Youth at Risk: Exploring Internet Addiction and Its Impact on South Asia’s Emerging Generation

Dr. Pankaj Jain, Sughosh Upasani,

doi:10.5281/zenodo.18118505

Research Paper | Open Access
Volume 03 | Issue 01 | Article Id IJSRT/250312175

Youth at Risk: Exploring Internet Addiction and Its Impact on South Asia’s Emerging Generation
Dr. Pankaj Jain* Sughosh Upasani
SES’s R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, 425405, Dhule, Maharashtra, India

Abstract

Background - The Indian subcontinent, a South Asian region with diverse cultural practices, linguistics varieties, religious beliefs, and geographical features, is home to India, Bhutan, Bangladesh, Nepal, Maldives, Sri Lanka, and Pakistan. An internet usage has increased globally, particularly in emerging nations like India. Internet addiction, a serious problem, is linked to mental health issues like ADD/ADHD, depression, anxiety, and alcoholism. Factors contributing to internet addiction include unstructured time, unrestricted access, cutting-edge equipment use, parental restrictions, virtual emotional expressions, and maintaining confidentiality. Methodology - An analytical investigation was conducted on the Indian subcontinent, involving 150 publications and a sample size of 58633. Data included demographics, internet usage, sample type, age, gender, and IA prevalence, analysed using relevant analytical tools. Result and Discussion - A study comparing 150 articles from Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka found that these countries have had the highest mean prevalence of internet addiction. The study suggests combining psychotherapy and pharmacological interventions, a "reality approach" to control behaviour, and a system to provide consistent assistance and alert users to internet usage restrictions. Educational institutions should focus on detecting and intervening before the problem escalates.

Keywords

Indian subcontinent, SAARC, Internet, Anxiety, Depression, Students

Introduction

Indian Subcontinent

The Indian subcontinent is a South Asian territory known for its many cultural traditions, linguistic variants, religious beliefs, and geographical qualities. This region is a subregion of Asia that includes the countries of India, Bhutan, Bangladesh, Nepal, the Maldives, Sri Lanka, and Pakistan. All of these countries, including Afghanistan, form the SAARC union. The South Asian Association for Regional Cooperation (SAARC) is South Asia's geographical union of states and regional intergovernmental organisation. India, one of the subcontinent's most populated countries, is well-known for its rich history, diverse cultural traditions, and multiple languages. New Delhi is the national capital of India. Pakistan is known for its diverse landscape, which includes mountains, plains, and a coastline, as well as its tangled history. Islamabad is Pakistan's capital city. Bangladesh is located to the east of India and is noted for its rivers, stunning landscapes, and a mix of urban and rural communities. Bangladesh's capital city is known as Dhaka. Nepal is a geographically isolated country situated mostly in the Himalayas. It is known for its breath-taking mountain beauty and many ethnic groups. Kathmandu is Nepal's major metropolis. Bhutan is a small country located in the eastern Himalayas and is landlocked. It is well-known for promoting Gross National Happiness as a measure of wealth. The capital city of Bhutan is known as Thimphu. Sri Lanka, an island nation to the south of India, is well-known not just for its beautiful beaches, but also for its rich history and diverse cultural influences. Colombo is Sri Lanka's major metropolis. The Maldives are a tropical archipelago in the Indian Ocean. They are noted for their coral reefs, white-sand beaches, and abundance of aquatic life. Male is the name of the capital of the Maldives. The Indian subcontinent has a long and complex history, moulded by numerous empires, religions, and cultures. The region is home to a sizable proportion of the world's population and is well-known for its cultural heritage, which includes Sikhism, Buddhism, Islam, and Hinduism. Furthermore, the subcontinent is home to a vast diversity of habitats, beginning with the Himalayan Mountain range and reaching all the way to coastal regions and fertile plains. [1-4]

