Technology Addiction and Social Connectedness in Psychiatric Illness: A Multicenter Study
Article information
Abstract
Objective
Technology addiction is an increasingly important public health problem all over the world that negatively affects people’s mental and physical health. In this study, we examined technology addiction and social connectedness levels of psychiatric patients who admitted to clinics in different geographical regions of Turkey.
Methods
A total of 642 people with a diagnosis of psychiatric illness who applied to psychiatry clinics in İstanbul, Elaziğ, Malatya, Yozgat, Adıyaman, and Bingöl provinces were included. Sociodemographic data form, Technology Addiction Scale (TAS), and Social Connectedness Scale (SCS) were applied to all participants.
Results
The total score of the TAS in patients diagnosed with anxiety disorder and somatoform disorder was significantly higher than the other patient groups (p<0.001). Patients diagnosed with anxiety disorder showed a significant difference from other patient groups in terms of SCS score (p<0.001). Anxiety disorder was found to be the highest in TAS total score and sub-dimensions and the lowest in SCS score, while major depressive disorder was found to be the lowest in TAS total score and sub-dimensions and the highest in SCS score. The multiple linear regression analysis showed that the total score of the TAS was predicted by the SCS score (β=-1.857, p<0.001) and the SCS score was predicted by age (β=0.046, p=0.049) and the total score of the TAS (β=-0.316, p<0.001).
Conclusion
As a result of this study, we can say that psychiatric patients have a moderate level of technology addiction, these people have high levels of social connectedness, and psychiatric patients with technology addiction have a high level of social belonging.
INTRODUCTION
In recent years, access to the internet and smartphones has become much easier due to the developments in technology. Today, it is thought that approximately 5.18 billion people can connect to the internet and 6.5 billion smartphones are actively used [1]. In addition to pleasurable things such as shopping, watching TV series-films, listening to music, playing games, the fact that official transactions can now be done online to a large extent has increased the use of the internet and smartphones and computers [2].
Loss of control over the substance or behavior, risky usage, causing social problems, and observing withdrawal symptoms in the absence of the addictive substance or behavior constitute the four main components of addiction. Behavioral addictions are a relatively new concept. Technology addiction is defined as a non-chemical (behavioral) addiction involving human-machine interaction. This addiction can be passive (television) or active (computer, smartphone) [3]. Technology addiction is a concept that includes addictions such as online shopping addiction, social media addiction, smartphone addiction, which are largely caused by problematic internet use [4]. Individuals with technology addiction show symptoms of mental preoccupation, mood changes, tolerance, withdrawal, relapse, and interpersonal conflict, as do individuals with substance or other behavioral addictions [5].
Although social media addiction, internet addiction, and smartphone addiction are not yet accepted as disorders with diagnostic criteria in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), internet gaming disorder has been included in the diagnostic manual [6]. Internet gaming disorder is defined with 9 diagnostic criteria in DSM-5: preoccupation with internet games (internet games become the dominant activity in everyday life), the need to devote more and more time to internet games (tolerance), withdrawal symptoms (irritability, anxiety or sadness), persistence (unsuccessful attempts to control participation in internet gaming), substitution (preferring internet games to old hobbies and pastimes), continuing to use internet games excessively despite the knowledge of psychosocial problems, deception (deceiving others about the amount of internet gaming), escape (using internet games to escape from a negative mood), and conflict/loss (lost educational or career opportunities). The presence of five or more criteria in the last year is considered sufficient for the diagnosis of internet gaming disorder [6]. Technology addiction has been shown to cause many biopsychosocial problems such as depression, impulsivity, loneliness, decreased well-being, impaired sleep quality, decreased self-esteem, and decreased academic performance [7-11].
Social connectedness is defined as the sense of belonging and subjective psychological bond that people feel towards other individuals and groups [12]. Maslow says that social connectedness is a basic human need [13]. As social connectedness increases and the social network diversifies, quality of life improves and mental health is positively affected [14]. Studies have shown that strong social connectedness reduces suicidal ideation, suicide attempts, depression, substance abuse, and offenses [15,16].
Social media and internet use may have positive effects on establishing and maintaining social connectedness [17]. While there are studies reporting that social media use and technology increase social connectedness and strengthen friendship, there are also studies reporting that problematic internet use disrupts social relations and decreases social connectedness [18,19]. In our literature review, we could not find any studies that examined technology addiction and social connectedness levels of psychiatric patients together. We think that the findings of this study may be useful in the recognition and treatment processes of psychiatric patients.
