The Effects of Sleep Duration and the Time to Fall Asleep on the

Short-term Memory Performance

Yuchen Du

Beijing National Day School, Beijing, China

Keywords: Short-Term Memory, Sleep Duration, Young Adults.

Abstract: Sleep is an issue that scientists have devoted to researching in recent decades, particularly with the

decreasing sleep time due to job obligations. The association between sleep and people's work performance

has become increasingly relevant. This study investigates how sleep duration and time to fall asleep affect

short-term memory performance in individuals aged 16 to 22. Data was collected throughout a 10-day sleep

quantitative experiment on 61 participants using the digit span test and sleep monitor app. By analyzing

using R studio, the original hypothesis was compared to reach the following conclusions: there is no

significant correlation between the time to fall asleep and short-term memory performance, with a

moderately positive correlation between 6.5-7.5h and a moderately negative correlation between 7.5-8.5h.

These findings show how young adults can enhance sleep duration to achieve the most spectacular

optimization of short-term memory performance, which is critical for supporting people in forming healthy

lifestyles.

1 INTRODUCTION

With the fast pace of social life becoming

increasingly popular, it is essential to pursue a

reasonable and healthy sleep habit, especially for

teenagers. Sleep is a state that can be easily reversed

when the human body is less responsive to the

surrounding environment and less interacting with

the environment. As an expected behaviour in higher

vertebrates, sleep deprivation will affect all aspects

of human life to a certain extent.

According to the statistics provided by Loessl

(Loessl, Valerius, Kopasz, Hornyak, Riemann, &

Voderholzer 2008), it is reasonable that the number

of sleep adolescents gets declines, and also the

Medical News Today suggests that about one-fifth of

students pull all-nighters at least once a month, and

even 35% of students stay up until 3 am at least once

a week on weekdays.

So far, researchers have taken sleep quality or

sleep duration as independent variables and

conducted multiple surveys on obesity, disease,

work efficiency and other aspects, even including

different age groups. To be specific, for example, the

researches conducted by both Hargens (Hargens,

Kaleth, Edwards, & Butner 2013) and Newman

(Newman, Nieto, Guidry, Lind, Redline, Pickering,

Quan 2001) prove that obesity and cardiovascular

disease influence it. Plus, as the conclusion obtained

from the quantitative data of 20,778 students, sleep

quality is directly proportional to study performance

，which is highly consistent with the conclusion

provided by Okano and Kaczmarzyk (Okano,

Kaczmarzyk, Dave, Gabrieli, & Grossman 2019). In

many studies, the relationship between sleep and

memory has always been a primary focus of

researchers because sleep plays a vital role in the

formation of memory and consolidation, like the

quantitative research provided by Durrant (Durrant,

Cairney, & Lewis 2013) and the findings uncovered

by Stickgold (Stickgold 2005). Specifically, REM

sleep and slow-wave sleep (SWS) are associated

with memory consolidation (Marshall, & Born 2007,

Moroni, Nobili, Curcio, De Carli, Tempesta,

Marzano, De Gennaro, Mai, Francione, Lo Russo, &

Ferrara 2008). However, in the existing research

results, few people focus on the relationship between

short-term memory and sleep duration, but more

focus on the neural mechanism of sleep on the

formation of long-term memory (Born 2010). Based

on the study of the class performance of 40

participants with uniform distribution of male and

female, compared with the sleep-deprived group, the

Du, Y.

The Effects of Sleep Duration and the Time to Fall Asleep on the Short-term Memory Performance.

DOI: 10.5220/0011371900003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 443-449

ISBN: 978-989-758-596-8

443

paired-associate test showed that the

non-sleep-deprived group had about a 20.6%

increase in long-term memory.

