Given the above varied theoretical perspectives, psychology focuses on technological change and the social environment that has affected human behaviour. The chain reactions that the internet triggered took a while to become effective. Though the internet was introduced to the public after the early 1990s [7], it did not have an immediate effect due to its inaccessibility for everyone and lack of corresponding technological advancements. However, with the invention of mobile phones and other electronic goods, the internet, which was restricted to computers and laptops, has become accessible to everyone, thus increasing its scope to a greater extent. Affordability, availability, user-friendly technology, and improved technical knowledge of the users have increased the extent of internet usage. However, all is not well with these developments as according to Dr. Kimberly Young, one of the first psychologists to identify problematic internet usage [8], the behaviour of an individual becomes problematic as the daily activities and responsibilities of the individual are being neglected due to intensive use of the internet. This assertion drew the attention of several psychologists to focus on the link between the internet and mental health and they sought various models for explaining problematic internet usage and internet addiction.

Several models explain internet usage and among them, the important ones are the cognitive-behavioural model of problematic internet use [9], the model of Anonymity, Convenience, and Escape (ACE) [10], Grohol’s three-stage model [11], and the Interaction of Person-Affect-Cognition (I-PACE) model [12] of specific Internet-use disorders.

The cognitive-behavioural model of problematic internet use explains the role of cognitions in internet usage. The model differentiates generalized problematic internet usage from specialized problematic internet usage, and the latter refers to using the internet for specified purposes like online gaming, e-sex, online gambling, etc. Generalized usage refers to using the internet for a wider range of purposes. In both of these usages of the internet, the common factors are the specific cognitions for the use and the withdrawal of the internet that cause behavioural problems in affected individuals. Cognitions in this model are further specified as maladaptive cognitions that refer to the fact that individuals have faulty cognitive perceptions either about themselves or the external world in general. These faulty cognitions would enable the individual to prefer the online world instead of the real world, thereby affecting the individual using the internet to a greater extent than is required and leading to internet addiction.

The Anonymity, Convenience, and Escape model presents the internet as a safe space for the user’s anonymity and escape by being the most convenient. The individual can be who he/she wants to be without being identified and can escape the harsh reality of life, that too at his/her fingertips via the mobile phone. The model explains these factors as being effective reasons for becoming addicted to the internet.

The three-stage model of Grohol emphasizes three stages that individuals go through in using the internet. In the initial or first stage, the individual explores the workings of the internet out of curiosity and as the individual improves his/her understanding of the application and benefits, he/she begins to be addicted to the usage and thus ushers the second stage. In the third phase, the individual begins to realize the perils of becoming an addict and tries to balance the time being spent on the internet.

The Person-Affect-Cognition-Execution model explains specific internet use disorders as a result of the interaction between predisposing variables and moderating and mediating variables. The predisposing variables are the neurobiological and psychological constitutions of the individual besides the cognitive and affective reactions to the internet as the mediating and the moderating variables. According to this model, the individual’s core characteristics, along with other components, such as cognitive aspects, decision-making, executive processes, and preferences of the individual all work together to reinforce the internet usage behavior, thus resulting in addiction.

All these models rely on the common feature of individual mental health and well-being without actually emphasising it. Therefore, the present study takes into consideration an individual’s mental health, gender, and internet use by adopting the model of Anonymity, Convenience, and Escape. It tries to find the predictive value of mental health on internet usage.

The World Health Organization (WHO) defines mental health as a state of well-being in which an individual realizes his or her abilities, can cope with the normal stresses of life, can work productively, and can make a contribution to his or her community [13]. The American Psychological Association (APA) defines mental health as a state of mind characterized by emotional well-being, good behavioural adjustment, relative freedom from anxiety and disabling symptoms, and a capacity to establish constructive relationships and cope with the ordinary demands and stresses of life [14]. Both these definitions imply that mental health is the state in which an individual is the best of who they are in all respects, such as behaviour, well-being, and psychological fitness. Mental health reflects the individual’s ability to think and behave in ways that enhance his/her well-being and potential.

There are various risk factors that impair mental health, and these are broadly grouped under genetic and environmental factors. The genetic factors comprise the biological traits carried to the individuals through genes on either side of the family. Environment factors comprise various sub-divisions, such as the home, neighbourhood, workplace, etc. Addictive behavior, especially alcoholism, was seen to be a coping mechanism among the UK police who were under occupational stress [15]. In today’s world, the environment is differentiated into the physical and virtual. It is established through research that the physical environment does impact to a great extent the mental health of individuals diagnosed with dementia, and certain changes in their physical environment do help them in their daily activities [16]. The virtual world, however, is another environmental factor that contributes to the mental state of the individual [17, 18].

