Technology-Based interventions to address internet addictive behaviors: systematic review

Technology-Based interventions to address internet addictive behaviors: systematic review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Technology-Based interventions to address internet addictive behaviors: systematic review Verónica Fernanda Peñafiel Mora, María Fernanda Granda Juca, Luis Otto Parra Gonzalez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5737257/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective Internet addictive behaviors result from abnormal internet use, including neglecting responsibilities and experiencing anxiety when offline. This systematic review analyzes technology-based interventions addressing these behaviors, focusing on effectiveness and user interaction. Methods A literature search was conducted across three digital libraries and two high-impact journals, focusing on peer-reviewed articles published in Q1 or Q2 journals between January 2022 and June 2024. Studies evaluating digital addiction interventions and user interaction were included, while reviews, editorials, gray literature, and studies without clear intervention descriptions were excluded. The review covered randomized controlled trials, comparative studies, wearables, and mobile health apps. Five research questions were addressed using 17 evaluation criteria. Data extraction answered the sub-questions. The review followed Barbara Kitchenham's guidelines, applying a rigorous selection and quality assessment process. Primary inclusion was verified using the Kappa coefficient for inter-rater agreement, and article quality was evaluated with established criteria. The content adhered to PRISMA guidelines. In total, 11 articles were included. Findings: The review found variability in intervention effectiveness, with personalized, real-time feedback interventions having the greatest impact on reducing screen time and addiction symptoms. Less effective interventions lacked personalization. Conclusions The study highlighted the most commonly used technology-based interventions and their effectiveness in reducing symptoms and screen time, as well as improving user satisfaction and treatment adherence. Research gaps were identified, including the need for data on quality characteristics and software requirements for personalizing interventions using new technology. Internet Addictive Behaviors Technology-Based Interventions Intervention Outcomes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Although connectivity has become essential for performing basic activities (1,2). Growing concern about excessive internet use and its potential behavioral consequences has emerged (3–6). This concern highlights the necessity of achieving a balance between connectivity and well-being through the development of user-centered digital strategies that effectively fulfill this goal(7). Internet-Related Disorders (IRD), also referred to as Internet Addiction (IA) or Internet Addictive Behaviors, are characterized by excessive and dysfunctional internet use, with symptoms such as intense craving, loss of control, tolerance, and withdrawal symptoms. (8,9).These disorders are similar to traditional addictions due to the lack of control and persistent negative effects. (8,10). The World Health Organization (WHO) has acknowledged these conditions with criteria Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and includes Internet Gaming Disorder and online gambling in the International Classification of Diseases 11th Revision (ICD-11) (11–14). Individuals affected by these disorders often show dependence on specific internet applications, such as video games, gambling, social networks, or online pornography, among others. The complexity of the condition, including alterations in brain structure and function, neurotransmitter systems, and genetic factors, highlights the need for a multidisciplinary approach (15,16). IA is on the rise. In 2020, it affected 5.8% of teenagers, while 2.8 % of young adults experienced an internet-related disorder (Geisel et al., 2021). During the pandemic, the overall prevalence was 36.7%, with severe addiction reaching 2.8% of individuals (17). In India, IA prevalence reached 53.6% in 2023 and increased to 64.1% in 2024 among university students (18). Traditional interventions like psychological and pharmacological approaches to address addictive behaviors have been studied for a long time and are recognized for their effectiveness (19–23). However technology-based interventions offer potential benefits, including accessibility, perceived anonymity, convenience, the continuous maturity and popularization of new technologies, and low costs (16,24). These interventions use technology to design, develop, and deliver health promotion content and strategies aimed at inducing or improving physical or psychological outcomes, as well as interactive and self-guided interventions on digital devices (16,25). While several systematic reviews examine the current state of issues related to internet addiction, including: terminology, assessment instruments, prevalence, predictive factors, the effectiveness of non-technological treatment interventions and the effects of excessive use of technology on mental well-being with a focus on reduce screen time (13,15,26,27). No updates have been published since the inclusion of related disorders in the ICD-11 by the WHO. To address this gap, a systematic review analysis was conducted on articles retrieved from digital libraries and specialized journals published from 2022 onward. The aim was to identify technology-based interventions targeting internet addictive behaviors, with a particular focus on their effectiveness, quality attributes, and user experience. Health-supporting technologies include telemedicine, wearable devices, AI-based diagnostics, and mobile health apps (mHealth) (28) . To ensure their effectiveness, user engagement is crucial and is achieved through various strategies. (29–32). Despite this, sustaining user engagement throughout the intervention is challenging (33). Digital assessment tools and digital interventions exemplify the role of digital medicine in addiction treatment (16,33–36). However, they require particular attention to users’ needs and preferences and ensuring they are adaptable and supportive of diverse levels of technology proficiency. (28,37). On other hand user experience is crucial in designing and implementing technology-based interventions, as it can influence adherence and effectiveness through personalization and adaptability It also improves adoption and continuous use, and allows for evaluating usability, acceptability, and satisfaction (38–42) Finally, validating technological interventions and report on the effectiveness in specific contexts through experiments or surveys is necessary to ensure their effectiveness and impact, refine the intervention, optimize overall engagement, and understand the complex relationship between engagement and clinical outcomes (35,43,44). Research Method This systematic review was conducted following the procedures for performing systematic reviews ( 45 , 46 ) and was customized to outline a search protocol consisting of three key phases: planning, execution, and results. Furthermore, the content of the report was developed in accordance with the recommendations of the PRISMA statement report ( 47 ). Planning The Review This systematic review was planned in six steps: i) establishment of the research question and sub-questions, ii) definition of search strategies, iii) selection of primary studies, iv) quality assessment, v) definition of data extraction, and vi) choice of synthesis methods. The research question formulated for this study was crafted in response to the gap identified in previous studies and the diversity of existing concepts and modalities: What have been the results of different technology-based interventions in addressing internet addictive behaviors regarding their effectiveness, quality attributes, and user experience? To answer this question, it was essential to establish the following sub-questions RQ1: What are the characteristics of research on technology-based interventions that address internet addictive behaviors? RQ2: What technology-based interventions exist to address internet addictive behaviors? RQ3: How effective have technology-based interventions addressed internet addictive behaviors? RQ4: What quality attributes are reported implicitly or explicitly about technology-based interventions? RQ5: What is the user experience reported about technology-based interventions ? Identification Of Data Sources And Search Strategy The research search was conducted in three digital libraries: Springer Link, Scopus, and Dimensions, due to focus on technology, engineering, and applied sciences, aligning with the study's objectives on technological interventions for cyberaddiction. While PubMed and PsycINFO are relevant in clinical fields, their scope does not reflect the technological focus of this work. Additionally, in two high-impact journals (Q1 according to SJR): the first was the Journal of Medical Internet Research, from Canada, belonging to the subject area of medicine and the category of Health Informatics, with an H-Index of 197, and the second was Computers in Human Behavior, a UK journal in the subject of Computer Science and the category of Human-Computer Interaction, with an H-Index of 251 For this study, the research string has been composed of the following terms: ("technology-based intervention" OR “digital health intervention”) AND (“internet use disorders” OR “internet addiction” OR “smartphone addiction” OR “cyberaddiction” OR “online addiction”) AND (“effectiveness” OR “trial”). The search was conducted by applying the search string to each article's metadata (i.e., title, abstract, and keywords) for all the sources (the search string syntax was adapted for it to be applied in each digital library). User experience and software quality attributes were not included in the search string because they are characteristics of the technologies of the intervention media ( 48 , 49 ). These search terms were also considered in the other sources that were manually inspected to perform a consistent search. The search's starting point was January 2022, when the WHO included Internet Gaming Disorder in the ICD-11, until June 2024. In each database and journal, filters were applied to determine year ranges, original articles, research articles in English published in Q1 and Q2 journals, and open access.Only Q1 and Q2 journals were selected due to the scientific rigor necessary for digital interventions involving human subjects, as well as the obligation to report results transparently and reliably due to the high risk involved ( 50 ), which is crucial for ensuring the validity and applicability of the findings. The studies that meet the following criteria were included. Full-text, peer-reviewed original articles published in Q1 or Q2 journals in English-language, from January 2022 to June 2024, on subareas of computation and medicine Studies whose objectives (at least one) where to evaluate intervention or treatment on digital addiction (smartphone, video game, social media) Studies that specify the type of intervention and provide a clear operational definition describing its implementation and the criteria used to measure its effectiveness. Studies whose objectives (at least one) were to evaluate the user's interaction with the intervention medium. The studies that meet the following criteria were excluded. Reviews, editorials, theoretical articles, grey literature, dissertations, books, and conferences, clinical guidelines, tutorials for researchers and secondary studies. Duplicate reports of the same study on different sources. Studies in which digital intervention was not defined and detailed Selection Criteria For Primary Studies IInitially, a screening of the studies retrieved from the automated and manual search was conducted (n = 623), This process began by removing duplicate articles and those that did not meet the first inclusion criterion or met the first and second exclusion