A Healthy Mind for Problematic Internet Use

Abstract

This article designed and tested a cognitive behavioral-based preventive intervention program for youths with problematic Internet use (PIU) behavior. The program is the Psychological Intervention Program–Internet Use for Youth (PIP-IU-Y). A cognitive-based therapy approach was adopted. A total of 45 secondary students from four schools completed the intervention program that was conducted in a group format by registered school counselors. Three sets of self-reported data on Problematic Internet Use Questionnaire (PIUQ), Social Interaction Anxiety Scale (SIAS), and Depression Anxiety Stress Scale (DASS) were collected at three time points: 1 week before the intervention, immediately after the last intervention session, and 1 month after the intervention. Paired t-test results showed that the program was effective in preventing negative progression into more serious Internet addiction stages, and reducing anxiety and stress and interaction phobia of the participants. The effect was evident immediately at the end of the intervention session and was maintained 1 month after the intervention. This study is among the first to develop and test a preventive intervention program for youths with PIU. The effectiveness of our program in preventing negative progression of PIU and its symptoms in problematic users has led us to postulate that the program will also prevent normal users from developing serious symptoms. The majority of the intervention programs reported in the literature merely tailor to those who already have problems.

Introduction

In recent years, the evolution of technology has contributed to the growth of Internet users and the subsequent negative impact on psychological and behavioral well-being. Individuals with problematic Internet use (PIU) are commonly described as being unable to control their Internet use, resulting in feelings of distress and even functional impairment.^1-4 Shapira et al.⁵ has defined PIU as (a) unmanageable preoccupation with the Internet; (b) distress and difficulties as a result of the behavior; and (c) unrelated to other disorders that might explain the behavior. Unsurprisingly, PIU shares characteristics with other substance-related addictions, particularly in areas of preoccupation, tolerance, and withdrawal.⁶ Consequently, researchers have begun to focus their investigative efforts on PIU, especially in Asian countries.^7–10

Over the years, researchers have acknowledged the association of PIU with problematic adolescent behavior. Physical symptoms are significantly associated with PIU, with substance abuse being the most common comorbid condition.¹¹ Lifestyle patterns and dietary behaviors have been shown to be affected by PIU, as are other physiological symptoms such as a lack of physical energy and weakened immunity.^12,13 The exacerbation of psychological conditions such as depression, hostility, and social anxiety in adolescents is also frequently observed in relation to PIU.^14–17

Evidence reveals depression as an antecedent of PIU behaviors.^7,18 Individuals who described themselves as Internet addicts or utilized the Internet for leisure were shown to exhibit higher levels of depression.^19,20 The common reason behind Internet usage was to alleviate loneliness and depression, but this dependency eventually resulted in a deterioration in the quality of social relationships, which further worsened depression.^21–23 From a developmental perspective, PIU may further worsen social anxiety and depressive symptoms, and interfere with the developmental period of identity formation.²⁴ These symptoms are expected to worsen with age, interfering with an individual's quality of life and interpersonal relationships with others, resulting in excessive and addictive reliance on the Internet.^25–27

Similarly, research indicates that social anxiety is an antecedent of PIU behaviors.^28–31 Researchers³² suggest that Internet usage allows adolescents to develop their ideal “self-identity,” compensating for their real-world imperfections. Caplan²⁸ further supports this by proposing that to reduce face-to-face related social anxiety, individuals seek low-risk communicative encounters (i.e., online platforms). While this temporarily alleviates a socially anxious individual's distress, quality of face-to-face interaction would eventually decline as dependency on the Internet increases. This would result in a cyclical avoidance of face-to-face interaction that would eventually result in PIU.^33,34 In addition, stress has been shown to be a risk factor in the development of symptoms of PIU. Studies show that Internet use is associated with stressful life events.^10,35–37 These associations vary according to personality and individual differences, such as coping style and satisfaction of psychological needs.^35,37 Researchers have shown that online behaviors may be a diversion and coping response to preexisting stress.^37,38

Hence, there is a need to develop a preventive model for PIU to encourage a healthy cyber culture and forestall the development of Internet addiction. The objective of this article is to develop and test a unique cognitive behavioral therapy (CBT) program integrating positive psychology to remedy PIU. The program is called the Psychological Intervention Program–Internet Use for Youth (PIP-IU-Y).

CBT and PIU

Treating PIU is the core objective of this study because it is the platform that breeds many other problems: psychologically, socially, and physically. CBT is a psychotherapy treatment that focuses on addressing current problems and educating clients on how to modify dysfunctional thinking and behavior.³⁹ It is built on the assumption that thinking processes both influence and are influenced by emotional and behavioral responses,⁴⁰ and concentrate on cognitive restructuring to bring about long-term emotional and behavioral change.⁴¹ Problematic behaviors such as excessive Internet use are best understood in relation to their role in reciprocal reinforcement within the vicious cycle of thought, feelings, and action. Behavioral responses often play the role of final link in the chain, locking and maintaining the whole sequence of thought, feelings, and behavior into persistent and unhelpful patterns.

