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ORIGINAL RESEARCH article

Front. Med., 16 December 2025

Sec. Healthcare Professions Education

Volume 12 - 2025 | https://doi.org/10.3389/fmed.2025.1719936

Knowledge, counseling practices, and educational gaps related to drug–food interactions among healthcare professionals in Saudi Arabia: a cross-sectional study

  • 1. College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia

  • 2. Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia

  • 3. Department of Pharmaceutical Practices, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia

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Abstract

Background:

Drug–food interactions (DFIs) can significantly affect therapeutic efficacy and patient safety. However, there is limited evidence regarding healthcare professionals’ knowledge and practices concerning DFIs, particularly in Saudi Arabia. This study aimed to evaluate the awareness, counseling practices, and perceived barriers among healthcare professionals regarding DFIs, with a focus on cultural and educational dimensions.

Methods:

A cross-sectional survey was conducted among 385 healthcare professionals in Saudi Arabia using a structured self-administered questionnaire. The survey assessed knowledge of DFIs, sources of information, counseling behavior, reporting practices, and the perceived need for additional training. Descriptive statistics were used to summarize findings.

Results:

A total of 231 respondents (60.0%) demonstrated limited knowledge regarding DFI mechanisms. More than half (51.4%) reported receiving no formal instruction on DFIs, and 146 participants (37.9%) relied primarily on informal sources. Only 164 professionals (42.6%) indicated that they routinely counseled patients about DFIs involving traditional foods or herbal products. Notably, only 73 participants (19.0%) had ever reported a DFI-related adverse event. These patterns suggest possible deficiencies in current curricular content, continuing professional education, and cultural alignment of counseling practices.

Conclusion:

The findings indicate potential gaps in healthcare professionals’ preparedness to manage DFIs effectively, particularly in relation to traditional food practices and pharmacovigilance. Tentatively, this may reflect shortcomings in both undergraduate curricula and post-graduate training programs. There may be value in integrating culturally sensitive DFI training into pharmacy and medical education, supported by continuing professional development initiatives. National guidelines tailored to local dietary contexts and patient beliefs could also enhance the quality of DFI-related counseling and reporting.

Introduction

Understanding drug–food interactions (DFIs) is essential to optimizing therapeutic outcomes and ensuring medication safety. DFIs can alter drug absorption, metabolism, and overall efficacy, potentially leading to therapeutic failures or adverse reactions. Comprehensive knowledge of DFIs is required among healthcare professionals, particularly pharmacists, physicians, and nurses, to ensure effective management and prevention of such interactions. Practical strategies, such as adjusting the timing of drug administration relative to meals, have been shown to reduce the likelihood of clinically significant interactions (1, 2).

The prevalence and clinical consequences of DFIs are well documented. For example, dietary fiber-rich foods have been shown to interfere with the absorption of levothyroxine, resulting in sub-therapeutic responses and poor disease control (3). Similarly, variations in the intake of omega-3 fatty acids can modify the pharmacological effects of warfarin, highlighting the importance of dietary consistency in patients receiving anticoagulant therapy (4). Such examples demonstrate the practical importance of DFI awareness for both healthcare providers and patients.

Globally, the integration of drug–food interaction (DFI) education into healthcare curricula has been recognized as a crucial step toward improving medication safety and therapeutic outcomes. Systematic reviews and comparative studies have indicated that structured DFI education, tailored to pharmacists, physicians, and other healthcare professionals, enhances clinical decision-making and patient counseling skills (58). Despite this, international analyses have revealed considerable variation in the inclusion and depth of DFI-related content across medical, pharmacy, and nursing programs (9, 10). Evidence also shows that healthcare providers frequently underestimate the impact of dietary components on drug efficacy and safety, highlighting the need for educational reform and continuing professional development in this area (1113). Collectively, these findings underscore the global call for standardized DFI modules and practical training integrated into undergraduate curricula and CPD programs to equip healthcare professionals with the knowledge and counseling competencies necessary for optimal patient care (1416).

Despite growing evidence, awareness of DFIs among healthcare professionals remains inconsistent across practice settings. Studies suggest that many practitioners possess limited knowledge of the clinical significance of DFIs or the mechanisms through which food components influence drug bioavailability (2, 17). Educational interventions targeting these knowledge gaps have proven effective in enhancing DFI-related competencies and improving medication safety outcomes. Therefore, incorporating DFI education into undergraduate curricula and continuing professional development programs is crucial to ensuring that healthcare professionals are well prepared to counsel patients appropriately (18).

Knowledge retention and practical application may be strengthened through the adoption of innovative educational strategies in healthcare training programs. Problem-based learning and case-based discussions can help students and professionals apply DFI concepts to real-world clinical scenarios. Additionally, simulation-based training and interactive digital modules have demonstrated effectiveness in improving comprehension and decision-making related to medication–food interactions (19, 20). Such approaches not only improve knowledge but also foster critical thinking and communication skills essential for patient counseling.

As the understanding of pharmacotherapy continues to evolve, the need to educate healthcare professionals about DFIs becomes increasingly urgent. A well-informed workforce can significantly reduce preventable medication errors, enhance therapeutic efficacy, and contribute to safer patient care practices. Strengthening DFI education within professional curricula and clinical training frameworks represents a vital step toward improving public health outcomes and advancing medication safety (21).

Therefore, this study aimed to assess the knowledge, counseling practices, and educational gaps related to drug–food interactions among healthcare professionals in Saudi Arabia. Specifically, it sought to evaluate healthcare providers’ understanding of common DFIs, identify demographic and professional predictors associated with knowledge levels, and explore the need for enhanced educational strategies to strengthen safe medication practices within the Saudi healthcare context.

Materials and methods

Study design and setting

A cross-sectional study was conducted between October 2024 and January 2025 to assess the knowledge, attitudes, and practices (KAP) of healthcare professionals (HCPs) in Saudi Arabia regarding drug–food interactions (DFIs). The study employed a structured, self-administered online questionnaire distributed across five major geographic regions: North, South, East, West, and Central Saudi Arabia.

