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Front. Psychol., 24 August 2023
Sec. Addictive Behaviors
This article is part of the Research Topic Addictive Behaviours, Risky Exposures, and the Psychosocial Health Outcomes: Policy Issues in Developing Countries View all 6 articles

Development and validation of a scale for streaming dependence (SDS) of online games in a Peruvian population

  • 1Escuela Profesional de Psicología, Facultad de Ciencias de la Salud, Universidad Peruana Unión, Lima, Peru
  • 2South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima, Peru
  • 3Escuela de Medicina Humana, Facultad de Ciencias de la Salud, Universidad Peruana Unión, Lima, Peru
  • 4Escuela de Posgrado, Universidad Peruana Unión, Lima, Peru
  • 5Facultad de Teología, Universidad Peruana Unión, Lima, Peru

Background: Addiction to online video game streaming has become one of the most appealing ways to occupy leisure time and is one of the most popular activities. The satisfaction it provides and the time invested in it are two of the main reasons why it is preferred. However, despite the clear benefits that this activity offers, in some cases, excessive use can lead to personal and/or family problems or abuse.

Objective: The objective of the study was to develop and validate a scale to measure potential traits of dependence on online game streaming. The participants were 423 Peruvian adults aged between 18 and 47 years (M = 22.87, SD = 5.02). The Streaming Dependence Scale (SDS) was developed based on a literature review, and exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted.

Results: The scale showed adequate internal consistency (α, CR, ω, and H > 80). Confirmatory analysis confirmed the one-dimensional structure (χ2 = 10.250, df = 5; p = 0.068; CFI = 0.98, TLI = 0.96, RMSEA = 0.06, SRMR = 0.05).

Conclusion: The brief SDS is a valid and reliable measure that can be used as a useful tool to identify and evaluate streaming dependence.

1. Introduction

In the age of digital transformation, traditional leisure patterns have undergone a profound metamorphosis. Video games and streaming platforms have become central players in this transformed landscape, altering youth interaction and socialization (Hu et al., 2017; Appel et al., 2020; Li et al., 2022; Islam et al., 2023). This technological revolution has prompted a deep rethinking of the concepts of leisure and entertainment, and their implications for personal growth and socialization (Jackson et al., 2011; Kaimara et al., 2022).

However, video game addiction is an emerging phenomenon that has become increasingly worrisome, given its effects extend to individual aspects disrupting family and social dynamics (Choìliz and Marco, 2016). Video games, once seen as an experimental form of entertainment, have evolved into an integral part of many people’s daily lives, especially among the youth (Boris, 2019; Dwivedi et al., 2022). While video games can be useful tools for cognitive and coordinative development, their excessive use can be detrimental, manifesting in addictions, social isolation, promotion of anger and violence, and harmful effects on physical and mental health (Zamani et al., 2009; Agbaria, 2022; T’ng et al., 2022; Zaman et al., 2022; Lérida-Ayala et al., 2023). Video game addiction is an integral part of Internet Gaming Disorder (IGD) and is emerging as a relevant challenge in the field of mental health (VandenBos and American Psychological Association, 2015). It manifests as a recurrent and poorly adapted pattern of gaming behaviors, continuing despite evident negative consequences. This addiction roots in motivational control issues and shares characteristics with gambling addiction (Kuss, 2013). The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) defines video game addiction as a constant and compulsive use of the internet for gaming, leading to clinically significant distress and psychological changes (Morrison, 2015). Studies show this addiction can induce alterations in specific brain areas like the prefrontal cortex, the ventral striatum, and the dorsal striatum (Kuss and Griffiths, 2012). These neurophysiological changes can manifest in a variety of symptoms ranging from sleep disorders and fatigue to anxiety and depression, affecting both the individual’s mental and physical health (Kircaburun et al., 2018).

Video game addiction has given rise to an even more worrying phenomenon: the addiction to video game streaming. The popularization of platforms like Twitch, which hosts an average daily audience of 26.5 million viewers, has shown a concerning increase in addiction to these streaming services (Speed et al., 2023). In this regard, addiction to online video games and, more recently, to their live streaming, have exacerbated the problem, increasing the risk of developing dependency (Diotaiuti et al., 2022b). The DSM-IV (1995) defines dependency as a pattern of video game use that causes significant impairment or distress. This manifests in various aspects, including an increase in tolerance, presence of withdrawal symptoms, excessive dedication to gaming, an uncontrollable craving to play, neglect of other vital activities, and difficulties limiting their use. In the DSM-5, Internet Gaming Disorder (IGD) has been introduced as a related entity that needs to be studied in greater depth (Morrison, 2015). Choìliz and Marco (2016) expand this definition by describing dependency as the excessive and inappropriate use of an initially enjoyable activity but that eventually generates serious personal and family complications. Thus, addiction to video game streaming can have severe repercussions for daily life, emotional stability and has been associated with impulsivity and codependency (Diotaiuti et al., 2022b). Susceptibility to streaming addiction is particularly acute among the youth. Young people who are entangled in the web may experience distress when denied access, which can trigger mental health problems like depression and rumination (Diotaiuti et al., 2022a). The proliferation of online video game streaming amplifies this dependency, especially when intertwined with real-time streams, increasing the risk of developing dependency (Li et al., 2020). Therefore, addiction to streaming or the internet can disrupt daily life management, relationships, and emotional stability. This dependency, often concurrent with other psychiatric symptoms and disorders, is associated with impulsivity and codependency. The similarities between video game addiction and addictive and obsessive-compulsive disorders highlight the need to consider different subtypes of addiction for adequate treatment (Petruccelli et al., 2014).