Psychopathology symptoms such as depression and anxiety have been found to significantly link to Internet addiction.^42–44 Studies have reported that CBT treatments significantly reduced clients' negative emotions, particularly anxiety.^45–47 Similarly, the severity of depression and depressive symptoms also showed significant reduction with or without medication following CBT intervention.^46–49 Other psychopathology symptoms such as hyperactivity, inattentiveness, and obsessive-compulsive symptoms also decrease with CBT intervention. Orzack et al.⁴⁸ explained that the improvements in psychological health are likely because clients are more active in seeking appropriate Internet content and are positively motivated by group therapy.

In addition, various researchers^50–52 have recognized the strong imperative of humans to relate to each other, and how adolescents perceive the online world as a platform for interpersonal relationships compared to the real world. CBT intervention programs for Internet addiction could rekindle offline relationships over time and give optimum results later in the therapy process as it is a complex issue.^45,53 By expanding offline social contacts, various psychosocial symptoms that are related to Internet addiction experience have reduced, for instance, social insecurity.^49,54 Also, collaborative sessions involving family and teacher support have been reported to increase the success rate and prevent relapses.⁵⁵

CBT approaches such as thought listing exercise, cognitive restructuring, and exposure therapy are useful in improving Internet addiction cases.⁷ Studies found that after CBT treatment, participants' compulsive thoughts and behaviors such as thinking of the Internet when away and cheating to get access to Internet improved.^47,53,54 There are also improvements in school and job performance, ability to regain proper daily structure, and engagement in proactive lifestyle.^45,46,49,54 All these improvements could be attributed to better user motivation, more positive thought processes and feelings, and adequate impulse control.^45,53,54 These improvements are most evident early in the treatment program and the effects could remain up to the 6-month follow-up period.^45,53,54 To avoid relapse, establishing techniques such as structuring leisure time with offline activities⁵⁴ has produced positive outcomes.

Positive psychology and PIU

Positive psychology emphasizes the attributes, traits, and experiences that enhance human well-being and quality of life.⁵⁶ The approach of using positive psychology techniques to counter PIU is relatively novel and this study would like to propose positive psychology as an effective method. A recent study⁵⁷ revealed positive psychology as an effective treatment for Internet addiction, by reducing use of Internet and enhancing social relationships. Researchers have also found that positive psychology interventions in school showed a decrease in adolescent distress and symptoms of anxiety and depression.⁵⁸ The same study also revealed increasing optimism, self-efficacy, and self-esteem among the participants.

In the proposed intervention program, the aspects of positive psychology incorporated are positive emotions, strengths, and emotional intelligence. Researchers have posited that positive emotions prompt broad mindsets that develop lasting personal resources, ensuring improved overall well-being and psychological health in the long term.⁵⁹ On the other hand, strengths of character are a focus on the positive traits of an individual, reflected in their actions, feelings, and thoughts.⁶⁰ Bar-On⁶¹ describes emotional intelligence as “an array of noncognitive capabilities, competencies, and skills that influence one's ability to succeed in coping with environmental demands and pressures.”

Psychological Intervention Program–Internet Use for Youth

The PIP-IU-Y program designed in this article incorporates CBT and positive psychology theory and approaches. The model suggests that PIU is the result of problematic cognitions and behaviors that support maladaptive responses. Previous research had highlighted that adolescents perceive the online world as a platform for interpersonal relationships. Hence, the PIP-IU-Y program contains a series of interpersonal skills to improve participants' face-to-face interactions. The intervention methods involve learning ways to reduce social anxiety and improve stress coping skills related to social interaction. Previous studies have proven that if therapies are conducted along with psychoeducation, it helps the participants gain greater insight into their self-perpetuating PIU cycle, which could then act as additional motivation to change their behavior.^55,62–64 Participants are also taught to acknowledge their own achievements and good qualities to instill positive thinking. At the end of the program, participants should exhibit assertive behavior as well as confidence in dealing with face-to-face interaction in replacing Internet communication. Table 1 shows the timeline and weekly topics of the program.

Table 1.

Timeline and Weekly Topics of the Intervention Program

Design	Week	Timeline	Activity/topics
Preintervention	1	Baseline (T₀)	Prestudy briefing Administration of questionnaire
Intervention (90-minute weekly CBT intervention session)	2	Session 1	Ice breaking/sharing
	3	Session 2	How you react to anxiety and stress
	4	Session 3	“Thought-Emotion-Action” (TEA)
	5	Session 4	Build your self confidence
	6	Session 5	Strengths and Emotional Intelligence
	7	Session 6	The exposure ladder
	8	Session 7	Assertiveness and confident thoughts
	9	Session 8 (T₁)	A Better Me Administration of questionnaire
Postintervention	10–13	Integration period	Participants integrate knowledge into daily practice
	13	(T₂)	Administration of questionnaire

CBT, cognitive behavioral therapy.

