In this narrative review, summaries are given of research published in 2024 on internet addiction in adults. The papers are focused on the prevalence of internet addiction, negative effects, comorbidities, predictors/risk factors, mechanisms and buffers. The prevalence of internet addiction ranged from 21-76% across cultures as well as within and across professions and by severity. The negative effects included depression, pain, and sleep problems. The comorbidities include anxiety, PTSD and ADHD. The predictors/risk factors can be categorized as personality characteristics, family problems, fear of missing out, and emotional disorders. The potential underlying biological mechanisms for internet addiction include dysfunction in multiple regions of the brain and the serotonin and dopamine neurotransmitter systems. The buffers include being married and belonging to an extended family. Surprisingly, online photography was the only intervention that appeared in this current literature. Methodological limitations include most of the studies being cross-sectional and the samples being almost exclusively young adults.

The internet has been very effective for school and work activities, for gathering information, communicating and for entertainment. However, as in other addictions, internet addiction or problematic internet use has negative effects, and the prevalence of adult internet addiction has ranged as high as 76% and is increasing in adults. Despite the increasing prevalence and serious effects for adults, much of the literature has been focused on internet addiction in adolescents. Thus, the purpose of this review is to summarize the current literature on internet addiction specifically in adults. A narrative review was selected as opposed to other types of reviews because, as a narrative review has been defined, it is a general descriptive overview of a topic that summarizes existing literature without a strict methodology. Other types of reviews such as scoping or systematic reviews require more rigorous, less variable research than the research on internet addiction in adults that was published in 2024.

The papers included in this review were derived from PubMed and PsycINFO by entering the terms internet addiction and the year 2024. Exclusion criteria for this review included proposed protocols, case studies and non-English language papers. This narrative review includes summaries of 41 papers on internet addiction research published during 2024. Most of the papers are empirical studies based on surveys or interviews. They are focused on prevalence, negative effects, comorbidities, predictors /risk factors or buffers for internet addiction in adults. Accordingly, this review is divided into sections that correspond to those categories. Although some papers can be grouped in more than one category, 15 papers are focused on prevalence, 8 papers are focused on negative effects, 8 papers on comorbidities, 18 papers on predictors/risk factors, 4 papers on potential underlying biological mechanisms, 2 papers on buffers and only 1 paper on an intervention. A discussion on methodological limitations of this current literature follows those sections.

Prevalence of internet addiction in adults

The prevalence of internet addiction in adults has been notably variable in this current literature. The variability of prevalence has been related to cross-cultural differences, variability within professions and across different professions, severity of psychological problems and exposure to parents’ problematic internet use (Table 1). 

Cross-cultural variability of internet addiction prevalence has been evident in this current literature. The prevalence has varied from a low of 21% in Iran to a high of 76% in Sudan. In the study from Iran (N= 314), the 21% incidence of internet addiction was associated with musculoskeletal disorders.¹ This relationship was not surprising given that excessive sitting at a computer can lead to musculoskeletal problems in several parts of the body. A slightly greater prevalence of 29% was noted in a study from Malaysia including a range of severity from moderate to severe.² The difference in prevalence across these countries could relate to cultural differences or simply their different ways of defining internet addiction based on hours at the computer or responses to surveys.

In a sample from Turkey (N= 390), 38% were problematic internet users and 26% were considered pathological Internet users.³ The defining properties of problematic versus pathological internet addiction were unclear. In a review of 10 epidemiological studies on internet addiction in India (N= 12,280 adult participants), the prevalence varied from 36% for mild internet addiction, 31% for moderate, .88% for severe internet addiction and 58% were said to have any level of internet addiction.⁴ Again, the criteria for the different severity categories were not clear and the labels applied for the different levels for the samples from India were different than those for Turkey. In addition, the report from India was based on 10 studies versus the one sample comprising the study from Turkey.

In research from Saudi Arabia (N= 338), 31% were considered to have moderate internet addiction and 21% severe.⁵ In this study, internet addiction was defined as six or more hours per day. In the study from Sudan already mentioned, internet addiction was as high as 76% (N= 307).²

Mild internet addiction was reported to occur in 40% of the sample, 33% had moderate internet addiction and 3% were classified as severe. In this study, the different levels of severity were defined by different numbers of hours.

