research paper for internet addiction

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Bentham Open Access

Internet Addiction: A Brief Summary of Research and Practice

Hilarie cash.

a reSTART Internet Addiction Recovery Program, Fall City, WA 98024

Cosette D Rae

Ann h steel, alexander winkler.

b University of Marburg, Department for Clinical Psychology and Psychotherapy, Gutenbergstraße 18, 35032 Marburg, Germany

Problematic computer use is a growing social issue which is being debated worldwide. Internet Addiction Disorder (IAD) ruins lives by causing neurological complications, psychological disturbances, and social problems. Surveys in the United States and Europe have indicated alarming prevalence rates between 1.5 and 8.2% [1]. There are several reviews addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [2-5], and some reviews [6-8] addressing the treatment of IAD. The aim of this paper is to give a preferably brief overview of research on IAD and theoretical considerations from a practical perspective based on years of daily work with clients suffering from Internet addiction. Furthermore, with this paper we intend to bring in practical experience in the debate about the eventual inclusion of IAD in the next version of the Diagnostic and Statistical Manual of Mental Disorders (DSM).

INTRODUCTION

The idea that problematic computer use meets criteria for an addiction, and therefore should be included in the next iteration of the Diagnostic and Statistical Manual of Mental Disorders (DSM) , 4 th ed. Text Revision [ 9 ] was first proposed by Kimberly Young, PhD in her seminal 1996 paper [ 10 ]. Since that time IAD has been extensively studied and is indeed, currently under consideration for inclusion in the DSM-V [ 11 ]. Meanwhile, both China and South Korea have identified Internet addiction as a significant public health threat and both countries support education, research and treatment [ 12 ]. In the United States, despite a growing body of research, and treatment for the disorder available in out-patient and in-patient settings, there has been no formal governmental response to the issue of Internet addiction. While the debate goes on about whether or not the DSM-V should designate Internet addiction a mental disorder [ 12 - 14 ] people currently suffering from Internet addiction are seeking treatment. Because of our experience we support the development of uniform diagnostic criteria and the inclusion of IAD in the DSM-V [ 11 ] in order to advance public education, diagnosis and treatment of this important disorder.

CLASSIFICATION

There is ongoing debate about how best to classify the behavior which is characterized by many hours spent in non-work technology-related computer/Internet/video game activities [ 15 ]. It is accompanied by changes in mood, preoccupation with the Internet and digital media, the inability to control the amount of time spent interfacing with digital technology, the need for more time or a new game to achieve a desired mood, withdrawal symptoms when not engaged, and a continuation of the behavior despite family conflict, a diminishing social life and adverse work or academic consequences [ 2 , 16 , 17 ]. Some researchers and mental health practitioners see excessive Internet use as a symptom of another disorder such as anxiety or depression rather than a separate entity [e.g. 18]. Internet addiction could be considered an Impulse control disorder (not otherwise specified). Yet there is a growing consensus that this constellation of symptoms is an addiction [e.g. 19]. The American Society of Addiction Medicine (ASAM) recently released a new definition of addiction as a chronic brain disorder, officially proposing for the first time that addiction is not limited to substance use [ 20 ]. All addictions, whether chemical or behavioral, share certain characteristics including salience, compulsive use (loss of control), mood modification and the alleviation of distress, tolerance and withdrawal, and the continuation despite negative consequences.

DIAGNOSTIC CRITERIA FOR IAD

The first serious proposal for diagnostic criteria was advanced in 1996 by Dr. Young, modifying the DSM-IV criteria for pathological gambling [ 10 ]. Since then variations in both name and criteria have been put forward to capture the problem, which is now most popularly known as Internet Addiction Disorder. Problematic Internet Use (PIU) [ 21 ], computer addiction, Internet dependence [ 22 ], compulsive Internet use, pathological Internet use [ 23 ], and many other labels can be found in the literature. Likewise a variety of often overlapping criteria have been proposed and studied, some of which have been validated. However, empirical studies provide an inconsistent set of criteria to define Internet addiction [ 24 ]. For an overview see Byun et al . [ 25 ].

Beard [ 2 ] recommends that the following five diagnostic criteria are required for a diagnosis of Internet addiction: (1) Is preoccupied with the Internet (thinks about previous online activity or anticipate next online session); (2) Needs to use the Internet with increased amounts of time in order to achieve satisfaction; (3) Has made unsuccessful efforts to control, cut back, or stop Internet use; (4) Is restless, moody, depressed, or irritable when attempting to cut down or stop Internet use; (5) Has stayed online longer than originally intended. Additionally, at least one of the following must be present: (6) Has jeopardized or risked the loss of a significant relationship, job, educational or career opportunity because of the Internet; (7) Has lied to family members, therapist, or others to conceal the extent of involvement with the Internet; (8) Uses the Internet as a way of escaping from problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, depression) [ 2 ].

