10.24265/horizmed.2024.v24n2.09
Original Article
Sleep quality
among School of Medicine students
of a Peruvian university during the return to in-person
classes after the COVID-19 social restrictions
Andrea Magaly
Martin-Osorio 1 0000-0003-0358-3312
Franco Romaní-Romaní 1 0000-0002-6471-5684
1Universidad de Piura. School
of Human Medicine. Lima, Peru.
a. Medical
student
b. Doctor of
Medicine, master in Epidemiology
*Corresponding
author
ABSTRACT
Objective: To evaluate the sociodemographic and academic characteristics associated with sleep quality among School of Human Medicine students.
Materials and methods: A cross-sectional study was carried out in a randomly selected sample of 184
School of Human Medicine students
from a private university located in the city of Lima, Peru. The study
was conducted between June and July 2022, a period of gradual return to in-person classes. The participants completed an anonymous virtual survey containing questions on
sociodemographic and academic factors as well as the Pittsburgh Sleep Quality Index (PSQI). This instrument comprised seven parameters with scores ranging from 0 to 21, where a good sleep quality was established from 0 to 5 points. The outcome was dichotomous (good and poor sleep quality), so binary logistic regression was used to evaluate
the associated factors.
Results:
Females accounted for
53.26 % and the mean age was 20.05 with a standard deviation of 1.73.
Concerning the hours of class time, 61.35 % were taught online. As for the sample, the prevalence of poor sleep quality was 69.02 %, out of which
76.53 % were females and 60.47 % males. The mean percentage of online classes
was higher among
those with poor sleep
quality compared to those with good sleep quality (63.48 % versus 56.58 %, p = 0.030).
Regardless of the percentage of online
classes, females were twice as likely to have poor sleep quality
(OR = 2.00, 95 % CI: 1.05 to 3.82).
Conclusions: Poor
sleep quality affected 7 out of 10 School of Human Medicine students in the
context of the gradual return to in-person classes after the COVID-19 social
restrictions. Females had a higher chance of poor sleep quality, regardless of
the percentage of online classes.
Keywords: Sleep Quality; Sleep; Students, Medical; Peru (Source: MeSH NLM).
INTRODUCTION
Sleep is a physiological process in humans with multiple effects
on the body (1). When disturbed, either quantitatively or qualitatively, both physical and mental health are affected;
hence, it is important to care for and preserve sleep (2,3).
Despite of this, poor sleep quality (SQ) is frequent among medical students
compared to the general population and to students of other professions (2,4,5). The evaluation of SQ is performed using objective methods-such as polysomnography
or actigraphy-as well as subjective methods, including clinical interviews
and questionnaires, inter alia, the Pittsburgh Sleep Quality Index
(PSQI) (1,6).
SQ can be influenced by demographic, social, academic,
environmental and psychological factors, and even by the restrictions imposed during the COVID-19 pandemic
(7). The COVID-19 pandemic was a period when
medical students had to adjust to online classes, a context that worsened SQ and increased the prevalence of anxiety and stress (5,8,9).
Poor SQ is associated with mental disorders among medical students; both conditions, in turn, play an antagonistic role in
academic performance (10,11).
In 2017, a meta-analysis estimated that the global prevalence of poor SQ was 52.7 %, and in the Americas, it was 59.9 % (12). Both prevalences are lower than those described in
Peruvian studies (13-15).
In February 2022, the Ministry of Education of Peru approved the
gradual return to in-person academic activities in public and private universities (16). This return was gradual
and was characterized by the implementation of a hybrid class model (online and in-person classes). Thus, there was a transition period in which students faced a new learning
scenario which could influence their SQ.
Due to the importance of sleep to health and the background
of the high prevalence of poor SQ among medical students in Peru, it became necessary to evaluate
the factors associated with SQ during a transition period between online
learning modality and the gradual return to in-person learning
modality. Based on the foregoing, the objective of the study was to determine
the demographic, social and academic
factors associated with poor SQ among medical
students at a private university in Lima.
MATERIALS AND METHODS
Study design and scope
A cross-sectional observational study was carried
out. The sample of medical
students was enrolled during June and July 2022. The study was conducted at a private
university located in Metropolitan Lima, Peru. This school started
its academic activities in 2017, and by the time of the study, there were six cohorts of incoming
students.
