Master's Thesis
LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
Academy of Medicine
The Department of Environmental and Occupational Medicine
An Evaluation of Digital Media usage as a Sleep-Aid
Instrument and its association with sleep quality
parameters and self-rated health among LSMU Students
By Kiefer Trenholm-Jensen
Scientific supervisor: Prof. dr. Ričardas Radišauskas
Table of contents
Abstract….………..Page 4 Summary ….………...Page 5 Acknowledgements, Conflict of interests, Authorization of ethics commit……….…..…Page 6 Introduction.………...…. Page 7 Literature Review .……….…….Page 9 Method ………...……...Page 18 Results .……….Page 23 Discussion.……….………...… Page 37 Strengths and Limitations……….Page 43 Conclusion .……….…….…….Page 44 Recommendations .……….…..Page 45 References……….Page 46
Explanations of short-forms seen in text
BTDMU= Bed-time Digital Media Users
Abstract:
Digital media, be it social media or digital entertainment of any sort, has become an unavoidable extension of the human experience. Digital media is a rapidly expanding
phenomenon helping us to convey, consume and communicate, effectively interweaving itself into almost every facet of our lives. There are undeniable benefits to these developments, and when used as a tool digital media has the power to enhance much of our day to day subsistence. However, are there any downsides to this seemingly beneficial technology? 2015 reports
estimate that some teenagers are now spending 9 hours per day consuming media of some sort1, that statistic intrigued me to ask the question, ―how is that affecting our health?‖ During the research for this thesis, the usage of digital media as a sleep aid instrument was investigated in the student body of LUHS. The focus was to identify how LUHS use digital media around their bedtime, why they use it and finally, how it is affecting them.
The study design isa cross-sectional quantitative methodology where the raw data was collected through a questionnaire. The questionnaire focused collecting information from four main groups of parameters; General Information, Sleep Quality, Digital Media Usage and General Physical and Mental Health. This data was then analyzed though various statistical analysis methods to explore any possible associations between self-reported sleep habits, digital media usage and perceived general health.
The analysis demonstrated several statistically significant associations related to bed-time digital media use, including increased anxiety frequency as well as heighted levels of sleep inertia (difficulty waking up in the morning) and sleep onset latency (extended period of time required to reach sleep state), shorter sleep duration and deceased perception of general health. Moreover, a qualitative thematic analysis of the motivation for digital media usage as a sleep instrument, provided by the participants was ―Fear of silence or fear of one's own anxious thoughts.‖
The study concludes that bed-time digital media is an overall negative influence on sleep hygiene, and presents a recommendation for science-based anxiolytic techniques may be of great value to students at LUHS.
The theme and topic of the thesis
To evaluate key self-rated general health metrics and the relationship with the use of digital media as a sleep-aid among LSMU students. Master thesis scientific supervisor Prof. dr. Ričardas Radišauskas; Lithuanian University of Health Sciences, Medical Academy, Academy of Medicine, Department of Environmental Medicine and Occupational Medicine at LSMU. Aim:To determine and evaluate the use of digital media as a sleep-aid instrument and its associations with sleep quality parameters and self-rated general health among LSMU students. Objectives:
1. To investigate self-reported digital media usage among LSMU students by sociodemographic characteristics.
2. To identify sleep quality parameters and self-rated general health among LSMU students.
3. To evaluate associations between digital media usage and sleep quality parameters self-rated general health.
Key Words: Sleep quality, Sleep onset latency, digital media, Sleep aid, sleep and health, sleep quality and technology, sleep and LSMU students
Summary
Analysis of student digital media use and the subsequentrelationship to self-reported sleep quality and perceived personal health in students from different disciplines at LSMU during December 2018. Author Kiefer Trenholm-Jensen. Final thesis of master’s degree/supervisor Prof. Dr. Ričardas Radišauskas.
The Aim of the thesis: To determine and evaluate the use of digital media as a sleep-aid instrument and its associations with sleep quality parameters and self-rated general health among LSMU students.
Objectives:
1. To investigate self-reported digital media usage among LSMU students by sociodemographic characteristics.
2. To identify sleep quality parameters and self-rated general health among LSMU students.
3. To evaluate associations between digital media usage and sleep quality parameters self-rated general health.
Materials and Methods
This paper acquired the LSMU Bioethics center permission. The data was collected in the form of an anonymous online questionnaire powered by Google forms and posted in various student groups on www.facebok.com. Students answered 30 questions which addressed their
personal/academic profile and well as self-reported sleeping habits, digital media usage and general overall health. Questions were inspired by existing sleep and health questionnaires. Statistical analysis was performed using SPSS. Data was collected from 295 LSMU students and evaluated using Pearson Chi-square, Mann-Whitney U and Binary Logistical regression. Results:Being a female was associated with lower rates of perceived general health, lower self-reported energy rates no exercise in the past month, and higher frequencies of perceived
anxiety, depression and headaches. Males were more likely to report longer internals of sleep onset and more difficulty waking up in the morning, which could indicate poorer sleep.
However self-rated sleep quality was evenly distributed among the sexes. Students in the years 1-3 have are more likely to have lower levels of perceived health and higher frequencies of anxiety. No weekly exercise was associated with lower levels of perceived general health sleep quality as well as increased frequencies of anxiety and depression. After evaluating
associations between Bed-time digital media use (BTDMU) and sleep quality parameters/self-rated general health, it was seen thatbedtime digital media usage was found to be associated with several variables includingsleep duration, perceived wake-up difficulty, perceived sleep onset latency, perceived general health status and exercise frequency.
Conclusion: BTDMU is used by more than 60% of LSMU students. It is predictive of shorter
sleep duration, longer sleep onset latency, greater frequencies of anxiety, lower levels of daytime energy, no weekly exercise and a lower level of perceived general health.
ACKNOWLEDGEMENTS
I would like to thank Prof. dr. Ričardas Radišauskas for his patience and interest in my work. His guidance, kindness and knowledge were a very valuable part of the creation of this thesis. I would like to thank all of the participants, who filled out the questionnaire, I am very grateful for your time. Thank you as well to the administrators for giving us a clear
understanding of what was required and when. Finally I want to thank my family friends, and and girlfriend for always supporting me.
CONFLICT OF INTERESTS
There was no conflict of interest for the author
AUTHORIZATION OF ETHICS COMMITTEE
Data analysis permission (Nr.BEC-MF-143) was issued by the Center of Bioethics LSMU. Permission was granted on the 6th of December, 2018.
1.
Introduction
Thomas Dekker once said ―Sleep is that golden chain that ties health and our bodies together‖ which underscores the tremendous importance of healthy sleep habits to a healthy functioning body. Sleep is a pervasive recovery process that affects every element of a person's health and a lack of sleep has been linked higher rates of obesity2, diabetes3,immune dysfunction4, memory and mood disorders and much more.
