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Abstract

This study aims to determine the burden of COVID19-induced internet addiction and related psychosocial factors among the Pakistani sample. A web-based cross-sectional survey was conducted from January to March 2021. An analytical cross-sectional survey was broadcast on the internet via a Google form completed by 1145 Pakistanis. Individuals aged 13 years and above with competency to comprehend English or Urdu language, currently residing in any province of Pakistan, having access to the questionnaire, and willing to participate were eligible to participate. Overseas Pakistanis were excluded from the study. The outcome is COVID19-induced internet addiction was measured using the validated tool Young’s Internet Addiction Test (IAT). In addition, symptoms of depression, anxiety, stress, and other psychosocial factors were assessed using the validated tool Depression, Anxiety, and Stress Scale-21 (DASS-21). Adjusted odds ratios with a 95% confidence interval were reported using multinomial logistic regression. Most participants were females and youth (between 20 and 24 years). The prevalence of problematic internet users (PIU) and addictive internet users (AIU) was 27.3% and 11.3%, respectively. The odds of extremely severe anxiety among AIU were approximately three times (Adj OR: 2.6 (1.1-7.1), followed by the odds of having extremely severe stress being about five times higher among AIU (Adj OR: 5.4 (1.6-17.6)) as compared to normal internet users (NIU). Amid COVID-19, the burden of internet addiction has surged among the Pakistani populace. This study identified that gender, marital status, depression, stress, anxiety, work situation, and mood changes during the COVID-19 pandemic are significantly correlated with problematic and addictive internet use. Preventative measures against the addictive use of the internet are needed to avoid or mitigate any serious mental health problems.

Keywords

Internet addiction survey COVID-19 depression anxiety stress

Study Summary

Strengths

This was a web-based cross-sectional study conducted during the peak time of the COVID-19 pandemic in Pakistan, reflecting the true impact of the pandemic.

The data collection method used (web-based survey) is an efficient and effective tool for screening public health conflicts.

The tools used (IAT and DASS-21) are previously validated tools in the Pakistani setting.

Limitations

Online surveys have inherent methodological constraints in that respondents with biases may choose themselves for the sample.

Online self-reporting surveys have limited generalizability to solely internet users.

Introduction

The 2019 coronavirus disease (COVID-19), undoubtedly the greatest public health catastrophe of recent times and a universal health concern for the past three years, has brought several challenges for the masses and governments alike.^1-5 From 1 h to the next, information transmission through digital means became the only available method for the total non-interruption of social and work collaboration.⁶ The COVID-19 pandemic, in addition to having a devastating influence on the healthcare system, has unquestionably impacted worldwide social structures and ties. To counteract the influence of social distancing and lockdown, the internet has become more popular and widespread among people of all social strata.⁶ Consequently, internet addiction and behavioral issues have been skyrocketed in these unprecedented times.⁷ Literature shows that usage of the internet has markedly increased in this period of lockdown.^8,9 Globally, studies done in many European countries have shown an increase of about 50% in the rates of internet addiction during the pandemic when compared to before.¹⁰ Similar trends have been observed in India, where excessive internet usage led to increasing amounts of people playing video games and Binge watching. According to surveys in India, during the pandemic, the number of people playing video games has increased 3 times on the Winzio games platform and 200% on Paytm first games.¹¹ During the pandemic, several reasons have contributed to the rise in internet addiction. The COVID-19 pandemic has imposed an overwhelming economic strain on communities and triggered emotional reactions among the general public.⁷ To combat COVID-19, the government imposed measures such as home confinement, constant lockdown, and the closure of all schools, colleges, universities, offices, businesses, and markets, which resulted in work from home and unemployment, all of which have significantly contributed to the factors that cause internet addiction.¹²

Problematic internet addiction is an undue obsession or craving for computer use and internet access.¹³ Behavioral addictions like this can take on various hidden and appealing forms. These may appear harmless at first, but they can lead to a vicious circle from which it is difficult to recover, often manifested by an increased compulsion to surf the internet, difficulty in withdrawal, dependability, and uncontrollable desires and may sometimes be associated with other comorbidities such as depression, anxiety, and stress.¹⁴ Kamolthip et al¹⁵ explained that problematic internet use was associated with several health issues in a fraction of the youth population and the COVID-19 pandemic may have led to internet addiction and subsequent health problems. The COVID-19 pandemic has led to an economic burden at the community level and propelled the masses to experience emotional reactions, psychological difficulties, and behavioral changes.⁷ The levels of depression, anxiety, and stress showed a rising trend during the past pandemics.^16,17 According to several studies, high levels of anxiety, depression, post-traumatic stress disorder, psychological distress, and stress were reported in the general population during the COVID-19 pandemic in countries across the USA, Asia, and Europe.^18-23

Regional countries such as developing Asian countries observed similar trends of depression, anxiety, stress, and internet addiction during the pandemic. A study of 7 middle-income countries in Asia showed the highest levels of depression, anxiety and stress in Thailand and the lowest in Vietnam.²⁴ Studies in China and Iran were conducted to see the prevalence of psychological disorders among their population and concluded that a rise was seen in these among the general population, especially the younger ones.^25,26 Chen et al conducted several studies among Chinese population across 3-time points: before, during the initial stages, and in the recovery period of the COVID-19 pandemic. They showed that children with moderate and high levels of problematic internet use had higher levels of fear of COVID-19 and perceived weight stigma.^27,28 They also proved that problematic smart phone and social media use served as pertinent mediators in the association between psychosocial distress and internet addiction during COVID-19.^29-33

A few studies have been conducted in Pakistan during pre-COVID-19 times to determine the burden of internet addiction and associated factors amongst the general population. A cross-sectional study was conducted among undergraduate students reported a severe internet addiction prevalence of 6.1%, with males having higher scores on IAT as compared to females.³⁴ Another study from pre-COVID times on youth between 18 and 25 years reported a mean internet addiction score of 48.5 among males and 40.1 among females, showing a higher addiction in males as compared to females.³⁵ The trend of internet addiction in times of the COVID-19 pandemic has also observed a rise in Pakistan. Various press articles and data from the telecom industry indicated a surge in internet usage. The Pakistan Telecommunication Authority claimed a 15% rise in internet usage, whereas telecom companies reported a 25% increase in data usage.^36,37 However, no evidence-based research has been conducted to observe the rising internet addiction during the pandemic and its associated factors. Therefore, it is crucial to understand the impact of the COVID-19 pandemic on the use of technology and whether there is a change in internet addiction prevalence before and after the pandemic. Although a few studies have found psychological and sociological risk factors associated with internet addiction, these studies have investigated only a minimal number of risk factors, which does not allow for full comprehension and understanding of the relationship. Therefore, this study aims to determine the burden of internet addiction among the Pakistani population amidst COVID-19 and the psychosocial factors associated with internet addiction, including sociodemographic, health-related, behavioral and environmental factors.

