Prevalence and associated factors of ADHD-like symptoms among
pharmacy students at Prince of Songkla University, Thailand in 2024: a
cross-sectional study
1Department of Social and Administrative Pharmacy, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Songkhla, Thailand
*Corresponding author: Amarawan
Pentrakan, Department of Social and Administrative Pharmacy, Faculty of
Pharmaceutical Sciences, Prince of Songkla University, 15 Kanchanavanit Road,
Hat Yai, Songkhla 90110, Thailand, E-mail:
amarawan.p@psu.ac.th
• Received: September 2, 2024 • Accepted: October 12, 2024
This is an Open-Access article distributed under the terms of the
Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Objectives: This study investigated the prevalence of
attention-deficit hyperactivity disorder (ADHD) and its associated factors among
pharmacy students at Prince of Songkla University in 2024. It was hypothesized
that the prevalence of ADHD would be associated with various demographic,
socioeconomic, historical, and behavioral factors.
Methods: This cross-sectional descriptive study involved pharmacy
students from years 1–5 at Prince of Songkla University in Thailand. Data
were gathered from 761 students using a self-administered questionnaire that
included the Adult ADHD Self-Report Scale (ASRS Screener V1.1). Descriptive
statistics, the chi-square test, the Fisher exact test, and multiple logistic
regression were employed for data analysis.
Results: In total, 526 students participated in the study
(participation rate: 69%), with an average age of 21±1.57 years. The risk
of ADHD was prevalent in 14.4% of the respondents (76 students; 95% CI:
11.4%–17.5%). Significant factors associated with an increased risk of
ADHD included identifying as not disclosed or preferring not to report gender
(adjusted OR [ORadj], 3.32; 95% CI, 1.04–10.57), having
insufficient monthly income (ORadj, 2.02; 95% CI, 1.13–3.61),
and recent traffic violations (ORadj, 2.02; 95% CI,
1.09–3.76). It was also found that difficulties with executive
functioning, such as organization and procrastination, were highly prevalent
among pharmacy students.
Conclusion: The study identified a substantial prevalence of ADHD
risk among pharmacy students, with factors including gender, financial
challenges, and behavioral patterns such as traffic violations significantly
associated with this risk. These findings underscore the necessity for targeted
mental health interventions in university settings.
Attention-deficit hyperactivity disorder (ADHD) is a neurological condition
primarily characterized by difficulties with inattention, hyperactivity, and
impulsivity. Although ADHD is most commonly associated with children and
adolescents, it can affect individuals of any age, making it a significant
global issue [1]. The COVID-19 pandemic
has exacerbated symptoms of ADHD, such as impulsivity, restlessness, and
concentration challenges, especially among students who are under increased
academic pressure. A study conducted in Japan [2] reported a notable increase in the prevalence of ADHD among
university students during the pandemic, reaching approximately 12%. This rise
was accompanied by worsened symptoms, including reduced focus, impaired
multitasking, emotional instability, and increased impulsivity and
hyperactivity, which negatively impacted social interactions and overall
functioning.
Similar trends have been observed in Thailand, where growing evidence suggests
that the COVID-19 pandemic has increased risk behaviors related to violence and
mental health issues among adolescents [3]. These impacts have led to physical inactivity, sedentary lifestyles,
and subsequent physical changes with long-term implications. Moreover, the
pandemic has contributed to the development of psychological conditions such as
emotional distress, conduct problems, hyperactivity, and inattention,
particularly among adolescents with chronic illnesses [4]. These negative effects may have lasting consequences,
including emerging ADHD symptoms, which could lead to further adverse physical
health outcomes over time.
Research has identified several factors that contribute to the risk of ADHD and
other mental health issues among students. These factors are typically
categorized into personal demographics, familial influences, and social
dimensions [5]. Notably, the COVID-19
pandemic has intensified certain behavioral changes, such as an increased
reliance on excessive digital technology, which have been linked to symptoms of
ADHD [6]. Furthermore, evidence suggests
that medical students exhibit a higher prevalence of ADHD-like symptoms compared
to their peers in other academic fields [7]. The shift to online classes due to COVID-19 has resulted in an
overall decline in academic performance [8]. Individuals experiencing significant academic stress are
particularly prone to displaying symptoms of ADHD.
