Abstract
Background
Combining the results of objective and subjective assessments of physical activity (PA) can provide a more complete assessment of PA patterns in medical students. This study aimed to determine the relationships between objective and subjective measured PA and its relation with socio-demographic characteristics among different stages of medical training.
Methods
This cross-sectional study was conducted on 186 students of four stages of medical training. The International Physical Activity Questionnaire Short-Form (IPAQ-SF) and pedometer were used.
Results
There were significant differences in total PA in terms of gender, weight, and serious illness. Differences between four stages of medical training in total (p = 0.002), vigorous (p < 0.001), and moderate PA (0.026) based on IPAQ-SF as well as pedometer-counted steps in Tuesday (p = 0.002) and Wednesday (p = 0.006) were significant. The results demonstrated a positive significant relationship between total PA based on IPAQ-SF and pedometer-determined steps in four days of the week.
Conclusion
There were positive low correlations between PA based on IPAQ-SF and pedometer among medical students. The PA of medical students at different years of medical training was different based on both IPAQ-SF and pedometer, which shows the need to pay attention to the special needs of students in terms of PA at each stage.
Keywords: Physical activity, Medical students, Pedometer, IPAQ, Self-report
INTRODUCTION
The beneficial effects of physical activity (PA) on health are widely acknowledged [1]. Enhancement of PA is a key strategy in reducing the burden of non-communicable diseases such as stroke, heart failure, and diabetes [2]. Although there are many studies showing the positive effects of PA on mental health, well-being [3], longevity [4], and quality of life [5], many people in the world have insufficient PA. Physical inactivity is a leading risk factor for global mortality [2].
PA of students, as future leaders of society, not only affects their health but also plays an important role in the general health of the community [6]. Students undergo a variety of stresses and pressures during the transition process from high school to university, which can affect PA levels in them [7]. Among students in various disciplines, medical students face more challenges including patients’ death and suffering, long-term educational period, and frequently rotate among various hospitals in the training system [8,9]. A study conducted in Poland found that medical students had the lowest PA and highest BMI among students in other medical sciences (22% severe, 52% moderate, 26% light PA) [10]. PA in medical students is associated with higher professional efficacy [11] and academic achievement [12].
Some socio-demographic factors can affect the levels of PA [13]. Previous studies have demonstrated an association between PA levels and gender in medical students [14,15]. In addition to gender, weight, stress, and year of study are the predictors of PA in college students [16,17]. PA level is associated with the year of study. In Sri Lanka, PA was significantly related to the academic year of medical training. It was found that students in the third year of study have the least PA [14].
There are various tools for measuring PA levels. Self-reporting of PA such as questionnaires and motion sensors such as pedometers are the most practicable PA measures [18]. The International Physical Activity Questionnaire (IPAQ) was developed in 1998 for self-reported measured PA which assesses people’s levels of PA subjectively over the past week [19]. Because of its low cost and ease of administration, researchers often use questionnaires to assess the levels of PA in their studies [18]. The limited answer choices and recall bias in the self-reported instruments are some of the disadvantages which may lead to inaccurate data [20]. A pedometer is used to measure the actual and objective PA which the self-report questionnaires cannot do accurately. It is an affordable motion detector that records the number of steps taken over a period of time [21]. So far, several studies have been conducted to assess PA levels in medical students, often through a questionnaire or pedometer alone. Combining the results of objective and subjective PA assessments gives a more complete assessment of PA patterns [22].
No studies have been conducted before to determine the relationships between self-reported PA, objectively measured PA and socio-demographic factors in medical students. Also, due to the different challenges that medical students face in pre-clinical and clinical years of medical training, this study was designed to compare PA levels among medical stages (Basic Sciences, physiopathology, the clinical clerkship, the internship) according to the objective and subjective measurement of PA (using the pedometer and questionnaire) and agreement between them as well as its relationship with socio-demographic features.
MATERIALS AND METHODS
1. Study design and population
This cross-sectional study was conducted on medical students of Tabriz University of Medical Sciences in 2017- 2018. Sampling began after approval by the Ethics Committee of Tabriz University of Medical Sciences (Code: IR.TBZMED, REC, 1396.77). Healthy male and female students were included in the study. Exclusion criteria were as follows: any movement limitations such as musculoskeletal disorders, contraindications for PA such as some physical ailments, participation in a championship sport, and pregnancy or breastfeeding for female students.
First, the total number of students in each stage was obtained from the Medical Education Department (710, 289, 468, 480 students in basic science, physiopathology, clerkship, and internship stages, respectively). Then, 186 students (45 students from three stages and 51 students from the internship stage with an equal sex ratio) were selected using stratified random sampling by online software (www.random. org). Due to the difficulty of accessing students at the internship stage and the possibility of a high drop-out rate, more students were selected from the internship stage. The researcher recruited the students to participate in the study and explained the aims. Students signed a written informed consent form. Participation in the study was anonymous and voluntary.