This study aims to examine the effect of the PIP-IU-Y in reducing symptoms of PIU, depression, anxiety, stress, and social interaction anxiety. We hypothesize that the PIP-IU-Y significantly reduces symptoms of (a) PIU, (b) depression, anxiety, and stress, and (c) social interaction anxiety.

Methods

Participants

Multisite recruitment was initiated in four government secondary schools asking for voluntary participation in a program to reduce PIU and social anxiety and increase social interaction in real life. Five groups with an average of 10 secondary school students between the ages of 13 to 18 years were identified and formed by registered school counselors based on their PIU score and motivation to change their behavior. Priority was given to participants with problematic PIU levels. A total of 45 participants completed the program.

Measures

The three scales utilized were the Problematic Internet Use Questionnaire (PIUQ),⁶⁵ Depression, Anxiety, Stress Scales (DASS),⁶⁶ and Social Interaction Anxiety Scale (SIAS).⁶⁷ All measurements were translated into Bahasa Malaysia to cater to government secondary school students who are more likely to be proficient in Bahasa Malaysia.

Problematic Internet Use Questionnaire

The PIUQ is a validated instrument that measures three dimensions of PIU: obsession, neglect, and control disorder.⁶⁵ All 18 items are rated on a 5-point Likert scale from 1 (Never) to 5 (Always). Obsession measures psychological fixation with the Internet, and symptoms of worry, anxiety, and depression due to inability in accessing the Internet. Neglect examines negligence of typical daily activities such as working and eating, while control disorder examines one's incompetence in decreasing the amount of time spent on the Internet. The cutoff scores for the PIUQ are 0–40 for “Normal” and 41 and above for “Problematic.”⁶⁵ Hence, participants who scored above 41 were rated as having problematic levels of Internet use. In this study, PIUQ showed high reliability throughout all three time points with Cronbach's alpha ranging from 0.794 to 0.889. This is consistent with past research showing similarly high reliabilities of 0.87 to 0.89.^65,68

Depression Anxiety Stress Scale

The full 42-item scale measures one's current emotional symptoms of depression, anxiety, and stress.⁶⁶ Items are rated on a 4-point Likert scale from 0 (Did not apply to me at all) to 3 (Applied to me very much, or most of the time). The participants were categorized into one of the five groups: normal, mild, moderate, severe, and extremely severe for each subscale, according to the cutoff scores.⁶⁶ Depression subscale scores were 0–9 (Normal), 10–13 (Mild), 14–20 (Moderate), 21–27 (Severe), and 28+ (Extremely severe). Anxiety subscale categorizes with 0–7 (Normal), 8–9 (Mild), 10–14 (Moderate), 15–19 (Severe), and 20+ (Extremely severe). Similarly, stress subscale group participants were categorized by 0–14 (Normal), 15–18 (Mild), 19–25 (Moderate), 26–33 (Severe), and 34+ (Extremely severe). Overall, a higher score implies severity of mental health. Reliabilities obtained from this study ranged from 0.924 to 0.954 for the overall scale, 0.904 to 0.924 for depression, 0.758 to 0.863 for anxiety, and 0.831 to 0.901 for stress. This is similar to past research showing high reliabilities.^69–71

Social Interaction Anxiety Scale

The SIAS is a 20-item instrument that measures distress level when interacting with other people.⁶⁷ The instrument may be helpful in assessing the presence of social phobia or other anxiety-related disorders, and in tracking symptoms over time. Scores within the range of 34–42 indicate social phobia, while scores of 43 and above indicate social anxiety.⁶⁷ Social phobia is the irrational fear of distinct social situations that result in avoidance and impairment, while social anxiety is a generalized fear of various social situations. For all three time points in this study, reliability is 0.788, 0.833, and 0.929, respectively, as evidenced by previous research.^67,72

Procedures

A single-subject experimental design method was adopted for this study. This method is well-established in clinical and counseling literature to evaluate the effects of behavioral interventions and change over time.^73,74 It is preferable to randomized controlled trial group design when researching the therapeutic effects of novel interventions on individuals.⁷³ It also more closely resembles clinical practice and therefore allows for greater transfer of research findings into practice applications.⁷³

The intervention program contains three phases (Table 1). Preintervention (T₀) involves administration of the three self-reported questionnaires and selection of candidates for intervention. During intervention, the manual-based CBT intervention program is delivered in a group format. The program consists of eight 90-minute weekly sessions. Immediately after the end of the eighth session (T₁), participants are measured again using the same three self-reported questionnaires. Follow-up (T₂) is conducted 1 month after completion of the intervention. Participants fill in the same questionnaires to ensure that any gain made during the intervention is maintained.

The eight weekly sessions of the intervention program are structured into three groups. The first group is the formulation stage and spans three sessions. It aims at setting the stage for the entire intervention program because it is pertinent to help participants acknowledge their PIU. They are asked to express their feelings with and without the Internet and recognize areas of potential improvement in social situations.

The second group restructures thoughts and behavior of the participants by addressing cognitive and behavioral methods involved in challenging preexisting thinking distortions and improving the participants' social confidence. It lasts two sessions. The third group, which lasts three sessions, concentrates on modification of thoughts and behavior. Here, the participants practice both cognitive and behavioral exercises learned in previous sessions.

