Unveiling the YouTube addiction: Understanding the spectrum of digital dependency

ABSTRACT

Background:

Excessive use of YouTube is increasingly being as a source of technological addiction in both adolescent and adult population worldwide.

Objective:

The aim of this study is to assess YouTube addiction among adolescents and adults and the ways to reduce its usage.

Methods:

After obtaining Institutional ethical clearance and informed consent, we collected data from adolescent and adult male and female using a 22-item YouTube addiction scale (YAS) questionnaire. Face-to-face interview was conducted by the principal investigator. The principal component analysis (PCA) of the YAS questionnaire was performed. Manual content analysis was conducted for the qualitative data.

Results:

We enrolled 54 (50.9%) male and 52 (49.1%) female participants. The majority of them use YouTube and Instagram. Severely addicted were 38.7%, who spent all seven days watching YouTube videos. A principal component analysis revealed a four-component YAS structure based on an eigenvalue cutoff (>1), Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was more than 0.5, and Bartlett’s test of sphericity was <0.001. We grouped 22 items into four dimensions: Impaired control, decrease alternate pleasure, intense desire, and measures taken to reduce usage.

Conclusion:

Behaviour modification needed to promote healthier YouTube usage habits.

Introduction

In today’s digital age, where online content consumption is ubiquitous, the concept of YouTube addiction has emerged as a significant concern. Overuse of social media is associated with low work performance, less healthy social relationships, sleep problems, low life satisfaction, and feelings of jealousy, anxiety, and depression. We have endeavoured to quantify and understand the extent of this addiction through the YouTube Addiction Scale (YAS).[1,2,3,4]

The YAS is a tool designed to assess individuals’ levels of dependence on the popular video-sharing platform. Developed by experts in psychology and digital behaviour, this scale aims to provide insights into the spectrum of YouTube addiction, helping both researchers and individuals gain a better understanding of the phenomenon. The scale typically consists of a series of questions or statements that participants respond to, reflecting their behaviours and attitudes toward YouTube usage. These questions may cover aspects such as the amount of time spent on the platform, the impact on daily life, and the ability to control usage. The spectrum of YouTube addiction is multifaceted, ranging from mild preoccupation to severe dependence. Individuals with low scores may exhibit healthy and controlled engagement with YouTube, using it as a source of entertainment or education without it significantly interfering with their daily lives.[5,6]

On the other end of the scale, individuals with high scores may experience negative consequences, such as neglecting responsibilities, sleep disturbances, and strained relationships due to excessive YouTube consumption. Understanding the different levels of addiction is crucial for developing targeted interventions and support mechanisms. Several factors contribute to YouTube addiction, including the platform’s design, personalized content recommendations, and the allure of constant novelty. The addictive nature of YouTube lies in its ability to cater to individual preferences, creating a personalized digital experience that can be difficult to resist.[7,8,9]

When delve deeper into the YAS, they aim to refine its questions, consider cultural variations, and adapt it to evolving digital landscapes. Additionally, mental health professionals can use the scale to identify and address problematic YouTube usage in clinical settings, providing tailored interventions for those struggling with addiction. Ultimately, the YAS serves as a valuable tool in unravelling the complexities of digital dependency. By acknowledging the spectrum of YouTube addiction, society can work toward fostering a healthy relationship with online platforms, ensuring that individuals can enjoy the benefits of digital content without succumbing to its addictive pitfalls. Hence, the present study was conducted to assess YouTube addiction among adolescents and adults and the ways to reduce it.[10,11,12]

Material and Methods

Study setting and study design

We did a mixed-method study (Quan-Qual) including a cross-sectional survey and In-depth interviews. Data was collected by the Department of Community Medicine for the period of five months, from December 2023 to April 2024, from the field practice villages of our tertiary care teaching hospital. Early intervention includes counselling services and care, were provided to all participants with higher levels of addiction.

Sample size and sampling

Quantitative

Using openEpi software, the sample size was calculated to be 106, with 80% power, a 95% confidence interval, 7.5% absolute precision, and 19.9% prevalence of internet addition.[5] The sample size of ≥100, with an item-to-respondent ratio of 1:5, is considered the minimum requirement for factor analysis. Finally, we included all the adolescent and adult male and females during the study period who were in the age group of 18 to 35 years. A consecutive sampling technique was applied.