Despite heightened awareness of ADHD, there is limited research on its prevalence
and associated factors among students in the post-COVID-19 era, particularly
within the pharmacy student population. The rigorous demands of pharmacy
programs, combined with the challenges of adapting to new learning environments,
may lead to an increase in ADHD-like symptoms in these students.
Objectives
This study aimed to investigate the prevalence of ADHD-like symptoms and their
associated factors among pharmacy students, focusing on first- to fifth-year
students in 2024 at Prince of Songkla University, Thailand.
Methods
Ethics statement
This study received approval from the Human Research Ethics Committee of the
Faculty of Pharmaceutical Sciences at Prince of Songkla University (Approval No.
68108/36). Additionally, participants were given an invitation letter to review
prior to deciding whether to participate in the research project.
Study design
A cross-sectional descriptive survey was conducted to explore the prevalence of
ADHD-like symptoms and their associated factors among pharmacy students at
Prince of Songkla University, Thailand.
Settings
The online questionnaire survey was conducted among pharmacy students at Prince
of Songkla University, Thailand, from January 15 to February 14, 2024.
Participants
This research involved all students from years 1 to 5 at the Faculty of
Pharmaceutical Sciences, Prince of Songkla University, who are currently
enrolled in both the Industrial Pharmacy and Pharmaceutical Care programs and
have consented to provide information.
Variables
Drawing from the literature review, we have categorized factors potentially
associated with ADHD-like symptoms into four domains. The first domain,
demographic factors, includes variables such as age and gender. The second
domain, socioeconomic status, covers elements like current education level,
field of study, living situation, and adequacy of monthly income. The third
domain, history, encompasses both personal and familial aspects, specifically
past behavioral issues and diagnoses of ADHD. The fourth domain, personal
behavior, examines factors such as history of traffic violations, alcohol
consumption, and smoking habits. Additionally, the category of online activities
explores patterns of internet usage, including duration and extent. The
framework outlining the relationship between these factors and ADHD is presented
in Fig. 1.
Fig. 1.
Conceptual framework for studying the relationship between various
factors and ADHD-like symptoms.
Data sources/measurement
The research employed an online questionnaire structured into three sections: (1)
Personal demographics, socioeconomic status, and past ADHD diagnosis: This
section collected data on various variables such as age, gender, academic year,
field of study, residence, adequacy of monthly income, and both personal and
familial history of ADHD. It provided a comprehensive demographic and
socioeconomic profile. (2) Personal behavior and online activities: This section
explored participants' behavior and online engagement through eight
questions covering topics such as traffic violations, alcohol consumption,
smoking, online learning hours, and media usage patterns. It aimed to examine
correlations between personal behaviors and online activities. (3) The Adult
ADHD Self-Report Scale (ASRS) V1.1: This reliable screener, with a sensitivity
of 0.93 and specificity of 0.71, was employed to assess potential ADHD symptoms
in adults [9]. The scale includes six
questions, each rated on a 5-point scale ranging from “never” to
“very often.” The questions are designed to measure the frequency
of symptoms, aiding in the initial screening for ADHD. To assess ADHD symptoms,
scores are assigned as follows: for items one through three, a score of 1 is
given if responses range from “sometimes” to “very
often”; otherwise, the score is 0. For items four through six, a score of
1 is assigned if responses are “often” or “very
often”; otherwise, the score is 0. A total score of 4 or higher indicates
potential ADHD symptoms.
The research data are available in Dataset 1.
Bias
This research took measures to prevent bias by anonymizing the identities of the
respondents. Participants have the freedom to choose whether or not they wish to
partake in this study.
Study size
The sample size was not estimated, as this study included the entire population
that consented to provide information.