As a society we are faced with an unprecedented amount of new technology and an endless supply of media is available just a click away. While this continual stream of digital media facilitates opportunities for readily available information, it is not without repercussions. One study found that nine of 10 Americans use technology in the hour before bed, leading to difficulties falling asleep and unrefreshing sleep in participants.5
Multiple studies have investigated the relationship between the use of various types of digital media and the quality/duration of sleep. However, little research has been done on the intentional use of digital media for the purpose of sleeping. The use of digital media as a sleep aid is becoming a popular phenomenon among all age groups as using movies, music, podcasts, social media etc. to help has become a common practice to help fall asleep.
Sleep is a complex physiological process which involves a carefully orchestrated cascade of neurotransmitters and very specific communication between different neural structures. With people now using technology that produces auditory, visual and cognitive stimulation, their sleep patterns must be investigated to see if this development has produced any sleep changes. Further research is required to investigate the effects on the quality, duration and continuity of sleep induced by technology.
This paper will investigate the self-reported general health and sleep quality of LSMU students as well as their relationship with sleeping and technology. At the time of writing this, little research has been done on the connection between sleep quality and digital media
consumption among LSMU students. Hopefully the results will yield a practical application to help make suggestions to impact and improve the student experience.
Furthermore, a brief thematic analysis will be performed to investigate the common themes present in the motivation for digital media usage, hopefully providing some valuable insight into why students are turning to digital media and technology to help them fall asleep.
2. Aim and objectives
The Aim of the thesis:
To determine and evaluate key self-rated health metrics and contract with the use of digital media as a sleep-aid instrument. Explore and evaluate the possible associations with sleep quality parameters and self-rated general health among LSMU students.
Objectives:
1. To investigate self-reported digital media usage among LSMU students by sociodemographic characteristics.
2. To identify sleep quality parameters and self-rated general health among LSMU students. 3. To evaluate associations between digital media usage and sleep quality parameters
3. Literature Review
3.1 Electronics usage and Sleep quality
At a passing glance, the way we use electronic devices and how we sleep may seem like distant concepts. However, the interplay of technology and daily life coupled with our seemingly ever-increasing dependency on digital media have indelibly shifted the paradigm of our existence. The staggering increase in our use of technology is well documented and shows no signs of slowing down. ―A decade of Digital Dependence‖ a 2018 report by Ofcom, the communication regulatory body of the UK states that in just 10 years smart phone ownership grew from 17% of all people to 78% of all people, and 95% among 16-25-year olds. That’s a 358.8% increase within the general population. The report states that the average UK citizen spends ―1 day per week online‖6
and 60% of all people 35 and under check their phones minutes before bed.
Proof of the tech explosion is not unique to the UK with 77% of US citizens owning a smart phone today, compared to 35% in 20117and the global amount of smart phone owners growing by over 1 billion users since 2014.8 The digital revolution isn’t simply leaving its mark on the adults and adolescents in the world, as a 2016 statistic from the American Academy of Pediatrics demonstrates; ―in 1970, children began to regularly watch TV at 4 years of age, whereas today, children begin interacting with digital media at 4 months of age9.
As with all advancements in technology there are pros and cons. Electronic devices has facilitated much of our modern amenities and ways of life, but they are not without their detractions. As our digital landscape continues to change at a rapid pace, new public health concerns such as decreased general sleep quality continue to emerge with a trajectory towards further decline.10
In recent decades the average sleep duration of healthy people has continued to decrease around the world, for example in the United States one third of all Americans sleeping less the than the recommended time in a 24 hour period, which the Center for Disease Control has now labeled ―a public health epidemic‖11 This trend is not devoid of side-effects, as lack of sleep has been identified as macroeconomic and health burden and associated with seven of the top 15 causes of mortality in the world12. With digital media consumptions astronomical rise and the global increase of insufficient sleep, possible correlations should be investigated between the two.
The relation between electronic use and sleep was evaluated in a study performed on Norwegian adolescents featuring over 9000 participants. The research documented and analyzed the relationship between the type of electronic device/frequency of use during the day and the self-reported sleep behavior of the participants (bedtime, rise time, sleep duration, sleep onset latency and wake after sleep onset).
What they found was that the adolescents (age 16-19) spent a considerable amount of time during the day and at bedtime using electronic devices. Moreover, both the daytime and bedtime electronics usage were associated the risk of shortened sleep duration, increased sleep onset latency and higher rate of sleep deficiency which represent a general decline in sleep efficiency and quality. The researchers found that the use of a personal computer was highly correlated to obtaining less than 5 hr. of sleep (OR=2.70, 95% CI 2.14 to 3.39)13. They concluded that the use of technology/ electronic media may possess a detrimental effect on sleep and suggested that restrictions on electronic devices may be beneficial to sleep quality.
Many studies have which have followed the electronic media habit and behaviors of children and adolescents have echoed similar results. In the 2018 cross-sectional observational study labeled ―To sleep, perchance to tweet‖ researched found over 70% of their 855 participants actively partake consume electronic media while in bed (before sleep) with 15% of participants reporting usage in excess of 1 hour. In fact, duration of media consumption played a significant role, with the heaviest digital media consumers reporting the highest rates of anxiety, shortened sleeping time and insomnia proclivity. Furthermore, the study probed into electronic media consuming partner’s bedtime usage and its effect on non-using partners. It was seen that the non-using partner is not affected at all, indicating that direct interaction with media is likely key to sleep disturbance.14
Building on the concept of digital media and sleep, a study performed in Belgium on which 894 Flemish adults (aged 19-94) examined the relationship between mobile phone usages specifically and sleep quality. Using the Pittsburgh Sleep Quality Index (PSQI), the Fatigue Assessment Scale (FAS) and the Bergen Insomnia Scale (BIS), the researchers investigated self-reported sleep quality, daytime fatigue and insomnia. The results showed over 50% of the studies’ partakersowned a smart phone and 60% of smart phone owners took their phones into their bedroom to send calls and text messages.15
This practice of mobile phone usage significantly affected the PSQI scores, manifesting as increased sleep disruption, longer sleep latency and decreased sleep quality which in turn produced reports of later rising from bed, higher rates of insomnia and increase fatigue. Furthermore, the article states that the age of the participant could predict the sleep disturbance,
with people aged <41.5 years experiencing increased daytime fatigue and people aged <40.8 years reporting later rise times. (People 60 years and older also saw a decrease in sleep duration).
The article concluded smart phone usage and sleep outcomes share a negative relationship to one another, suggesting further research on the matter is warranted as the digital media increased pervasiveness on our daily lives continues to grow.
Media users who frequently consume media in bed are also afflicted by a phenomenon called sleep displacement. A study published in the Journal of Sleep Research details sleep displacement in relation to electronic media, claiming that not only bedtime (when participant enters bed) but also shut-eye time (when participant attempts to fall asleep) are affected by media usage, as a greater ratio of bedtime media consumption resulted in a lengthened shut-eye latency.16
A review article from the Journal of Sleep Disorders and Therapy titled ―Media, Sleep and Memory in Children and Adolescents‖17
provides an interesting vista into memory consolidation caused by insufficient sleep, specifically insufficient sleep caused by digital media. Written as collaboration between a German pediatric department and Harvard medical school, the collaboration investigates the connection between media consumption and sleeping difficulties in children and adolescents. The authors of the article present research to support the three key factors that media consumption has on sleep quality.
