The impact of internet addiction on depression,physical activity level and trigger point sensitivity in Turkish university students

Abstract

BACKGROUND AND OBJECTIVES:

Internet addiction (IA), defined as excessive, time consuming, uncontrollable use of the internet, has become a widespread problem. In this study, we investigated the impact of internet addiction on depression, physical activity level, and latent trigger point sensitivity in Turkish university students.

METHODS:

A total of 215 university students (155 females and 60 males) who were between 18–25 years of age participated in the study. Using the Addiction Profile Index Internet Addiction Form (APIINT), we identified 51 people as non-internet-addicted (non-IA) (Group 1: 10 male/41 female) and 51 as internet-addicted (IA) (Group 2: 7 male/44 female). APIINT, International Physical Activity Questionnaire-Short-Form (IPAQ), Beck Depression Inventory (BDI), and Neck Disability Index (NDI) were administered to both groups, and the pressure-pain threshold (PPT) in upper/middle trapezius latent trigger points area was measured.

RESULTS:

The internet addiction rate was 24.3% in our students. Compared with the non-IA group, the daily internet use time and BDI and NDI scores were higher (all $p<$ 0.05), while the IPAQ walking ( $p<$ 0.01), IPAQ total ( $p<$ 0.05), and PPT values ( $p<$ 0.05) were lower in the IA group.

CONCLUSIONS:

IA is a growing problem. This addiction may lead to musculoskeletal problems and can have consequences involving the level of physical activity, depression, and musculoskeletal disorders, particularly in the neck.

Keywords

Internet addiction depression physical activity level Neck Disability Index pressure-pain threshold latent trigger points

1. Introduction

The internet has become one of the most accessible media worldwide. As time spent on the internet increases, problems related to the overuse/misuse of the internet have become apparent with behaviors such as seeking and craving in the absence of internet access. Spending excessive time on the internet and uncontrollable use of the internet is defined as internet addiction (IA). Based on the literature, the amount of the time spent online is the main symptom and cause for the use of the internet to be defined as an addiction [1, 2, 3]. Furthermore, a strong association between IA and depression has been demonstrated in the literature [4, 5, 6, 7, 8]. Ho et al. [4] published a meta-analysis and found that the global prevalence of depression among people with IA was 26.3%, and the risk to develop depression was 2.77 times higher than general population worldwide. In a study of Turkish university students, there were associations between internet addiction and depressive symptoms [5]. Depression is thought to be a major factor underlying the development of internet addiction. Therefore, depression may be considered both as an etiological factor and as a consequence. Any individual may become an internet addict due to a depressive disorder associated with any other sociological/psychological cause or may develop addiction-related depression after becoming an internet addict. Moreover, depressive mood may become apparent when the addict becomes unable to use the internet [9, 10, 11]. Therefore, comprehensive studies are needed to define the cause and effect relationship in this area.

Myofascial trigger points (MTrPs), which are an important cause of skeletal muscle pain, are defined as points that are painful with palpation in the muscles or fascia and hyperirritability in the tense muscle bands [12]. MTrPs are divided into two categories as active and latent. At the active trigger point, the pain continues both with movement and during rest. At the latent trigger points (LMTrPs), pain occurs only with pressure. Trigger points may not be active most of the time, and LMTrPs can be reactivated with a stimulus [13]. Pressure-pain threshold (PPT) indicates the presence of LMTrPs in the trapezius and scapular muscles. If the PPT was less than that of “normal” muscle tissue, it can be considered as LMTrP [14]. Although MTrPs are very common in the clinic and have been the cause of myofascial pain, it has been reported in the literature that it was not taken into consideration by clinicians [15, 16]. The prevalence of MTrPs is increasing among university students, and MTrPs may lead to myofascial pain syndrome [17]. In addition, there was a close relationship between LMTrPs and depression in a study conducted on healthy subjects among university students [18].

In a study conducted on university students and staff, Lucas et al. [19] reported that MTrPs first appeared in the trapezius muscle in this population. Another study reported that moderate physical activity was associated with a lower prevalence of MTrPs among university students [20]. This finding also indicated an association between hypo-activity and back pain. Moreover, the association between reduced physical activity and number of health problems is a well-known fact. Currently the number of people who spend their spare time on the internet is gradually increasing, and lower physical activity levels are reported in these people [21, 22]. In addition, increased use of the internet is associated with internet addiction, and the associations between this problem and physical activity or MTrPs sensitivity are not fully understood. Based on the above knowledge, we aimed to investigate internet addiction among Turkish university students and the impact of this addiction on depression, level of physical activity, and MTrPs sensitivity.