Qualitative

In-depth interviews were conducted among purposively selected adolescent and adult males and females (n = 20) who were vocal and willing to explore the ways to reduce YouTube usage.

Study participants

The study was conducted among adolescent and adult males and females from 18 to 35 years, after obtaining written, informed, and valid consent from the field practice villages of our tertiary care teaching hospital.

Data collection

Step 1: Quantitative survey

After obtaining institutional ethical clearance, the data were collected by using a YAS. Standardized and validated questionnaire was used for the assessment of addiction consists of six items corresponding to salience, mood modification, tolerance, withdrawal, conflict, and relapse, with a total of 22 variables. A face-to-face interview was conducted among study participants by the principal investigator (PI).SE The response to the each of the YAS items was rated with options being never, occasionally, sometimes, often, and always.[12]

Step 2: Qualitative assessment

The guidelines for in-depth interview was framed to explore the ways to reduce the YouTube usage. In-depth interviews were moderated by a trained person in qualitative research methods and well versed in the local language, Tamil. In-depth interviews were conducted at the times and places convenient to the participants. Interviews were audio-recorded with the participants’ permission, and each interview lasted for 20–30 min. The interview was conducted using an interview guide and consisted of open-ended questions on “What all were the good things you achieved on usage of YouTube”, “What all were the problems you faced during the usage of YouTube”, “What according to you are the ways to reduce the YouTube usage”. Discussion was audio-taped and transcribed as verbatim.

Statistical analysis

Quantitative

Data was entered using Epi info (version 7.2.2.6 developed by the Centre of Disease Control, Atlanta, USA, and WHO) and analysed using SPSS version 24 software (SPSS Inc., Chicago, Illinois, USA) package for analysis. Statistical measures like frequency and percentages, were obtained. We used a 5-point Likert scale to measure the frequency of usage of YouTube. We conducted a principal component analysis (PCA) of the YAS questionnaire. Eigen value and scree plot was used to decide on the number of factors to be extracted. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity the samples were adequate for factor analysis [Table 1]. The use of Eigen values, scree plots, KMO measure and Bartlett’s test in the study is justified, as they help ensure the appropriateness, validity, and reliability of the factor analysis, ultimately leading to more meaningful and scientifically rigorous results.

Table 1.

Values of KMO and Bartlett’s test

Variable	Value
Kaiser-Meyer-Olkin measure of sampling adequacy	0.880
Bartlett’s test of sphericity
Approximate Chi-Square	1553.389
df	231
Sig.	<0.001

Qualitative

Audio recordings were transcribed in English. The transcripts were carefully proofread and edited beforehand, ensuring accuracy. Two qualitative researchers independently performed inductive coding of the transcripts manually to enhance interpretation. To boost the study’s internal validity, the results were reviewed by another faculty member trained in qualitative research who was not involved in the study. Direct statements from participants are presented in italics. The findings were reported following the “Consolidation criteria for reporting qualitative research” (COREQ) guidelines.

To triangulate the data between two methods, we utilized a convergent mixed methods design. Initially, we analyzed the quantitative and qualitative datasets separately. Following this, we integrated the findings from both datasets during the interpretation of the results.

Results

The gender distribution shows a nearly equal split, with 50.9% male respondents and 49.1% female respondents. Most respondents fall within the age group of 18–23 years (70.8%), followed by 24–30 years (18.9%), with smaller percentages below 18 years (5.7%), and above 30 years (4.7%), a significant majority reside in urban areas (83%), while the remaining 17% reside in rural areas. In terms of educational attainment, the highest proportion of respondents have completed undergraduate (UG) education (71.7%), followed by completed school (15.1%), postgraduate (PG) education (7.5%), and other levels of education (4.7%). Additionally, a small percentage (0.9%) identify as illiterate [Table 2].

Table 2.