Study population and sampling

The target population included licensed pharmacists, physicians, and nurses practicing in Saudi Arabia. Eligible participants were required to have at least 1 year of clinical experience and to provide informed consent prior to participation. Medical students, interns, unlicensed individuals, or those unwilling to participate were excluded.

Sample size calculation was performed using the Raosoft sample size calculator with a 95% confidence level, 5% margin of error, and an assumed response distribution of 50%, yielding a required sample of 385 respondents. A multistage stratified sampling technique was utilized. In the first stage, healthcare facilities were selected across the five geographic regions. In the second stage, participants were recruited using systematic random sampling from those facilities.

Instrument development and validation

The survey instrument was developed following an extensive literature review and expert panel consultation. It consisted of six sections: demographic data, self-assessment of DFI familiarity, knowledge of DFIs, knowledge of medication timing, attitudes toward DFIs, and DFI-related practices.

Content validity was assessed by a panel of clinical pharmacy and pharmacology experts. A pilot test involving 20 healthcare professionals was conducted to assess clarity and feasibility; data from the pilot were excluded from the final analysis. Internal consistency was acceptable, with Cronbach’s alpha values of 0.78 for knowledge, 0.72 for attitudes, and 0.82 for practices.

Data collection

The final survey was hosted on Microsoft Forms and distributed electronically via professional networks and social media platforms. Data were collected anonymously over a 3-month period (15 October 2024 to 15 January 2025). Participation was voluntary, and responses were de-identified prior to analysis.

Scoring of outcomes

Knowledge of DFIs was assessed using nine true/false/don’t know items, with one point awarded per correct answer (range: 0–9). Knowledge of medication timing was assessed using seven items (range: 0–7). Attitude scores were calculated from four statements using a 3-point Likert scale (disagree = 1, unsure = 2, agree = 3), with a total score range of 4–12. Practice scores were based on five items rated on a 5-point Likert scale (never = 1 to always = 5), producing a total score range of 5–25.

Statistical analysis

Data were analyzed using R software (version 4.4.2) and RStudio (version 2024.9.1.394). Descriptive statistics included medians and interquartile ranges (IQRs) for non-normally distributed continuous variables, and frequencies and percentages for categorical variables. Normality was assessed using the Shapiro–Wilk test.

Inferential analysis was conducted using the Wilcoxon rank-sum test (for dichotomous variables) and the Kruskal–Wallis test (for variables with > 2 categories). Variables with a p-value < 0.05 in bivariate analysis were included in multivariable linear regression models to identify independent predictors of knowledge, attitude, and practice scores. Regression coefficients (β), 95% confidence intervals (CIs), and p-values were reported. All tests were two-sided, with a significance threshold set at p < 0.05.

Ethical considerations

The study protocol was reviewed and approved by the Biomedical Research Ethics Committee of Umm Al-Qura University, Makkah, Saudi Arabia (Approval No. HAPO-02-K-012-2025-01-2450). All methods were performed in accordance with the relevant guidelines and regulations, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained electronically from all participants prior to data submission. Data confidentiality and participant anonymity were maintained throughout the study.

Results

Demographic characteristics and self-perceptions on knowledge and practice of DFIs

A total of 385 participants were included in this cross-sectional study. Most respondents were aged between 30 and 39 years (46.8%), followed by those aged 22–29 years (37.1%). The sample consisted of 59.2% males and 40.8% females. Most participants were pharmacists (63.4%), followed by senior pharmacists (9.6%) and general practitioners (7.3%). The majority (88.6%) obtained their highest degree in Saudi Arabia. Regarding professional experience, 44.9% had more than 10 years of experience, while 29.1% had 1–5 years. The primary work settings were hospitals (40.9%) and pharmacies (40.4%). The southern region had the highest representation in the workplace (36.6%), followed by the western region (25.7%) and the central region (13.2%). Regarding familiarity with DFIs, 63.6% reported being somewhat familiar, and 31.2% indicated being very familiar. Most participants (68.3%) received formal training in DFIs during their undergraduate or graduate studies. DFIs were occasionally encountered by 54.8% of the respondents, and 64.9% reported being somewhat confident in identifying clinically significant DFIs (Table 1).

TABLE 1

Characteristic Description
Age
  22–29 years 143 (37.1%)
  30–39 years 180 (46.8%)
  40–49 years 58 (15.1%)
  50–59 years 3 (0.8%)
  Above 60 years 1 (0.3%)
Gender
  Male 228 (59.2%)
  Female 157 (40.8%)
Professional job
  Medical consultant 6 (1.6%)
  Medical specialist 5 (1.3%)
  General practitioner 28 (7.3%)
  Medical resident 11 (2.9%)
  Consultant pharmacist 12 (3.1%)
  Senior pharmacist 37 (9.6%)
  Pharmacist 244 (63.4%)
  Nurse 24 (6.2%)
  Others 18 (4.7%)
Country of the highest degree
  From Saudi Arabia 341 (88.6%)
  Outside of Saudi Arabia 44 (11.4%)
Years of experience in the current profession
   <1 year 42 (10.9%)
  1–5 Years 112 (29.1%)
  6–10 Years 58 (15.1%)
  More than 10 years 173 (44.9%)
Primary place of work*
  Hospital 157 (40.9%)
  Clinic 11 (2.9%)
  Hospital pharmacy 155 (40.4%)
  Community pharmacy 45 (11.7%)
  Others 16 (4.2%)
Region of the workplace
  Northern region 49 (12.7%)
  Southern region 141 (36.6%)
  Eastern region 45 (11.7%)
  Western region 99 (25.7%)
  Central region 51 (13.2%)
Familiarity with drug-food interactions
  Very familiar 120 (31.2%)
  Somewhat familiar 245 (63.6%)
  Not familiar 20 (5.2%)
Ever received formal training on drug-food interactions
  No 93 (24.2%)
  Yes, during undergraduate/graduate studies 263 (68.3%)
  Yes, through professional development programs 29 (7.5%)
Frequency of encountering drug-food interactions in practice
Never 25 (6.5%)
  Rarely 104 (27.0%)
  Occasionally 211 (54.8%)
  Frequently 45 (11.7%)
Confidence in identifying clinically significant drug-food interactions
  Not confident 34 (8.8%)
  Somewhat confident 250 (64.9%)
  Very confident 101 (26.2%)

Demographic characteristics and self-perceptions on knowledge and practice of drug-food interactions.

n (%); *The variable had one missing record.

