Relationships between problematic binge-watching behavior with psychological and social states

Abstract

Background

Binge-watching has been associated with various psychiatric and social issues. This study aims to examine the associations between binge-watching behavior and factors such as social inclusion, social interaction anxiety, loneliness, depression, anxiety, and stress among the general population in Türkiye.

Methods

A total of 894 participants (498 females and 396 males) from Türkiye participated in this study. Data were collected via a cross-sectional online survey conducted in 2024. The survey included five validated scales: the Binge-Watching Addiction Questionnaire (BWAQ), Social Inclusion Scale (SIS), Social Interaction Anxiety Scale (SIAS), UCLA Loneliness Scale, and Depression, Anxiety, and Stress Scale (DASS-21).

Results

The findings revealed significant positive correlations between binge-watching behavior and various psychological and social factors. A greater binge-watching frequency was associated with increased levels of depression, anxiety, and stress, as well as greater social interaction anxiety and loneliness. Additionally, binge-watching was linked to lower social inclusion.

Conclusions

The present study suggests that binge-watching is linked to increased depression, anxiety, and stress, as well as increased social interaction anxiety and loneliness. Moreover, individuals who engage in binge-watching tend to experience difficulties in social inclusion. These findings highlight the potential psychological risks associated with excessive media consumption and emphasize the need for further research on media use and mental health.

Introduction

Media effects are the intentional or unintentional consequences of mass communication tools for the masses [1]. Media exposure and its influence on viewers’ behaviors have been widely studied in the communication and behavioral sciences, with key theories such as cultivation theory, social learning theory, and uses and gratifications theory providing insight into how media shapes attitudes, perceptions, and behaviors. Technological advancements have changed the way we consume mass communication tools. For decades, watching television has been the most prevalent method of daily media consumption; however, this has changed, with time spent on the internet surpassing time spent watching television. The digital revolution has altered our TV viewing habits, leading to a decline in real-time viewing and a rise in binge-watching (BW) - BW refers to watching several episodes of a series or program consecutively in a single session through video-on-demand platforms [2]; [3]; [4]; [5]; [6].

Previous studies have identified BW as a significant risk factor affecting health, psychological well-being, and daily functioning [7]; [8]; [9]; with some research indicating that individuals who binge-watch frequently are more likely to report higher levels of stress, anxiety, and sleep disturbances than those who watch moderately. Research has linked excessive viewing with depression [10]; [6], as well as loneliness, psychological distress, and anxiety [11]; [12]; [13]; [14]; BW has also been associated with feelings of loneliness and diminished self-control [15]; [16]; [17]; and studies suggest that it is correlated with poorer mental health outcomes [18]; [19]; [13]. Contrary to these findings, Tefertiller and Maxwell [20] did not find a significant link between sequential viewing and factors such as self-control, depression, or loneliness.

Studies on binge-watching behavior in Türkiye focus on BW motivations and why and how participants binge-watch [21]; determining viewers’ motivations behind binge-watching decisions and the effects of binge-watching on viewers [22]; and understanding how the “new normal” consumption that develops with digital streaming affects or improves daily media activities with a focus on Netflix [23]. In our previous study titled The Association between Binge-Watching Behavior and Psychological Problems Among Adolescents [24], we examined mood disorders in 189 adolescents and reported that BW behavior was associated with increased emotional, behavioral, and cognitive problems, as well as higher DSM-IV Inattention scores. The digital revolution has affected our country as well as the rest of the world. There are no studies evaluating the distribution of binge-watching behavior throughout Türkiye. Unlike our previous study, we expand the scope beyond adolescents to include participants from each of the seven geographical regions of Türkiye. In this way, we aimed to assess the distribution of BW behavior in healthy individuals in Türkiye. In addition, we aimed to analyze social participation, social interaction anxiety, loneliness risk, and mental health concerns in this population and the correlations between BW behavior and these factors. This study represents the first large-scale investigation of binge-watching behavior in Türkiye, filling a critical gap in the literature by offering empirical insights into the social and psychological effects of binge-watching behavior in a non-Western context.

Literature review

Binge-watching has become a popular viewing model for subscription-based video-on-demand (SVOD) platforms such as Netflix, Amazon, Prime Video, and Hulu [6]. For decades, television has been the predominant medium for everyday consumption; however, this has shifted, as individuals now allocate more time to the digital content than traditional television broadcasts. The widespread use of the internet has transformed television viewing into a more layered, complex, and dynamic process.

Previous literature has focused on exploring BW behavior by measuring its causes, effects, underlying motivations, and impact on psychological well-being [16]; [25]; [2]; [26]; [27]; [28]. Some studies have focused on the potential risk of addiction associated with BW [29]; [27], whereas others have investigated its possible mental health consequences [19]; [18]. Despite increasing research, there is no clear consensus on whether BW functions as a coping mechanism for psychological distress or contributes to worsening mental health conditions.