2. Materials and methods

2.1 Study design and participants

Ethical approval (ATADEK: 2017/13/4) was obtained from the Local Ethics Committee of Acibadem University, and afterwards a total of 222 students (161 females, 61 males) were admitted as volunteers at the Faculty of Health Sciences of Acibadem University. There were only seven students (6 females, 1 male) who did not meet the inclusion criteria: two students because of psychiatric illness, and five students due to pain in the trapezius muscle (active MTrPs). A total of 215 students (155 females and 60 males) aged between 18–25 years participated in the study. Exclusion criteria were as follows:

(1) A history of orthopedic or neurological surgery, trauma, or disorders involving neck and shoulder region, (2) Receiving treatment for myofascial pain within 1 month prior to enrollment, (3) Use of painkillers for neck or shoulder pain within 3 months prior to enrollment, (4) Being diagnosed with fibromyalgia syndrome according to the American College of Rheumatology criteria [23], (5) Any severe psychiatric or systemic disease, use of antidepressants within 3 months prior to the enrollment, and (6) Current pain in the trapezius muscle.

The study was conducted between March 2017 and June 2017. Each student (215 students) filled out an Addiction Profile Index Internet Addiction Form (APIINT). The diagnosis of addiction was made in 51 subjects who scored equal to or higher than the cut-off point of 2 on APIINT, and these students were included in the group of internet addicts (Group 2; 7 males, 44 females). A total of 164 students (111 females, 53 males) who were determined to have no internet addiction were randomly selected using the Random Allocation Software 2.0 program and were assigned to a non-internet addiction group (Group 1; 10 males, 41 females). Sociodemographic data, including a potential family history of internet addiction, were obtained from each subject.

2.2 Outcome measures

2.2.1 Assessment of internet addiction

Subjects were asked about the time (in hours) they spent on the internet, and then they filled out an Addiction Profile Index Internet Addiction (APIINT) form. The validity and reliability of the APIINT form in university students was demonstrated by Ogel et al. [24], and the Cronbach’s alpha coefficient of this form is 0.90. The correlation coefficient between the Internet Addiction Scale [developed by Nichols [25, 26] and translated into Turkish by Canan et al. [27] and the APIINT is 0.81. The form includes 18 items assessing the preceding three months on a 5-point Likert type scale (0: never, 1: rare, 2: occasionally, 3: mostly, 4: almost every time). The form has a number of sub-dimensions including the diagnosis of addiction, its impact on daily life, strong urge, motivation, and the APIINT total scores (the intensity of addiction). The mean scores from the sub-dimensions are taken into consideration in the assessment. The cut-off point of the form is 2 for all scores [24].

2.2.2 Assessments of trigger points and pain-pressure thresholds

To evaluate MTrPs, subjects were asked to lay comfortably with their face looking down with their hands lying beside their body and their upper body exposed. Four specific criteria have been defined for the diagnosis of trigger point [14, 15, 16, 28, 29]: 1. The existence of a tender spot in a taut band of skeletal muscle; 2. The patient’s recognition of pain upon palpation of a tender spot; 3. The patient’s referred pain pattern (pain distribution expected from a trigger point in that muscle); 4. The existence of a local twitch response (transient local contraction of skeletal muscle fibers in response to palpation). The subject was asked “When this muscle was pressed, did you feel any pain or discomfort locally, and in other areas (referred pain)”. If the elicited local or referred pain did not produce the pain sensation as the patient suffered from before, the MTrPs was considered latent [18, 29].

