IS BLUE LIGHT DISRUPTING YOUR SLEEP?

THE PROBLEM & WHY ITS IMPORTANT

Our use of technology is ever expanding. Recent studies suggest that the average screen-time (smartphone, tablet, computer, TV) for adults is 11 hours per day (Nielson Report, 2018). But what are the true effects of all this technology usage?

The prevalence of insomnia and sleep deprivation is also increasing (Gradisar et al., 2013). One study found that 63% of Americans report not getting enough sleep to function properly (ibid). Poor sleep (too much, too little, or poor quality) is a known risk factor for many diseases such as obesity, diabetes, cardiovascular disease (CVD), depression and ultimately mortality. Furthermore, good quality sleep is known to promote concentration, improve learning and memory, increase energy and mood, regulate blood pressure and glucose levels, promote a strong immune system and, regulate weight (Ellenbogen, 2005). Similarly, a lack of sleep can affect the consolidation of information (Stickgold, 2005), which is likely to crucially affect student’s academic achievements (Medeiros, Mendes, Lima, & Araujo, 2001). This research highlights the importance of sleep in everyone’s everyday life, and therefore anything affecting sleep quality or duration should be studied.

Light emitted in the blue spectrum (i.e wavelength between 400-495 nm) such as the light emitted from most electronic devices, can suppress the production of melatonin, the primary hormone that regulates the sleep-wake cycles. This can lead to: decreased concentration, increased digital eye strain (which in turn can cause headaches), difficulty initiating sleep, and cause non-restorative sleep (Holman, 2010). Although the use of blue light can be a useful tool in the daytime, exposure to it shortly before bed can be detrimental. A common recommendation to promote good quality sleep is therefore limiting the use of TV and computer screens when nearing bed time (Cain & Gradisar, 2010).

However, with technology being an integral part of most people’s lives, limiting these usages, even if the benefits are clear, can be difficult. Especially for a population that craves connectivity and constant information. This is therefore a problem that needs to be addressed, particularly among the student population.

Whilst there is strong evidence for the negative effects of blue light coming from technological devices, we felt that from our own personal experiences, students do not take this into consideration as the information is not necessarily “common knowledge”. We therefore aimed to spread awareness of the problem and also introduce quick and easy fixes. In our observations, we found that students generally take certain measures to gain higher sleep quality such as not consuming any alcohol or caffeine shortly before bed, or not eating large amounts of junk food late at night. However, we found that they were either unaware of the effects of blue light, or unwilling to reduce their technology use late before bed. These reasons motivated our project; to find a way that students in particular, could still use technological devices late at night, without the adverse effects of blue light affecting their sleep.

Luckily, we have discovered new programs such as F.lux, which is downloadable for free on any computer, and Night Shift, a setting on iPhone. Both programs use your geolocation to determine the hours of light and darkness, to be able to filter the blue light to warmer colours when necessary (such as in the evening before bed). This ultimately reduces the harmful effects of blue light, and allows you to fall asleep faster, and to get a better night sleep overall. As a result, we wish to inform people of the ease at which you can use this kind of software in order to reduce the negative effects of blue light on sleep quality and duration, without having to drastically change their lifestyle.

TARGET AUDIENCE

Our target audience is young adults, in particular university students as they are especially at risk of the harmful effects of blue light, due to their high usage of technology. According to the Nielson report (2018), young adults (18 to 34) are those that use smartphones the most, spending 28% of their day on their smartphone, compared to 13% of the day for adults aged 65+. Equally, students will have deadlines that causes them to stay up late working, and therefore they are unable to refrain from using such devices before they go to sleep. This makes them more susceptible to the adverse effects of blue light. Our intervention was designed to not only target a small number of people directly, but also to indirectly target many students at the University of Warwick.

INTERVENTION

Our intervention is to increase students’ awareness of the problem of blue light, and how it could be affecting their daily life. We want to provide them with the information necessary to reduce the harmful effects and to promote high sleep quality.

Our intervention began by conducting a small scale study on students at the University of Warwick. We wanted to do this in order to see if we could replicate the common finding in the literature that reducing blue light does have positive effects on one’s wellbeing. In doing so, we also hope to persuade these individuals to make use of such softwares; changing their behaviour slightly in order to reduce the adverse effects on their sleep. Standard ethics protocol was used for this study; in that participants were first handed an information sheet and consent form (FIGURE 1). In the information sheet, participants were thanked for taking part, and told that the study was looking into the effects of blue light, from using electronic devices, on sleep quality. They were also informed what the study would entail; the initial questionnaire, followed by using the software for a week, and then completing another questionnaire by email. In terms of ethics; participants were told that their participation was voluntary, they did not have to answer any question they did not wish to, and that they could withdraw from the study at any time. The consent form then asked participants to note the date down; showing that they agreed to take part.

