Age and Sleep Variability as a Factor of Daytime Functioning

This study examines the relationship between sleep variability and daytime functioning in healthy adults. The study also investigates whether age affects this relationship. The results show that sleep variability affects daytime functioning, and the relationship remains the same after adding age as a covariate.

RUNNING HEADER: AGE AND SLEEP VARIABILITY AS A FACTOR OF DAYTIME FUNCTIONING 1
Age and sleep variability as a factor of daytime functioning
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Age and sleep variability as a factor of daytime functioning 2
Introduction
The relationship between poor daytime functioning and insufficient sleep is clear in the
population. Durations of short sleep are constantly linked with poor mental performance and
low moods and feeling of well-being. The variability in sleep schedule refers to the uniformity of
bed time and wake time. People who differ the time they go to bed every night and the time they
wake up have greater schedule variability compared to people who have a consistent bed and
wake time.
Recent studies in children suggest that sleep schedule variability impacts on daytime functioning
(Tomfohr, Ancoli-Israel & Dimsdale, 2010). In the adult population, variability in sleep schedule
has been considered especially when a clinical problem, insomnia for instance is present (Aly &
Moscovitch, 2010). Consequently, variability in sleep is related with a range of mental health
issues such as depression in theses population.
There are few studies that have been made which look specifically at variability in sleep
schedule in a representative and non-clinical adult sample. Thus, it is difficult to identify whether
variability in sleep schedule is linked to poor functioning in the daytime in non-clinical healthy
adults. Moreover, there is no distant idea of whether any demographic variables are related to
variability in sleep schedule. Thus, this study is aimed at answering the following research
questions:
 Does sleep variability affect daylight functioning in healthy adults?
 Does the relationship between sleep schedule variability and daytime functioning stay the
same, or change after adding age as a covariate?
Hypothesis

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To answer the above research questions identified, the following hypotheses were developed:
Question 1
H0: There is no difference in daylight functioning between the sleep variability groups
H1: There is a difference in daylight functioning between the sleep variability groups
Question 2
H0: There is no relationship between sleep schedule variability and daytime functioning
H1: There is a relationship between sleep schedule variability and daytime functioning
Data Analysis and Results
Hypothesis 1
To test hypothesis 1, a one-way ANOVA was employed. The one-way ANOVA (analysis of
variance) was chosen since it is used in determining whether there are any statistically significant
differences two or more independent groups’ means.
To carry on with the one-way ANOVA test, it was paramount to ascertain whether the data met
the necessary assumptions. It was found out that the data met all the 6 assumptions made for a
one-way ANOVA. In the first assumption, the dependent variable was measured in intervals
(continuous). In the second assumption, the independent variable consisted of three categorical,
independent groups. They included the low, moderate and high groups. To determine third
assumptions, the following histogram was used.
The syntax for the histograms are as shown below:

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It was evident that there were no significant outliers in the dataset with exemption to the
moderate score.

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From the three histograms above, it is evident that the dependent variable is normally distributed
for each category of the independent variable. Presence of homogeneity of variance was also
tested for the sixth and last assumption.
Table 1: Variance Test of Homogeneity
From table 1 above, the Levene’s test F value is 3.412 with a p-value of 0.044 (<.0.05). Since the
significance value is less than the critical value of 0.05, we choose to accept the null hypothesis
for the homogeneity of variance. Thus, we conclude that the data does not meet the assumption
of homogeneity of variance.
The syntax for the one-way ANOVA used was:
Table 2: Descriptive Statistics
From table 2 above, it is evident that the high category has the highest mean of 12.2 ± 3.19. The
low category was the lowest with a mean of 5.57 ± 2.17 while moderate had a mean of 9.19 ±
4.708. Overall, the mean of ESS was 8.68 ± 4.38.
Table 3: ANOVA

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From table 3 above, it is seen that the significate level is 0.00 (p = 0.00) which is below 0.05.
The decision is thus to not accept the null hypothesis. Thus, the difference in the daylight
functioning between the different sleep variability categories is statistically different.
Table 4: Multiple Comparison
From table 4, it is evident that there is a difference that is statistically significant in daylight
functioning between low and high variability (p = 0.000) and moderate and low (p = 0.026).
However, there is no difference between the groups of moderate and high (p=0.112).
Hypothesis 2
To test hypothesis 2, a one-way analysis of covariance (ANCOVA) was chose. The one-way
analysis of covariance is used in determining whether there are any significant differences
between two or unrelated groups on a dependent variable.
The syntax used to develop the ANCOVA is as shown:

