ABSTRACT
Background and Aims
Physical inactivity has become a critical public health issue worldwide, contributing to numerous chronic diseases. As future healthcare providers, medical students' understanding and practice of physical activity are essential both for their personal health and their professional capacity to guide patients.
This study examined physical activity knowledge, engagement levels, and determining factors among medical students at a Tunisian university.
Methods
A cross‐sectional analysis was conducted among 155 medical students (mean age 24.5 ± 2.8 years, 58.7% female). Participants completed a 25‐item online questionnaire that collected sociodemographic data (age, gender, academic year, BMI) and assessed physical activity knowledge, behaviors, and barriers using the validated Ricci‐Gagnon scale. The questionnaire evaluated both personal physical activity practices and understanding of activity guidelines for health promotion.
Results
Among 155 participants (91 female, 64 male), 62.6% (n = 97) correctly distinguished physical activity from sport. Most students (85.8%, n = 133) recognized that there is no age limit for participating in physical activity. While 86.5% (n = 134) were aware of physical activity guidelines, only 3.2% knew methods to assess their activity levels. The Ricci‐Gagnon scale classified 68.4% (n = 102) as physically active, though only 49% reported regular sports participation. The primary barriers included a lack of time (89.9%, n = 71) and motivation (22.8%, n = 18). Logistic regression identified independent predictors of higher activity levels: female gender (OR, 2.4), obesity status (OR, 2.6), being in the first or second cycle of medical study (OR, 2.6), and good physical activity knowledge (OR, 2.2).
Conclusion
These findings highlight essential gaps in medical students' physical activity knowledge despite adequate activity levels. The association between knowledge and activity suggests education could benefit both student health and clinical skills. Reported barriers, such as time constraints, call for structural changes in medical training. Curriculum reforms should prioritize prescribing, training, and assessing practical activities. Addressing these needs will better prepare future physicians to promote physical activity in patient care.
Keywords: attitudes, exercise, health behavior, health knowledge, Medical, medical education, practice, preventive medicine, self care, students
1. Introduction
Physical inactivity represents a significant and growing public health concern in modern societies. The shift toward sedentary occupations and reduced active transportation has contributed to a decline in activity levels worldwide. Current evidence identifies physical inactivity as the fourth leading mortality risk factor, responsible for approximately 3.2 million preventable deaths annually [1, 2, 3]. This health burden manifests through increased rates of non‐communicable diseases, including type 2 diabetes, cardiovascular conditions, obesity‐related disorders, and certain malignancies [1].
Global surveillance data indicate a concerning trend in the prevalence of physical inactivity, increasing from 23.4% in 2000 to 31.3% by 2022 [4]. This trend appears particularly pronounced in industrialized nations, though low‐ and middle‐income countries now face similar challenges [4]. Recent population studies estimate that nearly one‐third of adults worldwide fail to achieve recommended activity thresholds [5].
The World Health Organization establishes clear physical activity guidelines, recommending a weekly minimum of 150 min of moderate‐intensity exercise or 75 min of vigorous‐intensity exercise for adults aged 18–64 years [1]. Tunisian epidemiological research reveals particularly concerning patterns, with over 70% of adults failing to meet these basic activity requirements [6]. This situation demands effective public health strategies to promote physical activity at both the individual and population levels.
Healthcare professionals, particularly physicians, occupy a strategic position to influence physical activity behaviors. Research confirms that physically active clinicians are more likely to counsel patients about the benefits of exercise [7]. As future healthcare providers, the personal physical activity habits and related knowledge of medical students warrant careful evaluation. Their current practices and understanding may predict future patterns of patient counseling and professional effectiveness in the prevention of chronic diseases.
Based on existing research gaps regarding physical activity knowledge and practices among future healthcare providers in North Africa, our study aimed to: (i) assess physical activity knowledge levels among Tunisian medical students, (ii) evaluate their current physical activity practices, and (iii) identify key sociodemographic and behavioral factors influencing their activity patterns.
2. Methodology
2.1. Ethical Approval
This study adhered to the principles outlined in the Declaration of Helsinki. All participants provided informed consent before completing the questionnaire. The ethical committee of Hedi Chaker University Hospital, University of Sfax, Sfax, Tunisia, approved the study Protocol on May 21, 2025 (approval no. 35/25).