In the actual implementation, the researchers met with the lead school counselor and four school counselors who conducted the intervention program. The goal was to brief them on the objectives and weekly activities of the program to establish shared understanding on how to conduct the program. During the eight-session intervention, the same group of individuals met multiple times to share information on their group program and to iron out any issues and questions that arose. To reduce self-reporting bias by participants, counselors were asked to keep a report log of the participants. These reports were handed to the researchers at completion of the intervention program.

Ethics

The study was approved by the Malaysian Ministry of Education. All participants were informed about the study and all provided informed consent. Parental consent was sought for those younger than 18 years of age.

Results

Demographically, 64.4 percent of the participants were males, and the remaining 35.6 percent were females. They were between 13 and 18 years of age (M = 15.69, standard deviation [SD] = 1.08) and were of various ethnicities. Table 2 outlines Internet use behavior of the participants based on gender. Overall, most participants began using the Internet from the age of 11 years and above, and 37.8 percent spent a daily average of 6–9 hours on the Internet. The most common point of access was home, hostel, and apartment, which suggests that home provides a convenient environment to use the Internet. Social networking was the main purpose for using the Internet, followed by information search and online games. Laptops are used most to access the Internet, followed by desktop PCs, tablets, and mobile phones.

Table 2.

Internet Habits of the Participants Based on Gender

Internet habit	Males		Females		Total
Internet habit	n	%	n	%	n	%
Age when started using the Internet
<8 years old	5	17.2	0	0.0	5	11.1
9 years old	3	10.2	1	6.3	4	8.9
10 years old	1	3.4	2	12.5	3	6.7
11 years old	10	34.5	5	31.3	15	33.3
>12 years old	10	34.5	8	50.0	18	40.0
Average hours on Internet (daily)
<1 hour	0	0.0	1	6.3	1	2.2
2–3 hours	8	27.6	6	37.5	14	31.1
4–5 hours	9	31.0	4	25.0	13	28.9
6–7 hours	7	24.1	1	6.3	8	17.8
8–9 hours	5	17.2	4	25.0	9	20.0
Location
Home/hostel/apartment	28	96.6	16	100.0	44	97.8
Wi-Fi Hotspot	6	20.7	1	6.3	7	15.6
Activity
Search for information	8	27.5	6	37.5	14	31.2
Social networking	18	62.1	7	43.8	25	55.6
Play online games	3	10.3	2	12.5	5	11.1
Others	0	0.0	1	6.3	1	2.2
Device
Desktop PCs	5	17.2	4	25.0	9	20.0
Laptops	17	58.6	10	62.5	27	60.0
Tablets	4	13.8	1	6.3	5	11.1
Mobile phones	2	6.9	1	6.3	3	6.7
Others	1	3.4	0	0.0	1	2.2

Table 3 summarizes the results for PIU, depression, anxiety, stress, and social interaction anxiety at intervention onset. The results indicate that 80.0 percent of participants exhibited PIU behavior, 37.8 percent were depressed, 69.0 percent were anxious, and 46.7 percent were stressed. About 42.2 percent suffered from anxiety or phobia when interacting with others.

Table 3.

Responses at the Onset of Intervention

Category	Males		Females		Total
Category	n	%	n	%	n	%
PIU
Normal	8	17.8	1	2.2	9	20.0
Problematic	21	46.7	15	33.3	36	80.0
Depression
Normal	20	44.4	8	17.8	28	62.2
Mild	4	8.9	6	13.3	10	22.2
Moderate	2	4.4	1	2.2	3	6.7
Severe	3	6.7	1	2.2	4	8.9
Very severe	0	0.0	0	0.0	0	0.0
Anxiety
Normal	10	22.2	4	8.9	14	31.1
Mild	7	15.6	2	4.4	9	20.0
Moderate	9	20.0	7	15.6	16	35.6
Severe	2	4.4	1	2.2	3	6.7
Very severe	1	2.2	2	4.4	3	6.7
Stress
Normal	16	35.6	8	17.8	24	53.3
Mild	7	15.6	1	2.2	8	17.8
Moderate	3	6.7	5	11.1	8	17.8
Severe	3	6.7	2	4.4	5	11.1
Very severe	0	0.0	0	0.0	0	0.0
Social interaction anxiety
Normal	18	40.0	8	17.8	26	57.8
Phobia	6	13.3	7	15.6	13	28.9
Anxiety	5	11.1	1	2.2	6	13.3

PIU, problematic Internet use.