The target population consisted of 409 students from the
first to the sixth year of the Human Medicine program. The selection
criteria were the following: being over
18 years old, being enrolled in the first semester of the 2022 academic year
and providing an informed consent. We excluded students who reported a previous
diagnosis of a sleep disorder by a specialist and those who did not complete
all the questions.
The sample size for estimating a rate was calculated using OpenEpi, a
free and -open-source software (https://www.openepi.com/SampleSize/SSPropor. htm). An expected prevalence of poor SQ of 59.9
% (12),
a confidence level of 95 %, an absolute
precision of 5 %, and a population size of 349 students
over 18 years old were considered. The estimated sample size was 180. A non-
response rate of 10 % was assumed; thus, 198 students were invited. Simple
random sampling was used within a sampling frame obtained from the Academic
Secretary's Office of the School of Human Medicine.
Variables and measurements
The data collection instrument in its first part included
questions about age (in completed years), sex (female, male), number
of cigarettes smoked
per day over the last four weeks (no tobacco
use, 1-10, 11-20
and more than 20
cigarettes per day) and frequency
of alcohol use per week over the last four weeks (no alcohol
use, less than once, once or twice and, three or more times per week).
As to academic characteristics, the following were inquired about: year of study (first, second, third, fourth, fifth, sixth); academic load, which indicated
whether the student was enrolled in the required number
of courses (recategorized as full or partial);
belonging to the upper third, defined from the weighted average of grades and
the student's placement in the first third of these averages ordered
from highest to lowest (this variable was considered an indicator
of superior academic
performance); number of class hours per
week; and number
of hours of online classes
per week. The total
number of class hours per week (#cs) and the
number of online class hours per week (#csv) were used to calculate the
percentage of online classes: (#cvs
/ #cs)* 100 %.
The second part of the instrument included the PSQI, which
is a 19-question tool used to assess the SQ of the last four weeks. Measurement
with the PSQI is based on the sum of scores of seven sleep parameters evaluated
through responses to 18 of the 19 questions, four of them with open-ended numerical responses and the others
with multiple-choice responses on a Likert scale. SQ was rated based on
the scores obtained from the points for each of the parameters. Each parameter
was scored from 0 to 3, where 3 indicates greater dysfunction. The sum of the
scores of the seven parameters ranged from 0 to 21, where
0 to 5 points defined good SQ and 6 to 21 points poor SQ.
The PSQI parameters are subjective sleep
quality, latency (minutes it
takes a person to fall asleep after going to bed), sleep duration, sleep
efficiency (percentage that expresses the ratio of number of hours a person
spends in bed and the number of hours slept), disturbances, use of sleeping medication and daytime dysfunction (6). The study
used the PSQI validated for the Peruvian adult population by Luna et al., who
linguistically adapted the instrument and reported a Cronbach's alpha of 0.56 as an indicator of internal reliability (17).
Description of procedures
Expert judgement was sought to evaluate the adequacy of the Peruvian version
of the PSQI for medical
students. This stage was
performed in April 2022 by six
neurologists and two psychiatrists. The expert evaluation of the relevance of
the items had an Aiken’s V of 0.983. Subsequently, a pilot test was conducted
with 30 medical students from two private universities in Lima to evaluate the test-retest
reliability and internal
consistency of the instrument. The fist pilot measurement was carried
out in the first week of May 2022,
and the second measurement three weeks later with the same students.
Intra-observer agreement was measured using the Kappa coefficient and a value
of 0.524 was obtained. Concerning internal consistency, a Cronbach's alpha
value of 0.541 was estimated.
The online survey
was administered between
June and July 2022. Prior to this, an invitation
was sent to the selected students via institutional e-mail, along with the link to the online survey. The database
was cleaned in Microsoft Excel 2019.
Statistical analysis
The descriptive analysis
of the categorical variables
used frequencies and percentages, while the analysis of quantitative variables
used means and standard deviations.
The parameters of SQ were described for the entire sample
and by sex, for which absolute and relative frequencies
were presented. In the analysis of the factors associated with sleep quality,
Student's t-test was applied for
quantitative variables, while the uncorrected chi- square test or chi-square test for linear trend was used for categorical variables, as appropriate. Variables
with a p value < 0.05
(two-tailed) were included in the multivariate analysis. The assumption of no
collinearity was verified by calculating the variance inflation
factor (VIF), with a value < 2.5 being considered as a criterion for
no collinearity. Multivariate analysis was performed using binary logistic
regression, and the odds ratio with the corresponding 95 % confidence interval was estimated. JAMOVI software, version
2.3.16, was used for the described
analysis. A p value < 0.05 was
considered statistically significant.