2.2. Sample Size Calculation
The required number of participants was calculated using the equation: N = [(Z 2 α/2) × p × (1 − p)]/i 2, where n represents the sample size, p the anticipated prevalence, i the degree of precision, and zα/2 the critical value from the standard normal distribution for an error risk of α [8]. For an error risk of 5% and a precision of 10%, assuming a prevalence of physical activity among medical students of 29.6% based on previous research [9], the sample size was estimated at 79 participants. To account for possible refusals to participate or discontinuation, the sample size was increased by 10%, resulting in a total of 87 participants.
2.3. Participants
Medical students aged 18 years or older from the University of Sfax, Tunisia, were recruited between February and December 2022. A total of 155 students participated (91 female, 64 male; mean age 24.5 ± 2.8 years).
2.4. Study Design
This descriptive cross‐sectional study was conducted by the Department of Respiratory Medicine at Hedi Chaker University Hospital of Sfax, Tunisia. Data collection utilized a 25‐item French online questionnaire developed using Google Forms. To facilitate broad participation and easy access, the questionnaire link was distributed through Facebook, specifically within groups of medical students affiliated with the University of Sfax. Participation was voluntary, anonymous, and based on informed consent obtained electronically before completing the survey. The questionnaire comprised five sections:
Section I collected demographic data, including age, gender, weight, height, marital status, smoking status, presence of addictive behaviors, and level of medical study.
Section II assessed sport participation using closed‐ended questions, with non‐practitioners asked to justify their decision not to engage in sports.
Section III assessed sedentary lifestyle and physical activity level using the validated Ricci‐Gagnon scale (RGS), which comprises eight questions that assess sedentary behavior, leisure physical activity, and daily physical activity.
Section IV evaluated knowledge of physical activity through six questions, covering the understanding of physical inactivity prevalence in Tunisia, the distinction between sport and physical activity, age limits for physical activity, methods for evaluating physical activity, awareness of guidelines, and beliefs about physical activity prescription.
Section V assessed whether students had received courses relating to physical activity.
2.5. Data Collection Tools
2.5.1. Questionnaire
A 25‐item French online questionnaire was developed by the authors using Google Forms. This custom questionnaire comprised five sections, collecting demographic data, sport participation, physical activity assessment using the Ricci‐Gagnon scale, knowledge about physical activity, and information on physical activity education. The questionnaire was validated through pilot testing before full implementation [10]. A convenience sample of 10 students was recruited to complete the preliminary version of the questionnaire. Participants were asked not only to respond to all items but also to provide feedback regarding the clarity, wording, and comprehensibility of each question. The feedback obtained during this pilot phase indicated that all items were clear, understandable, and appropriately interpreted, with no major difficulties reported. Minor wording adjustments were considered where necessary to improve clarity. Given the absence of significant issues, the questionnaire was deemed suitable for use in the main study. Due to the exploratory nature of the study and the small sample size of the pilot phase, formal reliability testing (e.g., internal consistency measures such as Cronbach's alpha) was not performed.
2.5.2. Anthropometric Measurements
Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Participants were categorized as underweight (< 18.5), normal weight (18.5–24.9), overweight (25–29.9), and obese (> 30) [11, 12].
2.5.3. Physical Activity Assessment
The Ricci‐Gagnon scale was used to assess physical activity levels [10], with scores below 18 indicating sedentary behavior (Group 1) and scores of 18 or above indicating active status (Group 2).
2.5.4. Knowledge Assessment
Knowledge was evaluated using a six‐question assessment included in the custom questionnaire (Section IV). Questions covered understanding of the prevalence of physical inactivity in Tunisia, differentiation between sport and physical activity, age limits for physical activity, methods to evaluate physical activity, awareness of guidelines, and beliefs about physical activity prescription. To categorize knowledge levels, we applied a modified version of Bloom's taxonomy cut‐off points, which are commonly used in knowledge, attitude, and practice studies. According to this approach, scores are classified as follows: Good knowledge: 80%–100%, moderate knowledge: 60%–79%, and poor knowledge: < 60%. Given the limited number of items (n = 6), these thresholds were adapted proportionally. A score of ≥ 4 correct answers (≥ 66.7%) was considered indicative of “good knowledge,” while scores < 4 were categorized as “low knowledge.” Students were categorized as “good knowledgeable” if they answered more than four questions correctly and “less knowledgeable” if they answered fewer than four questions correctly.