Video game and streaming addiction is not a uniform experience and can vary enormously from one place to another. An example of this is found in Peru, where the use of the internet and video games has risen in recent years, particularly among the youth (INEI, 2023). Despite this growth, there is a significant gap in specific research on video game and streaming addiction in Peru, highlighting the need for a more detailed examination to fully understand this phenomenon in specific cultural and socioeconomic contexts. The analysis of streaming services and the interactive dynamics between streamers and the audience becomes a critical lens to unravel and combat the dependency on online video game streaming. Both, streamers and viewers, may experience a loss of control and seek to fulfill their affective needs through live streams, thereby fostering dependency (Sussman and Sussman, 2011; Choìliz and Marco, 2016). These factors can be influenced by a multitude of elements, among them, the way the streamer interacts with their audience, which can further reinforce this addictive bond (Sjöblom and Hamari, 2017; Zhao et al., 2018).

However, it is essential to note that although video game and streaming addiction present significant risks, it does not entirely discredit the potential benefits these services can offer. Streaming services can provide significant benefits in terms of learning, skill development, and social support (Li et al., 2020). Nevertheless, addressing the excessive and inappropriate use of these services is crucial, as it can stoke internet addiction and streaming dependency, leading to severe personal and family implications (Choìliz and Marco, 2016; Diotaiuti et al., 2022b). This streaming dependency can be linked to other psychiatric disorders, underlining the need to consider a variety of factors when addressing this addiction (Diotaiuti et al., 2022a). In Peru, as in many other countries, there are no national data shedding light on the prevalence of video game streaming addiction. However, it is reasonable to postulate that this problem is on the rise, considering the increasing popularity of video games and video game streaming platforms in these geographies. It is imperative that more research is conducted to decipher the risk factors and consequences of video game and video game streaming addiction, to conceive effective prevention and treatment strategies. This task is of critical importance in a world where video games and video game streaming are becoming an increasingly integrated part of the daily lives of countless people worldwide.

Therefore, the aim of this study is to develop and evaluate the psychometric properties of a scale for live-streamed online gaming addiction in adults.

2. Materials and methods

2.1. Design and participants

The present study, of an instrumental nature (Ato et al., 2013), was based on convenience sampling. For sample selection, we relied on an electronic calculator (Soper, 2022), considering variables such as: the number of observed and latent variables in the model, the anticipated effect size (λ = 0.10), the desired statistical significance (α = 0.05), and the level of statistical power (1–β = 0.90). With these criteria, the minimum required sample was 199 participants. However, we expanded the sampling and managed to recruit a total of 423 Peruvian adults from the three regions of the country (coast, highlands, jungle). The participants’ ages ranged between 18 and 47 years (M = 22.87, SD = 5.02). The study’s inclusion criteria stipulated that participant had to be over 18 years old, living in Peru, and have regular access to video game streaming platforms. We excluded individuals who did not meet the age and residency criteria, as well as those who did not have regular access to streaming platforms. The majority of participants (Table 1) in our sample were men (61.5%). In terms of geographical distribution, most lived in the coastal region of the country (71.4%). In terms of living arrangements, those living with their parents predominated (38.1%). As for the level of education, it was found that the largest proportion of respondents had an incomplete higher education (40.7%).


Table 1. Demographic characteristics.

2.2. Instruments

2.2.1. Streaming dependence

The meticulously designed Streaming Dependence Scale (SDS) was created based on a rigorous literature analysis. This analysis took into account the established diagnostic criteria present in the Diagnostic and Statistical Manual of Mental Disorders, fourth (DSM-IV, 1995), and fifth editions (DSM-V), as well as integrating findings from previous studies focused on gaming addiction (Choìliz and Marco, 2016). The literature exploration confirmed a preliminary set of 10 evaluative items. This initial scale underwent expert evaluation by three clinical psychologists who made adjustments based on their deep knowledge and experience in the field. These items assess multifaceted aspects of streaming dependence, covering the following domains: (a) salience, which refers to the amount of time individuals dedicate to streaming consumption and the extra time invested in related activities; (b) mood modification, considering whether the individual feels they are spending too much time watching streaming; (c) tolerance, evaluating if they can watch streaming for more than 3 h straight without being bothered by the lack of attention to other people or responsibilities; (d) withdrawal symptoms, analyzing if the individual primarily uses their free time for streaming consumption; (e) conflict, whether there have been arguments with loved ones due to the time spent in front of the screen; and (f) relapse, understood as sleep loss or decreased hours of rest as a result of streaming consumption. The items were crafted with three response options, following a Likert scale format, where “Almost never” corresponds to 1, “Sometimes” to 2, and “Almost always” to 3. This way, an assessment is achieved that reflects the individual nuances of streaming dependence, emphasizing personalization and detail in its study. The SDS, as an evaluation tool, aims to provide a quantifiable and reproducible measure of streaming dependence from an individual perspective. Its use allows for a deeper understanding of this emerging phenomenon, opening new avenues for research and clinical intervention (DSM-IV, 1995; Choìliz and Marco, 2016; DSM-V). By applying it, the goal is to gain a more profound understanding of how the digital world, and more specifically streaming, affects people’s daily lives and mental health.

