- Study protocol
- Open Access
Effectiveness of a mobile app-based educational intervention to treat internet gaming disorder among Iranian adolescents: study protocol for a randomized controlled trial
Trials volume 23, Article number: 229 (2022)
The use of video games, a hobby for many teenagers in their leisure time, has brought with it a new potential for concerns. Internet gaming disorder (IGD) is a mental condition classified as a disorder due to addictive behaviors. It may include use of video games, both online and offline. Consequences of IGD may include introversion, social anxiety, mood swings, loneliness, sleep problems, behavioral problems, depression, low self-esteem, and increased violence. In order to design an app-based intervention for adolescents, a transtheoretical model (TTM) has been used. This widely used model in the field of behavioral change is also practical for health education programs. In addition, cognitive-behavioral therapy (CBT) has been used to make people more aware of their behaviors, feelings and thoughts and how to achieve behavioral change. The present study seeks to determine the effectiveness of this app-based intervention in in the treatment of IGD among adolescents.
In this single-blinded, randomized, controlled trial, 206 high-school adolescents aged 13 to 18 years in Qazvin city will be recruited. Eligible adolescents will be randomly assigned into intervention and control groups. Eight consecutive sessions delivered over 2 months and based on the TTM and CBT will be delivered through the `app (named HAPPYTEEN) to the intervention group. The control group will receive a sleep hygiene intervention (8 consecutive sessions for 2 months) via the app. Data collection tools include the Internet Gaming Disorder Scale, Insomnia Severity Index, Depression, Anxiety, and Stress Scales, Stages of Change Questionnaire, Decision Balance, and Self-Efficacy. The study measures will be completed at baseline, post intervention, and 1 month and 3 months after the intervention.
The results of this intervention could be used as adjunct therapy for adolescents with IGD.
Clinical Trial Registration Center of Iran (IRCT) IRCT20181226042140N1. Registered on June 9, 2020.
In the setting of dramatic growth of communication technologies, computer games as a social phenomenon often attract children and adolescents . Some youth may experience difficulties regulating time spent playing video games, leading to addictive engagement [2, 3]. The 11th revision of the International Classification of Diseases (ICD-11) was officially adopted at the World Health Assembly . Gaming disorder (GD) was included in the ICD-11 (and internet GD (IGD) was included in the 5th Edition of the Diagnostic and Statistical Manual (DSM-5®)) to define criteria for disordered patterns of gaming . GD may include use of a variety of video games, whether online or offline, whereas IGD specifies the focus on gaming conducted online, often competitively in groups [5, 6]. Types and patterns of gaming may lie along a continuum from enjoyable to harmful and addictive .
Gaming in GD, like substance use in substance use disorders, may be motivated by desires to escape problems in life . According to the American Psychiatric Association, IGD is characterized by a persistent pattern of behavior and frequent use of the internet to engage in online gaming, leading to significant disruption or discomfort over a 12-month period. Five out of 9 inclusion criteria are needed for diagnosis. These criteria include preoccupation, withdrawal, tolerance, unsuccessful attempts to limit time spent gaming, diminished interests in non-gaming activities, continued gaming despite adverse consequences, deceiving family members, therapists and others about durations of gaming, using online games to reduce negative moods, and jeopardizing relations or educational or occupational opportunities due to internet gaming .
In Iran, a study by the National Youth Organization shows that the number of people using the internet has grown by 70% of the population last year . Zamani et al. found that of 564 seventh-graders, 17% experienced gaming problems , and another study reported that nearly 60% of 210 Iranian adolescents spend at least one or more hours a day playing video games . More than 35% of people using the internet in Iran are youth/young adults, and the average time spent on the internet is generally 52 min per week .
Globally, individuals aged 16 to 21 years appear among the most vulnerable for developing gaming problems . Approximately one in 21 adolescents may experience IGD . However, prevalence estimates of IGD vary across jurisdictions . In Spain, 0.6% of adolescents , and in the UK, 19.9%  experience gaming problems, and according to a study in Turkey, 4.5% of 16-year-olds met criteria for IGD . Approximately 1.5–3% of adolescents aged 13–16 years in the Netherlands had IGD . In East Asian countries, prevalence estimates may be higher , with males more frequently gaming and having gaming problems relative to females [4, 20]. This variability in prevalence estimates may reflect differences in assessment instruments, study populations, and the criteria for diagnosing IGD . In Iran, the prevalence of gaming in children and adolescents is reported to range between 30% and 72% . Another study found that 56% of young Iranians are very interested in computer games and use the internet in their spare time .
IGD may alter behavioral tendencies. For example, after prolonged gaming and IGD, people may experience introversion, social anxiety, mood swings, loneliness, sleep problems, behavioral problems, depression, aggression, and low self-esteem [22, 23]. However, longitudinal studies are needed to disentangle pre-existing vulnerabilities from consequences of gaming and IGD.
Successful educational interventions typically involve theoretical planning . The use of behavior change models/theories may help in recognizing the factors affecting behavior change and facilitating successful intervention . The transtheoretical model (TTM) is widely employed  and focuses on explaining how to change behavior over time . This model has many applications for behavioral change, including for physical activity, smoking cessation, alcohol withdrawal and IGD among adolescents, youth, and adults [28, 29].
Few studies have used cognitive-behavioral therapy (CBT) in the treatment of IGD . CBT has been used for many mental health-related illnesses including anxiety and depressive disorders and alcohol, drugs, gambling, and internet addictions [31,32,33,34,35]. CBT has demonstrated effectiveness in changing maladaptive behaviors [36, 37]. Due to the behavioral similarities between people who experience substance use and gambling disorders and those with IGD, some targeted domains of CBT for IGD may include stimulus control and learning of and enacting healthy coping responses when craving. Motivational understanding, self-monitoring, cognitive restructuring, and problem solving are also important considerations [30, 38, 39].