Figure 1: Information Sheet

Using volunteer sampling, we gave the 18 participants (6 male, 12 female) a questionnaire that we had designed ourselves (FIGURE 2 and 3). This questionnaire asked about how often they used electronic devices before they go to bed, and sleep quality using an adaption of Pittsburgh Sleep Quality Assessment (PSQI). This questionnaire asked participants to rate from ‘Not at all’ to ‘Three or more times a month’ on how much they have had trouble sleeping as a result of various things in the past month. This questionnaire also confirmed that they did not currently use such software, and that they were interested and would agree to take part in a trial for a week. Researchers then showed participants how to enable Night Shift on their phones, and instructed them how to download F.lux on their computers/laptops. Their email addresses were also collected.

Figure 2: Questionnaire

Figure 3: Questionnaire 

A week later, participants were emailed a second questionnaire (FIGURE 4), asking them to complete if they so wished. This questionnaire checked that they had successfully used it for the week, and how successful they found it in terms of their overall wellbeing. They were also given the adaptation of the Pittsburgh Sleep Quality Assessment (PSQI) again, but this time in reference to the past week, not the past month. Once participants had emailed back the second questionnaire, they were sent a debrief, where they were thanked for taking part, and again told the aim of the study (FIGURE 5). Participants were also told what we expected to find, and why such research into the effects of blue light is important.

Of the 18 participants that did the first questionnaire, 16 participants (4 male, 12 female) successfully completed the second questionnaire. Of these 16 participants that used the software for a week, 12 of them reported that on a scale from 1(Not successful at all), to 7 (extremely successful), they noted ‘6’ when asked how successful they found the software to be in term of your overall wellbeing. Another 3 participants rated ‘5’, and 1 participant rated ‘4’. Additionally, when analysing the data from the adaptation of the Pittsburgh Sleep Quality Assessment (PSQI), we found that there were higher reports of ‘once or twice a week’ in the initial questionnaire, whereas in the second questionnaire looking at the past week, more participants reported ‘Not at all’ or ‘Less than once a week’. We therefore believe that we have supported research into the negative effects of blue light, as participants reported better sleep quality following using software that reduced blue light.

Figure 4: Follow up

Figure 5: Debrief

It is important to note that the second questionnaire also asked participants whether they were going to continue to use the software on their electronic devices, and all 16 participants reported that they were going to continue to make use of it. Furthermore, a week after the study had finished, we contacted the 16 participants by email to see if they were still using the software. All 16 participants confirmed that they still had night shift enabled on their phones, with a few mentioning that it was also still on their laptops. From this, we can conclude that we persuaded the 16 participants to make a behaviour change; in that they have now permanently changed their behaviour to using this software, to reduce the negative effects of blue light.

Furthermore, in order to target a larger amount of students at the university, we put up a poster in many common areas, such as outside the library, in the humanities building and outside the arts centre (FIGURE 7). This poster not only informed readers of the negative effects of blue light, but also gave them a quick, easy and free solution, that requires very little effort. The posters will allow us to reach a wider audience, and to inform people of small ways to improve their health that they probably weren’t aware of, as the problem is rarely discussed.

Figure 6

Figure 7: Placement of Posters

PERSUASION TECHNIQUES USED

1.     ROBERT CIALDINI’S SIX PRINCIPLES

Our intervention used three of Robert Cialdini’s six principles (Cialdini, & Cialdini, 2007) to persuasion.

a)     Authority

On our poster, we put a quote from the Canadian Association of Optometrists that commented “exposure to too much blue light at night through screens may lead to poor sleep quality, difficulty falling asleep, and daytime fatigue’. This is because people are more likely to believe someone that is in a position of power; or has expertise in a certain field (Cialdini, 2007). Students would expect the Canadian Association of Optometrists to have a high level of expertise in regards to the effects of blue light; and as a result are more likely to be persuaded by information that has come from them.

b)    Liking/familiarity

Additionally, we commented that “as students, it works for us”. This is making use of the similarity effect; whereby research has found that people are more likely to believe something from someone who is similar to they are (Cialdini, 2007). As students, we are similar to our target audience in that they too are students, and therefore by indicating that using this software improved our sleep, other students are likely to assume that it will help them too. We also like people that are more similar to us, and the more we like someone, the more likely we are to look favourably upon things that they communicate.

c)     Consistency/commitment

In terms of the study that we conducted, we used the ‘Foot-in-the-door’ technique. We initially convinced participants to take part in a 1-week trial using the software on their phone, and on their laptop. Following this trial, we asked participants whether they were going to continue to use the software; a larger request, and all of them agreed that they would. This effect was still seen a week later, indicating a permanent change may have been made.