Firstly, the use of media late in the evening seem to create a state of increases physical arousal and heightened alertness, which are suspected to be associated with displaced shuteye time.
Secondly, from a far more physiological perspective, the specific type of blue light emitted by the devices used to consume media may be directly alter the secretion of melatonin which may have implications on the circadian rhythm.
Thirdly, the depending on the content of the consumed media, a fear reaction may be induced. If someone views media which creates fear or uneasiness which may result in asomeone sleeping later than they would without that fear reaction. These three powerful aspects of media use have potentially drastic ramifications on sleep.
Comparable evidence can be found that associates various forms of digital media consumption and overall decreased sleep quality for all ages. Nonetheless research reflects that people only seem to be increasing their bedtime media consumption18. These digital media and sleep behavior trends represent an increasingly sleep-deprived population. Fatigue may be the most easily connected side-effect to a poor night's sleep, but it by no means the only one. Sleep
deprivation is associated with a myriad of other conditions and may drastically affect the health and well-being of an individual, from cognitive function to reducing immune system capacity19.
3.2 Sleep and Cognitive Ability
Breakthrough research in 2013 demonstrates the vital importance of sleep as a restorative process with emphasized focus on the neurotoxin metabolite clearance in the cerebrum, sometimes colloquially referred to as ―Brain Drain‖. We know that extended sleep deprivation can be extremely harmful with the ability to cause the deterioration of general health20and a sustained state of wakefulness has even caused death in rodents21. The ―brain drain‖ research was an effort to explore the regenerative measures during the sleep state within the brain. Using a two-photon imaging technique the CSF influx in mice was measured and compared during natural sleep, during active anesthesia and in a state of wakefulness. Using tracers in the CSF, researchers could gauge tracer influx and interpret it as a reflection of CSF circulation. Tracer influx was measured while the mice slept versus directly after being awaken. Interestingly, tracer influx was reduced by 95% after the mice entered a state of wakefulness, indicating a sudden and drastic reduction in CSF circulation. An almost identical result was manifested in the tracer movement of mice anesthetized with a mixture of ketamine/xylazine, once slow wave sleep was initiated. 22
The distinct differences in CSF circulation in the conscious alert state versus the anesthetized/sleep state mice were drastically different, implying the grounds for the physiologically restorative role of sleep for toxic metabolite removal and subsequent cognitive function.
Sleep’s role in cognition may be far more insidious than we currently understand. Results from a Harvard study analyzing the correlation between the midlife sleep durations of female nurses to their cognitive function later in life seem to support this. The study in began in 1976 when researchers received 121,701 questionnaires from nurses concerning their health and lifestyle. The participants were requested to submit information about their sleep habits in 1986 and again in 2001. Following this, members of the group of nurses above the age of 70 who had not suffered a stroke began completing cognitive assessments over the phone. The cognitive tests repeated three times during two-year intervals. The study established that the women found that both longer and shorter sleep durations were associated with worse levels of cognition later in life, when compared to average midlife sleep duration23.
Similar findings have been echoed in a variety of studies which have investigated sleep duration and cognition in later life, some even identifying an association between poor midlife sleep and Alzheimer’s and dementia24.
3.3 Sleep and Memory.
A common associated condition to poor quality sleep is reduced memory recall25. Memory is the process of encoding and consolidating external stimuli for retrieval at a later date. It is probably inherently important for all living being to be able to form and retrieve memories as this builds the basis for learned behavior. There are a multitude of theories of the importance of sleep on memory. The waking hours of our lives are spent perceiving stimuli and encoding what’s called memory traces. These memory traces represent the initial data being encoded from an external stimulus into a knowledge network in the brain; however they are initially very sensitive to being lost or re-encoded with another memory. For this reason, memory traces require a process to increase the likelihood of them being accessible within the knowledge network in the future. The neurophysiologic data indicates that specifically slow wave sleep plays a vital role in this process, stabilizing and consolidating memory traces.26
To revisit ―Media, Sleep and Memory in Children and Adolescents‖, where researchers explored the relation of memory to quality of sleep, earlier work had been referenced. This work, done by the authors, investigated the effect of intense media use on the sleep quality and subsequent memory recall of school aged children. In the study, one group of children were permitted to consume media in the form of movies of video games and while the other group were provided no media. Their sleep quality of both groups was examined using polysomnographic diagnostics. The next day, the same children were presented with a set of audio/visual tests to assess the effect of the media consumption on their memory. The children whose media consumption exceeded 90 minutes were slower to enter a sleep state and experience a higher percentage of lighter, easily disrupted sleep known as stage 2 sleep. Moreover, the media consuming faction spent less time in deeper, slow-wave sleep, otherwise known as NREM sleep or stages 3 and 4. The next-day results indicated the media consumers, who experienced less deep sleep, exhibited inferior verbal memory recall ability.
Ultimately the research concluded that late-night media consumption was associated with less efficient sleep and worse verbal memory recall ability. This may suggest that the use
of media for sleep induction may be a perceived comfort for the individual, but ultimately be related to a poorer quality of sleep. 27
3.4 Sleep and Pain
Memory seems to have an irrefutable relationship with sleep quality and duration, but it is by no means the only aspect of human health and function that can suffer as a result of sleep deprivation. Pain is a universal human experience and every living person will most probably experience it in some form, yet some perceive it differently to it than others. Pain tolerance is the unique perception of noxious stimuli present in each individual. Pain is perceived very differently from person to person for a multitude of reasons, some known and some unknown28.
Using animal models, in this case mice, sleep deprivation effect on pain perception towards noxious stimuli was tested. By subjecting the mice to moderate daily sleep deprivation, scientists were able to establish a ―sleep debt‖ which altered the response to noxious stimuli. According to the report ―9 h of sleep deprivation increased nocifensivebehaviour (paw licking) after intraplantar injection of capsaicin.‖29
Capsaicin, a TRPV1 antagonist, is among other things the active ingredient in spicy food and it provides a painful thermal stimuli.30By demonstrating a heightened pain response in animals with a sleep deficit, the research suggest that a lack of sleep may be associated with a reduced pain threshold.
An emerging evidence for sleep being a pain perception modulator has also been illustrated in humans, as research in recent years has indicated sleep qualities’ integral role in the intensity and nature of pain perception.31This pain perception variance manifested itself in a Norwegian study on over 10,000 participants who were subjected to a pain tolerance exercise known as the ―cold-pressor‖ technique. This involves participants placing their hand in ice-water and recording the time of hand withdrawal. By plotting hand withdrawal time against the participants self-reported sleep hygiene, researchers discovered that all sleep parameters (onset latency, efficiency etc.) with the exception of duration had an effect on hand withdrawal time and pain tolerance. The study concluded as well that the frequency and severity of insomnia was directly linked to how the patient could withstand a noxious stimulus, an outcome baring a similar resemblance to the previously mentioned study performed on mice.