Social participation, defined as an individual’s engagement within their community, is an important social factor influenced by BW, as increased screen time can limit real-world interactions. Social interaction anxiety, characterized by fear or avoidance of social communication, is also linked to loneliness [30]. Loneliness is characterized by a feeling of disconnection from society and a lack of meaningful social ties. Many studies have shown a positive relationship between BW and increased loneliness [31]; [32]. Studies have shown that BW is a type of addiction motivated by certain factors such as stress, loneliness, social participation, and habits [16]; [33]; [12]. BW is often a solitary activity, that can increase social isolation and potentially promote feelings of loneliness [13].

Depression, another commonly studied psychological factor, is frequently associated with BW. Research suggests that people neglect other life responsibilities with excessive BW, which can lead to increased depressive symptoms [31]. Depression compels individuals to seek an escape from their pervasive unhappiness, often resulting in increased television consumption to alleviate these emotions [10]. BW is a way to escape negative emotions [34]. Some studies suggest that BW functions as a mood-regulating mechanism, with individuals reporting feeling more relaxed and cheerful afterward. These findings indicate that BW frequency may, in some cases, be negatively associated with depression levels [35]; [36], highlighting the complexity of its psychological impact.

Compared with studies on depression, fewer studies have explored the relationship between BW and anxiety. Studies have focused mostly on daily emotions rather than the clinical characteristics of anxiety. Barlow [37] defines anxiety as a future-oriented emotional state linked to the anticipation of potential adverse events (2002). Many studies have reveal a positive association between BW and anxiety levels, with anxious individuals spending more time watching TV [12]; [11]; [38]. However, Tefertiller and Maxwell (2018) [20] reported a positive correlation between anxiety levels and the likelihood of BW, but they did not definitively explain their results, emphasizing the lack of statistical significance.

Binge-watching behavior is also discussed in the literature as an escape from stress or a way to cope with negative emotions [39]; [12]; [40]. Stress, which manifests as mood changes, restlessness, sleep disturbances, anger, poor self-care, and difficulty concentrating [41], is a key factor influencing problematic digital media use. According to cognitive-behavioral theory, stress can act as a catalyst for increased media consumption and digital dependency [42]. However, research also suggests that prolonged BW does not necessarily harm well-being, and some studies indicate that the relationship between screen time and health outcomes remains ambiguous and context-dependent [35]; [43]. The variability of research findings indicates that ascertaining the nature of the association between mental health issues and BW is challenging [31].

Considering the ongoing debate on the social and psychological effects of BW, further research is essential to better understand its consequences and implications. Previous research has focused predominantly on BW’s potential health and social risks, yet few studies have provided a comprehensive evaluation of these risks in conjunction with one another. This study aims to bridge this gap by providing a comprehensive assessment of the psychological and social risks of BW. We also aimed to highlight the importance of proactive measures in understanding BW’s psychological and social implications.

These aims in the research lead to the following tracking research questions:

RQ1

What is the prevalence of BW among healthy individuals in Türkiye?

RQ2

Is there a correlation between BW behavior and symptoms of depression, anxiety, and stress, as well as social inclusion, social interaction anxiety, and loneliness?

Methods

Participants were recruited from a nationally representative sample of adults in Türkiye. The study targeted adults aged 18–60 years who regularly use video streaming platforms across Türkiye. An initial sample of 800 participants was designed to represent the adult population of Türkiye. To account for potential survey errors, such as incomplete responses, the final target sample size was increased to 900. After incomplete or erroneous surveys were eliminated, the final sample comprised 894 participants.

After informed consent was obtained, an anonymous and self-administered survey was delivered via an online platform. The participants were assured that the survey was voluntary, anonymous, and confidential. The survey was designed in Turkish and took approximately 20 min to complete. Participation in the study was voluntary, and all participants provided written informed consent. The Selçuk University Institutional Review Board approved all the study procedures.

The inclusion criteria were as follows: (1) healthy adults aged 18–60 years (2), no history of psychiatric illness, and (3) regular use of video streaming platforms. The target population of this study was participants over the age of 18. Participants over 60 years old were also excluded to ensure sample homogeneity. We also excluded participants with psychiatric disorders or who had received psychiatric treatment in the last three months because of its possible impact on the study results.

The data were collected via six validated Likert-type scales. The survey included six sections: (1) A participant information form covering sociodemographic characteristics (age, sex, occupation, income level, etc.), self-reported health status, and physical activity habits. We also defined the concept of BW and assessed the viewing behaviors of the participants. (2) Binge-Watching Addiction Questionnaire (BWAQ) [44]; (3) Depression, Anxiety, and Stress Scale (DASS-21) [45]; (4) Social Inclusion Scale (SIS) [46]; (5) Social Interaction Anxiety Scale (SIAS) [47]; [6] UCLA Loneliness Scale [48].