Description of the scores of different domains

Participants had a median score of 6.0 out of 9.0 (IQR = 4.0–8.0) for knowledge regarding DFIs. For knowledge of the best timing of drug intake, the median score was 5.0 out of 7.0 (IQR = 4.0–6.0). Details of the knowledge scores are summarized in Table 2. Attitudes toward DFIs had a median score of 10.0 out of 12.0 (IQR = 10.0–12.0). The median score for practices related to DFIs was 17.0 out of 25.0 (IQR = 14.0–20.0).

TABLE 2

Characteristic Median (Q1–Q3) Mean ± SD Min-Max N of items
 Knowledge regarding  drug-food interactions 6.0 (4.0–8.0) 5.6 ± 2.4 0.0–9.0 9
 Knowledge regarding the  best timing of drug intake 5.0 (4.0–6.0) 4.6 ± 1.6 0.0–7.0 7
 Attitudes toward drug-food  interactions 10.0 (10.0–12.0) 10.4 ± 1.4 5.0–12.0 4
 Practice of drug-food  interactions 17.0 (14.0–20.0) 16.7 ± 4.4 5.0–25.0 5

Description of the scores of different domains.

SD, standard deviation.

Knowledge regarding DFIs

Several key DFIs were correctly identified by the majority of participants. Specifically, 77.9% correctly reported that grapefruit juice increased atorvastatin levels, and 76.6% recognized that patients on spironolactone should avoid potassium-rich foods. Furthermore, 71.7% were aware that dairy products can reduce the efficacy of tetracyclines, and 70.4% acknowledged that caffeine can affect the effectiveness of diazepam. Correct responses were also recorded for interactions involving protein-rich foods and levodopa (59.2%) and MAOIs with aged cheeses (62.6%). However, only 44.9% correctly identified that excessive consumption of cauliflower affected the efficacy of levothyroxine. Notably, 57.7% of the participants correctly stated that amiodarone should not be taken with grapefruit juice, and 43.6% correctly indicated that patients on warfarin should not freely vary their intake of green leafy vegetables (Figure 1).

FIGURE 1

Bar graph titled “Knowledge Regarding Drug-Food Interactions” showing respondents’ awareness of drug-food interactions for MAOIs, warfarin, amiodarone, spironolactone, diazepam, levodopa, tetracycline, levothyroxine, and atorvastatin. Three response categories: No, Yes, Do not know. Highest awareness is grapefruit juice with atorvastatin at 77.9% responding “Yes”, and lowest awareness is amiodarone with grapefruit juice at 57.7% responding “No”.

Knowledge regarding drug-food interactions bar graph. It represents the percentage of respondents’ awareness of drug-food interactions for MAOIs, warfarin, amiodarone, spironolactone, diazepam, levodopa, tetracycline, levothyroxine, and atorvastatin. Three response categories: No, Yes, Do not know. Percentages inside each bar indicate the proportion of respondents selecting each option, with an asterisk (*) marking the correct answer.

Factors and predictors of high scores of general knowledge regarding DFIs

Compared with medical consultants, nurses had significantly lower general knowledge scores (β = −2.09, 95% CI = −4.04 to −0.15, p = 0.036). Participants working in clinics scored significantly lower than those working in hospitals (β = −2.27, 95% CI = −3.63, −0.91, p = 0.001). Participants who reported being “somewhat familiar” (β = −0.94, 95% CI, −1.51 to −0.37, p = 0.001) or “not familiar” (β = −2.04, 95% CI, −3.26 to −0.82, p = 0.001) with DFIs had lower knowledge scores than those who were “very familiar.” Those who received DFI training during undergraduate or graduate studies had higher scores than those who did not (β = 0.68, 95% CI, 0.11–1.25, p = 0.019). Compared to participants who never encountered DFIs in practice, those who rarely (β = 1.18, 95% CI, 0.20–2.16, p = 0.019) and occasionally (β = 0.99, 95% CI, 0.03–1.96, p = 0.044) encountered them had significantly higher knowledge scores (Table 3).