Admission to medical school in Iran is based on the national university entrance examination (Konkoor) after finishing high school [23]. Medical training in Iran lasts 7 years and is divided into 4 stages: basic science stage that students learn lessons such as biochemistry, anatomy, physiology, immunology, general pathology (including 5 semesters); the physiopathology stage in which internal medicine is taught (including 2 semesters); the clinical clerkship stage, in which students begin their clerkship in the clinical environment (including 4 semesters); the internship stage, which lasts 18 months and consists of 56 courses (including 3 semesters) [9]. Graduating from medical school depends on success in the practical clinical exam.
2. Study instruments
Three instruments were used for data collection: 1) a socio-demographic questionnaire to obtain information on age, gender, marital status, educational stage, BMI (Body Mass Index: was calculated from self-reports of height and weight), parental education and occupation, employment status, place of residence and family income. 2) A pedometer for objective assessment of participants’ PA levels. It is a simple and inexpensive device that measures the amount of movement of a person directly by counting the number of steps taken throughout a given period of time (usually per day) [24]. The validity and reliability of this tool were confirmed by Bassett et al. [25]. The Asaklit pedometer (China) was selected for use in this study. We instructed students on how to use the pedometer according to the manufacturer’s guidelines. It was attached to the subjects’ belt or waistband on the dominant foot (right or left). Participants wore a pedometer for 7 consecutive days except while sleeping or bathing. At the end of each day, before going to bed, we asked participants to record the number of steps taken per day on a log given to them. Each night text messages were sent to remind participants to record the number of steps. Students were instructed to reset the device to zero each morning. 3) IPAQ-Short Form (IPAQ-SF) is a self-administrated tool for assessing PA and its levels in individuals during a typical 7-day period. The short version has 7 questions that measure different types of PA, including vigorous, moderate, and mild. Activity counts were presented in the metabolic equivalents (METs). One MET is a unit for estimating energy expenditure during rest. The PA score for each vigorous, moderate, and mild PA was calculated in MET-minutes per week. Mild PA is equal to 3.3 METs, moderate PA is equal to 4 METs, and vigorous PA is equal to 8 METs. Students’ PA was categorized into three ranges of intensity: vigorous (8.0 METs), moderate (4.0 METs), and mild PA (3.3 METs), which were calculated using the following formula: MET level × minutes of activity/day × days per week. The sum of mild, moderate, and vigorous PA was considered as total PA [10,19]. In this study, we classified PA below 600 MET•min/week, 600- 3000 MET•min/week, and greater than 3000 MET•min/ week as mild, moderate, and sever activity respectively. The reliability of the Persian version of this questionnaire was reported in the study of BashiriMoosavi et al. with a correlation coefficient of 0.86 [26]. The validity and reliability of this tool have been assessed by Craig et al. in 12 countries [19].
3. Statistical analysis
Data were analyzed using SPSS/Ver 23. p < 0.05 was considered statistically significant. The Kolmogorov–Smirnov test was applied to assess the distribution of the quantitative data. Descriptive statistics including frequency, percentage, median (minimum, maximum), and median (range) were employed to express the data. Bivariate tests such as Spearman Rho correlation coefficient, Mann-Whitney, and Kruskal-Wallis were used to determine the relationship of PA with socio-demographic variables. A Kruskal Wallis Test was used to determine the relationship between quantitative PA (severe, moderate, mild) and steps per day with the educational stage. A Chi-square test was used to compare the groups in terms of frequency of vigorous, moderate, and mild PA. Spearman correlation test was used for investigating the relationship between pedometer-determined PA (steps per day) and total PA based on IPAQ-SF.
RESULTS
Socio-demographic characteristics of medical students were indicated in Table 1. The mean (SD) age and BMI were 22.6 (2.8) years and 23.7 (10.4) kg/m2. The gender ratio was 1:1, and 88% of students were single. There were significant differences in total PA of medical students in terms of gender (p < 0.001), weight (p = 0.009), serious illness (p < 0.014), and educational stages (p < 0.001) (Table 1).
Table 1.
