (2019), Choi (2017) was studying young adults whose bodies are still developing. For this reason, controlling the quality of sleep at this stage of life is especially important, as a better quality might be linked to better outcomes. Choi (2017) suggests that learning engagement in students depends on a multitude of factors with phone use before bedtime being one of them. The researcher has found that phone use before bed, little to no physical activity, and dissatisfaction with the chosen major (nursing in this study) were negatively correlated with academic success.

The question arises as to exactly what about phone usage is especially disruptive to sleep quality. Lowden et al. (2019) reveal the results of a double-blind study involving radiofrequency exposure to 1,930–1,990 MHz, UMTS 3G signaling standard, time‐averaged 10 g specific absorption rate of 1.6 W kg−1. Eighteen young participants aged 18-19 underwent supervised three-hour sessions on two consecutive days between 7 and 11 pm. After the exposure, the participants had a full-night, 7.5-hour sleep in a sleep laboratory (Lowden et al., 2019). The data analysis has shown that the exposure is likely to cause changes in the so-called sleep spindles. All the functions fulfilled by sleep spindles have yet to be established by science. However, at the moment, it is known that they are responsible for the retention of new information consolidation of memories. Drawing on this evidence, it is readily imaginable how exposure to 3G signaling can also impact academic performance that is largely relying on the ability to process new information.

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Moreover, recent studies suggest that quality sleep that is predicated on controlled mobile phone use is critical for maintaining good mental health. In their study, Thomee, Harenstam, and Hagberg (2011) sought to pinpoint an association between mobile phone use and stress, sleep disturbances, and symptoms of depression in young adults. Thomee et al. (2011) operationalized phone use in both qualitative (example: the need to be accessible) and quantitative terms (actual screen time). The researchers discovered that at a one-year follow-up, those participants who had been using mobile phones frequently scored suffered from more sleep disturbances and depressive symptoms. The group that proved to be at the most risk were individuals who were feeling stressed about having to be accessible via mobile phone at all times.

The question arises whether the opposite – that positive changes in lifestyle in terms of mobile phone use will correlate with better sleep quality – is true. He, Tu, Xiao, Su, and Tang (2020) provide evidence that confirms this hypothesis. In their randomized control trial, He et al. (2020) measured the effect of restricting bedtime mobile phone use. The participants in the intervention group enjoyed reduced sleep latency and pre-sleep arousal. Apart from that, they were able to sleep longer and memorize new information with more efficiency (He et al., 2020). The study suggests that such a simple change can have a profound positive effect on a person’s wellbeing.

Proposed Method

The main purpose of the current study is to understand smartphone use before bedtime among young adults and to pinpoint the relationship between phone use and the amount of sleep. The null hypothesis for this study is that the amount of sleep that a person receives during the night will not be affected by their phone use before bedtime. The alternative hypothesis, however, is that excessive phone use before sleep negatively impacts sleep duration.


College undergraduate and graduate students taking an introductory psychology course will be recruited for this study. The inclusion criteria will be current enrolment in an under-or graduate program and age over eighteen. The only exclusion criteria were as follows:

  1. self-reported severe mental and physical health conditions such as depression, schizophrenia, cardiovascular diseases, chronic, recurrent respiratory conditions, and active cancer that directly impact the quality of sleep;
  2. self-reported sleep disorders such as obstructive sleep apnea-hypopnea syndrome;
  3. use of any form of sleep assistance such as sleep medication, devices, and hypnosis;
  4. excessive caffeine (>400 mg) and alcohol use (> three cups) during the day;
  5. night shift workers;
  6. individuals being currently pregnant or lactating;
  7. individuals with self-reported daytime sleepiness that requires them to take frequent and lengthy (>90 minutes) naps during the day.

The exclusion criteria with this level of detail have been established to improve the precision of results by removing factors that could potentially skew them. This study will need approximately 80 students with gender and racial representation close to equal. The participation is voluntary; however, the study employs a reward system according to which the participating students will receive extra credit. The study design will be compliant with the ethical committee’s guidelines. All potential candidates will receive a detailed explanation regarding the purpose and the course of the study and sign consent forms if they agree to be a part of it.


The current study will employ a quantitative design with one independent and one dependent variable. The independent variable is phone use before bedtime. The dependent variable is the amount of sleep that a person is getting. Phone use before bedtime is operationalized as minutes of phone use four hours before the usual bedtime. The dependent variable is hours of sleep during the night. It should be noted that daytime naps are excluded from the total count.


All participants will be asked to install the Screen Time application on their smartphones to measure their phone use. Since this study focuses on phone use before bed, the participants will be asked to only use the app four hours before their usual bedtime. If some participants are not able to install the app on their mobile phones for some reason, they will be offered an alternative way to measure their exposure. To understand the phone use habits in young adults, the study will be taking place for a week, spanning both weekdays and weekends. This will be done on the premise that phone use habits might differ depending on the day of the week. The dependent variable, the amount of sleep, will be assessed via whether a fitness bracelet with a sleep tracking function or per participants’ self-reports.

