Sleep Monitoring Technology

Razan Al-Shaikh

School of Computer Science University of Birmingham

Birmingham, United Kingdom

Rma484@cs.bham.ac.uk

ABSTRACT

Sleep monitoring technology has been a new trend in the

last few years. In order to understand how these

technologies work and what they monitor, a review has

been conducted to discover important facts about sleep.

These facts contain a clear demonstration about how and

why we need quality sleep. After these facts, the most

popular and recent technology and research was reviewed

and evaluated. The results of the literature review revealed

that the current technology needs more enhancements in

order to be accurate and reliable. The enhancements should

be in both the physiology of sleep and the used technology.

Keywords

Sleep; Circadian rhythm; Sleep disorder;

Applications; Wearable; Sleep labs.

1. INTRODUCTION

Sleep is an important to maintain a healthy body.

Several mental and physical functions are managed

during sleep. Therefore, any disorder in sleep may

negatively affect these functions. In some cases, a

sleep disorder might not be obvious, which may lead

to making things worse. Sleep monitoring technology

aims to monitor the user during sleep and analyse it

with useful information and graphs. These

monitoring devices vary in their purpose, method,

tools and results. To understand how these tools

work, and to evaluate them, sleep physiology should

be considered. Human Computer Interaction (HCI)

research should be connected to other relevant fields

to meet the user’s needs and produce better

technology (Stawarz, 2015).

This paper will explore questions, such as what is

sleep, why we need to sleep, how we should sleep,

and when. After that, it will provide an example of a

sleep disorder to understand the procedure to treat it.

Then, current technology and recent researches will

be evaluated and try to bridge the gap between the

exciting tools and the users need. This paper will

conclude with lessons extracted from the evaluation.

2. BACKGROUND OF SLEEP

The main idea of sleep is to rest the body and recover

from the day’s work. During sleep the brain is still

active, and many procedures are only activated

during sleep (Lydic and Biebuyck, 1988). This means

that sleep is an essential activity for having a healthy

and balanced body. Sleep consists of sleep cycles,

and each cycle last about 90 minutes (Borbely, 1988).

Each cycle consists of two major phases: are Rapid

Eye Movement (REM), which is the dreaming phase;

and Non-Rapid Eye Movement (NREM) (Lydic and

Biebuyck, 1988). A quarter of the night’s sleep is in

the REM while the NREM has three phases (N1, N2

and N3) shifting between light and deep sleep

(Horne, 1988). Any distribution in the cycle can lead

to tiredness and sleepiness in the next day. Sleep is a

complex activity that affects and is affected by many

vital bodily functions. The following sections will

summarize some issues which should be understood

in order to understand how the sleep monitoring

technology works.

2.1 Circadian Rhythm

Circadian rhythm is an internal biological clock

under the control of the centres within the brain that

influences the whole body (Horne, 1988; Lydic and

Biebuyck, 1988). “Circadian” means “approximately

a day” (Hobson, 1989). This means that the body has

a repetitive cycle each day. If this cycle is been

disturbed, unpleasant issues might occur. The main

factors that can affect and disturb the circadian

rhythms are heat and light (Hobson, 1989). Circadian

rhythm disruption can lead to serious problems, such

as sleep disorders. Any change in the environment

can have a negatively effect on physical and mental

activities (e.g. jet-lag) (Facer-Childs and

Brandstaetter, 2015). It can adapt to an environment

change, such as changing work time or changing time

zone. However, the adaptation process may take

more than two weeks (Horne, 1988). Circadian

rhythm sleep disorder may lead to unpleasant

consequences, such as insomnia and extreme daytime

sleepiness, if left untreated (Kim et al., 2013). The

current findings have proven that total deprivation

from sleep and chronic circadian misalignment

control cortisol levels and raises plasma absorptions

of pro- and anti-inflammatory proteins (Wright,

2015). Figure 1 shows the location of the biological

clock in the brain and the relationship between it and

light.