Internet and Internet addiction

Worldwide, and especially in emerging nations like India, internet usage has skyrocketed since the turn of the century. [5-7] The internet has spread to every facet of human existence, from social communication and education to healthcare, finance, administration, retail, and entertainment, despite its humble beginnings as a tool for the exchange of information and study. [6] The internet has become an integral part of our daily lives to the point where we can't fathom a world without it. The number of people using the internet has surpassed three billion. [8] While technological advancement has undoubtedly improved and simplified our lives, it has also brought about the consequences. [5] Abuse of the internet and compulsive web surfing are real problems. Internet addiction, pathological internet use, online addiction disorder, pathological internet use, and online reliance are some of the terms used to describe this kind of unhealthy internet behaviour. [5,8-11] Although the negative consequences of spending too much time online were known for some time, it wasn't until the mid-1990s that internet addiction was officially acknowledged as a mental illness. [10] Researchers have paid close attention to it since then, and it is now thought of as a subset of behavioural addictions. The DSM-5 does not yet recognise online addiction as a distinct mental illness, although it does list internet gaming disorder as a "condition for further study”. [6, 9-11] Addiction to the internet can cause a host of physical, mental, and social issues, such as reduced productivity at work and in the classroom, poor nutrition, headaches, eye strain, social isolation, and problems in relationships. [10,11] There is strong evidence linking internet addiction to a variety of mental health issues, including ADD/ADHD, depression, anxiety, and alcoholism. [6, 9, 10, 12] The prevalence of internet addiction has been found to vary widely from 0.03% to 97.64 % in various studies, with the exact percentage varying widely across populations, research methods, and diagnostic tools. Additionally, due to their unique psychosocial and contextual traits, younger people, and particularly college students, are at a higher risk of vulnerability, according to the research. [10] Factors to be responsible -

Lot of time to spent unstructured.
Unrestricted internet access.
The need to make use of cutting-edge equipment
Fewer restrictions from parents.
Expressions of emotion for other people, virtually.
Maintaining confidentiality

Several personal, familial, and social traits, in addition to internet-related variables, have been postulated as potential predictors or risk factors for internet addiction by researchers. [10] The appropriate classification of Internet addiction has been debated. Some investigators have linked Internet addiction to addictive disorders, grouping it alongside alcohol and drug use disorders. [13] Others have linked Internet addiction to obsessive-compulsive disorder (OCD)[10] or to the impulse control disorders (ICDs).[14-16] The many names given to this phenomenon recognize the various ways in which it has been regarded:

Compulsive computer use, [17]
Pathological internet use, [18]
Problematic internet use, [19]
Internet dependency, [20]
Internet addiction [21] and even
Internet-mania. [22-23]

According to Young et al., [22] Internet addiction is a broad term covering a wide variety of behaviours and impulse control problems. The five subtypes of Internet addiction are as follows:

Computer addiction,
Cyber-relational addiction,
Cyber-sexual addiction,
Information overload, and
Net compulsions. [23]

Several screening instruments have been developed to assess Internet addiction, although none have emerged as the ‘gold standard’. Viz. Brenner, Egger and Rauterberg, Morahan-Martin and Schumacher, Shaw and Black, Young, etc. [23]

METHODOLOGY

For the Indian subcontinent, an analytical investigation was conducted. The study contained a total of 150 study publications, with a sample size of 58633. To avoid reporting bias, each study article was ensured that it would be coded, and that the information provided would not be the same or duplicate. The information gathered included demographic data, internet usage information, sample type, sample size, mean age of participants, gender preponderance, and IA prevalence. Data was statistically analysed using relevant tools and Microsoft Excel.

RESULTS AND DISCUSSION

Several personal, familial, and social traits, in addition to internet-related variables, have been postulated as potential predictors or risk factors for internet addiction by researchers. [10] Table No. 01 is of details of studies from total 150 articles included in this study from which Bangladesh (06), Bhutan (01), India (128), Nepal (07), Pakistan (06), and Shri Lanka (02). Figure No 01 shows the year of publication and number of studies.

Figure No 01 shows the year of publication and number of studies

Table No. 02 is of Details of studies included such as First author’s name (Surname/ Family name), Year of publication, Location of study sample, Sample type, sample size, prevalence in percentage, criteria used for determination of addiction or problematic use, and Gender preponderance. Table No. 02 is explaining diverse things amongst from which some are as follows. Our analysis, which takes gender differences into account, reveals that while Nepal has more women than men, Bangladesh, Pakistan, and Sri Lanka have more men than women. However, Bhutan and India are demonstrating an equal impact of men and women. In this study, we observed that, when we broke down the mean prevalence by nation, Bangladesh (31.16%), Bhutan (34.44%), India (27.71%), Nepal (40.38%), Pakistan (36.07%), and Sri Lanka (40.38%) were the highest. Bangladesh (27.25 years), Bhutan (27.6 years), India (21.35 years), Nepal (18.96 years), Pakistan (21.51 years), and Sri Lanka (16.65 years) are the countries with the highest mean ages. Comparing the sample size and population of country is as follows - Bangladesh (1115 out of 169,532,362), Bhutan (721 out of 787,424), India (47424 out of 1,425,775,850), Nepal (2227 out of 30,896,590), Pakistan (2905 out of 241,490,000), and Shri Lanka (4586 out of 21,893,579). The following countries are compared based on sample size and population: Bangladesh (1115 out of 169,532,362), Bhutan (721 out of 787,424), India (47424 out of 1,425,775,850), Nepal (2227 out of 30,896,590), Pakistan (2905 out of 241,490,000), and Sri Lanka (4586 out of 21,893,579). The internet and/or smartphone addiction can be measured using a variety of factors, but there isn't a single, definitive test to detect it with. The phrase addiction or overuse is highly subjective and psychological in nature, making it difficult to define and shape from a single perspective. The following tests or measurements, which were selected for this study after being mentioned in several publications: Chen Internet Addiction Scale - CIAS (01), Internet Addiction Scale - IAS (01), Internet Addiction Test - IAT (30), Internet Gaming Screening Questionnaire - IGSQ (02), Mobile Phone Involvement Questionnaire - MPIQ (01), Nomophobia Questionnaire - NQ (01), Smartphone Addiction Scale–Short Version - SAS-SV (12), Southampton mindfulness questionnaire - SMQ (02), Smartphone Addiction Inventory - SPAI (01), Young’s Internet Addiction Test - YIAT (93), and self-prepared Questionnaire (06).