In recent years, psychological treatments have undergone considerable changes given the widespread availability of digital technologies including computers, the internet, mobile devices such as smartphones, and mobile software applications . Many digital treatments are provided in the form of CBT . Increased convenience, privacy, and trust in this method have increased demand for such treatments . The internet is an emerging platform for mental health services worldwide [43,44,45]. The internet serves as a potential solution to geographic and transportation challenges and may facilitate the provision of mental health services to patients in their own homes. Internet-based CBT has shown promising results for addressing different mental problems [46,47,48]. Emerging evidence suggests that internet-based CBT is as effective as face-to-face CBT [48,49,50,51,52]. If the content of CBT-based technology that is disseminated via the internet, emails, or mobile phones is of good quality and has credible evidence-based efficacy and effectiveness, it may have widespread positive therapeutic effects and patient acceptance, similar to face-to-face CBT [53, 54]. Of note, given that gaming is often done online, appropriate precautions should be considered and used when conducting treatment for gaming problems via online platforms. Given the ubiquity of digital devices and their incorporation in many aspects of daily life for most people, such precautions will likely be beneficial in general for any intervention with IGD populations.
The study will aim to determine the effectiveness of an educational intervention based on the TTM model and CBT in the treatment of IGD in adolescents.
The overall aim of this trial is to determine the effectiveness of an educational intervention based on the TTM model and CBT in the treatment of online gambling addiction through an app in adolescents.
More specifically the study aims are to compare in the active control and intervention groups over time (before and one and three months after):
Stages of change
The balance of decisional choices
The intentions to perform preventive behaviors relating to online gaming
Levels of stress, anxiety and depression
Sleep health status
The implementation of the educational program and intervention will be online by using an app called HAPPYTEEN.
The stages of this research will briefly include educational planning, intervention implementation, and evaluation at three times (before the intervention and at 1-month and 3-month follow-ups after the intervention). The study flow diagram is provided in Fig. 1.
In order to select the sample, a one-stage sampling method will be used. For this purpose, a list of secondary schools will be prepared. Then, out of 25 high schools, 8 will be randomly selected and screened for gaming disorder in all students who play computer games (both online and offline) using the nine-item Internet Gaming Disorder Scale - Short Form (IGDS-SF9). An online questionnaire will be provided to adolescent students through school information channels so that those who volunteer may participate in this research. Eligible adolescents (n = 206) will be randomly assigned into action control (n = 103) and intervention groups (n = 103). An online questionnaire will be provided to adolescent students through school information channels so that those who volunteer can participate in this research.
After approval of the ethics Committee in Research of Qazvin University of Medical Sciences (IR.QUMS.REC.1398.358), a letter will be sent to the General Department of Education of Qazvin Province to obtain all high school lists and permission to conduct the research. The first author will hold coordination meetings with school principals and explain the research clearly. After the schools’ agreement, an announcement of the study will be posted in the schools’ channels. During the COVID-19 pandemic, a specific application (the SHAD web) was developed by the ministry of education to be used for all schools for teaching, parental involvement, and other activities. Each school has this application. Screening questionnaires will be provided to the target group through these school information channels (i.e., the SHAD WEB). In addition, in all stages of the research, to ensure the participants' satisfaction, they will be provided with an explanation that all their answers and statements will be kept completely confidential and tracked by numbers. After reviewing eligibility criteria, eligible adolescents will complete baseline measures and then will be randomly divided into two groups (active control and intervention) as detailed above and below. The intervention group will receive an educational app (HAPPYTEEN) based on the TTM model and CBT. The intervention will be conducted weekly in eight sessions with an average duration of 30 min, while the active control group will receive an intervention on sleep hygiene. It should be noted that individual participants in both groups will be assigned a unique code as username (phone number or email) to enter the app. Initial feedback from an independent sample of adolescents found that the app was understandable and acceptable to adolescents. All participants’ activities (including opening each page, duration of reading each page, the number of logins) will be monitored and evaluated by an independent researcher to ensure adherence to treatment among participants. In the app user environment, participants can also communicate with the support team via email and phone message to share any questions, criticisms, and suggestions with an independent support team. Messages related to reminder training tips and reminders to answer weekly questions are possible through the app.
Participants and procedure
Inclusion and exclusion criteria
Inclusion criteria for eligible adolescents to enter the trial will be as follows: voluntary participation of adolescents aged 13 to 18 years; studying in Abeyek high schools; full access to a computer or laptop, tablet or smartphone; internet access; scoring 32 or higher on the nine-item Internet Gaming Disorder Scale – Short Form (IGDS-SF9) ; and providing consent to participate in the study by both parents and adolescents. Adolescents with mental disabilities or severe mental health-related illness (such as schizophrenia and bipolar disorder) will be excluded. Moreover, the participants will be excluded if they do not receive more than 50% of the interventional materials. The procedure of recruitment and acquiring informed consent will be done by the second author (SFR).
Recruitment will begin on June 1, 2021. After screening the adolescents and having eligible adolescents complete the questionnaires, randomization will be conducted. This study is a two-group, parallel, single-blind randomized controlled trial with a sample size of 206 adolescents. From a total of 25 schools, 8 schools will be randomly selected by simple randomization methodology. A list of schools will be provided by referring to the Department of Education. All high schools will be considered eligible to have the opportunity to reach students of all different socioeconomic levels. After checking the eligibility of the adolescents, of the 206 participants’ schools, 103 adolescents will be randomly assigned to the intervention group, and the other 103 adolescents randomly assigned to the active control group by simple randomization methodology. The simple randomization will be done to assign participants for study groups by an independent statistician with a 1:1 allocation.