The cross-cultural variability noted across these studies may relate to real cultural differences, but also to other factors. Other factors have included availability of the internet, variability in study design (i.e. cross-sectional or longitudinal), age and gender of participants, measures/definitions of internet use (i.e. hours per day or scores on addiction scales), severity of use and data analyses. Until cultures are compared within the same study using the same measures/criteria for internet addiction, it will be difficult to know whether these differences are cross-cultural differences or simply differences in methodologies including sampling, definitions and measures.

Variable prevalence has been noted within the same profession, for example, across the medical profession. In one group of resident physicians from Ethiopia, the prevalence of internet addiction was 47% when addiction was defined as using the internet five or more hours per day (N= 417).⁷ The prevalence was significantly lower (29%) in a review of 13 cross-sectional studies on internet addiction among medical students (N=4787).⁸ The medical students possibly had less time to engage in internet activities than the physicians due to their heavy course load and study time. But also, the prevalence in physicians was specifically based on 5 or more hours per day while the lower prevalence in the review of 13 studies on medical students was based on many different criteria for prevalence across the multiple studies. An even lower prevalence of internet addiction (19%) was reported for house officers in a Malaysian Hospital (N= 143).⁸ This may have also related to less time available for internet use due to high patient load and supervision responsibilities.

Variable prevalence has also been reported for different professions which might again relate to time available for internet use. In a sample of commercial airline pilots from China, for example, the internet addiction rate was extremely low at 8% (N= 7055).⁹ Commercial airline pilots may have very limited time available for internet use or they may underestimate their internet time. A similarly low rate was reported among working age adults at 11% (N=1109).¹⁰ In this study, excessive internet use was referred to as problematic internet use rather than internet addiction possibly because working age adults would be expected to spend significant amounts of time on the internet for their work. In a group of older teachers in China the rate was significantly higher at 27% possibly because they were working less time or were retired and had more time available for internet use.¹¹

Another source of variable prevalence of internet addiction has been noted for severity of psychological disorders. Adults with different psychological disorders had different levels of internet addiction in at least one study.¹² In this sample of psychiatric outpatients with varying levels of pathology including depression, anxiety disorder, bipolar disorder and psychotic disorder (N= 143, Mean age = 49), the prevalence varied between 1% and 25% on problematic use of the internet. This variability may have related to the severity of their problem and/or the difference in their need to use the internet as a coping strategy or a way of finding social contacts and support.

Exposure to parents’ internet use was still another prevalence variable in this literature on problematic internet use in adults. For example, as many as 87% of a sample whose parents had problematic internet use had problematic internet use themselves as opposed to 41% of a sample whose parents did not have problematic use.¹³ Some have referred to this as modeling of behaviors by parents and others have called this phenomenon a contagion effect. Parents who engage in phubbing (phone snubbing or turning to their phones during interactions with their kids) have also been noted to have kids who spend more time on the internet.¹⁴

Negative effects of internet addiction

Although the internet has been valuable for work and school activities and for information, communication and entertainment, the current research has primarily focused on problematic internet use/internet addiction and its negative effects and risk factors. Several negative effects have been reported for internet addiction or problematic internet use including psychological distress, depression, pain, and sleep problems (Table 2).

In the study on older Chinese teachers (40+ years) already mentioned, problematic internet use led to psychological distress.¹¹ In that sample, “thwarting of psychological needs” and “occupational burnout” were mediators for explaining the relationship between problematic internet use and psychological distress.

Problematic internet use has also been a moderator variable. For example, problematic internet use exacerbated the relationship between alexithymia (difficulty recognizing and feeling emotions) and depression in a study from China (N= 594).¹⁵ In this sample, physical activity was also a moderator for that relationship. While problematic internet use enhanced the relationship between alexithymia and depression, physical activity decreased the strength of that relationship. Physical activity would be expected to lessen internet addiction simply by being physically removed from the computer. Surprisingly, this is the only study in this literature that addressed physical activity as a variable. Physical activity could act as a buffer or intervention to reduce internet addiction in the same way it reduced depression in this sample and in many other samples.¹⁶

Anxiety and stress have also been assessed along with depression in a few studies on internet addiction. In one study, depression, anxiety and stress were all comorbid with internet addiction (N=120).¹⁷

These comorbidities likely confounded or exacerbated the other symptoms reported including poor sleep, dry eye disease, loneliness, aggression, and musculoskeletal pain in the wrist, thumb, neck, and back.