2.2.2. Generalized anxiety

To assess anxiety, the Spanish version of the Generalized Anxiety Disorder Scale, GAD-2 (García-Campayo et al., 2012), was employed. This scale is a concise and abbreviated adaptation of the more extensive GAD-7, specifically designed to provide a quick and efficient measurement tool. The GAD-2 consists of two items, and its response scale follows a Likert-type format, with options ranging from 0 = never to 3 = almost every day. The version adapted to Spanish in Peru was used, with a reliability for internal consistency of α = 0.738 (95% CI, 0.699, 0.773) (Merino et al., 2017).

2.3. Procedure

This study faithfully adhered to the principles of integrity, transparency, and respect for human dignity, ensuring the fidelity and accuracy of the findings. The Ethics Committee of a Peruvian university (reference 2181-2022/UPEU-FCS-CF) meticulously examined and approved our research protocol. Each participant was fully and clearly informed about the nature of our study. Informed consent met legal requirements and reflected our conviction in respecting the individual’s autonomy and the right to decide about their participation in the research. Data collection was conducted in person, underscoring to each participant that their involvement in the study was entirely voluntary and anonymous.

2.4. Data analysis

We began our analysis with a total sample of participants divided into two groups to facilitate robust cross-validation. Sample 1, with 162 participants, served as the basis for developing the model in the exploratory factor analysis (EFA), while Sample 2, made up of 261 participants, was used to validate the model through confirmatory factor analysis (CFA) (VandenBos and American Psychological Association, 2015). A descriptive analysis of the items of the Streaming Dependency Scale (SDS) was carried out, calculating the mean, standard deviation, skewness, kurtosis, and performing a corrected inter-test correlation analysis. Skewness (g1) and kurtosis (g2) metrics were considered appropriate when values ranged between ± 1.5 (Pérez and Medrano, 2010). We implemented the corrected item-test correlation analysis to remove items when r(i-tc) < = 0.2 or when multicollinearity with (i-tc) < = 0.2 was detected (Kline, 2016).

To examine the factorial structure of the SDS, an EFA was performed using unweighted least squares with oblique rotation (promax). Parallel analysis allowed us to determine the optimal number of factors. We verified the suitability of the data with Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin (KMO) coefficient (Kaiser, 2016; Worthington and Whittaker, 2016).

Once the number of factors in the EFA was established, we proceeded with the CFA on the unifactorial scale using the WLSMV estimator, known for its robustness against deviations from inferential normality (Muthen and Muthen, 2017). We evaluated the model fit using the chi-square test (χ2), the Confirmatory Fit Index and Tucker-Lewis index (CFI and TLI ≥ 0.95) (Schumacker and Lomax, 2016), and the Root Mean Square Error of Approximation and Standardized Root Mean Square Residuals (RMSEA and SRMSR ≤ 0.05) (Kline, 2016). Additionally, to demonstrate internal validity through convergent validity, we calculated the average variance extracted (AVE) per factor (AVE > 0.50), which indicates that more than 50% of the variance is due to its indicators (Fornell and Larcker, 1981). We evaluated external validity by measuring the latent relationship between streaming dependence and anxiety through structural equation modeling (SEM).

Finally, we evaluated internal consistency using Cronbach’s alpha coefficient, composite reliability (CR), McDonald’s omega coefficient (McDonald, 1999), and the H coefficient (Hancock and Mueller, 2001), looking for values above 0.70 (Hancock and Mueller, 2001).

All statistical analysis was performed using R software 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria).1

3. Results

3.1. Content validity

Table 2 shows the results of the evaluation of the experts who analyzed the relevance, coherence, clarity, and context of the items of the SDS scale. It can be seen that items 1, 2, 3, 4, 6, 8, 9, and 10 received a favorable evaluation with a score greater than 0.80. However, items 5 and 7 received a score lower than 0.80, so they were decided to be eliminated based on the criteria established (Escurra Mayaute, 1988).


Table 2. Aiken V for the evaluation of the SDS items.