criteria (n = 248). A random sample equivalent to 10% (n = 38) of the final total (n = 375) was selected, following the approach of previous research ( 51 – 53 ). The three authors independently evaluated each study to determine its inclusion, considering the title, abstract, and keywords based on predefined inclusion and exclusion criteria, and agreements were analyzed using Cohen's Kappa coefficient with the interpretation suggested: < .2 poor agreement; .2 − .4 represents fair agreement; .41 − .60 moderate agreement; .61 − .80 substantial agreement and greater than 0,8 represents great agreement ( 54 , 55 ). Discrepancies were resolved through discussion after a thorough review of the entire document, and the agreement was established using the same coefficient. The agreement of criteria was evaluated using Cohen's Kappa coefficient. Initially, the overall agreement was K = 0,683, indicating moderate agreement ( 54 ). The pairwise agreements were as follows: Observer 1 (Ob1) and Observer 2 (Ob2) had K = .682 (SE = .171; 95% CI = .346–1.00); Ob1 and Observer 3 (Ob3) had K = .541 (SE = .177; 95% CI = .194 − .888) and Ob2 and Ob3 had K = 0,826 (SE = 0,118; 95% CI = .594–1.00) the results were discussed before starting the final review, which yielded a Cohen´s Kappa value of K = 0,847 indicating great agreement ( 54 ). The pairwise agreements were as follows: Ob1 and Ob2 and Ob1 and Ob3 had K = .771 (SE = .153; 95% CI = .470–1.00); Finally, Ob2 and Ob3 had K = 1.00. Quality Assessment Of The Primary Studies. In addition to the general inclusion/exclusion criteria, it is considered crucial to assess the "quality" of the primary studies. The following questions were considered to determine the inclusion of articles on a three-point scale: 0 = I do not agree, 0.5 = Partially agree and 1 = I Agree, If the total score is 4 or more, the article will be considered pertinent. Does the study identify technology-based interventions to address internet addictive behaviors? Does the study report the characteristics of the technology? Does the research report the study design? Does the study examine the effectiveness of technology-based interventions to address internet addictive behaviors? Does the study report on the integrative capacity of technology in treatment? Data extraction strategy The data extraction strategy was based on providing the set of possible answers for each research sub-question, using the theoretical information available to date and reported in the background of this systematic review. This strategy ensured consistency and comprehensiveness in the collection of relevant information. The same criteria were applied uniformly across all articles and search types, and seventeen key elements were considered. These classifications facilitated a systematic data analysis across different research sub-questions, enabling a comprehensive understanding of the elements considered. Regarding RQ1, the results were classified by topics that include the following extraction criteria (EC): (EC1) Research country, (EC2) Year of publication, (EC3) population, (EC4) intervention period, (EC5) type of validation and (EC6) type of experiment( 56 ). For RQ2, the classification included (EC7) technology( 28 ), (EC8) digital strategy( 31 ), (EC9) digital assessment( 16 ), (EC10) digital intervention( 16 ), and (EC11) personalization. RQ3 focused on: (EC12) Intervention type (treatment)( 57 , 58 ), (E13) symptom reduction( 11 ), (EC14) screen time reduction, and (EC15) intervention adherence( 59 ). RQ4 covered: (EC16) quality attributes of technology (ISO/IEC 25010:2023), and finally, RQ5 comprised (EC17) user experience( 40 ). Conducting the review A detailed search strategy was formulated to automatically identify studies from multiple sources, including Springer Link, Scopus, and Dimensions (Fig. 1). The search terms were selected and combined using Boolean operators to maximize article retrieval. In addition, a manual search was conducted in two journals with a Q1 ranking. The first was the Journal of Medical Internet Research from Canada in the discipline of Medicine and the subarea of Health Informatics, with an H-index of 197, and Computers in Human Behavior from the United Kingdom in the area of Computer Science: Human-Computer Interaction, with an H-index of 251. In the end, 11 studies (S1) met the criteria and category extraction requirements. A standardized form was used for data extraction to capture key information from the included studies: keywords, study design, participant characteristics, interventions, and outcomes. The paper from which the information was extracted came from three databases and one specialized journal: 5 from Dimensions, 4 from Springerlink, 1 from Scopus, and 1 from the International Journal of Mental Health and Addiction. The types of addictive behaviors related to the Internet that were addressed included: Internet addiction or general Internet use problems (n = 4), gambling (n = 3), and social media (n = 3). Additionally, interventions related to gaming (n = 2) were found. The average number of citations for each article ranged from 1 to 15, as detailed in Fig. 2. From the articles included in the analysis, 61 keywords were reported, of which 49 (80.3%) corresponded to unique (distinct) terms. The most frequent keywords were terms related to addiction, including: gambling, disorder, smartphone, addiction, internet, use and usage. And other technology-related terms were also identified, primarily: internet, mobile-based interventions, ecological momentary intervention, gambling tools, smartphone tools, machine learning, and telemedicine. Finally, terms related to the intervention were identified, such as: feasibility, treatment, controlled, intervention, and reduction. One reviewer independently conducted the final quality assessment of the research using the PRISMA checklist (S2). The synthesis used both qualitative and quantitative methods. Topic synthesis was conducted for quantitative synthesis, and the qualitative method was performed through a narrative synthesis approach summarizing the findings across studies, emphasizing key themes and discrepancies, and graphical elements and tables were employed to clarify and deepen the research. Results RQ1 - Results: What are the characteristics of research on technology-based interventions that address internet addictive behaviors? Regarding EC1 and EC2, two studies were published in 2022, seven in 2023, and two published between January and July 2024. The research was conducted across four continents, primarily in Europe, with studies carried out in Germany (n = 4); one was multicentric and also conducted in Austria and Switzerland (n = 1). Additionally, a study was recorded with participants from Spain, Mexico, and Colombia (n = 1). Research was also identified in Australia (n = 1), Canada (n = 1), China (n = 1), Denmark (n = 1), Norway(n = 1), and the USA (n = 1). Initially, the need to characterize the addressed addictive behaviors was identified, distinguishing between general behaviors (internet addiction and problematic internet use) and specific ones (social media, gambling, and gaming). Regarding EC3, EC4, EC5, and EC6, one study was validated through a survey, while the remaining ten were validated through experiments. The studied population in seven research studies consisted of adults, in three cases, university students, and, in one case, individuals over 16 years old. The sample sizes used in the different interventions ranged from 1 to 23,234, and the intervention periods varied from a minimum of 21 days to a maximum of 12 months. The survey was conducted to validate the intervention aimed at measuring perception. Four controlled clinical trials, one case report, four uncontrolled clinical trials, and one multicentric study with two groups using a simple blind design were carried out. It was also identified that the uncontrolled trials had shorter intervention periods than the controlled trials. The experiments directed at gambling were uncontrolled, while those focused on social media were controlled. No pattern was found between the type of behavior, population, and intervention period. Details are shown in Table 1 . Table 1 Key Characteristics of Research on Internet Addictive Behaviors Study Internet Addictive Behaviors Type of validation (EC5) Type of experiment (EC6) Population (EC3) Sample Size Intervention period (EC4) 1.( 60 ) Gaming Survey Perception Adults 1989 12 months 2.( 61 ) Internet addiction Experiment Case report University 160 6 weeks 3. ( 62 ) Problematic internet use Randomized controlled trials University and postgraduate students 1 21 days 4. ( 63 ) Internet addiction Randomized controlled trials Adults 642 7 weeks 5.( 64 ) Social media Randomized controlled trials University 11 8 weeks 6. ( 65 ) Social media Randomized controlled trials Adults 180 14 days 7. ( 30 ) Internet addiction Uncontrolled clinical trials Adults 94 2–6 weeks 8.( 66 ) Gambling Uncontrolled clinical trials Adults 23234 34 days 9. ( 67 ) Gambling Uncontrolled pilot study Adults 51 10 weeks 10.( 68 ) Gambling Uncontrolled trials Adults 60 39 days 11.( 69 ) Internet and Video Game Addiction Multicentre, two-arm, single-blinded trial ≥ 16 years 24 2 weeks To address RQ2 and RQ3, an initial analysis of the most frequent categories for each EC was conducted. Subsequently, the studies were grouped according to the type of technology used (EC7) and intervention type (EC12). RQ2 - Results: What technology-based interventions exist to address internet addictive behaviors? It was identified that the most frequently used type of technology (EC7) was websites, classified as “other type of technology.” On the other hand, the most commonly employed digital strategies (EC8) were alarms and notifications, followed by other strategies such as cognitive-behavioral therapies (CBT). Regarding the digital assessments used (EC9), the majority (n = 7) were digital assessment tools, and the most common digital intervention (EC10) was the application of digital psychotherapeutic lessons (n = 6). New technologies were recorded, such as strategies to encourage users to reduce smartphone use, like disabling notifications and setting the screen to grayscale, which are detailed later. Additionally, it was observed that personalization (EC11) was complete in 5 studies, partial in 1, while 4 did not personalize their intervention and 1 did not report it (See Fig. 3). All studies utilizing mobile health app technology implemented a digital strategy involving self-control tools, along with digital assessment tools, featuring different types of interventions and personalization. The studies that employed websites found that personalization was generally absent, except in one case. The main interventions were digital psychotherapeutic lessons, utilizing cognitive-behavioral strategies (CBT), alarms, and notifications, among others. One study combined websites with phone calls through alarms and notifications, as well as using Ecological Momentary Assessment (EMA) as part of their digital assessment, and Ecological Momentary Intervention (EMI) as part of their digital intervention. Telemedicine, in particular, was partially personalized, and in a study where a device (webcam) was used, telemedicine was also the most utilized treatment. Additionally, Table 2 presents the relevant digital interventions identified, including strategies that push users to reduce usage autonomously, as well as designs to incorporate interruption tools for gambling sessions. Table 2 Technology-based approaches to address internet addictive behaviors in studies Study Technology (EC7) Digital strategy (EC8) Digital assessment (EC9) Digital intervention (EC10) Personalization (EC11) (Sun, 2023) Mobile health app (mHealth) Self-control tools Digital Assessment tools Digital psychotherapeutic lessons Not reported (Olson et al., 2022) Self-control tools, alarms and notifications Not reported Other new technologies include disabling non-essential notifications and altering display settings to grayscale Yes (Aboujaoude et al., 2022) Self control tools Digital Assessment tools Tools to improve sleep time and