Methods

Study Design

A cross-sectional survey was carried out to determine the burden of internet addiction during the pandemic and the influence of various psychological factors, including depression, anxiety, stress, and other related factors on internet addiction. This open-linked web-based survey circulated from January 2021 using Google form links. Multiple popular Pakistani Facebook groups were targeted. All the people known to the investigators were sent the Google link, and they were requested to forward the same link to their potential contacts. The link was forwarded through the snowball effect. To ensure adequate survey translation in the local language, the English version was reviewed by the psychiatrist and translated into Urdu, and back-translated into English to substantiate comparability of meaning and context. Electronic consent was obtained from all the participants, including parental consent for participants under 18 years to participate in the study. Assent was further verified by taking the contact information of parents/guardians. Ethical approval was obtained from the Institutional Ethical Review Board.

Study Population

Individuals aged 13 years and above with competency to comprehend English or Urdu language, currently residing in any province of Pakistan, having access to the questionnaire, and willing to participate were eligible to participate. Overseas Pakistanis were excluded from the study.

Measures

Outcome

Internet addiction

The outcome of this study was internet addiction which was assessed by the “Young’s Internet Addiction Test (IAT)” to screen symptoms of internet addiction and related disruption in psychosocial functioning. This screening tool is a self-assessment scale with 20 questions, each of which is graded on a 6-point scale from 0 to 5. Participants who had an IAT score of 70 and above were categorized as an “addictive-internet-users (AIU)” coded as 2, IAT scores of 40 to 69 was categorized as “problematic-internet-users (PIU)” coded as 1, and IAT scores of 39 or less was classified as “normal-internet-users (NIU)” coded as 0.^38,39 This instrument has shown acceptable psychometric properties and was previously validated in a Pakistani setting showing Cronbach alpha of .88, indicating excellent reliability.³⁹ This tool has been used in adolescents and the adult population.²¹

Covariates

Depression, anxiety, and stress

Symptoms of depression, anxiety, and stress were screened using the “Depression, Anxiety, and Stress Scale-21 (DASS-21).” This scale has been used for the clinical assessment using the symptomatology approach of depression, anxiety, and stress; however, it doesn’t indicate the diagnosis of cases. This is a validated questionnaire that has previously been used during the COVID-19 pandemic to assess mental health in different countries.^40-44 This tool measures the severity/frequency of symptoms and is a 4-point scale from 0 to 3 where: 0 = “it does not apply to me at all,” 1 = “it applies to me to some degree or some of the time,” 2 = “it applies to me to a considerable degree or a good part of the time,” and 3 = “it applies to me very much, or most of the time.” To compute the final score for each psychological condition (depression, anxiety, and stress), scores for items related to each condition were summed and further multiplied by 2. Scores for all psychological conditions ranged from 0 to 42. Each participant’s responses were grouped into one of the five categories: normal, mild, moderate, severe, and extremely severe for depression, anxiety, and stress separately. For depression, participants with a score of 0 to 9 were categorized as “normal, a score of 10 to 13 as ‘mild’, a score of 14 to 20 as ‘moderate’,” a score of 21 to 27 as ‘severe’, and a score of 28 or higher as ‘extremely severe’.⁴⁵ For anxiety, participants with a score of 0 to 7 were categorized a score of 8 to 9 as ‘mild’, a score of 10 to 14 as ‘moderate’, a score of 15 to 19 as ‘severe’, and a score of 20 or higher as ‘extremely severe’.⁴⁵ For stress, participants with a score of 0 to 14 were categorized as ‘normal’, a score of 15 to 18 as ‘mild’ as ‘normal’,” a score of 19 to 25 as ‘moderate’, a score of 26 to 33 as ‘severe’, and a score of 34 or higher as ‘extremely severe’.⁴⁵ This instrument has shown excellent psychometric properties and has been previously used in Pakistani settings showing internal consistency (Cronbach’s α) of .84 to .97.⁴⁶ This tool has been used in adolescent and adult population.^47-49

Other covariates

Other variables, including sociodemographic, health-related, behavioral, and environmental factors related to the pandemic, were also collected. Details of study variables are explained in Supplemental Figure 1.

Sample Size and Sampling Strategy

The estimated sample size was a minimum of 1063 for determining the burden and related psychological factors of internet addiction. For the burden of internet addiction (PIU and AIU) among the Pakistani population, the burden was expected in the range from 9.7% to 47% with a 5% confidence interval and the significance level of 5%; found a minimum sample of 383 individuals was required.^50,51 For assessing the influence of depression, anxiety, and stress on internet addiction, a minimum sample of 924 individuals was required to achieve 80% power and at a 95% level of significance, with the range of non-diseased (normal internet users) with depression, anxiety and stress ranging from 28% to 33% and an anticipated odd ratio of 1.5.^52,53 As it was a web-based study, 15% of refusals and incomplete forms were anticipated. The final sample size was 1063, including 15% of refusals and incomplete forms. A non-probability purposive sampling technique was adopted.

Statistical Analysis

Statistical analysis was done using Stata version 16. Mean and standard deviation were computed for all continuous variables and frequencies, and percentages were computed for all categorical variables. All descriptive statistics were reported based on internet addiction. Overall mean and standard deviation were reported for depression, anxiety, and stress and were stratified based on internet addiction and gender. Multinomial logistic regression was used to assess the relationship of psychological conditions (Depression, Anxiety, and stress) and other factors with internet addiction amidst COVID-19. Adjusted odds ratio, along with the confidence interval of 95% were reported. A P-value of less than 5% was considered significant.

Ethical Considerations

Ethical approval was obtained from the Institutional Ethical Review Board (Aga Khan University Ethical Review Board). Participation was on a voluntary basis and participants were allowed to leave the study at any point in time. A referral list of mental health counseling services was also provided at the end of the questionnaire.