The Binge-Watching Addiction Questionnaire (BWAQ) scale, developed by Forte et al. (2021) [44] and validated in Turkish by Açıkel and Özkent [49], was used to assess BW behavior. The BWAQ consists of 20 items rated on a 5-point Likert scale, ranging from 0 (never) to 4 (always). The overall score from this questionnaire reflects the severity of addictive behavior in BW.

Lovibond et al. developed the DASS-21 as a shorter version of the original scale to assess depression, anxiety, and stress. The DASS-21 comprises 21 items, and Turkish validation was performed by Sarıcam H [50]. The scale comprises three self-report measures intended to assess the emotional states of depression, anxiety, and stress. The internal consistency of the DASS-21 was assessed using Cronbach’s alpha (α), with coefficients of 0.88 for depression, 0.82 for anxiety, and 0.90–0.93 for stress.

Participants exhibiting elevated and diminished levels of BW completed an evaluation of their social involvement levels using the Social Inclusion Scale (SIS). In 2009, Secker et al. created the SIS, and Turkish validation was performed by Ilgaz A et al. (2019). The instrument is a Likert-type scale of 18 items, with response options ranging from not at all (1 point), not particularly (2 points), yes a bit (3 points), to yes definitely (4 points). The minimum and maximum scores for SIS are 18 and 72 points, respectively. SIS measures an individual’s relationship with other people in the past month, and higher scores indicate more socialization.

Mattick et al. (1998) [47] developed the Social Interaction Anxiety Scale (SIAS) and validated it in 2011 to examine the relationship between social interaction anxiety and excessive viewing degrees. The 20-item SIAS scale is structured on a 5-point Likert scale ranging from 0 to 80.

We used the UCLA Loneliness Scale to assess the participants’ general feelings of loneliness. Russell, Peplau, and Cutrona (1980) [48] tested the UCLA Loneliness Scale. The reliability and validity of the Turkish version were assessed in 1989 [51]. The scale is a 20-item Likert-type self-report scale. Scale reports range from 20 to 80, where higher scores indicate higher levels of loneliness.

The combination of these scales aims to comprehensively examine the relationship between excessive BW behavior and its psychological and social correlates.

Statistical analysis

Confirmatory factor analysis (CFA) was performed to validate each scale. Internal consistency was assessed using Cronbach’s alpha coefficient. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s sphericity tests evaluated the adequacy of the sample for factor analysis. The Kolmogorov-Smirnov test determined the normality of the data distribution. Since the P value of the Kolmogorov Smirnov test was found to be > 0.05, parametric tests were applied for analysis. Parametric tests, including one-way ANOVA, Tukey and independent t-tests, were used for group comparisons. The Pearson correlation coefficient was applied to analyze relationships between variables. Version 22 of the SPSS software was used in the analyses of this study.

Results

This study enrolled 894 participants: 498 females (55.7%) and 396 males (44.3%). Among the total sample, 565 participants (63.2%) reported watching more than two episodes in a single sitting, classifying them as binge-watchers. The mean scores (M ± SD) for the scales were as follows: BWAQ (40.91 ± 13.64), SIS (39.20 ± 8.96), DASS-21 (42.54 ± 13.59), UCLA (29.56 ± 4.73), and SIAS (4.39 ± 15.96). Regarding marital status, 485 participants (71.3%) were married, while 195 (28.7%) were single. The majority of participants were aged 18–24 years (70.8%, n = 633), followed by those aged 25–34 years (13.5%, n = 120). A total of 7.3% (65) of the participants had secondary education, 14.3% (128) had high school education, 70.5% (630) had university (undergraduate/associate degree) education, and 7.9% (71) had postgraduate education (Table 1).

Table 1.

Characteristics of the participants

Characteristics of the participants	n	%
Sex
- Female - Male	498 396	55.7 44.3
Marital status
- Unmarried - Married	700 194	78.3 21.7
Age
- 18–24 years - 25–34 years - 35–44 years - > 45 years	633 120 78 63	70.8 13.5 8.7 7
Region
- Marmara - Aegean - Mediterranean - Central Anatolia - Black Sea - Eastern Anatolia - Southeastern Anatolia	109 141 156 253 89 70 76	12.2 15.8 17.4 28.3 10 7.8 8.5
Education Status
- Secondary Education - High School/College - University - Master	65 128 630 71	7.3 14.3 70.5 7.9
Number of people in the household
- ≤ 3 - 4 - 5 - ≥ 6	221 267 255 151	24.7 29.9 28.5 16.9
Total income
- < 20,000 TL - 20,000–40,000 TL - 40,000–60,000 TL - ≥ 60,000 TL	377 279 125 113	42.2 31.2 14 12.6
Regular exercise
- Yes - No	252 642	28.2 71.8
Digital media subscription
- Yes - No	541 353	60.5 39.5
Digital media subscription period
- ≤ 1 year - 2–3 years - 4–5 years - > 5 years	200 208 190 296	22.4 23.3 21.3 33.1
Digital media usage frequency
- Several times per day - Once a day - A few times a week - Once a week - More than once per month - Once a month	385 98 189 80 28 114	43.1 11 21.1 8.9 3.1 12.8
Digital platform tools
- Mobile devices - Computer - Smart TV - Tablet	573 190 82 49	64.1 21.3 9.2 5.5
Do you watch more than two episodes in one sitting?
- Yes - No	565 329	63.2 36.8