The pressure pain threshold (PPT) measurement was done bilaterally with Baseline ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ Dolorimeter A (Fabrication Enterprises, White Plains, NY, USA) to the areas that are defined as the trigger point [13, 29] of the upper and middle trapezius muscle. The dolorimeter consists of a metallic plunger with a 1-cm round plastic disc attached to the end and is connected to a quadrant that measures the pressure both in kilogram (kg) and in pounds (lbs.), with a range of 1–30 kg or 1–60 lb at intervals of 0.25 kg or 0.5 lb. The dolorimeter was applied vertically to the trigger point until the subject felt pain (subjects were asked to say yes when they felt pain), and the pressure was increased 1 kg/cm ${}^{2}$ every 3 seconds. The pressure that caused pain was repeated three times at intervals of 30 seconds, and the mean of three measurements was considered to be the pressure-pain threshold. The measurements were performed as defined by Fernandez-de-las-Penas et al. [31]. Inter-examiner reliability for the baseline dolorimeter is good to excellent (interclass correlation coefficient: 0.75–0.89) [32, 33].

2.2.3 Assessments of physical activity level

We used the International Physical Activity Questi-onnaire-Short Form (IPAQ) in the assessment of physical activity. IPAQ-short form was developed by Graig et al. [34], and the validity and reliability of the Turkish version of the form was demonstrated by (r:76) Öztürk [35] In IPAQ, physical activities that are performed for at least 10 minutes at a time are taken into consideration. The questionnaire interrogates the last 7 days regarding the following activities: (1) Time (minutes) spent for vigorous physical activities (football, basketball, aerobics, or fast bicycling, heavy lifting, carrying loads, etc.), (2) Time (minutes) spent for moderate physical activities (carrying light load, bicycling at a regular pace, folk dances, bowling, table tennis, etc.), and (3) Time spent for walking and time spent for sitting in a day.

Total physical activity scores (MET-min/week) were calculated by converting time spent for vigorous and moderate activities and walking into MET, which expresses basal metabolic rate, as follows:

•
Walking score (MET-min/week): 3.3 X time spent walking X walking days.
•
Moderate physical activity score (MET-min/ week): 4.0 X time spent on moderate activity X moderate activity days.
•
Vigorous activity score (MET-min/week): 8.0 X Time spent on vigorous activities X vigorous activity days.

Physical activity levels of the participants were classified into low (600 MET-min/week), moderate (600 to 3000 MET-min/week), and high ( $>$ 3000 MET-min/week) activity groups based on total physical activity scores [35, 36].

Table 1
Sociodemogragrapic characteristics and APIINT scores

Group 1 Group 2 $p$ value

Age 20.68 $\pm$ 1.07 20.7 $\pm$ 1.32 ${}^{\text{a}}p>$ 0.05

Female 41 44 ${}^{\text{b}}p>$ 0.05

Male 10 7 ${}^{\text{b}}p>$ 0.05

Body mass index 22.41 $\pm$ 4.03 21.17 $\pm$ 2.41 ${}^{\text{a}}p>$ 0.05

Time spent online per day 3.16 $\pm$ 2.03 7.3 $\pm$ 3.9 ${}^{\text{c}}p<$ 0.01 ${}^{}$

Frequency (%)

Smoking 23.5% 19.6% ${}^{\text{b}}p>$ 0.05

Family history 12% 46% ${}^{\text{b}}p<$ 0.001 ${}^{}$

APIINT scores

Impact on daily life 0.64 $\pm$ 0.44 1.59 $\pm$ 0.53 ${}^{\text{c}}p<$ 0.001 ${}^{}$

Strong urge 0.95 $\pm$ 0.85 2.45 $\pm$ 0.82 ${}^{\text{c}}p<$ 0.001 ${}^{}$

Motivation 0.7 $\pm$ 0.73 1.75 $\pm$ 0.8 ${}^{\text{c}}p<$ 0.001 ${}^{}$

Total score 1.17 $\pm$ 0.43 2.53 $\pm$ 0.53 ${}^{\text{c}}p<$ 0.001 ${}^{}$

Group 1: Non-addict group; Group 2: Internet addiction group; APIINT: Addiction Profile Index Internet Addiction Form; ${}^{\text{a}}$ Student’s $t$ -test; ${}^{\text{b}}$ Pearson Chi-Square Test; ${}^{\text{c}}$ Mann-Whitney U Test; ${}^{}p<$ 0.05; ${}^{}p<$ 0.01; ${}^{*}p<$ 0.001; Data are reported as means $\pm$ SD.

