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Abstract

BACKGROUND:

Internet addiction and physical inactivity are often a major public health problem.

OBJECTIVE:

This study aimed to determine the relationship between internet addiction (IA) and physical activity (PA) levels of university students in a province in eastern Turkey.

METHODS:

This cross-sectional study was conducted among 638 students. Internet Addiction Test (IAT), and International Physical Activity Questionnaire (IPAQ) were administered. Chi-square, independent sample t-test, correlation analysis, one-way analysis of variance tests (ANOVA), Tukey HSD test, and multivariate logistic regression analysis were performed.

RESULTS:

64.6% of the participants were female, with a mean age of 20.4±2.4 and a mean body mass index (BMI) of 22.3±3.5. 83.4% of the participants were identified as those asymptomatic, 15.2% showed limited symptoms, and 1.4% were pathological internet users according to IAT. A statistically significant difference was found between IAT scores and gender, mother’s education level, father’s education level, academic success, smoking status, and alcohol use (p < 0.05). According to IPAQ scores, 28.1% of the students were inactive, 56.3% were moderate PA and 15.7% had vigorous PA levels. IPAQ total scores of male participants, smokers, and participants with exercise habits were found to be significantly higher (p < 0.05). The mean score IAT and IPAQ was found to be 30.9±18.9 and 1697.7±1847.0. A negative, significant correlation was found between students’ PA and IA levels (p < 0.01).

CONCLUSION:

It has been observed that IA negatively affects PA. Seminars, conferences, and panels on the internet and physical activity should be organized for university students.

Keywords

Body mass index cross-sectional study exercise gender habits smoking

1 Introduction

Internet addiction (IA) is characterized by the clinical features of excessive and problematic internet use and behavioral addiction, that are: preoccupation, compulsive behavior, lack of control, and functional impairment [1]. Internet addiction is expressed in the literature with many terminologies such as pathological internet use, problematic internet use, excessive internet use, abuse of the internet, and internet use disorder [2]. Studies conducted in the United States and Europe reported that the prevalence of internet addiction varies between 1.5% –8.2%, and the prevalence in young people in Southeast Asia reaches 20–30% [3]. University students are in the age range in which internet use is most common. 80.7% of the Turkish population has home access to the internet [4]. IA, associated with many factors such as sleep, obesity, peer relationships, and academic burnout [5].

Many technological products in people’s daily life have reduced physical activity (PA), and therefore energy expenditure. In the literature, it was demonstrated that PA levels a decreased with the transition from high school to university [6]. More than a quarter of the world’s adult population (1.4 billion adults) are not active enough, around 1 in 3 women, and 1 in 4 men worldwide do not do enough PA to stay healthy. Globally, 28% of adults aged 18 and over are not active enough. Physical inactivity levels are twice as high in high-income countries compared to low-income countries [7]. However, adults aged 18 to 64 years should do at least 150–300 minutes of moderate-intensity or at least 75–150 minutes of vigorous aerobic PA or an equivalent combination of moderate to vigorous-intensity activity per week. Physical inactivity is one of the leading risk factors for death from noncommunicable diseases. People who are not active enough have a 20% to 30% higher risk of death compared to those who are sufficiently active [7].

Internet addiction can lead to undesirable consequences such as insomnia, weight gain due to inactivity, irregular nutrition, and health problems [8]. Increased sitting time, especially with internet use, causes a decrease in physical activity [9].

This study aimed to determine the relationship between internet addiction (IA) and physical activity (PA) levels of university students in a province in eastern Turkey.

2 Methods

2.1 Study design

The population of this cross-sectional study consisted of the students of Health Services Vocational School in a city, located in the east of Turkey. Absence of physical-mental diseases that may affect physical activity and internet addiction levels were accepted as inclusion criteria. Substance users, cancer patients, pregnant/breastfeeding women and those with serious psychiatric illness were excluded from this study. The study was carried out between March and May 2017. 638 students were reached, and included in the study. As a result, the rate of participation in the study was 91.5%. A questionnaire form consisting of three parts was applied to the participants. The questionnaire was applied face-to-face.

2.2 Data collection tools

The questionnaire form consisted of 3 sections: sociodemographic characteristics, Internet Addiction Test (IAT), and International Physical Activity Questionnaire (IPAQ-short form). In the first part, there were questions about sociodemographic characteristics, PA, and internet use of the participants. In the second part, the participants were asked to complete IAT, and the third part included the IPAQ. After the questionnaire form was filled in, the height and weight measurements of the participants were made using the calibration settings of Seca 769 –Seca 220, while they were wearing thin clothes, and without shoes. The evaluation of the World Health Organization (WHO) was taken as a criterion in the evaluation of the Body Mass Index (BMI). Those with a BMI below 18.5 were grouped as thin, those with 18.5–24.9 as normal, those with 25.0–29.9 as overweight, and those with a body mass index of 30, and above as obese [10].