3.2. Descriptive statistics of the items

In Table 3, the results of the descriptive statistics of the scale items are presented. It can be seen that item 6 had the highest mean (M = 1.65), while item 2 had the lowest mean (M = 1.44). In terms of dispersion, it was found that item 6 (SD = 0.65) had a higher dispersion compared to the other items. Skewness (g1) and kurtosis (g2) fluctuated within the acceptable values of ± 1.5 for all items, suggesting a multivariate normal distribution. In addition, it was found that the scale had item-total correlations greater than 0.30, indicating high homogeneity. Finally, an acceptable internal consistency was obtained for each item by calculating the Cronbach’s alpha, with values greater than 0.70 (>0.70).


Table 3. Descriptive statistics and reliability.

3.3. Internal structure evidence

The exploratory factor analysis (EFA) was carried out on the unidimensional scale to determine the underlying structure of the items. The results indicated an adequate fit of the data through the KMO coefficient (0.83) and the Bartlett’s test of sphericity (p < 0.001). Parallel analysis and the scree plot suggested the extraction of a single factor (Figure 1). The maximum likelihood extraction method and the varimax rotation method eliminated iteratively the items whose loads were lower than 0.50 in the proposed factors and with individual communality with loads lower than required (h2 < 0.30) (Costello and Osborne, 2005; Lloret-Segura et al., 2014) therefore it was considered to eliminate item 4, 6, and 8 (Table 4).


Figure 1. Parallel analysis.


Table 4. EFA, CFA, AVE, and reliability.

3.4. Validity based on internal structure

Insights derived from the exploratory factor analysis (EFA) allowed for the development of the confirmatory factor analysis (CFA) to assess the factor structure (Table 2). Initially, a model incorporating all items was performed. However, the goodness-of-fit indices [χ2 = 65.81, df = 20; p = 0.000; CFI = 0.91, TLI = 0.87, RMSEA = 0.09 (90% CI 0.07–0.065), SRMR = 0.05] did not reach the standards, indicating that this model might not be the optimal representation of our data structure. In addition, the factor loadings (λ) of items 6 and 8 were below the threshold (>0.50), suggesting that these items might not be strongly associated with the underlying factor. In light of these observations, a second model was developed, taking into account the preliminary evidence provided by the EFA. This analytical step indicated that the goodness-of-fit indices were adequate [χ2 = 10.250, df = 5; p = 0.068; CFI = 0.98, TLI = 0.96, RMSEA = 0.06 (90% CI 0.07–0.065), SRMR = 0.03], thus providing a more robust argument for the validity of our model. Furthermore, factor loadings exhibited satisfactory magnitudes (λ > 0.50), reinforcing our confidence in the role of each item in representing the underlying construct (Appendix).

3.5. Validity with convergent and reliability

In relation to internal consistency (Table 4) in Model 1, the alpha (α), omega (ω), and composite reliability (CR) were all above the recommended threshold of 0.70, indicating good internal consistency of the items. The H coefficient was 0.86, also indicating good reliability. However, the average variance extracted (AVE) was 0.40, which is below the recommended threshold of 0.50, suggesting that the items might not converge well on the latent factor. While in Model 2, the reliability indicators (α, ω, CR, and H) were all above 0.70, showing good internal consistency. In addition, the AVE was 0.56, exceeding the recommended threshold of 0.50, suggesting better convergence on the latent factor compared to Model 1.

3.6. Relationship between streaming dependence (SDS) and anxiety

To evaluate the relationship between the AFC and other constructs, a SEM model was proposed with two latent variables: streaming dependence and anxiety. This model showed a good fit: χ2 = 24.16, df = 13; p = 0.03; CFI = 0.98, TLI = 0.96, RMSEA = 0.06 (90% CI: 0.02–0.09), SRMR = 0.04 (Figure 2). Additionally, streaming dependence was positively related to anxiety (0.48; p < 0.001).


Figure 2. Predictive model of streaming dependence on anxiety.

4. Discussion

The digital world presents a universe of possibilities and opportunities, but also a vast field of challenges and threats to our physical and mental wellbeing. The use of online games, particularly those streamed live, has witnessed an unprecedented increase, raising concerns related to fatigue, insomnia, anxiety, and depression. The giants of this digital era, such as Twitch and YouTube, have experienced significant growth in their traffic, becoming the primary means of consuming online gaming content. This growing dependence on live-streamed online games on streaming platforms has become an enigma that oscillates between fascination with virtual immersion and fear of digital addiction. However, psychometric evidence supporting the existence of dependence on live-streamed online games, especially in regions like Peru and Latin America, remains scarce and insufficient. In the face of this challenge, the present study aimed to develop and evaluate a new psychometric assessment tool, the Streaming Dependence Scale (SDS).

The SDS was designed to capture the nuances of dependence on live-streamed online games in adults. Drawing inspiration from the video game dependence criteria of the DSM-IV (1995) and DSM-V (Morrison, 2015), as well as previous studies on gaming addiction (Choìliz and Marco, 2016), 10 items were created focusing on key aspects such as salience, mood modification, tolerance, withdrawal symptoms, conflicts, and relapse.

The findings of the Exploratory Factor Analysis (EFA) supported a one-dimensional structure, explaining 22.6% of the variance of the construct. However, the low communalities (h2 < 0.30) of items 4, 6, and 8 suggested their removal to improve the model’s robustness (Costello and Osborne, 2005; Lloret-Segura et al., 2014).

