Title: The Effects of Mobile Phone Usage Before Bedtime on Sleep Quality
Abstract
Mobile phone usage before bed has become a prevalent sleep-disrupting habit, especially among adolescents and young adults. Existing research has shown that exposure to blue light emitted from screens can suppress melatonin production and disrupt circadian rhythms. More research is needed to better understand the impact of specific mobile phone activities like social media, texting, and web browsing on sleep onset latency, duration and quality. This proposed study aims to investigate how different types of mobile phone usage in the hour before bedtime relate to self-reported and objectively measured sleep outcomes. Undergraduate students (N = 120) will be recruited to participate in a two-week study utilizing sleep diaries, actigraphy monitoring, and questionnaires. Findings will provide insight into how specific mobile phone activities most strongly undermine sleep and inform the development of targeted interventions to improve sleep hygiene practices related to technology use before bed.
Introduction
In modern society, mobile phone usage has become an integral part of daily life. The widespread adoption of mobile technology has introduced new potential disruptors to sleep, as phones are increasingly used in bedrooms and even while in bed (Gradisar et al., 2013). Existing research indicates that exposure to the blue light emitted from screens can suppress melatonin production and disrupt circadian rhythms that regulate sleep-wake cycles (Cain & Burris, 2013; Chang et al., 2015). This is problematic as melatonin facilitates sleep onset and quality (Rains et al., 2019). Meta-analyses have found small but significant associations between screen time before bed and longer sleep onset latency, poorer sleep efficiency, shorter duration, and more daytime impairment (van der Lely et al., 2015; Hale & Guan, 2015; Gamble et al., 2014).
While mobile phone usage before bed has been clearly linked to poorer sleep outcomes, existing research lacks specificity regarding which mobile phone activities pose the greatest risk. Common pre-bedtime phone activities like browsing social media, texting friends, watching videos, and web surfing potentially differ in levels of cognitive and emotional arousal they elicit. Higher arousal immediately before sleep has been shown to undermine sleep quality (Sung et al., 2016). Therefore, the impact of specific phone activities on sleep may differ based on their arousing effects. Understanding these differential relationships could help develop more targeted recommendations regarding limiting certain phone behaviors in the hours before bed to promote better sleep.
Additionally, few studies have utilized both subjective and objective measures of sleep. Most rely on self-report sleep diaries or questionnaires alone, which are subject to recall bias and estimation errors (Lauderdale et al., 2008). Wrist actigraphy allows for a more objective assessment of sleep metrics like sleep onset latency, wake after sleep onset, sleep efficiency and total sleep time compared to estimates (Marino et al., 2013). Incorporating both types of measures provides a more comprehensive understanding of how mobile phone usage influences sleep.
This study aims to address gaps in the current literature by investigating relationships between specific mobile phone activities in the hour before bed and both self-reported and objectively measured sleep outcomes. Findings will clarify which pre-bedtime phone behaviors pose the greatest risk to sleep and have implications for developing targeted interventions to improve sleep hygiene related to technology use.
Method
Participants and Design
This proposed study will utilize a within-subjects correlational design. Approximately 120 undergraduate students aged 18-25 years will be recruited from a large public university to participate. The sample will be predominantly female to reflect population proportions. Participants will be informed that the purpose is to study the effects of daily technology use on sleep, but not the specific hypotheses to reduce evaluation apprehension and demand characteristics.
Data Collection Procedure
After providing informed consent, participants will complete online screening measures of general technology use, sleep quality, and mental/physical health. Those meeting inclusion criteria will be asked to complete 14 days of daily sleep diaries and wear an actigraph monitor on their non-dominant wrist for the entire study period. Sleep diaries collect self-reported data on bedtime, rise time, number and length of awakenings, sleep quality ratings, and specific phone activities in the hour before bed. Participants will also complete online questionnaires at baseline and post-study assessing sleep quality, circadian preference, mental health, and mobile phone habits. They will be compensated with research credit or a $25 gift card upon completion of all study requirements.
Measures
Subjective Measures:
Pittsburgh Sleep Quality Index (PSQI): Global measure of sleep quality and disturbances over a 1-month period (Buysse et al., 1989)
Morningness-Eveningness Questionnaire (MEQ): Assessment of circadian preference (Horne & Östberg, 1976)
Depression Anxiety Stress Scales (DASS): Screening for negative emotional states (Lovibond & Lovibond, 1995)
Daily Sleep Diaries: Bedtime, rise time, number/length of awakenings, self-rated sleep quality, pre-bed phone activities
Objective Measure:
Actigraphy (Actiwatch-2): Wrist-worn device measuring sleep/wake patterns, validated against polysomnography (Marino et al., 2013)
Hypotheses
Higher levels of social media, video watching, and web surfing in the hour before bed will correlate with longer sleep onset latency, worse sleep efficiency, shorter duration, and lower sleep quality ratings on diaries and actigraphy.
Texting in the hour before bed will show weaker relationships to sleep outcomes due to lower arousing effects.
Effects will remain significant after controlling for covariates like gender, mental health, and circadian preference.
Data Analysis
Descriptive statistics will characterize sample and variables of interest
Bivariate correlations will examine relationships between specific pre-bed phone activities and subjective/objective sleep measures
Linear mixed models will assess within-subject effects of nightly phone behaviors on sleep while controlling for covariates
Data screening and assumptions testing will ensure model appropriateness prior to interpretation
Potential Limitations and Ethical Considerations
Convenience sampling limits generalizability and introduces selection biases
Self-report introduces biases like social desirability, recall errors
Lack of experimental control hinders causal inferences from correlational design
Possible reactivity effects from monitoring sleep may initially improve behaviors
Participant burden and confidentiality risks in daily diary completion are minimized
Benefits of contributing to knowledge and potentially improving sleep outweigh risks
Discussion
This study aims to advance understanding of how specific mobile phone activities in the hour before bed relate to sleep quality. Findings could clarify which behaviors pose the greatest risk and inform targeted interventions. For instance, recommendations may advise limiting social media, videos, and web surfing closer to bedtime more strongly than brief texting. If effects remain significant after accounting for potential confounds, stronger evidence will be provided linking these phone habits to impairments in sleep health. Overall, results could serve to optimize sleep hygiene recommendations regarding technology use before sleeping. Future studies may wish to experimentally manipulate pre-bed phone behaviors or utilize objective usage tracking. Nonetheless, this proposed study offers an important step towards better understanding the differential impacts of phone activities on sleep.
Andrew Stroud