2.6. Statistical Analysis
Data were analyzed with SPSS software version 26. Categorical variables were expressed as frequencies and percentages. The normality of the distribution of quantitative variables was assessed using the Shapiro–Wilk test. Normally distributed quantitative variables were expressed as mean ± standard deviation. Cross‐tabulations using the χ 2 test and Fisher's exact test were used to assess relationships between qualitative variables and physical activity knowledge or level. The student's t‐test was used to compare quantitative variables across knowledge and activity groups. Multivariate analysis was performed using binary logistic regression with a backward Wald selection method to identify independent predictors of physical activity level. Variables with a p value ≤ 0.20 in the univariate analysis were considered eligible for inclusion in the multivariate model [13]. In addition, variables of known clinical or theoretical relevance (e.g., age, sex, and medical study level) were included regardless of their univariate significance to account for potential confounding. Adjusted odds ratios (OR) with 95% confidence intervals were calculated. To assess multicollinearity among independent variables, variance inflation factors were examined, and no significant multicollinearity was detected. Potential confounders were handled by including them in the multivariate model, allowing for adjustment of their effects when estimating the association between independent variables and physical activity levels. All statistical tests were two sided, and a p value < 0.05 was considered statistically significant. Statistical analysis, reporting, and interpretation were conducted in accordance with the recommendations outlined in the Guidelines for Reporting of Statistics for Clinical Research by Assel et al. [14] and the SAMPL [15] (Statistical Analyses and Methods in the Published Literature) guidelines to ensure clarity, accuracy, and transparency of statistical reporting. Particular attention was given to the appropriate selection of statistical tests, transparent reporting of p values and confidence intervals, and clear presentation of results to ensure accuracy, reproducibility, and consistency with established standards for clinical research.
3. Results
3.1. Sample Characteristics
The study included 155 medical students with a mean age of 24.5 ± 2.8 years. Most participants were female (58.7%, n = 91), single (67.1%, n = 104), and had a normal body mass index (BMI) (mean 23.23 ± 2.93 kg/m2).
3.2. Physical Activity Knowledge Among Medical Students
Regarding knowledge of physical activity, 62.6% (n = 97) of participants correctly distinguished between physical activity and sport. Most students (85.8%, n = 133) recognized that there is no age limit for participating in physical activity. While 86.5% (n = 134) were aware of physical activity guidelines and 81.9% (n = 127) believed that physical activity could be prescribed like medication, only 3.2% knew methods to evaluate physical activity levels. Only 16.1% (n = 25) were aware of the prevalence of physical inactivity in Tunisia. Overall, 49% (n = 76) of participants were classified as having good knowledge about physical activity.
The majority of students (89.7%, n = 139) reported not receiving sufficient education about physical activity. When comparing knowledgeable and less knowledgeable students, those with greater knowledge were more physically active, as measured by the Ricci‐Gagnon scale (p = 0.01), and more likely to practice sports (p = 0.03). However, gender, age, marital status, BMI, and tobacco use did not significantly influence physical activity knowledge (Table 1).
Table 1.
Factors influencing the PA knowledge among medical students.
| Good knowledge | Low knowledge | Total | p | ||
|---|---|---|---|---|---|
| (n = 76) | (n = 79) | (n = 155) | |||
| Age (years) | 24.25 ± 2.57 | 24.49 ± 3.28 | 24.49 ± 3.28 | 0.61 | |
| BMI (kg/m2) | 23.24 ± 2.94 | 23.22 ± 2.94 | 23.2 3 ± 2.93 | — | 0.96 |