Table 4 presents the means and SDs of scores at various time points. The results show that participant scores on most measures decreased at the end of intervention, and upon follow-up. Paired sample t-tests were used to examine whether the changes were significant. Participants reported significant improvement in PIU after the last session, indicating a marked decline after intervention. Upon follow-up, participants reported a further decrement in their PIU. The effect size for this analysis, d = 0.59, 95 percent CI [3.04–9.81], was found to exceed Cohen's⁷⁵ convention for a medium effect (0.50), thus showing a medium to high effect. This shows a substantial decline in PIU symptoms reported by participants after the intervention. Besides that, anxiety, stress, and social interaction anxiety levels were observed to be significantly lower at follow-up, with a low to medium effect size. Hence, participants reported significantly lower scores of anxiety, stress, and social interaction anxiety 1 month after the intervention, with the exception of depression.

Table 4.

Comparison of Variables at Three Measurement Points

Variables	T₀ (preintervention)		T₁ (postintervention)		T₂ (follow-up)		t-Test			Cohen's d			95% CI [LL, UL]
	T₀ (preintervention)		T₁ (postintervention)		T₂ (follow-up)		T₀–T₁	T₁–T₂	T₀–T₂	T₀–T₁	T₁–T₂	T₀–T₂	T₀–T₁	T₁–T₂	T₀–T₂
	M	SD	M	SD	M	SD	T₀–T₁	T₁–T₂	T₀–T₂	T₀–T₁	T₁–T₂	T₀–T₂	T₀–T₁	T₁–T₂	T₀–T₂
PIU	50.54	10.72	47.18	9.31	44.11	11.13	2.60^*	2.53^*	3.83^**	0.33	0.30	0.59	0.76 to 5.96	0.62 to 5.51	3.04 to 9.81
Depression	9.3	7.56	9.47	8.48	8.09	8.41	−0.16	1.43	1.36	0.02	0.16	0.15	−2.19 to 1.87	−0.56 to 3.32	−0.58 to 3.01
Anxiety	10.22	5.4	9.91	5.45	8.22	5.46	0.39	2.33^*	2.57^*	0.06	0.31	0.37	−1.30 to 1.92	0.23 to 3.15	0.43 to 3.56
Stress	14.89	6.99	13.67	7.19	11.98	7.08	1.37	1.71	2.60^*	0.17	0.24	0.41	−0.57 to 3.02	−0.30 to 3.68	0.66 to 5.17
Social interaction anxiety	31.42	10.94	28.89	10.8	27.58	13.43	1.71	0.82	2.26^*	0.23	0.11	0.31	−0.45 to 5.51	−1.88 to 4.51	0.41 to 7.27

LL and UL represents the lower limit and upper limit of the CI.

p < 0.05, ^**p < 0.01.

CI, confidence interval; SD, standard deviation.

Overall, the results suggest that the majority of participants improved after eight weekly sessions of PIP-IU-Y, and further improvement was seen at 1-month follow-up. Specifically, participants were able to reduce their PIU and improve social interaction through a decrease in social anxiety and stress. In other words, symptoms of PIU were remedied through PIP-IU-Y and healthy Internet use was maintained postintervention, indicating that the PIP-IU-Y is a practical and meaningful program because the participants lives are improved.

Discussion and Conclusions

This study is among the first to develop and test a preventive intervention program for youths with PIU. It contributes significantly to the body of knowledge in Internet addiction and intervention programs. The results of this study show that symptoms of PIU, depression, anxiety, stress, and social interaction anxiety decreased with implementation of the program; hence the hypotheses are accepted.

In a validation study,⁷⁶ the PIP-IU-Y was implemented on 157 participants in 22 schools across Malaysia. The results showed that for all the variables (PIU, depression, anxiety, stress, and social interaction anxiety), scores significantly decreased from preintervention to postintervention, and further decreased at follow-up. The results of this validation study revealed effect sizes for various time points and variables that ranged between 0.5 to over 1, surpassing Cohen's⁷⁵ convention for large effect sizes. These results further support efficacy of the PIP-IU-Y in ameliorating PIU symptoms.

Although the overall depression levels at follow-up was lower than at preintervention in this study, the reduction was not shown to be statistically significant. This may be due to the fact that the depression levels were not high to begin with—a large majority of our participants fall in the “normal” category for depression, hence any differences observed after the program may not have been large enough to produce statistical significance.

The intervention program designed in this study was shown to be effective in ameliorating the common side effects of PIU: depression, anxiety, stress, and social interaction. In particular, those with PIU saw significant improvement in symptoms, which gave confidence to the value of the program. One-month follow-up results demonstrated that the knowledge gained from the program was continually integrated into their daily lives even after completion of the intervention program. As a majority of the participants in this study had problematic PIU levels at baseline measurement, the intervention program was shown to have secondary and tertiary preventive effectiveness by preventing negative progression of PIU and its correlated symptoms. The nonproblematic participants in this study were also observed to have a reduction in symptoms; hence, the primary preventive effectiveness of the PIP-IU-Y in preventing the development of PIU is inferred. This is because integrating knowledge learnt from the PIP-IU-Y program into daily Internet use would help the cultivation and practice of healthy cyber habits among youth. Past research^58,76 has shown that practicing healthy habits at an early age would hinder the development of psychological and behavioral changes as a result of PIU. The majority of the treatment programs reported in the literature merely tailor to those who already have problems.^8,77,78 Since prevention is better than cure, a program that could potentially help normal users from falling into the problematic group is unique and beneficial.