In fact, sleep disruption of all kinds may play a role in the bodies pain sensitivity. A study in Iran which looked at the musculoskeletal symptoms experienced by nurses indicated a disparity between day-workers and shift-workers in terms of pain prevalence. The study focused on pain prevalence in 8 specific body areas (lower back, neck, knees, upper back,
shoulder, wrist, buttock, and ankle) in 454 hospital nurses. When comparing the pain prevalence of shift worker and day workers, the data indicated a statistically significant increase in the musculoskeletal symptoms in the lower back and ankle regions in that of the shift workers.32Due to the nature of shift work the sleep cycle is disturbed, meaning the alteration of the average sleep cycle controlled by the circadian rhythm will probably affect the quality, duration, latency etc..
A 2018 study further investigated the sleep-pain phenomena, subjecting 35 healthy young adults to a fragmented night of sleep in their own homes, followed by a series of pain sensitivity tests to be compared against a control group. The researchers admitted the underlying mechanisms remain unknown but nonetheless the study further supported the importance of sleep on pain sensitivity, demonstrating that even a single night of suboptimal sleep could create palpable changes in pain tolerance in otherwise healthy subjects.33
3.5 Sleep and Mental Health
Sleep and mental health have an undeniable connection and how well we sleep affects how well we feel, and vice versa.34,35,36.Sleep is so intimately linked to our emotional state that even one night of suboptimal sleep can change significantly change mental and physiological response to a stressful stimulus.37
A study in the Elsevier’s Sleep Medicine journal followed 12 healthy adolescents with a history of good sleep through a 36-hour period of sleep deprivation to document if their mood profile underwent any type of alteration. The participants first had two consecutive nights with 10-hour sleep opportunities followed by one night of complete sleep deprivation. They were monitored throughout the sleeping and sleep deprivation periods and required to fill out the ―Profile of Mood States - short form‖38
every two hours. The baseline results were compared to those from the sleep deprivation state at the same clock times, (0900) and (1900).
The subscales of depression, anger, confusion, anxiety, vigor, and fatigue were all found to be significantly worsened following the induced sleep deprived state. The females demonstrated marked mood deficits in the form of increased states of anxiety and the onset of increased depressive feelings. Moreover, all participants reported an increased sense of confusion following lack of sleep. This provides sound evidence that healthy teenagers with no history of aberrant behavior can develop mood deficits after just one night.39
A larger scale cohort study published in a 2015 volume of Sleep examined the effect of depression on a metropolitan population of children and adolescents. The study followed 4175
individuals (ages 11-17 at baseline) over 4 years, amounting in 3134 follow-ups. Using the DSM-IV criteria for major depression and considering sleep deprivation as <6 hours, the study identified an unfortunate cyclic relationship between sleep deprivation and depression. The researchers made use of verified sleep questionnaires to quantify the participants sleep quality and the DISC-IV (Diagnostic Interview Schedule for Children) to identify both major depressive episodes and mood disturbances such as irritable/depressed moods and anhedonia in the last 12 months. The study discovered that sleep deprivation increased the risk for major depression by a factor of 3. Moreover, the results indicated the existence of a reciprocal effect in which major depression further exacerbated sleep deprivation in the participants. The reciprocal effect was not enough to be deemed truly statistically significant but was adequately present to not be overlooked; implying the existence of lack of sleep and major depression together may perpetuate each other.40
Depression and anxiety may represent some of the most common mood-related side effects of not getting enough sleep, but they are by no means the sole possibilities. Sound mental health is quite literally founded in adequate periods of the sleep. A 2018 study published in Front Psychiatry found that a severely sleep compromised state has the potential to evoke an uncompensated psychosis. Using a systematic-review approach on 21 includable articles all detailing the effects of extended sleep deprivation on healthy individuals, the authors found that all studies with the exception of one reported instances of visual distortion. 90% of participants reported experiences of visual distortion, ranging from simple distortions such as metamorphopsias to full visual hallucinations. Somatosensory (52%) and auditory hallucinations (33%) were experienced. The duration of the sustained wakefulness also affected the frequency and severity of noted symptoms, as patients usually began noticing rather inert symptoms such as irritability, anxiety and slight perceptual changes at the 24 hour mark. As the period of uninterrupted wakefulness drew longer, symptoms evolved into depersonalization, delusions and complex hallucinations, ultimately culminating in a mental state that resembled acute psychosis.41
The connection between sleep quality and duration and mental wellbeing has been thoroughly documented. Suboptimal sleep has been proven to evoke a range of symptoms both chronic and acute, from mild to debilitating.
3.6 Sleep and Electronics use as a sleep aid. (Intentional use)
Until this point the focus of this literature review has been to both identify and explore the relationship between overall sleep qualities in relation the use of electronics and explore the various ramifications of sleep deficit in terms of health. Much research has been performed on the use of electronic devices and digital media with reference to sleep. However less has been documented on the intentionality behind the phenomena. Little research has asked the question, why are people using electronics before bed?
According to Flemish research performed in 2014, people featured in the study used television (31.2%), music (26.0%), Internet (23.2%), and videogames (10.3%) as a sleep aid42A similar study entitled ―Nodding off or switching off? The use of popular media as a sleep aid in secondary-school children.‖ aimed to investigate whether or not children were using digital media as a sleep aid and found that 60.2% of the participating children were using music to help them fall asleep. 36.7% were watching television as a sleep aid and 28.7% of boys were using the computers a sleep aid.43The ―self-prescription‖ of media as a sleep aid is not unique to young people, one study indicated the most commonly used intervention for ages 60 and over was watching TV or listening to the radio (66.4%)
Based on the information that has been presented throughout this review, an approach to using digital media for any reason to reach a sleep state may be counterproductive and maybe even contraindicated for effectual, high quality sleep. To quote the American research paper which queried digital media use in the 2 h before bedtime ―using digital media, especially in the hour before sleep, contributes to poorer or disrupted sleep, and that sleep effects vary by the type of digital media used‖.44
Based on the data that has be provided thus far, it is fair to draw a few conclusions. Electronic media usage, especially near bedtime, is associated with less refreshing, lower quality sleep. Furthermore, a consistent lack of sleep is detrimental to the physical and mental health. Based on the data indicating that electronic media as a sleep aid is a popular tactic for multiple age demographics, it is entirely possible that a substantial portion of the population is engaging in behaviour with perceived benefits that does empirically proven detriment to their health. It is important to further investigate this practice to more populations and spread awareness.
4. Research Methodology and Methods
Using a cross-sectional quantitative methodology for binary results and qualitative, data was collected by means of an online survey powered by Google Forms™. The survey was
then posted in several forums exclusively for LSMU students, whom make up the target demographic for the study. The questionnaire was then published the following Facebook groups;―LSMU international students 2013‖, ―LSMU Medicine 2017/2018‖,‖LSMU Studentai‖, ‖LSMU Medicina2014m.‖, LSMU medicina 2017‖, ―LSMU Medicina 2018‖,‖LSMU Veterinarinė medicina 2017‖,‖LSMU International Students 2014‖,‖LSMU Medicina 2016‖,‖LSMU international students 2015‖,‖LSMU Farmacija'15‖ and ‖LSMU medicina'13‖.