quality 35,2% Tools to reduce notifications 48,9% Tools to reduce screen time 45,9% Yes (Brailovskaia et al., 2023) Other: Website Alarms and notifications Digital Assessment tools Instruction via e mails No (Dunbar et al., 2024) Ecological Momentary Assessment Digital psychotherapeutic lessons No (Hopfgartner et al., 2023) Automated usage control/Automated screen time management Alarms and notifications Digital Phenotyping Digital intervention with other new technologies: responsible gambling tools - used to interrupt long online gambling sessions No (Stenbro et al., 2023) Other (CBT) Digital Assessment tools Digital psychotherapeutic lessons No (Bernstein et al., 2023) Digital Assessment tools Digital psychotherapeutic lessons Yes (Diaz et al., 2024) Other: Website and call support Alarms and notifications Ecological Momentary Assessment Ecological momentary intervention Yes (Bernstein et al., 0223) Telemedicine and Other - Web Other (CBT) Digital Assessment tools Digital psychotherapeutic lessons Partially (main sessions and elective sessions) (Dieris-Hirche., 2023) Wearables device: webcam-based Telemedicine treatment Digital Assessment tools Digital psychotherapeutic lessons Yes RQ3 - Results: How effective have technology-based interventions addressed internet addictive behaviors? It was identified that the most commonly used intervention type (EC12) was clinical therapy. Additionally, studies were found that combined clinical intervention with self-control tools, entertainment, and reinforcement treatments. The therapy was found to reduce all symptoms (EC13) related to internet addictive behaviors, as well as loss of control. Additional elements such as anxiety, loneliness, and overall well-being were addressed. Two studies, in particular, managed to reduce the loss of control. Refer to Fig. 4 for details All studies identified significant reductions in symptoms and evaluated them with various instruments, primarily the CIUS. The studies that showed the most substantial changes were those where therapy was applied as a clinical intervention (EC12). In terms of reducing screen time (EC14), decreases ranged from 57 minutes to 4.79 hours per day. Regarding intervention adherence (EC15), rates fluctuated between 14.2% and 100%. The interventions with the highest adherence were therapies, while self-control tools had adherence rates ranging from 14–68%. Details can be found in Table 3 . Table 3 Effectiveness of technology-based interventions for addressing internet addictive behaviors. Study Intervention type (EC12) Reduction Treatment adherence (EC15) Symptom (EC13) Result (EC13) Screen time (EC14) (Aboujaoude et al., 2022) Self-control Sleep time 33,1% say yes Say yes (19%) Tools to: -Improve sleep time and quality 68,9% -Reduce notifications 44,8% -Reduce screen time 14,2% (Bernstein et al., 2023) Therapy All Decrease by 6,94 (CIUS) Not reported 42% (Bernstein et al., 2023) Therapy All Decrease by 17 (CIUS) 3 hours 100% (Brailovskaia et al., 2023) Entertainment All SM (13,86 − 13,16) hours at week PA( 12,97 − 13,11) hours at week Combination − (13,24 − 11,41) hours at week SM (131,47–85,40) PA - (121,58–102,34) Combination − (121,19–72,69) 83% social media group 80% physical activity group 74,5% combination group (Diaz et al., 2024) Therapy Loss of control Decreased from pre-treatment (Mdn = 7) to post-module 3 (Mdn = 2) (0–16 score) Reduce to 197,71 min (DE = 136,62) 73.3% (Dieris-Hirche., 2023) Therapy All Decrease by 8,5 (CIUS) 14.7 (DE = 21.0) per week 68,50% (Dunbar et al., 2024) Reinforcement treatment and Self-control All Decrease by 8,33 (IAT) 2 hours 43,6%% (Hopfgartner et al., 2023) Clinical treatment - Longer mandatory play breaks Loss of control Gamblers who used the “logout” button on the mandatory play break pop-up had the longest Time to Next Stake (TTNS), while those who waited to resume gambling had the shortest TTNS. Players took longer voluntary breaks from gambling the longer the mandatory play break lasted. 60% − 66% (Olson et al., 2022) Entertainment and Self-control Withdrawal symptoms Decrease 5.49 (CIUS) Decrease 57 minutes (Intervention group) Nudge 1: 98% Nudge 2: 83% Nudge 3:79% Nudge 4: 58% Nudge 5: 94% Nudge 6: 90% Nudge 7:40% Nudge 8: 83% Nudge 9:38% Nudge 10:88% (Stenbro et al., 2023) Therapy All Decrease 4.06 (NODS) Decrease 4,79 hours 82,30% (Sun, 2023) Therapy Withdrawal symptoms Anxiety Loneliness and wellbeing Anxiety (reduction) loneliness (reduction) wellbeing (increase) Not reported 100% Note : SM - Social Media; PA - Physical Activity; Nuge 1: Disable non-essential notifications, Nudge 2, Keep your phone on silent, face down, out of sight, and out of reach when not in use throughout the day. Nudge 3: Disable Touch ID. Nudge 4: Keep your phone on silent and out of reach when going to bed. Nudge 5: Change the color warmth to filter out blue light. Nudge 6: Hide social media and email apps in a folder of the home screen or even delete them. Nudge 7:If you can do the task on a computer, try to keep it on the computer. Nudge 8: Let your family, friends, or colleagues know that you will be replying less often unless they call you directly. Nudge 9: Set your phone screen to greyscale (black and white). Nudge 10: Overall, use your phone as little as possible. RQ4 and RQ5 - Results: What quality attributes are reported implicitly or explicitly about technology-based interventions? and What is the user experience reported about technology-based interventions ? All studies reported characteristics or results regarding the quality attributes of technology or the user experience. It was observed that reports on the quality attributes of technology were not explicitly expressed in all studies; for this reason, the characteristics of each technology were classified. For example, about: “An OMPRIS software environment was implemented using a protected database in Germany where study data were documented, monitored, and stored”( 69 ) the attribute was identified as “security.” Additionally, attributes of compatibility, functional suitability, and flexibility were reported. Finally, user experience was reported in seven studies, mainly regarding perceptions of usefulness and satisfaction. Medium and high levels were reported in all user experience elements. Details can be found in Fig. 5. Discussion During the development of the systematic review, the need to extract additional information beyond the previously established protocol was identified to ensure that the research was rigorous and accurate. This information pertains to the type of behavioral disorder addressed, the number of participants, and the documentation of the instruments used for diagnoses. While internet addiction is not formally recognized in all its forms ( 70 ), early diagnosis and intervention likely play a crucial role in influencing the course of the disease ( 71 , 72 ).The severity and progression of these disorders can vary significantly, and preventive measures for internet-related disorders resemble those used in substance addiction. According to this systematic review including access restrictions and resource-oriented primary prevention strategies. This research agrees that internet-based interventions are the most commonly used ( 24 , 73 ). For instance, online interventions have been effectively applied to treat gambling disorder ( 74 ) Additionally, telemedicine and web-based interventions, which facilitate real-time therapies and treatments, have been explored in several studies ( 61 , 63 , 65 , 68 , 69 ). Based on the current state of knowledge, cognitive-behavioral therapy, and abstinence have shown effectiveness in treating Internet-related disorders ( 8 , 64 , 66 ). But their implementation often depends on the clinical acceptance of Internet addiction as a treatable condition. For other hand variations in intervention time were observed, this variation depends not only on the type of intervention, assessment, or strategy but also on the resources and availability of the users ( 75 – 77 ). Thus, alternatives such as mobile applications and electronic devices are promising and effective options for complementing intervention with personalization ( 63 , 69 ). The integration of mobile health (mHealth) devices into behavioral health research has transformed data collection and the evaluation of intervention strategies. Through ecological momentary assessment methods, researchers capture psychological, emotional, and environmental factors related to behavioral outcomes in near real-time( 75 ). A framework for intervention based on the public health model suggests that interventions should aim to reduce risk factors and increase protective factors in these areas. This involves establishing accessibility restrictions and regulations based on content risk, as well as developing evidence-based services according to each individual’s problem severity and type( 72 ). A noteworthy example of design intervention focused on accessibility restrictions is 'Project Dopamine,' This initiative proposes the integration of features such as grayscale filtering, blur effects, the removal of shorts, notification suppression, and interface simplification to reducing the addictive stimuli associated with the YouTube platform ( 78 ). Although the project has not been clinically tested and is not included in the analysis of the systematic review, its design offers a promising approach to addressing compulsive usage and promoting healthier digital consumption habits. These features could serve as suggestions for developing new software. Moreover, when addressing digital well-being, it is essential to evaluate users' interaction with technological elements not only from the perspective of functionality but also from the viewpoint of user satisfaction. This dimension is crucial in influencing positive treatment adherence. ( 79 – 81 ). Highlighting the importance of a user-centered design approach to ensure the long-term success of digital interventions. In this context, the ISO/IEC 25010:2023 product quality model (SQuaRE) provides a comprehensive set of criteria that includes nine key attributes: functional suitability, performance efficiency, compatibility, interaction capability, reliability, security, maintainability, flexibility, and safety ( https://www.iso.org/es/contents/data/standard/07/81/78176.html).Thes e attributes were considered in the research to assess the effectiveness and quality of the interventions Despite the growing interest in using digital technologies to address addictive behaviors, several areas remain underexplored. First, personalization through artificial intelligence and its impact on mobile application interventions, as most studies adopt generalized approaches. Second, the need to assess technology's role in medical or psychological monitoring, with continuous tracking providing objective data beyond subjective perceptions. Additionally, more research is needed on the evaluation of technical aspects and intervention tools, as well as user interaction and satisfaction. Finally, there is a lack of studies considering the cultural and social context of users in the design and implementation of digital interventions, based on quality and software requirements analysis. These areas present opportunities for future research that could contribute significantly to the development of effective digital strategies for combating addictive behaviors. Quality Assessment Validation of the review protocol The systematic review protoco (S3)l was verified and adjusted to align with all the elements recommended by the preferred reporting items for systematic review and meta-analysis protocols PRISMA-P checklist (S4) ( 82 ) Validation of data extraction criteria and classification. The validation of the data extraction and classification criteria in this systematic review was crucial for ensuring the quality and relevance of studies These criteria were derived from a thorough conceptual review of current research on technological well-being and the role of technology in addiction treatment. Specific criteria included the type of intervention, technology characteristics, study design, target population, intervention period, and