| Gender | Male | 54.7% (n = 35) | 45.3% (n = 29) | 41.30% (n = 64) | 0.24 |
| Female | 45.1% (n = 41) | 54.9% (n = 50) | 58.70% (n = 91) | ||
| M study level | P + DCEM | 51.2% (n = 44) | 48.8% (n = 42) | 55.48% (n = 86) | 0.55 |
| TCEM | 46.4% (n = 32) | 53.6% (n = 37) | 44.51% (n = 69) | ||
| Marital status | Single | 51.0% (n = 53) | 49.0% (n = 51) | 67.1% (n = 104) | 0.50 |
| Married | 45.1% (n = 23) | 54.9% (n = 28) | 32.9% (n = 51) | ||
| Tabacco use | Smokers | 55.0% (n = 11) | 45.0% (n = 9) | 12.9% (n = 20) | 0.57 |
| Non smokers | 48.1% (n = 65) | 51.9% (n = 70) | 81.1% (n = 135) | ||
| Alcohol use | Users | 60.0% (n = 12) | 40.0% (n = 8) | 12.9% (n = 20) | 0.29 |
| Non‐users | 47.4% (n = 64) | 52.6% (n = 71) | 81.1% (n = 135) | ||
| Illicit substance | Users | 75.0% (n = 3) | 25.0% (n = 1) | 2.6% (n = 4) | 0.29 |
| Non‐users | 48.3% (n = 73) | 51.7% (n = 78) | 97.4% (n = 151) | ||
| PA level | Active | 77.6% (n = 59) | 22.4% (n = 17) | 68.4% (n = 106) | 0.01 |
| Inactive | 59.5% (n = 47) | 40.5% (n = 32) | 23.6% (n = 49) | ||
| Sport status | Yes | 57.3% (n = 47) | 42.7% (n = 35) | 49% (n = 76) | 0.03 |
| No | 39.7% (n = 29) | 60.3% (n = 44) | 51% (n = 79) | ||
Note: The PA knowledge was evaluated by six questions. Good knowledgeable: who answered more than four questions correctly. Less knowledgeable: those who answered fewer than four questions correctly. The PA level was evaluated according to the Ricci Gagnon scale. Active: RGS > 18. Inactive: RGS < 18, for sport status, yes: those who practiced sports regularly, no: those who did not practice sports. Associations between qualitative variables and knowledge were assessed using the χ 2 test. Continuous variables were compared across knowledge groups using Student's t‐test. p < 0.05 was significant.
Abbreviations: BMI, body mass index; M study, medical study; n, number; PA, physical activity; P + DCEM, the first (PCEM) and second cycle (DCEM) of medical study; TCEM, the third cycle of medical study.
3.3. Physical Activity Practices Among Medical Students
According to the Ricci–Gagnon scale, 68.4% (n = 106) of the students were classified as physically active. However, when considering self‐reported behaviors related specifically to structured exercise, only 49% (n = 76) reported regular participation in sports, and 59.4% (n = 92) indicated that they did not engage in regular physical activity. Among these, 55.5% (n = 86) reported exercising only 1–2 times per month. This apparent discrepancy may be explained by differences in the assessment methods. The Ricci–Gagnon scale captures overall physical activity, including daily and incidental activities (e.g., walking and routine movements), whereas the self‐reported measure refers specifically to planned and structured exercise. Sedentary behavior was also highly prevalent: 47.1% (n = 73) of students reported spending more than 5 h per day in front of screens (television, computer, or smartphone). In addition, 67.1% (n = 104) reported walking less than 30 min per day
The primary reasons for not practicing sports were a lack of time (89.9%, n = 71) and a lack of motivation (22.8%, n = 18), with 2.5% (n = 2) reporting health problems as a barrier.
3.4. Factors Influencing Physical Activity Levels
In multivariate binary logistic regression analysis, higher physical activity levels were independently associated with female gender (OR: 2.4, [1.24–5.58], p = 0.04), being in the first or second cycle of medical study (OR: 2.6, [1.24–5.58], p = 0.01), having obesity (OR: 2.6, [1.12–6.48], p = 0.03), and having good knowledge about physical activity (OR: 2.2, [1.004–4.502], p = 0.04).
Additional factors significantly associated with physical activity in univariate analysis included younger age (p = 0.03) and alcohol consumption (p = 0.03); however, marital status, tobacco use, and illicit substance use showed no significant associations (Table 2).
Table 2.
Factors influencing the PA level among medical students.