From each school, the students selected will receive a special code through SPSS software, and after analyzing the data, they will be selected randomly through these codes.
Blindness will not be performed in study participants due to the different natures of the approaches in the active control and intervention groups, with one group receiving educational training on IGD and the other receiving educational information on sleep hygiene. Study administration and the statistician will be blinded to group allocation.
The TTM model consists of six domains including (1) stages of change, (2) process of changes, (3) decisional balance, (4) change level, (5) self-efficacy, and (6) temptation. In this study, we will focus on three domains, as described below. The first domain is stages of change. While changing behavior and achieving the desired behavior, it is important to consider different stages . In the first stage (Stage 1: Precontemplation), due to lack of awareness or knowledge or experiences of failed attempts to change in the past, the person may not have enough motivation to consider behavioral change during the next 6 months. In the next stage (Stage 2: Contemplation), the person has the necessary knowledge regarding the results of performing the desired behavior and knows the benefits of performing the behavior and the costs of performing the behavior, but he/she may not have self-confidence or motivations to make changes in behavior during the next 6 months. In the third stage (Stage 3: Preparation), people may analyze more fully costs and benefits in performing the behavior. People in this stage value the benefits more than the obstacles, and by planning for the next 30 days, they typically feel one step closer to their goals. After the preparation stage, the person may be better able to resist temptations and persevere to adopt the new behavior. In the next stage (Stage 4: Action), a new behavior may be adopted by the person within the next 6 months. In the next stage (Stage 5: maintenance), positive changes in his/her lifestyle have typically occurred, and he/she has experienced a change in behavior for at least 6 months and a maximum of 5 years with more confidence and less temptation. The risk of unusual temptations and the consequences of false self-confidence may still precipitate relapse.
The second domain of the TTM model involves processes of changes. These processes include overt and covert activities that a person may use to progress through the stages of change. In other words, these include any activities that a person does to help modify the way he/she thinks, feels or behaves.
The next TTM domain considered is decisional balance. Decision-making is the process by which an individual considers the outcome of mental agreements (positive mental images, values, and beliefs) and oppositions to engage or not in behaviors. This domain is similar to that of perceived benefits and barriers in the health belief model . The balance between perceived agreements and disagreements often fluctuates throughout stages of change.
Self-efficacy is the third domain considered for the intervention group. This domain, which comes from Bandura's theory of social learning, is a person’s self-efficacy and has a linear and positive relationship with the domain of stages of change. If a person goes to higher stage of change, his/her self-efficacy also increases .
The present study will focus on three domains of the TTM model (i.e., stages of change, self-efficacy and decision balance) in order to investigate the role of the TTM model in the treatment of IGD. One of the reasons for selecting these three domains is their success in statistically predicting individuals’ addictive gaming behaviors, which has been examined and demonstrated in a previous study .
Beside the TTM model, CBT will be used for our intervention. CBT is a form of psychotherapy that helps people understand thoughts and feelings that influence their behaviors. During treatment, a person learns how destructive or disturbing thoughts may have negative effects on their behaviors , and they may learn coping skills to manage their thoughts and feelings in a more healthy fashion .
After installing the app, each individual enters into the study with a use code that can be a mobile phone number or email that is recorded in the initial screening of each person in the study. Thereafter, a confirmation code is received via SMS.
The main page of the app consists of 4 sections: 1. content of training sessions, 2. exercises and weekly assignments, 3. questionnaires, and 4. a progress chart. The trainings are designed for 8 consecutive weeks (one session per week), and the approximate duration of each session is 30 min. Training includes written texts, videos, and audio files (relaxation music, relaxation exercise, and audio stories). Table 1 provides an overview of the content of each session in the intervention group (Screenshots of the app is provided in Additional file 1).
The training time for each person is counted through the app from the day the sessions start until the content of the next session is activated, which is 7 days later. A reminder message will be sent to each participant to announce each new session. The content of the sessions includes raising awareness and examining the impact of online gaming on people's physical and mental health. It also identifies and corrects misconceptions about online games. This training also includes learning self-control, coping responses, and how to overcome temptation.
The third part of the app includes questionnaires. Participants will answer questions at baseline, 1 month, and 3 months after the end of intervention.
The fourth part of the app aims to evaluate the effectiveness of the intervention in each session according to the weekly completion of the IGDS-SF9 incorporated into exercises and assignments. Participants can monitor their progress in the chart.
Behavior-change interventions are usually complex, complicating research efforts and implementation of practical applications. A method proposed by Michie et al. characterizes the reliability of interventions in terms of behavior change techniques (BCTs) . BCTs are defined as visible, reproducible, and integral components of an intervention designed to change behavior. BCTs can be used alone or in combination and in different forms. We will use BCTs in intervention group to increase effectivity. Several BCTs have been used for this intervention in the app during each session. These BCTs are summarized in the Table 2.
The use of TTM model constructs in the intervention content of the HAPPYTEEN app is as follows:
Stages 1 and 2: Precontemplation and contemplation
The app intervention has been personalized based on the adolescent’s place in stages of change. For example, for those adolescents who are identified in precontemplation (Stage 1) and contemplation (Stage 2) stages, the app will direct them to start the intervention from session 1. In the stages of precontemplation (Stage 1) and contemplation (Stage 2), the aim is to help adolescents gain a better understanding of the definition of IGD and statistics on studies of IGD in Iran and globally. The prevalence of IGD and the similarity of IGD with substance use disorders will be comprehensively described to the adolescents. Moreover, an explanation about the positive and negative consequences of online games on people’s health will be described.