The Confirmatory Factor Analysis (CFA) supported the proposed one-dimensional structure, highlighting the model’s robustness. Although the first model did not show satisfactory fit indices (CFI = 0.91 and TLI = 0.87, RMSEA = 0.09) (Kline, 2016; Schumacker and Lomax, 2016), the second model–after the removal of the mentioned items–demonstrated excellent goodness-of-fit indices (Hu and Bentler, 1999). This implies that the SDS has a robust and suitable factorial structure for future applications.

The Streaming Dependence Scale (SDS) demonstrated high internal consistency, with McDonald’s Omega (ω) and coefficient H values exceeding 0.70 (Raykov and Hancock, 2005; Dominguez-Lara, 2016). This indicates the scale’s reliability in assessing latent variables, solidifying it as a reliable tool for measuring streaming dependence.

Convergent validity of the Streaming Dependence Scale (SDS) was also evaluated through the positive correlation between its items and other indicators of the same variable. Average Variance Extracted (AVE) was used as a measure to determine whether the factors explain a significant portion of the indicators’ variance, with an acceptable threshold of AVE greater than 0.5 (Fornell and Bookstein, 1982; Chin, 1998). In the first evaluated model, AVE was found to be less than 0.50, but in the second model, AVE surpassed the threshold with a value greater than 0.50. Additionally, the convergent validity of the second model was tested with another construct, finding that streaming dependence positively predicts anxiety. This suggests that higher levels of streaming dependence are associated with higher levels of anxiety, similar to previous studies (Lee et al., 2016). However, it is important to note that anxiety was measured in this study with only 2 items assessing the frequency of anxiety, so future studies should consider the duration and severity of anxiety.

4.1. Implications

The findings from our study on the Streaming Dependency Scale (SDS) have implications in several areas. First, from a professional practice perspective, the SDS emerges as an essential tool for mental health professionals. Specifically, when dealing with youth and young adults, the SDS offers an effective means to identify and assess dependency levels, paving the way for timely and effective interventions. Therefore, therapists, psychologists, and other healthcare professionals can use the SDS as a primary assessment tool, enabling them to design appropriate interventions and provide better guidance to their patients. Second, given the rising use of platforms such as Twitch and YouTube, streaming dependency is becoming an emerging public health issue. It is imperative that regulators and lawmakers collaborate with these platforms to formulate preventive policies. These policies can include awareness campaigns, limitations on consecutive streaming hours, and education about the risks associated with excessive use. Third, while theories on digital addiction have previously focused on video games and the Internet, the SDS introduces an additional dimension addressing streaming. This gives us a foundation to theorize about the unique characteristics of streaming, its distinct appeal, and how these factors can contribute to the development of a dependency. Fourth, in the realm of educational practice, the SDS could serve as a valuable resource for educators and school counselors. With the increasing integration of digital resources in the classroom, understanding the potential dependency on streaming can inform instructional design. For instance, educators might be cautious when incorporating streaming platforms as part of their curriculum, being aware of the potential for increased dependency.

Moreover, it is crucial to research the applicability of the SDS across different cultural and geographical contexts to ensure its universality. Additional factors, such as real-time social interactions and the relationship between personality, academic performance, social relations, and mental health, should be considered.

4.2. Limitations

This study also presents certain limitations that need to be acknowledged. One primary limitation lies in the sample size, especially when advanced analytical techniques are incorporated, which heavily depends on the magnitude of the sample. Various guidelines, such as those proposed by MacCallum et al. (2001), suggest that sample sizes for factor analysis should be considerably large, especially when the number of items is substantial. While Comrey and Lee (2013) have posited that a subject-item ratio of 10:1 might be suitable, some experts argue in favor of even higher ratios to ensure robust and reliable outcomes. For future research, we strongly recommend increasing the sample size. Another limitation is the study’s focus on a single geographical region, potentially constraining the generalizability of the findings to other populations and cultures. Cultural and socioeconomic diversity can significantly influence perceptions and experiences related to streaming dependence. Therefore, it’s recommended that future studies adopt a multicentric design, incorporating participants from a variety of regions. Moreover, even though the study drew inspiration from established diagnostic criteria such as DSM-IV and DSM-V (American Psychiatric Association, 1994, American Psychiatric Association, DSM-5 Task Force, 2013), the ever-evolving nature of digital media consumption implies that continuous adaptation and revision of these criteria are crucial (Griffiths, 2008). Additionally, the reliance on convenience sampling may have limited the generalizability of the results to the entire adult Peruvian population that engages in live-streamed online gaming. Future research should consider utilizing random sampling to procure a sample more representative of the total adult Peruvian population involved in live online game streaming.