| Parameters | G1 (active) | G2 (inactive) | p | |
|---|---|---|---|---|
| (n = 106) | (n = 49) | |||
| Age | 24.00 ± 2.5 | 25.1 ± 3.5 | 0.03 | |
| Gender | Male | 79.7% (n = 51) | 20.3% (n = 13) | 0.01 |
| Female | 60.4% (n = 55) | 39.6% (n = 36) | ||
| BMI (kg/m2) | 23.26 ± 2.71 | 23.17 ± 3.4 | 0.85 | |
| Obesity status | Underweight | 16.7% (n = 1) | 83.3% (n = 5) | 0.03 |
| Normal weight | 71.6% (n = 83) | 28.4% (n = 33) | ||
| Overweight | 70.4% (n = 19) | 29.6% (n = 8) | ||
| Obese | 50.0% (n = 3) | 50% (n = 3) | ||
| Marital status | Single | 69.2% (n = 72) | 30.8% (n = 32) | 0.74 |
| Married | 66.7% (n = 34) | 33.3% (n = 17) | ||
| M study level | P + DECEM | 76.7% (n = 66) | 23.3% (n = 20) | 0.01 |
| TCEM | 58.0% (n = 40) | 42.0% (n = 29) | ||
| Tabacco status | Smokers | 80.0% (n = 16) | 20.0% (n = 4) | 0.23 |
| Non smokers | 66.7% (n = 90) | 33.3% (n = 45) | ||
| Alcohol status | Users | 90.0% (n = 18) | 10.0% (n = 2) | 0.03 |
| Non_users | 65.2% (n = 88) | 34.8% (n = 47) | ||
| Illicit substance | Users | 100.0% (n = 4) | 0.0% (n = 0) | 0.16 |
| Non_users | 67.5% (n = 102) | 32.5% (n = 49) | ||
| PA knowledge | Good | 77.6% (n = 59) | 22.4% (n = 17) | 0.01 |
| Low | 59.5% (n = 47) | 40.5% (n = 32) |
Note: The PA knowledge was evaluated by six questions. Good: more than four answers were correct. Less: fewer than four answers were correct. The PA level was evaluated according to the Ricci–Gagnon scale (RGS). G1 (active): RGS ≥ 18. G2 (inactive): RGS < 18. Associations between qualitative variables and knowledge were assessed using the χ 2 test. Continuous variables were compared across active and inactive groups using Student's t‐test. p < 0.05 was significant.
Abbreviations: BMI, body mass index, according to BMI, obesity status was classified as follows: underweight: BMI < 18.5 kg/m2, normal weight: BMI 18.5–24.9 kg/m2, overweight: BMI 25.0–29.9 kg/m2, obese: BMI ≥ 30.0 kg/2; M study, medical study; n, number; PA, physical activity; P + DCEM, the first (PCEM) and second cycle of medical study (TCEM); TCEM, the third cycle of medical research.
4. Discussion
The present investigation revealed that 68.4% of Tunisian medical students were physically active, as measured by the Ricci‐Gagnon scale, exceeding the activity levels of the general Tunisian population [10]. This higher prevalence likely reflects medical students' greater awareness of the importance of regular physical activity for health [16].
The prevalence of physical activity varies significantly across studies due to different assessment tools, prior exercise habits, educational interventions, and cultural contexts. The findings align with those of Cruz et al. [17], who found that 55.6% of medical students were physically active, and with a study from India reporting that 41.3% of medical students were active [18]. However, when examining sports participation specifically, only 49% of students practiced sports regularly, a finding similar to that of Nardelli et al. [19], who reported that 35.5% of medical students engaged in sports, and Nóbrega [20], who found a 41% participation rate.
The primary barriers to physical activity identified were a lack of time (89.9%) and a lack of motivation (22.8%), consistent with findings from Makkawy et al. [21]. Srirangaramasamy et al. [22] similarly observed that most medical students engaged in only moderate levels of physical activity, with few reaching high activity levels, likely due to limited time available for exercise because of heavy workloads and long hours associated with medical training.
Several factors were found to be associated with physical activity levels in this cohort. Interestingly, female students were more likely to be classified as physically active compared to their male counterparts. While this finding contrasts with commonly reported results in the literature, it may reflect context‐specific factors such as evolving sociocultural norms, increased awareness of health and body image among female students, or differences in the types of activities captured by the assessment tool. It is also possible that female students engage more frequently in light‐to‐moderate or incidental physical activities, which may be better captured by the measurement scale used in this study. Similarly, overweight and obese students were more likely to be classified as physically active compared to those with normal or low body weight. This association should be interpreted with caution. One possible explanation is that individuals with higher body weight may be more motivated to engage in physical activity due to increased health awareness or medical advice. Alternatively, this finding may reflect reporting bias or limitations of the measurement tool, which may not fully distinguish between activity intensity and overall energy expenditure. Importantly, given the cross‐sectional design of this study, these associations do not imply causality. It cannot be determined whether higher physical activity levels influence body weight or whether body weight influences engagement in physical activity. Residual confounding factors, such as dietary habits, socioeconomic status, or underlying health conditions, may also have contributed to the observed associations.
While this finding aligns with Bergier et al. [16], it contrasts with Ball et al. [23], who considered high BMI an essential physical and cognitive barrier to physical activity, as excess weight can lead to fatigue that reduces mobility and makes exercise more difficult.