TABLE 3

Variable Inferential analysis Multivariable regression
Median (IQR) P-value Beta 95% CI P-value
Age 0.003
  22–29 years 7.0 (5.0, 8.0) Reference Reference
  30–39 years 6.0 (4.0, 8.0) –0.45 –0.98, 0.08 0.096
  40–49 years 5.0 (3.0, 7.0) –0.30 –1.02, 0.43 0.424
  50 years or more 5.0 (3.0, 7.5) 0.46 –1.63, 2.54 0.669
Gender 0.001
  Male 6.0 (3.0, 7.5) Reference Reference
  Female 6.0 (5.0, 8.0) 0.36 –0.12, 0.85 0.139
Professional job < 0.001
  Medical consultant 6.0 (5.0, 8.0) Reference Reference
  Medical specialist 4.0 (4.0, 6.0) –0.32 –2.83, 2.18 0.800
  General practitioner 5.5 (2.5, 8.0) –0.66 –2.60, 1.28 0.505
  Medical resident 8.0 (2.0, 8.0) –1.09 –3.23, 1.05 0.319
  Consultant pharmacist 7.0 (6.0, 8.0) 0.32 –1.81, 2.45 0.770
  Senior pharmacist 7.0 (6.0, 8.0) 0.65 –1.27, 2.57 0.506
  Pharmacist 6.0 (4.0, 8.0) –0.19 –2.03, 1.66 0.844
  Nurse 3.0 (1.0, 5.0) –2.09 –4.04, –0.15 0.036
  Others 4.5 (4.0, 6.0) –0.79 –2.84, 1.26 0.451
Country of the highest degree 0.934
  From Saudi Arabia 6.0 (4.0, 8.0)
  Outside of Saudi Arabia 6.5 (4.0, 7.5)
Years of experience in the current profession 0.069
  <1 year 7.0 (6.0, 8.0)
  1–5 years 6.0 (4.0, 8.0)
  6–10 years 6.0 (4.0, 8.0)
  More than 10 years 6.0 (3.0, 8.0)
Primary place of work < 0.001
  Hospital 6.0 (4.0, 8.0) Reference Reference
  Clinic 2.0 (0.0, 5.0) –2.27 –3.63, –0.91 0.001
  Hospital pharmacy 6.0 (4.0, 8.0) –0.19 –0.74, 0.36 0.498
  Community pharmacy 7.0 (6.0, 8.0) 0.82 –0.01, 1.64 0.053
  Others 5.5 (3.0, 6.0) –1.06 –2.16, 0.04 0.061
Region of the workplace 0.002
  Northern region 6.0 (4.0, 8.0) Reference Reference
  Southern region 5.0 (3.0, 7.0) –0.16 –0.86, 0.55 0.661
  Eastern region 6.0 (4.0, 8.0) 0.04 –0.82, 0.90 0.929
  Western region 6.0 (5.0, 8.0) 0.20 –0.54, 0.95 0.589
  Central region 6.0 (4.0, 8.0) –0.15 –0.98, 0.68 0.720
Familiarity with drug-food interactions < 0.001
  Very familiar 8.0 (6.0, 8.0) Reference Reference
  Somewhat familiar 6.0 (4.0, 7.0) –0.94 –1.51, –0.37 0.001
  Not familiar 2.0 (1.0, 5.0) –2.04 –3.26, –0.82 0.001
Ever received formal training on drug-food interactions < 0.001
  No 5.0 (2.0, 7.0) Reference Reference
  Yes, during undergraduate/graduate studies 7.0 (5.0, 8.0) 0.68 0.11, 1.25 0.019
  Yes, through professional development programs 5.0 (3.0, 7.0) 0.33 –0.62, 1.29 0.494
Frequency of encountering drug-food interactions in practice < 0.001
  Never 4.0 (0.0, 6.0) Reference Reference
  Rarely 6.0 (4.0, 7.5) 1.18 0.20, 2.16 0.019
  Occasionally 6.0 (4.0, 8.0) 0.99 0.03, 1.96 0.044
  Frequently 7.0 (5.0, 8.0) 0.98 –0.14, 2.09 0.086
Confidence in identifying clinically significant drug-food interactions < 0.001
  Not confident 5.0 (2.0, 6.0) Reference Reference
  Somewhat confident 6.0 (4.0, 7.0) 0.03 –0.82, 0.89 0.939
  Very confident 8.0 (6.0, 8.0) 0.99 –0.02, 2.00 0.056

Factors and predictors of high general knowledge scores regarding drug-food interactions.

IQR, interquartile range. Kruskal-Wallis rank sum test; Wilcoxon rank sum test. CI, Confidence Interval.

Knowledge regarding the best timing of drug intake

Most participants demonstrated a good knowledge of the appropriate timing of drug administration. The correct timing was most frequently identified for levothyroxine tablets and metformin Immediate Release (IR) tablets, with 88.3% of respondents selecting “on an empty stomach” and “after a meal,” respectively. Similarly, 72.2% correctly indicated that indomethacin capsules should be taken after a meal, and 66.8% identified that the correct timing for alendronate tablets was on an empty stomach. The correct responses were lower for isotretinoin capsules (60.3%) and carbamazepine IR tablets (51.9%). For calcium lactate, 36.4% correctly selected “after a meal,” while 37.9% incorrectly selected “on an empty stomach” (Figure 2).

FIGURE 2

Bar chart titled “Knowledge Regarding the Best Timing of Drug Intake” showing responses for seven drugs: Calcium Lactate, Carbamazepine IR, Indomethacin, Isotretinoin, Metformin IR, Alendronate, and Levothyroxine Tablets. Responses are divided into three categories: “On empty stomach,” “After a meal,” and “Do not know.” Levothyroxine has the highest percentage for “On empty stomach” at 88.3%. Metformin IR is mostly recommended “After a meal” at 88.3%. Indomethacin is also high for “After a meal” at 72.2%.

Knowledge regarding the best timing of drug bar graph. It is showing the responses for seven drugs: Calcium Lactate, Carbamazepine IR, Indomethacin, Isotretinoin, Metformin IR, Alendronate, and Levothyroxine Tablets. Responses are divided into three categories: “On empty stomach,” “After a meal,” and “Do not know.” Percentages inside each bar indicate the proportion of respondents selecting each option, with an asterisk (*) marking the correct answer.

Factors and predictors of high knowledge scores regarding the best timing of drug intake

Significantly higher knowledge scores regarding the best timing of drug intake were observed among female participants compared with males (β = 0.37, 95% CI, 0.03–0.71, p = 0.032). Compared to those working in hospitals, participants working in hospital pharmacies had slightly lower scores (β = −0.41, 95% CI, −0.82 to 0.00, p = 0.049). Additionally, participants who reported not being familiar with DFIs had significantly lower scores than those who were very familiar (β = −1.20, 95% CI, −2.12 to −0.28, p = 0.011) (Table 4).