The mechanism by which one gets addicted to substances has been identified to be the same as in addiction to internet gaming and gambling. Hence, among the various mental health disorders included in the DSM, internet gaming has been added recently to the DSM 5 alongside internet gambling and other addictive disorders [19]. The inclusion confirms that this particular component of the environment, the virtual world, is impacting mental health.

The advancements in the fields of science and technology, especially those relating to the internet have been instrumental in a revolutionary change in the social ecosystem in the recent past. In this regard, the pandemic played an incremental role to an extent that even those individuals who were not influenced much earlier have been drawn to the internet user fold. However, it is interesting to note that every individual is not affected the same way and neither is every individual using the internet. In this context, the situation of students in Indian academia is a specimen as the conventional face-to-face teaching environment suddenly changed to online, thus resulting in high dependency on the internet. Given the changed socio-eco and educational system, it would be appropriate to examine internet usage among postgraduate students as they are the ones who have the compulsion to use the internet for academic purposes. As the prior research indicates a negative association between internet usage and mental health, the present study is intended to identify whether the mental status of the individuals moderates internet usage. This gives rise to the question, is the internet a boon to those with distorted cognitive thinking and helping them to get through life? A brief review of the literature in this regard is presented in this article.

Postgraduate students use social networking sites on the internet for many reasons and among them, the prominent one is to make new friends besides getting in touch with past friends [20] for academic purposes [21]. For this, they mainly use the internet. It has been reported in the literature review that women engaged in this more than men [22]. Moreover, they further use it to take help in homework, expand the horizons of research, and promote self-learning [23]. Meerah [24] emphasized that becoming well acquainted with technology and improving one’s skill and knowledge in this field is very important for researchers as the internet and technology are being upgraded and updated at an exceedingly fast pace. Students, if aware of only a few methods and ways to access the internet, would not be able to use the internet to the fullest in accessing all the information that is made available to them.

The internet has not been used uniformly among students. It has been found that students belonging to the Department of Business Administration used the internet most compared to the Department of Library and Information Science and the Department of Mass Communication [20]. Similarly, doctoral students use the internet more than the masters’ level students [22].

The period for which they used the internet was found to be an average of 1-2 hours before the COVID-19 pandemic [21]. For the reasons best known during and post-pandemic, the time spent must have been more. The post-pandemic effects on internet usage were found to be both positive and negative. The positive consequences were that the students learned new technical skills, did courses online, and gathered a wealth of information. Along with academic improvement, there was also career advancement through internet usage. The primary negative consequence was that the students wasted a large amount of time just scrolling aimlessly.

The pandemic brought about a huge change in the education system in the form of online teaching, e-libraries, and e-classrooms. This change was assumed to break down barriers and be of advantage to all students, including those with mental health problems. However, research has made it evident that this is not the case, and e-learning has not addressed the problems of those with mental health disabilities [25].

From the above studies, it can be inferred that internet usage among postgraduate students is quite prevalent. The internet among this particular population is used primarily for research and academic purposes. Furthermore, the usage of the internet also varies with discipline as well as gender.

Almost half of the students were found with emotional or stress-related problems and more than half of the respondents reported knowing someone having an emotional or stress-related problem in a study in the USA. These problems were largely due to financial status, interpersonal relationships with academic advisors or fellow students and marriage [26], changes in the social environment and health policies [27], academic culture, and structural inequalities [28].

A review of literature on the mental health of college students reported four primary topics viz., the current state of mental health issues among students, risk factors, worsening of mental health in recent years, and nicotine as a substance of abuse. Furthermore, males suffered from suicidal tendencies and suicidal ideation, whereas females suffered from depression and anxiety [29].

Among the Danish young people aged 16-29 years, 24% out of the sample of 3146 presented with poor mental health. The risk of poor mental health differed between the genders, with females having a higher adjusted risk of poor mental health [30]. Though the incidences of suicide due to depression and other mental health issues are increasing with time, young individuals with mental health are not reaching out for help in the conventional way of approaching mental health counsellors or psychologists rather, they are using mobile app-based help to address their problems. However, these applications are not used to their fullest extent as the most affected individuals look for personalization, information availability, and ease of use on the application [31].

In an Indian sample of 300 postgraduate students, mental health discrepancies among the genders, especially in the humour component were found, and there was a significant relationship between mental health and success [32]. In a longitudinal study in China, 28% of postgraduate students were found to have been affected by mental distress, and there was an increase of 16% in this distress between 2000-2019 [27].

The above studies reflect that there are mental health issues that are very prevalent among young students. The extent of the mental health issues varies with gender and other socio-cultural factors, academic culture, substance abuse, etc. It was also found that the type of mental health problems differs with gender and other socio-demographic variables.