Significantly, physical activity level was associated with physical activity knowledge, contrary to the findings of Schlickmann and Kock [3]. While 49% of participants demonstrated good overall knowledge of physical activity, specific knowledge gaps were evident. Only 3.2% knew methods for evaluating physical activity levels, considerably lower than the 98.9% of medical students with high knowledge levels regarding physical activity reported at the University of Cyberjaya [24]. Similarly, while medical students at the University of Khartoum demonstrated good knowledge of the cardiovascular and mental health benefits of physical activity, only 19.4% were aware of the WHO‐recommended activity levels [25]. Mandic et al. [26] also found that only 19% of medical students correctly knew the minimum physical activity recommendations for healthy adults. These knowledge gaps may be explained by insufficient education about physical activity, as reported by 89.7% of participants.
4.1. Limitations
This study has several limitations that should be considered when interpreting its findings. First, the assessment of physical activity levels relied on the Ricci‐Gagnon scale, a self‐reported tool. While such scales are convenient for evaluating extensive samples, their results may be inconsistent due to over‐ or underreporting of physical activity [25]. Device‐based measurements, such as accelerometry or pedometry, can provide more accurate results; however, these approaches have limitations in terms of availability, comfort, and suitability across different populations [27]. Second, the cross‐sectional design prevents establishing causal relationships between the identified factors and physical activity levels. Longitudinal studies would better elucidate how knowledge and behaviors related to physical activity evolve throughout medical education and into professional practice. Third, recruiting participants through social media may have introduced selection bias, potentially attracting students already interested in physical activity and health‐related topics, thereby overestimating the true prevalence of physical activity in this population. Fourth, the study was conducted at a single institution in southern Tunisia, which limits its generalizability to medical students across different regions and cultural contexts within the country and beyond. Finally, while we assessed students' knowledge about physical activity, we did not evaluate the quality or content of their medical curriculum regarding physical activity education, which could provide valuable insights for educational interventions.
5. Conclusion
This study highlights that, while Tunisian medical students demonstrated reasonable levels of physical activity, significant knowledge gaps exist regarding physical activity assessment methods and specific guidelines. Female gender, early stage of medical education, obesity status, and knowledge of good physical activity were independently associated with higher activity levels. The primary barriers identified—lack of time and motivation—point to the need for structural changes in medical education to support active lifestyles among future physicians. These findings have important implications for medical education. First, medical curricula should incorporate comprehensive physical activity education, including practical training in assessment methods and prescription guidelines. Second, academic institutions should consider implementing wellness programs that address time management and provide accessible exercise opportunities for medical students. Third, the association between knowledge and behavior regarding physical activity suggests that educational interventions could improve both student health and future clinical practice. Future research should explore effective educational interventions to enhance learning and practice of physical activity among medical students, investigate how physical activity behaviors transition from student to professional life, and examine how cultural contexts influence these patterns across different regions. By addressing these knowledge gaps and barriers, medical education can better prepare future physicians to promote physical activity effectively in their patient care.
Author Contributions
Rim Kammoun: conceptualization, investigation, funding acquisition, writing – original draft, methodology, validation, visualization, writing – review and editing. Walid Feki: methodology, validation, visualization, writing – review and editing, software, formal analysis, project administration. Rim Khemakhem: software, formal analysis, project administration, data curation, supervision, resources. Halil Ibrahim Ceylan: data curation, supervision, resources, conceptualization, investigation, funding acquisition, writing – original draft. Asma Haddar: conceptualization, investigation, funding acquisition, writing – original draft, methodology, validation, visualization, writing – review and editing. Aiman Jmal: methodology, validation, visualization, writing – review and editing, software, formal analysis, project administration. Ismail Dergaa: software, formal analysis, project administration, data curation, supervision, resources. Hatem Ghouili: data curation, supervision, resources, conceptualization, investigation, funding acquisition, writing – original draft. Omar Hammouda: conceptualization, investigation, funding acquisition, writing – original draft, methodology, validation, visualization, writing – review and editing. Kaouthar Masmoudi: methodology, validation, visualization, writing – review and editing, software, formal analysis, project administration. Raul Ioan Muntean: software, formal analysis, project administration, data curation, supervision, resources, writing – review and editing, writing – original draft, visualization. Samy Kammoun: methodology, validation, formal analysis, software, visualization, writing – review and editing, supervision.
Funding
The authors have nothing to report.