The questionnaire was open for participation from the 18th to the 25th of December 2018. Participants were only allowed to participate in the questionnaire once. Already graduated students and students from different educational facilities other than LSMU were removed from the raw data in order to maintain demographic contained to geographic and educational groupings. The questionnaire was anonymous, and participants of the questionnaire were informed that by answering any questions, they were providing consent for the data to be collected and analyzed for the use of this thesis.
Statistical Analysis
When the raw data was collected, several forms of statistical analysis were performed using the IBM software SPSS (build 1.0.0.1131.). To investigate variable independence, a chi-square analysis was performed on questionnaire results from different questions. Using a 95% confidence rate, two variables consisting of two sets of grouped data were analyzed by means of a Pearson chi-square test. (Error Bars indicate 95% C.I.)
Due to the abnormal distribution of results obtained from a Likert scale, using only parametric tests such as the Pearson Chi-square may not fully capture the essence of the all of the data. This is why the Likert scale data obtained has also been analyzed by non-parametric means, in this case a Mann-Whitney U test. Research suggests that a Mann-Whitney U test is ideal abnormal distributions of ordinal data, allowing it to extrapolate the upper limit of the effectiveness of the Likert scale.45 Analysis by Binary Logistic Regression (Backward LR)was also performed as suggested by the papers supervisor.
Study Instrument
The survey consisted of 30 questions divided into 4 sections, entitled ―1. Participant Information‖, ―2. Sleep Quality‖, ―3. Digital Media Usage‖ and ―General Health and Wellbeing.‖ Table XX on the following page contains all of the questions that included in the survey and a brief motivation why they were included.
Table 1 presents each question that was included in the questionnaire and a short reasoning of why the question was important to add.
Table 1
Question Significance of Question
The following questions required participants to select one (or sometimes several) options from a list of multiple choice answers.
1. Sex To investigate potential differences in
symptoms and behavior associated with participant sex.
2. Faculty To investigate potential differences in
symptoms and behavior associated with participant faculty.
3.Study Year To investigate potential differences in
symptoms and behavior associated with the year of study.
4. On average, how you rate your quality of sleep?
Self-reported sleep quality was important to establish to explore potential associations with other behavior and technology usage.
5. Have you been diagnosed with a sleep disorder?
Sleep disorder prevalence was analyzed to investigate relationship between officially diagnosed sleep problems and digital media use.
6. On average, how many hours do you sleepat night?
Sleep duration was explored to provide objective sleep quality parameters as well as self-reported.
7. Approximately what time do you fall asleep?
Sleep time was investigated to investigate if sleep chronotype was associated with health and technology use.
8. How long does it take for you to fall asleep at night?
A subjective sleep parameter known as sleep onset latency has been shown to be indicative of sleep quality in other studies.
9. On average, how often do you wake upduring the night?
Sleep disruption was explored to evaluate possible associations with health and technology use.
10. What other things do you use to help you sleep? (select one for more)
This question was posed to gain an understanding of students sleep hygiene habits and the possible associations with tech. use.
11. Do you find it difficult to wake up in the morning?
Difficulty with morning waking has been shown to indicate overall quality of sleep as well as duration.
12. As of right now do you wish you had a better quality of sleep?
This question is meant to gauge the desire to sleep better, in an attempt to demonstrate motivation for media use.
13. Do you have electronic devices in your bedroom? If yes, which do you have? (select one or more)
This question was meant to provide more context for whether the physical technological unit itself possessed any statistically significant relation to health and sleep quality
14. Do you ever use digital media to help youfall asleep? (e.g. movies, podcasts, music etc)
One of the cornerstones of the research was to investigate bed-time media usage in relation to sleep quality and general health.
15. What kind of digital media do you use to help you sleep (select one or more)
This question was meant to provide further context and detail into the specific use of digital media.
16.Why do you use digital media to help you sleep? (select one or more)
To perform at thematic analysis of the motivation of digital media usage, the rationale for each participant was assessed.
17. How often do you use digital media to help fall asleep?
Frequency is an important factor when attempting to ascertain the nature of bed-time digital media use.
18. Do you turn the digital media off before you sleep?
Turning digital media off or falling asleep with it on is an important distinction in the nature of its use.
19. Does the digital media ever cause you to wake up? (to turn it off, to lower the volume, change the media type)
This question was asked to examine the relationship between bed-time digital media use and sleep disturbances
20.How would you describe your health? Self-reported general health was another cornerstone in this thesis, and an important
landmark to compare to bed-time digital media use and sleep quality.
21. In the past month, how often did you exercise?
Exercise is an important health parameter and interesting to gauge the relation between media usage and sleep quality.
22. Do you suffer from chronic pain? As presented in the literature review, pain perception is a heavily influenced by sleep quality.
23. In the past month, how often did youexperience headaches?
Headaches are a common manifestation of sleep deprivation and pain, therefore and important parameter to measure.
24. On average, how often do you experiencefeelings of anxiety?
Anxious mood are very influenced by sleep quality and health, therefore an important variable to measure.
25. On average, how often do you experience feelings of depression?
Depressive mood are very influenced by sleep quality and health, therefore an important variable to measure.
The following questions required participants to provide an answer on a scale of 1-7, 1 being completely disagree and 7 being completely agree.
26. "In the past month, I feel the quality of my sleep affected my memory "
Sleep has an influence of memory performance and therefore was measured to explore possible associations.
27. "In the past month, I have had troublestaying awake while driving, eating meals or socializing"
These questions represent different repetitive social activities that may produce higher levels of fatigue if sleep deprived.
28. "In the past month, I feel the quality of my sleep affected my ability to focus on tasks"
Sleep has an influence of focus capacityand therefore was measured.
29. "On average, I often experience a lack of energy"
Self-reported energy levels can be a product of sleep and health quality and therefore were measured to explore possible associations
30. "I often not attend social events due tofatigue"
A potential association between fatigue and decreased social interaction was of interest.
4.2 Participants
A total of 295 LSMU students responded from to the questionnaire. The participant pool was 74% (N=21) female and 26% (N=75) male, with the respective faculties represented in Tab. 2.
Tab. 2
Participants by Faculty N from each Faculty (%)
Faculty N(%)
Medical Faculty 220 (75%)
Odontology Faculty 23 (7.8%)
Nursing Faculty 15 (5.1%)
Public Health Faculty 9 (3.1%)
Veterinary Faculty 19 (6.5%)
Animal Science Faculty 3 (1.0%)
Pharmacy Faculty 4 (1.5%)
Total 293
Tab. 3
Participants by Study Year N from each Study Year (%)
Study Year N(%) 1st Year 25 (8.6%) 2nd Year 42 (14.4%) 3rd Year 68 (23.3%) 4th Year 26 (8.9%) 5th Year 81 (27.7%) 6th Year 41 (14.1%) Graduate Studies 9 (3.0%) Total 293
5.Results
Graph 1 represents the multiple choice responses to Question 16 ―why do you use Digital Media to Sleep‖ chosen by the participants.