2.2.1 Internet Addiction Test (IAT)

The internet addiction levels of the participants were determined with the 20-question IAT developed by Young and validated in Turkish by Bayraktar. The standardized Alpha value was .91 and the Spearman-Brown value was .87. According to the score obtained in IAT, which is a six-point Likert scale, those with a total score of 80, and above are classified as “Pathological Internet Users”, those between 50–79 as “Limited Symptoms”, 49 and below as “ Asymptomatic” [11].

2.2.2 International Physical Activity Questionnaire (IPAQ-short form)

The PA status of the participants was evaluated with the IPAQ –short form. The IPAQ-short form was develeloped by Craig et al. [12], and adopted to Turkish by Ozturk [13]. The IPAQ-short form consists of 7 questions. The criterion is that each activity should be done at least 10 minutes at a time. The calculation of the total score of the IPAQ includes the sum of the duration (minutes) and frequency (days) of low-intensity activity (walking), moderate-intensity activity, and vigorous activity in the last seven days. From these calculations, a score in MET-minutes is obtained. According to the total IPAQ scores, PA levels are classified as low (inactive) of 599 MET-min/week and below, 600–2999 MET-min/week as moderate (minimally active) and 3000 MET-min/week and above as vigorous [13].

2.3 Statistical analysis

SPSS 22.0 package program was used data analysis. Depending on the characteristics of the variables, chi-square (χ²), independent sample t-test, Spearman correlation analysis, one-way analysis of variance tests (ANOVA), and post hoc Tukey HSD test were performed. Means were presented with standard deviation, p < 0.05 was considered significant. Multivariate logistic regression analysis was also utilized. Only variables with significant associations (i.e. p-value <0.05) with participant’s PA status in the χ² tests were considered in the logistic regressions. Moderate and vigorous PA were collected in one group. They were compared for risk factors with participants of physically active (inactive PA: 1, moderate/vigorous PA: 0). The odds ratio (OR) and its 95% confidence interval (CI) were calculated for each categorical variable.

3 Results

A total of 638 students, 64.6% (n = 412) of the participants were female, 97.3% (n = 621) were single, with a mean age of 20.4±2.4 (min = 17, max = 50) and, a mean BMI of 22.3±3.5 (min = 15.2, max = 39.3). The distribution of students according to their sociodemographic characteristics is given in Table 1.

Table 1
Distribution of students by sociodemographic characteristics

Sociodemographic characteristics (n = 638) n %

Gender

Female 412 64.6

Male 226 35.4

Age

17–18 70 11.0

19–20 339 53.1

21–22 161 25.2

23–24 42 6.6

≥25 26 4.1

Marital status of parents

Married 587 92.0

Widow 33 5.2

Divorced 18 2.8

Grade

First 380 59.6

Second 258 40.4

Department

Anesthesia 106 16.6

Physiotherapy 88 13.8

Autopsy assistant 60 9.4

Medical imaging techniques 124 19.4

Medical laboratory techniques 101 15.8

Medical documentation and secretarial

Family monthly income 159 24.9

≤1000 TL 65 10.2

1001–2000 TL 273 42.8

2001–3000 TL 158 24.8

≥3001 TL 142 22.3

Socioeconomic status

Poor 44 6.9

Medium 426 66.8

Good 168 26.3

Sociodemographic characteristics (n = 638)	n	%
Gender
Female	412	64.6
Male	226	35.4
Age
17–18	70	11.0
19–20	339	53.1
21–22	161	25.2
23–24	42	6.6
≥25	26	4.1
Marital status of parents
Married	587	92.0
Widow	33	5.2
Divorced	18	2.8
Grade
First	380	59.6
Second	258	40.4
Department
Anesthesia	106	16.6
Physiotherapy	88	13.8
Autopsy assistant	60	9.4
Medical imaging techniques	124	19.4
Medical laboratory techniques	101	15.8
Medical documentation and secretarial
Family monthly income	159	24.9
≤1000 TL	65	10.2
1001–2000 TL	273	42.8
2001–3000 TL	158	24.8
≥3001 TL	142	22.3
Socioeconomic status
Poor	44	6.9
Medium	426	66.8
Good	168	26.3

83.4% (n = 532) of the participants were identified as those asymptomatic, 15.2% (n = 97) showed limited symptoms, and 1.4% (n = 9) were pathological internet users according to IAT (Table 2). The IAT mean score of the participants was 30.94±18.97, IPAQ mean score was 1697.7±1847.0 MET min/week.