Ethical considerations
The Institutional Review Board of the Universidad de Piura approved the research protocol
(file PREMED04202202).
The research adhered to the current ethical standards, an online informed
consent was required
applied, and the analysis was performed on an
anonymized database.
RESULTS
Characteristics of the sample
A total of 198 students were invited; nevertheless, the
analysis was conducted on data from 184 students. The selection process and
reasons for exclusion are shown in Figure
1. Of the participants, 53.26 % were females,
the age range was 18-26 years, while the mean (standard deviation [SD]) was 20.05
(1.73) years; additionally,
75.54 % had a full academic load. The mean percentage of online classes was 61.35 % (SD = 19.97) (Table
1). Among those with a full academic load, the mean percentage
of online classes
was 60.78 % (SD = 20.19), while among those without a full academic
load, the mean was 63.09 %
(SD = 19.40) (p = 0.501).
Figure 1. Flowchart of the sample
selection process
Table 1. Sociodemographic and academic characteristics of medical students
(N =184)
|
Variables |
n
(%) |
|
Age (years), mean (SD) |
20.05 (1.73) |
|
Sex |
|
|
Female |
98 (53,26) |
|
Male |
86 (46.74) |
|
Year of study |
|
|
First |
37 (20.11) |
|
Second |
45 (24.46) |
|
Third |
41 (22.28) |
|
Fourth |
30 (16.30) |
|
Fifth |
14 (7.61) |
|
Sixth |
17 (9.24) |
|
Academic load |
|
|
Full |
139 (75.54) |
|
Partial |
45 (24.46) |
|
Class hours per week, mean (SD) |
31.90 (6.65) |
|
Online class hours per week, mean (SD) |
19.29 (6.71) |
|
Percentage of online classes |
61.35 (19.97) |
|
Upper third * |
|
|
Yes |
52 (35.37) |
|
No |
95 (64.63) |
|
Daily tobacco use |
|
|
No |
161 (87.50) |
|
Yes |
23 (12.50) |
|
Alcohol use per week |
|
|
No alcohol use |
78 (42.39) |
|
< once |
85 (46.20) |
|
Once or twice |
19 (10.33) |
|
≥ 3 times |
2 (1.09) |
Data available from 147 students
Pittsburgh Sleep Quality Index (PSQI)
Subjective quality was reported as poor in 44.57 % of the
students, with a higher value among females compared to males (52.04
% versus 36.05
%). Sleep latency
was greater than 60 minutes
in 11.96 % of students, with a higher
rate among females (15.31
% versus 8.14 %). In addition, 33.70 % indicated that they slept seven or more hours, 79.89 % reported a sleep efficiency ≥ 85 %. Furthermore, 90.76 % reported
that they did not take sleeping
medication. A total of 28.80 % reported
feeling quite drowsy while driving, eating or
doing other activity; this affected more females (37.76 %) than males (18.60 %)
(Table 2).
The
PQSI score had a minimum
of 1 and a maximum
of 18; the median was 7, and
the interquartile range (IQR) was from 5 to 9.
The median was 8 (IQR: 6-10) among females
and 6 (IQR: 5-8) among males.