Stage 3: Preparation
In the sixth week, a list of problems and difficult situations related to gaming will be provided for the adolescents to consider to prevent relapse. The adolescents will also write out different approaches for controlling online gaming and give a score of 1 to 10 out for all approaches presented. They will then choose an approach with respect to implementation and achievability.
Stage 4: Action
In the sixth week, adolescents will be encouraged to consider obstacles that may disrupt their plans. The adolescents will be asked to consider approaches to prevent them from being tempted to game online. Motivational videos will be provided to enhance adolescents’ confidence and motivation to perform better.
Factors that lead to maintaining good behavior and helping adolescents to achieve their goals are addressed at the beginning of the seventh session. Regarding the choice of a healthy lifestyle as a measure to prevent addiction to online games, it will be suggested that alternative behaviors could be tried such as having a sports program instead of going to gaming clubs, connecting with friends and attending parks and cinemas in groups, stress management training, and relaxation techniques.
Stage 5: Maintenance
In the seventh week, a worksheet of enjoyable activities will be provided to adolescents to prevent them from being tempted to play online games. Motivational videos will also be presented to enhance efforts to maintain their new behaviors. To enhance decisional balance, adolescents will receive information on the consequences of online games (both positive and negative consequences) including physical, mental, and emotional impacts in shorter and longer terms. In order to reduce stress and improve adolescents’ self-efficacy, training in relaxation techniques including body relaxation, breathing relaxation, yoga (as an audio file), mindfulness training, and mental imagery will also be used in the HAPPYTEEN app.
Active control condition
Eight educational sessions on “sleep hygiene” will be performed for the participants in the active control group. Sleep hygiene intervention was chosen for the control group based on a recent study on the targeted sample . This study found that insomnia is prevalent among Iranian adolescents, and it can affect adolescents’ quality of life. Moreover, it was found that IGD severity was significantly associated with insomnia. Therefore, this intervention will be conducted to help adolescents in the active control group increase their quality of life and mental health.
The sleep hygiene condition includes video, text, and audio files that explain each section. In each training session, the purpose of that session and the previous content will be reviewed, and by providing complete explanations, the following content will be taught: sleep hygiene, study of sleep habits, sleep management, identifying incorrect patterns of behavior related to sleep and the impacts of environment, time, physical activity, and nutrition on sleep. The details of the intervention for the active control group are shown in Table 3.
Sample size/power calculation
The sample size for the study was estimated based on a pilot study of 12 adolescents with IGD. The results of the pilot study showed that adolescents reported lower scores on the IGDS-SF9 at post-treatment follow-up (pre-treatment M = 34.59, SD = 16.91; post-treatment M = 25.37, SD = 17.94). However, 2 adolescents did not complete the post-treatment assessments. It is hypothesized that the app-based intervention will have at least a moderate mean effect size of 0.50 (Cohen’s d) compared to the control group. Using G*Power , sample size was calculated assuming a power of 90% for a two-group comparison analysis with three measures over time, the significance level of 0.05 and loss to follow-up of approximately 20%. The final sample size was set at 103 participants for each group.
Assessments and outcomes
Data collection methods
Data collection from demographic information to measuring the severities of Internet Gaming Disorder (IGDS-SF9), insomnia (ISI), and depression, anxiety and stress (DASS-21), determining the status of the adolescents in stage of change in the TTM model (SOCQ), the degree of self-efficacy and decision-making balance in adolescents will be done with an online questionnaire. At the beginning of the study, participants will be assured that their information is and will remain confidential within the research team. Figure 2 provides a SPIRIT depiction of the schedule of enrollment, interventions, and assessments.
Demographic questions will include information on participants’ age, gender, educational level, type of school, parents’ education, type of gaming device (console, personal computer, cellphone, etc.), and duration of using online games in weekdays and weekends. This information will be collected by an online questionnaire.
Primary outcome measure
The primary outcome measure will be the IGDS-SF9. The IGDS-SF9 assesses the severity of IGD in adolescents [6, 62]. These activities include any game, whether online or offline performed through consoles, computers, laptops, tablets, and smartphones. The IGDS-SF9 has 9 items correspond to the nine IGD criteria defined by the DSM-5. All items are rated a five-point Likert type scale from 1 (never) to 5 (always). The total score is obtained by summing all 9 responses and range from 9 to 45 with higher scores indicating a higher risk of developing IGD. The Persian version of the IGDS-SF9 was found to be highly valid and reliably among Iranian adolescents .
Secondary outcome measures
Secondary outcome measures will include the Depression, Anxiety and Stress Scale (DASS-21), Insomnia Severity Index (ISI), Stages of Change Questionnaire (SOCQ), Decisional Balance Scale, and Self-efficacy Scale.
The Depression, Anxiety, and Stress Scale (DASS-21)
To assess stress, anxiety, and depression in adolescents, the DASS-21 will be used . The DASS-21 is a 21-item self-reported measure that consists of 3 subscales of stress, anxiety, and depression. Each subscale includes 7 items, and each item is rated on a four-point Likert type scale from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). The subscale scores can be computed through the sum of the 7 responses and range from 0 to 21, with higher scores indicating greater anxiety, stress or depression. The validity and reliability of the Persian version of the DASS-21 was found to be acceptable in previous studies [64, 65].
