Keywords

1 Introduction

In our current generation, the easy accessibility of the internet has led to excessive usage of smartphone in our daily lives for various tasks. Approximately a billion people spend 50% of their time on any digital media like smartphone, tablet, laptop computer or television [9]. Globally, many surveys were conducted and stated that smartphone is circulated over 92.5% in 2019 [41]. The survey also confirms that Indians are amongst the second-highest smartphone users in all Nations. It was reported that most of the Indian teenagers spend an average of 4 h daily on their mobile phone [41]. The dissemination of smartphones in India started in the year 2008 with devastating results. Excessive usage of smartphone can cause physiological problems, including eye pain, decreased visual acuity, blurred vision, dry eyes, headache, neck stiffness, wrist pain, back pain, etc. If the visual fatigue continues in everyday life, it will significantly affect the attention, focus, and also create functional impairment to the individuals. The excessive usage of internet and smartphone also causes negative effect on individuals and their mental health. Recent studies confirmed that addiction to social media and the internet lead to compulsive behaviour and cognitive emotions. It is identified that empathy, severity and life satisfaction are also affected by social media and internet addiction. Various psychological aspects including anxiety and depression are positively correlated with addiction to social media and the internet [9, 18, 30, 38].

Considering that smartphones share many common aspects with the Internet, the physical and psychosocial problems are caused by the smartphone addiction that are similar to those caused by the Internet. Physical problems are posture and neck issues caused by utilizing smart phones or tablets for longer duration, also known as “text neck”, pain and discomfort of eyes, eye burning, itching and headache caused by digital eye strain [42]. Psychosocial effects of addiction are depression, obsessive compulsive disorder, relationship problems, anxiety, sleep disturbances like the chances of insomnia increases by utilizing any digital medium for longer duration before bed, thereby reducing sleep quality and could increment the measure of time taken to fall asleep [42]. In particular, the addiction of the Internet and smartphone utilization over socio-graphic groups have become a current research domain. Significantly, several studies have focused on adolescents who are especially susceptible to excessive mobile phone and Internet use. Studies noted that excessive smartphone use could affect the mental health of an adolescent as much as Internet and computer use. The study also states that addiction of smartphone increases due to the larger distribution of smartphone. Furthermore, various convenient functions and applications of smartphones may be the primary factors that contribute to excessive usage [31].

Among researchers, adolescents have received considerable attention as the most vulnerable group of having addictive behavior than other demographic user groups. However, no studies have presented the characteristics of adolescents’ addictive behavior and how it is distinctive from other demographic user groups. Considering the high circulation of smartphones, the instant messaging, number of times checking their smartphones of adolescents helps to reveal the different levels of smartphone addiction [16]. Also, studies have examined by conducting a set of questionnaires with score from 15–60 range based on the amount of smartphone utilization and the smartphone activities type and revealed that a score above 42 are considered as addicts and below are non-addicts or mild level of addiction and also stated internet is not only the core symptoms of excessive use but the user’s time reliance on online activities. Addicts tend to use the Internet differently than non-addicts. For example, addicted users utilize smartphone for applications such as gaming, fun and entertainment compared to non-addicts. The finding shows that the mild level of Internet users are utilizing the Internet for communication and messaging. Entertainment and gaming are the major activities that addicts are frequently exposed to [27].

Any kind of addiction to digital media like addiction of smartphone, social media, internet, gaming are considered as the problematic smartphone usage which causes anxiety, loneliness, depression, low self-esteem and so on. These behavioral addicts lead to mental disorders and reducing in the level of psychological well-being. Psychological well-being refers to the mental health of an individual, it is about how the choices are made, behave, feel, think, engage with others and so on. It is identified by 6 important components, which are (i) the condition of a feeling of purpose and meaning in life, (ii) competence, (iii) personal growth and development, (iv) autonomy, (v) personal mastery and (vi) having positive relation with others [40]. Psychological well-being is a state of balance achieved by attaining both rewarding and challenging life events. As the addiction of smartphone increases, the level of psychological well-being will be reduced which causes mental disorders.