Table 2. Sleep quality
parameters of the PSQI among medical students
(N = 184)
|
Sleep quality paraments |
Male n (%) |
Female n
(%) |
p value |
Total N (%) |
|
Subjective quality |
|
|
|
|
|
Fairly good |
7 (8.14) |
2 (2.04) |
0.059a |
9 (4.89) |
|
Good |
45 (52.33) |
41 (41.84) |
|
86 (46.74) |
|
Poor |
31 (36.05) |
51 (52.04) |
|
82 (44.57) |
|
Fairly poor |
3 (3.49) |
4 (4.08) |
|
7 (3.80) |
|
Latency (minutes) |
|
|
|
|
|
≤ 15 |
29 (33.72) |
16 (16.33) |
0.002b |
45 (24.46) |
|
16-30 |
34 (39.53) |
30 (30.61) |
|
64 (34.78) |
|
31-60 |
16 (18.60) |
37 (37.76) |
|
53 (28.80) |
|
> 60 |
7 (8.14) |
15 (15.31) |
|
22 (11.96) |
|
Sleep duration (hours) |
|
|
|
|
|
≥ 7 |
31 (36.05) |
31 (31.63) |
0.145b |
62 (33.70) |
|
< 7 and ≥ 6 |
31 (36.05) |
25 (25.51) |
|
56 (30.43) |
|
< 6 and ≥ 5 |
13 (15.12) |
27 (27.55) |
|
40 (21.74) |
|
< 5 |
11 (12.79) |
15 (75.31) |
|
26 (14.13) |
|
Efficiency (%) |
|
|
|
|
|
≥ 85 % |
68 (79.07) |
79 (80.61) |
0.182a |
147 (79.89) |
|
75 %-84 % |
14 (16.28) |
8 (8.16) |
|
22 (11.96) |
|
65 %-74 % |
3 (3.49) |
7 (7.14) |
|
10 (5.43) |
|
< 65 % |
1 (1.16) |
4 (4.08) |
|
5 (2.72) |
|
Disturbances (scores) |
|
|
|
|
|
0 |
16 (18.60) |
3 (3.06) |
< 0.001a |
19 (10.33) |
|
≥ 1 y ≤ 9 |
68 (79.07) |
78 (79.59) |
|
146 (79.35) |
|
≥ 10 y ≤ 18 |
2 (2.33) |
16 (16.33) |
|
18 (9.78) |
|
≥ 19 y ≤ 27 |
0 (0) |
1 (1.02) |
|
1 (0.54) |
|
Use of sleeping medication |
|
|
|
|
|
(times a week) |
|
|
|
|
|
0 |
79 (91.86) |
88 (89.80) |
0.150a |
167 (90.76) |
|
< 1 |
7 (8.14) |
5 (5.10) |
|
12 (6.52) |
|
1-Feb |
0 (0) |
4 (4.08) |
|
4 (2.17) |
|
≥ 3 |
0 (0) |
1 (1.02) |
|
1 (0.54) |
|
Daytime dysfunction |
|
|
|
|
|
None |
3 (3.49) |
1 (1.02) |
0.012a |
4 (2.17) |
|
Mild |
23 (26.74) |
15 (15.31) |
|
38 (20.65) |
|
Moderate |
44 (51.16) |
45 (45.92) |
|
89 (48.37) |
|
Severe |
16 (18.60) |
37 (37.76) |
|
53 (28.80) |
|
Scoring according to the PQSI |
|
|
|
|
|
Good |
34 (39.53) |
23 (23.47) |
0.019b |
57 (30.98) |
|
Poor |
52 (60.47) |
75 (76.53) |
|
127 (69.02) |
a Fisher’s exact test, b Pearson’s chi-square test
A total of 69.02 % (95 % CI: 62.30 to 75.75 %) had poor SQ. Poor
SQ was reported by 76.53 % (95 % CI: 68.14 to 84.92) of females, compared to 60.47 % (95 % CI: 50.13 to 70.79) of
males. Significant association was found between poor SQ
with
the year of study and the percentage of online classes;
a higher rate of students
with poor SQ was seen in the early
years of study. Moreover, the percentage of online classes was higher among
those with poor SQ (Table 3).