Therefore, this paper focuses on the two Young

adult groups, 16-18 (high school students) and 19-22

(college students). This work analyzed the literature

on factors affecting short-term memory. In

particular, many researchers in the past few decades

have suggested that interference is the only cause of

short-term memory forgetting (Oberauer, &

Lewandowsky 2008). Nevertheless, recent data

suggest that short-term memory loss is actually due

to time-based forgetting, known as the Trace Decay

theory (Altmann, & Schunn 2012). In particular,

direct evidence was found that the cause of

short-term memory forgetting was traced decay after

72 college students tested visual images (Deliens,

Schmitz, Caudron, Mary, Leproult, & Peigneux

2013). On the other hand, the current literature

supports the dynamic relationship between sleep and

memory trace (Ricker, Spiegel, & Cowan 2014).

Most importantly, according to Sadeh's study in

2003 (Sadeh, Gruber, & Raviv 2003), appropriate

sleep supplementation can significantly improve

people's digit span, reflecting the improvement of

their short-term memory. In summary, it is

reasonable to assume the correlation between sleep

duration and short-term memory. This paper

addresses the following hypothesis:

Hypothesis-1 The time to fall asleep is negatively

correlated with short-term memory

Hypothesis-2 Sleep duration is positively

correlated with short-term memory

However, it is worth noting that there is still a

gap between theory and reality, and further

investigation is needed to verify the hypothesis

about the influence of sleep duration and short-term

memory. Therefore, a 10-day group experiment was

conducted to verify the hypothesis using the Digit

Span test and other technologies. In addition, this

work also collected age, gender, education level and

other data through online questionnaires for further

hypothesis and analysis. In this experiment,

researchers adopted two proven techniques to

measure short-term memory and sleep-related

variables, respectively. Since Richardson

(Richardson 2007) elaborated on the Digit Span test

as a quantitative measurement tool, recognized and

validated by many other researchers (Jones, &

Macken 2018). In addition, for the Sleep Recorder

app, its working principle has been proved feasible

by literature. Since actigraphy is the core technology

for sleep monitoring, and all muscles except the eye

muscles are inhibited during REM sleep, even when

the brain is active (University of Toronto. 2012). By

recording changes in body movement and sound

decibel levels caused by slight muscle changes

during sleep, the software can use programmed

programs to calculate the user's sleep duration and

other sleep-related indicators (Mohr 2015).

2 PARTICIPANTS

Participants were recruited on Wechat Moment using

posts, which described the research study, research

benefits, and inclusion criteria. Inclusion criteria

included people who were either high school

students or college students. Before completing the

study, participants were instructed to sign a consent

form acknowledging their willingness to participate.

A guardian’s signature was required if the participant

was under 18 years of age. See Appendix 1 for the

sample consent form.

A handbook including a sleep monitor app

tutorial and an online numeric working memory test

tutorial was sent to all participants via Wechat.

Participants were instructed to send the sleep report

and online numeric working memory test to the

researchers via Wechat in 10 consecutive days. To

protect participants’ privacy, each participant was

assigned a unique code. Specifically, participants

with high school education levels were assigned to

group A coded from A01 to Axx, while participants

with college education levels were assigned to group

B coded from B01 to Bxx. All research notes and

documents adopted the unique codes.

3 MATERIALS AND METHODS

Designing the experiment to support the stated

hypothesis proved to be challenging as research

ethics had to be carefully considered alongside the

rigour of the experimental design.

Specifically, limited resources, coupled with the

test cycle of half a month, proved to be an obstacle.

Researchers consulted as part of the initial

information collection played a vital role in

determining the outline of the subsequent design of

the experiment. Research participants completed a

study to investigate the correlation between sleep

duration and short-term memory performance and

between time to fall asleep and short-term memory

performance. They filled out an online

questionnaire, used the sleep monitor app, and took

online numeric working memory tests.

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

444

3.1 Online Questionnaire

Volunteers are recruited by the posts that were

posted on Wechat Moment. The posts provided

potential participants with a brief description of the

research, the benefits of the program, requirements

for volunteers, and the work this research need

volunteers to perform. Prospective participants can

scan the QR code to access the online

pre-experiment questionnaire for signing up for

participation, and the questionnaire collected basic

demographics information, including gender, age,

and education level. See Appendix 1 for specific

questions in the questionnaire.