2.2 Sleep Hygiene

Sleep hygiene relates to behaviours and surrounding

conditions that can be modulated in order to enhance

sleep quality (Stepanski and Wyatt, 2003). Poor sleep

hygiene can lead to a perpetuation or aggravation of

circadian rhythm sleep disorder, and make it difficult

to treat (Kim et al., 2013), whereas good sleep

hygiene is associated with healthier sleep quality

(Mindell, 2009). Following sleep hygiene rules can

enhance sleep quality. The rules are summarized as

follows: establish a regular sleep time, avoid naps, eat

a light sleep-time snack, avoid caffeine; and create

encouraging conditions for sleep (Borbely, 1988;

Stepanski and Wyatt, 2003). The effectiveness of

these rules might vary from person to person.

2.3 Sleep’s Influence on Health

As mentioned earlier, sleep can be affected by many

factors, such as circadian rhythm, diet, caffeine,

exercise, age, weight and time of sleep (Andretic and

Shaw, 2005). In order to enhance sleep quality by

improving these factors, recent studies have been

focused their different aspects. Zhou et al. (2016)

studied the effect a high protein diet and weight loss

had on sleep quality. The study found empirical

evidence that protein intake affects the sleep quality

of obese adults. Another study supports the previous

research: Del Brutto et al. (2016) found that daily fish

intake significantly improves sleep quality.

Furthermore, Moraes et al. (2014) studied the age

factor on sleep. The results of the study showed a

significant relationship between age and sleep

quality. Age can affect sleep based on several factors,

such as the sleep time, REM, sleep latency and

apnoea/hypopnoea. Based on the previous study,

Parkes (2016) studied older employees and work

environment as factors to measure sleep quality. In

most cases, sleep quality may be affected by multiple

factors, which makes the situation more complicated.

For example, the caffeine effect depends on genetic

tendency, age, weight, sex and other factors (Clark

and Landolt, 2016). Knowing this can answer the

question why some people can drink coffee or tea and

sleep immediately.

On the other hand, quality of sleep can affect several

functions in the body. Poor sleep quality can lead to

depression, obesity, type two diabetes and suicide.

Alsaggaf et al. (2016) studied a group of medical

students to find out how sleep deprivation affected

their stress levels and academic achievement. The

study discovered that stress and grade average were

associated with poor sleep quality and insomnia

symptoms. Stress ratings were amplified during acute

sleep deprivation while it stayed low across the study

for both the misaligned and synchronized control

groups (Wright et al., 2015). In addition, Lee et al.

(2016) found that poor sleep quality may self-

sufficiently raise the occurrence of diabetes despite

other factors, such as lifestyle and a family history of

Figure 1 Biological Clock in the Brain

Source: www.slideshare.net

diabetes. Sleep quality is a key element in insulin

resistance for type two diabetes (Arora et al., 2015).

Kodaka et al. (2014) studied the relationship between

suicide in the Japanese and sleep disturbances. They

found that 56.4% of people who tried to commit

suicide did so because of sleep disturbances, more

than those who did so due to mental disorders

(35.3%), which makes it an important risk factor for

suicide in Japan. Jeon et al. (2014) revealed from a

study of train drivers that poor sleep quality is

associated with increasing human errors partially

those who suffer from stress. Poor sleep quality

affects not only the current situation; it can also affect

the future. Based on a study of pregnant women,

those who had the poorest sleep quality during

pregnancy had the highest depression levels in the

postpartum period (Tomfohr et al., 2014).

The longest study of staying without sleep in humans

was carried out in California in 1966. The volunteer

stayed 11 days without sleep. The volunteer suffered

from several issues during the experience, such as

mood changes, difficulties with speech, lapses of

memory, illusions and other problems. More details

about the experiment can be found in the references

(Gulevich et al., 1966 cited in Horne, 1988).

3. EXAMPLE OF SLEEP DISORDERS

Obstructive sleep apnoea (OSA) is a sleep disorder

which stops breathing during sleep. Locking the

airway cause a drop in the oxygen levels of the body,

which leads the brain to wake the patient up to

recover their breath (Jordan et al., 2014). This can

occur several times in one night. In some cases, OSA

does not wake up the patient, which makes the patient

suffer from tiredness all of the following day without

knowing the reason. The main risk factors for OSA

are obesity, male gender, age, nasal obstruction and

genetics. The diagnosis is usually run in a sleep lab

in a hospital to monitor the sleep with a

polysomnogram as shown Figure 2. A more detailed

description of the device will be given in the next

section.