measured outcomes. The extraction was conducted by the authors, who underwent an observer calibration process that was refined based on the initial results. The categories used were deemed sufficient to enable accurate classification. A limitation of the study is the diversity of objectives, measurement instruments, and intervention designs complicates the comparison of effectiveness between different studies, which hinders the ability to conduct meta-analyses with the information obtained during this period. However, this diversity allows for identifying the current state of research, which can enhance it by including the most relevant elements and those that have shown positive results so far. Conclusions And Future Work All the research sub-questions have been addressed to answer the general question. The results of various technology-based interventions have shown variability in effectiveness, user interaction, and approaches. In general, the most effective interventions have been those that integrate personalization strategies, real-time feedback, and clinical follow-up. These interventions have not only managed to reduce screen time and addiction symptoms but have also generated greater user satisfaction. highlighting the urgent need for the development of technology-based interventions aimed at reducing addictive behaviors Additionally, it was identified that treatment adherence could represent one of the most significant challenges. However, motivational strategies can be implemented, such as economic and non-economic incentives through motivational phrases. On the other hand, the least effective interventions were those that lacked interactive elements and did not personalize the process. Through this research, future work has the potential to address scientific gaps in two broad areas. The first area involves empirical and experimental studies aimed at generating frameworks, processes, and evaluations related to personalization using artificial intelligence, integrating technology with continuous monitoring, assessing the effectiveness of primary and personalized prevention intervention tools, and conducting contextualized research. The second area focuses on in-depth investigations of requirements and software quality assessment in compliance with the ISO/TS 82304-2:2021 quality standard ( https://www.iso.org/standard/78182.html ). This standard provides guidelines for evaluating the quality and reliability of health and wellness applications and should be considered in both research and practical applications. Barriers in the development of digital behavioral interventions frequently stem from inadequate interdisciplinary and multidisciplinary approach understanding, which hinders the effective conceptualization of interventions and leads to unrealistic expectations concerning costs and development processes, while also neglecting user needs. Consequently, integrating engineering and behavioral sciences is essential for overcoming these challenges( 76 ). Declarations Data Availability Statements The database (S1), supplementary materials (S2,S3 and S4), and the search compilation and process (S5) have been deposited under the title Technology-based Interventions to Address Internet Addictive Behaviors: Supplementary Material and are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.27738081 Author Contribution All authors contributed to the study conception and design. Conceptualization, investigation, formal analysis and first draft of the manuscript were performed by Verónica Fernanda Peñafiel Mora. Validation, writing – review & editing, supervision and Funding Acquisition by María Fernanda Granda Juca and Luis Otto Parra Gonzales.All authors commented on previous versions of the manuscript. And all authors read and approved the final manuscript Acknowledgement The University of Cuenca funded the review as part of the project "Assessment of the Impact of Cyber Addictions on the Academic Performance, Health, and Well-being of University Students in the City of Cuenca. Other Information The protocol for the systematic review was developed following the PRISMA P guidelines and can be found in S4. It was not registered on any platform. Amendments were made in the review of the protocol regarding the type of addiction, the sample size, and the explicit results of symptom reduction. The execution of the review has been funded by the University of Cuenca, as part of the research project 'Assessment of the Impact of Cyber Addictions on the Academic Performance, Health, and Well-being of University Students in the City of Cuenca.' 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Selected research (n=11)\u003c/p\u003e\n\u003cp\u003eS2 - File 2. PRISMA Checklist\u003c/p\u003e\n\u003cp\u003eS3 - File 3. Review protocol\u003c/p\u003e\n\u003cp\u003eS4 - File 4. Prisma P- Checklist\u003c/p\u003e\n\u003cp\u003eS5 - File 5. Search compilation\u003c/p\u003e","description":"","filename":"27738081.zip","url":"https://assets-eu.researchsquare.com/files/rs-5737257/v1/5e151c7edf26c274e5c45a35.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Technology-Based interventions to address internet addictive behaviors: systematic review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlthough connectivity has become essential for performing basic activities (1,2). Growing concern about excessive internet use and its potential behavioral consequences has emerged (3\u0026ndash;6). This concern highlights the necessity of achieving a balance between connectivity and well-being through the development of user-centered digital strategies that effectively fulfill this goal(7).\u003c/p\u003e\n\u003cp\u003eInternet-Related Disorders (IRD), also referred to as Internet Addiction (IA) or Internet Addictive Behaviors, are characterized by excessive and dysfunctional internet use, with symptoms such as intense craving, loss of control, tolerance, and withdrawal symptoms. (8,9).These disorders are similar to traditional addictions due to the lack of control and persistent negative effects. (8,10).\u003c/p\u003e\n\u003cp\u003eThe World Health Organization (WHO) has acknowledged these conditions with criteria Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition \u0026nbsp;(DSM-5) and includes Internet Gaming Disorder and online gambling in the International Classification of Diseases 11th Revision (ICD-11) (11\u0026ndash;14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividuals affected by these disorders often show dependence on specific internet applications, such as video games, gambling, social networks, or online pornography, among others. The complexity of the condition, including alterations in brain structure and function, neurotransmitter systems, and genetic factors, highlights the need for a multidisciplinary approach \u0026nbsp;(15,16).\u003c/p\u003e\n\u003cp\u003eIA is on the rise. In 2020, it affected 5.8% of teenagers, while 2.8 % of young adults experienced an internet-related disorder (Geisel et al., 2021). During the pandemic, the overall prevalence was 36.7%, with severe addiction reaching 2.8% of individuals (17). In India, IA prevalence reached 53.6% in 2023 and increased to 64.1% in 2024 among university students (18).\u003c/p\u003e\n\u003cp\u003eTraditional interventions like psychological and pharmacological approaches to address addictive behaviors have been studied for a long time and are recognized for their effectiveness (19\u0026ndash;23). However technology-based interventions offer potential benefits, including accessibility, perceived anonymity, convenience, the continuous maturity and popularization of new technologies, and low costs (16,24). These interventions use technology to design, develop, and deliver health promotion content and strategies aimed at inducing or improving physical or psychological outcomes, as well as interactive and self-guided interventions on digital devices (16,25).\u003c/p\u003e\n\u003cp\u003eWhile several systematic reviews examine the current state of issues related to internet addiction, including: terminology, assessment instruments, prevalence, predictive factors, the effectiveness of non-technological treatment interventions and the effects of excessive use of technology on mental well-being with a focus on reduce screen time (13,15,26,27). No updates have been published since the inclusion of related disorders in the ICD-11 by the WHO.\u003c/p\u003e\n\u003cp\u003eTo address this gap, a systematic review analysis was conducted on articles retrieved from digital libraries and specialized journals published from 2022 onward. The aim was to identify technology-based interventions targeting internet addictive behaviors, with a particular focus on their effectiveness, quality attributes, and user experience.\u003c/p\u003e\n\u003cp\u003eHealth-supporting technologies include telemedicine, wearable devices, AI-based diagnostics, and mobile health apps (mHealth) (28) . To ensure their effectiveness, user engagement is crucial and is achieved through various strategies. (29\u0026ndash;32). Despite this, sustaining user engagement throughout the intervention is challenging \u0026nbsp;(33).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDigital assessment tools and digital interventions exemplify the role of digital medicine in addiction treatment (16,33\u0026ndash;36). However, they require particular attention to users\u0026rsquo; needs and preferences and ensuring they are adaptable and supportive of diverse levels of technology proficiency. (28,37).\u003c/p\u003e\n\u003cp\u003eOn other hand user experience is crucial in designing and implementing technology-based interventions, as it can influence adherence and effectiveness through personalization and adaptability \u0026nbsp; It also improves adoption and continuous use, and allows for evaluating usability, acceptability, and satisfaction (38\u0026ndash;42)\u003c/p\u003e\n\u003cp\u003eFinally, validating technological interventions and report on the effectiveness in specific contexts \u0026nbsp;through experiments or surveys is necessary to ensure their effectiveness and impact, refine the intervention, optimize overall engagement, and understand the complex relationship between engagement and clinical outcomes (35,43,44).\u0026nbsp;\u003c/p\u003e"},{"header":"Research Method","content":"\u003cp\u003eThis systematic review was conducted following the procedures for performing systematic reviews (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) and was customized to outline a search protocol consisting of three key phases: planning, execution, and results. Furthermore, the content of the report was developed in accordance with the recommendations of the PRISMA statement report (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePlanning The Review\u003c/h3\u003e\n\u003cp\u003eThis systematic review was planned in six steps: i) establishment of the research question and sub-questions, ii) definition of search strategies, iii) selection of primary studies, iv) quality assessment, v) definition of data extraction, and vi) choice of synthesis methods.\u003c/p\u003e \u003cp\u003eThe research question formulated for this study was crafted in response to the gap identified in previous studies and the diversity of existing concepts and modalities: What have been the results of different technology-based interventions in addressing internet addictive behaviors regarding their effectiveness, quality attributes, and user experience? To answer this question, it was essential to establish the following sub-questions\u003c/p\u003e \u003cp\u003eRQ1: What are the characteristics of research on technology-based interventions that address internet addictive behaviors?\u003c/p\u003e \u003cp\u003eRQ2: What technology-based interventions exist to address internet addictive behaviors?\u003c/p\u003e \u003cp\u003eRQ3: How effective have technology-based interventions addressed internet addictive behaviors?\u003c/p\u003e \u003cp\u003eRQ4: What quality attributes are reported implicitly or explicitly about technology-based interventions?\u003c/p\u003e \u003cp\u003eRQ5: What is the user experience reported about technology-based interventions ?