Insomnia Severity Index (ISI)
The ISI is a brief self-reported instrument that measures adolescents' perceptions regarding the severity and the effects of insomnia. The ISI consists of seven items that are rated on a five-point Likert scale from 0 (no problem) to 4 (very severe problem). The total score is obtained by summing all 7 responses and the score ranges from 0 to 28 where 0–7 indicates absence of insomnia, 8–14 indicates subthreshold insomnia, 15–21 indicates moderate insomnia, and 22–28 indicates severe insomnia . The Persian version of the ISI was found to be valid and reliable among both clinical and general populations of Iranians .
Stage of Change Questionnaire (SOCQ)
Stage of change will be measured using a single item: “Did you play online games for 20 hours or more a week during last month?”. Reponses options are as follows: “A. Yes, and I do not plan to reduce my digital game use. B. Yes, and I plan to reduce my digital game use in the next 6 months. C. Yes, and I plan to reduce my digital game use in the next 30 days. D. Not this month, but I have played digital games for less than 20 hours a week in the past 6 months. E. No, and I have not played digital games more than 19 hours a week in the past 6 months. F. No, and I have NEVER played digital games more than 19 hours a week”.
Based on the answers given, the participants will be classified into one of the five stages of change described in the present study: A = (Precontemplation), B = (Contemplation), C = (Preparation), D = (Action), E = (Maintenance), and F = (A participant that is unlikely to be struggling from IGD) .
The Decision Balance Scale consists of 24 items that cover two subscales of the pros and cons of decreasing online gaming. The Decision Balance Scale has been developed based on previous studies of smoking and weight reduction . Adolescents will be asked to rate their agreements for their decision to play or to avoid playing digital games. An example of Pro questions includes “Digital games are a good way for me to relieve stress.” An example of Con questions includes, “I am unable to focus on activities that are not related to digital games.” All items are rated on a five-point Likert scale from 1 (not important) to 5 (very important). The total subscale is obtained by summing all 12 responses with a range from 12 to 60 .
The Self-efficacy Scale consists of 14 items that are rated on a five-point Likert scale from 1 = (strongly disagree) to 5 (strongly agree). This scale has two subscales assessing situational confidence and situational temptations. Each subscale has 7 items. The total subscale is obtained by summing all 7 responses and the total score ranges from 7 to 35 .
Assessment of adherence
Participants’ adherence to the interventions will be assessed by calculation the length of time using the app and number of log-ins using an online database. Moreover, the online database will be used to assess each participant’s viewing of each page and time spent on it.
All educational content were prepared under auditing of the first author (AHP) who is a professor in the field of health psychology and behavioral change. Also, all research processes including recruitment, intervention implementation, and assessments will be conducted under his supervision.
Ancillary and post-trial care
After completing the follow-up assessments, if any individuals have IGD, they will be referred to a psychologist to receive further necessary counseling and or treatment.
A blinded biostatistician will analyze the data using SPSS version 25 and MLwiN version 2.27 software. An intention-to-treat analysis will be used to compare the intervention effects between the active control and intervention groups. The Kolmogorov–Smirnov test will be used to assess normality distribution of study variables. Multilevel linear models (MLMs) using a random-effects model will be conducted to compare primary and secondary outcome measures across the two groups over baseline and at 1 month and 3 months after the intervention. Within-group intraclass correlations will be calculated, and a two-level mixed model will be used with adolescents nested in time.
The present study will aim to investigate the effectiveness of an educational intervention based on the TTM model and CBT in the treatment of IGD in adolescents. To the best of our knowledge, this study will be the first online intervention to aim to treat IGD and decrease the duration of online gaming and severity of IGD among adolescents. As described above, the present study uses a combination of TTM and CBT models for the intervention.
In general, cognitive, emotional, and physiological processes are involved in the development of addictions. These processes manifest in addictive behaviors. Therefore, theories and models of behavioral change have an important role in developing interventions [68, 69].
In the present study, in addition to using the CBT framework, the TTM is also used for developing the intervention. Using BCTs, the study attempts to increase motivation in adolescents in order to move from the early stages (i.e., precontemplation and contemplation) to later ones (e.g., maintenance). For people in the preparation and action stages, an attempt will be made to help them to the action and maintenance stages by using planning and increasing self-efficacy.
The app-based intervention was chosen for treating gaming disorder among adolescents for several reasons . Despite help-seeking for a behavioral addiction typically being advantageous for affected people to obtain external assistance to facilitate dealing with the behavioral addiction, treatment-seeking is relatively infrequent in gaming disorder compared to other behavioral addictions . Therefore, new modes of delivery (e.g., internet-based or app-based interventions) are warranted to increase dissemination. This approach is cost-effective and often easier to deliver compared to face-to-face delivery . There is a concern for internet-related behavioral addictions with respect to internet-based modes of intervention (i.e., internet- or app-based interventions) may lead to worsening of the disorder in individuals. However, past studies have shown that such interventions can also be useful for internet-related behavioral addictions (including gaming addiction [71, 72]). Considering how people with internet-use concerns function with respect to internet use in general is important, particularly given the many aspects of daily life that involve internet use . Due to the COVID-19 pandemic, many face-to-face interventions have shifted to online modes of delivery, given benefits that they offer.
The present study has some limitations. In the present study, only high-school students will be assessed. Therefore, the results of the study may not be generalized to younger age groups. Also, despite the use of a behavior-change model in the present study, the follow-up time (i.e., 3 months) to evaluate maintenance in students is relatively short. Nonetheless, the results of the present study should be useful to assess the efficacy of an online treatment for adolescents with IGD.