Figure 2 Polysomnogram for an OSA Patient

Source: www.nhlbi.nih.gov

4. SLEEP QUAILTY

The previous section raised the importance of having

good night’s sleep to avoid several mental and

physical problems. Sleep duration and sleep quality

are not to be confused. Sleep duration is one of

several aspects that measures sleep quality. Sleep

quality can be measured by different methods. This

paper will survey the most popular method:

4.1 Pittsburgh Sleep Quality Index (PSQI)

Most of the sleep quality in the previous section was

measured by the PSQI. It is the most popular self-

rated questionnaire, and it consists of seven aspects:

“subjective sleep quality, sleep latency, sleep

duration, habitual sleep efficiency, sleep disturbance,

sleep medication usage and daytime dysfunction”

(Buysse et al., 1989; Andretic and Shaw, 2005). PSQI

is designed to be suitable for research and psychiatric

practice. Accordingly, sleep quality does not refer to

sleep duration only; it is a result of many parts. The

questionnaire should have a specific score for each

aspect. After that, the scores are totalled to form the

global score. This score has a range from 0 to 21 to

refer to the sleep quality (the higher the score, the

worse the sleep quality). High scores may result from

physical or mental problems such as insomnia,

bipolar disorder or OSA. By knowing this

information, the diagnosis and treatment can be more

accurate and useful. For further understanding,

Appendix A contains the full questionnaire.

4.2 Technology Usage

Sleep quality doesn’t just attract sleep physiologists.

It is an active topic in technology and HCI as well.

HCI researchers have provided several innovations

and research to enhance sleep quality. There are

many different types of sleep technology based on the

purpose, the place and the task. Sleep technology is

divided into five categories: diagnosis, treatment,

monitoring, waking, and sleep inducing (Choe et al.,

2011). This paper will study the sleep monitoring

technology and who it helps. The following sections

will survey the currently available technology and

recent researches.

4.2.1 In-Lab Technology

There are specialized sleep labs in some hospitals to

monitor the sleep and detect problems. The

University of Birmingham has a sleep clinic to

monitor the patients and a sleep laboratory deal with

healthy cases for research purposes. The author

conducted a brief interview with a Ph.D. student

(Shoug Al-Homod SXA509@bham.ac.uk) in the

Sport, Exercise and Rehabilitation Science

department at the University of Birmingham (the full

interview can be found in Appendix B). Her Ph.D. is

about OSA and she has run experiments to monitor

the brain, chest, eyes and heart rate signals during

OSA. As shown in Figure 3, the sleep lab in the

university has an environment to make the participant

feel comfortable, as sleep hygiene recommends. The

participant sleeps in the room to the do the

experiment, and the researcher monitors her/him

through a camera and polysomnography sensors. The

test usually takes two to three days to be done. The

first day in the experiment called first night effect for

familiarization with the environment. They do not

record measurements. The second and third day is for

the control and intervention as the experiment

requires. The amplifier is used to increase the signal

power and connect the sensor to the computer, in

order to extract the graphs.

As mentioned in the OSA example,

polysomnography is used to monitor several

important signals, such as the oxygen level, heart

rate, eye movements, flow nose, EEG, and other

related data (Jordan et al., 2014). The monitoring

should be done by a medical and technical person.

The essential reading during sleep is the EEG. It

consists of waves that can be measured in terms of

frequency (Lydic and Biebuyck, 1988). To measure

the REM, electrodes have to be placed around the

eyes. Usually, this process is expensive and needs a

long time in the waiting list to have the test (Leger et

al., 2012). In addition, the efficiency of the test may

be reduced due to several factors, such as the sensors,

the wires and changing the sleep environment. Figure

2.A shows the hardware used in the test, while Figure

2.B shows the resulting graphs from this hardware.

Figure 3 The Sleep Lab at the University of

Birmingham

4.2.2 Applications

Sleep Cycle in iOS

This application is designed to calculate the user’s

sleep cycles and to wake them up at the end of a

cycle. As mentioned earlier, waking up in the middle

of a cycle can lead to the desire to sleep and tiredness

all that day. Beside the alarm, the application claims

that it can analyse sleep through sound analysis. In

order to understand practically how the application

works, the author downloaded the application and

used it for five days. At the beginning, the application

asks the user for the desired waking time, and the

alarm will go on any time the previous half an hour.