Table 3. Sleep quality
according to the sociodemographic and academic characteristics
|
Characteristics |
Sleep
quality |
p valuea |
|
|
Good (57) n (%) |
Poor (127) n (%) |
||
|
Age – mean (SD) |
20.42 (1.77) |
19.88 (1.69) |
0.051b |
|
Sex |
|
|
|
|
Female |
23 (23.47) |
75 (76.53) |
0.019 |
|
Male |
34 (39.53) |
52 (60.47) |
|
|
Year of study |
|
|
|
|
First |
9 (24.32) |
28 (75.68) |
0.037c |
|
Second |
11 (24.44) |
34 (75.56) |
|
|
Third |
13 (31.71) |
28 (68.29) |
|
|
Fourth |
10 (33.33) |
20 (66.67) |
|
|
Fifth |
6 (42.86) |
8 (57.14) |
|
|
Sixth |
8 (47.06) |
9 (52.94) |
|
|
Academic load |
|
|
|
|
Full |
45 (32.37) |
94 (67.63) |
0.472 |
|
Partial |
12 (26.67) |
33 (73.33) |
|
|
Percentage of online classes -
mean (SD) |
56.58 (21.79) |
63.48 (18.79) |
0.03 |
|
Upper third |
|
|
|
|
Yes |
17 (32.69) |
35 (67.31) |
0.994 |
|
No |
31 (32.63) |
64 (67.37) |
|
|
Tobacco use |
|
|
|
|
No tobacco use |
51 (30.91) |
114 (69.09) |
0.952 |
|
1-10 cigarettes per day |
6 (31.58) |
13 (68.42) |
|
|
Alcohol use per week |
|
|
|
|
No alcohol use |
23 (29.49) |
55 (70.51) |
0.166c |
|
< once |
24 (28.24) |
61 (71.76) |
|
|
Once or twice |
8 (42.11) |
11 (57.89) |
|
|
≥ 3 times |
2 (100) |
0 (0) |
|
The values are expressed as n (%) or mean, a chi-square test,
b Student’s t-test, c chi-square test for linear trend
Factors associated with SQ
The variables found to be associated with SQ in the bivariate
analysis were sex, year of study and percentage of online classes (Table 3). The association between them was analyzed
before including them in the binary logistic regression model. Age and year of study were found to be associated with each other (Kruskal-Wallis test =
108, degrees of freedom = 5, p <
0.001). The median of the percentage of online classes decreased with increasing year of study:
first year (median
[Mdn] = 73.5), second (Mdn = 78.8), third (Mdn = 75.0),
fourth (Mdn = 43.9), fifth (Mdn = 37.8) and sixth (Mdn = 23.5). Since the year of study was correlated with age and the
percentage of online classes,
only the last variable was included in the
model. Finally, the multivariate analysis revealed that, regardless of the
percentage of online classes, females had twice the chance of poor SQ compared to males (95 %
CI: 1.05 - 3.82) (Table 4).
Table 4. Binary logistic
regression analysis for factors associated with poor sleep
quality among medical
students.
|
Characteristic |
Odds
ratio |
Lower
limit |
Upper
limit |
p
value |
|
Sex |
|
|
|
|
|
Male |
1 |
|||
|
Female |
2 |
1.05 |
3.82 |
0.035 |
|
Percentage of online classes |
1.02 |
1 |
1.03 |
0.057 |
Variance inflation
factor (sex and percentage of online classes)
= 1.01
Hosmer-Lemeshow test (chi-square = 6.407, p = 6.02), Nagelkerke’s R2 = 6.8 %
DISCUSSION
It was found that seven out of 10 medical students at a Peruvian
university had poor SQ. This is a higher rate than that reported
worldwide (52.7 %) and in the Americas (59.9 %) (12), but consistent with studies from Peru (83.9 %) (14), Colombia (79.3 %) (18) and Chile (91.8 %) (19).
The poor SQ found was lower than what has been reported in other studies in Peru in years prior to the quarantine (20,21), during the COVID-19 health emergency (14) and the
progressive return to in-person learning at a university in Lambayeque (22).
It is suggested that the transition period, during which in-person classes were resumed, had a
positive influence on SQ; however, this hypothesis cannot be confirmed due to
the absence of pre-pandemic baseline data to contrast our results. The
provisions of the Peruvian Ministry of Education resulted in transition period in which online and in-person classes coexisted (16). We consider that it will not
be feasible to replicate this context for further research and that our results
will serve to report what happened in this particular juncture.
Females were more affected by poor SQ, and this association
that has been seen in other studies (20,23,24).
Hormonal changes during their menstrual cycle, even without causing
symptoms or discomfort, influence SQ negatively (25,26).
These same hormonal factors also contribute to a higher prevalence of mental health
disorders-such as depression and anxiety-which are usually
pathologies associated with poor
SQ (27,28). This finding indicates that they form a group susceptible to poor SQ and its
consequences on academic performance and mental health.
Early-year students were younger and had a higher number of online classes compared to
upper-year students. It has been observed that online classes during the
COVID-19 pandemic affected the mental health and SQ of medical students, and
our results would be consistent with what has been reported in the literature (5,29).