Current protocol version: 2.0 dated April 9, 2021. This trial has not yet started. It is anticipated to start 19 April 2021. Anticipated end date for recruitment: 31 May 2021.
The authors will disseminate the study results via publishing one or more articles in peer-reviewed journals in related fields.
Behavior change techniques
Cognitive behavioral therapy
Consolidated Standards of Reporting Trials
Depression Anxiety Stress Scales
Diagnostic and Statistical Manual of Mental Disorders, version 5
Internet gaming disorder
Internet Gaming Disorder Scale–Short-Form
Insomnia Severity Index
Randomized controlled trial
Stage of change
Fazeli S, Zeidi IM, Lin C-Y, Namdar P, Griffiths MD, Ahorsu DK, et al. Depression, anxiety, and stress mediate the associations between internet gaming disorder, insomnia, and quality of life during the COVID-19 outbreak. Addict Behav Rep. 2020;12:100307.
Essential facts about the computer and video game industry. Entertainment Software Association; 2015. Available from: http://www.theesa.com/wpcontent/ uploads/2014/10/ESA_EF_2014.pdf. Accessed 7 Jul 2021.
Facts about the computer and video game industry. Entertainment Software Association; 2019. Available from: https://www.theesa.com/wp-content/uploads/2019/05/2019-Essential-Facts-About-the-Computer-and-Video-Game-Industry.pdf. Accessed 10 Jul 2021.
APA. Diagnostic and statistical manual of mental disorders: DSM-5. Arlington: American Psychiatric Association; 2013.
King DL, Haagsma MC, Delfabbro PH, Gradisar M, Griffiths MD. Toward a consensus definition of pathological video-gaming: A systematic review of psychometric assessment tools. Clin Psychol Rev. 2013;33(3):331–42.
Pontes HM, Griffiths MD. Measuring DSM-5 Internet gaming disorder: development and validation of a short psychometric scale. Comput Hum Behav. 2015;45:137–43.
Kuss DJ, Griffiths MD. Internet gaming addiction: a systematic review of empirical research. Int J Mental Health Addict. 2012;10(2):278–96.
Young KS, De Abreu CN. Internet addiction: a handbook and guide to evaluation and treatment. Canad: John Wiley & Sons Inc; 2010.
Rooij AV, Schoenmakers T, Eijnden RV, Mheen DV. Compulsive internet use: the role of online gaming and other internet applications. J Adolesc Health. 2010;47(1):51–7.
Kemp S. Digital 2020. Iran: DataReportal: Kepios; 2020. Available from: https://datareportal.com/reports/digital-2020-iran. Accessed 30 June 2021.
Zamani E, Kheradmand A, Cheshmi M, Abedi A, Hedayati N. Comparing the social skills of students addicted to computer games with normal students. Addict Health. 2010;2(3-4):59.
Mohammadi M, RezaeiDehaghani A, Mehrabi T, RezaeiDehaghani A. Association between playing computer games and mental and social health among male adolescents in Iran in 2014. Iran J Nurs Midwifery Res. 2016;21(2):153.
Navabakhsh M, F H, V ZP. Effects of Internet and mobile phones changed the identity of the youth of 15-20 years. J Sociol. 2010;1(1):145–70.
Mentzoni RA, Brunborg GS, Molde H, Myrseth H, Skouverøe KJM, Hetland J, et al. Problematic video game use: estimated prevalence and associations with mental and physical health. Cyberpsychol Behav Soc Netw. 2011;14(10):591–6.
Wong ILK, Lam MPS. Gaming behavior and addiction among Hong Kong adolescents. Asian J Gambl Issues Public Health. 2016;6(1):1–16.
Müller KW, Janikian M, Dreier M, Wölfling K, Beutel M, Tzavara C, et al. Regular gaming behavior and internet gaming disorder in European adolescents: results from a cross-national representative survey of prevalence, predictors, and psychopathological correlates. Eur Child Adolesc Psychiatr. 2015;24(5):565–74.
Griffiths MD, Hunt N. Dependence on computer games by adolescents. Psychol Rep. 1998;82(2):475–80.
Comert I, Ogel K. Prevalence factors for Internet and computer addiction in [sic] Istanbul sample. Turk Klin J Forensic Med Forensic Sci. 2009;6:9–16.
Van Rooij AJ, Schoenmakers TM, Vermulst AA, Van Den Eijnden RJ, Van De Mheen D. Online video game addiction: identification of addicted adolescent gamers. Addiction. 2011;106(1):205–12.
Desai RA, Krishnan-Sarin S, Cavallo D, Potenza MN. Video-gaming among high school students: health correlates, gender differences, and problematic gaming. Pediatrics. 2010;126(6):e1414–e24.
Amiri H, Afsharinia K, editors. The role of performing computer games and children’s behavioral disorders. Regional Conference on Child and Adolescent Psychological Issue; 2011.
Anderson CA, Shibuya A, Ihori N, Swing EL, Bushman BJ, Sakamoto A, et al. Violent video game effects on aggression, empathy, and prosocial behavior in eastern and western countries: a meta-analytic review. Psychol Bull. 2010;136(2):151.
Young KS. When gaming becomes an obsession: help for parents and their children to treat online gaming addiction. Retrieved October. 2010;17:2013.
Robinson M, Aventin Á, Hanratty J, Ruane-McAteer E, Tomlinson M, Clarke M, et al. Nothing so practical as theory: a rapid review of the use of behaviour change theory in family planning interventions involving men and boys. Reprod Health. 2021;18(1):1–26.