2.2.4 Assessment of depression levels

	Group 1	Group 2	$p$ value
Age	20.68 $\pm$ 1.07	20.7 $\pm$ 1.32	${}^{\text{a}}p>$ 0.05
Female	41	44	${}^{\text{b}}p>$ 0.05
Male	10	7	${}^{\text{b}}p>$ 0.05
Body mass index	22.41 $\pm$ 4.03	21.17 $\pm$ 2.41	${}^{\text{a}}p>$ 0.05
Time spent online per day	3.16 $\pm$ 2.03	7.3 $\pm$ 3.9	${}^{\text{c}}p<$ 0.01 ${}^{**}$
Frequency (%)
Smoking	23.5%	19.6%	${}^{\text{b}}p>$ 0.05
Family history	12%	46%	${}^{\text{b}}p<$ 0.001 ${}^{***}$
APIINT scores
Impact on daily life	0.64 $\pm$ 0.44	1.59 $\pm$ 0.53	${}^{\text{c}}p<$ 0.001 ${}^{***}$
Strong urge	0.95 $\pm$ 0.85	2.45 $\pm$ 0.82	${}^{\text{c}}p<$ 0.001 ${}^{***}$
Motivation	0.7 $\pm$ 0.73	1.75 $\pm$ 0.8	${}^{\text{c}}p<$ 0.001 ${}^{***}$
Total score	1.17 $\pm$ 0.43	2.53 $\pm$ 0.53	${}^{\text{c}}p<$ 0.001 ${}^{***}$

The Beck Depression Inventory (BDI) was developed by Beck in 1961 to measure the risk for depression and the severity and intensity of depressive symptoms in adults [37]. The Turkish validity (r: 74) and reliability ( $\alpha$ : 80) of the scale was established by Hisli [38]. The inventory is a 21-item, single dimension, 4-point Likert-type scale. Each item is scored from 0 to 3, based on the severity of depression. Scores higher than the cutoff of 17 indicate a pathological condition, and total scores may range from 0 to 63. Scale scores from 0 to 9 indicate no depression, while scores 10 to 16 indicate mild depression, 17 to 24 indicate moderate depression, and scores higher than 25 indicate severe depressive symptoms.

2.2.5 Assessment of neck disability

Neck Disability Index (NDI) was administered to each subject to measure neck disability. This index was modified from the Oswestry Low Back Pain Disability Questionnaire. This is a 10-item questionnaire, and 5 items were from the Oswestry Disability Questionnaire. The other 5 items were defined according to recommendations from physicians, patients, and literature reviews. The items are pain intensity, self-care, carrying, reading, headaches, concentration, work, driving, sleep, and leisure activity. Each item is scored from 0 (no disability) to 5 (total disability). The sum of scores ranges from 0 (no disability) to 50 (total disability). Higher scores are associated with higher levels of limitation and lower scores indicate less limitation [39, 40].

2.3 Statistical analysis

The Number Cruncher Statistical System (NCSS) 2007 (Kaysville, UT, USA) software was used for statistical analyses. In the comparisons of quantitative data between two groups, the Student’s $t$ -test was used for the normally distributed variables, and the Mann-Whitney U test was used for non-normally distributed data. in addition to descriptive statistics (mean, standard deviation, frequency, rate, minimum and maximum). Pearson’s Chi-Square Test was used in the comparisons of qualitative data. For multivariate analysis, the backward stepwise (conditional) logistic regression analysis was used to assess the effects of other factors on internet addiction. Linear Regression analysis was used in the assessment of the effects of APIINT sub-scores on the IPAQ, BDI, NDI and LMTrPs-PPT variables. A $p$ value $<$ 0.05 was considered statistically significant.

3. Results

Only 24 (12%) of the students who participated in the study did not have LMTrPs and 5 had active MTrPs (2%) in the trapezius muscle. Based on APIINT scores, 51 (24.3%) out of 210 subjects were defined as internet addicts. Demographic data are shown in Table 1. No significant difference was found between the two groups in demographic data ( $p>$ 0.05). The number of subjects who responded affirmatively when they were asked if they believed that any family member might be an internet addict was significantly higher in Group II than in Group I ( $p<$ 0.001). Time spent online and APIINT sub-scores were significantly higher among the students in Group II than the students in Group I, as expected (Table 1; $p<$ 0.001).