Table 01 – Details of studies included.

Authors	Year	Location	Sample type	sample size	PRV (%)	Criteria used	Gender preponderance	Ref
Macro	2004	India	MED	165	58.1	IAT	Not Clear	6
Dixit	2010	India	MED	200	18.5	IAT	Not Clear	6
Neha	2012	India	MED	212	58	IAT	Not Clear	6
Aggarwal	2013	India	MED	192	23.9	SQ	Not Clear	6
Jain	2013	India	MED	200	69.5	IAT	Not Clear	6
Subba.,	2013	India	MED	335	78.8	IAT	Not Clear	6
Srijampana	2014	Andhra Pradesh	MED	211	0.4	YIAT	F > M	24
Karim	2014	Bangladesh	UGS	177	36	IAT	Not Clear	24
Malviya	2014	Madhya Pradesh	MED	242	9.5	YIAT	M > F	24
Sharma	2014	Madhya Pradesh	UGS	391	0.3	YIAT	M > F	24
Sulania	2015	Delhi	MED	202	15.4	YIAT	M > F	24
Chaudhari	2015	India	MED	300	52.5	YIAT	F > M	24
Krishnamurthy	2015	Karnataka	UGS	515	8.9	YIAT	Not Clear	24
Bhatt	2015	Kashmir	UGS	130	30	YIAT	Not Clear	24
Kawa	2015	Kashmir	UGS	100	4	YIAT	Not Clear	24
Chaudhari	2015	Maharashtra	MED	282	7.4	YIAT	M > F	24
Nath	2016	Assam	MED	188	0.50%	YIAT	M > F	24
Islam	2016	Bangladesh	UGS	573	24	IAT	Not Clear	24
Uddin	2016	Bangladesh	UGS	475	38.3	IAT	Not Clear	24
Nagori	2016	Gujrat	MED	525	0.90%	YIAT	M > F	24
Sharma	2016	India	SCH	1386	26.96	NQ	M > F	24
Prabhakaran	2016	India	UNCL	724	8.7	IAT	Not Clear	24
Bhat	2016	Karnataka	UGS	1763	0.80%	YIAT	Not Clear	24
Gedam	2016	Maharashtra	MED	390	2.30%	YIAT	M > F	24
kshatri	2016	Odisha	MED	506	0.60%	YIAT	M > F	24
Subhaprada	2017	Andhra Pradesh	MED	95	24.2	YIAT	M > F	24
Afrin	2017	Bangladesh	SCH	279	67.4	IGSQ	Not Clear	24
Prakash	2017	India	MDS	169	13	SQ	F > M	24
Patel AV	2017	India	UGS	200	32	SAS-SV	Not Clear	24
Mutalik	2017	Karnataka	MED	934	0.60%	YIAT	M > F	24
Kumar	2017	Madhya Pradesh	MED	349	6	YIAT	F > M	24
Gedam	2017	Maharashtra	MED	846	0.40%	YIAT	M > F	24
Pati	2017	Maharashtra	MED	488	3.70%	YIAT	M > F	24
Bhandari	2017	Nepal	UNCL	937	35.4	IAT	Not Clear	24
Niranjjan	2017	Pondicherry	MED	200	16.5	YIAT	M > F	24
Priya	2017	Uttar Pradesh	MED		5.80%	YIAT	Not Clear	24
Gupta	2018	Delhi	UGS	380	25.3	YIAT	M > F	24
Patel	2018	Gujarat	MED	139	16.6	YIAT	M > F	24
Damor	2018	Gujrat	MED	313	0.30%	YIAT	M > F	24
Ammati	2018	India	MED	328	36.8	SMQ	M > F	24
Mangot AG	2018	India	MED	93	41	SAS-SV	Not Clear	24
Nida N	2018	India	MED	236	34.4	SAS-SV	Not Clear	24
Prasad S	2018	India	MED	140	36	SAS-SV	Not Clear	24
Shankar V	2018	India	STD	193	40.93	MPIQ	M > F	24
Chaudhari P	2018	India	UGS	222	70	IAS	Not Clear	24
Nayak JK	2018	India	UGS	429	45	IAT	F > M	24
Anand	2018	Karnataka	UGS	2776	0.5	YIAT	Not Clear	24