Socio-demographic characteristic of medical students and its relationship with physical activity (N = 186)
| Socio-demographic characteristics | No. (%) | Physical activity median (range) | p |
|---|---|---|---|
| Age/mean (SD) | 22.6 (2.8%) | r = -0.006 | 0.943* |
| Gender | < 0.001† | ||
| Male | 93 (50.0%) | 2431.5 (19998) | |
| Female | 93 (50.0%) | 1182 (30879) | |
| Marital status | 0.154† | ||
| Single | 162 (88%) | 1732.5 (30879) | |
| Married | 12 (22%) | 1155.7 (5544) | |
| Educational grade | < 0.001‡ | ||
| Basic sciences | 45 (24.2%) | 2445 (7893) | |
| Physiopathology | 45 (24.2%) | 870.7 (4158) | |
| Clinical clerkship | 45 (24.2%) | 1481.2 (5683.5) | |
| Internship | 51 (27.4%) | 1794 (39879) | |
| Weight (kg)/m (SD) | 67.4 (12.6%) | r = 0.214 | 0.009* |
| BMI (kg/m2)/m (SD) | 23.7 (10.4%) | r = 0.051 | 0.543* |
| Underweight (< 18.5) | 17 (9.9%) | 3358.5 (5683.5) | 0.096 |
| Normal (18.5-24.9) | 123 (71.9%) | 1186.5 (19998) | |
| Overweight (25-29.9) | 28 (16.4%) | 1231.2 (6024) | |
| Obese (≥ 30) | 3 (1.8%) | 4906 (3559.5) | |
| Employment | 0.112† | ||
| Yes | 9 (5.4%) | 2365.5 (6784.5) | |
| No | 159 (94.6%) | 1550.5 (19998) | |
| Inhabitant | 0.105‡ | ||
| Dormitory | 48 (26.1%) | 1182 (7626) | |
| Parent’s home | 93 (50.5%) | 1550.5 (7893) | |
| Personal | 22 (12.0%) | 1455 (19998) | |
| Leased | 20 (10.9%) | 2735.2 (5040) | |
| Depression history | 0.973† | ||
| Yes | 7 (3.8%) | 1786.5 (6024) | |
| No | 178 (96.2%) | 1611.0 (30879) | |
| Family history of depression | 0.685† | ||
| Yes | 15 (8.2%) | 1485 (6024) | |
| No | 169(91.8%) | 1732.5 (30879) | |
| Father’s education | 0.950‡ | ||
| Primary-secondary | 8 (4.3%) | 1546.5 (4239) | |
| Diploma | 31 (16.8%) | 1786.5 (6933.05) | |
| University | 145 (78.8%) | 1550 (19998) | |
| Mother’s education | 0.730‡ | ||
| Primary-secondary | 17 (9.2%) | 1786.5 (7626) | |
| High school-diploma | 48 (26%) | 1455.0 (6933) | |
| University | 119 (64.7%) | 1717.5 (19998) | |
| Father’s occupation | 0.456‡ | ||
| Unemployed | 2 (1.1%) | 3885 (0.0) | |
| Employee | 88 (47.8%) | 1546.5 (7893) | |
| Self-employment | 54 (29.3%) | 1477.5 (19998) | |
| Others | 40 (21.7%) | 1786.5 (6024) | |
| Mother’s occupation | 0.546† | ||
| House keeper | 99 (54.7%) | 1639.5 (7893) | |
| Occupied | 82 (45.3%) | 1717.5 (19998) | |
| Income | 0.235† | ||
| Sufficient | 113 (63.1%) | 1786.5 (7893) | |
| Somehow sufficient and insufficient | 66 (36.9%) | 1380.7 (19998) | |
| Death of loved ones | 0.635† | ||
| Yes | 5 (2.7%) | 1346.5 (5685) | |
| No | 179 (97.3%) | 1664.2 (30879) | |
| Serious illness | 0.014† | ||
| Yes | 15 (8.2%) | 3999 (6933) | |
| No | 167 (91.8%) | 1548.5 (30879) | |
| Stressful events | 0.176† | ||
| Yes | 34 (18.6%) | 2365.5 (6024) | |
| No | 149 (81.4%) | 1477.5 (19998) | |
| Examinations period | 0.110† | ||
| Yes | 69 (38.3%) | 1786.5 (19998) | |
| No | 111 (61.7%) | 1466.2 (30879) | |
| History of mood disorders | 0.110† | ||
| Yes | 7 (3.8%) | 346.5 (462) | |
| No | 177 (96.2%) | 1732.5 (30879) |
SD: Standard deviation.
*Pearson correlation coefficient, †Mann-Whitney, ‡Kruskal-Wallis.
The frequency of PA based on IPAQ-SF in two categories of vigorous (< 0.001) and moderate was significantly different among medical students in different educational stages (0.002). The most vigorous (80%) and moderate (57.8%) PA during the past week was related to basic science and the least (vigorous: 26.7%, moderate: 20.0%) to physiopathology stages. All students stated that they had mild PA (walking) during the past week (Table 2).
Table 2.