Table 2

Distribution of students’ internet addiction status

	Internet Addiction Status
Variables (n = 638)	Asymptomatic		Limited		Pathological		Total		Statistics
			Symptoms		Internet User
	n	% *	n	% *	n	% *	n	% **
Gender
Female	352	85.4	58	14.1	2	0.5	412	64.6	8.615^a p = 0.013
Male	180	79.6	39	17.3	7	3.1	226	35.4
BMI
Underweight and normal weight	428	83.8	79	15.5	4	0.8	511	80.1	7.327^a p = 0.026
Overweight and obese	104	81.9	18	14.2	5	3.9	127	19.9

^aChi-square test; *Percentage of row; **Percentage of column.

A statistically significant difference was found between IAT scores and gender (p = 0.028), mother’s education level (p = 0.007), father’s education level (p = 0.026), academic success (p < 0.001), smoking status (p < 0.001), and alcohol use (p = 0.019, Table 3). There was no significant difference between IAT scores and age, marital status, and place of residence.

Table 3

Distribution of students’ IAT scores according to various variables

Variables (n = 638)	n	IAT Score (Mean±SD)	Statistics	p
Gender
Female	412	29.6±17.7	–2.201^a	0.028
Male	226	33.2±20.8
Age
17–19	237	31.0±18.4
≥20	401	30.8±19.3	0.121^a	0.903
Marital status
Single/divorced	623	31.0±19.0
Married	15	25.2±14.3	1.172^a	0.242
Mother’s education
No education	144	27.9±18.7*
Primary school graduate	237	30.1±18.2*
Secondary school graduate	123	31.8±18.9	3.583^b	0.007
High school graduate	96	32.6±20.2
University graduate and above	38	40.1±18.2*
Father’s education
No education	24	26.3±16.8
Primary school graduate	163	27.9±18.4*
Secondary school graduate	143	32.3±18.9	2.776^b	0.026
High school graduate	209	30.8±19.6
University graduate and above	99	35.0±18.0*
Family monthly income
≤1000 TL	65	28.2±18.3
1001–2000 TL	273	28.9±18.0*	4.581^b	0.011
≥2001 TL	300	33.3±19.6*
Place of residence
Home	443	30.0±18.8
Dormitory	195	33.0±19.2	–1.891^a	0.059
Academic success
High	229	27.5±18.6*
Moderate	361	32.2±18.4*	7.339^b	<0.001
Low	48	37.2±21.5*
Smoking
Yes	159	35.4±20.3
No	479	29.4±18.2	3.516^a	<0.001
Alcohol
Yes	63	36.2±21.1
No	575	30.3±18.6	2.342^a	0.019

^aIndependent sample t-test; ^bOne-way analysis of variance tests (ANOVA); *Groups that make the difference with the Tukey HSD test.

The distribution of the IAT scores of the participants according to the internet and telephone-related variables is shown in Table 4. IAT scores of the participants who have social media accounts and internet connections were significantly higher (p < 0.05).

Table 4

Distribution of students’ scores of the IAT by internet and telephone-related variables

Variables (n = 638)	n	IAT Score (Mean±SD)	Statistics	p
Social network account
Yes	539	32.1±19.2
No	99	24.1±16.1	3.934^a	<0.001
Internet connection
Yes	509	32.0±19.2
No	129	26.5±17.1	2.928^a	0.004
Having own computer
Yes	364	32.2±19.5	2.046^a	0.041
No	274	29.1±18.0
Smart phone
Yes	603	31.1±18.9	1.301^a	0.194
No	35	26.8±19.8
Duration of daily internet use
≤60 min	87	24.4±17.1*	8.706^b	<0.001
61–180 min	176	27.8±18.9*
181–240 min	77	31.7±18.9
≥241 min	298	34.4±18.7*
Total years internet used
<3 years	104	29.7±20.3	–0.682^a	0.495
≥3 years	534	31.1±18.7

^aIndependent sample t-test; ^bOne-way analysis of variance tests (ANOVA); *Groups that make the difference with the Tukey HSD test.

IPAQ total scores of male participants, smokers, exercise habits and gym memberships were found to be significantly higher (p < 0.05, Table 5). There was no significant relationship between alcohol use and chronic disease and IPAQ total scores.