This paper discusses about the studies in developed countries to India and explains what are the factors and prevalence for addiction of smartphone, internet, social media and gaming. Also, it illustrates what are the effects caused by the increase in addiction level and report on different types of scales available to measure and rate the addiction levels.

2 Methods

A bibliographic search was conducted through IEEE, science direct, PUBMED, PMC with the keywords’ physiological disorders, mental activity, usage duration, smartphone addiction and were limited to recent 10 years from 2010–2019. The search results were analyzed critically to select the appropriate papers. A total of forty papers are selected from fifty papers by reading the full paper for the review on mental or physiological disorders caused by excessive usage of smartphone.

3 Result and Discussions

In this paper, forty papers are reviewed, which are concerned about the mental fatigue or physiological disorders caused by smartphone addiction or digital medium addiction. In this study, the factors and prevalence for addiction of smartphone, internet, social media and gaming are examined. Also, what are the effects caused by the increase in addiction level and scales available to measure and rate the addiction levels are analyzed. The main focus of this paper is to study about mental or physiological disorders in India.

3.1 Reasons for Smartphone Addiction

Here, the reasons and prevalence for addiction of smartphone, social media, internet and games are discussed. Namsu Park [39] studied on correlation of smartphone use and physiological well-being. They Identified that caring for others, trend, communication, information, accessibility and time pass are the reasons for incremental smartphone usage or smartphone addiction, which also leads to low self-esteem, loneliness, depression. Suliman S. Aljomaa [33] also examined that there exists a difference in addiction of smartphones based on independent factors like age, gender, social status, educational level, marital status, monthly income, hours of utilization. They also forwarded a set of questionnaires to subjects for measuring smartphone addiction, which comprises of basic details and five specific categories: health, overuse of smartphone, technological advancement, the psychological-social advancements and preoccupation with smartphones. Concluded that gender differences, marital status plays a vital role in the addiction of smartphone. It states that, unmarried subjects were ascertained in a higher degree of addiction levels, as unmarried males were more addicted to internet gaming or gambling, besides females are addicted to social networking sites (SNS). Yusong Gao [32] examined the association between smartphone usage, loneliness and social anxiety. They collected the subject’s app-usage duration, frequency, text messages and call log data through MobileSens Application which directly collects and stores data in the server. After analyzing the data, they drew the inference that a high level of loneliness and social anxiety leads to frequent usage of text messages, calls, camera apps, and other SNS. Ahmet Rıfat Kayis [30] proposed five different factors of internet addiction and performed a meta-analysis. The five factors are neuroticism, extraversion, openness, agreeableness, and conscientiousness, moreover, the study determined that internet addiction is positively related to neuroticism and other factors are negatively related. Hatice Kumcagiz [29] investigated the correlation between levels of psychological well-being with addiction to digital medium for university scholars. By utilizing smartphone addiction scale and psychological well-being scale, obtained perception, gender, parental attitudes, grade and family’s economic status as the factors that affect the range of psychologically well-being and digital medium addiction. Yongsuk Hwang [27] distinguished the users of smartphone-based on addiction levels and smartphone usage pattern by questionnaire data and categorized into 3 groups like addicts, potential addicts, and non-addicts’ group. Furthermore, the study stated that adolescents are more prone to smartphone addiction by utilizing mobile games and SNS, whereas adults use smartphone for gambling, videos, SNS, mobile games and SNS. Zaheer Hussain [25] studied on anxiety, personality factors and narcissism, and how it is