Comparison the frequency of physical activities (vigorous, moderate, mild) basedon IPAQ-SF between medical students in different educational stages (N = 186)
| Physical activity | Categories | Basic science | Physiopathology | Clinical clerkship | Internship | Total | p* |
|---|---|---|---|---|---|---|---|
| n (%) | |||||||
| Vigorous | Positive | 36 (80%) | 12 (26.7%) | 22 (48.9%) | 21 (41.2%) | 91 (48.9%) | < 0.001 |
| Negative | 9 (20%) | 33 (73.3%) | 23 (51.1%) | 30 (58.8%) | 95 (51.1%) | ||
| Moderate activity | Positive | 26 (57.8%) | 9 (20.0%) | 20 (44.4%) | 26 (51%) | 81 (43.5%) | 0.002 |
| Negative | 19 (42.2%) | 36 (80.0%) | 25 (55.6%) | 25 (49%) | 105 (56.5%) | ||
| Mild activity (walking) | Positive | 45 (100%) | 45 (100%) | 45 (100%) | 51 (100%) | 186 (100%) | - |
| Negative | - | - | - | - | - | ||
Physical activity level less than 600 METs-min/week: low physical activity, between 600-3000: moderate physical activity, and more than 3000: vigorous physical activity.
*Chi-square test.
The median (min, max) of total PA according to IPAQ- SF among medical students was 1725 (346.5, 31225.5) MET min/week. There was a statistically significant difference between the four groups of students in terms of total (p = 0.002), vigorous (p < 0.001), and moderate PA (346.5, 8239.5)]. The highest median (min, max) of PA was related to basic science [2445 (346.5, 8239.5)] and the lowest to physiopathology [935 (346.5, 11208.0)] (Table 3).
Table 3.
Median (maximum, maximum) of physical activity based on IPAQ-SF among medical students in different educational stages (N = 186)
| Physical activity (MET min/week) | Basic science | Physiopathology | Clinical clerkship | Internship | Total | p* |
|---|---|---|---|---|---|---|
| Total physical activity | 2445 (346.5, 8239.5) | 935 (346.5, 11208.5) | 1481.2 (346.5,13608.0) | 1794 (346.5, 31225.5) | 1725 (346.5, 31225.5) | 0.002 |
| Vigorous physical activity | 720 (0, 4800) | 0 (0, 1920) | 0 (0, 3600) | 0 (0, 28800) | 40 (0, 28800) | < 0.001 |
| Moderate physical activity | 180 (0, 3600) | 0 (0, 2160) | 0 (0, 4320) | 60 (0, 14400) | 0 (0, 14400) | 0.026 |
| Mild physical activity (walking) | 1039.5 (346.5, 3861) | 594 (346.5, 7128.0) | 668.2 (346.5, 7128.0) | 717.7 (346.5, 8316.0) | 742.5 (346.5, 8316.5) | 0.131 |
*Kruskal-Wallis Test.
There were significant differences between pedometer- counted steps of medical students in different educational stages in Tuesday (p = 0.002) and Wednesday (p = 0.006). The most median (min, max) of steps [9085.5 (1543, 17000)] was related to basic science similar to total PA according to IPAQ-SF (Table 4).
Table 4.
Median (maximum, maximum) of pedometer counted steps per day among medical students in different educational stages (N = 186)
| Day | Basic science | Physiopathology | Clinical clerkship | Internship | Total | p* |
|---|---|---|---|---|---|---|
| Monday | 8204.5 (814, 16122) | 6182 (1648, 14543) | 4114 (1650, 13883) | 5060 (998, 12033) | 6045 (814, 16122) | 0.236 |
| Tuesday | 9085.5 (1543, 17000) | 7670 (1288, 21220) | 5868 (1220, 13459) | 4474 (750, 14506) | 6134 (750, 21220) | 0.002 |
| Wednesday | 7750.4 (2388, 12352) | 7050 (1202, 15053) | 2980 (1200, 6000) | 5675 (721, 13000) | 5776 (721, 15053) | 0.006 |
| Thursday | 6000 (1213, 14375) | 8575 (699, 15322) | 5734 (700, 12545) | 4500 (897, 10675) | 6000 (699, 15322) | 0.103 |
| Friday | 5863 (561, 8763) | 5120 (498, 16500) | 4085 (500, 8561) | 6251 (600, 12000) | 5700 (498, 16500) | 0.514 |
| Saturday | 4902.5 (2946, 15626) | 8000 (780, 14291) | 7000 (800, 11857) | 6728 (323, 33163) | 6632 (323, 33163) | 0.463 |
| Sunday | 4997.3 (2009, 15670) | 8700 (778, 15608) | 7321 (1100, 22400) | 5266 (750, 16179) | 5266 (750, 22400) | 0.156 |
*Kruskal-Wallis Test.
The results demonstrated a positive significant relationship between total PA based on IPAQ-SF and pedometer- determined steps in four days of the week (Monday, Thursday, Friday, and Saturday) (Table 5).