Graph.1 Results of Question 16: Why do you use Digital Media to Sleep?
The frequency of causes for using digital media to sleep among respondents Free Fields from Question 16.
In Question 16, participants were offered a variety of reasons why they use digital media as a sleep aid (shown in fig. 1) However, question 16 also featured an option entitled ―other‖ where participants could provide additional reasons for opting to use Digital media as a sleep aid. Their responses are provided below. A thematic analysis will be provided in the discussion.
1. ‖ It’s so boring that make me sleep‖
2. ‖ when all other things that "help you sleep" aint working, you eventually get bored and use Digital Media as a last resort.‖
3. ‖Helps me get bored and sleepy‖
4. ‖I am interested in what i’m doing and cant stop‖
5. ‖It helps me to focus on something else than my thoughts‖ 6. ‖Makes me focus on something else than my thoughts‖
7. ‖The constant noise makes my brain disconnect, instead on focusing on the small noises which can activate my brain on thinking what can it be ‖
8. ‖It makes my eyes tired and therefore I sleep‖ 9. ‖I hope i will get tired by using it‖
10. ‖I use it when I can't sleep‖
11. ‖I get more tired so eventually I sleep‖ 12. ‖To think less‖
13. ‖It helps me get sleepier‖ 14. ‖addiction to my phone‖
5.a Identification of sleep quality parameters and self-rated general health
among LSMU (students)
Table 4 demonstrates the statistical distributions of several key variables that were collected from the questionnaire. Anxiety Frequency Depression Frequency Percieved
General Health Difficulty waking in the morning (Sleep Inertia)
On average, how you rate your quality of sleep? Difficulty falling alseep(Sleep onset latency) N (Valid) 293 293 293 293 293 293 N (Missing) 0 0 0 0 0 0 Mean 2.9181 2.5802 3.0205 3.6792 4.68 3.0068 Std. Error of Mean 0.06173 0.06071 0.05183 0.06339 0.073 2.0000 Median 3.0000 3.0000 3.0000 4.0000 5.00 3.00 Std. Deviation 1.05667 1.03924 0.88727 1.08515 1.241 0.98270 Variance 1.117 1.080 0.787 1.178 1.541 0.966 Range 5.00 5.00 5.00 5.00 6.00 5.00 Minimum 1.00 1.00 1.00 1.00 1.00 1.00 Maximum 5.00 5.00 5.00 5.00 7.00 5.00 Significance of Value
1.0 = Never 1.0 = Never 1.0 = Very Poor 1.0 =Very Difficult 1= Very Poor 1= <15 minutes Significance of Value
5.0 = Always 5.0 = Always 5.0=Excellent 5.0 = No Difficulty
7= Excellent 5=>60 minutes
Fig. 1 Self-reported Health Score among Respondents by sex
When comparing self-rated general health scores, it was seen that 75.6% of females selected a ―higher score‖ (4-7 out of 7 on a scale where 7 represents ―Excellent‖ health) whereas 68.1% of males selected a higher score. This distribution demonstrated a statistical association (P=0.022) between gender and perceived general health.
Fig 2. Self-reported Health Score among Respondents by Sex
Participants were asked to rate the frequency of perceived anxiety in the last month of out of 5. Higher and lower anxiety frequencies were almost perfectly distributed among males, whereas females were significantly more likely to select the higher frequencies, ―Sometimes‖, ―Often‖ or ―Always‖ (P=0.001). 75.6% 24.4% 68.1% 31.9% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% Higher Self-Rated Health Scores Lower Self-Rated Health Scores Female Students Male Students X2=5.268,df=1,P=0.022 69.5% 29.5% 51.3% 49.7% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% Higher Frequencies of Anxiety Lower Frequencies of Anxiety Female Students Male Students X2=10.821, df=1, P=0.001
Fig 3. Self-reported Depression Score among Respondents by Sex
Figure 3 illustrates the data showing that 58% of females reported higher frequencies of depression(Sometimes-Often-Always)in the last month, while 63% of males reported lower frequencies (Rarely-Never) in the last month. The association between sex and self-reported depression were statistically significant (P=0.001).
Fig 4. Self-reported Energy levels among Respondents by Sex
Participants were asked how much they agree with the statement ―On average, I often experience a lack of energy‖ by choosing from 1 (Completely disagree) to 7 (Completely Agree). The distribution is seen below in graph 4, with 59.5% of males rating their agreeance with a higher value (4-7) comparing to only 41.1% of females. (P=0.004)
58,10% 41,20% 36,80% 63,20% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% Higher Frequencies of Depression Lower Frequencies of Depression Female Students Male Students X2=10.167, df=1, P=0.001 41,10% 59,90% 59,50% 40,50% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% Higher Levels of Percieved Energy
Lower Lower Levels of percieved energy
Female Students Male Students
X2=8.362, df=1 P=0.004
Fig 5. Self-reported Exercise Frequency among Respondents by Sex
The Females were 2x more likely to select ―I do not Exercise‖ in response to the question ―In the past month, how often did you exercise?‖demonstrating a statistically significant association between the sexes and exercise frequency (P=0.037).
Fig 6. Self-reported Headache Frequency among Respondents by Sex
The proportion of females experiencing >1 headache per week (42.5%) was
significantly higher than the proportion of males experiencing one headache per week (25%). Figure 6 displays the significant discrepancy (P=0.011) between males and females and headache frequency. 30,90% 69,10% 18,40% 81,60% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00%
No Weekly Exercise Some Form of Weekly Exercise Female Students Male Students X2=4.365, df=1, P=0.037 42,50% 58,50% 25,00% 75,00% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00%
>1 Headache per Week <1 Headache per Week
Female Students Male Students
X2=6.539,df=1, P=0.011
Fig 7. Self-reported Sleep Onset Latency among Respondents by Sex
3 out of 4 males believe they require more than 15 minutes to fall asleep, compared to only 60% of females. When reviewing the data, a significant association between sex and sleep onset latency was seen (P=0.026).
Fig 8. Self-reported Wake-up Difficulty among Respondents by Sex
89.9% of Females responded ―Sometimes‖, ―Often‖ or ―Always‖ in response to the question ―Do you find it difficult to wake up in the Morning?‖ While both sexes reported higher rates of wake-up difficulty, this was a higher percentage than wake-up difficulty among males (80.3%). 60,80% 39,20% 75,00% 25,00% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00%
>15 min SOL <15 min SOL
Female Students Male Students X2=4.936,df=1 P=0.026 89,90% 10,10% 80,30% 19,70% -20,00% 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% 120,00% Higher Frequencies of Wake-up Difficulty Lower Frequencies of Wake-up Difficulty Female Students Male Students X2=4.700, df=1 P=0.030
Fig 9. Self-reported Anxiety Frequency Difficulty among Respondents by Study
Year
71.1% of the Students from 1st-3rd year selected higher frequencies of anxiety (―Sometimes‖, ―Often‖ or ―Always‖) compared to 59.4% of 4th
-6th years. The data represents an association between whether a student studies in 1st-3rd year and how often they feel anxious (P=0.036).