Anand	2018	Karnataka	UGS	1086	0.4	YIAT	Not Clear	24
Sharma	2018	Karnataka	UGS	1304	0.5	YIAT	M > F	24
Suresh	2018	Karnataka	MED	150	0.60%	YIAT	Not Clear	24
Anand	2018	Karnataka & Kerala	MED	1763	0.8	YIAT	Not Clear	24
Thakur	2018	Madhya Pradesh	UGS	425	1.3	YIAT	M > F	24
Singh	2018	Punjab	MED	122	19.7	YIAT	Not Clear	24
Kumar	2018	West Bengal	UGS	200	39.50%	YIAT	Not Clear	24
Tenzin	2019	Bhutan	ADS	721	34.44	IAT	F = M	24
Bhatt	2019	Himachal Pradesh	MED	320	23.4	YIAT	Not Clear	25
Abilash	2019	India	UGS	300	49	YIAT	Not Clear	25
Grover	2019	India	MDS	376	8.24	IAT	M > F	25
Dharmadhikari	2019	India	MED	195	46.15	SAS-SV	Not Clear	25
Nagori	2019	India	MED	525	9.3	IAT	M > F	8
Daniel	2019	India	MED	81	12.6	YIAT	Not Clear	26
Kamate	2019	India	UGS	500	61	SQ	Not Clear	27
Kandasamy	2019	India	UGS	200	26	YIAT	Not Clear	28
Jain P	2019	India	MED	146	24.65	SAS-SV	Not Clear	28
Kannan	2019	India	MED	201	17.4	IAT	Not Clear	28
Kumar VA	2019	India	MED	150	44.7	SAS-SV	F = M	29
Padmanabha	2019	India	MED	115	63.48	IAT	M > F	6
Patel	2019	India	MED	172	5.73	IAT	M > F	24
Sancheti	2019	India	UGS	360	2.56	IAT	F > M	7
Andrusha	2019	India	MED	27	51.9	YIAT	Not Clear	9
Asokan	2019	India	MED	381	61.4	YIAT	Not Clear	6
Bagdey	2019	India	UGS	400	26.86	YIAT	Not Clear	11
chaudhari	2019	India	UGS	201	73.7	YIAT	Not Clear	24
Ghanate	2019	India	UGS	700	19.1	YIAT	Not Clear	5
Kulkarni	2019	India	ADS	469	29.63	SQ	M > F	6
Kumar	2019	India	STD	426	31.69	YIAT	M > F	30
Kunnathoor	2019	India	STD	278	17	SQ	M > F	31
Mavatkar	2019	India	MED	138	36.23	IAT	F > M	32
Mohandas	2019	India	UGS	390	18.4	IAT	F > M	33
Renuka	2019	India	GNP	214	27.6	SAS-SV	M > F	34
Javaeed	2019	Jammu Kashmir	MED	210	52.4	YIAT	Not Clear	35
Javaeed	2019	Jammu Kashmir	MED	316	87.1	YIAT	Not Clear	10
Ghanate	2019	Karnataka	MED	700	1.7	YIAT	Not Clear	36
Veena	2019	Karnataka	UGS	455	0.7	YIAT	M > F	12
Asokan	2019	Kerala	MED	381	0.5	YIAT	Not Clear	37
Kishore	2019	Kolkata	MED	147	2.7	YIAT	Not Clear	38
Kannan	2019	Puducherry	MED	201	97.4	YIAT	M > F	39
Hassan	2020	Bangladesh	UNS	545	27.1	IAT	Not Clear	40
Nathawat	2020	Goa	UGS	200	15	YIAT	F = M	41
Kandre	2020	Gujarat	MED	247	0.9	YIAT	Not Clear	42
Sai	2020	India	MED	233	37.06	YIAT	F > M	43
Abhinaya	2020	India	MED	172	54.6	YIAT	F > M	44
Gupta	2020	India	UGS	205	23	YIAT	Not Clear	45
Awasthi	2020	India	MED	221	5.9	YIAT	F > M	46
Jain	2020	India	MED	466	31.96	YIAT	M > F	47
Kaur	2020	India	MED	250	1.2	YIAT	M > F	48
Sharma., et al	2020	India	CDS	2	0	SQ	Not Clear	49
Chattopadhyay	2020	India	MED	200	52	YIAT	Not Clear	50