Table 5.
Relationship between pedometer-counted steps and total physical activity on the basis of IPAQ-SF in medical students (N = 186)
| Pedometer-counted steps per day | Total physical activity (MET min/week) | Mild physical activity (MET min/week) | ||
|---|---|---|---|---|
| r | p* | r | p* | |
| Monday | 0.281 | 0.017 | 0.133 | 0.273 |
| Tuesday | 0.139 | 0.247 | 0.118 | 0.331 |
| Wednesday | 0.204 | 0.088 | 0.160 | 0.185 |
| Thursday | 0.350 | 0.003 | 0.215 | 0.075 |
| Friday | 0.447 | < 0.001 | 0.282 | 0.022 |
| Saturday | 0.325 | 0.005 | 0.384 | 0.001 |
| Sunday | 0.206 | 0.079 | 0.161 | 0.159 |
*Spearman rho correlation coefficient.
DISCUSSION
The purpose of this study was to evaluate the subjective and objective measures of PA and its relationship with socio-demographic factors between different stages of medical training. The results of this study showed a significant relationship between some socio-demographic characteristics such as gender, weight, serious disease, and year of study with the level of PA. Previous studies have shown a gender difference in the level of PA in medical students [14,27,28]. In our study, female students showed less PA than male students and this finding is consistent with previous studies across 17 countries out of 23 geographical regions [29]. Perhaps the reasons for this gender difference are due to cultural issues, social norms, and low self-efficacy [30,31]. In terms of the objective and subjective comparison of PA, Mestek and Colleagues also reported that there was a better agreement between pedometer-measured and self-reported PA in college-aged men than women [22].
Our finding revealed a relationship between weight and PA levels but there was no significant difference in terms of BMI. A study by Yousif et al. [32] showed that there was no significant relationship between BMI and PA among medical students, which is consistent with our findings. However, the significant relationship between weight and levels of PA remains largely unjustifiable. In addition, in the study of Hsu et al. [20] comparison of the objective and subjective measurement of PA showed higher correlations between different activity modalities among lower body fat college students than those higher body fat.
Our findings showed that serious illness had a significant relationship with PA levels. We could not find any study that examined the relationship between serious illness and PA in medical students. In Connolly et al. [33] study, low PA behavior between critical illness patients was observed. It seems that the relationship between serious illnesses or a history of chronic illnesses with levels of PA needs to explore in medical students. However, the impact of chronic disease on physical activity seems reasonable.
In our study, the levels of PA varied with different years of study, based on both IPAQ-SF and pedometer-counted PA (in just two days). It can be said that there was a little relative agreement between the two types of measurements of PA in terms of the study year. In both types of measurements, the highest PA level was observed in the basic sciences. The highest level of PA based on IPAQ-SF which was observed in the stage of the basic science is not consistent with previous studies [11]. Also, we did not find any study based on the pedometer-counted PA to compare our findings. In previous studies, the year of study was one of the predictors of students’ level of PA [14,17] but, this has been reported differently. For example, Macilwrait et al. study [11] reported that the highest PA level was observed in the third year and the lowest in the final year. In contrast, Medagama’s study [14] reported the lowest PA in the third year and the highest in the fourth year of medical training. These discrepancies may be originated from the different study conditions at medical schools between countries. In our study, the lowest level of PA was observed in the physiopathology stage. The physiotherapy stage coincides with the second half of the third year and the fourth year of medical training, which shows that our findings are in line with Medagama et al. study based on IPAQ-SF. However, it should be noted that the mentioned study examined only the third, fourth and fifth-year medical students. The different challenges that medical students face in their training years especially greater academic pressure of theory courses during the third year of study can be explained the discrepancy in PA levels among academic stages [14]. Due to insufficient evidence in this regard, there is a need for further studies to compare objective and subjective measures of PA in different years of study in medical students.
The results of the present study showed that there is a positive correlation between total PA based on IPAQ-SF and pedometer in 4 days of the week (rs = 0.28-0.44), which is consistent with the previous studies. In a study by De Cocker et al. [34] conducted on 310 adults, a positive low correlation was reported between step counts measured by the pedometer and IPAQ-SF (rs = 0.33). The results of a systematic review, conducted to the validation of IPAQ-SF, showed that the correlation of IPAQ-SF with pedometer in the included studies was 0.25-0.33, median = 0.28 [35]. But, on the other hand, this relationship has not always been positive and reported negatively or positively correlated in other studies. In a systematic review, the correlations between self-report and direct measures of PA were generally low to moderate and ranged from -0.71 to 0.96, without any clear pattern [36]. A review study also found that the agreement between self-reported PA and pedometer, varied depending on the questionnaire used, individuals assessed and how to report pedometer outputs [37].