Patient and Public Involvement

The general public of Pakistan was involved in this study as participants via a web-based survey. After the end of the survey, a list of toll-free telephonic counseling services was shared by all participants.

Results

A total of 1145 complete surveys were received from January to March 2021.

Internet Addiction

Among 1145 participants, 703 (61.4%) had IAT score of 39 or below; found to be normal internet users, 313 (27.3%) were problematic internet users (IAT scores of 40-69), and 129 (11.3%) were addictive internet users having IAT scores 70 or above.

Socio-Demographic and Psychosocial Characteristics

The majority of the participants were females (about 60%) in each of the three groups of internet users, and the majority were youth that is, aged 20 to 24 years in each group. Most of the participants were living in urban areas during the pandemic. Among normal internet users, 79.8% were single, that is, never married, whereas, among problematic and addictive users, the proportions of never-married participants were 87.5% and 84.5%, respectively (P < .01). Most of the participants belonged to the nuclear family system, and was not found to be significantly different in the three groups (P = .65). About 21% of the normal users, about 16% of the problematic users, and 18.6% of the addictive users had a post-graduate degree (P = .03). Moreover, 13.2% of the addictive users had a family income of rupees less than 50 000 compared to 23.3% of problematic users and 33.7% of normal users (P < .001) (Table 1). Seven hundred ninety-three (69.3%) study participants were found to be students. Among addictive internet users, 72.1% were students and among problematic internet users, 76.4% were students. As far as depression, anxiety and stress are concerned, among students, 10.3% were severely depressed, 17.7% were extremely severely depressed, 7.8% were severely anxious, 23.5% were extremely severely anxious, 11.10% were severely stressed, and 7.1% extreme severely stressed.

Table 1.

Shows Sociodemographic Factors of Participants (n = 1145).

	Normal internet users N = 703 (61.4%)	Problematic internet users N = 313 (27.3%)	Addictive internet users N = 129 (11.3%)	^χ2	P-value
Socio-demographic factors
	n (%)	n (%)	n (%)
Gender
Male	286 (40.7)	119 (38.0)	54 (41.9)	0.8	.66
Female	417 (59.3)	194 (62.0)	75 (58.1)
Age
Mean years ± SD	24.43 (7.79)	23.19 (6.24)	23.89 (7.18)	3.14*	.04
Teenager (≤ 19 years)	140 (19.9)	76 (24.3)	28 (21.7)	5.6	.46
Youth (20-24 years)	349 (49.6)	160 (51.1)	62 (48.1)
Young adult (25-30 years)	133 (18.9)	50 (16.0)	27 (20.9)
Middle/older adult (≥ 31 years)	81 (11.5)	27 (8.6)	12 (9.3)
Living area
Rural	118 (16.79)	43 (13.74)	13 (10.08)	7.8	.09
Suburban	96 (13.66)	47 (15.02)	27 (20.93)
Urban	489 (69.56)	223 (71.25)	89 (68.99)
Marital status
Never married	561 (79.8)	274 (87.5)	109 (84.5)	9.4	<.01
Ever married	142 (20.2)	39 (12.5)	20 (15.5)
Education
Less than higher secondary	57 (8.1)	11 (3.5)	12 (9.3)	14.0	.03
Higher Secondary	234 (33.3)	126 (40.3)	46 (35.7)
Undergrad degree	265 (37.7)	126 (40.3)	47 (36.4)
Postgrad degree	147 (20.9)	50 (16.0)	24 (18.6)
Family system**
Extended	250 (35.6)	120 (38.3)	49 (38.0)	0.8	.65
Nuclear family	453 (64.4)	193 (61.7)	80 (62.0)
Family income
<50 k	237 (33.7)	73 (23.3)	17 (13.2)	28.9	.00
50-100 k	134 (19.1)	63 (20.1)	32 (24.8)
100-200 k	124 (17.6)	65 (20.8)	30 (23.3)
>200 k	208 (29.6)	112 (35.8)	50 (38.8)
Family size
Small (≤ 4 people)	162 (23.0)	72 (23.0)	30 (23.3)	8.1	.08
Medium (5-7 people)	419 (59.6)	166 (53.0)	68 (52.7)
Large (≥ 8 people)	122 (17.4)	75 (24.0)	31 (24.0)
Work situation before the pandemic
Employed	161 (22.90)	58 (18.53)	26 (20.16)	15.8	<.01
Unemployed	81 (11.52)	16 (5.11)	10 (7.75)
Students	461 (65.58)	239 (76.36)	93 (72.09)

Indicates F-test statistics to see the association of age with the degree of internet addiction using a one-way ANOVA test.

Nuclear families are comprised of married partners and their offspring, while Extended families include at least three generations: grandparents, married offspring, and grandchildren.

Depression, stress, and anxiety

DASS-21 results showed that 47.3% of the addictive internet users were found to be extremely severely depressed as compared to only 6.8% of the normal internet users. Similarly, 55.8% of the addictive internet users versus 11.4% of the normal internet users had extremely severe anxiety, and 25.6% of the addictive versus 2.3% of the normal internet users were extremely severely depressed. All these differences were found to be highly significant (P < .01) (Table 2)

Table 2.

Shows Health-Related Factors and Behavioral and Environmental Factors of Participants (n = 1145).