Just Asking – In doing so, we saw the influence of ‘just asking’. Purely asking participants to do something such as download a software, or make use of a current setting on their phone, meant they engaged in that behaviour. This technique was therefore highly effective in persuading the participants in the study to change their behaviour.

2.     THE ELABORATION LIKELIHOOD MODEL

Petty, R. E., & Cacioppo, J. T. (1979).

The central route to persuasion was used in the poster not only because of the problem at hand; that of one of aversive effects on one’s wellbeing, but also because of our audience; that of students. Students not only have the ability, but also the motivation to pay attention to our persuasive message. We believe that students would be highly motivated to improve their sleep, since there has been a large amount of research to suggest that students suffer from poor quality sleep and duration (Buboltz Jr, Brown, & Soper, 2001; Carney, Edinger, Meyer, Lindman, & Istre, 2006). Students also have the ability to pay attention to such a message; as educated students they are able to understand the negative effects and make their own opinion on them.

3.     MERE EXPOSURE EFFECT

Since it has been found that hearing one message from a single person multiple times is just as persuasive as hearing multiple people tell you the same message (Weaver, Garcia, Schwarz, & Miller, 2007), we made use of this in terms of our poster. Posters were put up in various places around campus; the library, the humanities building, the arts centre. This meant that those exposed to one of the posters, were presumably exposed to another poster when walking around campus. Research has also been found that constant ads causes familiarity, resulting in liking, which relates to Calidini’s liking/familiarity principle (Fang, Singh, & Ahluwalia, 2007). By seeing the same message over and over again, students would not only start to recognise it more, but also due to its familiarity, they would see it more favourably, and therefore be more persuaded to take the message on board (ibid).

FUTURE DIRECTIONS

Our study was done on a very small scale, only changing the behaviour of 16 individuals. In the future, larger scale studies should be conducted, persuading participants to make use of these free software’s that filter the blue light. Equally, this will allow researchers to track the impact that their intervention is having, since we are unable to know how many people went and downloaded this software as a result of seeing our posters on campus. Further studies could also give people alternative methods such a blue light filter glasses, which are available in most opticians as part of prescription lenses, and also available to order online with no magnification. However these methods are not free, which is why we chose not to propose them in our initial study since it was focused on students who have a limited budget.

References:

Buboltz Jr, W. C., Brown, F., & Soper, B. (2001). Sleep habits and patterns of college students: a preliminary study. Journal of American college health, 50(3), 131-135.

Buysse, D. J., Reynolds III, 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.

Cain, N., & Gradisar, M. (2010). Electronic media use and sleep in school-aged children and adolescents: A review. Sleep medicine, 11(8), 735-742.

Carney, C. E., Edinger, J. D., Meyer, B., Lindman, L., & Istre, T. (2006). Daily activities and sleep quality in college students. Chronobiology international, 23(3), 623-637.

Cialdini, R. B., & Cialdini, R. B. (2007). Influence: The psychology of persuasion (pp. 173-174). New York: Collins.

Ellenbogen, J. M. (2005). Cognitive benefits of sleep and their loss due to sleep deprivation. Neurology, 64(7), E25-E27.

Fang, X., Singh, S., & Ahluwalia, R. (2007). An examination of different explanations for the mere exposure effect. Journal of consumer research, 34(1), 97-103.

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Holzman, D. C. (2010). What’s in a color? The unique human health effects of blue light.

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Medeiros, A. L. D., Mendes, D. B., Lima, P. F., & Araujo, J. F. (2001). The relationships between sleep-wake cycle and academic performance in medical students. Biological Rhythm Research, 32(2), 263-270.

Petty, R. E., & Cacioppo, J. T. (1979). Issue involvement can increase or decrease persuasion by enhancing message-relevant cognitive responses. Journal of personality and social psychology, 37(10), 1915.

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature, 437(7063), 1272.

The Canadian Association of Optometrists. (2019). Blue Light – Is there risk of harm?.

Retrieved from (01/03/19):  https://opto.ca/health-library/blue-light-is-there-risk-of-harm?fbclid=IwAR3oyLdlfdVJbNvpRTEv0NJNfjQwkPPI-kQ8QWXvOfdo3H-u3cpb7VjFRws.

Weaver, K., Garcia, S. M., Schwarz, N., & Miller, D. T. (2007). Inferring the popularity of an opinion from its familiarity: A repetitive voice can sound like a chorus. Journal of personality and social psychology, 92(5), 821.