As mentioned above, the assessed individuals can influence the concordance between self-reported PA and pedometer. College students can be considered as a special group in the population because the university environment creates new conditions for them [38]. Entering adulthood, having a new social life, and increased pressure to focus on academic performance can add a lot of stress to students and lead to a significant change in their PA pattern [7,39]. A study by Hsu et al. [20], examined students’ objective and subjective measures of PA, found that there were low positive correlations (rs = 0.20-0.47) in light and vigorous PA between subjective and objective assessments of PA (accelerometer) which is consistent with our finding. We did not find any other studies that examined the correlation between IPAQ-SF and pedometer exclusively in medical students. Medical students in pre-clinical and clinical years of training are facing more problems than other college students. Prolonged medical education, high-stakes examinations, exposure to death and suffering of patients, rotation in different hospitals during clinical years, and full-time clinical education create special conditions for medical students [8,9,40]. However, it seems that subjective assessment and recall of physical activity during the past week in the medical students is more accurate due to being young and having a high level of education. Further studies are needed to confirm positive correlation, as shown in our study, between IPAQ-SF and pedometer in medical students.
Despite the positive correlations observed between IPAQ- SF and pedometer in 4 days in our study, these correlations were not very high. In a review study of Lee and colleagues [35], it was reported that the correlation between the IPAQ- SF and objective measures of PA in the most included studies was lower than the acceptable standard (0.09-0.39). The modest correlations in our study may be because IPAQ-SF and pedometer capture different dimensions of PA [20]. Each of these instruments has advantages and limitations that need to be addressed simultaneously. The pedometer counts individual’s steps directly and reduces the subjectivity inherent in surveys. So a pedometer is a reliable instrument for measuring walking-type movements [25]. But pedometers cannot measure all types of PA, such as swimming, cycling, and weight lifting [37]. The IPAQ-SF is based on respondents’ ability to remember their PA over the past week and their honesty in the report [41]. In addition, the IPAQ-SF overestimated PA level by 36 to 173 percent. Overestimation is one of the key limitations of such self-reported questionnaires. For these reasons, the IPAQ-SF cannot be used as an indicator of relative or absolute PA [35]. Therefore, it is not possible to say exactly which one of these instruments is a good tool for measuring PA. More precise tools should be developed to minimize the limitations mentioned above. For example, although a pedometer is a proper instrument for measuring PA, it needs to be upgraded so that it can measure other forms of PA.
Our study was limited to one medical school, which eliminates the possibility of comparing results with other medical colleges. One of the potential limitations of the present study was the type of cross-sectional study, allowing us only to assess activity levels for a short period of time. In this study, we applied only one type of objective and subjective measures and did not use other instruments such as accelerometer or Long form IPAQ. The relatively small sample size was also one of the limitations of our study that may affect statistical power.
CONCLUSION
There were positive low correlations between PA based on IPAQ-SF and pedometer-counted steps among medical students. Further large, multi-institution studies are needed to confirm positive correlation, as shown in our study, between IPAQ-SF and pedometer in medical students as a special group in the population. The PA of medical students at different years of medical training was different based on both IPAQ-SF and pedometer, which shows the need to pay attention to the special needs of students in terms of PA at each stage. Moreover it seems necessary to investigate the main reasons for the low physical activity in female students and eliminate the underlying factors to promote the health of this group.
ACKNOWLEDGEMENTS
Hereby, we thank the Research Deputy of Tabriz University of Medical Sciences and all of the medical students who participated in this study.
Footnotes
CONFLICTS OF INTERESTS
None to declare.
REFERENCES
- 1.World Health Organization. [Accessed date: Feb 5, 2021];Global Strategy on Diet, Physical Activity and Health 2004. Available from: https://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf .
- 2.Global status report on noncommunicable diseases 2014. World Health Organization; 2014. [Google Scholar]
- 3.Hawker CL. Physical activity and mental well-being in student nurses. Nurse Educ Today. 2012;32(3):325–31. doi: 10.1016/j.nedt.2011.07.013. [DOI] [PubMed] [Google Scholar]
- 4.Blair SN, Morris JN. Healthy hearts-and the universal benefits of being physically active: physical activity and health. Ann Epidemiol. 2009;19(4):253–6. doi: 10.1016/j.annepidem.2009.01.019. [DOI] [PubMed] [Google Scholar]
- 5.Joseph RP, Royse KE, Benitez TJ, Pekmezi DW. Physical activity and quality of life among university students: exploring self-efficacy, self-esteem, and affect as potential mediators. Qual Life Res. 2014;23(2):659–67. doi: 10.1007/s11136-013-0492-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Keating XD, Guan J, Piñero JC, Bridges DM. A meta-analysis of college students' physical activity behaviors. J Am Coll Health. 2005;54(2):116–26. doi: 10.3200/JACH.54.2.116-126. [DOI] [PubMed] [Google Scholar]
- 7.Bray SR, Born HA. Transition to University and Vigorous Physical Activity: Implications for Health and Psychological Well-Being. J Am Coll Health. 2004;52(4):181–8. doi: 10.3200/JACH.52.4.181-188. [DOI] [PubMed] [Google Scholar]
- 8.Dyrbye LN, Thomas MR, Shanafelt TD. Medical student distress: causes, consequences, and proposed solutions. Mayo Clin Proc. 2005;80(12):1613–22. doi: 10.4065/80.12.1613. [DOI] [PubMed] [Google Scholar]
- 9.Jamali A, Tofangchiha S, Jamali R, Nedjat S, Jan D, Narimani A, et al. Medical students' health-related quality of life: roles of social and behavioural factors. Med Educ. 2013;47(10):1001–12. doi: 10.1111/medu.12247. [DOI] [PubMed] [Google Scholar]
- 10.Dabrowska-Galas M, Plinta R, Dabrowska J, Skrzypulec-Plinta V. Physical activity in students of the Medical University of Silesia in Poland. Phys Ther. 2013;93(3):384. doi: 10.2522/ptj.20120065. [DOI] [PubMed] [Google Scholar]
- 11.Macilwraith P, Bennett D. Burnout and Physical Activity in Medical Students. Ir Med J. 2018;111(3):707. [PubMed] [Google Scholar]
- 12.Al-Drees A, Abdulghani H, Irshad M, Baqays AA, Al-Zhrani AA, Alshammari SA, et al. Physical activity and academic achievement among the medical students: a cross-sectional study. Med Teach. 2016;38(Suppl 1):S66–72. doi: 10.3109/0142159X.2016.1142516. [DOI] [PubMed] [Google Scholar]
- 13.Moniruzzaman M, Ahmed MM, Zaman MM. Physical activity levels and associated socio-demographic factors in Bangladeshi adults: a cross-sectional study. BMC Public Health. 2017;17(1):59. doi: 10.1186/s12889-016-4003-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Medagama A, Galgomuwa M, Silva C. Physical activity patterns and socio-demographic correlates of physical activity among medical undergraduates in Sri Lanka: an observational study. Postgrad Med J. 2018;94(1107):32–6. doi: 10.1136/postgradmedj-2017-135008. [DOI] [PubMed] [Google Scholar]
- 15.Rejali M, Mostajeran M. Assessment of physical activity in medical and public health students. J Educ Health Promot. 2013;2:19. doi: 10.4103/2277-9531.112690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Al-Tannir MA, Kobrosly SY, Elbakri NK, Abu-Shaheen AK. Prevalence and predictors of physical exercise among nurses. A cross-sectional study. Saudi Med J. 2017;38(2):209–12. doi: 10.15537/smj.2017.2.15502. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Blake H, Stanulewicz N, McGill F. Predictors of physical activity and barriers to exercise in nursing and medical students. J Adv Nurs. 2017;73(4):917–29. doi: 10.1111/jan.13181. [DOI] [PubMed] [Google Scholar]
- 18.Ainsworth B, Cahalin L, Buman M, Ross R. The current state of physical activity assessment tools. Prog Cardiovasc Dis. 2015;57(4):387–95. doi: 10.1016/j.pcad.2014.10.005. [DOI] [PubMed] [Google Scholar]
- 19.Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95. doi: 10.1249/01.MSS.0000078924.61453.FB. [DOI] [PubMed] [Google Scholar]
- 20.Hsu YW, Liu CC, Chang YJ, Tsai YJ, Tsai WC, Fu Y. Comparisons of Subjective and Objective Measures of Free-Living Daily Physical Activity and Sedentary Behavior in College Students. SSEJ . 2020:1–9. doi: 10.1007/s42978-019-00047-z. [DOI] [Google Scholar]
- 21.Behren TK, Hawkins SB, Dinger MK. Relationship between objectively measured steps and time spent in physical activity among free-living college students. Meas Phys Educ Exerc Sci. 2005;9(2):67–77. doi: 10.1207/s15327841mpee0902_1. [DOI] [Google Scholar]
- 22.Mestek ML, Plaisance E, Grandjean P. The relationship between pedometer-determined and self-reported physical activity and body composition variables in college-aged men and women. J Am Coll Health. 2008;57(1):39–44. doi: 10.3200/JACH.57.1.39-44. [DOI] [PubMed] [Google Scholar]