Singh	2020	India	UGS	297	62.28	SMQ	M > F	51
Agrawal	2020	India	ADS	450	46.77	YIAT	M > F	52
Basu	2020	India	MED	424	37	IGSQ	M > F	53
BHNADARI	2020	India	ADS	267	44.2	IAT	M > F	54
Kandre	2020	India	MED	427	15.09	YIAT	M > F	55
Oswal	2020	India	MED	523	22.2	SPAI	F = M	56
Prakash	2020	India	GNP	350	20	IAT	Not Clear	57
Pathak	2020	Karnataka	MED	150	4	YIAT	M > F	58
Parvathy	2020	Kerala	MED	368	22.8	YIAT	Not Clear	59
Mukherjee	2020	Kolkata	MED	150	19.3	YIAT	M > F	60
Murarkar	2020	Maharashtra	MED	303	0.3	YIAT	Not Clear	60
Sohail	2020	Pakistan	MED	87	43	IAT	F > M	61
Jain	2020	Rajasthan	UGS	957	15.5	YIAT	M > F	62
Jaiswal	2020	Rajasthan	MED	307	3.3	YIAT	Not Clear	63
Gayathri	2020	Tamil Nadu	MED	300	2.3	YIAT	M > F	64
Aqeel	2020	Uttar Pradesh	MED	488	0.8	YIAT	M > F	65
Srivastava	2020	Uttar Pradesh	UGS	133	0.7	YIAT	Not Clear	66
Awasthi	2020	Uttarakhand	MED	221	5.9	YIAT	Not Clear	67
Dhamnetiya	2021	India	MED	201	41.2	YIAT	M > F	68
Raveendran	2021	India	ADS	227	59	YIAT	M > F	69
Mariavinifa	2021	India	UGS	500	24.2	YIAT	F > M	70
Razik	2021	India	UGS	497	18.7	YIAT	F > M	71
Mengistu	2021	India	UGS		18.88	IAT	Not Clear	72
Salunkhe	2021	India	ADS	200	67.5	YIAT	Not Clear	73
Yamuna	2021	India	ADL	50	18	YIAT	F = M	74
Patil	2021	India	SCH	50	52	IAT	Not Clear	75
Kumar	2021	India	UGS	235	4..68	YIAT	M > F	76
Sebastian	2021	India	MED	487	67.6	SAS-SV	F > M	77
Singh H	2021	India	ADS	479	22.31	YIAT	Not Clear	78
Chatterjee	2021	India	MED	224	40	SAS-SV	M > F	79
Gupta	2021	India	MED	77	77	SAS-SV	F > M	80
Nayak A	2021	India	MED	148	87.16	IAT	Not Clear	81
Pal	2021	India	MED	280	58.57	YIAT	M > F	82
MengistuNE	2021	Nepal	UGS	220	42	IAT	Not Clear	83
Singh S	2021	Nepal	SCH	144	50.65	YIAT	M > F	84
Singh B	2021	Nepal	MED	506	39.33	YIAT	F > M	85
Lakhdir	2021	Pakistan	GNP	1145	38.8	YIAT	F > M	86
Memon	2021	Pakistan	MED	263	85.17	YIAT	M > F	88
MengistuPK	2021	Pakistan	UGS	125	16.7	IAT	Not Clear	87
Naseem	2021	Pakistan	MED	345	34	YIAT	F = M	88
Gunathillaka	2021	Shri Lanka	SCH	2800	8	YIAT	Not Clear	89
Chowdhury	2022	Bangladesh	ADL	315	39.7	YIAT	Not Clear	90
Chauhan	2022	India	MED	250	51.6	YIAT	M > F	91
Ghogare	2022	India	GNP	412	45.1	SAS-SV	Not Clear	92
Patel	2022	India	SCH	384	32.5	YIAT	M > F	93
Dawadi	2022	Nepal	MED	229	25.62	YIAT	M > F	94
Din	2022	Pakistan	UGS	440	30	CIAS	M > F	95
Ariyadasa	2022	Shri Lanka	ADS	1351	17.2	IAT	M > F	96
Goel	2023	India	UGS	987	25.5	YIAT	F > M	97
Shahi	2023	India	MED	200	28	YIAT	M > F	98
Acharya	2023	Nepal	UGS	344	29.9	YIAT	F > M	99