There has been also been a variety of assessment tools used in evaluation. Young’s Internet Addiction Test [ 16 ], the Problematic Internet Use Questionnaire (PIUQ) developed by Demetrovics, Szeredi, and Pozsa [ 26 ] and the Compulsive Internet Use Scale (CIUS) [ 27 ] are all examples of instruments to assess for this disorder.

The considerable variance of the prevalence rates reported for IAD (between 0.3% and 38%) [ 28 ] may be attributable to the fact that diagnostic criteria and assessment questionnaires used for diagnosis vary between countries and studies often use highly selective samples of online surveys [ 7 ]. In their review Weinstein and Lejoyeux [ 1 ] report that surveys in the United States and Europe have indicated prevalence rates varying between 1.5% and 8.2%. Other reports place the rates between 6% and 18.5% [ 29 ].

“Some obvious differences with respect to the methodologies, cultural factors, outcomes and assessment tools forming the basis for these prevalence rates notwithstanding, the rates we encountered were generally high and sometimes alarming.” [ 24 ]

There are different models available for the development and maintenance of IAD like the cognitive-behavioral model of problematic Internet use [ 21 ], the anonymity, convenience and escape (ACE) model [ 30 ], the access, affordability, anonymity (Triple-A) engine [ 31 ], a phases model of pathological Internet use by Grohol [ 32 ], and a comprehensive model of the development and maintenance of Internet addiction by Winkler & Dörsing [ 24 ], which takes into account socio-cultural factors ( e.g. , demographic factors, access to and acceptance of the Internet), biological vulnerabilities ( e.g. , genetic factors, abnormalities in neurochemical processes), psychological predispositions ( e.g. , personality characteristics, negative affects), and specific attributes of the Internet to explain “excessive engagement in Internet activities” [ 24 ].

NEUROBIOLOGICAL VULNERABILITIES

It is known that addictions activate a combination of sites in the brain associated with pleasure, known together as the “reward center” or “pleasure pathway” of the brain [ 33 , 34 ]. When activated, dopamine release is increased, along with opiates and other neurochemicals. Over time, the associated receptors may be affected, producing tolerance or the need for increasing stimulation of the reward center to produce a “high” and the subsequent characteristic behavior patterns needed to avoid withdrawal. Internet use may also lead specifically to dopamine release in the nucleus accumbens [ 35 , 36 ], one of the reward structures of the brain specifically involved in other addictions [ 20 ]. An example of the rewarding nature of digital technology use may be captured in the following statement by a 21 year-old male in treatment for IAD:

“I feel technology has brought so much joy into my life. No other activity relaxes me or stimulates me like technology. However, when depression hits, I tend to use technology as a way of retreating and isolating.”

REINFORCEMENT/REWARD

What is so rewarding about Internet and video game use that it could become an addiction? The theory is that digital technology users experience multiple layers of reward when they use various computer applications. The Internet functions on a variable ratio reinforcement schedule (VRRS), as does gambling [ 29 ]. Whatever the application (general surfing, pornography, chat rooms, message boards, social networking sites, video games, email, texting, cloud applications and games, etc.), these activities support unpredictable and variable reward structures. The reward experienced is intensified when combined with mood enhancing/stimulating content. Examples of this would be pornography (sexual stimulation), video games (e.g. various social rewards, identification with a hero, immersive graphics), dating sites (romantic fantasy), online poker (financial) and special interest chat rooms or message boards (sense of belonging) [ 29 , 37 ].

BIOLOGICAL PREDISPOSITION

There is increasing evidence that there can be a genetic predisposition to addictive behaviors [ 38 , 39 ]. The theory is that individuals with this predisposition do not have an adequate number of dopamine receptors or have an insufficient amount of serotonin/dopamine [ 2 ], thereby having difficulty experiencing normal levels of pleasure in activities that most people would find rewarding. To increase pleasure, these individuals are more likely to seek greater than average engagement in behaviors that stimulate an increase in dopamine, effectively giving them more reward but placing them at higher risk for addiction.

MENTAL HEALTH VULNERABILITIES

Many researchers and clinicians have noted that a variety of mental disorders co-occur with IAD. There is debate about which came first, the addiction or the co-occurring disorder [ 18 , 40 ]. The study by Dong et al . [ 40 ] had at least the potential to clarify this question, reporting that higher scores for depression, anxiety, hostility, interpersonal sensitivity, and psychoticism were consequences of IAD. But due to the limitations of the study further research is necessary.

THE TREATMENT OF INTERNET ADDICTION

There is a general consensus that total abstinence from the Internet should not be the goal of the interventions and that instead, an abstinence from problematic applications and a controlled and balanced Internet usage should be achieved [ 6 ]. The following paragraphs illustrate the various treatment options for IAD that exist today. Unless studies examining the efficacy of the illustrated treatments are not available, findings on the efficacy of the presented treatments are also provided. Unfortunately, most of the treatment studies were of low methodological quality and used an intra-group design.