It has statistics about the sleep, such as a diagram of

the sleep contains the hours and how deep the sleep

is, the duration of sleep and the sleep quality. Figure

4 shows the main interfaces for the application on the

iOS platform.

Figure 4 Sleep Cycle App (iOS)

Source: www.sleepcycle.com

Sleepbot in Android

This application is similar to the Sleep Cycle

application. It monitor sleep through the microphone

and collects statistics about the sleep night. In

addition, it is considered as a smart alarm as it wakes

up the user between two cycles to avoid tiredness.

The application needs to improve its usability. The

interface (shown in Figure 5) was not easy to use,

which increases the error rate. The application is free

in the Google Play store, and has been downloaded

one million times. The application ranking is 4.0

from 48,594 users.

Both applications require that the phone has to be

near the bed to analyse the sleep. On account of the

battery concern, the phone has to be plugged into the

charger during the sleep. This method is a power

consumption and not safe. After closing the alarm,

the application stops the monitoring and analysing. In

case the user did not wake up, two limitations can be

found. The first limitation is the sleep duration will

be incomplete, which makes the analysing

inaccurate. Another limitation is that the alarm

technique is not effective enough because it did not

wake up the user (the second limitation is out of this

paper’s scope). Besides that, both applications claim

that they can calculate sleep quality. However, it is

not sufficient to calculate it through the sound sensor

only, as mentioned earlier in the sleep quality

measurements section.

The number of downloads of these applications can

be interpreted that people are seeking solutions to

enhance their sleep quality. There are problems that

need to be addressed. Secondly, the users like these

applications because they are free (or cheap),

comfortable, easy to use, and work reasonably well.

In the meanwhile, the applications cannot replace in-

hospital sleep monitoring devices. However, they can

give a general sign to detect sleep disorders.

4.2.3 Unconstrained Sleep Monitoring

Regarding the previous limitations to sleep

applications, recent studies have tried to improve

them to be more reliable, accurate and easy to use

(Hao et al., 2013; Jayarajah et al., 2015; Lobato et al.,

2015; Papakostas et al., 2015). The recent researches

have focused on how to measure sleep quality

Figure 5 Sleepbot App (Android)

Source: https://mysleepbot.com/

without contact with the user. Regarding the limit of

this paper it will survey three recent studies:

Nandakumar et al. (2015) developed algorithms that

can detect several sleep apnoea events at distance of

up to a metre. The idea is to transform the smartphone

into a sonar system to detect chest and abdomen

movement (if the chest motion is reduced by more

than 30%). As mentioned in previous sections, in

sleep apnoea, the muscles lock the airway which

makes chest movement difficult. In addition, a chest

belt should be used in the sleep lab test to detect that

change. The study transforms the phone by using the

microphone sensor. Therefore, the results depend on

the microphone quality and position.

Kay et al. (2012) studied room environment and its

effect on sleep. Regarding the study, the authors

implement a device called “Lullaby” with several

sensors such as temperature, audio, light, photo,

motion and an off-the-shelf sleep sensor to monitor

these aspects. The purpose of this implementation is

to detect the potential cause of poor sleep quality.

Understanding the user’s intention is the main aim for

smart homes. By analysing each movement in

advanced algorithms, the system can predict the

user’s needs. For example, if the user reaches the

home, the system will automatically turn on the light

and heater to reach the preferred temperature, and

perform other related functions.Abid et al. (2015)

have developed a method to monitor the breathing

and heart rate. The study used a similar technique

with Nandakumar et al. (2015) study that mentioned

earlier. The FMCW has been used for the detection

the chest and heart rate in order to detect OSA.

4.2.4 Wearable devices

Usually, the wearable device has an application or

website to retrieve information. The device should be

worn during sleep.

Zeo Sleep Manager Pro

Zeo is a fabric-electrode band worn on the forehead

during sleep to monitor and send signals to an

application through a wireless connection (Jeon and

Finkelstein, 2015). The application contains sleep

graphs, and they can be uploaded to a specialist in the

company for a consultation. After the company

closed down in 2013, the website and the

consultation vanished. Zeo is currently unavailable in

the market. The reason for including it in the paper is

that the device was the most reliable in-home

monitor. They have used high quality sensors to

detect sleep.