High correlations were again reported between internet addiction, depression, anxiety, and stress in a sample of adults who were older than 24 years-old.¹⁸ These negative emotions were also highly correlated in a group comparison of outpatients with internet addiction and those without internet addiction.¹⁹

Surprisingly, the internet addiction group had the lowest level of depression and anxiety symptoms, as well as greater life satisfaction and adaptive emotional regulation. However, the internet group also had greater “risk behavior urges”.

Although musculoskeletal disorders might be expected to result from internet addiction, they have only been reported by two research groups. In the study already described, musculoskeletal pain was noted in the wrist, thumb, neck, and back.¹⁷ In another sample that had a prevalence of 21% internet addiction (N= 314), high pain levels were reported as well as discomfort, burning or numbness in the neck, wrist, upper back, hips, and thighs.¹ Surprisingly, these musculoskeletal problems that might be expected from prolonged time at the computer were only addressed in these two studies in this current literature.

Given the psychological and physical problems just described, it is not surprising that sleep disturbances have also been reported. Negative effects of internet use on sleep quality have been noted in at least two studies on medical students. In one study, excessive internet use contributed to poor sleep quality in medical students (N=181).²⁰ As in many of these studies, the Internet Addiction Test was administered to diagnose internet addiction.

In another sample of medical students (N=338 students from Saudi Arabia), sleep quality was negatively affected by internet addiction.⁵ In this sample, 21% had severe internet addiction and 31% had moderate addiction. Internet addiction (defined as six or more hours per day) was negatively correlated with sleep quality. Sleep quality, in turn, explained 75% of the variance in internet addiction. In this sample, internet addiction was more prevalent in males, and, not surprisingly, it also led to inferior academic performance.

Comorbidities of internet addiction

Several comorbidities have been noted for internet addiction that likely contribute to and confound the effects of internet addiction (Table 3). These include depression, anxiety, PTSD, and substance use disorders. Typically, multiple comorbidities have been noted in each study. For example, in the study on commercial airline pilots from China, internet addiction occurred in 8% of the pilots, depression was noted in 23% and poor sleep was reported by 33% of the pilots in this sample (N= 7055).⁹ In another study on medical students (N=506 from Saudi Arabia), the prevalence of internet addiction was 31%.²¹ Those students who had a PTSD diagnosis were 1.7 times more likely to have internet addiction and those with a depression diagnosis were 2.2 times more likely to experience internet addiction. Other risk factors in this sample were low-income levels and being single.

In a comparison between patients with and without opioid use disorder, most opioid addicted patients also had problematic internet use.²² Other comorbidities in this sample included depression, anxiety, ADHD symptoms, and impulsivity. Sampling patients with disorders is often convenient but the disorders and the internet addiction are confounded. The relative contribution of these comorbidities to internet addiction needs to be determined by regression analyses or structural equations modeling. Similar analyses could be used to determine the contributions of the comorbidities plus the internet addiction to the negative effects being attributed to the internet addiction. These sampling and data analyses problems are characteristic of many of the internet addiction studies reviewed here.

In a sample of psychiatric outpatients (N=143, mean age=49) the prevalence of internet addiction was surprisingly only low to moderate (between 1% and 25%).¹² This prevalence was, also surprisingly, evenly distributed across individuals with varying pathology including depression, bipolar disorder, anxiety, and psychotic behavior. Problematic internet use was also correlated with personality and eating disorders and other addictions including alcohol and substance abuse. And several forms of internet addiction were reported including social media, online shopping, and video streaming. This large number of comorbidities in a sample size this small suggests that this group of psychiatric outpatients had multiple comorbidities that likely contributed to/exacerbated their internet addiction.

Eating disorders were also a confounding/exacerbating condition in the study on internet addiction in Malaysia.² In this sample of young adults, 29% had moderate to severe Internet addiction and 33% were at risk for eating disorders. The correlation between internet addiction and eating disorders was very high at .79.