Fig 10. Self-reported Anxiety Frequency Difficulty among Respondents by Study
Year
Self-rated health scores appear to be higher among 1st-3rd year student (P=0.001), with over 80% of them rating their health as from Neutral to Excellent.
71,10% 28,90% 59,40% 40,60% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% Higher Frequencies of Percieved Anxiety Lower Frequencies of Percieved Anxiety 1st-3rd Year Students 4th-6th Year Students X2+4.379, df=1 P=0.036 81,50% 18,50% 63,90% 36,10% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% Higher Self-Rated Health Score Lower Self-Rated Health Score 1st-3rd Year Students 4th-6th Year Students X2=11.117, df=1 P=0.001
Fig 11. Self-reported Exercise Frequency among Respondents by Study Year
More than 3 out of 4 medical students report performing some form of weekly exercise, while 62% of other the other included faculties perform weekly exercise, indicating a
significant difference (P=0.028).
Fig 12. Self-reported Exercise Frequency among Respondents by Study Year
79% of students who reported having no weekly exercise schedule described their anxiety frequency as ―Sometimes, ―Often‖ or ―Always‖, compared to 59% of student who exercise once per week, demonstrating a significant association between weekly exercise and anxiety. 75,50% 24,50% 62,00% 38,00% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00%
Weekly Exercise No Weekly Exercise
Medical Students Other X2=4.858, df=1 P=0.028 59,40% 79,00% 40,60% 21,00% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00%
Some Form of Weekly Exercise No Weekly Exercise Higher Frequencies of Percieved Anxiety Lower Frequencies of Percieved Anxiety X2=9,855, df=1 P=0.002
Fig 13. Self-reported Depression Frequency among Respondents by Study Year
In a similar yet slightly less pronounced distribution as exercise and anxiety, 65.4% of students who do not exercise weekly report higher levels of depression, a statistically
significant change.
Fig 14. Self-reported Energy Levels among Respondents by Study
Year 71.3% of students who do not exercise responded with a 5, 6 or 7 (7 representing ―Strongly Agree‖) to the question ―On average, I often experience a lack of energy‖, representing a much wider distribution than students who maintain weekly exercise (P=0.001).
47,60% 65,40% 52,40% 34,60% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00%
Some Form of Weekly Exercise No Weekly Exercise Higher Frequency of Depression Lower Frequency of Depression X2=7.439, df=1, P=0.006 51,20% 28,70% 48,80% 71,30% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00%
Some Form of Weekly Exercise
No Weekly Exercise
Higher Energy Levels Lower Energy Levels
X2=11.796, df=1 P=0.001
5.b Evaluation of associations between digital media usage and sleep
quality parameters and self-rated general health
Fig 15. Self-reported Energy Levels among Respondents by Study
This finding was a particularly interesting aspect of digital media use The population of Students who leave their digital media turned on during the period of sleep onset had a greater percentage of high anxiety frequency (75.4%) compared to the students who turn digital devices off (p=0.044) (Fig. 15).
75,40% 61,80% 24,60% 38,20% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00%
Device ON Device OFF
Higher Frequency Anxiety
Lower Frequency of Anxiety
X2=4.069, df=1 P=0.044
Table 5. The odds of sleep quality parameters and self-rated health among respondents depended by bedtime digital-media usage (multivariate logistic regression analysis)
Table 5
Variables
95% CI for Exp(B)B Sig Exp(B) Lower Higher
Morning Wake Up Difficulty (REF=Never) Rarely 1.340 0.085 3.818 0.829 17.576 Sometimes 17.576 0.028 4.676 1.181 18.510 Often 1.792 0.011 6.000 1.512 18.510 Always 1.869 0.08 6.480 1.614 26.016
Sleep Onset Latency (REF=<15 min) Sleep Onset Latency (>15 Min) .524 .037 1.689 1.033 2.760 Self-Reported Health Rating (REF=Higher Values) LowerSelf-Reported Health Rating 0.532 .045 1.703 1.013 2.863 Exercise Frequency (REF=>4 time per week)
Exercise 2-3 times/Week .324 .404 1.382 .646 2.958 Exercise 1 time/Week .714 .076 2.042 .927 4.494 No Weekly Exercise .786 .040 2.194 1.035 4.650 Sleep Hours (REF=8 or more) 7 -<8 hours 1.417 .081 4.125 .839 20.283 7-6 hours 1.858 .008 6.414 1.615 25.470 5-<6 hours 1.599 .022 4.946 1.258 19.444 <5 Hours 1.691 .017 5.423 1.348 21.810
Odds the sleep quality parameters among respondents by bedtime digital-media usage in logistic regression analysis are presented in Table 5 (above). The respondents who usage the digital-media due to sleep always having perceived difficulty of morning wake up (OR=6.48, 95% CI 1.61-26.01).This reflects a strong correlation between bedtime digital-media use and increased probability of perceiving difficulty when waking in the morning.
Self-reported health scores were grouped into lower values (1, 2 and 3) and higher values(4,5,6 and 7).It was seen that digital media users were more likely to select a health rating of 3 or lower on a scale of 7(OR=1.703, 95% CI 1.01-2.86)
Sleep Onset Latency scores were grouped into <15 mintues and higher >15 minutes. The results demonstrated that a digital media user was more likely to select a sleep onset interval longer longer than 15 minutes. (OR=1.689, 95% CI 1.03-2.760)
Bed –time digital media user’s were more likely to report no weekly exercise in reference to users who exercise 4 or more times a week (OR=2.194, 95% CI 1.03-4.65)
Bed –time digital media usage was more predictive for a sleep duration less than 7 hour, even prodiving an statistically significant odds ratio for <5 hours of sleep (OR=5.423, 95% CI 1.35-21.81)
The following statements represent the all the variables from the raw data that were statistically significant for the grouping variable ―Do you ever use digital media to help you fall asleep‖ after running a Mann-Whitney U test. According to these results the participants whom use digital media to help them sleep were statistically more likely to experience more a higher frequency of anxiety, sleep inertia (difficulty waking up), a longer period of sleep onset latency and lower self-reported general health. Below are the full statistical reports from the data.
Self-reported Anxiety Frequencies among Respondents by BTDMU
Mean ranks in self-reported Anxiety in the groups ―Digital media instrument users‖ and ―Non-users‖ were 155.01 and 133.48; the difference in the groups differed significantly.
Percieved frequency of anxiety was greater among digital media instrument users. (Mann-Whitney U= 8554,000, z=-2.186, P = 0.029).