Figure 6 shows the band and the smartphone app.

Figure 6 Zeo Sleep Manager

Source: www.digitaltrends.com

Basis

The Basis smart watch claims that it gives an analysis

of sleep with realistic and simple techniques to

enhance it. The application monitors sleep duration,

sleep start time, sleep end time, REM sleep, deep

sleep, light sleep, toss-and-turn events, interruptions

and sleep quality. The watch detects the information

through an optical heart rate engine, 3-axis

accelerometer, galvanic skin response, skin

temperature sensor, vibration and haptic feedback

motor, ambient temperature sensor, monochrome

touchscreen display with backlight and automatic on-

wrist detection sensor. Shown in Figure 7 is the

Figure 7 Basis Peak Sleep Tracker

Source: www.mybasis.com/

accelerometer from the back of the watch. The watch

costs £129.99.

Fitbit

Fitbit is a general activity tracker which tracks all-day

activity and sleep. The tracker records the sleep and

sets goals to enhance it. It uses the accelerometer

sensor to predict sleep. Information from the sensor

will be transmitted to the application through a

wireless connection (as shown in Figure 8). The

tracker has several models, and they cost around

£79.99 to £159.99.

Figure 8 Fitbit Sleep Tracker App Source:

https://www.fitbit.com/

The previous wearable devices share common

features. Excluding Zeo, they can be used to track all-

day activities and are easy to buy from online stores.

The price is considered to be relatively expensive for

what they offer. Most of their features can be

replaced with cheaper options and convergent results

such as the smartphone apps.

5. CONCERNS ABOUT TECHNOLOGY

USAGE

Increasing use of smartphones has increased the

controversy about using them on the health (Mann

and Röschke, 2004). Scientific literature revision has

been made on the effect of high-frequency devices on

sleep. The results concluded with no evidence of

sleep disturbance. However, some studies revealed

that long usage of phones may effect on sleep

duration, sleep time and quality of sleep (Al-Khlaiwi

and Meo, 2004; Exelmans and Van den Bulck, 2016).

From the available data, no clear conclusion can be

shared about health concerns of using smartphones

beside or on the bed.

6. LESSONS

Most of the current in-home sleep monitoring are not

reliable because of several reasons. First, HCI

research in sleep monitoring has been improved in

the last FEW years. However, it needs to be improved

based on full understanding OF HOW people sleep,

what is sleep quality, what can make a difference in

the sleep, how we measure sleep and the most

important point after analyzing sleep quality is to

provide the user with useful advice and information

to enhance the sleep. HCI needs studies such as Kay

et al (2012) to be developed. As seen in the previous

sections, the analysis usually contains graphs and

numbers without methods about how can the user

enhance sleep (except Zeo) and what caused the

disorder. Second reason is, the technology used to

monitor. Although there are many recent efforts to

develop an accurate monitor, more research can be

done to bridge the gap in this area.

The monitoring has to be during all the day not only

monitor the sleep. In addition, it should include all

the relevant factors that can affect sleep. For

example, Parent et al. (2016) studied the effect of

using electronic devices before sleep can reduce the

sleep duration of the children up to 45 minutes and

increase sleep disturbance. Therefore, the monitoring

system should prompt the user to leave the

smartphone away before two hours of bedtime.

Another example, using PSQI to guide the

monitoring to the correct bath.

This paper can be considered as an initial guide to

sleep monitoring for further work. To produce better

service Choe et al. (2011) framework is

recommended to be followed:

Goal: the goal of the monitoring should be

clearly defined.

Features: what are the technology features

should be included. For example, monitor to

increase the user awareness of sleep quality or

monitor to send the data to the doctor.

Technology platform: as previously mentioned,

there are many platforms to apply the

monitoring such as, smartphone applications,

wearable devices or ubiquitous computing.

Stakeholder: who will be the user for the

device? Normal user, user with sleep order or

researcher?

Input Mechanism: how the device will monitor?

Does it require an interaction with user or not.