Sociodemographic details of the participants (n=106)

Variables	n (%)
Gender
Male	54 (50.9)
Female	52 (49.1)
Age group (In years)
<18	6 (5.7)
18–23	75 (70.8)
24–30	20 (18.9)
>30	5 (4.7)
Residence
Urban	88 (83)
Rural	18 (17)
Highest level of education
Illiterate	1 (0.9)
Completed school	21 (19.8)
UG	76 (71.7)
PG	8 (7.5)

97.2% of respondents use social media. The most used social media platforms are Instagram (53.8%), followed by YouTube (39.6%), Facebook (4.7%), and Twitter (1.9%). All respondents who use social media also use YouTube. Among those who use YouTube, the majority (88.7%) watch videos on their mobile phones, while smaller percentages use laptops (3.8%) or tablets (7.5%). Less than half of the respondents (38.7%) spent all seven days watching YouTube videos, and 21.7% spending more than 5 hours in a day. The types of videos watched on YouTube are diverse, with (25.5%) watching films/short films and daily routine vlogs (23.6%). Other categories include food reviews (21.7%), educational videos (12.3%), spiritual/devotional videos (5.7%), and other unspecified categories (12.2%) [Table 3].

Table 3.

Details on frequency of social media usage (n=106)

Variable	n (%)
Use of social media like YouTube, Facebook, Instagram, twitter
Yes	103 (97.2)
No	3 (2.8)
Most used social media
Facebook	5 (4.7)
YouTube	42 (39.6)
Instagram	57 (53.8)
Twitter	2 (1.9)
Do you use YouTube
Yes	103 (97.2)
No	3 (2.8)
Gadget used for seeing YouTube videos
Mobile phone	94 (88.7)
Laptop	4 (3.8)
Tablet	8 (7.5)
Number of days spent in a week for seeing YouTube videos
<3 days	28 (26.4)
≥3 days	78 (73.6)
Hours spent in a day in YouTube
<3 hours	63 (59.4)
3–5 hours	20 (18.8)
>5 hours	23 (21.8)
Frequency of visiting social media in a day
1–3 times a day	22 (20.8)
4–6 times a day	34 (32.1)
7–9 times a day	13 (12.3)
>9 times a day	37 (34.9)
Kind of videos seen in YouTube
Food reviews	23 (21.7)
Daily routine vlogs	25 (23.6)
Educational videos	13 (12.3)
Films/short films	27 (25.5)
Spiritual/devotional videos	6 (5.7)
Others	6 (12.2)

The scree plot shows the components as the x-axis and the corresponding Eigen values as the Y-axis [Figure 1]. The first four components are considered whose Eigen value are 10.065, 1.710, 1.370, and 1.185. Since all these four factors are having Eigen value greater than 1 and sharing the maximum variance hence, they are essential in the present study [Table 4].

Figure 1.

Scree plot of a 4-component YouTube addiction scale questionnaire. Keynote: Four components are having Eigen value greater than 1

Table 4.

Rotated structure matrix for principal component analysis with varimax rotation of a 4-component YouTube addiction scale questionnaire

Factor	Components	Item description	Rotated loading	% of variance	Eigen value
I	Impaired control	Less control over life	0.435	45.748	10.065
		Physically inactive	0.616
		Less priority to hobbies and leisure activities	0.762
		Difficult to manage life	0.579
		Feel disturbed if network is low	0.511
		Irritable if prohibited from use	0.526
		Searching for excuse during work for using YouTube	0.624
		Restless if prohibited from use	0.581
II	Decreased alternate pleasure	Not listened to anyone to reduce usage	0.650	7.773	1.710
		Quitting social engagement to use YouTube	0.730
		Thinking a lot about what has happened in YouTube	0.703
		Feeling sad if spent less time	0.535
		Fantasize being on YouTube when offline	0.749
		Thinking about planned use of YouTube	0.681
		Keep looking for next session on YouTube	0.561
III	Intense desire	Used YouTube to reduce feelings	0.496	6.227	1.370
		Use to forget about problem	0.736
		Spend more time than intended	0.643
		Urge to use	0.756
IV	Measures taken to reduce usage	Tried to reduce use but failed	0.851	5.387	1.185
		Time spent is exciting	0.811
		Decided to use less frequently	0.792

Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser Normalization

In-depth interview generated responses on the categories of concentration on other works, self-control activities, and family support. Increasing physical activity by joining a gym, deleting the YouTube app, and Self-control were the most common ways suggested to reduce You Tube usage [Table 5].