\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification Of Data Sources And Search Strategy\u003c/h2\u003e \u003cp\u003eThe research search was conducted in three digital libraries: Springer Link, Scopus, and Dimensions, due to focus on technology, engineering, and applied sciences, aligning with the study's objectives on technological interventions for cyberaddiction. While PubMed and PsycINFO are relevant in clinical fields, their scope does not reflect the technological focus of this work. Additionally, in two high-impact journals (Q1 according to SJR): the first was the Journal of Medical Internet Research, from Canada, belonging to the subject area of medicine and the category of Health Informatics, with an H-Index of 197, and the second was Computers in Human Behavior, a UK journal in the subject of Computer Science and the category of Human-Computer Interaction, with an H-Index of 251\u003c/p\u003e \u003cp\u003eFor this study, the research string has been composed of the following terms: (\"technology-based intervention\" OR \u0026ldquo;digital health intervention\u0026rdquo;) AND (\u0026ldquo;internet use disorders\u0026rdquo; OR \u0026ldquo;internet addiction\u0026rdquo; OR \u0026ldquo;smartphone addiction\u0026rdquo; OR \u0026ldquo;cyberaddiction\u0026rdquo; OR \u0026ldquo;online addiction\u0026rdquo;) AND (\u0026ldquo;effectiveness\u0026rdquo; OR \u0026ldquo;trial\u0026rdquo;). The search was conducted by applying the search string to each article's metadata (i.e., title, abstract, and keywords) for all the sources (the search string syntax was adapted for it to be applied in each digital library). User experience and software quality attributes were not included in the search string because they are characteristics of the technologies of the intervention media (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). These search terms were also considered in the other sources that were manually inspected to perform a consistent search. The search's starting point was January 2022, when the WHO included Internet Gaming Disorder in the ICD-11, until June 2024. In each database and journal, filters were applied to determine year ranges, original articles, research articles in English published in Q1 and Q2 journals, and open access.Only Q1 and Q2 journals were selected due to the scientific rigor necessary for digital interventions involving human subjects, as well as the obligation to report results transparently and reliably due to the high risk involved (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), which is crucial for ensuring the validity and applicability of the findings.\u003c/p\u003e \u003cp\u003eThe studies that meet the following criteria were \u003cb\u003eincluded.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFull-text, peer-reviewed original articles published in Q1 or Q2 journals in English-language, from January 2022 to June 2024, on subareas of computation and medicine\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudies whose objectives (at least one) where to evaluate intervention or treatment on digital addiction (smartphone, video game, social media)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudies that specify the type of intervention and provide a clear operational definition describing its implementation and the criteria used to measure its effectiveness.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStudies whose objectives (at least one) were to evaluate the user's interaction with the intervention medium.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe studies that meet the following criteria were \u003cb\u003eexcluded.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e Reviews, editorials, theoretical articles, grey literature, dissertations, books, and conferences, clinical guidelines, tutorials for researchers and secondary studies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDuplicate reports of the same study on different sources.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStudies in which digital intervention was not defined and detailed\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSelection Criteria For Primary Studies\u003c/h3\u003e\n\u003cp\u003eIInitially, a screening of the studies retrieved from the automated and manual search was conducted (n\u0026thinsp;=\u0026thinsp;623), This process began by removing duplicate articles and those that did not meet the first inclusion criterion or met the first and second exclusion criteria (n\u0026thinsp;=\u0026thinsp;248). A random sample equivalent to 10% (n\u0026thinsp;=\u0026thinsp;38) of the final total (n\u0026thinsp;=\u0026thinsp;375) was selected, following the approach of previous research (\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The three authors independently evaluated each study to determine its inclusion, considering the title, abstract, and keywords based on predefined inclusion and exclusion criteria, and agreements were analyzed using Cohen's Kappa coefficient with the interpretation suggested: \u0026lt; .2 poor agreement; .2 \u0026minus;\u0026thinsp;.4 represents fair agreement; .41 \u0026minus;\u0026thinsp;.60 moderate agreement; .61 \u0026minus;\u0026thinsp;.80 substantial agreement and greater than 0,8 represents great agreement (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Discrepancies were resolved through discussion after a thorough review of the entire document, and the agreement was established using the same coefficient.\u003c/p\u003e \u003cp\u003eThe agreement of criteria was evaluated using Cohen's Kappa coefficient. Initially, the overall agreement was K\u0026thinsp;=\u0026thinsp;0,683, indicating moderate agreement (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The pairwise agreements were as follows: Observer 1 (Ob1) and Observer 2 (Ob2) had K\u0026thinsp;=\u0026thinsp;.682 (SE\u0026thinsp;=\u0026thinsp;.171; 95% CI\u0026thinsp;=\u0026thinsp;.346\u0026ndash;1.00); Ob1 and Observer 3 (Ob3) had K\u0026thinsp;=\u0026thinsp;.541 (SE\u0026thinsp;=\u0026thinsp;.177; 95% CI\u0026thinsp;=\u0026thinsp;.194 \u0026minus;\u0026thinsp;.888) and Ob2 and Ob3 had K\u0026thinsp;=\u0026thinsp;0,826 (SE\u0026thinsp;=\u0026thinsp;0,118; 95% CI\u0026thinsp;=\u0026thinsp;.594\u0026ndash;1.00) the results were discussed before starting the final review, which yielded a Cohen\u0026acute;s Kappa value of K\u0026thinsp;=\u0026thinsp;0,847 indicating great agreement (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The pairwise agreements were as follows: Ob1 and Ob2 and Ob1 and Ob3 had K\u0026thinsp;=\u0026thinsp;.771 (SE\u0026thinsp;=\u0026thinsp;.153; 95% CI\u0026thinsp;=\u0026thinsp;.470\u0026ndash;1.00); Finally, Ob2 and Ob3 had K\u0026thinsp;=\u0026thinsp;1.00.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality Assessment Of The Primary Studies.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn addition to the general inclusion/exclusion criteria, it is considered crucial to assess the \"quality\" of the primary studies. The following questions were considered to determine the inclusion of articles on a three-point scale: 0\u0026thinsp;=\u0026thinsp;I do not agree, 0.5\u0026thinsp;=\u0026thinsp;Partially agree and 1\u0026thinsp;=\u0026thinsp;I Agree, If the total score is 4 or more, the article will be considered pertinent.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the study identify technology-based interventions to address internet addictive behaviors?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the study report the characteristics of the technology?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the research report the study design?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the study examine the effectiveness of technology-based interventions to address internet addictive behaviors?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the study report on the integrative capacity of technology in treatment?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eData extraction strategy\u003c/h3\u003e\n\u003cp\u003eThe data extraction strategy was based on providing the set of possible answers for each research sub-question, using the theoretical information available to date and reported in the background of this systematic review. This strategy ensured consistency and comprehensiveness in the collection of relevant information. The same criteria were applied uniformly across all articles and search types, and seventeen key elements were considered. These classifications facilitated a systematic data analysis across different research sub-questions, enabling a comprehensive understanding of the elements considered.\u003c/p\u003e \u003cp\u003eRegarding RQ1, the results were classified by topics that include the following extraction criteria (EC): (EC1) Research country, (EC2) Year of publication, (EC3) population, (EC4) intervention period, (EC5) type of validation and (EC6) type of experiment(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). For RQ2, the classification included (EC7) technology(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), (EC8) digital strategy(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), (EC9) digital assessment(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), (EC10) digital intervention(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and (EC11) personalization. RQ3 focused on: (EC12) Intervention type (treatment)(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), (E13) symptom reduction(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), (EC14) screen time reduction, and (EC15) intervention adherence(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). RQ4 covered: (EC16) quality attributes of technology (ISO/IEC 25010:2023), and finally, RQ5 comprised (EC17) user experience(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eConducting the review\u003c/h3\u003e\n\u003cp\u003eA detailed search strategy was formulated to automatically identify studies from multiple sources, including Springer Link, Scopus, and Dimensions (Fig.\u0026nbsp;1). The search terms were selected and combined using Boolean operators to maximize article retrieval. In addition, a manual search was conducted in two journals with a Q1 ranking. The first was the \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e from Canada in the discipline of Medicine and the subarea of Health Informatics, with an H-index of 197, and \u003cem\u003eComputers in Human Behavior\u003c/em\u003e from the United Kingdom in the area of Computer Science: Human-Computer Interaction, with an H-index of 251.\u003c/p\u003e\u003cp\u003eIn the end, 11 studies (S1) met the criteria and category extraction requirements. A standardized form was used for data extraction to capture key information from the included studies: keywords, study design, participant characteristics, interventions, and outcomes.\u003c/p\u003e \u003cp\u003eThe paper from which the information was extracted came from three databases and one specialized journal: 5 from Dimensions, 4 from Springerlink, 1 from Scopus, and 1 from the International Journal of Mental Health and Addiction. The types of addictive behaviors related to the Internet that were addressed included: Internet addiction or general Internet use problems (n\u0026thinsp;=\u0026thinsp;4), gambling (n\u0026thinsp;=\u0026thinsp;3), and social media (n\u0026thinsp;=\u0026thinsp;3). Additionally, interventions related to gaming (n\u0026thinsp;=\u0026thinsp;2) were found. The average number of citations for each article ranged from 1 to 15, as detailed in Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eFrom the articles included in the analysis, 61 keywords were reported, of which 49 (80.3%) corresponded to unique (distinct) terms. The most frequent keywords were terms related to addiction, including: gambling, disorder, smartphone, addiction, internet, use and usage. And other technology-related terms were also identified, primarily: internet, mobile-based interventions, ecological momentary intervention, gambling tools, smartphone tools, machine learning, and telemedicine. Finally, terms related to the intervention were identified, such as: feasibility, treatment, controlled, intervention, and reduction.