Table 2
Assessment of IPAQ scores, BDI, NDI, and trigger point pressure-pain thresholds according to internet addiction

	Group 1	Group 2	$p$
IPAQ sitting	371.40 $\pm$ 126.86	409.60 $\pm$ 129.77	${}^{\text{a}}$ 0.140
IPAQ walking	1007.64 $\pm$ 385.64	752.42 $\pm$ 377.51	${}^{\text{a}}$ 0.001 ${}^{**}$
IPAQ total	1820.86 $\pm$ 1128.68	1320.34 $\pm$ 884.93	${}^{\text{a}}$ 0.015 ${}^{*}$
IPAQ Levels	$n$ (%)	$n$ (%)
Low	9 (31.0)	20 (69.0)	$\chi^{2}=$ 6.877
Moderate	30 (54.5)	25 (45.5)	${}^{\text{b}}$ 0.032 ${}^{*}$
High	12 (68.8)	6 (31.3)
BDI	1.86 $\pm$ 2.58	8.88 $\pm$ 9.34	${}^{\text{a}}$ 0.001 ${}^{**}$
NDI	11.81 $\pm$ 5.79	14.76 $\pm$ 8.15	${}^{\text{a}}$ 0.041 ${}^{*}$
DT-upper trapezius-PPT	3.97 $\pm$ 2.21	2.42 $\pm$ 1.79	${}^{\text{a}}$ 0.001 ${}^{**}$
DT-middle trapezius-PPT	4.97 $\pm$ 2.20	3.27 $\pm$ 1.69	${}^{\text{a}}$ 0.001 ${}^{**}$
NDT-upper trapezius-PPT	3.82 $\pm$ 2.17	3.15 $\pm$ 1.72	${}^{\text{a}}$ 0.091
NDT-middle trapezius-PPT	4.06 $\pm$ 2.14	3.20 $\pm$ 1.77	${}^{\text{a}}$ 0.030 ${}^{*}$

Group 1: Non-addict group; Group 2: Internet addiction group; IPAQ: International Physical Activity Questionnaire-Short-Form; BDI: Beck Depression Inventory; NDI: Neck Disability Index; DT-upper trapezius-PPT: Pain-pressure threshold in the dominant side upper trapezius muscle; DT-middle trapezius-PPT: Pain-pressure threshold in the dominant side middle trapezius muscle; NDT-upper trapezius-PPT: Pain-pressure threshold in the non-dominant side upper trapezius muscle; NDT-middle trapezius-PPT: Pain-pressure threshold in the non-dominant side middle trapezius muscle; ${}^{\text{a}}$ Student-t Test; ${}^{\text{b}}$ Pearson Chi-Square Test; ${}^{*}p<$ 0.05; ${}^{**}p<$ 0.01.

Walking and total IPAQ scores were significantly lower in Group II than those in Group I ( $p<$ 0.01, $p<$ 0.05, respectively, Table 2). No significant difference was found between the two groups in IPAQ scores for time spent sitting ( $p>$ 0.05; Table 2). The number of subjects who obtained IPAQ scores indicating moderate activity levels was lower in Group II than in Group I ( $p<$ 0.05). The prevalence of internet addiction was significantly higher among subjects who obtained low scores in the IPAQ in comparison to those who obtained moderate and high scores ( $p<$ 0.05; Table 2).

BDI and NDI scores were significantly higher in Group II than in Group I ( $p<$ 0.01, $p<$ 0.05, respectively, Table 2). The PPT of the middle and upper trapezius in the dominant side and the middle trapezius in the non-dominant side were significantly higher in Group I than in Group II (Table 2).

The effects of risk factors for internet addiction were assessed with logistic regression analysis. The variables maintaining their significance in the model are shown in Table 3. The assessment of the effects of variables on IA using backward stepwise (conditional) logistic regression analysis revealed that the model was significant and the model had a good explanatory power (87.5% accuracy to predict subjects without internet addiction; a prediction accuracy of 82% for subject with IA and an overall prediction accuracy of 84.7%). The odds ratio for the effect of the time spent online on IA was found to be 1.770 (95% CI: 1.303–2.405); the odds ratio for the BDI scores was 1.292 (95% CI: 1.086–1.538); the odds ratio for the NDI scores was 1.121 (95% CI: 1.011–1.243) and the odds ratio for the DT-middle trapezius-PPT was 0.645 (95% CI: 0.463–0.899). Other variables were found to be significant in univariate analyses ( $p<$ 0.05) but not in multivariate analyses ( $p>$ 0.05).