TABLE 4

Variable Inferential analysis Multivariable regression
Median (IQR) P-value Beta 95% CI P-value
Age 0.371
  22–29 years 5.0 (3.0, 6.0)
  30–39 years 5.0 (4.0, 6.0)
  40–49 years 5.0 (3.0, 6.0)
  50 years or more 3.5 (3.0, 5.0)
Gender 0.007
  Male 5.0 (3.0, 6.0) Reference Reference
  Female 5.0 (4.0, 6.0) 0.37 0.03, 0.71 0.032
  Professional job 0.011
  Medical consultant 4.5 (4.0, 6.0) Reference Reference
  Medical specialist 4.0 (4.0, 6.0) 0.67 –1.22, 2.56 0.485
  General practitioner 5.0 (3.0, 6.0) –0.03 –1.49, 1.44 0.973
  Medical resident 6.0 (4.0, 6.0) 0.37 –1.25, 1.98 0.658
  Consultant pharmacist 5.5 (2.5, 6.0) –0.12 –1.74, 1.50 0.883
  Senior pharmacist 5.0 (4.0, 6.0) 0.82 –0.63, 2.27 0.269
  Pharmacist 5.0 (4.0, 6.0) 0.69 –0.70, 2.07 0.332
  Nurse 4.0 (2.0, 5.0) –0.70 –2.16, 0.77 0.352
  Others 3.0 (3.0, 5.0) –0.12 –1.67, 1.42 0.874
Country of the highest degree 0.052
  From Saudi Arabia 5.0 (4.0, 6.0)
  Outside of Saudi Arabia 4.0 (3.0, 6.0)
Years of experience in the current profession 0.139
  < 1 year 5.0 (3.0, 6.0)
  1–5 years 5.0 (3.0, 6.0)
  6–10 years 5.0 (4.0, 6.0)
  More than 10 years 5.0 (4.0, 6.0)
Primary place of work 0.024
  Hospital 5.0 (4.0, 6.0) Reference Reference
  Clinic 4.0 (2.0, 5.0) –0.80 –1.83, 0.22 0.126
  Hospital pharmacy 5.0 (4.0, 6.0) –0.41 –0.82, 0.00 0.049
  Community pharmacy 5.0 (4.0, 6.0) –0.04 –0.61, 0.53 0.893
  Others 4.0 (3.0, 5.0) –0.83 –1.66, 0.00 0.051
Region of the workplace 0.234
  Northern region 6.0 (4.0, 6.0)
  Southern region 4.0 (3.0, 6.0)
  Eastern region 5.0 (4.0, 6.0)
  Western region 5.0 (3.0, 6.0)
  Central region 5.0 (4.0, 6.0)
Familiarity with drug-food interactions < 0.001
  Very familiar 6.0 (5.0, 6.0) Reference Reference
  Somewhat familiar 4.0 (3.0, 6.0) –0.31 –0.74, 0.11 0.149
  Not familiar 3.0 (1.5, 5.0) –1.20 –2.12, –0.28 0.011
Ever received formal training on drug-food interactions 0.015
  No 4.0 (3.0, 6.0) Reference Reference
  Yes, during undergraduate/graduate studies 5.0 (4.0, 6.0) 0.18 –0.24, 0.61 0.402
  Yes, through professional development programs 4.0 (3.0, 6.0) 0.03 –0.69, 0.75 0.936
Frequency of encountering drug-food interactions in practice < 0.001
  Never 4.0 (2.0, 5.0) Reference Reference
  Rarely 4.0 (3.0, 6.0) 0.44 –0.30, 1.18 0.248
 Occasionally 5.0 (4.0, 6.0) 0.73 0.00, 1.46 0.052
  Frequently 5.0 (4.0, 6.0) 0.39 –0.45, 1.24 0.360
Confidence in identifying clinically significant drug-food interactions < 0.001
  Not confident 4.0 (3.0, 5.0) Reference Reference
  Somewhat confident 5.0 (3.0, 6.0) –0.08 –0.72, 0.57 0.820
  Very confident 6.0 (4.0, 6.0) 0.42 –0.34, 1.18 0.281

Factors and predictors of high knowledge scores regarding the best timing of drug intake.

IQR, interquartile range; CI, Confidence Interval.Kruskal-Wallis rank sum test; Wilcoxon rank sum test.

Attitudes toward DFIs

The majority of participants demonstrated positive attitudes toward DFIs. Most notably, 90.4% agreed that informing patients about DFIs was part of their professional role. Similarly, 77.4% of patients agreed that DFIs contributed to poor therapeutic outcomes. Additionally, 70.4% of the participants supported the notion that mandatory training on DFIs is necessary for all HCPs. By contrast, only 41.6% agreed that the healthcare system provides adequate resources for managing DFIs, indicating a perceived gap in institutional support (Figure 3).

FIGURE 3

Bar chart titled “Attitudes Towards Drug-Food Interactions” showing responses from professionals. Categories include mandatory DFI training, healthcare resources, DFI impact on outcomes, and informing patients. Agreement is highest for informing patients (90.4%), lowest for adequate resources (41.6%). Disagreement is low across all categories. Unsure responses range from 7.5% to 24.9%.

Attitudes towards drug-food interactions bar chart. It shows the responses from healthcare professionals who disagreed, were unsure, or agreed with each statement. The statments include mandatory DFI training, healthcare resources, DFI impact on outcomes, and informing patients.

Factors and predictors of high scores of attitudes toward DFIs

Female participants had significantly more positive attitudes toward DFIs than male participants (β = 0.35, 95% CI = 0.06–0.64, p = 0.017). Compared to those who were very familiar with DFIs, somewhat familiar participants had lower attitude scores (β = −0.50, 95% CI = −0.84 to −0.15, p = 0.005). Participants who rarely (beta = 0.88, 95% CI, 0.28–1.48, p = 0.004), occasionally (β = 1.34, 95% CI, 0.75–1.93, p < 0.001), or frequently (β = 1.04, 95% CI, 0.35–1.73, p = 0.003) encountered DFIs in practice had significantly higher attitude scores than those who never experienced them (Table 5).