7. CONCLUSION

This paper briefly answered pressing questions about

sleep and sleep monitoring technology.

Understanding sleep and sleep-related issues will

lead to better sleep monitoring inventions. The

current research in HCI mainly focuses on the

technology and their algorithms to run the

monitoring. Further research needs to focus on sleep

and how to enhance its quality.

8. REFERENCES

Adib, F., Mao, H., Kabelac, Z., Katabi, D., & Miller,

R. C. (2015). Smart homes that monitor

breathing and heart rate. In Proceedings of

the 33rd Annual ACM Conference on Human

Factors in Computing Systems (pp. 837-

846). ACM.

Al-Khlaiwi, T., & Meo, S. A. (2004). Association of

mobile phone radiation with fatigue,

headache, dizziness, tension and sleep

disturbance in Saudi population. Saudi

medical journal, 25(6), 732-736.

Alsaggaf, M. A., Wali, S. O., Merdad, R. A., &

Merdad, L. A. (2016). Sleep quantity,

quality, and insomnia symptoms of medical

students during clinical years. Relationship

with stress and academic performance. Saudi

medical journal, 37(2), 173-182.

Andretic, R., & Shaw, P. J. (2005). Essentials of sleep

recordings in Drosophila: moving beyond

sleep time. Methods in enzymology, 393,

759-772.

Arora, T., Chen, M. Z., Omar, O. M., Cooper, A. R.,

Andrews, R. C., & Taheri, S. (2015). An

investigation of the associations among sleep

duration and quality, body mass index and

insulin resistance in newly diagnosed type 2

diabetes mellitus patients. Therapeutic

Advances in Endocrinology and Metabolism,

2042018815616549.

Basis Advanced Sleep Analysis: REM, Deep, Light

Sleep Tracking. (n.d.). Retrieved March 03,

2016, from http://www.mybasis.com/en-

GB/advanced-sleep-analysis/

Borbely, A. (1988). Secrets of sleep. Basic Books.

Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman,

S. R., & Kupfer, D. J. (1989). The Pittsburgh

Sleep Quality Index: a new instrument for

psychiatric practice and research. Psychiatry

research, 28(2), 193-213.

Choe, E. K., Consolvo, S., Watson, N. F., & Kientz,

J. A. (2011). Opportunities for computing

technologies to support healthy sleep

behaviors. In Proceedings of the SIGCHI

Conference on Human Factors in Computing

Systems (pp. 3053-3062). ACM.

Clark, I., & Landolt, H. P. (2016). Coffee, Caffeine,

and Sleep: A Systematic Review of

Epidemiological Studies and Randomized

Controlled Trials. Sleep Medicine Reviews.

Del Brutto, O. H., Mera, R. M., Ha, J. E., Gillman, J.,

Zambrano, M., & Castillo, P. R. (2016).

Dietary fish intake and sleep quality: a

population-based study. Sleep Medicine, 17,

126-128.

Exelmans, L., & Van den Bulck, J. (2016). Bedtime

mobile phone use and sleep in adults. Social

Science & Medicine, 148, 93-101.

Facer-Childs, E., & Brandstaetter, R. (2015).

circadian phenotype composition is a major

predictor of diurnal physical performance in

teams.Frontiers in neurology, 6.

Gulevich, G., Dement, W., & Johnson, L. (1966).

Psychiatric and EEG observations on a case

of prolonged (264 hours)

wakefulness. Archives of General

Psychiatry, 15(1), 29.

Hao, T., Xing, G., & Zhou, G. (2013). iSleep:

unobtrusive sleep quality monitoring using

smartphones. In Proceedings of the 11th

ACM Conference on Embedded Networked

Sensor Systems (p. 4). ACM.

Hobson, J. A. (1989). Sleep. New York: Scientific

American Library.

Horne, J. (1988). Why we sleep: the functions of sleep

in humans and other mammals. Oxford

University Press.

Jayarajah, K., Radhakrishnan, M., Hoi, S., & Misra,

A. (2015). Candy crushing your sleep.

In Proceedings of the 2015 ACM

International Joint Conference on Pervasive

and Ubiquitous Computing and Proceedings

of the 2015 ACM International Symposium

on Wearable Computers (pp. 753-762).