Reliability analyses demonstrated high internal consistency for all scales, with Cronbach’s alpha values exceeding 0.70. The Cronbach’s alpha values of the BWAQ, DASS-21, SIS, UCLA, and SIAS scales are given in Table 2 as 0.926, 0.972, 0.930, 0.946, and 0.970, respectively. Since the values ​​obtained were greater than 0.70, the scales were found to be quite reliable (Table 2).

Table 2.

Confirmatory factor analysis fit values ​​of the scales

Scales	X2	df	X2/df	GFI	CFI	RMSEA
BWAQ	604.901	127	4.763	0.909	0.919	0.073
DASS-21	864.626	186	4.649	0.913	0.941	0.064
SIS	319.896	72	4.443	0.938	0.951	0.071
UCLA	275.751	63	4.377	0.941	0.954	0.070
SIAS	481.644	102	4.722	0.919	0.954	0.072
Good Match Values*			≤ 3	≥ 0.90	≥ 0.90	≤ 0.05
Acceptable Compatibility Values*			≤ 3–5	0.89 − 0.85	≥ 0.85	0.06–0.08

Confirmatory factor analysis (CFA) indicated that some items required removal due to poor model fit. In the BWAQ, item 15 from the desire subscale and item 5 from the foresight subscale are removed. We removed items 1 and 10 from the Factor 1 subdimension of the SIS scale and items 7 and 14 from the Factor 2 sub-dimension. Again, items 1, 5, 10, 15, 16, 19, and 20 are removed from the UCLA scale. The DASS-21 and SIAS scales retained all items, as their fit indices are within acceptable limits. (Table 2). The results of the Kolmogorov‒Smirnov test indicated that the scale scores and their subdimensions followed a normal distribution.

Correlation analysis demonstrated a positive and moderate relationship between the BWAQ and DASS-21 scores. Again, a weak but significant positive correlation is observed between the BWAQ and UCLA scores, and finally, the BWAQ showed a weak positive correlation with the SIS scores, except for the avoidance and desire subdimensions (Table 3).

Table 3.

Correlation analysis between the scales

		DASS-21	SIS	UCLA	SIAS
BWAQ	Correlation coefficient	0.462	0.076	0.268	0.441
	P value	0.000	0.024	0.000	0.000
Craving	Correlation coefficient	0.484	0.055	0.277	0.468
	P value	0.000	0.099	0.000	0.000
Dependence	Correlation coefficient	0.401	0.126	0.187	0.377
	P value	0.000	0.000	0.000	0.000
Prediction	Correlation coefficient	0.308	0.166	0.151	0.310
	P value	0.000	0.000	0.000	0.000
Avoidance	Correlation coefficient	0.294	-0.045	0.247	0.266
	P value	0.000	0.177	0.000	0.000

Scale scores was analyzed in relation to demographic variables. Accordingly, female participants scored significantly higher than male participants did on the BWAQ, DASS-21, SIS, and SIAS scales (p value < 0.050). No significant sex differences were detected for the UCLA scale (p value > 0.185; T test; Table 4).

Table 4.