5. Conclusion

The Streaming Dependence Scale (SDS) offers a valid and reliable tool within the current landscape of digital mental health research. Not only does the SDS allow for a rigorous evaluation of streaming dependence, but it also lays the groundwork for a deeper understanding of how the intrinsic features of live streaming, such as its real-time interactivity and the formation of virtual communities, can influence the onset of addictive behaviors. This understanding, in turn, is crucial for drawing parallels and distinctions between streaming dependence and other recognized forms of digital addiction, like video game addiction or social media addiction. Consequently, it’s imperative to continue research in this direction, not just to assess the prevalence and severity of streaming dependence across various populations and cultural contexts but also to identify associated risk and protective factors. Furthermore, it’s vital to develop, implement, and evaluate evidence-based intervention programs that address both the prevention and treatment of this emerging form of dependence.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The study was carried out by the Ethics Committee of the Universidad Peruana Unión (reference 2181-2022/UPEU-FCS-CF). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

LS-S, AC-V, and WM-G participated in the conceptualization of the idea. LS-S and WM-G were in charge of the methodology and software and commissioned the data curation, and resources. All authors contributed the validation, formal analysis, and research, carried out the writing of the first draft, review and editing, visualization, and supervision and have read and approved the final version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


  1. ^


Agbaria, Q. (2022). Cognitive behavioral intervention in dealing with internet addiction among Arab teenagers in Israel. Int. J. Ment. Health and Addict. 1–15. doi: 10.1007/s11469-021-00733-6

PubMed Abstract | CrossRef Full Text | Google Scholar

American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Association.

Google Scholar

American Psychiatric Association, DSM-5 Task Force (2013). Diagnostic and statistical manual of mental disorders: DSM-5™, 5th Edn. Washington, DC: American Psychiatric Publishing, Inc. doi: 10.1176/appi.books.9780890425596

CrossRef Full Text | Google Scholar

Appel, G., Grewal, L., Hadi, R., and Stephen, A. T. (2020). The future of social media in marketing. J. Acad. Mark. Sci. 48, 75–95. doi: 10.1007/s11747-019-00695-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Ato, M., López, J. J., and Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en psicología. Anal. Psicol. 29, 1038–1059. doi: 10.6018/analesps.29.3.178511

CrossRef Full Text | Google Scholar

Boris, B. (2019). The impact of social media on video game communities and the gaming industry, department of computer science. Varna: University of Economics – Varna.

Google Scholar

Chin, W. W. (1998). “The partial least squares approach for structural equation modeling,” in Modern methods for business research, ed. G. A. Marcoulides (Mahwah, NJ: Lawrence Erlbaum Associates Publishers), 295–336.

Google Scholar

Choìliz, M., and Marco, C. (2016). ADITEC: Evaluacioìn y prevencioìn de la adiccioìn a Internet, Moìvil y Videojuegos. Madrid: TEA Ediciones.

Google Scholar

Comrey, A. L., and Lee, H. B. (2013). A first course in factor analysis in a first course in factor analysis. New York, NY: Psychology Press, doi: 10.4324/9781315827506

PubMed Abstract | CrossRef Full Text | Google Scholar

Costello, A. B., and Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Pract. Assess. Res. Eval. 10:7. doi: 10.7275/jyj1-4868

CrossRef Full Text | Google Scholar

Diotaiuti, P., Mancone, S., Corrado, S., De Risio, A., Cavicchiolo, E., Girelli, L., et al. (2022b). Internet addiction in young adults: The role of impulsivity and codependency. Front. Psychiatry 13:893861. doi: 10.3389/fpsyt.2022.893861

PubMed Abstract | CrossRef Full Text | Google Scholar

Diotaiuti, P., Girelli, L., Mancone, S., Corrado, S., Valente, G., and Cavicchiolo, E. (2022a). Impulsivity and depressive brooding in internet addiction: A study with a sample of Italian adolescents during COVID-19 lockdown. Front. Psychiatry 13:941313. doi: 10.3389/fpsyt.2022.941313

PubMed Abstract | CrossRef Full Text | Google Scholar

Dominguez-Lara, S. A. (2016). Evaluación de la confiabilidad del constructo mediante el coeficiente H: Breve revisión conceptual y aplicaciones. Psychol. Avan. Discip. 10, 87–94.

Google Scholar

DSM-IV (1995). Manual diagnoìstico y estadiìstico de los trastornos mentales. Mason, IW: DSM-IV.

Google Scholar

Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., et al. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inform. Manag. 66:102542. doi: 10.1016/j.ijinfomgt.2022.102542

CrossRef Full Text | Google Scholar

Escurra Mayaute, L. M. (1988). Cuantificación de la validez de contenido por criterio de jueces. Rev. Psicol. 6, 103–111.

Google Scholar

Fornell, C., and Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J. Mark. Res. 19:440. doi: 10.2307/3151718

CrossRef Full Text | Google Scholar

Fornell, C., and Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 18, 382–388. doi: 10.1177/002224378101800313

CrossRef Full Text | Google Scholar

García-Campayo, J., Zamorano, E., Ruiz, M. A., Pérez-Páramo, M., López-Gómez, V., and Rejas, J. (2012). The assessment of generalized anxiety disorder: Psychometric validation of the Spanish version of the self-administered GAD-2 scale in daily medical practice. Health Qual. Life Outcomes 10:114. doi: 10.1186/1477-7525-10-114

PubMed Abstract | CrossRef Full Text | Google Scholar

Griffiths, M. D. (2008). Videogame addiction: Further thoughts and observations. Int. J. Mental Health Addict. 6, 182–185. doi: 10.1007/s11469-007-9128-y

CrossRef Full Text | Google Scholar

Hancock, G. R., and Mueller, R. O. (2001). “Rethinking construct reliability within latent variable systems,” in Structural equation modeling: Present and future—a festschrift in Honor of Karl Joreskog, eds R. Cudeck, S. D. Toit, and D. Soerbom (Skokie, IL: Scientific Software International), 195–216.