Autism spectrum disorder (ASD) and Attention Deficit /Hyperactivity Disorder (ADHD) have also been comorbid with internet addiction. In one sample (N= 248), ASD led to ADHD symptoms impulsivity and negative moods which led to internet addiction symptoms.²³ In another sample of individuals with ADHD (N= 96), both the attention deficit symptoms and the hyperactivity symptoms led to internet addiction.²⁴ Internet addiction in samples with these disorders may reflect the participants’ attempts to cope with their attention and hyperactivity symptoms by focusing on the internet.

Predictors/risk factors for internet addiction

The severity of the negative effects of internet addiction and the related comorbidities highlights the need for identifying predictors/risk factors for internet addiction. Several predictors/risk factors for internet addiction have been the focus of research in this current literature. They include personality factors, family problems, fear of missing out experiences, and negative emotions including social anxiety, loneliness and depression (Table 4).

The personality factors that have contributed to internet addiction include narcissism, neuroticism and extraversion. In a study, entitled “Digital reflections: narcissism, stress, social media, internet addiction, and homophobia” (N = 559, mean age = 27), narcissism led to homophobia and social media addiction which, in turn, led to stress.²⁵ In the study on neuroticism (N = 151), neuroticism led to the fear of missing out which, in turn, led to problematic internet use.²⁶ These research groups clearly had different theories about mediating variables prior to their selection of the variables and the use of mediation/moderation analysis.

In a study entitled “Problematic Internet use and personality traits: results in working age adults” (N=1109), 11% of the sample had problematic internet use. The personality traits associated with problematic internet use included extraversion and openness to experience.¹⁰ These were surprising results given that extraversion and openness to experience are typically considered positive personality traits, although those individuals with extraversion (defined as the preference for socially engaging with others) may have led them to use the internet for additional social engagement. Qualitative studies that explored underlying motives and perceived benefits of internet use might further inform these reported relationships between personality traits and problematic internet use.

Family risk factors have been referred to as “family climate”, family communication, and family health. In a study on the impact of family climate on problematic internet use (N= 21,854), a prevalence of 31% problematic internet use was noted.²⁷ In a mediation analysis, poor family communication led to loneliness which, in turn, led to problematic internet use. In this analysis, loneliness explained an extremely high amount of the variance in problematic internet use (91%). In a second mediation analysis, poor family health led to loneliness which, in turn, led to problematic internet use. In this analysis, loneliness contributed to 39% of the variance in problematic internet use.

That loneliness has rarely appeared as a risk variable for internet addiction in this literature is surprising. Loneliness typically results from perceived isolation and is often experienced as social pain. This might then lead to the use of the internet for seeking social contacts. In addition, the motives for internet use, e.g. seeking social contact, have rarely been discussed in this literature.

Modeling of internet use by parents and the parents’ excessive use has also been a risk factor for problematic internet use. For example, in a group comparison between parents with problematic internet use and those without problematic internet use (N=4568 parents), the prevalence of problematic internet use was 61%.¹³

Eighty-seven percent of adolescents whose parents had problematic internet use went on to have problematic internet use themselves versus 41% of adolescents who did not have parents with problematic internet use. These data are not surprising and are consistent with data reported in the literature on phubbing (phone snubbing or turning away from a social interaction to the internet).¹⁴

The fear of missing out (FOMO) has been a predictor/risk factor for Internet addiction. In one study, FOMO led to interpersonal and intrapersonal emotion dysregulation which, in turn, led to internet addiction which the authors called “doomscrolling”.²⁸ Fear of missing out was also a notable mediator in the study already described on neuroticism predicting problematic internet use.²⁶

Emotional problems have been risk factors for internet addiction in several studies in this current literature. Those problems include emotional dysregulation, emotional distress, loneliness and social anxiety.

In one of the few observational studies entitled “Social media misuse explained by emotional dysregulation”, internet use was measured four times per day for seven days.²⁹ The results of this intensive study are given in the title of the paper. In this sample that had the greatest prevalence of internet addiction in the current literature (76%), those with emotional distress had 6.4 times greater risk for internet addiction.³⁰

And females had a 1.9 times greater risk for internet addiction. In a meta-analysis of 32 studies (N= 35,623 participants), loneliness was the most consistent risk factor for internet addiction.³¹ The frequency of loneliness as a risk factor for internet addiction in the earlier studies included in the meta-analysis may explain why loneliness has rarely appeared in empirical studies in the current literature. Researchers tend to select unique variables for their studies rather than replicating already frequently studied variables.