METHODS
This study was conducted in accordance with the ethical standards set out in the Declaration of Helsinki, 1983 revision. This cross-sectional study was conducted in 6 provinces of Turkey between January 1, 2023 and June 30, 2023. Approval for the study was obtained from Firat University Non-Interventional Research Ethics Committee with the decision numbered 2022/15-09 on 15.12.2022. Institutional permissions were obtained from Elazığ Fethi Sekin City Hospital, Yozgat Bozok University Faculty of Medicine, Adıyaman Training and Research Hospital, İstanbul Training and Research Hospital, Bingöl State Hospital, Malatya İnönü University Turgut Özal Medical Centre.
Participants
The sample size was calculated in Open Epi software version 3 (https://www.OpenEpi.com) based on the formula; n= [DEFF×Np(1-p)]/[(d2/Z21-α/2×(N-1)+p×(1-p)]. We required a sample size of 768 patients in order to achieve a confidence interval of 95% with a 50% prevalence of technology addiction. A total of 683 people participated in the study. However, 35 people later refused to participate in the study. and 6 people were excluded from the study due to incomplete completion of the scales. As a result, 642 (83.6%) patients were reached in this study. Patients were selected from Adıyaman (n=103), Bingöl (n=34), Elaziğ (n=100), İstanbul (n=179), Malatya (n=112), and Yozgat (n=114) provinces (Figure 1). The sample group consists of patients who applied to the outpatient clinic in the hospitals where the authors worked during the 6 months (from January 1, 2023 to June 30, 2023) in which the study was conducted.
Distribution of patients according to provinces.
Instruments
Diagnostic interview
All patients who provided consent to engage in the research underwent structured interviews based on DSM-5 guidelines, conducted face to face by a psychiatrist, with each interview lasting around 30 minutes. Sociodemographic data form, Technology Addiction Scale (TAS), and Social Connectedness Scale (SCS) were administered to all participants after obtaining their signed written informed consent. Literate individuals between the ages of 18–65, who applied to a psychiatry outpatient clinic were included in the study. The exclusion criteria were illiteracy, hearing and speech impairment, history of alcohol and substance abuse in the last 6 months, any significant physical pathology or any neurological disease (such as epilepsy, cerebrovascular disease) that would affect the distribution of psychiatric symptoms in the patient, history of head trauma and cognitive dysfunction.
Self-reported scales
Sociodemographic data form
It is an evaluation form created by the researchers and includes data such as sex, age, marital status, economic status, diagnosis of psychiatric illness, and duration of illness.
TAS
It was developed by Aydın [20] to determine the level of technology addiction. It is a likert-type scale (1-never, 2-rarely, 3-medium, 4-very often, and 5-always) consisting of four subdimensions: instant messaging (6 items), using social networks (6 items), using websites (6 items), and playing online games (6 items). The lowest score that can be obtained from the sub-dimensions of the scale is 6 and the highest score is 30. The lowest score that can be obtained from the total score of the TAS is 24 and the highest score is 120. There are no reverse scored items in the scale. While interpreting the arithmetic averages of the whole scale, 0–24 point range was accepted as “Not dependent,” 25–48 point range as “Slightly dependent,” 49–72 point range as “Moderately dependent,” 73–96 point range as “Highly dependent,” and 97–120 point range as “Completely dependent”. As a result of the analyses, the reliability of the scale was calculated with internal consistency and Cronbach alpha value was 0.861. The internal consistency coefficients of the sub-dimensions were found as using social networking (0.786), instant messaging (0.806), playing online games (0.897), and using websites (0.861) respectively. In our sample, according to the Cronbach alpha analysis, the value of the total score of the scale was found to be 0.941, the value of the sub-dimension of using social networks was 0.835, the value of the sub-dimension of instant messaging was 0.855, the value of the sub-dimension of playing online games was 0.913 and the value of the sub-dimension of using websites was 0.9.
SCS
It was developed by Lee and Robbins [21] to measure the individual’s sense of belonging. SCS consists of 8 items. Turkish validity and reliability study was conducted by Duru [22]. The questions of the scale can be answered from “Strongly agree” to “Strongly disagree”. A score of 1 for “Strongly agree,” 2 for “Mostly agree,” 3 for “Agree,” 4 for “Disagree,” 5 for “Mostly disagree” and 6 for “Strongly disagree”. The minimum score for the scale is 6 and the maximum score is 48. A high score from the scale is accepted as an indicator of a high sense of commitment. In the original validity and reliability study of the scale, the internal consistency coefficient was found to be 0.91. In our study, Cronbach alpha value was calculated as 0.940.