Table 3

Logistic regression analysis of risk factors for internet addiction

	$P$	ODDS	95% CI
			Lower	Upper
Time spent online per day	0.001 ${}^{**}$	1.770	1.303	2.405
BDI	0.004 ${}^{**}$	1.292	1.086	1.538
NDI	0.031 ${}^{*}$	1.121	1.011	1.243
DT-middle trapezius-PPT	0.010 ${}^{*}$	0.645	0.463	0.899

BDI: Beck Depression Inventory; NDI: Neck Disability Index; DT-middle trapezius-PPT: Pain-pressure threshold in the non-dominant side middle trapezius muscle; ${}^{*}p<$ 0.05; ${}^{**}p<$ 0.01.

The assessment of the effects of time spent online and APIINT sub-scores on the BDI and NDI scores revealed that APIINT total scores and time spent online had significant effects on these scores ( $p<$ 0.05; Table 4). Risk factors for internet addiction had effects on BDI scores at a rate of 28.1% (R ${}^{2}$ : 0.281); this rate was 7.8% for the NDI scores (R ${}^{2}$ : 0.078). Risk factors for internet addiction affected the DT-upper trapezius-PPT values at a rate of 22.5% (R ${}^{2}$ : 0.225) and the DT-middle trapezius-PPT values at a rate of 7.8% (R ${}^{2}$ : 0.078) (Table 4).

Table 4

Associations between risk factors for internet addiction and BDI, NDI, and PPT scores

	Unstandardized coefficients	$p$	95% confidence interval for B
	B		Lower bound	Upper bound
BDI
APIINT total score	0.221	0.025 ${}^{*}$	0.028	0.414
Time spent online per day	0.053	0.017 ${}^{*}$	0.010	0.096
NDI
APIINT total score	3.396	0.006 ${}^{**}$	1.006	5.786
Time spent online per day	$-$ 0.465	0.083	$-$ 0.992	0.062
DT-upper trapezius-PPT
APIINT motivation	$-$ 0.775	0.009 ${}^{**}$	$-$ 1.349	$-$ 0.201
APIINT total score	1.897	0.001 ${}^{**}$	0.872	2.921
Time spent online per day	$-$ 0.171	0.023 ${}^{*}$	$-$ 0.317	$-$ 0.025
DT-middle trapezius-PPT
APIINT total score	$-$ 0.947	0.001 ${}^{**}$	$-$ 1.480	$-$ 0.415

BDI: Beck Depression Inventory; APIINT: Addiction Profile Index Internet Addiction Form; NDI: Neck Disability Index; DT-upper trapezius-PPT: Pain-pressure threshold in the non-dominant side upper trapezius muscle; DT-middle trapezius-PPT: Pain-pressure threshold in the non-dominant side middle trapezius muscle; ${}^{*}p<$ 0.05; ${}^{**}p<$ 0.01.

4. Discussion

This study aimed at investigating the effects of internet addiction and time spent online on depression, physical activity level, and trigger point sensitivity in Turkish university students, the prevalence of IA was found to be 24.3%. IPAQ walking, IPAQ total, and PPT scores were lower (except NDT-upper trapezius-PPT) while time spent online per day and BDI and NDI scores were higher in the IA Group. Based on the results of regression analysis, risk factors for IA include time spent online, BDI and NDI scores, and DT-middle trapezius-PPT. In addition, the APIINT total score and time spent online per day were lower risk factors for BDI, NDI, and trapezius PPT values in dominant side. In other words, internet addiction and time spent online were low risk factors for depression and musculoskeletal problems of the neck (cause and effect).

The prevalence of internet addiction has been reported to be high among young people due to the features of immature personality, with a reported range from 1.5% to 30.1% [41, 42, 43, 44]. In studies conducted in Turkey, the prevalence of IA amongst the young population is reported to vary between 1.1–23.2% [5, 45, 46, 47, 48, 49]. In the current study, this rate was found to be 24.3%. This is may be due to the increase in internet accessibility and usage over the years and it is thought that the variability between reported prevalence rates from different studies in the literature is related to invalid and unreliable research methods and scales used, differences in target population, culture, and social structure [42].