Sleet DA, Gielen AC. Behavioral interventions for injury and violence prevention. In: Handbook of injury and violence prevention. USA: Springer; 2008. p. 397–410.
Tseng H-M, Liao S-F, Wen Y-P, Chuang Y-J. Stages of change concept of the transtheoretical model for healthy eating links health literacy and diabetes knowledge to glycemic control in people with type 2 diabetes. Prim Care Diabetes. 2017;11(1):29–36.
Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38–48.
Lee Y-M, Park H-S. A study on the stage of change in exercise behaviors, exercise self efficacy, depression and stress in middle aged women. Korean J Women Health Nurs. 2004;10(3):218–25.
Oh HS, Kim YR. Changing mechanisms corresponding to the changing stages of smoking cessation. J Nurses Acad Soc. 1996;26(4):820–32.
Griffiths MD, Meredith A. Videogame addiction and its treatment. J Contemp Psychother. 2009;39(4):247–53.
Carlbring P, Degerman N, Jonsson J, Andersson G. Internet-based treatment of pathological gambling with a three-year follow-up. Cogn Behav Ther. 2012;41(4):321–34.
Du Y-s, Jiang W, Vance A. Longer term effect of randomized, controlled group cognitive behavioural therapy for Internet addiction in adolescent students in Shanghai. Aust N Z J Psychiatry. 2010;44(2):129–34.
Dimiceli L. Depression, methamphetamine use, and alcohol consumption among Thai youth in Chiang Mai province: the exploration of perceptions of depression and associations between substance use and depressive symptoms. Dissertation for the degree of Doctor of Public Health, USA: Johns Hopkins University; 2013.
Ladouceur R, Boisvert J-M, Dumont J. Cognitive-behavioral treatment for adolescent pathological gamblers. Behav Modif. 1994;18(2):230–42.
King DL, Delfabbro PH, Griffiths MD, Gradisar M. Assessing clinical trials of Internet addiction treatment: a systematic review and CONSORT evaluation. Clin Psychol Rev. 2011;31(7):1110–6.
Liu C, Liao M, Smith DC. An empirical review of internet addiction outcome studies in China. Res Soc Work Pract. 2012;22(3):282–92.
Walker MB. Irrational thinking among slot machine players. J Gambl Stud. 1992;8(3):245–61.
King DL, Delfabbro PH, Griffiths MD. Cognitive behavioral therapy for problematic video game players: conceptual considerations and practice issues. J CyberTher Rehabil (JCR). 2010;3(3)
Young KS. Cognitive behavior therapy with Internet addicts: treatment outcomes and implications. Cyberpsychol Behav. 2007;10(5):671–9.
Fairburn CG, Patel V. The impact of digital technology on psychological treatments and their dissemination. Behav Res Ther. 2017;88:19–25.
Andersson G. The internet and CBT: a clinical guide. USA: CRC Press; 2014.
Blom K, Jernelöv S, Kraepelien M, Bergdahl MO, Jungmarker K, Ankartjärn L, et al. Internet treatment addressing either insomnia or depression, for patients with both diagnoses: a randomized trial. Sleep. 2015;38(2):267–77.
Alimoradi Z, Lin C-Y, Broström A, Bülow PH, Bajalan Z, Griffiths MD, et al. Internet addiction and sleep problems: a systematic review and meta-analysis. Sleep Med Rev. 2019;47:51–61.
Alimoradi Z, Lin C-Y, Imani V, Griffiths MD, Pakpour AH. Social media addiction and sexual dysfunction among Iranian women: the mediating role of intimacy and social support. J Behav Addict. 2019;8(2):318–25.
Babson KA, Ramo DE, Baldini L, Vandrey R, Bonn-Miller MO. Mobile app-delivered cognitive behavioral therapy for insomnia: feasibility and initial efficacy among veterans with cannabis use disorders. JMIR Res Protocols. 2015;4(3):e87.
Lancee J, van den Bout J, van Straten A, Spoormaker VI. Internet-delivered or mailed self-help treatment for insomnia? A randomized waiting-list controlled trial. Behav Res Ther. 2012;50(1):22–9.
Ritterband LM, Bailey ET, Thorndike FP, Lord HR, Farrell-Carnahan L, Baum LD. Initial evaluation of an Internet intervention to improve the sleep of cancer survivors with insomnia. Psycho-Oncology. 2012;21(7):695–705.
Kiropoulos LA, Klein B, Austin DW, Gilson K, Pier C, Mitchell J, et al. Is internet-based CBT for panic disorder and agoraphobia as effective as face-to-face CBT? J Anxiety Disord. 2008;22(8):1273–84.
Andrews G, Davies M, Titov N. Effectiveness randomized controlled trial of face to face versus Internet cognitive behaviour therapy for social phobia. Aust N Z J Psychiatry. 2011;45(4):337–40.
Axelsson E, Andersson E, Ljótsson B, Björkander D, Hedman-Lagerlöf M, Hedman-Lagerlöf E. Effect of internet vs face-to-face cognitive behavior therapy for health anxiety: a randomized noninferiority clinical trial. JAMA Psychiatry. 2020;77(9):915–24.
Vallejo MA, Ortega J, Rivera J, Comeche MI, Vallejo-Slocker L. Internet versus face-to-face group cognitive-behavioral therapy for fibromyalgia: a randomized control trial. J Psychiatr Res. 2015;68:106–13.
Andersson G, Rozental A, Rück C, Carlbring P. Guided Internet-delivered CBT: can it really be as good as seeing a therapist? Behav Ther. 2015;
Ben-Zeev D, Davis KE, Kaiser S, Krzsos I, Drake RE. Mobile technologies among people with serious mental illness: opportunities for future services. Adm Policy Mental Health Mental Health Serv Res. 2013;40(4):340–3.