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For each participant, the average screen time before bed and average sleep duration will be calculated. The study will also compute other descriptive statistics such as median and quartiles. The association between phone use before bedtime and amount of sleep will be calculated using simple linear regression with the level of significance at p < 0.05.

Proposed Results

 Relationship between phone use before bedtime and the amount of sleep
Graph 1. Relationship between phone use before bedtime and the amount of sleep

As seen from Graph 1, there is a negative correlation between phone use before bedtime and the amount of sleep a person is getting (p < 0.05). In other words, the more time a person spends surfing the Internet or reading from their phone before going to sleep, the less they are likely to sleep at night. The linear regression model explains up to 22% of the variation in the dependent variable (amount of sleep) (R2 = 0.217).

Mean 77.24489796
Standard Error 6.104429264
Median 80
Mode 100
Standard Deviation 42.73100485
Minimum 10
Maximum 180

Table 1. Descriptive statistics of phone use before bedtime

Table 1 demonstrates the descriptive statistics of phone use before bedtime. As seen from Table 1, the average time that participants were spending on their phone before going to bed was 77 minutes, or 1 hour and 17 minutes. The median of the array almost equals its average: 80 minutes or one hour and twenty minutes. The smallest amount of time that participants were spending using electronic gadgets was 10 minutes and the largest – 180 minutes or three hours.

Mean 392.1836735
Standard Error 11.88420869
Median 400
Mode 420
Standard Deviation 83.18946084
Minimum 240
Maximum 600

Table 2. Descriptive statistics for the amount of nighttime sleep

Table 2 contains the descriptive statistics for the amount of nighttime sleep. As seen from Table 2, the average sleep duration was 392 minutes, or six hours and 32 minutes. The median of the array almost equals its average: 400 minutes or six-hour and forty minutes, which shows the absence of significant outliers. The smallest sleep duration observed in participants was four hours and the largest – ten hours.


The results of this study confirmed the alternative hypothesis that phone use before bedtime does harm the amount of sleep that phone users are getting during the night. The study results are significant due to the confidence level of p < 0.05 and R2 = 0.217, those explaining up to 22% of the variation in the dependent value. What this research has revealed is consistent with previous research on this topic. As mentioned in the literature review, an ample body of evidence suggests that excessive phone use is detrimental to sleep quality. Thus, this study contributes to the growing body of research on the effects of phone use, especially in young adults.

For all its advantages, the current study is not devoid of limitations. Firstly, the proposed procedure of the study relies on self-reporting. Since there is no possibility to put participants in a sleeping laboratory, it is difficult to estimate the precision of their self-observations and measurements. Moreover, some of them do not have gadgets for more precise measurements, which means that the results are likely to be contingent on their perception. The second limitation comes from rather small sample size and its homogeneity: while it has males and females in an almost 1:1 proportion, it is not representative of all races. Because of that, the results cannot be inferred from broader populations. On top of that, the data analysis did not control for other factors that might have been contributing to sleep quality such as physical activity and fitness.

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Subsequent studies in this domain should tackle the limitations of the current one. There needs to be research that recruits a greater number of young adults that are more racially and ethnically diverse. Aside from that, it would be compelling to understand which types of phone use are the most disruptive to sleep quality. For instance, future research could try to find differences between texting, calling, surfing, and reading in their potential to disrupt normal sleep patterns.


  1. Akca, N. K., PhD., Senturk, S., PhD., & Alsac, S. Y., PhD. (2019). The effect of cell phone use in adolescents on sleep qualıty: Central anatolia case. International Journal of Caring Sciences, 12(3), 1752-1760.
  2. Choi, S. (2019). Relationships between Smartphone Usage, Sleep Patterns, and Nursing Students’ Learning Engagement. Journal of Korean Biological Nursing Science, 21(4), 231–238.
  3. Exelmans, L., & Van den Bulck, J. (2016). Bedtime mobile phone use and sleep in adults. Social Science & Medicine, 148, 93–101.
  4. Fossum, I. N., Nordnes, L. T., Storemark, S. S., Bjorvatn, B., & Pallesen, S. (2014). The association between use of electronic media in bed before going to sleep and insomnia symptoms, daytime sleepiness, morningness, and chronotype. Behavioral Sleep Medicine, 12(5), 343–357.
  5. He, J., Tu, Z., Xiao, L., Su, T., & Tang, Y. (2020). Effect of restricting bedtime mobile phone use on sleep, arousal, mood, and working memory: A randomized pilot trial. PLoS ONE, 15(2), 1–13.
  6. Lowden, A., Nagai, R., Åkerstedt, T., Hansson Mild, K., & Hillert, L. (2019). Effects of evening exposure to electromagnetic fields emitted by 3G mobile phones on health and night sleep EEG architecture. Journal of Sleep Research, 28(4), N.PAG.
  7. Nathan, N., & Zeitzer, J. (2013). A survey study of the association between mobile phone use and daytime sleepiness in California high school students. BMC Public Health, 13(1), 1–5.
  8. Thomee, S., Härenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults – a prospective cohort study. BMC Public Health, 11(1), 66–76.

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