Abbreviations used – Sample Type - ADL = Adult, ADS = Adolescents, CDS = Case study, GNP = General population/ random sampling, MDS = Faculties of health sciences and Medical education, MED = Medical students, SCH = Secondary / high school students, STD = Students, UGS = under graduates, UNCL = Unclear, and UNS = unspecified. Criteria Used - CIAS = Chen Internet Addiction Scale, IAS = Internet Addiction Scale, IAT = Internet Addiction Test, IGSQ = Internet Gaming Screening Questionnaire, MPIQ = Mobile Phone Involvement Questionnaire, NQ = Nomophobia Questionnaire, SAS-SV = Smartphone Addiction Scale–Short Version, SMQ = Southampton mindfulness questionnaire, SPAI = Smartphone Addiction Inventory, YIAT = Young’s Internet Addiction Test, and SQ = self-prepared Questionnaire. Gender – M = Male, F = Female.

Table 02 – Country wise demonstration of sample size and prevalence.

Country	Sample size	Mean Prevalence %	Age (Average)	Gender preponderance
Bhutan	721	34.44	27.6	F = M
Bangladesh	1115	31.16	27.25	M > F
Nepal	2227	40.38	18.96	F > M
Pakistan	2905	36.07	21.51	M > F
Shri Lanka	4586	40.38	16.65	M > F
India	47424	27.71	21.35	F = M
Total/ Mean	58978	34.77	22.22	M > F

A significant amount of progress is being made in the field of smartphone technology, and the number of individuals who use these devices is continuously increasing. Over the course of the last decade, the addiction to cell phones has been acknowledged as a type of behavioural addiction known as smartphone addiction. [36] The most recent revelation is that addiction to smart phones and/or the internet is a widespread phenomenon that can be found throughout all sectors of civilization. However, the number of people who are addicted to these technologies varies from one sector to another. Out of 150 research included in this study most of studies have been done on Medical students and Faculties of health sciences and Medical education 82 (54.66 %), then higher number of under graduates 41 (27.33 %). Other studies are Adult 02 (1.33%), Adolescents 08 (05.33 %), Case study 01 (0.67 %), General population/ random sampling 04 (2.67 %), Secondary / high school students / Students 09 (06 %), and Unclear/ unspecified 03 (02 %). The average prevalence of Medical students and Faculties of health sciences and Medical education is 27.23%, but higher found in Adult studies which is 57.7 %. There is a possibility that a combination of psychotherapy and specific pharmacological therapies could play a significant role in the decrease of the addiction that was mentioned earlier. In addition, it is strongly recommended to follow what is known as "the approach to reality," which entails requesting that the patient focus on his or her own actions and use motivational interviewing techniques. Keeping a person's score under control can be accomplished by encouraging them to take part in activities such as meditation and activities that take place outside. The topic of how to prevent it is a very difficult one to answer; one alternative is to design a system that gives users with regular help and informs them if they exceed the restriction. This is one of the possibilities. [11] It is probable that the free and uninterrupted availability of Internet facilities throughout the campus is the reason why everything on campus has a smartphone and why there has been a growth in the number of students using smartphones. [31]

CONCLUSION

According to the most recent data, the number of people who are addicted to the internet is growing at an alarming rate, which is a significant issue that requires immediate attention and resolution. The higher percent of Indians, older than eighteen years, are addicted to their smart phones. It is of greater importance to educational and research-based organisations to identify individuals who are addicted to the internet. It is therefore necessary for college administrators and parents alike to pay a greater amount of attention to identify students who are at danger and to act before the situation becomes out of control. There is a pressing need to emphasise the significance of instructing children on how to use the internet in a manner that is both healthy and secure.

ACKNOWLEDGEMENT

The authors are thankful to Principal, R.C. Patel Institute of Pharmaceutical Education and Research Shirpur, Dist. Dhule (MS) India- 425 405 for providing necessary library facilities.

DECLARATION OF INTERESTS

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics Approval Statements That Refer to Your Institution

Not Applicable.