Statistical analysis
The analyses were carried out using the SPSS 22 software package (Statistical Package for Social Sciences; IBM Corp., Armonk, NY, USA). In the study, descriptive data were presented as n and percentages for categorical data and as mean±standard deviation for continuous data. Chi-square analysis (Pearson chi-square) was used to compare categorical variables between groups. The Kolmogorov–Smirnov test was used to assess the suitability of continuous variables for normal distribution. The Mann–Whitney U-test was used to compare two variables, and the Kruskal–Wallis test was used to compare more than two variables. Spearman correlation test was used to examine the relationship between continuous variables. Linear regression analysis was applied to determine the predictors of TAS and SCS scale. The Enter method was used to build the model and those with a significant correlation in the correlation test were included in the model. Statistical significance was accepted as p<0.05 in the analyses.
RESULTS
A total of 642 patients, 331 (51.6%) women and 311 (48.4%) men, were enrolled. The mean age of the patients was 36.8±12.7 (min=18, max=88) years. 51.4% of patients were single and 48.6% were married. 16.2% of the participants lived in rural areas and 83.8% in urban areas. 30.4% of the participants had completed middle school or less and 69.6% had completed high school or more. Other patient characteristics are shown in Table 1.
Sociodemographic characteristics of the patients (N=642)
While 32.7% of the patients were newly diagnosed, 67.3% had an old diagnosis. The mean duration of diagnosis for those with an old diagnosis was 5.1±5.0 years. When analyzing the diagnoses of the patients, 303 (47.2%) were anxiety disorders, 144 (22.4%) were depressive disorders, 71 (11.1%) were schizophrenia spectrum disorders and other psychotic disorders, 60 (9.3%) were bipolar disorders, 39 (6.1%) were obsessive-compulsive disorders, and 25 (3.9%) were somatoform disorders (Figure 2).
Distribution of patients’ psychiatric diagnoses.
Of the patients included in the study, 89.1% used smartphones, 27.7% used televisions, 26.3% used laptops, 23.2% used desktop computers, and 14.2% used tablets.
92.5% of patients use internet sites, spending an average of 2.3±1.7 hours per day. 90.7% of patients use social networking sites, spending an average of 2.7±1.9 hours per day. 88.2% of patients use messaging services, spending an average of 2.1±1.8 hours per day. 36.1% of patients play online games, spending an average of 1.8±1.5 hours per day. 95.8% of patients use at least one online program, spending an average of 9.7±4.0 hours per day. A detailed analysis of the types of websites used by the 594 patients who use websites shows that 58.1% use music websites, 57.2% use shopping websites, 56.2% use search engines, 47.8% use film websites, 26.8% use technology websites, 23.2% use newspaper websites, 21.7% use dating websites, 20.7% use fashion and beauty websites, 18.5% use travel websites, 15.2% use financial websites, 12.5% use blogs, and 7.9% use erotic websites.
The mean TAS subscale scores of the patients were 14.7±7.1 for “using social networks,” 15.3±8.0 for “instant messaging,” 12.6±8.1 for “playing online games,” and 14.5±8.0 for “using websites”. The mean TAS total score was 57.2±28.5. According to the total score, 12.5% of the patients were not dependent, 34.1% were slightly dependent, 22.4% were moderately dependent, 14.2% were highly dependent and 16.8% were completely dependent. The mean SCS score of the patients was found to be 31.0±12.4. Men’s “using social networks” (p=0.006), “instant messaging” (p=0.033), “playing online games” (p<0.001), “using websites” (p=0.044), and TAS total score (p=0.003) were significantly higher than women’s, while SCS total score (p=0.005) was significantly lower.
The TAS total score and the subscale scores of single people were found to be significantly higher and the SCS total score was found to be significantly lower than those of married people (p<0.001). The TAS total score and the subscale scores of those living in urban areas were significantly higher and the SCS total score was significantly lower than those living in rural areas (p<0.001). The scores for “using social networks” (p<0.001), “instant messaging” (p<0.001), “using websites” (p=0.001), and “TAS total” (p<0.001) were significantly higher for those with a high school degree or higher than for those with a middle school degree or lower, while the score for “SCS total” (p=0.019) was significantly lower. The “using social networks” (p=0.001), “instant messaging” (p=0.009), “using websites” (p=0.037), and “TAS total” (p=0.012) scores of employed patients were significantly higher than those of non-employed patients, whereas the “SCS total” score (p=0.041) was significantly lower. The “using social networks” (p=0.017), “using websites” (p=0.014), “TAS total” (p=0.047) scores of patients with a previous diagnosis were significantly higher than those of patients with a new diagnosis, while the “SCS total” score (p=0.014) was significantly lower. There was no significant difference between the scale scores for economic status, alcohol and smoking status (p>0.05) (Table 2).