Depression has been suggested to be an important etiological factor associated with pathological use of internet. Moreover, a depressive mood has been observed in internet addicts, and a strong association has been reported between depression and IA. Depression may be considered both as a causative factor and as a consequence. Adolescents with internet addiction state that they consider the internet as a medium that alleviates their depressive symptoms [4, 5, 6, 7, 8]. A strong positive relationship was found between IA and depression levels in a study of 3442 Turkish university students conducted by Orsala et al. [5]. In addition, in a study conducted by Çelik and Mutlu amongst the Turkish university students and employees, a close relationship between LMTrPs and depression was shown [18]. Consistent with prior research, the current study, depression levels were high in the IA group and PPT levels in the trapezius muscle were low. Association between BDI scores and PPT levels as a risk factor have been shown at a low level [4, 5, 6, 7, 8, 18].

The use of the internet and computers might have the potential for negatively impacting health, independent of sedentary behaviors. Extensive use of the internet and computers may replace physical activities in leisure time [21]. Several studies have demonstrated associations between extensive use of the internet/computers in leisure time and BMI and lower physical activity levels [21, 22, 50]. In addition, studies conducted in recent years have shown that the level of physical activity decreases as internet addiction increases [51, 52, 53]. Similar results have been demonstrated in a study conducted on 100 Turkish adults [54]. The current study found lower physical activity scores in IA group in line the literature.

In the current study, neck disability index scores were high in the IA Group. This consequence may be expected in internet addicts in association with the use of the internet for extended periods of time. As a result of maintaining the same position for extended periods of time, poor posture and low muscle strength have been reported in people who work online and who continuously use their cell phone to access the internet [55, 56]. These conditions may cause pain in the neck and MTrPs. Some studies have shown that increased use of smartphone and internet results in high neck pain scores similar the current study [57, 58].

Park and colleagues, in a study of university students in Korea, investigated sternocleidomastoid and upper trapezius muscles PPT levels in 20 students whose heavy smartphone users compared to a control group according to smartphone addiction proneness. They showed that the pressure pain threshold in these muscles decreases with the increase in smartphone use [59]. The number of subjects we recruited in the current study was higher than that study. However, like the study by Park et al., we found that internet addiction in Turkish university students decreased PPT levels as well. However, internet addiction, depression, neck disability scores and PPT levels as a risk factor have been shown at a low level (cause and effect).

One of the limitations of our study was that some of the data were self-reported and thus subject to various biases, selective memory, exaggeration etc., and cannot be independently verified. The study was conducted during the spring pre-examination period and the students had few holidays and free time. Therefore, this situation may affect the results of the study. In addition, our study is limited to a finite age group. Further studies with a larger sample size and various age groups are required for more definite results. Nevertheless, we obtained a lot of data from this study. Physical activity scores and pressure-pain thresholds were low, and depression and neck disability scores were higher in the IA group. These findings suggest that internet addiction is a growing problem today; considering that this addiction may lead to musculoskeletal problems and can have consequences involving the level of physical activity, depression and musculoskeletal disorders, particularly in the neck, we believe that the emphasis should be put on raising awareness and on studies investigating how to avoid internet addiction.

Footnotes

Acknowledgments

The author is very grateful to the students of the Faculty of Health Sciences of Acibadem University who participated in this study and to the 4th year students of the Physical Therapy and Rehabilitation for their support in the conduct of this study.

Conflict of interest

No competing interests exist.

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The impact of internet addiction on depression,physical activity level and trigger point sensitivity in Turkish university students

Abstract

BACKGROUND AND OBJECTIVES:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Study design and participants

2.2 Outcome measures

2.2.1 Assessment of internet addiction

2.2.2 Assessments of trigger points and pain-pressure thresholds

2.2.3 Assessments of physical activity level

2.2.5 Assessment of neck disability

2.3 Statistical analysis

3. Results

Table 2 Assessment of IPAQ scores, BDI, NDI, and trigger point pressure-pain thresholds according to internet addiction

Footnotes

Acknowledgments

Conflict of interest

References

Table 2
Assessment of IPAQ scores, BDI, NDI, and trigger point pressure-pain thresholds according to internet addiction