Consent
All participants provided consent for the use of their anonymous data for research purposes and publication.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors of this manuscript thank all individuals who participated in this study. All authors approved of the final version to be published and agree to be accountable for any part of the work. In preparing this paper, the authors used the ChatGPT model 4 on November 15, 2024, to revise specific passages of the manuscript, double‐check for grammatical errors, and improve academic English. After using this tool, the authors have reviewed and edited the content as necessary and take full responsibility for the publication's content [28, 29]. Rim Kammoun affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Contributor Information
Halil Ibrahim Ceylan, Email: halil.ibrahimceylan60@gmail.com.
Raul Ioan Muntean, Email: muntean.raul@uab.ro.
Data Availability Statement
The data that support the findings of this study are openly available upon request from the corresponding author.
References
- 1. Bull F. C., Al‐Ansari S. S., Biddle S., et al., “World Health Organization 2020 Guidelines on Physical Activity and Sedentary Behaviour,” British Journal of Sports Medicine 54, no. 24 (2020): 1451–1462. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Rao C. R., Darshan B., Das N., Rajan V., Bhogun M., and Gupta A., “Practice of Physical Activity Among Future Doctors: A Cross Sectional Analysis,” International Journal of Preventive Medicine 3, no. 5 (2012): 365–369. [PMC free article] [PubMed] [Google Scholar]
- 3. Schlickmann D. W. and Kock K. S., “Level of Physical Activity Knowledge of Medical Students in a Brazilian University,” Journal of Lifestyle Medicine 12, no. 1 (2022): 47–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Strain T., Flaxman S., Guthold R., et al., “National, Regional, and Global Trends in Insufficient Physical Activity Among Adults From 2000 to 2022: a Pooled Analysis of 507 Population‐Based Surveys With 5·7 Million Participants,” Lancet Global Health 12, no. 8 (2024): e1232–e1243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Guthold R., Stevens G. A., Riley L. M., and Bull F. C., “Worldwide Trends in Insufficient Physical Activity From 2001 to 2016: A Pooled Analysis of 358 Population‐Based Surveys With 1·9 Million Participants,” Lancet Global Health 6, no. 10 (2018): e1077–e1086. [DOI] [PubMed] [Google Scholar]
- 6. Msaad S., Ketata N., Kammoun N., et al., “Sleep Habits and Disturbances Among Tunisian Adults: A Cross‐Sectional Online Survey,” Nature and Science of Sleep 16 (2024): 675–698. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Lobelo F., Duperly J., and Frank E., “Physical Activity Habits of Doctors and Medical Students Influence Their Counselling Practices,” British Journal of Sports Medicine 43, no. 2 (2009): 89–92. [DOI] [PubMed] [Google Scholar]
- 8. Methnani J., Latiri I., Dergaa I., Chamari K., and Ben Saad H., “ChatGPT for Sample‐Size Calculation in Sports Medicine and Exercise Sciences: A Cautionary Note,” International Journal of Sports Physiology and Performance 18, no. 10 (2023): 1219–1223. [DOI] [PubMed] [Google Scholar]
- 9. Joy V. and Vincent D. J., “The Prevalence of Physical Activity Among MBBS Students in a Medical College in Kerala,” Public Health Review: International Journal of Public Health Research 7, no. 4 (2020): 28–34. [Google Scholar]
- 10. Reychler G., Petit A.‐S., Caty G., et al., “The Construct Validity of the Ricci and Gagnon Questionnaire to Assess the Physical Activity Level: A Prospective Study,” European Rehabilitation Journal 3, no. 1 (2023): 1–5. [Google Scholar]
- 11. WHO , Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation (World Health Organization Technical Report Series, 2000), 894: i‐xii, 1–253. [PubMed] [Google Scholar]
- 12. Ghouili H., Ben Khalifa W., Ouerghi N., et al., “Body Mass Index Reference Curves for Tunisian Children,” Archives de Pédiatrie 25, no. 8 (2018): 459–463. [DOI] [PubMed] [Google Scholar]
- 13. Hosmer D. W., Lemeshow S., and Sturdivant R. X., “Applied Logistic Regression [Internet],” in Wiley Series in Probability and Statistics, 1re éd. (Wiley, 2013), 10.1002/9781118548387. [DOI] [Google Scholar]