ACM.

Jeon, H. J., Kim, J. H., Kim, B. N., Park, S. J., Fava,

M., Mischoulon, D., ... & Lee, D. (2014).

Sleep quality, posttraumatic stress,

depression, and human errors in train drivers:

a population-based nationwide study in

South Korea.Sleep, 37(12), 1969.

Jeon, L. E. E., & Finkelstein, J. (2015). Consumer

sleep tracking devices: a critical

review. Digital Healthcare Empowering

Europeans: Proceedings of MIE2015, 210,

458.

Jordan, A. S., McSharry, D. G., & Malhotra, A.

(2014). Adult obstructive sleep apnoea. The

Lancet, 383(9918), 736-747.

Kay, M., Choe, E. K., Shepherd, J., Greenstein, B.,

Watson, N., Consolvo, S., & Kientz, J. A.

(2012). Lullaby: a capture & access system

for understanding the sleep environment.

In Proceedings of the 2012 ACM Conference

on Ubiquitous Computing (pp. 226-234).

ACM.

Kim, M. J., Lee, J. H., & Duffy, J. F. (2013).

Circadian rhythm sleep disorders. Journal of

clinical outcomes management:

JCOM, 20(11), 513.

Kodaka, M., Matsumoto, T., Katsumata, Y.,

Akazawa, M., Tachimori, H., Kawakami, N.,

... & Takeshima, T. (2014). Suicide risk

among individuals with sleep disturbances in

Japan: a case–control psychological autopsy

study. Sleep medicine, 15(4), 430-435.

Lee, J. A., Sunwoo, S., Kim, Y. S., Yu, B. Y., Park,

H. K., Jeon, T. H., & Yoo, B. W. (2016). The

Effect of Sleep Quality on the Development

of Type 2 Diabetes in Primary Care

Patients. Journal of Korean Medical

Science,31(2), 240-246.

Leger, D., Bayon, V., Laaban, J. P., & Philip, P.

(2012). Impact of sleep apnea on

economics. Sleep medicine reviews, 16(5),

455-462.

Liskowsky, D. R. (1991). Circadian Rhythms. In

Biological rhythms: Implications for the

worker.Washington, DC: US Government

Printing Office, Office of Technology

Assessment.

Lobato, F., Silva, B., de Bem, R., & Miranda, D.

(2015). Non-invasive sleep-environment

monitoring system. In Proceedings of the 8th

ACM International Conference on PErvasive

Technologies Related to Assistive

Environments (p. 75). ACM.

Lydic, R., & Biebuyck, J. F. (Eds.). (1988). Clinical

physiology of sleep. American Physiological

Society.

Mann, K., & Röschke, J. (2004). Sleep under

exposure to high-frequency electromagnetic

fields. Sleep medicine reviews, 8(2), 95-107.

Min, J. K., Doryab, A., Wiese, J., Amini, S.,

Zimmerman, J., & Hong, J. I. (2014).

Toss'n'turn: smartphone as sleep and sleep

quality detector. In Proceedings of the

SIGCHI Conference on Human Factors in

Computing Systems (pp. 477-486). ACM.

Mindell, J. A., Meltzer, L. J., Carskadon, M. A., &

Chervin, R. D. (2009). Developmental

aspects of sleep hygiene: findings from the

2004 National Sleep Foundation Sleep in

America Poll. Sleep medicine, 10(7), 771-

779.

Moraes, W., Piovezan, R., Poyares, D., Bittencourt,

L. R., Santos-Silva, R., & Tufik, S. (2014).

Effects of aging on sleep structure

throughout adulthood: a population-based

study. Sleep medicine, 15(4), 401-409.

Nandakumar, R., Gollakota, S., & Watson, N. (2015).

Contactless sleep apnea detection on

smartphones. In Proceedings of the 13th

Annual International Conference on Mobile

Systems, Applications, and Services (pp. 45-

57). ACM.

Need help choosing a tracker? (n.d.). Retrieved

March 03, 2016, from

https://www.fitbit.com/uk

Papakostas, M., Staud, J., Makedon, F., & Metsis, V.