Findings of T and ANOVA tests between participants of the scales

	BWAQ (X ± SS)	DASS-21 (X ± SS)	SIS (X ± SS)	SIAS (X ± SS)	UCLA (X ± SS)
Gender
Female Male	42.02 ± 0.59 39.51 ± 0.71	44.26 ± 0.58 40.36 ± 0.71	39.89 ± 0.39 38.32 ± 0.47	44.76 ± 0.71 41.66 ± 0.81	29.38 ± 0.20 29.80 ± 0.25
p value	0.006	0.000	0.009	0.004	0.185
Marital status
Unmarried Married	41.46 ± 0.51 38.47 ± 0.99	43.32 ± 0.52 39.71 ± 0.91	39.36 ± 0.34 38.61 ± 0.64	44.07 ± 0.62 40.93 ± 1.02	29.58 ± 0.18 29.49 ± 0.33
p value	0.005	0.001	0.307	0.090	0.822
Age
18–24 years 25–34 years 35–44 years > 45 years	41.77 ± 0.53 39.05 ± 1.24 39.72 ± 1.96 36.16 ± 1.40	43.16 ± 0.52 39.49 ± 1.20 41.74 ± 1.45 37.96 ± 1.82	39.09 ± 0.35 41.41 ± 0.89 38.98 ± 0.96 38.77 ± 1.12	43.83 ± 0.64 41.98 ± 1.31 42.02 ± 1.79 40.60 ± 1.98	29.56 ± 0.19 28.50 ± 0.37 30.44 ± 0.54 29.65 ± 0.61
p value	0.008	0.000	0.000	0.065	0.019
Region
Marmara Aegean Mediterranean Central Anatolia Black Sea Eastern Anatolia Southeastern Anatolia	41.73 ± 1.22 40.96 ± 1.22 41.44 ± 1.12 39.82 ± 0.87 40.62 ± 1.12 41.84 ± 1.77 41.61 ± 1.53	42.70 ± 1.33 43.82 ± 1.15 41.72 ± 1.22 40.60 ± 0.81 46.28 ± 1.26 43.52 ± 1.52 42.65 ± 1.44	39.98 ± 0.87 37.60 ± 0.74 38.13 ± 0.75 40.11 ± 0.54 39.43 ± 0.95 40.17 ± 0.90 38.93 ± 1.12	44.27 ± 1.50 42.58 ± 1.41 41.59 ± 1.31 42.55 ± 1.00 43.15 ± 1.29 50.20 ± 1.71 44.03 ± 2.06	29.45 ± 0.41 29.63 ± 0.41 29.01 ± 0.38 29.14 ± 0.28 29.65 ± 0.53 31.32 ± 0.49 30.35 ± 0.58
p value	0.833	0.028	0.085	0.012	0.013
Education Status
Secondary Education High School/College University Master	34.73 ± 1.67 44.75 ± 1.34 41.21 ± 0.52 36.88 ± 1.44	41.61 ± 2.03 44.92 ± 1.28 42.73 ± 0.52 37.30 ± 1.18	35.55 ± 1.04 37.76 ± 0.78 39.62 ± 0.35 41.29 ± 0.96	43.67 ± 2.11 45.98 ± 1.52 43.29 ± 0.63 39.28 ± 1.45	30.80 ± 0.60 30.95 ± 0.47 29.34 ± 0.18 27.81 ± 0.44
p value	0.000	0.002	0.000	0.043	0.000
Number of people in the household
≤ 3 4 5 ≥ 6	40.15 ± 1.00 39.71 ± 0.77 42.18 ± 0.86 41.97 ± 1.03	43.26 ± 1.06 42.05 ± 0.81 42.09 ± 0.81 43.04 ± 0.94	39.22 ± 0.62 39.48 ± 0.55 39.18 ± 0.55 38.66 ± 0.68	42.36 ± 1.13 42.02 ± 0.89 44.60 ± 1.05 45.23 ± 1.23	29.62 ± 0.33 29.37 ± 0.27 29.69 ± 0.30 29.57 ± 0.36
p value	0.119	0.697	0.847	0.095	0.882
Total income
< 20,000 TL 20,000–40,000 TL 40,000–60,000 TL ≥ 60,000 TL	41.37 ± 0.70 41.41 ± 0.77 38.24 ± 10.2 41.04 ± 10.3	43.64 ± 0.76 42.79 ± 0.73 40.58 ± 1.16 40.36 ± 1.13	38.58 ± 0.45 39.55 ± 0.52 39.60 ± 0.87 39.88 ± 0.84	45.20 ± 0.90 43.17 ± 0.85 41.96 ± 1.48 39.40 ± 1.23	29.91 ± 0.26 29.40 ± 0.26 29.36 ± 0.41 29.01 ± 0.40
p value	0.133	0.044	0.377	0.005	0.247
Regular exercise
Yes No	39.68 ± 0.92 41.38 ± 0.52	40.32 ± 0.87 43.40 ± 0.52	38.92 ± 0.66 39.29 ± 0.32	40.30 ± 1.01 44.59 ± 0.62	29.11 ± 0.30 29.73 ± 0.18
p value	0.093	0.002	0.578	0.000	0.079
Digital media subscription
Yes No	42.53 ± 0.58 38.42 ± 0.70	43.30 ± 0.60 41.35 ± 0.67	39.42 ± 0.39 38.84 ± 0.45	43.71 ± 0.71 42.87 ± 0.79	29.41 ± 0.20 29.78 ± 0.24
p value	0.000	0.037	0.341	0.432	0.252
Digital media subscription period
≤ 1 year 2–3 years 4–5 years > 5 years	37.89 ± 0.95 42.01 ± 0.89 43.12 ± 0.89 40.75 ± 0.85	41.13 ± 0.89 43.34 ± 1.01 43.22 ± 0.95 42.47 ± 0.79	39.32 ± 0.63 39.36 ± 0.64 39.36 ± 0.61 38.88 ± 0.52	43.70 ± 1.14 43.94 ± 1.08 43.54 ± 1.15 42.68 ± 0.94	29.15 ± 0.27 29.27 ± 0.33 30.05 ± 0.35 29.72 ± 0.29
p value	0.001	0.342	0.910	0.820	0.199
Digital media usage frequency
Several times per day Once a day A few times a week Once a week More than once per month Once a month	42.37 ± 0.70 40.83 ± 1.35 41.26 ± 0.96 37.67 ± 1.31 41.32 ± 3.05 37.57 ± 1.31	44.32 ± 0.71 41.79 ± 1.38 40.86 ± 0.91 42.40 ± 1.44 42.14 ± 2.03 40.09 ± 1.34	38.73 ± 0.45 37.32 ± 0.99 41.02 ± 0.61 38.01 ± 1.01 39.17 ± 1.51 40.16 ± 0.78	45.20 ± 0.87 38.90 ± 1.49 42.42 ± 1.05 42.87 ± 1.66 46.03 ± 3.54 42.40 ± 1.33	29.71 ± 0.24 29.06 ± 0.45 29.66 ± 0.34 29.33 ± 0.55 29.50 ± 0.82 29.50 ± 0.41
p value	0.007	0.019	0.007	0.012	0.881
Digital platform tools
Mobile devices Computer Smart TV Tablet	40.71 ± 0.54 41.64 ± 1.01 37.58 ± 1.77 45.89 ± 2.02	42.68 ± 0.55 41.18 ± 0.96 41.45 ± 1.79 47.79 ± 1.91	39.55 ± 0.35 38.64 ± 0.68 39.14 ± 1.08 37.18 ± 1.40	43.10 ± 0.65 42.87 ± 1.11 41.08 ± 1.75 52.44 ± 2.69	29.64 ± 0.20 29.52 ± 0.31 28.29 ± 0.53 30.83 ± 0.67
p value	0.007	0.020	0.248	0.000	0.022