Table 5

Distribution of students’ IPAQ scores according to various variables

Variables (n = 638)	n	Low Intensity PA MET (min/week)	n	Moderate Intensity PA MET (min/week)	n	Vigorous Intensity PA MET (min/week)	n	Total IPAQ score MET (min/week)
Gender
Female	137	258.4±209.2	235	1400.8±644.7	40	4460.5±1837.1	412	1318.0±1382.3
Male	42	161.7±211.5	124	1762.3±668.8	60	5247.1±2655.8	226	2390.0±2328.7
		2.613^a		–4.986^a		–1.630^a		–6.335^a
		p = 0.010		p < 0.001		p = 0.106		p < 0.001
Age
17–18	16	250.2±199.8	42	1616.2±670.0	12	5897.1±4544.9	70	2037.8±2647.2
≥19	163	234.3±214.9	317	1513.7±675.3	88	4800.9±1926.0	568	1655.8±1721.6
		0.284^a		0.925^a		0.825^a		1.635^a
		p = 0.777		p = 0.355		p = 0.426		p = 0.103
Grade
First	112	225.4±211.3	222	1514.6±664.7	46	5081.1±2889.0	380	1566.3±1814.9
Second	67	252.9±216.6	137	1543.6±692.3	54	4805.8±1869.1	258	1891.2±1880.2
		–0.835^a		–0.396^a		0.574^a		–2.172^a
		p = 0.405		p = 0.692		p = 0.568		p = 0.030
BMI
Underweight-normal weight	150	228.5±210.7	295	1515.7±681.4	66	4845.6±2553.2	511	1567.9±1740.9
Overweight and obese	29	272.7±225.3	64	1571.6±645.3	34	5101.1±2040.8	127	2219.9±2153.2
		–1.022 ^a		–0.601^a		–0.506^a		–3.165^a
		p = 0.308		p = 0.549		p = 0.614		p = 0.002
Smoking
Yes	40	195.4±226.7	87	1672.2±719.8	32	5231.4±2037.5	159	2017.0±2026.6
No	139	247.3±208.5	272	1478.8±653.9	68	4791.7±2532.5	479	1591.7±1773.0
		–1.359^a		2.342 ^a		0.859 ^a		2.363 ^a
		p = 0.176		p = 0.020		p = 0.392		p = 0.019
Chronic disease
Yes	8	313.9±245.9	31	1524.6±728.3	7	5303.7±1760.7	46	1889.1±1767.1
No	171	232.0±211.6	328	1525.8±670.4	93	4904.5±2429.4	592	1682.9±1853.7
		1.062^a		–0.010^a		0.425 ^a		0.729 ^a
		p = 0.290		p = 0.992		p = 0.671		p = 0.466

^aIndependent sample t-test.

Multivariate logistic regression showed that being a limited symptom or pathological internet user increased the risk of physical inactivity (OR 0.32, 95% CI 0.20–0.51, Table 6).

Table 6

Comparison of inactive participants with moderate and vigorous participants according to sociodemographic variables and habits: Multivariate logistic regression analysis

Factors	Odds ratio	95% Confidence interval	p
Gender
Female	1
Male	0.542	0.354–0.830	0.005
Grade
First	1
Second	0.946	0.650–1.376	0.771
BMI
Underweight-normal weight	1
Overweight and obese	0.770	0.476–1.246	0.287
Exercise habit
No	1
Yes	0.237	0.116–0.486	<0.001
Gym membership
No	1
Yes	0.990	0.400–2.451	0.983
Internet addiction status
Limited symptoms-pathological internet user	1
Asymptomatic	0.325	0.205–0.516	<0.001

According to IPAQ scores, 28.1% of the students were inactive, 56.3% were moderate PA and 15.7% had vigorous PA levels. The distribution of IAT mean scores with regard to the PA levels is shown in Table 7.

Table 7

Distribution of IAT score according to the PA levels of the students

Physical Activity Levels (n = 638)	n	IAT Score (Mean±SD)	Statistics	p
Low (Inactive) PA	179	35.5±20.0*
Moderate PA	359	29.2±17.9*	7.509^a	<0.001
Vigorous PA	100	28.6±19.3*

^aOne-way analysis of variance tests (ANOVA); *Groups that make the difference with the Tukey HSD test.

While the IAT mean score of the inactive participants was 35.54±20.06, this score decrees significantly in the vigorous PA level (p = 0.001, Table 7).