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Chaudhari B, Menon P, Saldanha D, Tewari A, Bhattacharya L. Internet addiction and its determinants among medical students. Ind Psychiatry J 2015; 24:158-62.
Nowreen N, Ahad F. Effect of smartphone usage on quality of sleep-in medical students. Natl J Physiol Pharm Pharmacol 2018;8(10):1366-1370.
Jain P, Gedam SR, Patil PS. Study of smartphone addiction: prevalence, pattern of use, and personality dimensions among medical students from rural region of central India. Open J Psychiatry Allied Sci. 2019; 10:132-8. doi: 10.5958/2394-2061.2019.00029.6.
Shankar V, Singh K, Jangir MK, NOMOPHOBIA: Detection and Analysis of Smartphone Addiction in Indian Perspective, IJAER, 2018, (13), 11593-11599
Kumar VA, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Ind Psychiatry J. 2019 Jan-Jun;28(1):82-85. doi: 10.4103/ipj.ipj_56_19. Epub 2019 Dec 11.
Christenson GA, Faber RJ, de Zwaan M, Raymond NC, Specker SM, Ekern MD, Mackenzie TB, Crosby RD, Crow SJ, Eckert ED, et al. Compulsive buying: descriptive characteristics and psychiatric comorbidity. J Clin Psychiatry. 1994 Jan;55(1):5-11.
Whang LS, Lee S, Chang G. Internet over-users' psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychol Behav. 2003 Apr;6(2):143-50. doi: 10.1089/109493103321640338.
Greenfield DN. Psychological characteristics of compulsive internet use: a preliminary analysis. Cyberpsychol Behav. 1999;2(5):403-12. doi: 10.1089/cpb.1999.2.403.
Treuer T, Fábián Z, Füredi J. Internet addiction associated with features of impulse control disorder: is it a real psychiatric disorder? J Affect Disord. 2001 Oct;66(2-3):283. doi: 10.1016/s0165-0327(00)00261-5.
Yoo HJ, Cho SC, Ha J, Yune SK, Kim SJ, Hwang J, Chung A, Sung YH, Lyoo IK. Attention deficit hyperactivity symptoms and internet addiction. Psychiatry Clin Neurosci. 2004 Oct;58(5):487-94. doi: 10.1111/j.1440-1819.2004.01290. x.
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Belsare TJ, Gaffney GR, Black DW. Compulsive computer use. Am J Psychiatry. 1997 Feb;154(2):289. doi: 10.1176/ajp.154.2.289a.
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Joseph J, Varghese A, VR V, et al. Prevalence of internet addiction among college students in the Indian setting: a systematic review and meta-analysis. General Psychiatry 2021;34: e100496. doi:10.1136/gpsych-2021-100496
Mamun, M.A.; Griffiths, Mark D. (2019). The assessment of internet addiction in Bangladesh: Why are prevalence rates so different? Asian Journal of Psychiatry, doi: 10.1016/j.ajp.2019.01.017
Sharma M, Amandeep, Mathur DM, Jeenger J. Nomophobia and its relationship with depression, anxiety, and quality of life in adolescents. Ind Psychiatry J 2019; 28:231-6
Prabhakaran MA, Patel VR, Ganjiwale DJ, Nimbalkar MS. Factors associated with internet addiction among school-going adolescents in Vadodara. Journal of family medicine and primary care. 2016 Oct;5(4):765.
Buneviciene I, Bunevicius A. Prevalence of internet addiction in healthcare professionals: Systematic review and meta-analysis. International Journal of Social Psychiatry. 2021;67(5):483-491. doi:10.1177/0020764020959093
Patel AV, Patel AP, Patel DV, Patel DM, Patel DR, Patel JB, Suresh V. Assessment of smartphone addiction among undergraduate students of Sumandeep Vidyapeeth, Vadodara. International Journal of Nursing Education and Research. 2017;5(1):16-8.
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Chaudhari B, Menon P, Saldanha D, Tewari A, Bhattacharya L. Internet addiction and its determinants among medical students. Ind Psychiatry J 2015; 24:158-62.
Nowreen N, Ahad F. Effect of smartphone usage on quality of sleep-in medical students. Natl J Physiol Pharm Pharmacol 2018;8(10):1366-1370.
Jain P, Gedam SR, Patil PS. Study of smartphone addiction: prevalence, pattern of use, and personality dimensions among medical students from rural region of central India. Open J Psychiatry Allied Sci. 2019; 10:132-8. doi: 10.5958/2394-2061.2019.00029.6.
Shankar V, Singh K, Jangir MK, NOMOPHOBIA: Detection and Analysis of Smartphone Addiction in Indian Perspective, IJAER, 2018, (13), 11593-11599
Kumar VA, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Ind Psychiatry J. 2019 Jan-Jun;28(1):82-85. doi: 10.4103/ipj.ipj_56_19. Epub 2019 Dec 11.
Christenson GA, Faber RJ, de Zwaan M, Raymond NC, Specker SM, Ekern MD, Mackenzie TB, Crosby RD, Crow SJ, Eckert ED, et al. Compulsive buying: descriptive characteristics and psychiatric comorbidity. J Clin Psychiatry. 1994 Jan;55(1):5-11.