Social Anxiety has been a risk factor for Internet addiction in at least three papers. In a meta- analysis (N= 11,746), social anxiety was a mediator for the relationship between anxious attachment and greater problematic internet use.³² Social anxiety was also a mediator in a study from Chile that involved self-report measurements five times a day for 10 days (N= 122).³³ In this study, a reciprocal relationship was noted between internet addiction and social anxiety, which was mediated by loneliness. Trait anxiety has also been a predictor of internet addiction in one of the few longitudinal studies in this literature.³⁴

Other risk factors for internet addiction noted in this literature include compulsivity and low distress tolerance. In a study entitled “Examining the unique relationship between problematic use of the internet and impulsive and compulsive tendencies”, the participants were Australian (N =370, mean age= 30).³⁵ The results suggested that compulsivity and negative urgency symptoms of impulsivity were the most significant predictors of problematic use of the internet. In research on several forms of problematic internet use, low distress tolerance was the most noted risk factor (N= 1956).³⁶

Researchers who have explored multiple risk factors for internet addiction have typically found that multiple variables have contributed to the addiction. In the study from Turkey that had a prevalence of 38% problematic internet use, for example, the significant predictors included anxious arousal and online dissociation (detachment from reality).³

In a study investigating the relationship between internet addiction, domestic violence, and emotional divorce among married women in Tehran (N=400), as many as 46% of the women experienced emotional divorce.³⁷ As in many of the studies in this current literature, scores on the Young Internet Addiction Test were the criteria for internet addiction. The results of this study on women suggested that emotional divorce led to internet addiction which led to domestic violence. Internet addiction was also negatively related to age, duration of marriage, and employment status.

In a meta-analysis on 10 epidemiological studies from India that showed an average prevalence of 58% internet addiction, several risk factors were noted.⁴ These included male gender, being from a single parent family, stress, anxiety, and loneliness. Unfortunately, once again, the relative significance of these variables was not determined.

Potential underlying biological mechanisms for internet addiction

Only a few potential underlying biological mechanisms for internet addiction have been addressed in this literature, likely because of their complexity and costliness. They include dysfunction in multiple regions of the brain and in the serotonin and dopamine neurotransmitter systems (Table 5). 

Altered functional networks were noted in a study on resting state fMRIs (N=59).³⁸ In this study decreased sensory processing was noted which involved language, visual, auditory, and sensorimotor networks in the brain. 

In another fMRI study entitled “Altered local intrinsic neural activity and molecular architecture in internet use disorders”, similar functional impairments were noted in audiovisual and inhibitory control circuits.³⁹ The authors suggested that these may be associated with underlying neurotransmitter system alterations.

Neurotransmitter systems alterations were implicated in a genetic study on problematic internet use (N=407 men).⁴⁰ In this study, serum was analyzed for several hormones, neurotransmitters and genes. An association was noted between ANKK1 polymorphisms and serotonin and dopamine neurotransmitter alterations. Although behaviors aside from internet addiction were not recorded in these fMRI studies, the neurotransmitter alterations would likely contribute to many of the negative internet addiction effects and comorbidities, for example, depression which is related to depleted serotonin and dopamine. 

Buffers and intervention for internet addiction

Surprisingly, only two studies on buffers and one on intervention have appeared in this current literature on internet addiction. The buffers include being married and belonging to an extended family and the intervention involved online photography (Table 6).

In a sample of resident physicians, the prevalence of problematic internet use was high at 47%.⁶ Problematic internet use was correlated with five or more hours per day of internet use and less than seven hours of sleep. Being married was a buffer in this research. In the study on family climate effects on problematic internet use, 31% prevalence of internet addiction was noted.³⁵ The buffer in this study was having an extended family. These results are not surprising given that being married and/or having an extended family would be expected to be a buffer against loneliness which has been a notable risk factor, mediator and moderator variable for internet addiction in this current literature.