TABLE 5

Variable Inferential analysis Multivariable regression
Median (IQR) P-value Beta 95% CI P-value
Age 0.005
  22–29 years 11.0 (10.0, 12.0) Reference Reference
  30–39 years 11.0 (10.0, 12.0) 0.08 –0.22, 0.38 0.609
  40–49 years 10.0 (10.0, 10.0) –0.41 –0.83, 0.01 0.055
  50 years or more 10.0 (10.0, 11.0) –0.10 –1.38, 1.19 0.884
Gender < 0.001
  Male 10.0 (10.0, 11.0) Reference Reference
  Female 11.0 (10.0, 12.0) 0.35 0.06, 0.64 0.017
Professional job 0.372
  Medical consultant 11.0 (10.0, 12.0)
  Medical specialist 11.0 (10.0, 11.0)
  General practitioner 11.0 (10.5, 12.0)
  Medical resident 11.0 (10.0, 12.0)
  Consultant pharmacist 11.5 (10.0, 12.0)
  Senior pharmacist 11.0 (10.0, 11.0)
  Pharmacist 10.0 (10.0, 12.0)
  Nurse 10.0 (10.0, 11.0)
  Others 11.0 (9.0, 12.0)
Country of the highest degree 0.573
  From Saudi Arabia 10.0 (10.0, 12.0)
  Outside of Saudi Arabia 10.0 (10.0, 11.0)
Years of experience in the current profession 0.484
  <1 year 10.0 (9.0, 11.0)
  1–5 years 10.0 (10.0, 12.0)
  6–10 years 10.5 (10.0, 12.0)
More than 10 years 10.0 (10.0, 12.0)
Primary place of work 0.295
  Hospital 11.0 (10.0, 12.0)
  Clinic 10.0 (8.0, 11.0)
  Hospital pharmacy 10.0 (10.0, 12.0)
  Community pharmacy 11.0 (10.0, 12.0)
10.0 (8.0, 11.0)
Region of the workplace 0.063
  Northern region 10.0 (10.0, 12.0)
  Southern region 10.0 (10.0, 12.0)
  Eastern region 11.0 (10.0, 12.0)
  Western region 11.0 (10.0, 11.0)
  Central region 10.0 (10.0, 12.0)
Familiarity with drug-food interactions < 0.001
  Very familiar 11.0 (10.0, 12.0) Reference Reference
  Somewhat familiar 10.0 (10.0, 11.0) –0.50 –0.84, –0.15 0.005
  Not familiar 9.0 (8.0, 12.0) –0.71 –1.44, 0.02 0.059
Ever received formal training on drug-food interactions 0.007
  No 10.0 (8.0, 11.0) Reference Reference
  Yes, during undergraduate/graduate studies 11.0 (10.0, 12.0) 0.11 –0.24, 0.46 0.532
  Yes, through professional development programs 11.0 (10.0, 12.0) 0.01 –0.55, 0.56 0.981
Frequency of encountering drug-food interactions in practice < 0.001
Never 9.0 (8.0, 10.0) Reference Reference
  Rarely 10.0 (9.0, 11.0) 0.88 0.28, 1.48 0.004
  Occasionally 11.0 (10.0, 12.0) 1.34 0.75, 1.93 < 0.001
  Frequently 10.0 (10.0, 12.0) 1.04 0.35, 1.73 0.003
Confidence in identifying clinically significant drug-food interactions 0.010
  Not confident 10.0 (8.0, 11.0) Reference Reference
  Somewhat confident 10.0 (10.0, 12.0) 0.01 –0.52, 0.54 0.981
  Very confident 11.0 (10.0, 12.0) –0.22 –0.85, 0.41 0.489

Factors and predictors of high scores of attitudes toward drug-food interactions.

IQR, interquartile range; CI, Confidence Interval. Kruskal-Wallis rank sum test; Wilcoxon rank sum test.

Practice of DFIs

A total of 55.4% of the participants reported that they usually or always counsel patients about potential DFIs. Similarly, 41.6% indicated that they usually or always documented DFIs when they occurred. Drug databases were commonly used to check for interactions, with 49.6% reporting frequent use. Collaboration with other HCPs on DFI management was reported by 43.2% of participants, while only 22.4% reported that they usually or always provided written materials about DFIs to patients (Figure 4).

FIGURE 4

Bar chart illustrating the practice of drug-food interactions among healthcare providers. Categories include using a drug database, collaborating with healthcare providers, providing written materials, documenting, and counseling patients about potential interactions. Response options range from never to always. The highest frequency for using a drug database is “sometimes” at 38.2 percent, while counseling patients is “rarely” at 34.5 percent.

Practice of drug-food interactions encounters among healthcare providers bar chart. It shows the percentage of respondents who answered “Never,” “Rarely,” “Sometimes,” “Usually,” or “Always” for each practice Categories include using a drug database, collaborating with healthcare providers, providing written materials, documenting, and counseling patients about potential interactions.

Factors and predictors of high scores of practices of DFIs

Compared to medical consultants, higher practice scores were observed among medical specialists (β = 5.09, 95% CI, 0.21–9.96, p = 0.042), general practitioners (β = 4.27, 95% CI, 0.46–8.08, p = 0.029), medical residents (β = 5.43, 95% CI, 1.20–9.66, p = 0.012), consultant pharmacists (β = 6.96, 95% CI, 2.73–11.2, p = 0.001), senior pharmacists (β = 5.40, 95% CI, 1.65–9.16, p = 0.005), pharmacists (β = 7.35, 95% CI, 3.77–10.9, p < 0.001), nurses (β = 5.48, 95% CI, 1.61–9.35, p = 0.006), and those classified as “others” (β = 5.59, 95% CI, 1.56–9.62, p = 0.007). Participants who were somewhat familiar with DFIs had lower scores than those who were very familiar (β = −2.33, 95% CI, −3.45 to −1.22, p < 0.001). Compared to those who never encountered DFIs, higher practice scores were reported by participants who rarely (β = 2.51, 95% CI, 0.58–4.44, p = 0.011), occasionally (β = 2.73, 95% CI, 0.83– 4.62, p = 0.005), and frequently (β = 3.09, 95% CI, 0.89–5.30, p = 0.006) encountered them. Those who reported being somewhat confident (β = 2.12, 95% CI, 0.42–3.82, p = 0.015) or very confident (β = 2.71, 95% CI, 0.73–4.68, p = 0.008) in identifying clinically significant DFIs also had significantly higher practice scores than those who were not confident (Table 6).