related to problematic smartphone usage. Developed a web-based questionnaire and obtained a significant relationship among time spent on smartphones, anxiety, age, openness, conscientiousness, emotional stability. Also, concluded that all these factors also lead to internet gaming disorder. Cheng Kuang-Tsan [24] analyzed the interrelation between life satisfaction and smartphone addiction among university students by multiple regression analysis, correlation analysis of product-moment and descriptive statistics. Also suggested that stress due to academics, family-stress, career issues and love-affair would cause addiction and reduces life-satisfaction. Jon D. Elhai [23] reviewed on causes of problematic smartphone usage and examined the connection between low self-esteem, chronic stress, anxiety, depression, and smartphone addiction. The study ranked the causes for smartphone addiction and usage in the order of high to low impact from depression, stress, lack of confidence and impatience. Jose de-Sola [22] predicted the problematic smartphone usage by daily use, level of education, gender and age, also classified the user’s as problematic, at risk, regular and casual usage with obtained results. In total at-risk users are 15.4% and problematic users are 5.1%. Factors for problematic smartphone usage are Social environment dependence, loss of control, craving, abuse, and dependence. Eunhyang Kim [20] investigated the connection between attachment and addiction of smartphones through self-esteem, anxiety within the age of adolescents by generating structural equation modelling (SEM). They recommended that, attachment of smartphone increases the level of anxiety and lowers the level of self-esteem, and in turn may prompt to addiction of smartphone. Aljohara A. Alhassan [19] studied on the interrelation between depression and smartphone addiction, factors and their similarity associated with depression. They came to an inference that there is a high level of depression and addiction between high school students than college students or university students. Among depression and addiction there is a positive and linear relationship. Here Hye-Jin Kim [17] inspected the connection between addiction of smartphone in adolescents, and, their status and surroundings (parental addiction o digital media, domestic violence, quality of friendship and self-control). Finally stated that family dysfunction leads to an increase in the addiction of phone, and, friendship and self-control reduces the level of addiction. Mi Jung Rho [13] identified the psychiatric symptoms and problematic usage types of smartphone for adults by utilizing the decision tree method and Korean smartphone addiction proneness scale. They classified the subjects by utilizing the scale: 26% as smartphone independent and 74% as smartphone-dependent group. Also, observed that dependent smartphone users of about 74% are prone to psychiatric signs (dysfunctional impulsivities, depression, anxiety and self-control). Qiufeng Gao [9] predicted the bond between parent-child, and, its effect on education level, quality of life (QOL), physiological perspective and smartphone usage disorder (SUD). The results suggested that subjects with the good-parent-child association have a negative relation with SUD and positive relation with QOL. Also, as the education level increases the SUD and parent-child association gradually reduces. Marc Nahas [8] determined the problematic smartphone usage and the extent among subjects with age of 18–65, by generating a questionnaire data based on types of smartphone usage, positions and other demographic data to observe the symptoms of obsessive-compulsive disorder and depression. They obtained results showing that subjects of 18–34 years with internet subscription and unmarried are more addicted and chatting was the most utilized function. Sharon Horwood [7] analyzed the correlation between psychological well-being and problematic smartphone usage with the measure of life scale and psychological well-being scales. Subjects with problematic smartphone usage have low psychological well-being scores which reflect to lower well-being. Psychological well-being is linked with compulsive behavior, autonomy, maladaptive coping, lack of control, negative emotions and anxiety which is negatively associated. As a reduction in psychological well-being score leads to problematic smartphone usage.