Google Scholar

Hu, L., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Modeling 6, 1–55.

Google Scholar

Hu, M., Zhang, M., and Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Comput. Hum. Behav. 75, 594–606. doi: 10.1016/j.chb.2017.06.006

CrossRef Full Text | Google Scholar

INEI (2023). El 91,3% de la población de 6 y más años de edad que usa internet accedió a través de un teléfono celular. London: INEI.

Google Scholar

Islam, M. R., Hasan Apu, M. M., Akter, R., Tultul, P. S., Anjum, R., Nahar, Z., et al. (2023). Internet addiction and loneliness among school-going adolescents in Bangladesh in the context of the COVID-19 pandemic: Findings from a cross-sectional study. Heliyon 9:e13340. doi: 10.1016/j.heliyon.2023.e13340

PubMed Abstract | CrossRef Full Text | Google Scholar

Jackson, L. A., Von Eye, A., Witt, E. A., Zhao, Y., and Fitzgerald, H. E. (2011). A longitudinal study of the effects of internet use and videogame playing on academic performance and the roles of gender, race and income in these relationships. Comput. Hum. Behav. 27, 228–239. doi: 10.1016/j.chb.2010.08.001

CrossRef Full Text | Google Scholar

Kaimara, P., Oikonomou, A., and Deliyannis, I. (2022). Could virtual reality applications pose real risks to children and adolescents? A systematic review of ethical issues and concerns. Virtual Real. 26, 697–735. doi: 10.1007/s10055-021-00563-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaiser, H. F. (2016). The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151. doi: 10.1177/001316446002000116

CrossRef Full Text | Google Scholar

Kircaburun, K., Jonason, P. K., and Griffiths, M. D. (2018). The dark tetrad traits and problematic online gaming: The mediating role of online gaming motives and moderating role of game types. Pers. Individ. Diff. 135, 298–303. doi: 10.1016/j.paid.2018.07.038

CrossRef Full Text | Google Scholar

Kline, R. B. (2016). Principles and practice of structural equation modeling (Cuarta Ed.). New York, NY: Guilford Press.

Google Scholar

Kuss, D. J. (2013). Internet gaming addiction: Current perspectives. Psychol. Res. Behav. Manag. 6, 125–137. doi: 10.2147/PRBM.S39476

PubMed Abstract | CrossRef Full Text | Google Scholar

Kuss, D. J., and Griffiths, M. D. (2012). Internet and gaming addiction: A systematic literature review of neuroimaging studies. Brain Sci. 2, 347–374. doi: 10.3390/brainsci2030347

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, K. E., Kim, S. H., Ha, T. Y., Yoo, Y. M., Han, J. J., Jung, J. H., et al. (2016). Dependency on smartphone use and its association with anxiety in Korea. Public Health Rep. 131, 411–419. doi: 10.1177/003335491613100307

PubMed Abstract | CrossRef Full Text | Google Scholar

Lérida-Ayala, V., Aguilar-Parra, J. M., Collado-Soler, R., Alférez-Pastor, M., Fernández-Campoy, J. M., and Luque-de la Rosa, A. (2023). Internet and video games: Causes of behavioral disorders in children and teenagers. Children 10:86. doi: 10.3390/children10010086

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Wang, C., and Liu, J. (2020). A systematic review of literature on user behavior in video game live streaming. Int. J. Environ. Res. Public Health 17:3328. doi: 10.3390/ijerph17093328

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Xu, Z., Hao, Y., Xiao, P., and Liu, J. (2022). Psychosocial impacts of mobile game on K12 students and trend exploration for future educational mobile games. Front. Educ. 7:843090. doi: 10.3389/feduc.2022.843090

CrossRef Full Text | Google Scholar

Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A., and Tomás-Marco, I. (2014). Exploratory item factor analysis: A practical guide revised and updated. Anal. Psicol. 30, 1151–1169. doi: 10.6018/analesps.30.3.199361

CrossRef Full Text | Google Scholar

MacCallum, R. C., Widaman, K. F., Preacher, K. J., and Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behav. Res. 36, 611–637. doi: 10.1207/S15327906MBR3604_06

PubMed Abstract | CrossRef Full Text | Google Scholar

McDonald, R. P. (1999). Test theory: A united treatment. Mahwah, NJ: Lawrence Erlbaum.