3.2 Sleep Monitor App

Participants were instructed to use the Snail Sleep

app (Apple or Android supported) to monitor their

time to fall asleep and sleep duration every night for

a total of 10 consecutive days, from August 15,

2021, to August 25, 2021. All participants were

instructed to send the screenshots of sleep duration

and the time to fall asleep to the researchers.

3.3 Short-term Memory Test

In the experiment, this research used the online digit

span test to test participants’ short-term memory

levels. Participants were instructed to learn a series

of numbers (Human Benchmark 2007). Participants

completed an online numeric working memory test

twice in a row one hour after waking up for 10

consecutive days, from August 15, 2021, to August

25, 2021. The numerical average of two consecutive

memory test levels represented participants’

performances each experimental day. All

participants were instructed to send the screenshots

of memory test levels to the researchers after

finishing the memory test.

Microsoft Excel was used to record the data and

organize data manipulations. R studio was used to

statistically analyze. Shapiro test was used to test the

normality of data. Spearman test was used to test the

association between sleep duration and memory test

level and the association between the time to fall

asleep and memory test level.

4 RESULT AND ANALYSIS

4.1 Participants

According to the online questionnaire, as shown in

Figure 1.1 and Figure 1.2, 61 qualified participants

participated in the experiment, of which men

accounted for 41%, and women accounted for 59%.

Regarding age distribution, people with high school

education levels accounted for 52%, and those with

college education levels accounted for 48%. The

distribution is relatively even. Specifically,

21-year-old participants accounted for the largest

proportion, accounting for 23.0% of the total. See

Figure 1 for detailed variable distribution data.

The Effects of Sleep Duration and the Time to Fall Asleep on the Short-term Memory Performance

445

Figure.1.1(left) the distribution of gender; Figure.1.2(right) the distribution of the age

4.2 The Completion of the Experiment

The experiment lasted for 10 days. A total of 61

participants participated in the study, except for one

participant in the university group who dropped out

due to personal reasons. The completion rate of high

school participants was 96.9%, and that of college

participants was 96.4%, which could be used for

data collection and analysis.

4.3 The Time to Fall Asleep and the

Short-term Memory Performance

In terms of data analysis, Microsoft Excel was used

to record the data and organize data manipulations.

Researchs used R studio to analyze statistically.

Shapiro test was used to test the normality of data,

and the result showed that the time to fall asleep was

not normally distributed. Thus, Spearman’s test was

used to test the association between the time to fall

asleep and memory test level. A Spearman

correlation coefficient revealed no significant

correlation between weight and age, rho(61)

=-0.067, p =0.11. The result was as follows:

Table 1: Correlation between the time to fall asleep and memory test performance.

Whole group

Condition p rho

0.11 -0.067

As shown in table 1, as p value > 0.05, there is

no correlation between the time to fall asleep and the

short-term memory performance, regardless of the

rho value (Spearman's correlation coefficient). The

result is essential because it suggests that it does not

matter what time people sleep, breaking the

stereotype in modern society.

4.4 Sleep Duration and the Short-term

Memory Performance

Researchers further analyzed the impact of sleep

duration on short-term memory. Similarly, R studio

was used to perform Spearman's test after the

disposal of data. A Spearman correlation coefficient

revealed no significant correlation between weight

and age, rho(61) =-0.006, p =0.8843. The result was

as follows:

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

446

Table 2: Correlation between the sleep duration and

memory performance.

Whole group

Condition p rho

0.8843 -0.006

As shown in table 2, as p value > 0.05, there is

no correlation between the time to fall asleep and the

short-term memory performance, regardless of the

rho value.

5 FURTHER FINDING AND

ANALYSIS

As analyzed above, the results do not fit with the

original hypothesis, but by analyzing the graph of

sleep duration and memory test performance and

using the technique of fitting asymptotes, it can be

found that sleep duration has a linear relationship at

6.5-8.5h. As shown in Figure 2, the red line

represents the positive correlation, and the black line

represents the negative correlation. As a result, this

paper put forward a new hypothesis:

During 6.5-7.5h and 7.5-8.5h, there is a

correlation between sleep duration and short-term

memory performance.