Statistical methods
This study utilized descriptive statistics to analyze personal data, including
frequency distributions, percentages, means, and SDs. To investigate the
relationships among variables, inferential statistics are employed, focusing on
the analysis of single-variable relationships. Depending on the data, either the
chi-square test or the Fisher exact test was used, with the latter being applied
when expected values were fewer than 5 in more than 20% of cases. For the
analysis of multiple variables, multiple logistic regression was conducted, and
the results are presented as adjusted OR (ORadj) and 95% CIs. All
statistical analyses were performed using SPSS software, with the significance
threshold set at 0.05. For data visualization, Tableau software was used.
Results
Participants
ADHD was evaluated in 761 pharmacy students across years 1 to 5 at Prince of
Songkla University using the ASRS screener. Out of these students, 526 (69%)
voluntarily participated, with an average age of 21±1.57 years. The
majority of the participants were female (73%), enrolled in the industrial
pharmacy program (59.9%), did not drink alcohol (66.2%), did not smoke (98.1%),
and had no previous ADHD diagnosis (95.3%). The screening identified 76 students
(14.4%; 95% CI, 11.4%–17.5%) as meeting the criteria for ADHD.
Table 1 presents the frequency
distributions of subgroup variables and analytical factors associated with ADHD.
The study employed the chi-square or Fisher exact test to identify significant
associations between the occurrence of ADHD and various variables. These
variables included gender, adequacy of monthly income, history of prior ADHD
diagnosis, frequency of traffic rule violations in the past 3 months, daily
internet or smartphone use, and duration of online study sessions.
Table 1.
Preliminary test results for the relationship between screening
factors and ADHD assessment results
Time spent on the
internet/smartphone per day (hr)
0.040
Less than 4
70 (13.3)
65 (92.4)
5 (7.1)
4–6
134 (25.5)
119 (88.8)
15 (11.2)
More than ٦
322 (61.2)
266 (82.6)
56 (17.4)
Length of online
study/learning session (hr)
0.027
Less than or equal to
١
40 (7.6)
34 (85.0)
6 (15.0)
More than 1 up to 2
288 (54.8)
246 (85.4)
42 (14.6)
More than ٢ up to
٣
140 (26.6)
127 (90.7)
13 (9.3)
More than 3
58 (11.0)
43 (74.1)
15 (25.9)
Most frequent time of
using online media
0.053
Daytime
33 (6.3)
32 (97.0)
1 (3.0)
Nighttime
486 (92.4)
413 (85.0)
73 (15.0)
Both
7 (1.3)
5 (71.4)
2 (28.6)
ADHD , attention-deficit hyperactivity disorder.
1)Chi-square test or Fisher exact test (for expected values below 5 in
more than 20% of cases).
The results revealed a statistically significant difference in ADHD rates based
on gender (P=0.017). Furthermore, individuals with insufficient income were more
likely to have ADHD compared to those with sufficient income (P=0.021).
Additionally, there was a significant association concerning individuals with a
prior ADHD diagnosis, who were much more likely to be deemed at risk for ADHD in
this assessment (P=0.005). Furthermore, a statistically significant association
was observed between ADHD assessment results and behavioral factors, including
traffic violations (P=0.013), internet usage (P=0.040), and the duration of
study sessions (P=0.027).
Main results
Fig. 2 presents a bar chart that displays
responses to the ASRS screener for six key questions. Each bar represents the
frequency of ADHD-related symptoms as rated by different groups of students. The
x-axis indicates the number of students, and the colored segments within each
bar show varying frequency responses, from "never" to "very
often". Key observations reveal that many individuals often struggle to
complete tasks after finishing the challenging parts, suggesting issues with
task follow-through. Similarly, numerous respondents frequently encounter
difficulties in organizing tasks that require careful planning, indicating
problems with executive functioning.
Fig. 2.
The distribution of responses to the ASRS screener questions among
pharmacy students. ASRS, Adult ADHD Self-Report Scale; ADHD,
attention-deficit hyperactivity disorder.
Memory issues were prevalent, with a significant number of respondents reporting
that they sometimes or often forgot appointments or obligations. Tasks requiring
substantial mental effort frequently led to avoidance and procrastination, with
many respondents admitting they delayed starting such tasks “very
often” or “often.” Restlessness was another common issue,
with numerous respondents experiencing fidgeting or squirming when required to
sit for extended periods. Although responses varied, fidgeting was frequently
reported by a significant portion of respondents. Additionally, feelings of
hyperactivity and the urge to stay in motion were apparent, with many
respondents experiencing this “sometimes.”