Self-reported Morning wake-up Difficulty among Respondents by BTDMU
Mean Ranks of average self-reported rate of morning wake-up difficulty in the groups ―Digital media instrument users‖ and ―Non-users‖ were 155.15 and 133.23; the difference in the groups differed significantly.Wake-up difficulty was more frequent among digital media instrument users (Mann-Whitney U= 8527,500, z=-2.226, P = 0.026).
Self-reported SOL among Respondents by BTDMU
Medianranks of self-reported Sleep Onset Latency in the groups ―Digital media instrument users‖ and ―Non-Users‖ were 158.11 and 128.25; the difference in the groups differed significantly. Perceived sleep latency was longer among digital instrument users (Mann-Whitney U=7984.50, z=-3.086, P=0.002).
Self-reported General Health among Respondents by BTDMU
MeanRank in self-reported General Health in the groups ―Digital media instrument users‖ and ―Non-users‖ were 141.03 and 157.07; the difference in the groups differed significantly. General Health ratings were higher among non-users
6.1 Discussion
Several interesting statistical significances can be seen in the data. The first intriguing result was seen in the result which evaluated the relationship between digital media as a sleep aid and self-reported wake up difficulty.
6.1 Statistical associations between sleep and health and participant sex,
faculty, exercise habits and study year.
Looking at the trends that emerged throughout the statistical analysis, it appears that several participant variableseffect the perception of health and sleep hygiene.
Sex of participant was associated with large array of health parameters. 75.6% of female participants perceive their health as 4 or higher on a scale out of 7(P=0.022). This is a finding that contradicts some research performed in the past where levels of self-reported health were reportedly lower among women.A 2018 study on 1,106 Spanish retirees demonstrated that the retired men report significantly better health then the retired women (P=0.05), despite there being no statistical difference between objective health (P=0.39).46 The age groups clearly differ with that of the Spanish study, as do the results of perceived health. This suggests the role of participant age may be an influentialvariable for self-reported health of females, potentially suggestive that further research tracking the perceived health among women of young age until adulthood may demonstrate a downward trend. An unfortunate dimension of the research for this thesis is lack of objective health data, thereby making a comparison between objective and subjective health unviable.
Women were also more likely to report higher frequencies of anxiety (P=0.001) and depression (P=0.002), which reflects the current stance taken by the World Health
organization47, who claims that women predominate the sufferers of ―depression, anxiety and somatic complaints‖48However studies have also proven the women are typically more comfortable expression emotions such as sadness or vulnerability49, inferring women may be more comfortable expressing concerns over mental health. This may play a role in how they report their perception on their mental health.
Being female also carried a higher association with lower energy levels (graph 4, P=0.004) andwomen were more likely to be among the students who reported not exercising at all, (graph 5, P=0.037). Studies have shown in the past that exercise dependence is higher
among males50, yet the results of the research performed on LSMU students indicates females are much more likely to not perform weekly exercise. Seeing as exercise is an extremely valuable practice for mental and physical health, something that female LMSU students are likely to have worse perceptions of, a program which focuses on female physical activity may be very beneficial.
On the other hand, male LSMU students were more likely to report longer self-reported periods of sleep onset latency (P=0.026) and a more difficulty with morning waking (P=0.030) Both of these variables are indicative of poorer sleep quality, however self-reported sleep quality is almost entirely evenly distributed among the sexes(P=0.917). This is an interesting finding which may inspire further research in this area, as to whether the sleep requirements are different or a discrepancy lies in the rate of self-reporting or the objective health of males and females.
Year of study (1-6) among participants yielded two specific associations when compared mental and physical health. When grouping students into 1st+2nd+3rd years and 4th+5th+6th years, the data reflected that 81.5% of 1st-3rd years score among the higher rates of self-perceived health , opposed to 63.9% of 4th-6th year students(P=0.001). However 71.1% of the 1st-3rd year also reported higher rates of anxiety. More so than their older counterparts at63.9 % (P=0.036). If one was to extrapolate a trend from these numbers, it would seem that both general health and anxiety rates decrease as a student matures. Further research on the effect on health related topics and their development throughout the academic careers of students may provide valuable insight on areas for improvement. Past research has indicated young students are more likely to engage in risky behavior and consume higher levels of alcohol51, two known potent influences on anxiety. It may be beneficial to further research this to ascertain whether information on stress management coping skills could be beneficial, as it has been proven to be so in the past52.
Exercise was seen to be one of the most undeniablypervasive influences on the health and sleep of LSMU students. No weekly exercise was associated with lower sleep quality (P=0.024), lower self-reported energy levels (P=0.001), increased frequency of anxiety
(P=0.002), increased frequency of depression (P=0.006) and a lower self-reported health rating (P=0.005). Health and exercise have a well documented the mutually beneficial relationship, and numerous studies have reinforced the importance of exercise for improved sleep, stress relief, mood improvement, increased energy and increased mental alertness. 53Truly, no weekly exercise was among most predictive parameters of overall health, and according to the data 27.9% of LSMU students reported no exercise in the last month. Moreover, 30.9% of the
female population reported they do no exercise, almost twice as much compared to the 18.4% of non-exercising males(P=0.037). Also, 38% of the students belonging to non-medical faculties reported no exercise as opposed to only 25% of non-exercising medical students (P=0.028).
Knowing the importance of exercise and its vast array of benefits, more than a quarter of participants have no weekly schedule for exercise. We can also see that being female at LSMU and not studying medicine is predictive of a larger percentage of non-exercisers. With this in mind, further research should be conducted to establish how to effectively promote exercise among more sedentary groups.
6.2 Digital Media Sleep-Aid Usage vs. Perceived Morning Wake-up
Difficulty
The logistic regression analysis demonstrated an interesting pattern among BTDMU’s and frequency of wake-up difficulty. As the frequency progressed from ―sometimes‖ to ―often‖ to ―always‖, so did the significance level of the P value and the value ofExp(B). This indicated not only a statistical association, but a dose dependant relationship between BTDMU and increased odds of difficulty waking up. The results of the Mann-Whitney test also provide evidence to support a significant difference in median latencies of wake-up difficulty, being higher in the BTDMU group (P=0.026).
Thisrelationship between BTDMU’s and perceived morning wake-up difficulty may possess profound implications for the relationship between tech and health for LMSU students. The process of awakening and period of adjustment that follows directly is referred to as ―Sleep Inertia‖, a period of grogginess upon awakening from a sleep state. According to research, partial sleep deprivation has a dose-dependent negative effect on cognitive
performance during sleep inertia54, which reflects difficulty ―waking up‖ in the morning. This means that decreased sleep duration negatively affects sleep inertia, implying that increased sleep inertia may be a factor that could indicate objective sleep quality.
Naturally, a self-reported prolonged period of sleep inertia among bed-time digital media users does not equate toBTDMUs having a decreased over-all quality of sleep.However, a sleep poll conducted in the United States also made established that night time digital media use caused unrefreshing sleep55 and unrefreshing sleep has been associated with excess sleep inertia56. Data from the logistic regression also indicates that sleep duration was affected in LSMU BTDMU’s, indicating that users were 86% more likely to sleep 7 hours or less, which has been to be suboptimal.57