Ruggiero AR, Peach HD, Gaultney JF. Association of sleep attitudes with sleep hygiene, duration, and quality: a survey exploration of the moderating effect of age, gender, race, and perceived socioeconomic status. Health Psychol Behav Med. 2019;7(1):19–44.
Qin L, Cheng L, Hu M, Liu Q, Tong J, Hao W, et al. Clarification of the cut-off score for nine-item Internet Gaming Disorder Scale–Short Form (IGDS9-SF) in a Chinese context. Front Psychiatry. 2020;11:470.
Glanz K, Rimer BK, Viswanath K. Health behavior and health education: theory, research, and practice. USA: Wiley; 2008.
Laforge RG, Greene GW, Prochaska JO. Psychosocial factors influencing low fruit and vegetable consumption. J Behav Med. 1994;17(4):361–74.
Faust KA. Applying the transtheoretical model to problematic digital game use. A dissertation for the degree of Ph.D in Psychology. USA: University of Rhode Island; 2017.
McMullin RE. The new handbook of cognitive therapy techniques: WW Norton & Company; 2000.
Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81–95.
Faul F, Erdfelder E, Lang A-G, Buchner A. G* Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–91.
Wu T-Y, Lin C-Y, Årestedt K, Griffiths MD, Broström A, Pakpour AH. Psychometric validation of the Persian nine-item Internet Gaming Disorder Scale–Short Form: does gender and hours spent online gaming affect the interpretations of item descriptions? J Behav Addict. 2017;6(2):256–63.
Crawford JR, Henry JD. The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample. Br J Clin Psychol. 2003;42(2):111–31.
Asghari A, Saed F, Dibajnia P. Psychometric properties of the Depression Anxiety Stress Scales-21 (DASS-21) in a non-clinical Iranian sample. Int J Psychol. 2008;2(2):82–102.
Lin C-Y, Broström A, Nilsen P, Griffiths MD, Pakpour AH. Psychometric validation of the Persian Bergen Social Media Addiction Scale using classic test theory and Rasch models. J Behav Addict. 2017;6(4):620–9.
Morin CM, Bastien C, Guay B, Radouco-Thomas M, Leblanc J, Vallières A. Randomized clinical trial of supervised tapering and cognitive behavior therapy to facilitate benzodiazepine discontinuation in older adults with chronic insomnia. Am J Psychiatry. 2004;161(2):332–42.
Yazdi Z, Sadeghniiat-Haghighi K, Zohal MA, Elmizadeh K. Validity and reliability of the Iranian version of the insomnia severity index. Malays J Med Sci MJMS. 2012;19(4):31.
Michie S, Carey RN, Johnston M, Rothman AJ, De Bruin M, Kelly MP, et al. From theory-inspired to theory-based interventions: a protocol for developing and testing a methodology for linking behaviour change techniques to theoretical mechanisms of action. Ann Behav Med. 2018;52(6):501–12.
Webb TL, Sniehotta FF, Michie S. Using theories of behaviour change to inform interventions for addictive behaviours. Addiction. 2010;105(11):1879–92.
Thege BK, Woodin EM, Hodgins DC, Williams RJ. Natural course of behavioral addictions: a 5-year longitudinal study. BMC Psychiatry. 2015;15(1):1–14.
Hing N, Russell AMT, Gainsbury SM, Blaszczynski A. Characteristics and help-seeking behaviors of Internet gamblers based on most problematic mode of gambling. J Med Internet Res. 2015;17(1):e3781.
Park JJ, Booth N, Bagot KL, Rodda SN. A brief internet-delivered intervention for the reduction of gaming-related harm: a feasibility study. Comput Hum Behav Rep. 2020;2:100027.
The Qazvin University of Medical Sciences (no. IR.QUMS.REC.1399.103) funds the clinical trial. The study proposal was reviewed by the Qazvin University of Medical Sciences research department prior to the starting the study. The Qazvin University of Medical Sciences is not implicated in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Ethics approval and consent to participate
The Ethics Committee of Qazvin University of Medical Sciences (no.IR.QUMS.REC.1399.103) and the Organization for Education in Qazvin has approved the current version of the protocol. Informed consent will be obtained from all study participants and their parents or their legal representatives.
Consent for publication
The authors declare that they have no conflict of interest with respect to the content of this manuscript. The authors alone are responsible for the content and writing of the manuscript. Dr. Potenza has the following disclosures. He has consulted for and advised Opiant Pharmaceuticals, Idorsia Pharmaceuticals, AXA, Game Day Data, and the Addiction Policy Forum; has been involved in a patent application with Yale University and Novartis; has received research support from the Mohegan Sun Casino, the Connecticut Council on Problem Gambling, and the National Center for Responsible Gaming; has participated in surveys, mailings, or telephone consultations related to drug addiction, impulse control disorders, or other health topics; and has consulted for law offices and gambling entities on issues related to impulse control or addictive disorders.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Pakpour, A.H., Fazeli, S., Zeidi, I.M. et al. Effectiveness of a mobile app-based educational intervention to treat internet gaming disorder among Iranian adolescents: study protocol for a randomized controlled trial. Trials 23, 229 (2022). https://doi.org/10.1186/s13063-022-06131-0
- Internet gaming disorder
- Transtheoretical model
- Randomized controlled trial
- Cognitive-behavioral therapy
- Addictive behaviors
- Video games
- Internet addiction disorder