Our program has implications for parents and governments. It provides a solution to an imminent issue that affects our youth, highlighting the value of an intervention program to assist them in correcting their PIU behaviors and preventing them from developing more serious symptoms. Since the program was conducted by school counselors to their students, the Ministry of Education could consider incorporating the program into the school curriculum to prevent youths from developing PIU, and ultimately cultivate responsible Internet use behavior and a healthy cyber culture.

Limitations and Future Research

Despite the empirical support, this study has limitations, which calls for additional research. First, the timeline to carry out the intervention program was rather restrictive. Since it was not possible to conduct the program during the examination period and during school holidays, counselors may feel the pressure and rush to complete the program within a certain period. Moreover, since the program can only be conducted after school hours, some eligible participants were not able to take part due to timing and extracurricular activities. If the intervention program could be incorporated as part of the school curriculum, it will be even more effective.

Other than that, this study was conducted in Malaysia and the sample used was predominantly Malaysian. As the sample nationality in this study is restricted to one country, future research could test the effectiveness of the intervention program on people of other nationalities by implementing the program in other countries.

Future research could also investigate treatment differences among the various forms of PIU, such as online gaming and Internet pornography viewing. The program can also be customized and tested in younger groups such as children in elementary schools.

With rising PIU among adolescents, an effective intervention program ought to be in place to assist these individuals. Our research designed and tested a cognitive behavioral-based preventive intervention program for youths. Results show that the program was effective in preventing negative progression into more serious Internet addiction.

Footnotes

Acknowledgments

The authors would like to extend appreciation to the school counselors involved for conducting and monitoring the aforementioned intervention program in their respective secondary schools. This study was supported by the grant from the Malaysian Communications and Multimedia Commission (MCMC). Approval to conduct this research was obtained from the Malaysian Ministry of Education. There is no ethics code provided.

Authors' Contributions

All the authors have contributed equally to this research.

Author Disclosure Statement

No competing financial interests exist.

References

Young

. Psychology of computer use: XL. Addictive use of the Internet: a case that breaks the stereotype. Psychological Reports, 1996; 79:899–902.

Young

. Internet addiction: the emergence of a new clinical disorder. CyberPsychology and Behavior, 1998; 1:237–244.

Young

, Rogers

. The relationship between depression and Internet addiction. CyberPsychology and Behavior, 1998; 1:25–28.

, O'Brien

, Snyder

, et al. Diagnostic criteria for problematic Internet use among U.S. university students: a mixed-methods evaluation. PLoS One, 2016; 11:e0145981.

Shapira

, Goldsmith

, Keck

, et al. Psychiatric features of individuals with problematic internet use. Journal of Affective Disorders, 2000; 57:267–272.

Griffiths

. A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 2005; 10:191–197.

Davis

. A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 2001; 17:187–195.

Young

. CBT-IA: the first treatment model for internet addiction. Journal of Cognitive Psychotherapy, 2011; 25:304–312.

, Wong

, Marsh

. “Problematic Internet Use among University Students in Malaysia,”. media matters: networked media content research report, 2013; 2:68–81.

10.

Tang

, Yu

, Du

, et al. Prevalence of internet addiction and its association with stressful life events and psychological symptoms among adolescent internet users. Addictive Behaviors, 2014; 39:744–747.

11.

Liu

, Desai

, Krishnan-Sarin

, et al. Problematic Internet use and health in adolescents: data from a high school survey in Connecticut. The Journal of Clinical Psychiatry, 2011; 72:836.

12.

Kim

, Park

, Kim

, et al. The effects of Internet addiction on the lifestyle and dietary behavior of Korean adolescents. Nutrition Research and Practice, 2010; 4:51–57.

13.

Cao

, Sun

, Wan

, et al. Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health, 2011; 11:802.

14.

Kraut

, Patterson

, Lundmark

, et al. Internet paradox: a social technology that reduces social involvement and psychological well-being?. American Psychologist, 1998; 53:1017.

15.

, Yoo

, Cho

, et al. Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. Journal of Clinical Psychiatry, 2006; 67:821.

16.

, Yen

, Liu

, et al. The associations between aggressive behaviors and Internet addiction and online activities in adolescents. Journal of Adolescent Health, 2009; 44:598–605.

17.

, Liu

, Wang

, et al. The exacerbation of depression, hostility, and social anxiety in the course of internet addiction among adolescents: a prospective study. Comprehensive Psychiatry, 2014; 55:1377–1384.

18.

Caplan

. Preference for online social interaction: a theory of problematic Internet use and psychosocial well-being. Communication Research, 2003; 30:625–648.

19.

Bessiere

, Kiesler

, Kraut

, et al. Effects of Internet use and social resources on changes in depression. Information, Community and Society, 2008; 11:47–70.

20.

Morrison

, Gore

. The relationship between excessive Internet use and depression: a questionnaire-based study of 1,319 young people and adults. Psychopathology, 2010; 43:121–126.