Google Scholar

Merino, C., Dominguez, S., and Fernández, M. (2017). Validación inicial de una escala breve de satisfacción con los estudios en estudiantes universitarios de Lima. Educacion Medica 18, 74–77. doi: 10.1016/j.edumed.2016.06.016

CrossRef Full Text | Google Scholar

Morrison, J. (2015). DSM-5 Guia para el diagnostico clínico, Vol. 7. Mexico City: Manual Moderno.

Google Scholar

Muthen, L., and Muthen, B. (2017). MPlus user’ guide, 8th Edn. Los Angeles, CA: Muthén & Muthén

Google Scholar

Pérez, E. R., and Medrano, L. (2010). Análisis factorial exploratorio: Bases conceptuales y metodológicas. Rev. Argentina Ciencias Comportamiento 2, 58–66.

Google Scholar

Petruccelli, F., Diotaiuti, P., Verrastro, V., Petruccelli, I., Carenti, M. L., De Berardis, D., et al. (2014). Obsessive-compulsive aspects and pathological gambling in an Italian sample. BioMed Res. Int. 2014:167438. doi: 10.1155/2014/167438

PubMed Abstract | CrossRef Full Text | Google Scholar

Raykov, T., and Hancock, G. R. (2005). Examining change in maximal reliability for multiple-component measuring instruments. Br. J. Math. Stat. Psychol. 58, 65–82. doi: 10.1348/000711005X38753

PubMed Abstract | CrossRef Full Text | Google Scholar

Schumacker, R. E., and Lomax, R. G. (2016). A beginner’s guide to structural equation modeling, 4th Edn. Milton Park: Taylor & Francis.

Google Scholar

Sjöblom, M., and Hamari, J. (2017). Why do people watch others play video games? An empirical study on the motivations of Twitch users. Comput. Hum. Behav. 75, 985–996. doi: 10.1016/j.chb.2016.10.019

CrossRef Full Text | Google Scholar

Soper, D. (2022). A-priori sample size calculator for structural equation models [Software].

Google Scholar

Speed, A., Burnett, A., and Robinson, T. (2023). Beyond the game: Understanding why people enjoy viewing Twitch. Entertain. Comput. 45:100545. doi: 10.1016/j.entcom.2022.100545

CrossRef Full Text | Google Scholar

Sussman, S., and Sussman, A. N. (2011). Considering the definition of addiction. Int. J. Environ. Res. Public Health 8, 4025–4038. doi: 10.3390/ijerph8104025

PubMed Abstract | CrossRef Full Text | Google Scholar

T’ng, S. T., Ho, K. H., and Pau, K. (2022). Need frustration, gaming motives, and internet gaming disorder in mobile multiplayer online battle arena (MOBA) games: Through the lens of self-determination theory. Int. J. Mental Health Addict. 1–21. doi: 10.1007/s11469-022-00825-x

PubMed Abstract | CrossRef Full Text | Google Scholar

VandenBos, G. R., and American Psychological Association (2015). APA dictionary of psychology, 2nd Edn. Washington, DC: American Psychological Association.

Google Scholar

Worthington, R. L., and Whittaker, T. A. (2016). Scale development research: A content analysis and recommendations for best practices. Couns. Psychol. 34, 806–838. doi: 10.1177/0011000006288127

CrossRef Full Text | Google Scholar

Zaman, M., Babar, M. S., Babar, M., Sabir, F., Ashraf, F., Tahir, M. J., et al. (2022). Prevalence of gaming addiction and its impact on sleep quality: A cross-sectional study from Pakistan. Ann. Med. Surg. 78:103641. doi: 10.1016/j.amsu.2022.103641

PubMed Abstract | CrossRef Full Text | Google Scholar

Zamani, E., Chashmi, M., and Hedayati, N. (2009). Effect of addiction to computer games on physical and mental health of female and male students of guidance school in city of Isfahan. Addict. Health 1, 98–104.

Google Scholar

Zhao, Q., Chen, C.-D., Cheng, H. W., and Wang, J. L. (2018). Determinants of live streamers’ continuance broadcasting intentions on Twitch: A self-determination theory perspective. Telemat. Inform. 35, 406–420. doi: 10.1016/j.tele.2017.12.018

CrossRef Full Text | Google Scholar


Appendix Table 1

Appendix Table 1. Streaming Dependence Scale (EDS) in Spanish.

Keywords: streaming, dependence, validation, games, online

Citation: Sairitupa-Sanchez LZ, Collantes-Vargas A, Rivera-Lozada O and Morales-García WC (2023) Development and validation of a scale for streaming dependence (SDS) of online games in a Peruvian population. Front. Psychol. 14:1184647. doi: 10.3389/fpsyg.2023.1184647

Received: 12 March 2023; Accepted: 07 August 2023;
Published: 24 August 2023.

Edited by:

Jesús-Nicasio García-Sánchez, University of León, Spain

Reviewed by:

Stefania Mancone, University of Cassino, Italy
Ira Darmawanti, Surabaya State University, Indonesia

Copyright © 2023 Sairitupa-Sanchez, Collantes-Vargas, Rivera-Lozada and Morales-García. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wilter C. Morales-García,

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