3.2 Effects of Smartphone Addiction

Here, the effects caused by the addiction of smartphone, social media, internet and games are discussed. Joshua Harwood [37] examined the link between mental health and smartphone usage by considering call, text, email and application usage data by subjective evaluation. They assumed that increased usage of smart-device causes weaker mental health. Stress levels, anxiety and depression are evaluated and based on those scores’ subjects were divided into 3 categories like more addicted (score > 80), moderate addiction (score 50–79) and no addiction (score 0–49). Yu-Kang Lee [36] studied the interconnection between compulsive behaviors and behavioral characteristics of smartphone users and determine the strain due to compulsive behavior. They analyzed different behavioral characteristics are needed for touch, materialism, social anxiety and locus of control by subjective analysis utilizing SEM with competing models. They validated that smartphones provoke various social ills; also, psychological traits are positively related to technostress and compulsive utilization of smartphone. Jian Li [35] inspected the locus of control over the mobile phone and its negative outcomes associated due to excessive usage of smartphone i.e., more often to use while studying, in class and at bed-time. A subjective assessment was conducted that determines how smartphone addiction effects for different parameters like phone usage duration, academic performance and quality of sleep. A path model was generated to monitor mobile phone usage and locus of control, which obtained results showing that mobile phone usage prompts negative effect on psychological behavior. Maya Samaha [34] identified how risk of smartphone addiction is associated with life satisfaction by academic performance and stress. By conducting a web-based subjective assessment, they concluded that life satisfaction reduces and stress increases with an addiction of smartphone which also affects the academic performance through addiction scale measure. Jacob E. Barkley [31] monitored the cell-phone usage position and examined how cell-phone usage interrupts with physical activities or exercising behavior. They concluded that any digital medium like smartphone or television leads to sedentary leisure behavior which leads to reduced physical activities and exercise, also, frequent users of the digital medium are more prone to health issues and have fewer fitness levels. Jocelyne Matar Boumosleh [26] studied on smartphone addiction symptoms and its prevalence by developing a web-based assessment on various smartphone-related variables, personality trait, lifestyle, academic, sociodemographic. They observed that anxiety and depression are positively related to addiction of smartphone and lifestyle habits, work duration, work type and gender are not associated with addiction levels, but decreased sleep quality, duration and tiredness as effects of addiction. Yeon-Jin Kim [18] examined on addiction of smartphone and internet on anxiety and depression for sociodemographic factors. They classified the subjects into 3 classes: normal users, internet adductors, smartphone adductors based on the web-based survey and concluded internet adductors have less range of anxiety and depression when compared with smartphone adductors. Andr«e O Werneck [16] inspected the correlation between television watching, time spent sitting, levels of physical activity and insomnia among Brazilian subjects. They analyzed based on the above parameters by subjective assessment and, concluded that there will be a higher risk of insomnia when sitting time exceeds 4 h, which also leads to difficulties in sleep irrespective of their age and physical activities. Jessica Peterka-Bonetta [7] validated internet usage disorder (IUD) and SUD with specific personality traits. They procured that impulsivity, social anxiety as effects of IUD and SUD with positive correlation. Thiago Paulo Frascareli Bento [6] determined the mental issues and prevalence of lower back pain associated with electronic devices, habitual practice of physical activity and sociodemographic variables. Multivariate, bivariate logistic regression and descriptive analysis are conducted on data collected and confirmed that daily smartphone or tablet or television or laptop computer usage for more than 3 h or watching in inappropriate position leads to mental health problems and lower back pain. Leonardo S. Fortes [1] analyzed decision-making performance in professional soccer athletes and how it is affected by smartphone usage. They conducted a set of experiments with soccer athletes under 4 different conditions like watching television, using a smartphone for 15 min, 30 min and 45 min before the game. Each subject has gone through subjective assessment and objective assessment of perceived recovery before game, urine, heart rate variability and decision-making index (DMI). Decision making relies on memory, anticipation, attention, perception and DMI is based on the number of appropriate actions to the total number of actions. They concluded there is not much effect with 15 min smartphone usage but 30 min and 45 min usage before the game reduces the DMI.