For the BWAQ and DASS-21 scales, single individuals outscored married individuals (T test; p value < 0.050). Although single individuals had higher scores than did married individuals on the SIAS scale, the difference is not statistically significant (p value > 0.050). Marital status had no significant effect on the SIS or UCLA score (T test; p value > 0.050; Table 4).

18–24 age groups had the highest scores compared to other age groups in the BWAQ, DASS-21, and UCLA scales (p value < 0.050). In the SIS scale, the highest value is in the 25–34 age group, and there is a statistically significant difference compared to other age groups (p value < 0.050). In the SIAS scale, the highest score is in the 18–24 age group, but the difference is not statistically significant (p value > 0.050; ANOVA; Tukey; Table 4).

For the BWAQ scale, individuals with high school and university education had higher scores (44.750 ± 1.35 and 41.219 ± 0.52), while individuals with postgraduate education had average scores (36.887 ± 1.44), and individuals with high school education had the lowest scores (34.738 ± 1.67) (p value < 0.050). Similarly, individuals with high school (44.921 ± 1.29) and university (42.733 ± 0.53) had higher scores for the DASS-21 scale (p value < 0.050). Similarly, the highest score for the SIAS scale is obtained with high school education (45.984 ± 1.53) (p value < 0.050; ANOVA; Tukey; Table 4).

It was observed that the number of people living in the household did not make a difference for all scales. While it was observed that the scores decreased as the individual’s income increased in the DASS-21 scale and the SIAS scale, it was observed that the individual’s income did not make a difference in the other scales (p value < 0.050; ANOVA; Tukey; Table 4).

It was observed that individuals who do not exercise regularly (43.401 ± 0.53) have higher scores than individuals who do (40.325 ± 0.88) on the DASS-21 scale. Similarly, it was observed that individuals who do not exercise regularly (44.598 ± 0.62) have higher scores than individuals who do (40.301 ± 1.01) on the SIAS scale, while it was observed that whether the individual exercises or not does not make a difference for the other questionnaires (p value < 0.050; ANOVA; Tukey; Table 4).

The study found that the scales varied based on the individual’s living region. Accordingly, it was observed that individuals living in the Black Sea region (46.280 ± 1.27) have higher scores than those in other regions on the DASS-21 scale. The DASS-21 scores of the other regions were nearly identical. In the UCLA scale, it was observed that the individuals with the highest scores were in the Eastern Anatolia Region (31.328 ± 0.50) and the Southeastern Anatolia Region (30.355 ± 0.583) and other regions had similar scores. In the SIAS scale, the highest score was in the Eastern Anatolia Region (50.200 ± 1.72) and other regions had similar scores. No regional differences were observed for BWAQ and SIS (p value < 0.050; ANOVA; Tukey; Table 4).