Multiple logistic regression analysis of factors related to the prevalence of
ADHD identified significant associations after adjusting for the influence of a
past ADHD diagnosis. The factors found to be statistically significant included
not disclosed or unspecified gender (ORadj, 3.32; 95% CI,
1.04–10.57), insufficient monthly income relative to expenses
(ORadj, 2.02; 95% CI, 1.13–3.61), and a history of traffic
violations within the past 3 months (ORadj, 2.02; 95% CI,
1.09–3.76), as detailed in Table
2.
Adequacy of monthly income
(reference: sufficient)
Insufficient
2.019
(1.129–3.611)
0.018
Traffic violation behavior
(reference: never violated traffic rules in the past 3
months)
Ever violated traffic rules in
the past 3 months
2.021
(1.086–3.759)
0.026
Unsure
1.189
(0.470–3.007)
0.714
ADHD, attention-deficit hyperactivity disorder.
1)Multiple logistic regression, with a significance level at 0.05.
Discussion
Key results
This research investigated the prevalence of ADHD and its associated factors
among pharmacy students in 2024. The study involved 526 pharmacy students from
first to fifth years at Prince of Songkla University, who participated
voluntarily. The findings indicate that 14.4% of the students showed signs of
ADHD. The initial analysis examined the relationship between ADHD and various
factors, including gender, monthly income sufficiency, previous ADHD diagnoses,
recent traffic violations, daily internet and smartphone usage, and the length
of online study sessions. Further analysis on the impact of previous ADHD
diagnoses revealed several risk factors that increase the likelihood of an ADHD
diagnosis. Individuals of unspecified gender were found to have higher odds of
ADHD than female students, although no significant difference was found between
male and female students. Additionally, students with insufficient income were
more than twice as likely to have ADHD than those with adequate income.
Moreover, students who had committed traffic violations in the past 3 months
were approximately twice as likely to have ADHD compared to those who had
not.
Interpretation
Current pharmacy students appear to have a higher risk of developing ADHD-like
symptoms, with a prevalence exceeding the 8% reported before the COVID-19
pandemic by the Mental Health Institute in Thailand [10]. Fig. 2 visualizes
the prevalence of these behaviors among students. The cognitive symptoms
reported in pharmacy students include impaired attention, difficulty in task
management, forgetfulness, and reduced executive functioning—consistent
with common ADHD challenges. Difficulties in executive functioning, such as
organization and procrastination, were highly prevalent. While hyperactivity and
impulsive symptoms were also present, they were less pronounced compared to the
challenges associated with inattention. The findings suggest that several
factors may contribute to this increased ADHD risk among pharmacy students,
including mental health struggles related to gender identity, financial stress,
and environmental influences. Notably, students with insufficient income were
twice as likely to exhibit ADHD-like behaviors compared to those with sufficient
income. Furthermore, students who violated traffic rules show a doubled risk of
ADHD, with even those unsure about their traffic behavior showing an increased
likelihood. This may be linked to traits such as impulsivity, inattention, and
difficulties with executive functioning, which can contribute to higher rates of
risky or rule-breaking behaviors, particularly in risky driving.
Comparison with previous studies
Our findings are consistent with increased rates of ADHD observed in other
countries since the onset of the COVID-19 pandemic. For example, a study in
Europe noted an increase in the consumption of ADHD medications following the
pandemic [11]. Additionally, our results
align with studies indicating that not disclosed and transgender individuals
experience significantly higher rates of mental health challenges than their
cisgender counterparts [12]. There is
also substantial evidence suggesting that chronic financial stress can
contribute to cognitive and behavioral issues typically associated with ADHD
[13]. Moreover, our findings
corroborate the connection between traffic violations and unmedicated ADHD
drivers, especially in situations that require low levels of attention [14]. However, our study identifies a
correlation between the duration of online learning and the manifestation of
ADHD symptoms, which contradicts previous research suggesting no link between
learning styles and mental health issues [15].