21.

Griffiths

. Does Internet and computer “addiction” exist? Some case study evidence. CyberPsychology and Behavior, 2000; 3:211–218.

22.

Leung

. Impacts of Net-generation attributes, seductive properties of the Internet, and gratifications-obtained on Internet use. Telematics and Informatics, 2003; 20:107–129.

23.

Bessière

, Pressman

, Kiesler

, et al. Effects of internet use on health and depression: a longitudinal study. Journal of Medical Internet Research, 2010; 12:e6.

24.

Lai

, Mak

, Watanabe

, et al. The mediating role of Internet addiction in depression, social anxiety, and psychosocial well-being among adolescents in six Asian countries: a structural equation modelling approach. Public Health, 2015; 129:1224–1236.

25.

Odacı

, Kalkan

. Problematic Internet use, loneliness and dating anxiety among young adult university students. Computers and Education, 2010; 55:1091–1097.

26.

Young

. Treatment outcomes using CBT-IA with Internet addicted patients. Journal of Behavioral Addictions, 2013; 2:209–215.

27.

Cheng

, Li

AYL

. Internet addiction prevalence and quality of (real) life: a meta-analysis of 31 nations across seven world regions. Cyberpsychology, Behavior, and Social Networking, 2014; 17:755–760.

28.

Caplan

. Relations among loneliness, social anxiety, and problematic Internet use. CyberPsychology and Behavior, 2006; 10:234–242.

29.

, Yen

, et al. Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study. CyberPsychology and Behavior, 2007; 10:545–551.

30.

Liu

, Kuo

. A study of Internet addiction through the lens of the interpersonal theory. CyberPsychology and Behavior, 2007; 10:799–804.

31.

Bernardi

, Pallanti

. Internet addiction: a descriptive clinical study focusing on comorbidities and dissociative symptoms. Comprehensive Psychiatry, 2009; 50:510–516.

32.

Allison

, von Wahlde

, Shockley

, et al. The development of the self in the era of the internet and role-playing fantasy games. American Journal of Psychiatry, 2006; 163:381–385.

33.

Whang

LSM

, Lee

, Chang

. Internet over-users' psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychology and Behavior, 2003; 6:143–150.

34.

Ebeling-Witte

, Frank

, Lester

. Shyness, Internet use, and personality. CyberPsychology and Behavior, 2007; 10:713–716.

35.

Yan

, Li

, Sui

. The relationship between recent stressful life events, personality traits, perceived family functioning and Internet addiction among college students. Stress and Health, 2014; 30:3–11.

36.

Xie

, Zhou

, Xu

, et al. Prevalence of internet addiction and the related factors in middle school students in Guangzhou. Journal of Southern Medical University, 2010; 30:1801–1804.

37.

, Zhang

, Li

, et al. Stressful life events and adolescent Internet addiction: the mediating role of psychological needs satisfaction and the moderating role of coping style. Computers in Human Behavior, 2016; 63:408–415.

38.

Snodgrass

, Lacy

, Francois Dengah II

, et al. A vacation from your mind: problematic online gaming is a stress response. Computers in Human Behavior, 2014; 38:248–260.

39.

Beck

. Thinking and depression: II. Theory and therapy. Archives of General Psychiatry, 1964; 10:561–571.

40.

Wills

, Sanders

. (2013) Cognitive behaviour therapy: foundation for practice. Thousand Oaks, CA: SAGE Publications.

41.

Beck

. (2011) Cognitive behavior therapy: basics and beyond. New York: Guilford Press.

42.

, Kim

, Bae

, et al. Depression and Internet addiction in adolescents. Psychopathology, 2007; 40:424–430.

43.

Alavi

, Maracy

, Jannatifard

, et al. The effect of psychiatric symptoms on the internet addiction disorder in Isfahan's University students. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 2011; 16:793.

44.

Younes

, Halawi

, Jabbour

, et al. Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: a cross-sectional designed study. PLoS One, 2016; 11:e0161126.

45.

, Jiang

, Vance

. Longer term effect of randomized, controlled group cognitive behavioural therapy for Internet addiction in adolescent students in Shanghai. Australian and New Zealand Journal of Psychiatry, 2010; 44:129–134.

46.

Kim

, Han

, Lee

, et al. Combined cognitive behavioral therapy and bupropion for the treatment of problematic on-line game play in adolescents with major depressive disorder. Computers in Human Behavior, 2012; 28:1954–1959.

47.

, Wang

. The role of cognitive distortion in online game addiction among Chinese adolescents. Children and Youth Services Review, 2013; 35:1468–1475.

48.

Orzack

, Voluse

, Wolf

, et al. An ongoing study of group treatment for men involved in problematic Internet-enabled sexual behavior. CyberPsychology and Behavior, 2006; 9:348–360.

49.

Wölfling

, Beutel

, Dreier

, et al. Treatment outcomes in patients with internet addiction: a clinical pilot study on the effects of a cognitive-behavioral therapy program. Biomed Research International, 2014; 2014:425924.

50.