Figure.2: The effect of sleep duration on the memory test performance. Each point represents the daily sleep duration and

the memory test performance of 61 participants in 10 consecutive days.

In order to verify whether this hypothesis is true,

this work used Spearman’s test to test its correlation

by importing the interval data into R studio. The

results are as follows:

Table 3: Correlation between the sleep duration and memory performance (sleep duration 6.5-8.5h).

Whole group 6.5-7.5h group 7.5-8.5h group

Condition p rho p rho p rho

0.88 -0.01 < .001 0.41 < .001 -0.47

It is worth noting that when the sleep duration is

between 6.5 and 7.5h, the p value is less than 0.05,

and the rho value is 0.41 as shown in table 3. During

this period, there is a moderate correlation between

sleep duration and short-term memory performance.

In addition, according to the research by David

Spurgeon (Spurgeon 2002), he conducted a study of

the sleep habits of more than one million people

The Effects of Sleep Duration and the Time to Fall Asleep on the Short-term Memory Performance

447

over six years. The result showed that people who

slept for six or seven hours had a lower death rate

than those who regularly slept eight or more hours.

Plus, the best survival rates were found among those

who slept seven hours a night. A group sleeping

eight hours was 12% more likely to die within the

six years than those sleeping seven hours. Thus, the

sleep duration between 6.5-7.5h shows the positive

implication corresponding to the further finding.

6 CONCLUSIONS

According to the ten days of experiment and data

analysis, the following conclusions can be

determined. First, no significant correlation between

the time to fall asleep and short-term memory

performance, and there are no significant correlation

between the sleep duration and short-term memory

performance if all the periods are permitted. Plus, if

it is limited to a specific sleep duration range, there

is a positively moderate correlation between

6.5-7.5h; there is a negatively moderate correlation

between 7.5-8.5h.

The research discussed the relationship between

short-term memory and sleep-related variables of

16-22-year-old young adults by collecting data from

participants for up to 10 days. However, due to the

limitations of experimental equipment and

experimental period, the researcher cannot perform

any interventions such as sleep deprivation. The

sleep duration data provided by each participant is

not evenly distributed, and most of the data come

from the interval of 6.5-8h. This leads to the

possibility that rho in the result obtained using

Spearman's test in R studio may increase, making

the conclusion biased.

Figure.3: The scatter diagram of sleep duration and memory test performance. The red line encloses the concentrated data

set.

All the participants in the study are Chinese

nationals, especially those in the high school

education level group who are students from the

International Department, and they face the pressure

of applying for foreign undergraduates. This extra

pressure makes the sleep duration of the overall data

smaller to some extent. In the distribution of male to

female ratio, female participants are 18% more than

males, which may lead to the data provided by

women occupying most of the theoretical basis of

the conclusion.

In terms of future research directions, researchers

can focus on sleep duration in the 6.5-8.5h period

and conduct further analysis by controlling other

variables except for age. Because the major group

analyzed in this experiment is young adults, recent

studies have also shown that sleep duration has a

non-negligible effect on other age groups; future

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

448

related research can allow an analysis of both the

elderly and middle-aged people. In addition, future

research can pay extra attention to people's

occupations and rest habits, making the research

group more specific and detailed. Considering the

technology limits of the experiments, it is

recommended that future researchers can apply for

permission from relevant international human rights

organizations to conduct laboratory experiments,

that is, recruit volunteers to closed laboratories,

while using EEG and other technologies to perform

more precise measurements.

As shown in Figure.4, on the other hand, the

average range of the collected sleep duration data is

6.89h, which means that the participants' sleep

duration will differ by about 7 h at most. The uneven

distribution of such massive data implies irregular

sleep habits of the studied group, so it is

recommended that future research directions control

the range of sleep-related variables provided by the

subjects.

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