It was found that, for every 1 %
increase in online learning modality, students
increased their probability of having poor SQ by
2 %,
which would explain
the higher frequency
of poor SQ among early-year
students. The juncture in which the study was conducted allowed measuring a
percentage of online classes, thereby enabling an evaluaton
of its role concerning SQ. This analysis would not be feasible in in-person learning modality. No prior
studies were found that investigated the percentage of online classes or any
similar indicator. Although the strength of the association between the percentage of
online classes and poor SQ was marginal, it was suggested that a sample
with greater statistical
power could confirm this association.
Belonging to the upper third is an indicator of good academic performance. Evidence
suggests that good SQ is associated
with better academic performance (30,31);
nevertheless, in our study and in a Peruvian study,
no such association was found (13). A full academic
load was associated with poor SQ in a 2016 Peruvian study (32).
In our study, students with a partial academic load-which involved fewer class
hours- had a similar percentage of online classes compared to those with a full academic load. This fact may have led to the academic load not being associated
with SQ.
As for alcohol and/or tobacco use, these have been
associated with a higher rate of poor SQ, as well as alterations in mental
health (33,34).
In our study, the lack of association
between poor SQ and tobacco use could be explained by the low rate of smokers (10.3 %). No association
was found between
alcohol use and SQ. In the sample
only
1.1 % consumed alcohol three or more times per week, and
the amount of consumption was not inquired about, which would have helped
to better explore
the association between poor SQ and alcohol use. This association that has been reported
as significant by other studies (33,35).
Within the sleep parameters, it is important
to point out that two out of
three students sleep less than seven hours per night, i.e., they do not meet
the minimum amount of sleep recommended for their age (seven to nine hours per day) (36). This finding is repeated in other studies (21,37) and is alarming because
long-term effects are associated with
an increased risk of hypertension, type 2 diabetes mellitus, obesity,
depression and cardiovascular disease, inter alia (38). These data support the relevance of seeking solutions
to prevent the consequences of
not getting enough hours of sleep.
Daytime dysfunction is a consequence of inadequate rest, and we found that 77.1 % of the
students had moderate to severe
daytime dysfunction, which
can result in serious
outcomes such as workplace or car accidents (39). This result may appear contradictory when
compared to the rate of students reporting good to fairly good subjective sleep
quality (51.6 %). The foregoing reveals that while half of the students
considered their SQ to be good to fairly good, three out of four had daytime
dysfunction in the moderate
to
severe range and seven out of 10 were classified as having poor SQ. These findings
could be explained
by the fact that there are
students who underestimate their behaviors prone to poor SQ, and subjectively consider that they sleep
well, and do not perceive
the need to implement measures to improve their sleep.
The study had the following limitations: we did not perform stratified
sampling by year, which would have been the most efficient sample selection for
the characteristics of the study population; responses were susceptible to recall bias and social desirability among the participants. Finally, the results cannot be extrapolated to other university
student populations because it was a single-center study conducted in a very
specific context.
In conclusion, poor SQ is a prevalent
condition that affects 7
seven out of 10 medicine
students at a private university located in Lima, Peru. Being
female was found to be an independent risk factor for poor SQ, regardless of
the percentage of online classes. Therefore, it is recommended
to study and implement interventions based on sleep hygiene to improve
and maintain adequate SQ among university medical students, especially among
females.
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Acknowledgment: We
express our gratitude to Dr. Liesel Ludowieg for her advice
and support in conceptualizing the idea and to Dr. Billy Sánchez for his
assistance with the pilot test and analysis of the pilot data.
Author contributions: AMO
and FRR participated in the study design. Moreover, AMO was responsible for
data collection, and AMO and FRR developed
the database and conducted the
statistical analysis. In addition, AMO prepared the first draft of the
manuscript. AMO and FRR then carried out the successive revisions of the manuscript
and approved the final version.
Both assume responsibility for the published article.
Funding sources:
This article was funded by the authors.
Conflicts of interest: The authors declare no conflicts of interest.
Corresponding author:
Andrea Magaly
Martin Osorio
Address: C/ Mártir José Olaya 162, Miraflores. Lima, Perú.
Telephone: +51 945 595 510
E-mail: andrea.martin@alum.udep.edu.
Reception
date:
June 16, 2023
Evaluation
date:
July 7, 2023
Approval
date:
July 26, 2023