A statistically significant and negative correlation was found between the participants’ PA levels and IAT scores (r=–0.137, p < 0.001). As the PA levels of the participants increased, their IAT scores decreased.

4 Discussion

According to IAT, 83.4% of the students were asymptomatic, 15.2% showed limited symptoms, and 1.4% were pathological internet users. In the study of Gunduz et al. [14], 1.7% of the participants and Abdel-Salam et al. [15] 1.9% were found to be pathological internet users. Generally, the rate of pathological internet users was low and the rate of those with limited symptoms can be considered as a harbinger of serious problems in the future.

In our study, the mean score of the IAT of the students was found to be 30.9±18.9. Similar to our findings; it was found to be 30.3±15.5 in Tayhan et al. [16], 30.0±18.4, and 37.1±8.4 in Bhandari et al. [17].

In our study, IAT scores of male students were found to be significantly higher than female students. Our findings were consistent with the literature [16 , 19]. In contrast, internet addiction in women was found to be significantly higher in Haroon et al. [20]. In a study conducted with medical students in Pakistan, no significant relationship between gender and internet addiction was found [21]. The higher IAT scores of male students in our study may be related to the more frequent use of the Internet by males in Turkey [22]. It can be thought that the differences in the studies are due to sociocultural variables.

Internet addiction was found to be significantly higher in overweight and obese students compared to underweight and normal-weight students. Similar to our study, in the study of Tayhan et al. [16], as BMI increased, internet addiction increased significantly. Saud et al. [23], reported, a positive and significant correlation between BMI and internet addiction. Besides that, in the study of Gunduz et al., a negative correlation was found between BMI and internet addiction [14]. High BMI was predictive of internet addiction [14].

In our study, when the education level of the parents of the students within the scope of the research increased, the IAT scores increased significantly. Although, Khan et al. reported similar results [21], in Salama’s study [18], there was no significant difference between IAT scores and parents’ education levels.

In our study, when the monthly income levels of the families of the students increased, the IAT scores increased significantly. In the literature, no significant relationship was found between family income level and internet addiction [15, 20]. In our study, this finding may be related to the fact that families having higher income levels provide more opportunities such as computers and the internet to their children.

The IAT scores of the students staying in the dormitory within the scope of the study were found to be higher than the students staying at home, but the results were not statistically significant. In the literature, there was no significant relationship between the places where students stay during their education and their total internet addiction scores [14 , 19]. The need to provide social support by spending time on the internet, trying to eliminate the perception of loneliness that may arise due to being away from their families through sharing sites and interactive chats, the feeling of euphoria, pleasure, and relaxation may be positive reinforces of addictive behaviors.

The IAT scores of the participants who perceived their academic success as low were found to be significantly higher. Similar to our study, Haroon et al. found a significant correlation with low academic success and excessive internet [20]. The study by Sarialioglu et al. [24] in adolescents was also parallel to our study. In this study, students with low levels of academic success were found to be more addicted to the internet.

In this study, IAT scores of students who smoked were found to be significantly higher than those of non-smokers. Similarly, IAT scores were significantly higher in smokers in the literature [18, 25]. In the study of both Khan et al. [21] and Seki et al. [26], there was no significant relationship between smoking and internet addiction. In our study, IAT scores of participants who use alcohol were found to be significantly higher than those who did not. The research of Salici et al. also reported similar results to our study [19]. Some studies did not find a relationship between alcohol consumption and internet addiction [26]. Based on these findings, it may be said that pathological internet use, alcohol use, and cigarette addiction provide susceptibility to addiction with similar mechanisms, which is supported by the results of our study.

Internet addiction test scores of the participants who have an internet connection were found to be significantly higher. Similar to our study, Salama [18] stated that there was a significant relationship between IAT scores and having an internet connection. Having an internet connection may cause people to spend more time on the internet as it will facilitate access to the internet.

Internet addiction test scores of the participants having personal computers were found to be significantly higher. In the study of Hassan et al. [25], the internet addiction status of those having personal computer was found to be higher, but it was not significant. It is thought that having a personal computer allows them to access the internet more easily and this may be a risk factor in terms of internet addiction.

As the daily internet usage time increased, IAT scores increased significantly. This finding is in line with those in the literature [14 , 25]. It can be thought that spending a lot of time on the internet will cause internet addiction.