	Normal Internet Users N = 703 (61.4%)	Problematic Internet Users N = 313 (27.3%)	Addictive Internet Users N = 129 (11.3%)	^χ2	P-value
Health-related factors
Depression Anxiety Stress Scale-21 items (DASS-21)
Depression
Normal	367 (52.2)	78 (24.9)	26 (20.2)	219.4	<.01
Mild	119 (16.9)	33 (10.5)	8 (6.2)
Moderate	119 (16.9)	79 (25.2)	17 (13.2)
Severe	50 (7.1)	44 (14.1)	17 (13.2)
Extremely severe	48 (6.8)	79 (25.2)	61 (47.3)
Anxiety
Normal	350 (49.8)	80 (25.6)	24 (18.6)	199.6	<.01
Mild	83 (11.8)	22 (7.0)	4 (3.1)
Moderate	156 (22.2)	67 (21.4)	19 (14.7)
Severe	34 (4.8)	39 (12.5)	10 (7.8)
Extremely severe	80 (11.4)	105 (33.6)	72 (55.8)
Stress
Normal	532 (75.7)	144 (46.0)	39 (30.2)	234.3	<.01
Mild	51 (7.3)	35 (11.2)	4 (3.1)
Moderate	63 (9.0)	54 (17.3)	14 (10.9)
Severe	41 (5.8)	53 (16.9)	39 (30.2)
Extremely severe	16 (2.3)	27 (8.6)	33 (25.6)
History of any mental health problems
No	366 (52.1)	119 (38.0)	58 (45.0)	17.9	<.01
Yes	260 (37.0)	146 (46.7)	53 (41.1)
I don’t know	77 (11.0)	48 (15.3)	18 (14.0)
Family members suffering from mental problems during COVID-19
No	485 (69.0)	173 (55.3)	74 (57.4)	23.4	<.01
Yes	146 (20.8)	82 (26.2)	36 (27.9)
I don’t know	72 (10.2)	58 (18.5)	19 (14.7)
Behavioral and environmental factors during the pandemic
Work situation during the pandemic
No, I do not work	321 (45.7)	172 (55.0)	69 (53.5)	21.8	<.01
No, looking for a job	62 (8.8)	19 (6.1)	5 (3.9)
No, short-time work	55 (7.8)	29 (9.3)	16 (12.4)
Yes, on the worksite and teleworking	98 (13.9)	46 (14.7)	12 (9.3)
Yes, on the worksite exclusively	167 (23.8)	47 (15.0)	27 (20.9)
Job-status during pandemic
Same status as before	692 (98.4)	311 (99.3)	127 (98.4)	1.5	.47
Lost the job	11 (1.6)	2 (0.7)	2 (1.6)	1.5	.47
Workload during pandemic
Not working	221 (31.4)	121 (38.7)	48 (37.2)	11.3	.18
Higher than before	137 (19.5)	62 (19.8)	27 (20.9)
Highly variable	66 (9.4)	31 (9.9)	15 (11.6)
Less than before	164 (23.3)	51 (16.3)	23 (17.8)
Same as before	115 (16.4)	48 (15.3)	16 (12.4)
Financial impact on the budget due to pandemic
No	214 (30.4)	82 (26.2)	33 (25.6)	7.0	.13
Yes a little	336 (47.8)	146 (46.7)	57 (44.2)
Yes a lot	153 (21.8)	85 (27.2)	39 (30.2)
Mood during the pandemic
Negative	321 (45.7)	178 (56.9)	96 (74.4)	43.6	<.01
No change	224 (31.9)	70 (22.4)	14 (10.9)
Positive	158 (22.5)	65 (20.8)	19 (14.7)

Medical and Family History

About 41% of the addictive internet users (vs. 37% of the normal internet users) had a history of mental health problems. Moreover, 27.9% of the addicted users versus 20.7% of the normal users reported that their family members suffered from mental issues during COVID (Table 2).

Behavioral and Environmental Factors

The work situation of the participants was also found to be significantly different (P < .01) among the three groups of internet users, showing 53.5% of the addictive users were not working as compared to 45.7% of the normal users who were not working. Overall, 1.31% (n = 15) of the participants got laid off during the pandemic which was insignificant in terms of the degree of internet addiction. Also, workload and financial impact on the budget due to the pandemic were not found to be significantly different in the three groups (Table 2).

The mood of participants during the pandemic was significantly different (P < .001) in the three groups of internet users, with 74.4% of addictive users having negative moods compared to 45.7% of the normal users (Table 2).

Figure 1 describes the degree of internet addiction with the internet use for different activities from January to March 2020. The majority of the percentage of addictive internet users are found to be those participants that invested their maximum time on the internet for either stock marketing, gaming gambling, or adult entertainment. Participants who used the internet for online classes, educational or work-related activities, shopping, social networking, and recreational activities are comparatively less likely to be found as problematic or addictive internet users.

Figure 1.

Graphical representation of the degree of internet addiction in % by the usage of different activities from January to March 2020.

Overall, the mean depression score of participants among addictive users was 23.4 ± 12.9 compared to 18.2 ± 11.4 and 10.5 ± 9.5 in the problematic and normal internet users, respectively. Similarly, the overall mean anxiety score was much higher in addictive internet users than in problematic and normal users (20.2 vs 15.1 and 8.6). Overall mean stress score was also found to be significantly higher in addictive internet users as compared to problematic and normal internet users (P < .01) (Table 3). Moreover, we found that mean scores for depression, anxiety, and stress were significantly higher in females than males (Table 3 and Figure 2).

Table 3.

Showing DASS-21 Scores of Participants as a Function of Gender.

	Normal internet users mean (SD)			Problematic internet users mean (SD)			Addictive internet users mean (SD)			F factor			P-value
	M	F	Overall	M	F	Overall	M	F	Overall	M	F	Overall	M	F	Overall
Depression Anxiety Stress Scale-21 (DASS-21) scores
Depression score	9.1 (8.2)	11.4 (10.1)	10.5 (9.5)	17.3 (10.8)	18.8 (11.8)	18.2 (11.4)	21.0 (13.3)	25.2 (12.4)	23.4 (12.9)	53.1	67.1	117.4	<.01	<.01	<.01
Anxiety score	7.8 (7.1)	9.2 (8.5)	8.6 (8.0)	14.3 (9.2)	15.6 (10.5)	15.1 (10.0)	17.9 (12.2)	22.0 (11.9)	20.2 (12.1)	48.0	72.2	118.6	<.01	<.01	<.01
Stress Score	9.5 (7.9)	11.6 (9.4)	10.8 (8.9)	17.0 (10.0)	18.3 (10.6)	17.8 (10.4)	20.6 (12.8)	25.5 (11.8)	23.5 (12.4)	50.2	75.7	123.2	<.01	<.01	<.01

M = male; F = female; SD = standard deviation.

Figure 2.

Graphical representation of mean DASS scores of participants as a function of gender.