The research design adapted for the present study was a descriptive correlational study. The study intended was not experimental, it required a large sample for discerning the variability in internet usage and mental health. Therefore, a survey was organized wherein the self-reporting of participants was adopted.

The study seeks to describe mental health issues and internet usage as well as to examine the relationship among these variables. Understanding the relationship between the two is mandatory as the virtual world is penetrating the physical world to such an extent that both have become indistinguishable. The population chosen are the postgraduate students of Andhra Pradesh and Telangana. The particular population chosen as the young adults are those being impacted most by society, internet, and subject to mental health issues. The study is not associated with any funding and has been approved by the Research and Development cell of Sri Venkateswara University, Tirupati, India, as part of Ph.D. research work (Reference number for ethics committee approval: 2462023001).

The sample (N = 1040) was randomly drawn from the postgraduate students pursuing master’s programmes in Sciences, Social Sciences, Humanities, Commerce, Business Administration, and others in six universities located in Andhra Pradesh and Telangana states in India. The total 1040 comprised of N = 507 males (48.8%), a total of N = 530 females (51%), and N = 3 of the third gender. The sampling method adopted is proportion to the population in terms of gender, that is, the male and female students pursuing postgraduate studies in the university.

The gender distribution of the sample matches with the data provided by the Government of India [33] All India Survey on Higher Education 2020- 2021, Ministry of Education, Department of Higher Education, New Delhi, for universities in Andhra Pradesh and Telangana, reported male 49.35% and female 50.64%.

Furthermore, since the socio and economic variations in Indian society are significant, the sample of students drawn for the study represents the proportion of the socio-economic groups. According to the 2011 Census of India, the state of Andhra Pradesh before its bifurcation into two states: Andhra Pradesh and Telangana, had the scheduled caste and scheduled tribe population proportion of 16.4% and 5.6%, respectively, as the total population. In the present sample of students, the proportion of scheduled caste and scheduled tribes is 17.0% and 6.1%, respectively.

The details of the sample are mentioned below in Table 1.

The students were briefed about the survey and those who were willing to take part in the survey were administered the tools for obtaining data. The tool consisted of a consent form that each participant had to sign before responding to the tool. It was followed by a section containing details about the socio-demographic variables, such as age, gender, religion, social status, discipline of study, etc. The Mental Health Inventory (MHI 5) was used to collect data about mental health, and the Internet Addiction Test (IAT) was used to collect data about internet usage.

Mental health was measured using the Mental Health Inventory (MHI-5). It is a brief five-item questionnaire to measure general mental health. It can be used to screen depressive symptoms as well as anxiety. Each of the five questions has six options from which the respondent can choose one and for which a score is given between 1 to 6. The total raw score ranges from 6 to 30 and is transformed into a variable score ranging from 0-100 using a linear transformation, with higher scores indicating poorer mental health.

Positive scoring is done for questions a, b, and d.

Negative scoring is done for c and e.

Internet usage was calculated using a checklist citing the various reasons for using the internet along with the time spent in hours using the internet. This checklist was in addition to the Internet Addiction Test, which is a 20-item inventory developed by Dr. Kimberly Young. The IAT is a self-reported measure for experienced internet users, which can estimate the amount of addiction, dependency, and social functioning.

The score ranges for the IAT are as follows:

Normal Level of Internet Usage: 0-30 points.

Mild Addiction: 31-49 points.

Moderate Addiction: 51-79 points.

Severe Addiction: 80-100 points.

The IAT also has a score for a pattern of symptoms which are:

Items 10, 12, 13, 15, and 19 reflect salience.

Individuals who score high on salience are preoccupied with the virtual world and use it to escape from the real world.

Items 1, 2, 14, 18, and 20 correspond to excessive internet use.

High scores on this indicate that the individual would be subject to mental health issues, such as aggression and depression if internet usage is restricted or withdrawn.

Items 6, 8, and 9 are regarding neglect of work.

Scores on this are indicative of neglecting work that the individual is supposed to do in favour of the internet.

Items 7 and 11 reflect anticipation.

Anticipation indicates the individual thinking about online activities through offline and the individual is eager for online activity.

Items 5, 16, and 17 are on lack of control.

A high score on this indicates that the individual is unable to control the time spent on the internet.

Items 3 and 4 indicate neglect of social life.

Higher scores on these items indicate the individual preference for online social connections compared to the real ones.

H1: There is a significant difference in mental health among the genders.

H2: There is a significant difference in internet usage among genders.

H3: There is a significant relationship between mental health and internet usage among postgraduate students.

H4: Internet usage can be significantly predicted by mental health.