It was observed that individuals with digital media membership had higher scores than those without in the BWAQ and DASS-21 scales (p value < 0.050; ANOVA; Tukey; Table 4). It is observed that the scores are similar in the SIS, UCLA, and SIAS scales depending on whether the individuals had a digital media membership or not (p value > 0.050; ANOVA; Tukey; Table 4). The BWAQ scale revealed that individuals with a digital media membership of less than 1 year scored lower than those with a membership of more than 1 year (p value < 0.050; ANOVA; Tukey; Table 4), whereas the other scales showed no significant difference in the duration of digital media membership (p value > 0.050; ANOVA; Tukey; Table 4).

The frequency of using digital media several times a day caused BWAQ and DASS-21 scores to be higher than those of individuals who use digital media less frequently. It was observed that other usage frequencies had similar scores. While the scores of individuals whose digital platform tool is a tablet are the highest in the BWAQ scale and SIAS scale, they were found to be lower in those whose digital platform tool is a smart TV (p value < 0.050; ANOVA; Tukey; Table 4). Similarly, the UCLA scale and DASS-21 scale revealed the highest scores for individuals using a tablet as their digital platform tool (p value < 0.050; ANOVA; Tukey; Table 4). On the other hand, it is observed that the digital platform tool did not make a difference in the SIS Scale (p value > 0.050; ANOVA; Tukey; Table 4).

As a result of multiple regression analysis in which the BWAQ variable is taken as the dependent variable and the SIS, SIAS, UCLA Loneliness Scale, and DASS-21 variables are taken as independent variables, the model was found to be significant. (P value < 0.05). Accordingly, while DASS-21 (0.322, P value < 0.05), SIS (0.116, P value < 0.05), and SIAS (0.215, P value < 0.05) had significant and positive effects, the effect of the UCLA loneliness scale (0.053, P value > 0.05) variable is insignificant (Table 5).

Table 5.

Regression analysis of the scales

	Beta	SD	T	P value
DASS-21	0.322	0.035	9.111	0.000
SIS	0.116	0.044	2.624	0.000
UCLA	0.053	0.096	0.559	0.576
SIAS	0.215	0.031	6.873	0.000
Constant	11.793	3.055	6.873	0.000

Discussion

This study utilized a national online survey to examine the correlation between binge-watching (BW) and various social and psychological factors, using the BWAQ, SIS, SIAS, UCLA Loneliness Scale, and DASS-21. To the best of our knowledge, this is the first large-scale epidemiological study investigating the associations between these scales and BW in the general population. The prevalence of BW addiction in our sample was 63.2%. The findings suggest a moderate association between BW tendencies and depression, anxiety, and stress levels. Among these, the DASS-21 score exhibited the strongest association with BW after controlling for other variables. Additionally, a positive correlation was identified between social engagement, social interaction anxiety, and BW, whereas loneliness demonstrated a weaker positive correlation with BW.

Our results align with previous research indicating a link between social interaction anxiety and BW [12]. Studies on adolescents have also demonstrated a positive association between screen use and anxiety [52]; [53]. These findings reinforce prior research highlighting the role of social interaction in television viewing [54]; [14]; [5], and the strong association between social anxiety disorder and internet addiction [55]. Watching television can serve as a means for individuals to engage in virtual social interactions [12]. The accessibility of digital platforms makes them an effective means for shaping individuals’ social behaviors.

The study supports a weak correlation between BW and loneliness, while no significant association was found in the regression analyses. Some prior studies reported no significant association between these variables [10]; [34], our findings extend previous research suggesting that individuals may engage in BW as a coping mechanism for loneliness [17]; [12]. Earlier studies evaluated the similar relationship between BW and social interaction anxiety, which is associated with feelings of loneliness. Factors such as boredom, stress, loneliness, social participation, and habitual behavior contribute to BW [16]. Lack of social support, feelings of isolation, and loneliness are considered vulnerability factors in the development of excessive internet use [56]. Moreover, engaging in BW alone may lead to the avoidance of real-life social interactions, further contributing to social isolation and loneliness. Interestingly, the study revealed a stronger association between BW dependence and loneliness among younger individuals.

Consistent with previous studies [13]; [57], our study found a significant association between BW and depression. Additionally, our study observed a stronger correlation between BW addiction and symptoms of depression, anxiety, and stress in younger individuals. These results align with studies indicating that depression is a major risk factor for internet and smartphone addiction [58]. According to Panek (2014) [15], individuals who rely on media to control their irritable moods may find it challenging to stop using them. Depressed individuals may turn to BW as a means of escaping frustration and negative emotions. This tendency, on the one hand, distracts attention from negative thoughts and emotions, and on the other hand, suggests that social problems may arise.