3.3 Assessment Scales

Here the scales available to evaluate addiction levels are explained. Yu-Hsuan Lin [38] proposed Smartphone Addiction Inventory (SPAI) in which tolerance, withdrawal, functional impairment, compulsive behavior was analyzed, based on Chinese internet addiction. Maya Samaha [34] utilized satisfaction with life scale, perceived stress scale and smartphone addiction scale in which smartphone addiction rates are computed by considering various parameters. Yusong Gao [32] analyzed loneliness and anxiety through University of California Los Angeles Loneliness Scale (UCLA-LS) and Interaction Anxiousness Scale (IAS) which depends on subject’s smartphone application usage like health apps, text messages, calls. Julia Machado Khoury [28] adapted, validated and translated SPAI for Brazilian citizens and named it as SPAI-BR. They collect the data through SPAI-BR and determine addiction levels. For ranging addiction, Jocelyne Matar Boumosleh [26] updated the SPAI scale with 26-item questionnaires. Yeon-Jin Kim [18] developed scale of symptom checklist with 90-items, addiction proneness scale and scale for internet addiction based on sociodemographic variables. Halley M. Pontes [8] measured gaming disorder by developing gaming disorder test (GST) and validated with internet gaming disorder scale (IGDS) through concurrent validity, convergent and discriminant validity, nomological validity and factorial validity. I-Hua Chen [2] determined smartphone addiction by Internet Gaming Disorder Scale (IGDS), Bergen Social Media Addiction Scale (BSMAS) and Smartphone Application-Based Addiction Scale (SABAS) in which IGDS evaluates amount of time spent daily on gaming, BSMAS evaluates amount of time spent daily on social media and SABAS evaluates total time spent daily on applications of smartphone.

3.4 Other Methods Available to Measure Mental Fatigue

Here other two methods available for evaluating mental fatigue are given. Mei-Lien Chen [21] studied on air traffic controllers and the correlation between physiological stress symptoms and fatigue. They concluded 50% of subjects felt weary and tired after work and results suggested that ATC job is a stressful job with excess work stress and mental disorders. Yasunori Yamada [15] proposed two models to identify mental fatigue while watching videos for adults and younger people. In the first model, they developed a novel method to measure mental fatigue from the picture of the eye captured and in the second model they adapted the feature selection method. They gathered the data of eye-tracking from older and younger subjects before and after cognitive tasks while watching videos, and, eye-tracking data comprises of gaze allocation, saliency-based metrics, eye-movement direction, oculomotor-based metrics, blink behavior, pupil measures. Shitong Huang [14] studied on smart electrocardiogram (ECG) to measure mental fatigue state by subjective assessment which is interconnected to smartphone and transferred the data through Bluetooth transmission. They utilized KNN to classify the variables which have 8 heart rate variability indicators, collecting data at 5 min interval.

3.5 Study on Physiological Disorders in India

Anjali Sharma [10] examined how mobile phone radiations affects the human nervous system and leads to illness. They conducted experiments on animals by dividing them into two categories: one under normal conditions and other they exposed to microwave radiations, and understand that microwave radiations have toxic effects which affects central nervous system and leads to illness. Yatan Pal Singh Balhara [11] studied on digital medium which acts as the main source of behavioral addicts and suggested that there is a need in developing measuring equipment for behavioral addicts. Yatan Pal Singh Balhara [12] also examined how problematic internet usage is correlated with students of college and university in 8 different countries including India and obtained results stating that 5 Asian countries have higher score among participants for generalized problematic internet usage scale than 3 European countries. Yatan Pal Singh Balhara [13] studied on school students’ pattern and extent of problematic internet usage in Delhi from Cyber awareness program. Concluded that problematic internet usage was recorded for 19% and for mood regulations utilized internet was almost 37%.

4 Summary

Finally, as an inference of this study, it is observed that most of the study is from other nations, the research on this domain in India is less and it is needed to be done. In this research, the factors, effects and prevalence for addiction of smartphone, internet, social media, gaming and, what are the scales available to measure the addiction levels are discussed. There are five major factors which leads to increase in addiction level of digital medium are parent-child relationship, self-esteem, depression, loneliness and anxiety which affects the life satisfaction, academic performance, sleep quality and also interrupts the physical activities or exercising behavior, in turn, all these leads to weaker mental health. Also, the addiction levels are different for different age groups, gender, socio-status, education level and work-type. These addiction levels can be reduced by engaging themselves in other physical activities like outdoor games, by interacting with people and by locus of control. The smartphone addiction can be reduced up to some extent by locus of control, which is to reduce smartphone usage at unwanted times by the individual ability to control or avoid usage of smartphone. Also, different techniques like an electroencephalogram (EEG), electrocardiogram (ECG) can be utilized to monitor the mental behavior of the subject other than the subjective assessment for more appropriate results.