Our final multinomial logistic regression model showed that gender was significantly associated with internet use. The odds of being male were higher (Adj OR: 1.7 (1.1-2.7)) in addictive users compared to normal users when controlled for other variables. Similarly, being single was found to have higher odds with problematic internet use (Adj OR: 1.6 (1.1-2.5)). Moreover, the odds of having extremely severe depression were 3.1 (95% CI: 1.5-6.4) times greater in problematic users than in normal internet users. Anxiety was also significantly associated with internet use, with the odds of extremely severe anxiety being about three times higher in addictive users (Adj OR: 2.6 (1.1-7.1)) compared to normal users. Stress was also significantly associated with internet use, with the odds of extremely severe stress being about five times higher in addictive users (Adj OR: 5.4 (1.7-17.7)). Furthermore, participants who do not work had greater odds of being addictive users (Adj OR: 2.2 (1.1-4.6)). Mood changes during the pandemic were also significantly associated with internet use, with the odds of negative mood change being greater (Adj OR: 2.9 (1.5-5.6)) in addictive internet users as compared to normal internet users (Table 4).

Table 4.

Shows the Adjusted Odds Ratio (Adj OR) Along with the 95% Confidence Interval (CI) of Factors Associated with Internet Addiction Using Multinominal Logistic Regression.

Variable	Problematic internet user (PIU)	Addictive internet user (AIU)
Variable	Adj OR (95% CI)	Adj OR (95% CI)
Sociodemographic factors
Gender
Male	1.2 (0.8-1.6)	1.7 (1.1-2.7) *
Marital status
Never married	1.6 (1.0-2.5)*	1.5 (0.8-2.8)
Family income
50-100 k	1.3 (0.9-2.1)	2.8 (1.4-5.7)*
100-200 k	1.3 (0.9-2.1)	2.6 (1.3-5.2)*
>200k	1.3 (0.9-2.0)	2.3 (1.2-4.4)*
Family size
Small (<= 4 people)	1.2 (0.8-1.7)	1.1 (0.7-2.0)
Large (>= 8 people)	1.8 (1.3-2.7)*	1.8 (1.1-3.2)*
Health-related factors
Depression Anxiety Stress Scale-21 (DASS-21)
Depression
Mild	1.1 (0.7-1.8)	0.8 (0.3-1.8)
Moderate	2.0 (1.3-3.2)*	1.1 (0.4-2.3)
Severe	2.1 (1.1-3.9)*	1.1 (0.4-3.3)
Extremely severe	3.1 (1.5-6.4)*	1.9 (0.7-5.7)
Stress
Mild	1.2 (0.7-2.0)	0.5 (0.2-1.7)
Moderate	1.1 (0.6-1.9)	1.2 (0.5-3.0)
Severe	1.2 (0.6-2.5)	3.3 (1.2-8.9)*
Extremely severe	1.2 (0.5-3.2)	5.4 (1.7-17.7)*
Anxiety
Mild	1.1 (0.6-1.7)	0.7 (0.2-2.2)
Moderate	1.2 (0.8-1.9)	1.4 (0.7-3.1)
Severe	2.6 (1.4-4.9)*	2.1 (0.7-6.3)
Extremely severe	2.0 (1.1-3.9)*	2.6 (1.1-7.1)*
Family members suffering from mental problems during COVID-19
Yes	1.4 (1.0-2.0)	1.1 (0.7-1.9)
I don’t know	1.7 (1.1-2.6)*	1.3 (0.7-2.5)
Behavioral and environmental factors during the pandemic
Work situation during the pandemic
Do not work	1.3 (0.8-2.0)	2.2 (1.0-4.6)*
Looking for job	0.7 (0.3-1.3)	0.7 (0.2-2.2)
Part-time job	1.2 (0.7-2.3)	2.6 (1.0-6.5)*
Work in person	0.7 (0.4-1.1)	1.5 (0.7-3.4)
Describe your mood changes during the pandemic
Negative	1.2 (0.8-1.7)	2.9 (1.5-5.6)*
Positive	1.1 (0.7-1.8)	1.5 (0.7-3.3)

Indicates results with P < .05.

Discussion

This study indicates that most of participants are normal internet users (61.4%), whereas the prevalence of problematic and addictive internet use is 27.3% and 11.3%, respectively amidst COVID-19. Previous Pakistani studies concluded that the proportion of addictive internet users ranged from 0.6% to 1.1%, and the proportion of problematic internet users ranged from 16% to 37% before the COVID-19 pandemic.^54-56 The findings from the current study are showing higher proportions among problematic and addictive internet users as compared to pre-COVID times. The possible reason for the rise in internet addiction could be the measures taken to curb the COVID-19 pandemic such as lockdowns, closure of educational institutions, closure of entertainment clubs, and promotion of work from home practices have led to an increase in time spent at home and have contributed remarkably in increased and possibly addictive use of the internet.^8,9,12

The current study shows males to be more addictive users. This finding is consistent with many previous studies^50,51,57 with the possible reason that males are more involved in online gaming and are more prone to use the internet for entertainment and leisure activities in comparison to females. However, some studies show females are more addicted to internet use attributing it to online shopping addiction, especially in recent years with the advent of more online shopping websites.^56,58

This study further revealed that single participants have greater odds of being addicted internet users. This finding is compatible with a few studies.⁵⁹ The possible explanation could be that married life poses certain responsibilities and demands more time toward family, limiting many leisure activities like internet use. On the other hand, unmarried individuals may spend more time on the internet to compensate for family life and social interaction, especially during the lockdown in pandemic where internet use can serve to communicate and develop virtual friendships. However, we also found studies where marital status had no association with internet use.⁶⁰

Our results show that severe depression, severe stress, and negative mood changes during a pandemic are significantly associated with addictive internet use. These findings are in line with the literature.^61,62 Behaviors like internet addiction may serve as a coping strategy to reduce stress and depression and avoid negative thoughts.⁶³ The negative impact of addictive internet use on psychosocial distress was identified before the COVID-19 pandemic.^64,65 Studies have shown that internet gaming disorder was a significant mediator between self-stigma and anxiety and stress.⁶⁶ Other studies have discovered a positive correlation between internet addiction and insomnia and alcohol use.⁶⁷ Further, internet gaming has been associated with decreased physical activity and psychological quality of life.⁶⁸ The present study extends prior findings to emphasize the importance of taking care of internet addiction during the COVID-19 pandemic. Another finding of this study is that not working situation is significantly associated with addictive use of the internet. Literature shows that unemployment during the COVID-19 pandemic has markedly contributed to the factors responsible for internet addiction.¹²