Comparison of patients’ scale scores according to sociodemographic characteristics
A significant difference was observed between psychiatric diagnoses in terms of social network use score (p<0.001). This difference was due to the difference between the anxiety disorder group and the depressive disorder, schizophrenia spectrum disorder and other psychotic disorders, and bipolar disorder groups, with the anxiety disorder group having the highest score. A significant difference was observed between psychiatric diagnoses regarding instant messaging score (p<0.001). This difference was due to the difference between the anxiety disorder group and the depressive disorder, schizophrenia spectrum disorder and other psychotic disorder, and bipolar disorder groups; and between the somatoform disorder group and the depressive disorder, schizophrenia spectrum disorder and other psychotic disorder, and bipolar disorder groups, with the anxiety disorder and somatoform disorder groups having the highest scores. A significant difference was observed between psychiatric diagnoses in terms of playing online game score (p<0.001). This difference was due to the difference between the anxiety disorder group and the depressive disorder group, with the anxiety disorder group having the highest score. In terms of using website scores, a significant difference was observed between psychiatric diagnoses. (p<0.001). This difference was due to the difference between the anxiety disorder group and the depressive disorder, schizophrenia spectrum disorder and other psychotic disorders and bipolar disorder groups, with the anxiety disorder group having the highest score. A significant difference was observed between psychiatric diagnoses regarding the total TAS score (p<0.001). This difference was due to the difference between the anxiety disorder group and the depressive disorder, schizophrenia spectrum disorder and other psychotic disorders and bipolar disorder groups, and between the somatoform disorder and depressive disorder groups, with the anxiety disorder and somatoform disorder groups having the highest scores.
With regard to the SCS score, a significant difference was observed between psychiatric diagnoses (p<0.001). This difference was due to the difference between the anxiety disorder group and depressive disorders, schizophrenia spectrum disorders and other psychotic disorders, with the anxiety disorder group having the highest score. According to the results, the anxiety disorder had the highest TAS total score and sub-dimensions and the lowest SCS score, whereas the depressive disorder had the lowest TAS total score and sub-dimensions and the highest SCS score (Table 3).
Comparison of patients’ scale scores according to diagnoses
There was a significant positive correlation between age and duration of diagnosis and SCS total score, and a significant negative correlation between age and TAS total score and sub-dimensions. There is a significant positive correlation between the duration of diagnosis and playing online game scores. A significant negative correlation was found between SCS and TAS and its sub-dimensions (Table 4).
Correlation of scale scores
According to the multiple linear regression analysis, SCS score (β=-1.857, p<0.001) predicted TAS total score. Age (β=0.046, p=0.049) and TAS total score (β=-0.316, p<0.001) predicted the SCS score (Table 5).
Lineer regression analysis of factors associated with TAS and SCS
DISCUSSION
In recent years, technological developments have been applied to many areas of life, making everyday life much easier. Computers, televisions, and smartphones are used as much in our country as anywhere else in the world. In this study, the prevalence of technology addiction among people presenting to psychiatric outpatient clinics in different geographical regions of our country, as well as demographic characteristics such as age, sex, educational status, psychiatric diagnosis, what device, how often and for what purpose they most frequently connect to the internet, and the relationship of this situation to people’s levels of social connectedness were examined. As a result of our findings, we can say that psychiatric patients have a moderate level of technology addiction and a high level of social connectedness. In addition, psychiatric patients with technology addiction have higher levels of social connectedness.