According to IPAQ, 28.1% of the students were inactive, 56.3% had moderate PA and 15.7% had vigorous PA levels. In a study conducted in Turkey, 18.6% of the students were inactive, 43.9% had moderate PA and 37.3% had vigorous PA levels [27]. In a study conducted in Vietnam, 23.1% were inactive, 28.5% had moderate PA and 48.4% had vigorous PA levels [28]. In a study conducted in Cyprus, 6.3% of the students were inactive, 32.9% had moderate PA, and 60.8% had vigorous PA levels [29]. In our study, it was found that only 15.7% of the students participated in PA were at a level that would protect and increase their health.

The IPAQ mean score was 1697.7±1847.0 MET-min/week. In Akova’s [30] study, IPAQ mean score was found to be 1618.1±1934.3, and in Ozturk et al. [9] study, it was found to be 2448.2±3868.7. These changes in the level of PA among countries were thought to be due to differences in socioeconomic development, technology, and urbanization levels.

The IPAQ total score of the male participants was found to be significantly higher than female students. Similar to our study, men’s IPAQ scores were significantly higher in the literature [29 –31]. In our study, it was found that women were more inactive than men. In most countries, women are less active than men, and there are significant differences in physical activity levels within and between countries and regions. These differences may be due to inequalities in access to opportunities to be physically active [32].

Among the participants, the IPAQ total scores of the 17–18 age group were found to be higher than those in the age group 19 and over, but the results were not statistically significant. Blake et al. reported also similar findings [33]. There were also studies in the literature in which no significant difference was found between age and PA scores [31, 34]. It is thought that the fact that the age of university students, is close to each other and that they are in young adulthood may reveal this result.

The IPAQ total scores of the participants in the second-grade were found to be significantly higher than those in the first grade. There were also studies in the literature in which no significant difference was found between the grade and IPAQ scores [34, 35]. It can be thought that first-grade students are not well aware of the fact that enough amounts of PA is required for health compared to second-grade students. It is thought that second-graders can contribute to being more physically active than first-year students due to the physical education course they take at the university.

The IPAQ total scores of overweight and obese students were found to be significantly higher. In a study conducted with medical students, the IPAQ total scores of obese students were high but not significant [31]. PA is important in preventing weight gain. However, it alone is not effective in reducing body weight. PA is only one factor in the complexity of a weight management program. Nutrition, genetic and behavioral factors all affect body composition. However, PA provides health-beneficial metabolic adaptations without a measurable reduction in body weight. The increasing interest of students in body images in recent years suggests that this may be the main reason for this situation.

The IPAQ total scores of smokers among the students included in the study were found to be higher than the non-smokers. It is thought that the reason for this difference may be explained by the effect of cigarettes on energy expenditure. It was shown that smoking may have a minor and temporary effect on energy expenditure, but it was not proven whether it had a chronic effect. It was been shown that there was a 10% increase in energy expenditure following a cigarette (after about 30 minutes) [13].

The IPAQ total scores of the students with chronic diseases were found to be higher, but the results were not statistically significant. A study conducted in Iran also reported similar results to our study [31]. It can be thought that individuals with chronic diseases do more to be healthy in PA.

A negative, significant but weak correlation was found between PA and IA levels. As the PA levels of the participants increased, their IAT scores decreased. This finding is consistent with the literature [25, 27].

4.1 Limitations

Limitations in this study include the fact that information was collected based on a self-reported survey. It is possible that the participants did not accurately report their physical activity levels and internet use. The results of our study cannot be generalized to all students, since our research is single-centered and does not cover all health services vocational schools in Turkey. Further studies are needed on this subject.

5 Conclusion

It was observed that internet addiction negatively affects PA. Thus, measures should be taken to prevent IA and to increase the PA levels of university students. Students should be prevented from starting to smoke and use alcohol, and users should be directed to quit. Seminars, conferences, and panels on the internet, planned use of technological environments, pathological internet use, physical activity, physical inactivity, leisure time, and recreation should be organized for university students. Activities can be organized on campus to make the social lives of students healthier and thus prevent their social use of the internet. Since doing physical activity regularly during childhood and youth is important for health at later ages, university students should be given the habit of doing sports and sports should be turned into a lifestyle. Sports facilities should be increased both in the city and on the university campus.

Footnotes

Acknowledgments

We thank all the participants who contributed to this study.

Ethical approval

The study was approved by the Fırat University Non-invasive Clinical Research Ethics Committee (approval no. 189530; 28/02/2017). Informed consent was obtained from all participants. The study was carried out in accordance with the Declaration of Helsinki.

Conflict of interest

The authors declare there were no potential conflicts of interest concerning the research, authorship, and/or publication of this article.

Funding

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

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