Our study was unique in our settings, with quite a large sample size and considering several variables. However, it has some limitations. Firstly, it was a web-based survey, so the sample may not be representative, and the findings may not be generalized to the entire population. Also, information bias and selection bias cannot be eliminated in a web-based study. Moreover, due to the cross-sectional design of the study, the temporal relationship cannot be studied. Furthermore, the current study uses Young’s Internet Addiction Test (IAT) which cannot distinguish between different forms of internet addiction (eg, social media addiction, gaming addiction) which might impact psychosocial well-being in a different manner. Therefore, future studies are warranted to use internet addiction scales specific to different forms of internet addiction such as Bergan Social Media Scale, Internet Gaming Disorder Scale-Short Form, and Smartphone Application Based Addiction Scale.^69-71

However, future research in this area can use perspective and longitudinal designs to establish temporal associations. Literature suggested excellent reliability of DASS21 in adolescents; however, few studies commented on stress/tension items of the tool being questionable. As adolescence is a phase where emotions are constantly emerging, this is why the tool might be considered questionable by certain studies.⁴⁷

Conclusion

While the Internet is increasingly becoming an integral part of our lives in the modern era, its excessive use has potentially addictive effects that lead to serious mental health problems. Moreover, with the COVID-19 pandemic, increased use of the internet possibly leads to addictive behaviors that need to be carefully monitored. This study, unique of its kind in Pakistan, identified that gender, marital status, depression, stress, anxiety, work situation, and mood changes during the COVID-19 pandemic are significantly correlated with problematic and addictive internet use. These findings could serve as a preliminary step toward early awareness and the implementation of preventive measures against the addictive use of the internet to avoid or mitigate any serious mental health problems that we may run into, especially during these crucial times of the COVID-19 pandemic.

Supplemental Material

sj-tif-1-inq-10.1177_00469580221138671 – Supplemental material for Demographic and Psychosocial Factors associated with Internet Addiction among the Pakistani Population during COVID-19: A Web-Based Survey

Supplemental material, sj-tif-1-inq-10.1177_00469580221138671 for Demographic and Psychosocial Factors associated with Internet Addiction among the Pakistani Population during COVID-19: A Web-Based Survey by Maryam Pyar Ali Lakhdir, Ayesha Nasir Hameed, Fauzia Basaria Hasnani, Meher Angez, Muhammad Talha Nawaz, Malik Muhammad Hamza Khan, Ghazal Peerwani, Apsara Ali Nathwani and Syed Iqbal Azam in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

Special thanks to Wahab Fazal, Fatima Zehra, and Naila Qurban for their participation in the broadcasting of the google link during the data collection process.

Author Contributions

Conception or design of the work: MPL; Proposal development: MPL, ANH, MA, MTN, MMHK; Proposal review: GP, AA, SIA; Data collection: MPL, ANH, MA, MTN, MMHK, GP; Data Cleaning: MPL, ANH, MA, MTN, MMHK, AA, and Data Coding: MPL, ANH, MA, MTN, MMHK, SIA; Data analysis and interpretation: MPL, ANH, FBH; Table formulation: MPL, ANH, FBH; Prepared figure: MPL, ANH; Drafting the article: MPL, ANH, FBH; Critical revision of the article: MPL, GP, SIA; Final approval of the version to be published: MPL, ANH, MA, MTN, MMHK, FBH, GP, AA, SIA. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethics Approval and Consent to Participate

Ethical approval was taken from The Aga Khan University Ethical Review Committee (2020-5750-15165). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee.

Informed Consent

Before data collection (access to electronic form), all participants were asked to electronically sign a form of consent to be included in this study.

Consent for Publication

All authors consent for publication of this paper. All authors have read and approved the final manuscript. This manuscript has not been published and is not under consideration for publication elsewhere.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to information that could compromise the privacy of research participants.

ORCID iDs

Maryam Pyar Ali Lakhdir

Apsara Ali Nathwani

Supplemental Material

Supplemental material for this article is available online.

References

Khajehnasiri

Zaroushani

Errors and preventive measures of health-care systems during COVID-19 pandemic. Asian J Soc Heal Behav. 2022;5(1):49.

Prasiska

Muhlis

Megatsari

Effectiveness of the emergency public activity restrictions on COVID-19 epidemiological parameter in East Java Province, Indonesia: an ecological study. Asian J Soc Heal Behav. 2022;5(1):33.

Haq

Nayan

Uddin

, et al. Comparison of the performance of machine learning-based algorithms for predicting depression and anxiety among university students in Bangladesh: A result of the first wave of the COVID-19 pandemic. Asian J Soc Heal Behav. 2022;5(2):75.

Sangma

Kumar

Nerli

Khanna

Vasavada

Tiwari

Social stigma and discrimination in Coronavirus disease-2019 survivors and its changing trend: a longitudinal study at tertiary care center Gujarat, India. Asian J Soc Heal Behav. 2022;5(2):68.

Shetty

Sandya

Jaise

Manikappa

Pai

Stress and burden among caregivers of persons with bipolar affective disorder during the COVID-19 pandemic in India. Asian J Soc Heal Behav. 2022;5(2):51.

Deslandes

Coutinho

The intensive use of the internet by children and adolescents in the context of COVID-19 and the risks for self-inflicted violence. Cien Saude Colet. 2020;25:2479-2486.

Dubey

Ghosh

Chatterjee

Biswas

Chatterjee

Dubey

COVID-19 and addiction. Diabetes Metab Syndr. 2020;14(5):817-823.

Király

Potenza

Stein

, et al. Preventing problematic internet use during the COVID-19 pandemic: consensus guidance. Compr Psychiatry. 2020;100:152180.

King

Delfabbro

Billieux

Potenza

MN.

Problematic online gaming and the COVID-19 pandemic. J Behav Addict. 2020;9(2):184-186.

10.

Rolland

Haesebaert

Zante

Benyamina

Haesebaert

Franck

Global changes and factors of increase in caloric/salty food intake, screen use, and substance use during the early COVID-19 containment phase in the general population in France: survey study. JMIR Public Health Surveill. 2020;6(3):e19630.

11.

Amin

Griffiths

Dsouza

DD.

Online gaming during the COVID-19 pandemic in India: strategies for work-life balance. Int J Ment Health Addict. 2022;20:296-302.

12.