TABLE 6

Variable Inferential analysis Multivariable regression
Median (IQR) P-value Beta 95% CI P-value
Age 0.605
  22–29 years 17.0 (14.0, 20.0)
  30–39 years 17.0 (14.0, 20.0)
  40–49 years 16.0 (13.0, 19.0)
  50 years or more 14.5 (13.5, 18.5)
Gender 0.811
  Male 17.0 (14.0, 20.0)
  Female 17.0 (14.0, 19.0)
  Professional job < 0.001
  Medical consultant 11.5 (10.0, 12.0) Reference Reference
  Medical specialist 11.0 (10.0, 22.0) 5.09 0.21, 9.96 0.042
  General practitioner 15.0 (13.0, 18.0) 4.27 0.46, 8.08 0.029
  Medical resident 15.0 (14.0, 19.0) 5.43 1.20, 9.66 0.012
  Consultant pharmacist 17.0 (16.0, 22.0) 6.96 2.73, 11.2 0.001
  Senior pharmacist 17.0 (14.0, 20.0) 5.40 1.65, 9.16 0.005
  Pharmacist 17.0 (15.0, 20.0) 7.35 3.77, 10.9 < 0.001
  Nurse 14.5 (12.5, 17.0) 5.48 1.61, 9.35 0.006
  Others 15.5 (13.0, 17.0) 5.59 1.56, 9.62 0.007
Country of the highest degree 0.452
  From Saudi Arabia 16.0 (14.0, 20.0)
  Outside of Saudi Arabia 17.0 (14.5, 20.0)
Years of experience in the current profession 0.961
  < 1 Year 17.0 (13.0, 20.0)
  1–5 Years 16.5 (14.0, 19.5)
  6–10 Years 16.0 (14.0, 20.0)
  More than 10 years 16.0 (14.0, 20.0)
Primary place of work 0.168
  Hospital 16.0 (13.0, 19.0)
  Clinic 17.0 (14.0, 22.0)
  Hospital pharmacy 17.0 (15.0, 19.0)
  Community pharmacy 17.0 (15.0, 20.0)
  Others 14.0 (9.5, 19.5)
Region of the workplace 0.109
  Northern region 17.0 (15.0, 19.0)
  Southern region 16.0 (13.0, 20.0)
  Eastern region 18.0 (15.0, 21.0)
  Western region 17.0 (14.0, 20.0)
  Central region 15.0 (13.0, 18.0)
Familiarity with drug-food interactions < 0.001
  Very familiar 19.0 (16.0, 22.0) Reference Reference
  Somewhat familiar 15.0 (13.0, 19.0) –2.33 –3.45, –1.22 < 0.001
  Not familiar 15.5 (10.0, 20.0) –1.04 –3.41, 1.32 0.388
Ever received formal training on drug-food interactions 0.010
  No 16.0 (12.0, 18.0) Reference Reference
  Yes, during undergraduate/graduate studies 17.0 (15.0, 20.0) 0.42 –0.68, 1.53 0.454
  Yes, through professional development programs 16.0 (12.0, 19.0) 0.87 –1.02, 2.75 0.367
Frequency of encountering drug-food interactions in practice < 0.001
  Never 13.0 (10.0, 16.0) Reference Reference
  Rarely 16.0 (12.0, 20.0) 2.51 0.58, 4.44 0.011
  Occasionally 17.0 (15.0, 19.0) 2.73 0.83, 4.62 0.005
  Frequently 18.0 (15.0, 21.0) 3.09 0.89, 5.30 0.006
Confidence in identifying clinically significant drug-food interactions < 0.001
  Not confident 14.5 (9.0, 17.0) Reference Reference
  Somewhat confident 16.0 (14.0, 20.0) 2.12 0.42, 3.82 0.015
  Very confident 18.0 (15.0, 21.0) 2.71 0.73, 4.68 0.008

Factors and predictors of high scores of practice of drug-food interactions.

IQR: interquartile range; CI, Confidence Interval. Kruskal-Wallis rank sum test; Wilcoxon rank sum test.

Discussion

Substantial deficiencies in healthcare professionals’ knowledge and practices concerning drug–food interactions were revealed (DFIs), particularly within the context of pharmacy practice in Saudi Arabia. Among the 385 participants, 231 (60.0%) demonstrated limited awareness of DFI mechanisms. This indicates a broader shortfall in education and training, possibly due to limited curricular coverage of DFIs and insufficient continuing professional development opportunities. Comparable deficits have been reported regionally, as community pharmacists in Palestine struggled to identify common DFIs despite recognizing their clinical relevance, and similar limitations were observed among healthcare workers in Ethiopia (22, 23). Collectively, these patterns reflect a systemic educational challenge across the Middle East and Africa that warrants comprehensive reform.

The lack of structured education emerges as a plausible explanation for these knowledge gaps. In this study, 198 participants (51.4%) reported never receiving formal instruction on DFIs, while 146 (37.9%) indicated that their knowledge was primarily acquired informally. These results align with international evidence highlighting insufficient curricular integration of DFI topics within pharmacy education (24, 25). Similar concerns have been raised in Malaysia and Poland, where inadequate DFI instruction contributes to knowledge gaps and medication errors (26, 27). The importance of embedding DFI content into pharmacy curricula to enhance awareness and practical counseling skills is underscored by these educational deficiencies.

Counseling practices also appear to be influenced by educational background. Only 164 respondents (42.6%) reported routinely discussing DFIs related to traditional foods or herbal remedies with patients. This pattern mirrors international findings showing that, while pharmacists generally recognize their responsibility to counsel patients, many lack sufficient confidence or depth of knowledge to provide culturally sensitive advice (28, 29). Moreover, reliance on unverified online resources for DFI information—reported in other community pharmacy studies—raises concerns about the accuracy and safety of patient counseling (30, 31). These findings highlight the need for structured, evidence-based counseling frameworks that integrate both clinical and cultural knowledge.

Underreporting of DFI-related adverse events represents another critical issue. In this study, only 73 participants (19.0%) had ever submitted a pharmacovigilance report. This aligns with previous research documenting low levels of reporting among Saudi healthcare professionals, often linked to limited awareness of reporting systems and uncertainty regarding procedures (32, 33). Regionally, Alshammari et al. and Garashi et al. noted that underdeveloped pharmacovigilance infrastructure and training deficiencies remain major barriers, while Alzubiedi et al. reported that the COVID-19 pandemic further disrupted reporting processes (3436). Addressing these gaps through education, standardized reporting protocols, and integration of DFI-specific examples in training programs could improve the safety surveillance system and reporting culture.