- 23.Farrokhi-Khajeh-Pasha Y, Nedjat S, Mohammadi A, Rad EM, Majdzadeh R, Monajemi F, et al. The validity of Iran's national university entrance examination (Konkoor) for predicting medical students' academic performance. BMC Med Educ. 2012;12(1):60. doi: 10.1186/1472-6920-12-60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hackmann DJ, Mintah JK. Pedometers: a strategy to promote increased physical activity among college students. JIP. 2010;4:1. [Google Scholar]
- 25.Bassett DR, Jr Validity and reliability issues in objective monitoring of physical activity. Res Q Exerc Sport. 2000;71(2 Suppl):S30–6. doi: 10.1080/02701367.2000.11082783. [DOI] [PubMed] [Google Scholar]
- 26.BashiriMoosavi F, Farmanbar R, Taghdisi M, AtrkarRoshan Z. Level of Physical Activity among Girl High School Students In Tarom County and Relevant Factors. Iran J Health Educ Health Promot. 2015;3(2):133–40. [Google Scholar]
- 27.Uddin R, Khan A, Burton NW. Prevalence and sociodemographic patterns of physical activity among Bangladeshi young adults. J Health Popul Nutr. 2017;36(1):31. doi: 10.1186/s41043-017-0108-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Fagaras SP, Radu LE, Vanvu G. The Level of Physical Activity of University Students. Procedia Soc Behav Sci. 2015;197:1454–7. doi: 10.1016/j.sbspro.2015.07.094. [DOI] [Google Scholar]
- 29.Pengpid S, Peltzer K, Kassean HK, Tsala JPT, Sychareun V, Müller-Riemenschneider F. Physical inactivity and associated factors among university students in 23 low-, middle-and high-income countries. Int J Public Health. 2015;60(5):539–49. doi: 10.1007/s00038-015-0680-0. [DOI] [PubMed] [Google Scholar]
- 30.Samara A, Nistrup A, Al-Rammah TY, Aro AR. Lack of facilities rather than sociocultural factors as the primary barrier to physical activity among female Saudi university students. Int J Womens Health. 2015;7:279–86. doi: 10.2147/IJWH.S80680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Clemente FM, Nikolaidis PT, Martins FML, Mendes RS. Physical activity patterns in university students: Do they follow the public health guidelines? PloS one. 2016;11(3):e0152516. doi: 10.1371/journal.pone.0152516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Yousif MM, Kaddam LA, Humeda HS. Correlation between physical activity, eating behavior and obesity among Sudanese medical students Sudan. BMC Nutr. 2019;5(1):6. doi: 10.1186/s40795-019-0271-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Connolly BA, Mortimore JL, Douiri A, Rose JW, Hart N, Berney SC. Low Levels of Physical Activity During Critical Illness and Weaning: The Evidence-Reality Gap. J Intensive Care Med. 2019;34(10):818–27. doi: 10.1177/0885066617716377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.De Cocker KA, De Bourdeaudhuij IM, Cardon GM. What do pedometer counts represent? A comparison between pedometer data and data from four different questionnaires. Public Health Nutr. 2009;12(01):74–81. doi: 10.1017/S1368980008001973. [DOI] [PubMed] [Google Scholar]
- 35.Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8(1):115. doi: 10.1186/1479-5868-8-115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5(1):56. doi: 10.1186/1479-5868-5-56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Tudor-Locke C, Williams JE, Reis JP, Pluto D. Utility of pedometers for assessing physical activity. Sports Med. 2002;32(12):795–808. doi: 10.2165/00007256-200232120-00004. [DOI] [PubMed] [Google Scholar]
- 38.Liu X, Ping S, Gao W. Changes in Undergraduate Students' Psychological Well-Being as They Experience University Life. Int J Environ Res Public Health. 2019;16(16):2864. doi: 10.3390/ijerph16162864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Alkhateeb SA, Alkhameesi NF, Lamfon GN, Khawandanh SZ, Kurdi LK, Faran MY, Khoja AA, Bukhari LM, Aljahdali HR, Ashour NA, Bagasi HT, Delli RA, Khoja OA, Safdar OY. Pattern of physical exercise practice among university students in the Kingdom of Saudi Arabia (before beginning and during college): a cross-sectional study. BMC Public Health. 2019;19(1):1716. doi: 10.1186/s12889-019-8093-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Domantay J. Health-related quality of life of future physicians at a medical school in the Philippines: a cross-sectional study. Sage Open. 2014;4(3):1–9. doi: 10.1177/2158244014545459. [DOI] [Google Scholar]
- 41.Ahmad MH, Salleh R, Mohamad Nor NS, Baharuddin A, Rodzlan Hasani WS, Omar A, Jamil AT, Appukutty M, Muda WMAW, Aris T. Comparison between self-reported physical activity (IPAQ-SF) and pedometer among overweight and obese women in the MyBFF@ home study. BMC Womens Health. 2018;18(1):100. doi: 10.1186/s12905-018-0599-8. [DOI] [PMC free article] [PubMed] [Google Scholar]