\u003c/p\u003e \u003cp\u003eOne reviewer independently conducted the final quality assessment of the research using the PRISMA checklist (S2). The synthesis used both qualitative and quantitative methods. Topic synthesis was conducted for quantitative synthesis, and the qualitative method was performed through a narrative synthesis approach summarizing the findings across studies, emphasizing key themes and discrepancies, and graphical elements and tables were employed to clarify and deepen the research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eRQ1 - Results: What are the characteristics of research on technology-based interventions that address internet addictive behaviors?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRegarding EC1 and EC2, two studies were published in 2022, seven in 2023, and two published between January and July 2024. The research was conducted across four continents, primarily in Europe, with studies carried out in Germany (n\u0026thinsp;=\u0026thinsp;4); one was multicentric and also conducted in Austria and Switzerland (n\u0026thinsp;=\u0026thinsp;1). Additionally, a study was recorded with participants from Spain, Mexico, and Colombia (n\u0026thinsp;=\u0026thinsp;1). Research was also identified in Australia (n\u0026thinsp;=\u0026thinsp;1), Canada (n\u0026thinsp;=\u0026thinsp;1), China (n\u0026thinsp;=\u0026thinsp;1), Denmark (n\u0026thinsp;=\u0026thinsp;1), Norway(n\u0026thinsp;=\u0026thinsp;1), and the USA (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eInitially, the need to characterize the addressed addictive behaviors was identified, distinguishing between general behaviors (internet addiction and problematic internet use) and specific ones (social media, gambling, and gaming). Regarding EC3, EC4, EC5, and EC6, one study was validated through a survey, while the remaining ten were validated through experiments. The studied population in seven research studies consisted of adults, in three cases, university students, and, in one case, individuals over 16 years old. The sample sizes used in the different interventions ranged from 1 to 23,234, and the intervention periods varied from a minimum of 21 days to a maximum of 12 months. The survey was conducted to validate the intervention aimed at measuring perception. Four controlled clinical trials, one case report, four uncontrolled clinical trials, and one multicentric study with two groups using a simple blind design were carried out. It was also identified that the uncontrolled trials had shorter intervention periods than the controlled trials. The experiments directed at gambling were uncontrolled, while those focused on social media were controlled. No pattern was found between the type of behavior, population, and intervention period. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eKey Characteristics of Research on Internet Addictive Behaviors\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet Addictive Behaviors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType of validation (EC5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType of experiment (EC6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation (EC3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntervention period (EC4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGaming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 months\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProblematic internet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandomized controlled trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUniversity and postgraduate students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandomized controlled trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandomized controlled trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandomized controlled trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncontrolled clinical trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u0026ndash;6 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGambling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncontrolled clinical trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGambling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncontrolled pilot study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGambling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncontrolled trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet and Video Game Addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMulticentre, two-arm, single-blinded trial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo address RQ2 and RQ3, an initial analysis of the most frequent categories for each EC was conducted. Subsequently, the studies were grouped according to the type of technology used (EC7) and intervention type (EC12).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRQ2 - Results: What technology-based interventions exist to address internet addictive behaviors?\u003c/h2\u003e \u003cp\u003eIt was identified that the most frequently used type of technology (EC7) was websites, classified as \u0026ldquo;other type of technology.\u0026rdquo; On the other hand, the most commonly employed digital strategies (EC8) were alarms and notifications, followed by other strategies such as cognitive-behavioral therapies (CBT). Regarding the digital assessments used (EC9), the majority (n\u0026thinsp;=\u0026thinsp;7) were digital assessment tools, and the most common digital intervention (EC10) was the application of digital psychotherapeutic lessons (n\u0026thinsp;=\u0026thinsp;6). New technologies were recorded, such as strategies to encourage users to reduce smartphone use, like disabling notifications and setting the screen to grayscale, which are detailed later. Additionally, it was observed that personalization (EC11) was complete in 5 studies, partial in 1, while 4 did not personalize their intervention and 1 did not report it (See Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eAll studies utilizing mobile health app technology implemented a digital strategy involving self-control tools, along with digital assessment tools, featuring different types of interventions and personalization. The studies that employed websites found that personalization was generally absent, except in one case. The main interventions were digital psychotherapeutic lessons, utilizing cognitive-behavioral strategies (CBT), alarms, and notifications, among others. One study combined websites with phone calls through alarms and notifications, as well as using Ecological Momentary Assessment (EMA) as part of their digital assessment, and Ecological Momentary Intervention (EMI) as part of their digital intervention. Telemedicine, in particular, was partially personalized, and in a study where a device (webcam) was used, telemedicine was also the most utilized treatment. Additionally, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the relevant digital interventions identified, including strategies that push users to reduce usage autonomously, as well as designs to incorporate interruption tools for gambling sessions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTechnology-based approaches to address internet addictive behaviors in studies\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology (EC7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital strategy\u003c/p\u003e \u003cp\u003e(EC8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital assessment\u003c/p\u003e \u003cp\u003e(EC9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital intervention\u003c/p\u003e \u003cp\u003e(EC10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePersonalization (EC11)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Sun, 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMobile health app (mHealth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital psychotherapeutic lessons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Olson et al., 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-control tools, alarms and notifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther new technologies include disabling non-essential notifications and altering display settings to grayscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Aboujaoude et al., 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf control tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTools to improve sleep time and quality 35,2%\u003c/p\u003e \u003cp\u003eTools to reduce notifications 48,9%\u003c/p\u003e \u003cp\u003eTools to reduce screen time 45,9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Brailovskaia et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOther: Website\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlarms and notifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInstruction via e mails\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Dunbar et al., 2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEcological Momentary Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital psychotherapeutic lessons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Hopfgartner et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomated usage control/Automated screen time management Alarms and notifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Phenotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital intervention with other new technologies: responsible gambling tools - used to interrupt long online gambling sessions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Stenbro et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther (CBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital psychotherapeutic lessons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Bernstein et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital psychotherapeutic lessons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Diaz et al., 2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther: Website and call support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlarms and notifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEcological Momentary Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEcological momentary intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Bernstein et al., 0223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelemedicine and Other - Web\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther (CBT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital psychotherapeutic lessons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePartially (main sessions and elective sessions)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Dieris-Hirche., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWearables device: webcam-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTelemedicine treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Assessment tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital psychotherapeutic lessons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRQ3 - Results: How effective have technology-based interventions addressed internet addictive behaviors?\u003c/h2\u003e \u003cp\u003eIt was identified that the most commonly used intervention type (EC12) was clinical therapy. Additionally, studies were found that combined clinical intervention with self-control tools, entertainment, and reinforcement treatments. The therapy was found to reduce all symptoms (EC13) related to internet addictive behaviors, as well as loss of control. Additional elements such as anxiety, loneliness, and overall well-being were addressed. Two studies, in particular, managed to reduce the loss of control. Refer to Fig.\u0026nbsp;4 for details\u003c/p\u003e\u003cp\u003eAll studies identified significant reductions in symptoms and evaluated them with various instruments, primarily the CIUS. The studies that showed the most substantial changes were those where therapy was applied as a clinical intervention (EC12). In terms of reducing screen time (EC14), decreases ranged from 57 minutes to 4.79 hours per day. Regarding intervention adherence (EC15), rates fluctuated between 14.2% and 100%. The interventions with the highest adherence were therapies, while self-control tools had adherence rates ranging from 14\u0026ndash;68%. Details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEffectiveness of technology-based interventions for addressing internet addictive behaviors.