Building on these findings, pharmacy curricula in Saudi Arabia may not adequately address the complexities of DFIs. The high proportion of participants lacking formal DFI instruction reinforces the need for curricular reform. Existing programs across the MENA region prioritize biomedical and regulatory content while neglecting applied patient-facing competencies such as counseling and communication (25, 26). Simulation-based learning, cultural case studies, and integration of traditional medicine examples into pharmacotherapy teaching could bridge this gap and prepare students for real-world counseling.

Internationally, pharmacy education models in high-income countries have successfully incorporated interprofessional learning and cultural competence modules (27, 37). Adapting these frameworks to the Saudi context could enhance patient-centered education while respecting local dietary and cultural practices. Cultural alignment is particularly crucial, as pharmacists working in regions with prevalent traditional herbal use must tailor counseling to patients’ dietary customs to improve adherence and trust (38, 39). Regional calls for culturally contextualized DFI counseling guidelines further support this approach (40).

Continuing professional development (CPD) offers a practical mechanism to address knowledge retention and practice gaps post-graduation. CPD participation has been shown to enhance pharmacist competence and confidence in areas such as dietary counseling and adverse event reporting (41, 42). Effective CPD should be grounded in local dietary habits and patient communication norms to ensure relevance and sustainability. Research suggests that structured CPD initiatives directly improve professional performance, particularly when aligned with competency-based standards (43, 44). Integrating mandatory, DFI-specific modules into existing CPD frameworks could therefore enhance the ability of pharmacists to manage complex medication regimens safely.

Cultural competence and interprofessional collaboration represent vital dimensions of safe medication practice. Studies emphasize that pharmacists’ understanding of patients’ cultural and dietary patterns significantly affects counseling quality and patient adherence (4547). Collaborative learning models, including interprofessional education (IPE), foster shared responsibility among healthcare providers, reducing the likelihood of DFI-related errors (22). Integrating cultural competence and interprofessional training within pharmacy curricula ensures that healthcare professionals can navigate complex interactions involving traditional diets, supplements, and prescribed medications effectively (38, 39).

While these findings provide valuable insights, several limitations should be acknowledged. The cross-sectional design limits causal inference, and self-reported data may be subject to recall and social desirability bias, especially concerning counseling behaviors. The convenience sampling approach constrains generalizability, and the study did not assess participants’ personal cultural beliefs or traditional medicine practices, which may influence DFI counseling performance. Additionally, although participants were recruited from all major Saudi regions using a stratified approach, regional representation was uneven, with a higher proportion of respondents from the Southern region (36.6%). This imbalance may reflect differential response rates and could introduce non-response bias, meaning that the findings may be more reflective of the Southern region’s healthcare professionals than of the entire national population. Future studies should consider proportional stratification or weighting to achieve a more balanced representation across regions.

Collectively, the results suggest that pharmacy education and professional training in Saudi Arabia would benefit from explicit instruction on DFI identification, management, and culturally competent counseling. Integrating case-based and simulation learning, pharmacovigilance modules, and patient communication exercises can strengthen applied knowledge and skills. Expanding CPD programs to include DFI-specific content and incorporating culturally relevant examples would further reinforce pharmacist readiness. At the policy level, developing national DFI counseling guidelines and reporting protocols under the Saudi Commission for Health Specialties (SCFHS) could standardize practice and align with Vision 2030’s commitment to healthcare excellence. Future research should adopt mixed-methods designs to explore how traditional beliefs and cultural factors influence DFI counseling, ultimately informing evidence-based educational interventions and safer pharmacotherapy practices.

Conclusion

This study identified significant gaps in healthcare professionals’ knowledge and counseling practices regarding drug–food interactions (DFIs) in Saudi Arabia. The findings highlight the need for structured educational reform and continuous professional development focusing on DFI management, pharmacovigilance, and culturally competent counseling. Applied knowledge and patient communication may be strengthened through the integration of simulation-based learning, pharmacovigilance training, and case-based instruction into pharmacy curricula, together with tailored CPD programs. Strengthening healthcare provider competency in these areas will ultimately enhance medication safety, patient adherence, and overall therapeutic outcomes.

Statements

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Biomedical Research Ethics Committee of Umm Al-Qura University, Makkah, Saudi Arabia. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

AS: Conceptualization, Writing – review & editing, Investigation, Methodology, Software, Formal analysis, Writing – original draft, Data curation, Resources, Visualization. SW: Investigation, Supervision, Project administration, Writing – review & editing, Validation, Methodology. MA: Conceptualization, Methodology, Investigation, Validation, Project administration, Supervision, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

We wish to thank all the healthcare professionals who participated in this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare Generative AI was used in the creation of this manuscript. ChatGPT (OpenAI) was used solely for language editing and formatting assistance. All analytical decisions, data interpretation, and final text were reviewed and verified by the author prior to submission, in accordance with journal policy.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

CI, Confidence Interval; CPD, Continuing Professional Development; CYP450, Cytochrome P450; DFI, Drug–Food Interaction; HCP, Healthcare Professional; IQR, Interquartile Range; IR, Immediate Release; KAP, Knowledge, Attitudes, and Practices; AOI, Monoamine Oxidase Inhibitor; Q1, Q3, First Quartile, Third Quartile; SD, Standard Deviation.

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Summary

Keywords

drug–food interactions, pharmacist counseling, cultural competency, pharmacovigilance, healthcare education

Citation

Shalali A, Wali SM and Aldurdunji MM (2025) Knowledge, counseling practices, and educational gaps related to drug–food interactions among healthcare professionals in Saudi Arabia: a cross-sectional study. Front. Med. 12:1719936. doi: 10.3389/fmed.2025.1719936

Received

07 October 2025

Revised

30 October 2025

Accepted

25 November 2025

Published

16 December 2025

Volume

12 - 2025

Edited by

Zhiyao He, Sichuan University, China

Reviewed by

Nabil Albaser, Al-Razi University, Yemen

Abeer Abdelkader, Minia University, Egypt

Updates

Copyright

*Correspondence: Mohammed M. Aldurdunji,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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