Czeisler

MÉ

Lane

Petrosky

, et al. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic—United States, June 24–30, 2020. Morb Mortal Wkly Rep. 2020;69(32):1049-1057.

13.

Weinstein

Lejoyeux

Internet addiction or excessive internet use. Am J Drug Alcohol Abuse. 2010;36(5):277-283.

14.

Jorgenson

Hsiao

Yen

C-F.

Internet addiction and other behavioral addictions. Child Adolesc Psychiatr Clin N Am. 2016;25(3):509-520.

15.

Kamolthip

Chirawat

Ghavifekr

, et al. Problematic Internet use (PIU) in youth: a brief literature review of selected topics. Curr Opin Behav Sci. 2022;46:101150.

16.

Stanton

Khalesi

, et al. Depression, anxiety and stress during COVID-19: Associations with changes in physical activity, sleep, tobacco and alcohol use in Australian adults. Int J Environ Res Public Health. 2020;17(11):4065.

17.

Lee

Shin

Park

, et al. Depression as a mediator of chronic fatigue and post-traumatic stress symptoms in middle east respiratory syndrome survivors. Psychiatry Investig. 2019;16(1):59-64.

18.

Xiong

Lipsitz

Nasri

, et al. Impact of COVID-19 pandemic on mental health in the general population: a systematic review. J Affect Disord. 2020;277:55-64.

19.

Wang

Chudzicka-Czupała

Tee

, et al. A chain mediation model on COVID-19 symptoms and mental health outcomes in Americans, Asians and Europeans. Sci Rep. 2021;11(1):6481. doi:10.1038/s41598-021-85943-7

20.

Barzilay

Moore

Greenberg

, et al. Resilience, COVID-19-related stress, anxiety and depression during the pandemic in a large population enriched for healthcare providers. Transl Psychiatry. 2020;10(1):291-298.

21.

Rossi

Socci

Talevi

, et al. COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. Front Psychiatry. 2020;11:790.

22.

Renaud-Charest

Lui

LMW

Eskander

, et al. Onset and frequency of depression in post-COVID-19 syndrome: a systematic review. J Psychiatr Res. 2021;144:129-137.

23.

Ceban

Nogo

Carvalho

, et al. Association between mood disorders and risk of COVID-19 infection, hospitalization, and death: a systematic review and meta-analysis. JAMA Psychiatr. 2021;78(10):1079-1091.

24.

Wang

Tee

Roy

, et al. The impact of COVID-19 pandemic on physical and mental health of Asians: a study of seven middle-income countries in Asia. PLoS One. 2021;16(2):e0246824.

25.

Huang

Zhao

Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res. 2020;288:112954.

26.

Salari

Hosseinian-Far

Jalali

, et al. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Global Health. 2020;16:57.

27.

Chen

I-H

Chen

C-Y

Liu

C-H

, et al. Internet addiction and psychological distress among Chinese schoolchildren before and during the COVID-19 outbreak: a latent class analysis. J Behav Addict. 2021;10(3):731-746.

28.

Chen

C-Y

Chen

I-H

O’Brien

Latner

Lin

C-Y.

Psychological distress and internet-related behaviors between schoolchildren with and without overweight during the COVID-19 outbreak. Int J Obes. 2021;45(3):677-686. doi:10.1038/s41366-021-00741-5

29.

Fung

XCC

Siu

AMH

Potenza

, et al. Problematic use of internet-related activities and perceived weight stigma in schoolchildren: a longitudinal study across different epidemic periods of COVID-19 in China. Front Psychiatry. 2021;12:675839.

30.

Chen

C-Y

Chen

I-H

Hou

W-L

, et al. The relationship between children’s problematic Internet-related behaviors and psychological distress during the onset of the COVID-19 pandemic: a longitudinal study. J Addict Med. 2022;16(2):e73-e80.

31.

Chen

C-Y

Chen

I-H

Pakpour

Lin

C-Y

Griffiths

MD.

Internet-related behaviors and psychological distress among schoolchildren during the COVID-19 school hiatus. Cyberpsychol Behav Soc Netw. 2021;24(10):654-663.

32.

Chen

I-H

Chen

C-Y

Pakpour

, et al. Problematic internet-related behaviors mediate the associations between levels of internet engagement and distress among schoolchildren during COVID-19 lockdown: a longitudinal structural equation modeling study. J Behav Addict. 2021;10(1):135-148.

33.

Chen

I-H

Chen

C-Y

Pakpour

Griffiths

Lin

C-Y.

Internet-related behaviors and psychological distress among schoolchildren during COVID-19 school suspension. J Am Acad Child Adolesc Psychiatry. 2020;59:1099-1102.e1.

34.

Waqas

Rehman

Malik

, et al. Exploring the association of ego defense mechanisms with problematic internet use in a Pakistani medical school. Psychiatry Res. 2016;243:463-468.

35.

Zafar

Suneel

Relationship between personality traits, attachment styles and internet addiction among Pakistani University Students. Int J Soc Sci Educ Stud. 2018;5(1):96.

36.

Data usage - Profit by Pakistan Today [Internet]. Accessed April 9, 2021. https://profit.pakistantoday.com.pk/2020/09/10/data-usage-jumped-25pc-revenue-from-voice-traffic-declined-10pc-during-lockdown/data-usage/

37.

Pakistan’s internet use surges amid COVID-19 lockdown [Internet]. Accessed April 9, 2021. https://www.aa.com.tr/en/asia-pacific/pakistan-s-internet-use-surges-amid-covid-19-lockdown/1807118

38.

Jelenchick

Becker

Moreno

MA.

Assessing the psychometric properties of the Internet Addiction Test (IAT) in US college students. Psychiatry Res. 2012;196(2–3):296-301.

39.

Ndasauka

Pitafi

Kayange

GM.

Psychometric properties of Young’s Internet Addiction Test (IAT) in Urdu language. Asian J Psychiatr. 2019;40:39-44.

40.

XTT

Dang

Toweh

, et al. Evaluating the psychological impacts related to COVID-19 of Vietnamese people under the first nationwide partial lockdown in Vietnam. Front Psychiatry. 2020;11:824.

41.

Tee

Anlacan

, et al. Psychological impact of COVID-19 pandemic in the Philippines. J Affect Disord. 2020;277:379-391.

42.

Wang

Pan

Wan

, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health. 2020;17(5):1729.

43.