Whang LS, Lee S, Chang G. Internet over-users' psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychol Behav. 2003 Apr;6(2):143-50. doi: 10.1089/109493103321640338.
Greenfield DN. Psychological characteristics of compulsive internet use: a preliminary analysis. Cyberpsychol Behav. 1999;2(5):403-12. doi: 10.1089/cpb.1999.2.403.
Treuer T, Fábián Z, Füredi J. Internet addiction associated with features of impulse control disorder: is it a real psychiatric disorder? J Affect Disord. 2001 Oct;66(2-3):283. doi: 10.1016/s0165-0327(00)00261-5.
Yoo HJ, Cho SC, Ha J, Yune SK, Kim SJ, Hwang J, Chung A, Sung YH, Lyoo IK. Attention deficit hyperactivity symptoms and internet addiction. Psychiatry Clin Neurosci. 2004 Oct;58(5):487-94. doi: 10.1111/j.1440-1819.2004.01290. x.
Brenner V. Psychology of computer use: XLVII. Parameters of Internet use, abuse and addiction: the first 90 days of the Internet Usage Survey. Psychol Rep. 1997 Jun;80(3 Pt 1):879-82. doi: 10.2466/pr0.1997.80.3.879.
Holden C. 'Behavioral' addictions: do they exist? Science. 2001 Nov 2;294(5544):980-2. doi: 10.1126/science.294.5544.980.
Belsare TJ, Gaffney GR, Black DW. Compulsive computer use. Am J Psychiatry. 1997 Feb;154(2):289. doi: 10.1176/ajp.154.2.289a.
Stein DJ. Internet addiction, Internet psychotherapy. Am J Psychiatry. 1997 Jun;154(6):890. doi: 10.1176/ajp.154.6.890.
Aboujaoude E, Koran LM, Gamel N, Large MD, Serpe RT. Potential markers for problematic internet use: a telephone survey of 2,513 adults. CNS Spectr. 2006 Oct;11(10):750-5. doi: 10.1017/s1092852900014875.
Shaw M, Black DW. Internet addiction: definition, assessment, epidemiology and clinical management. CNS Drugs. 2008;22(5):353-65. doi: 10.2165/00023210-200822050-00001.
Joseph J, Varghese A, VR V, et al. Prevalence of internet addiction among college students in the Indian setting: a systematic review and meta-analysis. General Psychiatry 2021;34: e100496. doi:10.1136/gpsych-2021-100496
Mamun, M.A.; Griffiths, Mark D. (2019). The assessment of internet addiction in Bangladesh: Why are prevalence rates so different? Asian Journal of Psychiatry, doi: 10.1016/j.ajp.2019.01.017
Sharma M, Amandeep, Mathur DM, Jeenger J. Nomophobia and its relationship with depression, anxiety, and quality of life in adolescents. Ind Psychiatry J 2019; 28:231-6
Prabhakaran MA, Patel VR, Ganjiwale DJ, Nimbalkar MS. Factors associated with internet addiction among school-going adolescents in Vadodara. Journal of family medicine and primary care. 2016 Oct;5(4):765.
Buneviciene I, Bunevicius A. Prevalence of internet addiction in healthcare professionals: Systematic review and meta-analysis. International Journal of Social Psychiatry. 2021;67(5):483-491. doi:10.1177/0020764020959093
Patel AV, Patel AP, Patel DV, Patel DM, Patel DR, Patel JB, Suresh V. Assessment of smartphone addiction among undergraduate students of Sumandeep Vidyapeeth, Vadodara. International Journal of Nursing Education and Research. 2017;5(1):16-8.
Dharmadhikari SP, Harshe SD, Bhide PP, Prevalence and correlates of excessive smartphone use among medical students: A cross sectional study. Indian J Psychol Med 2019; 41:549-55.
Karki K, Singh DR, Maharjan D, KC S, Shrestha S, Thapa DK. Internet addiction and sleep quality among adolescents in a peri-urban setting in Nepal: A cross-sectional school-based survey. PLoS One. 2021 Feb 18;16(2):e0246940.. DOI: http://dx.doi.org/10.18203/2320-6012.ijrms20192903
Daniel P. Pattern of internet use and prevalence of internet addiction among interns of a medical college in Kerala. Int J Community Med Public Health 2019; 6:5, 1-5. 10.18203/2394-6040.ijcmph20191508
Kamate R, Baliga S, Mallapur MD. Internet addiction among engineering students in Belagavi city: cross sectional study. International Journal of Community Medicine and Public Health. 2019 Apr;6(4):1488. 10.18203/2394-6040.ijcmph20191372
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Dr. Pankaj Jain

Corresponding author

SES’s R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, 425405, Dhule, Maharashtra, India

Sughosh Upasani

Co-author

SES’s R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, 425405, Dhule, Maharashtra, India

Dr. Pankaj Jain*, Sughosh Upasani, Youth at Risk: Exploring Internet Addiction and Its Impact on South Asia’s Emerging Generation, Int. J. Sci. R. Tech., 2025, 2 (12), 1-13. https://doi.org/10.5281/zenodo.18118505

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Youth at Risk: Exploring Internet Addiction and Its Impact on South Asia’s Emerging Generation

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Dr. Pankaj Jain

Sughosh Upasani

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