Limitations/generalizability
While our study offers valuable insights, its scope was limited to a single
university in Southern Thailand, potentially affecting its generalizability. The
cross-sectional design provided only a snapshot in time and lacked the depth of a
longitudinal study. Furthermore, the reliance on self-reported data could introduce
biases, and the sample size for certain subgroups, such as male students, was
relatively small. Future research should aim to include larger sample sizes to
address these limitations.
Suggestions
To advance research on ADHD prevalence among pharmacy students, further studies
are essential to fully comprehend their behavioral patterns and coping
strategies. This understanding is crucial for developing effective support
mechanisms tailored to their specific needs. Additionally, it is important to
explore targeted interventions that address specific risk factors identified in
the study, including insufficient income, gender identity, and recent traffic
violations. Addressing these factors will improve our understanding of ADHD in
this group and inform the development of more precise and effective intervention
strategies.
Conclusion
Several factors increase the likelihood of experiencing ADHD-like symptoms.
Individuals of unspecified gender, those with insufficient monthly income, and
those with recent traffic rule violations were more prone to exhibit ADHD-like
symptoms. Notably, symptoms such as difficulties in executive functioning,
including organization and procrastination, were highly prevalent among pharmacy
students.
Authors' contributions
Project administration: Pentrakan A
Conceptualization: Rakchat K, Eadcharoen S, Pentrakan A
Methodology & data curation: Rakchat K, Eadcharoen S, Pentrakan A
Funding acquisition: not applicable
Writing – original draft: Rakchat K, Eadcharoen S, Pentrakan A
No potential conflict of interest relevant to this article was reported.
Funding
This work was supported by the Prince of Songkla University research fund
(PHA6704028S). The funder had no role in the study design, data collection or
analysis, decision to publish, or preparation of the manuscript.
Dataset 1 . Raw response data from 526 participants in Thailand
Acknowledgments
We extend our sincere gratitude to the Faculty of Pharmaceutical Sciences at Prince
of Songkla University for their invaluable support in providing access to relevant
information essential to this research.
Supplementary materials
Not applicable.
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Prevalence and associated factors of ADHD-like symptoms among
pharmacy students at Prince of Songkla University, Thailand in 2024: a
cross-sectional study
Fig. 1.
Conceptual framework for studying the relationship between various
factors and ADHD-like symptoms.
Fig. 2.
The distribution of responses to the ASRS screener questions among
pharmacy students. ASRS, Adult ADHD Self-Report Scale; ADHD,
attention-deficit hyperactivity disorder.
Fig. 1.
Fig. 2.
Prevalence and associated factors of ADHD-like symptoms among
pharmacy students at Prince of Songkla University, Thailand in 2024: a
cross-sectional study
Preliminary test results for the relationship between screening
factors and ADHD assessment results
Factors
n (%)
ADHD assessment results
(n=526)
P-value1)
No ADHD n (%)
ADHD n (%)
Age (yr)
0.793
≤20
116 (22.1)
100 (86.2)
16 (13.8)
21–23
322 (61.2)
273 (84.8)
49 (15.2)
≥23