Safran

, Muran

. (2000) Negotiating the therapeutic alliance: a relational treatment guide. New York: Guilford Press.

51.

Leahy

. The therapeutic relationship in cognitive-behavioral therapy. Behavioural and Cognitive Psychotherapy, 2008; 36:769–777.

52.

Wills

. (2008) Skills in cognitive behaviour counselling & psychotherapy. Los Angeles: Sage.

53.

Young

. Cognitive behavior therapy with Internet addicts: treatment outcomes and implications. CyberPsychology and Behavior, 2007; 10:671–679.

54.

Rooij

AJV

, Zinn

, Schoenmakers

, et al. Treating internet addiction with cognitive-behavioral therapy: a thematic analysis of the experiences of therapists. International Journal of Mental Health and Addiction, 2012; 10:69–82.

55.

King

, Delfabbro

, Griffiths

, et al. Cognitive‐behavioral approaches to outpatient treatment of Internet addiction in children and adolescents. Journal of Clinical Psychology, 2012; 68:1185–1195.

56.

Seligman

MEP

, Csikszentmihalyi

. (2014) Positive psychology: an introduction. In: Flow and the foundations of positive psychology. Netherlands: Springer, pp. 279–298.

57.

Khazaei

, Khazaei

, Ghanbari-HB. Positive psychology interventions for internet addiction treatment. Computers in Human Behavior, 2017; 72:304–311.

58.

Shoshani

, Steinmetz

. Positive psychology at school: a school-based intervention to promote adolescents' mental health and well-being. Journal of Happiness Studies, 2014; 15:1289–1311.

59.

Fredrickson

. The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. American Psychologist, 2001; 56:218–226.

60.

Park

, Peterson

, Seligman

MEP

. Strengths of character and well-being. Journal of Social and Clinical Psychology, 2004; 23:603–619.

61.

Bar-On

. (1997) BarOn emotional quotient inventory. Toronto: Multi-Health Systems.

62.

King

, Delfabbro

, Griffiths

. Cognitive behavioral therapy for problematic video game players: conceptual considerations and practice issues. Journal of CyberTherapy and Rehabilitation, 2010; 3:261–273.

63.

King

, Delfabbro

. Internet gaming disorder treatment: a review of definitions of diagnosis and treatment outcome. Journal of Clinical Psychology, 2014; 70:942–955.

64.

Sakuma

, Mihara

, Nakayama

, et al. Treatment with the self-discovery camp (SDiC) improves internet gaming disorder. Addictive Behaviors, 2017; 64:357–362.

65.

Koronczai

, Urbán

, Kökönyei

, et al. Confirmation of the three-factor model of problematic internet use on off-line adolescent and adult samples. Cyberpsychology, Behavior, and Social Networking, 2011; 14:657–664.

66.

Lovibond

, Lovibond

. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 1995; 33:335–343.

67.

Mattick

, Clarke

. Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour Research and Therapy, 1998; 36:455–470.

68.

Demetrovics

, Szeredi

, Rózsa

. The three-factor model of Internet addiction: the development of the Problematic Internet Use Questionnaire. Behavior Research Methods, 2008; 40:563–574.

69.

Brown

, Chorpita

, Korotitsch

, et al. Psychometric properties of the depression anxiety stress scales (DASS) in clinical samples. Behaviour Research and Therapy, 1997; 35:79–89.

70.

Antony

, Bieling

, Cox

, et al. Psychometric properties of the 42-item and 21-item versions of the depression anxiety stress scales in clinical groups and a community sample. Psychological Assessment, 1998; 10:176–181.

71.

Crawford

, Henry

. The depression anxiety stress scales (DASS): normative data and latent structure in a large non-clinical sample. British Journal of Clinical Psychology, 2003; 42:111–131.

72.

Olivares

, García-López

, Hidalgo

. The social phobia scale and the social interaction anxiety scale: factor structure and reliability in a Spanish-speaking population. Journal of Psychoeducational Assessment, 2001; 19:69–80.

73.

Borckardt

, Nash

, Murphy

, et al. Clinical practice as natural laboratory for psychotherapy research: a guide to case-based time-series analysis. American Psychologist, 2008; 63:77.

74.

Morgan

, Morgan

. (2008) Single-case research methods for the behavioral and health sciences. Thousand Oaks, CA: SAGE Publications.

75.

Cohen

. (1988) Statistical analysis for the behavioral sciences. Hillsdale: Lawrance Erlbaum.

76.

, Wong

. Outcome of the Psychological Intervention Program: Internet Use for Youth. Journal of Rational-Emotive and Cognitive-Behavior Therapy, 2018; 36:187–200.

77.

Shek

, Tang

, Lo

. Evaluation of an Internet addiction treatment program for Chinese adolescents in Hong Kong. Adolescence, 2009; 44:359.

78.

King

, Delfabbro

, Griffiths

, et al. Assessing clinical trials of Internet addiction treatment: a systematic review and CONSORT evaluation. Clinical Psychology Review, 2011; 31:1110–1116.