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntervention type (EC12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eReduction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment adherence\u003c/p\u003e \u003cp\u003e(EC15)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptom (EC13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003cp\u003e(EC13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScreen time\u003c/p\u003e \u003cp\u003e(EC14)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Aboujaoude et al., 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSleep time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,1% say yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSay yes (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTools to:\u003c/p\u003e \u003cp\u003e-Improve sleep time and quality 68,9%\u003c/p\u003e \u003cp\u003e -Reduce notifications 44,8%\u003c/p\u003e \u003cp\u003e -Reduce screen time 14,2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Bernstein et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease by 6,94 (CIUS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Bernstein et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease by 17 (CIUS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Brailovskaia et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSM (13,86\u0026thinsp;\u0026minus;\u0026thinsp;13,16) hours at week\u003c/p\u003e \u003cp\u003e PA( 12,97\u0026thinsp;\u0026minus;\u0026thinsp;13,11) hours at week\u003c/p\u003e \u003cp\u003e Combination \u0026minus;\u0026thinsp;(13,24\u0026thinsp;\u0026minus;\u0026thinsp;11,41) hours at week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSM (131,47\u0026ndash;85,40)\u003c/p\u003e \u003cp\u003ePA - (121,58\u0026ndash;102,34) Combination \u0026minus;\u0026thinsp;(121,19\u0026ndash;72,69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83% social media group\u003c/p\u003e \u003cp\u003e 80% physical activity group\u003c/p\u003e \u003cp\u003e 74,5% combination group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Diaz et al., 2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoss of control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecreased from pre-treatment (Mdn\u0026thinsp;=\u0026thinsp;7) to post-module 3 (Mdn\u0026thinsp;=\u0026thinsp;2) (0\u0026ndash;16 score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduce to 197,71 min (DE\u0026thinsp;=\u0026thinsp;136,62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Dieris-Hirche., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease by 8,5 (CIUS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.7 (DE\u0026thinsp;=\u0026thinsp;21.0) per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68,50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Dunbar et al., 2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforcement treatment and Self-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease by 8,33 (IAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43,6%%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Hopfgartner et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical treatment - Longer mandatory play breaks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoss of control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGamblers who used the \u0026ldquo;logout\u0026rdquo; button on the mandatory play break pop-up had the longest Time to Next Stake (TTNS), while those who waited to resume gambling had the shortest TTNS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlayers took longer voluntary breaks from gambling the longer the mandatory play break lasted.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60% \u0026minus;\u0026thinsp;66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Olson et al., 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntertainment and Self-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithdrawal symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease 5.49 (CIUS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecrease 57 minutes (Intervention group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNudge 1: 98%\u003c/p\u003e \u003cp\u003e Nudge 2: 83%\u003c/p\u003e \u003cp\u003e Nudge 3:79%\u003c/p\u003e \u003cp\u003e Nudge 4: 58%\u003c/p\u003e \u003cp\u003e Nudge 5: 94%\u003c/p\u003e \u003cp\u003e Nudge 6: 90%\u003c/p\u003e \u003cp\u003e Nudge 7:40%\u003c/p\u003e \u003cp\u003eNudge 8: 83%\u003c/p\u003e \u003cp\u003e Nudge 9:38%\u003c/p\u003e \u003cp\u003e Nudge 10:88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Stenbro et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease 4.06 (NODS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecrease 4,79 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82,30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Sun, 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithdrawal symptoms Anxiety Loneliness and wellbeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety (reduction) loneliness (reduction) wellbeing (increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: SM - Social Media; PA - Physical Activity; Nuge 1: Disable non-essential notifications, Nudge 2, Keep your phone on silent, face down, out of sight, and out of reach when not in use throughout the day. Nudge 3: Disable Touch ID. Nudge 4: Keep your phone on silent and out of reach when going to bed. Nudge 5: Change the color warmth to filter out blue light. Nudge 6: Hide social media and email apps in a folder of the home screen or even delete them. Nudge 7:If you can do the task on a computer, try to keep it on the computer. Nudge 8: Let your family, friends, or colleagues know that you will be replying less often unless they call you directly. Nudge 9: Set your phone screen to greyscale (black and white). Nudge 10: Overall, use your phone as little as possible.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ4 and RQ5 - Results: What quality attributes are reported implicitly or explicitly about technology-based interventions? and What is the user experience reported about technology-based interventions ?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll studies reported characteristics or results regarding the quality attributes of technology or the user experience. It was observed that reports on the quality attributes of technology were not explicitly expressed in all studies; for this reason, the characteristics of each technology were classified. For example, about: \u0026ldquo;An OMPRIS software environment was implemented using a protected database in Germany where study data were documented, monitored, and stored\u0026rdquo;(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) the attribute was identified as \u0026ldquo;security.\u0026rdquo; Additionally, attributes of compatibility, functional suitability, and flexibility were reported. Finally, user experience was reported in seven studies, mainly regarding perceptions of usefulness and satisfaction. Medium and high levels were reported in all user experience elements. Details can be found in Fig.\u0026nbsp;5.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDuring the development of the systematic review, the need to extract additional information beyond the previously established protocol was identified to ensure that the research was rigorous and accurate. This information pertains to the type of behavioral disorder addressed, the number of participants, and the documentation of the instruments used for diagnoses.\u003c/p\u003e \u003cp\u003eWhile internet addiction is not formally recognized in all its forms (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), early diagnosis and intervention likely play a crucial role in influencing the course of the disease (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e).The severity and progression of these disorders can vary significantly, and preventive measures for internet-related disorders resemble those used in substance addiction. According to this systematic review including access restrictions and resource-oriented primary prevention strategies.\u003c/p\u003e \u003cp\u003eThis research agrees that internet-based interventions are the most commonly used (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). For instance, online interventions have been effectively applied to treat gambling disorder (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) Additionally, telemedicine and web-based interventions, which facilitate real-time therapies and treatments, have been explored in several studies (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the current state of knowledge, cognitive-behavioral therapy, and abstinence have shown effectiveness in treating Internet-related disorders (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). But their implementation often depends on the clinical acceptance of Internet addiction as a treatable condition. For other hand variations in intervention time were observed, this variation depends not only on the type of intervention, assessment, or strategy but also on the resources and availability of the users (\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, alternatives such as mobile applications and electronic devices are promising and effective options for complementing intervention with personalization (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). The integration of mobile health (mHealth) devices into behavioral health research has transformed data collection and the evaluation of intervention strategies. Through ecological momentary assessment methods, researchers capture psychological, emotional, and environmental factors related to behavioral outcomes in near real-time(\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA framework for intervention based on the public health model suggests that interventions should aim to reduce risk factors and increase protective factors in these areas. This involves establishing accessibility restrictions and regulations based on content risk, as well as developing evidence-based services according to each individual\u0026rsquo;s problem severity and type(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA noteworthy example of design intervention focused on accessibility restrictions is 'Project Dopamine,' This initiative proposes the integration of features such as grayscale filtering, blur effects, the removal of shorts, notification suppression, and interface simplification to reducing the addictive stimuli associated with the YouTube platform (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Although the project has not been clinically tested and is not included in the analysis of the systematic review, its design offers a promising approach to addressing compulsive usage and promoting healthier digital consumption habits. These features could serve as suggestions for developing new software.\u003c/p\u003e \u003cp\u003eMoreover, when addressing digital well-being, it is essential to evaluate users' interaction with technological elements not only from the perspective of functionality but also from the viewpoint of user satisfaction. This dimension is crucial in influencing positive treatment adherence. (\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Highlighting the importance of a user-centered design approach to ensure the long-term success of digital interventions. In this context, the ISO/IEC 25010:2023 product quality model (SQuaRE) provides a comprehensive set of criteria that includes nine key attributes: functional suitability, performance efficiency, compatibility, interaction capability, reliability, security, maintainability, flexibility, and safety (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iso.org/es/contents/data/standard/07/81/78176.html).Thes\u003c/span\u003e\u003cspan address=\"https://www.iso.org/es/contents/data/standard/07/81/78176.html).Thes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee attributes were considered in the research to assess the effectiveness and quality of the interventions\u003c/p\u003e \u003cp\u003eDespite the growing interest in using digital technologies to address addictive behaviors, several areas remain underexplored. First, personalization through artificial intelligence and its impact on mobile application interventions, as most studies adopt generalized approaches. Second, the need to assess technology's role in medical or psychological monitoring, with continuous tracking providing objective data beyond subjective perceptions.