Table 5.

Ways to reduce you tube usage (n=30)

Themes	Codes
Concentration on other works	Increase physical activity by joining gym (11)
	Concentrate on other household works like gardening, cooking, and baking (10)
	Reading newspapers (10)
	Doing art works (8)
	Sleep, yoga and meditation (4)
Self-control activities	Deleting YouTube app (20)
	Self-control by minimize or avoid phone usage (16)
	Set alarm for limiting usage (6)
	Keep data limit for usage (6)
Family support	Increase conversation with family members, children, and friends (7)
	Decrease screen time usage by telling family members to intimate to stop usage (6)

“I spend less time in YouTube when I get involved myself in other activities such as by increasing physical activities and also by joining gym”. (20 years Male)

“By indulging in self-control activities such as setting alarm for limited usage and keeping data limit for usage I could reduce the YouTube usage”. (23 years Female)

Discussion

Gender distribution shows a near-equal split, with 50.9% male and 49.1% female respondents. Most respondents are aged 18–23 (70.8%) and reside in urban areas (83%). Educational attainment is primarily undergraduate (71.7%). Nearly all (97.2%) use social media, with Instagram (53.8%), and YouTube (39.6%) being most popular. YouTube usage is high, with 38.7% watching daily and 21.7% spending over 5 hours a day. The first four components of the scree plot are essential due to Eigenvalues above 1.

The survey findings indicate a high prevalence of social media usage, particularly YouTube, with over 97% of respondents actively using it. Instagram emerges as the most popular platform, followed closely by YouTube, highlighting the platform’s significant role in contemporary social media engagement. Similar results were found in the study conducted by Shyam et al. and Barthakur et al.[7,8]

YouTube usage patterns reveal that mobile phones are the preferred device for accessing the platform, with most respondents (88.7%) spending a considerable amount of time watching videos, often averaging more than five hours per day. Adiele et al.[9] had stated that reasons for online addiction was mainly influenced by extrinsic reason for internet use. Mak et al.[10] assessed internet addiction using the internet addiction test (IAT) and the revised chen internet addiction scale (CIAS-R) and found the prevalence of smartphone usage as 62 percentage. The frequency of visiting social media websites is notably high, indicating frequent and habitual engagement with online content. Further exploration into respondents’ attitudes and behaviours regarding YouTube usage uncovers several noteworthy trends. These include a significant proportion of individuals spending considerable time contemplating YouTube, experiencing urges to use it more frequently, and resorting to the platform for mood modification purposes.

Additionally, instances of relapse, withdrawal symptoms, and conflicts arising from excessive YouTube usage underscore the potential addictive nature of the platform and its impact on various aspects of users’ lives, such as hobbies, leisure activities, exercise, perceived control, and life management. The survey concludes by suggesting various strategies to reduce YouTube usage, including decreasing screen time, engaging in alternative activities such as art or physical exercise, limiting data usage, increasing social interactions, and practicing mindfulness through sleep and meditation. Studies also suggested that engaging in alternate activities, such as spending time with elders, aerobic exercise will reduce the usage of internet.[13,14,15,16,17,18]

Overall, the findings highlight the complex interplay between sociodemographic factors, online behaviours, and psychological responses to YouTube usage. They underscore the need for greater awareness, moderation, and intervention strategies to promote healthier online habits and mitigate the potential negative consequences associated with excessive YouTube usage.

Ours is one of the few studies assessed the YouTube addiction among adolescents and adults using a validated standard questionnaire. Limitation includes a limited sample size, response bias due to the face-to-face interview format, where participants might feel pressured to provide socially acceptable answers.

Conclusion

The results underscore various conflicts individuals experience because of their YouTube usage, including prioritization, perceived loss of control, reduced physical activity, and difficulties in life management. These findings highlight potential areas for intervention or behaviour modification to promote healthier YouTube usage habits. Hence, organizing digital well-being workshops and support groups to address social media addiction. Promote healthy habits and screen time breaks. Provide counselling services and parental guidance on monitoring usage. Advocate for policy changes to regulate screen time and support further research on addiction’s long-term effects.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.