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The dataset was found to have met all the assumptions of ANCOVA. The dependent (ESS
Score) variable and the covariate variable (age) were measured on a continuous scale. On the
other hand, the independent variable consisted of three categorical and independent groups. The
table below are the results used to evaluate the presence of homogeinity of variance.
Table 5: Error Variance Levene’s Test of Equality
From table 5 above, the Leven’s test F value is 3.287 with a p-value of 0.049 (<.0.05). Since the
significance value is less than the alpha value of 0.05, we choose to accept the null hypothesis
for the homogeneity of variance assumption. Thus, we conclude that the variance does not meet
the assumption of homogeneity.
The descriptive statistics will remain as earlier shown in table 2.
Table 6: Tests of Between-Subjects Effects

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From table 6 above, it is seen that there is statistically significant difference between the adjusted
means (p < 0.05). The new adjusted means are as seen in table 7 below.
Table 7: Estimates
A pairwise comparison was then carried out to determine whether the statistically difference
between the adjustment means is true.
Table 8: Pairwise Comparison

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From table 8, it was seen that difference between the low moderate and low high groups were
statistically significant (p = 0.046, p = 0.00). However, the moderate high group was not
statistically significant (p = 1.53).
Discussion
The data analysis made use of three variables, sleep variability, age and ESS score. Sleep
variability was categorical in nature having been divided into three groups, low, moderate and
high. Age category was continuous, ranging from 16 years to 55 years of age. The ESS (Epworth
Sleepiness Scale) score was also continuous measuring on a scale of 0 to 3. A score of 10 and
above show pathological and abnormal sleepiness.
Effect of sleep variability on daylight functioning in healthy adults
From the results, it was found out that there is statistically significant difference in the daylight
functioning between the various sleep variability categories. Consequently, it was found out that
there was variance between low and high variability and the moderate and high variability. Thus,
quality sleep is relatable to good cognitive performance (Yang et al., 2012). However, there was
no difference between the daylight functioning between low and moderate. This proves that sleep

Age and sleep variability as a factor of daytime functioning 10
deprivation leads to excessive sleepiness during the daytime thus leading to poor daytime
alertness (Gaultney, 2010).
Change in relationship between sleep schedule variability and daytime functioning after adding
age as a covariate
The means of the ESS Score between the categories changed though with a marginal increase
after adding age as a covariate. According to Rodriguez, Dzierzewski & Alessi (2015), sleep
problems are more common in elderly people therefore explaining the increase in the ESS scores
for the low and high categories. However, from the results, it was seen that the relationship
between the sleep schedule variability and daytime functioning did not change. This is in
contrast to Smolensky et al. (2011) who claimed that sleep problems are more prone to the
elderly in recent days. The difference between low and high variability and the moderate and
high variability were still statistically significant while there was no difference between the
daylight functioning between low and moderate.
Conclusion
For one to remain keen and active during the day, sufficient sleep is paramount. The study has
shown that an individual who has a low or moderate sleep variability is able to work efficiently
but when his sleep variability is high, they will not be able to function properly. Low and
moderate sleep variability are associated with low ESS scores while high sleep variability are
associated with very high ESS scores.

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Reference
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Gaultney, J. F. (2010). The prevalence of sleep disorders in college students: impact on academic
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Rodriguez, J. C., Dzierzewski, J. M., & Alessi, C. A. (2015). Sleep problems in the elderly. The
Medical clinics of North America, 99(2), 431.
Smolensky, M. H., Di Milia, L., Ohayon, M. M., & Philip, P. (2011). Sleep disorders, medical
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Tomfohr, L. M., Ancoli-Israel, S., & Dimsdale, J. E. (2010). Childhood socioeconomic status
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Yang, P. Y., Ho, K. H., Chen, H. C., & Chien, M. Y. (2012). Exercise training improves sleep
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