(2015). Monitoring breathing activity and

sleep patterns using multimodal non-

invasive technologies. In Proceedings of the

8th ACM International Conference on

PErvasive Technologies Related to Assistive

Environments (p. 78). ACM.

Parent, J., Sanders, W., & Forehand, R. (2016).

Youth Screen Time and Behavioral Health

Problems: The Role of Sleep Duration and

Disturbances. Journal of developmental and

behavioral pediatrics: JDBP.

Parkes, K. R. (2016). Age and work environment

characteristics in relation to sleep: Additive,

interactive and curvilinear effects. Applied

Ergonomics, 54, 41-50.

Startpage - Sleep Cycle alarm clock. (n.d.). Retrieved

March 03, 2016, from

http://www.sleepcycle.com/

Stawarz, K. M., & Cox, A. L. (2015). Designing for

Health Behavior Change: HCI Research

Alone Is Not Enough.

Stepanski, E. J., & Wyatt, J. K. (2003). Use of sleep

hygiene in the treatment of insomnia. Sleep

medicine reviews, 7(3), 215-225.

Tomfohr, L. M., Buliga, E., Letourneau, N. L.,

Campbell, T. S., & Giesbrecht, G. F. (2014).

Trajectories of Sleep Quality and

Associations with Mood during the Perinatal

Period. Sleep, 38(8), 1237-1245.

Wright, K. P., Drake, A. L., Frey, D. J., Fleshner, M.,

Desouza, C. A., Gronfier, C., & Czeisler, C.

A. (2015). Influence of sleep deprivation and

circadian misalignment on cortisol,

inflammatory markers, and cytokine

balance. Brain, behavior, and immunity, 47,

24-34.

Zhou, J., Kim, J. E., Armstrong, C. L., Chen, N., &

Campbell, W. W. (2016). Higher-protein

diets improve indexes of sleep in energy-

restricted overweight and obese adults:

results from 2 randomized controlled

trials. The American Journal of Clinical

Nutrition, ajcn124669.

Appendix A. Pittsburg Sleep Quality Index (PSQI)

Appendix B: INTERVIEW TRANSCRIPT: AUDIO RECORDED INTERVIEW

Razan: How do you run your experiments?

Shoug: I usually run the experiment on three days. The first day call a first night effect. I don’t take any measurement

form it. You know that’s because the participant sleep in a new place and may be the sensors does not him/her feel

comfortable. The second night for control, and the last night is the intervention night.

Razan: is necessary to run the experiment three days or you can reduce it?

Shough: it depends on the study. Some studies run the first night effect. Then, they divide the second day to control

and intervention. As I said, it can be reduced but you can’t skip the familiarization night.

Razan: what do measure in the experiment?

Shoug: the most important measures to my experiment is the EEG, ECG and muscles tone.

Razan: why do you measure them?

Shough: we measure EEG and ECG because they are the most accurate measurement to know if the person is

sleeping or not. The muscle tone to detect if OSA has occurred

Razan: Do you use the same sensors for all participant or use it once and throw it?

Shoug: No, it is one for all participants. But we wash it after each use

Razan: great! Where do you place these sensors in the body?

Shoug: on the head. First, we have to scrub the area to remove the dead skin. Then, swipe it with alcohol swap to

remove the oils. After that, we place a conductive paste to place them. To me I prefer to put a hair pin. It is easier.

Then, connect these sensors to the amplifier and connect the amplifier to computer. After that, we will have sleep

records.

Razan: how do you know if apnoea has happened?

Shoug: the flow in the nasal canal will change. Also, the chest level and the oxygen level in the body.

Razan: ok. After that?

Shoug: the patient had had arousal. It means that either he/she wakes up and transform from deep sleep to light

sleep. If they wake up, the diagnosis will be easier because they know they are not breathing. But if they did not

wake up they do not that they have apnoea. They sleep for a long duration with tiredness in the next day. Moreover,

it may cause serious problems such as accident when they driving. As I said before, because they do not know they

have a problem.

“Shoug was wearing Fitbit tracker, and I asked her about it”

Razan: what do you thing about this tracker?

Shoug: The main reason for wearing it to monitor my activity during the day. Most nights I take it off. It is a good

tracker. But, sometimes I was watching a movie and it thought me sleep. It is good but not reliable