This study reinforces the growing body of evidence linking BW behaviour with symptoms of anxiety and depression. It highlights the existence of an important relationship between stress and problematic watching. However, it is important to note that BW itself is not inherently problematic-only excessive viewing poses concerns. As Flayelle et al. suggest BW may serve as a beneficial coping mechanism for individuals experiencing anxiety and stress [29]; [22]. Our results indicate that BW tendencies increase as mood disorders worsen, which may lead to further social challenges.

Another noteworthy finding, which is consistent with our previous research [49] is the role of gender in BW behavior. Women presented higher BW addiction rates than men did and scored higher on all scales except loneliness. These findings suggest that future studies should explore gender differences in BW behavior in greater depth.

This study, which obtained comprehensive findings on problematic media consumption behavior in a large population, improved our understanding of the social and psychological processes underlying BW. These findings can inform digital media usage habits, well-being, and public health initiatives. Conducted across all regions of Türkiye, our research aligns with global studies, further underscoring the universality of the BW phenomenon.

Limitations and recommendations for future research

This study has several limitations. First, it does not establish causal relationships between BW and psychological or social factors. It remains unclear whether BW leads to psychological and social challenges or vice versa. Prospective studies are needed to explore these causal pathways. Second, the study relies on self-reported data collected through an online survey, introducing the possibility of selection bias and response bias. Future research should incorporate objective behavioral measures or experimental designs to validate these findings. Third, while this study examined the association between BW and various psychological and social factors, incorporating open-ended qualitative studies could provide more in-depth insights into individual motivations and experiences related to BW. Given the increasing prevalence of screen-based entertainment in modern society, further investigation into the psychological and behavioral implications of BW is warranted. Additionally, these findings highlight the need for educational strategies aimed at helping adolescents and adults navigate evolving media consumption habits. Despite these limitations, this study offers valuable perspectives on the BW phenomenon and its potential psychological and social consequences.

Conclusions

This study advances the understanding of BW by situating it within a broader psychosocial framework, demonstrating significant associations with variables such as social participation, social interaction anxiety, depression, anxiety, stress, and loneliness. These findings align with and extend previous research that has identified links between excessive media consumption and various psychological challenges [2]; [33]. By using a large and diverse sample, this study conducted a comprehensive review of concerns raised in the literature about the potential of BW to disrupt social functioning and emotional well-being. The findings highlight that recent advancements in media entertainment and the consequent alterations in usage patterns pose both difficulties and opportunities for media consumers in their daily lives.

Moreover, the results support the conceptualization of BW not merely as a leisure activity but as a behavioral pattern with implications akin to other forms of problematic media use, as discussed in the works of Starcevic and Billieux (2017) and Tefertiller and Maxwell (2018). This reinforces the need to view BW through the lens of media psychology and behavioral science.

The study also contributes to ongoing debates on the role of digital platforms in shaping user habits. In particular, raising awareness of the risks associated with BW, such as its potential for addiction and its impact on daily life, is crucial. Additionally, video streaming platforms could implement features that promote healthier viewing habits. In line with prior calls for platform accountability [5], the proposed interventions—such as screen-time reminders and customizable limits—underscore the importance of limited TV consumption strategies and suggest providing customizable settings that allow users to set personal screen-time limits. Academics in the field of new media should focus more on BW as a modern television norm and educate society about its potential negative consequences.

Future research should adopt longitudinal designs to explore the causal pathways and long-term psychological effects of BW. Additionally, cross-cultural studies could shed light on how sociocultural norms influence BW behaviors and their consequences. By anchoring BW within established theoretical frameworks and empirical trends, this study provides a foundation for more nuanced inquiries into the interplay between digital media use and mental health.

Abbreviations

BW

Binge-watching

Author contributions

Conceptualization, Y.Ö; Methodology, Y.A. Datacuration, Y.A.; Investigation, Y.Ö.; Resources, Y.Ö.; Writing – original draft Y.Ö; Writing – Review and Editing, Y.Ö. and Y.A.; Project administration, Y.Ö. and Y.A. All authors read and approved the final manuscript.

Funding

This study was supported by scientific research fund from Selcuk University, 2024.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study received ethical approval from the Selçuk University Scientific Ethics Evaluation Committee report number of 559280/24 July 2023. All participants were informed of the study protocol. Informed consent was obtained from all the participants. For study, consent to use the anonymized data for publication purposes was provided was the owners. All procedures conducted in this present study were in accordance with the Declaration of Helsinki for the use of humans in experimental research or comparable ethical standards. Clinical trial number: not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.