- 14. Assel M., Sjoberg D., Elders A., et al., “Guidelines for Reporting of Statistics for Clinical Research in Urology,” BJU International 123, no. 3 (2019): 401–410, 10.1111/bju.14640. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Lang T. A. and Altman D. G., “Statistical Analyses and Methods in the Published Literature: The SAMPL Guidelines*,” in Guidelines for Reporting Health Research: A User's Manual [Internet], eds. Moher D., Altman D. G., Schulz K. F., Simera I., and Wager E., 1re éd. (Wiley, 2014), 264–274, 10.1002/9781118715598.ch25. [DOI] [Google Scholar]
- 16. Bergier B., Tsos A., and Bergier J., “Factors Determining Physical Activity of Ukrainian Students,” Annals of Agricultural and Environmental Medicine 21, no. 3 (2014): 613–616. [DOI] [PubMed] [Google Scholar]
- 17. Cruz M. A. F., Guimarães M. K. H., Macena L. B., Silva L. N. S., and Cruz J. F., “Nível De Atividade Física E Índice De Massa Corpórea Em Acadêmicos De Medicina Da Universidade Tiradentes Em Aracaju‐Se,” Caderno de Graduação‐Ciências Biológicas e da Saúde‐Unit‐Sergipe 3, no. 1 (2015): 101–112. [Google Scholar]
- 18. Padmapriya K., Krishna P., and Rasu T., “Prevalence and Patterns of Physical Activity Among Medical Students in Bangalore, India,” Electronic Physician 5, no. 1 (2013): 606–610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Nardelli G. G., Gaudenci E. M., Garcia B. B., Carleto C. T., Gontijo L. M., and Pedrosa L. A. K., “Perfil Dos Alunos Ingressantes Dos Cursos Da Área Da Saúde De Uma Universidade Federal,” Revista de Enfermagem e Atenção à Saúde 2, no. 1 (2013): 3–12. [Google Scholar]
- 20. Nóbrega E. C. M., “História Familiar De Doenças Crônicas, Atividade Física E Hábitos Alimentares Em Estudantes Da Área Da Saúde,” Revista Brasileira em Promoção da Saúde 27, no. 3 (2014): 333–340. [Google Scholar]
- 21. Makkawy E., Alrakha A. M., Al‐Mubarak A. F., et al., “Prevalence of Overweight and Obesity and Their Associated Factors Among Health Sciences College Students, Saudi Arabia,” Journal of Family Medicine and Primary Care 10, no. 2 (2021): 961–967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Srirangaramasamy J., Karthikeyan V., Ramanathan R., et al., “Assessment of Physical Activity and Sleep Quality Among Doctors and Medical Students: A Cross‐Sectional Study From South India,” Cureus 16, no. 7 (2024): e65807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Ball K., Crawford D., and Owen N., “Obesity as a Barrier to Physical Activity,” Australian and New Zealand Journal of Public Health 24, no. 3 (2000): 331–333. [DOI] [PubMed] [Google Scholar]
- 24. Halim A. A., Shakaran B., and Ezanee N. S., “Knowledge, Attitude and Practice of Physical Activity Among Medical Students at University of Cyberjaya, Malaysia,” International Journal of Science and Healthcare Research 8 (2023): 1–9. [Google Scholar]
- 25. Fadul M. H., Fadul A., Yasir H Eissa A., Mohamed Elhassan A. Z. W., Manhal G. A. A., and Abdelgafour R. H., “Physical Activity Among Medical Students at the University of Khartoum, Sudan, 2022: Knowledge, Practice, and Barriers,” Cureus 15, no. 9 (2023): e45914. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Mandic S., Wilson H., Clark‐Grill M., and O'Neill D., “Medical Students' Awareness of the Links Between Physical Activity and Health,” Montenegrin Journal of Sports Science & Medicine 6, no. 2 (2017): 5–12, 10.26773/mjssm.2017.09.001. [DOI] [Google Scholar]
- 27. Skender S., Ose J., Chang‐Claude J., et al., “Accelerometry and Physical Activity Questionnaires—A Systematic Review,” BMC Public Health 16 (2016): 515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Dergaa I., Ben Saad H., Glenn J. M., et al., “A Thorough Examination of ChatGPT‐3.5 Potential Applications in Medical Writing: A Preliminary Study,” Medicine 103, no. 40 (2024): e39757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Dergaa I., Fekih‐Romdhane F., Glenn J. M., et al., “Moving Beyond the Stigma: Understanding and Overcoming the Resistance to the Acceptance and Adoption of Artificial Intelligence Chatbots,” New Asian Journal of Medicine 1, no. 2 (2023): 29–36. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are openly available upon request from the corresponding author.