88 (16.7)
77 (87.5)
11 (12.5)
Gender
0.017
Male
122 (23.2)
102 (83.6)
20 (16.4)
Female
384 (73.0)
335 (87.2)
49 (12.8)
Undisclosed gender
20 (3.8)
13 (65.0)
7 (35.0)
Year of study
0.137
1
126 (24.0)
106 (84.1)
20 (15.9)
2
137 (26.0)
122 (89.1)
15 (10.9)
3
99 (18.8)
78 (78.8)
21 (21.2)
4
96 (18.3)
82 (85.4)
14 (14.6)
5
68 (12.9)
62 (91.2)
6 (8.8)
Program of study
0.897
Industrial pharmacy
315 (59.9)
270 (85.7)
45 (14.3)
Clinical pharmacy
211 (40.1)
180 (85.3)
31 (14.7)
Residence
0.307
Home
86 (16.3)
69 (80.2)
17 (19.8)
Off-campus dormitory
214 (40.7)
185 (86.4)
29 (13.6)
On-campus dormitory
226 (43.0)
196 (86.7)
30 (13.3)
Living arrangement
0.226
With family
90 (17.1)
73 (81.1)
17 (18.9)
With friends
215 (40.9)
190 (88.4)
25 (11.6)
Living alone
221 (42.0)
187 (84.6)
34 (15.4)
Adequacy of monthly
income
0.021
Sufficient
350 (66.5)
310 (88.6)
40 (11.4)
Insufficient
176 (33.5)
140 (79.5)
36 (20.5)
Personal history of ADHD
diagnosis
0.005
Yes
4 (0.8)
2 (50.0)
2 (50.0)
No
502 (95.4)
433 (86.3)
69 (13.7)
Unsure
20 (3.8)
15 (75.0)
5 (25.0)
Family history of ADHD
diagnosis
0.189
Yes
6 (1.1)
4 (66.7)
2 (33.3)
No
498 (94.7)
429 (86.1)
69 (13.9)
Unsure
22 (4.2)
17 (77.3)
5 (22.7)
Traffic violation behavior
(in the past 3 months)
0.013
Ever violated traffic
rules
166 (31.6)
133 (80.1)
33 (19.9)
Never violated traffic
rules
303 (57.6)
271 (89.4)
32 (10.6)
Unsure
57 (10.8)
46 (80.7)
11 (1.3)
Frequency of drinking (in
the past 3 months)
0.318
Do not drink
349 (66.3)
304 (87.1)
45 (12.9)
Drink less than or equal to
٣ times per week
43 (8.2)
38 (88.4)
5 (11.6)
Drink more than ٣ times
per week
115 (21.9)
94 (81.7)
21 (18.3)
Smoking
0.625
Non-smoker
517 (98.3)
443 (85.7)
74 (14.3)
Smoker
9 (1.7)
7 (77.8)
2 (22.2)
Time spent on the
internet/smartphone per day (hr)
0.040
Less than 4
70 (13.3)
65 (92.4)
5 (7.1)
4–6
134 (25.5)
119 (88.8)
15 (11.2)
More than ٦
322 (61.2)
266 (82.6)
56 (17.4)
Length of online
study/learning session (hr)
0.027
Less than or equal to
١
40 (7.6)
34 (85.0)
6 (15.0)
More than 1 up to 2
288 (54.8)
246 (85.4)
42 (14.6)
More than ٢ up to
٣
140 (26.6)
127 (90.7)
13 (9.3)
More than 3
58 (11.0)
43 (74.1)
15 (25.9)
Most frequent time of
using online media
0.053
Daytime
33 (6.3)
32 (97.0)
1 (3.0)
Nighttime
486 (92.4)
413 (85.0)
73 (15.0)
Both
7 (1.3)
5 (71.4)
2 (28.6)
ADHD , attention-deficit hyperactivity disorder.
1)Chi-square test or Fisher exact test (for expected values below 5 in
more than 20% of cases).
Factors related to the prevalence of ADHD
Factor
ORadj
95% CI
P-value1)
Gender (reference:
female)
Male
1.264
(0.627–2.546)
0.512
Unspecified gender
3.317
(1.041–10.574)
0.043
Adequacy of monthly income
(reference: sufficient)
Insufficient
2.019
(1.129–3.611)
0.018
Traffic violation behavior
(reference: never violated traffic rules in the past 3
months)
Ever violated traffic rules in
the past 3 months
2.021
(1.086–3.759)
0.026
Unsure
1.189
(0.470–3.007)
0.714
ADHD, attention-deficit hyperactivity disorder.
1)Multiple logistic regression, with a significance level at 0.05.
Table 1.
Preliminary test results for the relationship between screening
factors and ADHD assessment results
ADHD , attention-deficit hyperactivity disorder.
Chi-square test or Fisher exact test (for expected values below 5 in
more than 20% of cases).
Table 2.
Factors related to the prevalence of ADHD
ADHD, attention-deficit hyperactivity disorder.
Multiple logistic regression, with a significance level at 0.05.