Given the negative effects of internet addiction on both psychological and physical health, it is surprising that only one intervention study appeared in this current literature.⁴¹ In this study (N= 125, mean age = 23), several forms of internet addiction were noted including social media, online shopping, and video streaming. For the intervention, the participants were requested to photograph for ten days "the things that make you feel a sense of control in life”. At a follow-up assessment of this online photography intervention seven days later, the participants reported an increased sense of control and decreased internet addiction symptoms as well as decreased symptoms of other addictions including alcohol and substance abuse.

Methodological limitations of this literature

​Several limitations can be noted for the current literature on internet addiction in adults. They include variable definitions of internet addiction by the different researchers, various sampling methods, and different measures across studies, making it difficult for meta-analyses to be conducted.

Although many studies were focused on prevalence data and on predictors/risk factors for internet addiction, very few studies appeared on negative effects of internet addiction. In addition, underlying mechanism research was scarce. Although expensive fMRI studies were conducted suggesting dysfunction in different regions of the brain, less expensive measures could have been used, for example, saliva assays of cortisol to assess stress related to internet addiction, and urine for assays of dopamine and serotonin neurotransmitter dysfunction. Surprisingly, given the negative psychological and physical effects of excessive internet use, only one intervention study appeared in this literature.

In terms of the sampling, frequently those with internet addiction or psychiatric problems comprised the samples. Several young adult samples were also prevalent and different age samples were not compared. The findings were mixed on gender differences with some suggesting greater prevalence in women and others reporting a greater rate in men. In addition, a few samples were limited to one gender, thus limiting the generalizability of the data. Further, most of the studies came from the eastern continent raising, again, the question of generalizability of the data. Surprisingly, no data came from the U.S., the U.K. or Europe where internet addiction is highly prevalent, especially in children and in adolescents.^42,43

The U.S., U.K. and European studies on internet addiction may have come earlier than this 2024 literature.

Although the same measure of internet addiction was used by most researchers (the Young Internet Addiction Test), the definitions of internet addiction varied as they were often based on the amount of time the internet was excessively used rather than the self-report Internet Addiction Test scores. And the time spent on the internet as a criterion for internet addiction varied from study to study. The samples also varied on the prevalence of internet addiction within the sample with some samples being exclusively comprised of internet addicted individuals and others being a comparison between individuals with and without problematic internet use. And the availability of time available for internet use, for example, across different professions, was not considered as a risk factor. Those having more time available for internet use would likely be more vulnerable to internet addiction, as was mentioned earlier as a potential interpretation for the results of several risk factor studies.

Within samples there was confounding of the internet use variable by serious comorbidities including depression, anxiety and PTSD as well as other problems that could have led to the negative effects or added to the severity of the negative effects. Some variables, for example depression and anxiety, were treated as negative effects by some researchers and as risk factors by other researchers. Surprisingly, different types of internet addiction, for example, screen use versus texting, were typically not compared.

Despite these methodological limitations, this literature has been informative. The large number of studies on predictors and risk factors will likely inform more intervention research to prevent the negative effects of problematic internet use or problematic internet use itself.⁴⁴

The 2024 publications that are included in this review are focused on the prevalence of internet addiction, negative effects, comorbidities, predictors/risk factors, mechanisms or buffers. The prevalence of internet addiction has ranged from 21-76% but has varied across cultures as well as within and across professions and by severity. The few negative effects that have been reported in this current literature include depression, pain, and sleep problems. However, the negative effects have been confounded and likely exacerbated by comorbidities including anxiety, PTSD and ADHD. The predictors/risk factors can be categorized as personality characteristics, family problems, fear of missing out, and emotional disorders. The few potential underlying biological mechanisms for internet addiction that have been explored include dysfunction in multiple regions of the brain and the serotonin and dopamine neurotransmitter systems. Only two buffers for internet addiction were published during 2024 including being married and belonging to an extended family. Surprisingly, online photography was the only intervention that appeared in this current literature. Methodological limitations include most of the studies sampling young adults, most being cross-sectional, and variability in the measurement of internet addiction. Despite these limitations, the data will likely inform future research on risk factors and interventions for internet addiction in adults.⁴⁵

None.

None.

Author declares that there are no conflicts of interest.

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