Wang

Tripp

Sears

, et al. The impact of the COVID-19 pandemic on physical and mental health in the two largest economies in the world: a comparison between the United States and China. J Behav Med. 2021;44(6):741-759.

44.

Hao

Tan

Jiang

, et al. Do psychiatric patients experience more psychiatric symptoms during COVID-19 pandemic and lockdown? A case-control study with service and research implications for immunopsychiatry. Brain Behav Immun. 2020;87:100-106.

45.

Gomez

A Guide to the Depression, Anxiety and Stress Scale (DASS 21). Cent East Sydney Prim Heal networks; 2016.

46.

Azim

Baig

. Frequency and perceived causes of depression, anxiety and stress among medical students of a private medical institute in Karachi: a mixed method study. J Pak Med Assoc. 2019;69(6):840-845.

47.

Silva

HAD

Passos

MHPD

Oliveira

VMAD

Palmeira

Pitangui

ACR

Araújo

RCD

. Short version of the Depression Anxiety Stress Scale-21: is it valid for Brazilian adolescents? Einstein (Sao Paulo). 2016;14(4):486-493.

48.

Szabó

The short version of the Depression Anxiety Stress Scales (DASS-21): factor structure in a young adolescent sample. J Adolesc. 2010;33(1):1-8.

49.

Necho

Tsehay

Birkie

Biset

Tadesse

Prevalence of anxiety, depression, and psychological distress among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Int J Soc Psychiatr. 2021;67(7):892-906.

50.

Buneviciene

Bunevicius

Prevalence of internet addiction in healthcare professionals: systematic review and meta-analysis. Int J Soc Psychiatr. 2021;67(5):483-491.

51.

Siste

Hanafi

Sen

, et al. The impact of physical distancing and associated factors towards Internet addiction among adults in Indonesia during COVID-19 pandemic: a nationwide web-based study. Front Psychiatry. 2020;11:580977.

52.

Kim

Bae

, et al. Depression and Internet addiction in adolescents. Psychopathology. 2007;40(6):424-430.

53.

Young

Rogers

RC.

The relationship between depression and Internet addiction. Cyberpsychol Behav. 1998;1:25-28.

54.

Khan

Shabbir

Rajput

TA.

Effect of gender and physical activity on Internet addiction in medical students. Pak J Med Sci. 2017;33(1):191-194.

55.

Waqas

Farooq

Bhatti

Javed

Ghumman

Raza

, et al. Exploring the psychometric properties of the English version of the Internet Addiction Test in the Pakistani population: a cross-sectional survey. Peer J Pre Prints. 2015.

56.

Ahmer

Tanzil

Internet addiction among social networking sites users: Emerging mental health concern among medical undergraduates of Karachi. Pak J Med Sci. 2018;34(6):1473.

57.

Masaeli

Farhadi

Prevalence of Internet-based addictive behaviors during COVID-19 pandemic: a systematic review. J Addict Dis. 2021;39(4):468-488. doi:10.1080/10550887.2021.1895962

58.

Tang

Koh

YY.

Online social networking addiction among college students in Singapore: comorbidity with behavioral addiction and affective disorder. Asian J Psychiatr. 2017;25:175-178.

59.

Prakash

Yadav

Singh

TB.

An online cross-sectional study to assess the prevalence of internet addiction among people staying at their home during lockdown due to COVID-19. Int J Indian Psychol. 2020;8(3):424-432.

60.

Dib

Haddad

Sacre

, et al. Factors associated with problematic internet use among a large sample of Lebanese adolescents. BMC Pediatr. 2021;21(1):148-212.

61.

Garcia-Priego

Triana-Romero

Pinto-Galvez

Duran-Ramos

Salas-Nolasco

Reyes

, et al. Anxiety, depression, attitudes, and internet addiction during the initial phase of the 2019 coronavirus disease (COVID-19) epidemic: a cross-sectional study in Mexico. medRxiv. 2020.

62.

Obeid

Saade

Haddad

, et al. Internet addiction among Lebanese adolescents: the role of self-esteem, anger, depression, anxiety, social anxiety and fear, impulsivity, and aggression—a cross-sectional study. J Nerv Ment Dis. 2019;207(10):838-846.

63.

Blasi

Giardina

Giordano

, et al. Problematic video game use as an emotional coping strategy: evidence from a sample of MMORPG gamers. J Behav Addict. 2019;8(1):25-34.

64.

Chang

K-C

Chang

Y-H

Yen

C-F

, et al. A longitudinal study of the effects of problematic smartphone use on social functioning among people with schizophrenia: mediating roles for sleep quality and self-stigma. J Behav Addict. 2022;11(2):567-576.

65.

Chen

I-H

Pakpour

Leung

, et al. Comparing generalized and specific problematic smartphone/internet use: longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J Behav Addict. 2020;9(2):410-419.

66.

Chang

Y-H

Chang

K-C

Hou

W-L

Lin

C-Y

Griffiths

MD.

Internet gaming as a coping method among schizophrenic patients facing psychological distress. J Behav Addict. 2020;9(4):1022-1031.

67.

Oluwole

Obadeji

Dada

Surfing over masked distress: internet addiction and psychological well-being among a population of medical students. Asian J Soc Heal Behav. 2021;4(2):56.

68.

Fung

Kwok

Leung

Poon

The effects of internet gaming and social media use on physical activity, sleep, quality of life, and academic performance among university students in Hong Kong: a preliminary study. Asian J Soc Heal Behav. 2021;4(1):36.

69.

Chen

I-H

Strong

Lin

Y-C

, et al. Time invariance of three ultra-brief internet-related instruments: smartphone application-based addiction scale (SABAS), Bergen social media addiction scale (BSMAS), and the nine-item Internet Gaming Disorder Scale- Short Form (IGDS-SF9) (Study Part B). Addict Behav. 2020;101:105960.

70.

Leung

Pakpour

Strong

, et al. Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen social media addiction scale (BSMAS), smartphone application-based addiction scale (SABAS), and internet gaming disorder scale-short form (IGDS-SF9) (Study Part A). Addict Behav. 2020;101:105969.

71.

Poon

LYJ

Tsang

HWH

Chan

TYJ

, et al. Psychometric properties of the Internet Gaming Disorder Scale-short-form (IGDS9-SF): systematic review. J Med Internet Res. 2021;23(10):e26821.

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