\u003c/p\u003e \u003cp\u003eAdditionally, more research is needed on the evaluation of technical aspects and intervention tools, as well as user interaction and satisfaction. Finally, there is a lack of studies considering the cultural and social context of users in the design and implementation of digital interventions, based on quality and software requirements analysis. These areas present opportunities for future research that could contribute significantly to the development of effective digital strategies for combating addictive behaviors.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuality Assessment\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eValidation of the review protocol\u003c/h2\u003e \u003cp\u003eThe systematic review protoco (S3)l was verified and adjusted to align with all the elements recommended by the preferred reporting items for systematic review and meta-analysis protocols PRISMA-P checklist (S4) (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation of data extraction criteria and classification.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe validation of the data extraction and classification criteria in this systematic review was crucial for ensuring the quality and relevance of studies These criteria were derived from a thorough conceptual review of current research on technological well-being and the role of technology in addiction treatment. Specific criteria included the type of intervention, technology characteristics, study design, target population, intervention period, and measured outcomes. The extraction was conducted by the authors, who underwent an observer calibration process that was refined based on the initial results. The categories used were deemed sufficient to enable accurate classification.\u003c/p\u003e \u003cp\u003eA limitation of the study is the diversity of objectives, measurement instruments, and intervention designs complicates the comparison of effectiveness between different studies, which hinders the ability to conduct meta-analyses with the information obtained during this period. However, this diversity allows for identifying the current state of research, which can enhance it by including the most relevant elements and those that have shown positive results so far.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Conclusions And Future Work","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003cp\u003eAll the research sub-questions have been addressed to answer the general question. The results of various technology-based interventions have shown variability in effectiveness, user interaction, and approaches. In general, the most effective interventions have been those that integrate personalization strategies, real-time feedback, and clinical follow-up. These interventions have not only managed to reduce screen time and addiction symptoms but have also generated greater user satisfaction. highlighting the urgent need for the development of technology-based interventions aimed at reducing addictive behaviors\u003c/p\u003e \u003cp\u003eAdditionally, it was identified that treatment adherence could represent one of the most significant challenges. However, motivational strategies can be implemented, such as economic and non-economic incentives through motivational phrases. On the other hand, the least effective interventions were those that lacked interactive elements and did not personalize the process.\u003c/p\u003e \u003cp\u003eThrough this research, future work has the potential to address scientific gaps in two broad areas. The first area involves empirical and experimental studies aimed at generating frameworks, processes, and evaluations related to personalization using artificial intelligence, integrating technology with continuous monitoring, assessing the effectiveness of primary and personalized prevention intervention tools, and conducting contextualized research. The second area focuses on in-depth investigations of requirements and software quality assessment in compliance with the ISO/TS 82304-2:2021 quality standard (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iso.org/standard/78182.html\u003c/span\u003e\u003cspan address=\"https://www.iso.org/standard/78182.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This standard provides guidelines for evaluating the quality and reliability of health and wellness applications and should be considered in both research and practical applications.\u003c/p\u003e \u003cp\u003eBarriers in the development of digital behavioral interventions frequently stem from inadequate interdisciplinary and multidisciplinary approach understanding, which hinders the effective conceptualization of interventions and leads to unrealistic expectations concerning costs and development processes, while also neglecting user needs. Consequently, integrating engineering and behavioral sciences is essential for overcoming these challenges(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003eData Availability Statements\u003c/h2\u003e\n\u003cp\u003eThe database (S1), supplementary materials (S2,S3 and S4), and the search compilation and process (S5) have been deposited under the title Technology-based Interventions to Address Internet Addictive Behaviors: Supplementary Material and are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.27738081\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization, investigation, formal analysis and first draft of the manuscript were performed by Ver\u0026oacute;nica Fernanda Pe\u0026ntilde;afiel Mora. Validation, writing \u0026ndash; review \u0026amp; editing, supervision and Funding Acquisition by Mar\u0026iacute;a Fernanda Granda Juca and Luis Otto Parra Gonzales.All authors commented on previous versions of the manuscript. And all authors read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The University of Cuenca funded the review as part of the project \"Assessment of the Impact of Cyber Addictions on the Academic Performance, Health, and Well-being of University Students in the City of Cuenca.\u003c/p\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOther Information\u003c/h2\u003e \u003cp\u003e The protocol for the systematic review was developed following the PRISMA P guidelines and can be found in S4. It was not registered on any platform. Amendments were made in the review of the protocol regarding the type of addiction, the sample size, and the explicit results of symptom reduction. The execution of the review has been funded by the University of Cuenca, as part of the research project 'Assessment of the Impact of Cyber Addictions on the Academic Performance, Health, and Well-being of University Students in the City of Cuenca.' The authors of the review declare that there are no conflicts of interest, and the following materials are publicly available: Summary of the included studies, and data extracted from the included studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGajić J, Đorđević A. DIGITAL COMMUNICATON AND CONNECTIVITY IN OVERCOMING THE WIDER EFFECT OF THE PANDEMIC CRISIS. In: Proceedings of the 8th International Scientific Conference - FINIZ 2021 [Internet]. Belgrade, Serbia: Singidunum University; 2021 [cited 2024 Oct 9]. p. 76\u0026ndash;81. 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Leveraging Providers\u0026rsquo; Preferences to Customize Instructional Content in Information and Communications Technology\u0026ndash;Based Training Interventions: Retrospective Analysis of a Mobile Phone\u0026ndash;Based Intervention in India. JMIR MHealth UHealth. 2020;8(3):e15998.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349(jan02 1):g7647\u0026ndash;g7647.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Internet Addictive Behaviors, Technology-Based Interventions, Intervention Outcomes","lastPublishedDoi":"10.21203/rs.3.rs-5737257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5737257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eInternet addictive behaviors result from abnormal internet use, including neglecting responsibilities and experiencing anxiety when offline. This systematic review analyzes technology-based interventions addressing these behaviors, focusing on effectiveness and user interaction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA literature search was conducted across three digital libraries and two high-impact journals, focusing on peer-reviewed articles published in Q1 or Q2 journals between January 2022 and June 2024. Studies evaluating digital addiction interventions and user interaction were included, while reviews, editorials, gray literature, and studies without clear intervention descriptions were excluded. The review covered randomized controlled trials, comparative studies, wearables, and mobile health apps. Five research questions were addressed using 17 evaluation criteria. Data extraction answered the sub-questions. The review followed Barbara Kitchenham's guidelines, applying a rigorous selection and quality assessment process. Primary inclusion was verified using the Kappa coefficient for inter-rater agreement, and article quality was evaluated with established criteria. The content adhered to PRISMA guidelines. In total, 11 articles were included.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eThe review found variability in intervention effectiveness, with personalized, real-time feedback interventions having the greatest impact on reducing screen time and addiction symptoms. Less effective interventions lacked personalization.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study highlighted the most commonly used technology-based interventions and their effectiveness in reducing symptoms and screen time, as well as improving user satisfaction and treatment adherence. Research gaps were identified, including the need for data on quality characteristics and software requirements for personalizing interventions using new technology.\u003c/p\u003e","manuscriptTitle":"Technology-Based interventions to address internet addictive behaviors: systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-07 12:33:16","doi":"10.21203/rs.3.rs-5737257/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-09T18:21:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-04T04:06:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-02-28T04:23:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-23T14:51:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338887822156967012458450699994665248410","date":"2025-01-21T09:57:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55336491395043279828207494440336317907","date":"2025-01-21T08:54:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142988083174205509119108767628356533230","date":"2025-01-17T10:01:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-17T03:11:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-09T20:31:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-08T11:54:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Mental Health","date":"2024-12-30T17:42:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6a6c2af-eb9d-4f8a-b7c5-13e3fd6f9706","owner":[],"postedDate":"January 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-09T10:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-07 12:33:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5737257","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5737257","identity":"rs-5737257","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}