As a result of our study, the mean TAS total score of psychiatric patients was 57.2±28.5. This was interpreted to mean that internet addiction was not at a high level in psychiatric patients in general. The internet has been linked to a reduction in stress, anxiety and depressive symptoms as a result of its access to information and communication [23]. Some studies have reported strong links between technology addiction and mental health disorders [24,25]. We found that the total TAS score of the group diagnosed with anxiety and somatoform disorders was statistically higher than the other disorders. It has been shown that people with anxiety and social anxiety disorders are prone to internet addiction [26-28]. Cerruti et al. [29] reported that internet use was high among people with high levels of somatic complaints. Again, the fact that patients diagnosed with anxiety and somatoform disorders had higher subscale scores for using social networks and websites suggests that these individuals were seeking treatment for their physical and mental complaints and were more likely to use technology for these purposes. It is known that there is a significant positive relationship between depression and technology addiction and that depressive symptoms are high in people with technology addiction [30,31]. The fact that the TAS total score was lower in patients with depressive disorder than in the other patient groups suggests that mental complaints such as apathy and lack of energy may sometimes cause this condition. The SCS score was higher in patients diagnosed with depressive disorder than in the other patient groups, while it was lowest in patients with anxiety disorder. A high score on this scale indicates that people have a high sense of belonging, high self-esteem, harmony, and self-regulation. It forms the opinion that people with depressive disorders attach more importance to social life than to the virtual world, such as the internet environment, and that they are more inclined to establish face-to-face contact with people than with the technological environment. The fact that the TAS total score is low, especially for patients in İstanbul, suggests that psychiatric patients have problems accessing technology due to work intensity and financial difficulties. Patients in the provinces of Elaziğ, Malatya, Adıyaman, and Bingöl were found to have higher TAS total and online gaming subscale scores and lower SCS scores than patients in other provinces. It is known that technology addiction is high in rural areas [32]. This situation suggests that our study was conducted in the winter and spring months in these areas, which are particularly involved in agriculture and animal husbandry, and that they may be playing online games because they have more free time and spend more time at home due to seasonal conditions.
There was a significant positive correlation between patients’ age and duration of diagnosis and SCS total score; and a significant negative correlation between age and TAS total score and sub-dimensions. In a study conducted among university students in Japan, a negative correlation was found between depression and SCS scores [33]. This may indicate that in psychiatric patients the sense of social belonging increases as the disease becomes chronic, whereas in healthy people the presence of subclinical disease symptoms may be a factor in preferring social isolation. Again, there was a significant positive correlation between length of diagnosis and online gaming score, supporting the study that people with mental health problems have an online gaming disorder [34]. Our findings show that the SCS score (β=-1.857, p<0.001) predicted the TAS total score. In a study of 201 healthy adolescents conducted by Savci and Aysan [35] it was reported that those with technological addiction in four areas, namely internet addiction, social media addiction, digital game addiction, and smartphone addiction, significantly predicted social connectedness. This difference between the studies may be due to the fact that the sample groups were made up of two different groups, one healthy adolescents and the other psychiatric patients.
It was found that psychiatric patients were the most likely to access the internet via a smartphone, with a rate of 89.1%, and that they used the internet mainly for instant messaging. It is also known that university students often use smartphones (76%) among technological communication tools [36]. The easy portability and accessibility of smartphones may be a major contributor to this situation. Again, in line with the literature, we found that psychiatric patients who were single [37], urban dwellers, highly educated and employed were more likely to be dependent on technology. We know that socio-cultural and economic levels influence access to the internet and related devices.
Our limitations are that our study was cross-sectional, we did not have a control group and some patients were in partial remission with medication.
To summarize, excessive use of the internet, which is widely used by many people, leads to addiction. Although psychiatric patients are highly dependent on technology, we have observed that they prefer social communication to social isolation. There are concerns that the use of technology, which may increase in the future, may isolate psychiatric patients and that this situation may have a negative impact on psychological treatments. However, the fact that internet use is increasing may benefit psychiatric patients in organizing psychiatric treatment and online group psychotherapy when it is difficult to meet face-to-face. Studies with larger samples are needed to clarify this situation.
Notes
Availability of Data and Material
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Osman Kurt, Sevler Yıldız, Burcu Sırlıer Emir, Neslihan Cansel. Data curation: Osman Kurt, Neslihan Cansel. Formal analysis: Osman Kurt, Meltem Oktay, Şeyma Sehlikoğlu. Investigation: all authors. Methodology: Osman Kurt, Muhammed Fatih Tabara, Aslı Kazğan Kılıçaslan, Neslihan Cansel, Şeyma Sehlikoğlu. Project administration: Osman Kurt, Sevler Yıldız. Resources: Osman Kurt, Muhammed Fatih Tabara, Burcu Sırlıer Emir. Software: Osman Kurt, Muhammed Fatih Tabara, Şeyma Sehlikoğlu. Supervision: Osman Kurt, Muhammed Fatih Tabara, Sevler Yıldız, Meltem Oktay. Validation: Osman Kurt, Sevler Yıldız, Aslı Kazğan Kılıçaslan, Burcu Sırlıer Emir. Visualization: Osman Kurt, Aslı Kazğan Kılıçaslan, Meltem Oktay, Neslihan Cansel. Writing—original draft: Osman Kurt, Muhammed Fatih Tabara, Sevler Yıldız, Neslihan Cansel. Writing—review & editing: Osman Kurt, Sevler Yıldız, Aslı Kazğan Kılıçaslan, Burcu Sırlıer Emir.
Funding Statement
None
Acknowledgements
We would like to thank all participants.