The general lack of treatment studies notwithstanding, there are treatment guidelines reported by clinicians working in the field of IAD. In her book “Internet Addiction: Symptoms, Evaluation, and Treatment”, Young [ 41 ] offers some treatment strategies which are already known from the cognitive-behavioral approach: (a) practice opposite time of Internet use (discover patient’s patterns of Internet use and disrupt these patterns by suggesting new schedules), (b) use external stoppers (real events or activities prompting the patient to log off), (c) set goals (with regard to the amount of time), (d) abstain from a particular application (that the client is unable to control), (e) use reminder cards (cues that remind the patient of the costs of IAD and benefits of breaking it), (f) develop a personal inventory (shows all the activities that the patient used to engage in or can’t find the time due to IAD), (g) enter a support group (compensates for a lack of social support), and (h) engage in family therapy (addresses relational problems in the family) [ 41 ]. Unfortunately, clinical evidence for the efficacy of these strategies is not mentioned.

Non-psychological Approaches

Some authors examine pharmacological interventions for IAD, perhaps due to the fact that clinicians use psychopharmacology to treat IAD despite the lack of treatment studies addressing the efficacy of pharmacological treatments. In particular, selective serotonin-reuptake inhibitors (SSRIs) have been used because of the co-morbid psychiatric symptoms of IAD (e.g. depression and anxiety) for which SSRIs have been found to be effective [ 42 - 46 ]. Escitalopram (a SSRI) was used by Dell’Osso et al . [ 47 ] to treat 14 subjects with impulsive-compulsive Internet usage disorder. Internet usage decreased significantly from a mean of 36.8 hours/week to a baseline of 16.5 hours/week. In another study Han, Hwang, and Renshaw [ 48 ] used bupropion (a non-tricyclic antidepressant) and found a decrease of craving for Internet video game play, total game play time, and cue-induced brain activity in dorsolateral prefrontal cortex after a six week period of bupropion sustained release treatment. Methylphenidate (a psycho stimulant drug) was used by Han et al . [ 49 ] to treat 62 Internet video game-playing children diagnosed with attention-deficit hyperactivity disorder. After eight weeks of treatment, the YIAS-K scores and Internet usage times were significantly reduced and the authors cautiously suggest that methylphenidate might be evaluated as a potential treatment of IAD. According to a study by Shapira et al . [ 50 ], mood stabilizers might also improve the symptoms of IAD. In addition to these studies, there are some case reports of patients treated with escitalopram [ 45 ], citalopram (SSRI)- quetiapine (antipsychotic) combination [ 43 ] and naltrexone (an opioid receptor antagonist) [ 51 ].

A few authors mentioned that physical exercise could compensate the decrease of the dopamine level due to decreased online usage [ 52 ]. In addition, sports exercise prescriptions used in the course of cognitive behavioral group therapy may enhance the effect of the intervention for IAD [ 53 ].

Psychological Approaches

Motivational interviewing (MI) is a client-centered yet directive method for enhancing intrinsic motivation to change by exploring and resolving client ambivalence [ 54 ]. It was developed to help individuals give up addictive behaviors and learn new behavioral skills, using techniques such as open-ended questions, reflective listening, affirmation, and summarization to help individuals express their concerns about change [ 55 ]. Unfortunately, there are currently no studies addressing the efficacy of MI in treating IAD, but MI seems to be moderately effective in the areas of alcohol, drug addiction, and diet/exercise problems [ 56 ].

Peukert et al . [ 7 ] suggest that interventions with family members or other relatives like “Community Reinforcement and Family Training” [ 57 ] could be useful in enhancing the motivation of an addict to cut back on Internet use, although the reviewers remark that control studies with relatives do not exist to date.

Reality therapy (RT) is supposed to encourage individuals to choose to improve their lives by committing to change their behavior. It includes sessions to show clients that addiction is a choice and to give them training in time management; it also introduces alternative activities to the problematic behavior [ 58 ]. According to Kim [ 58 ], RT is a core addiction recovery tool that offers a wide variety of uses as a treatment for addictive disorders such as drugs, sex, food, and works as well for the Internet. In his RT group counseling program treatment study, Kim [ 59 ] found that the treatment program effectively reduced addiction level and improved self-esteem of 25 Internet-addicted university students in Korea.

Twohig and Crosby [ 60 ] used an Acceptance & Commitment Therapy (ACT) protocol including several exercises adjusted to better fit the issues with which the sample struggles to treat six adult males suffering from problematic Internet pornography viewing. The treatment resulted in an 85% reduction in viewing at post-treatment with results being maintained at the three month follow-up (83% reduction in viewing pornography).

Widyanto and Griffith [ 8 ] report that most of the treatments employed so far had utilized a cognitive-behavioral approach. The case for using cognitive-behavioral therapy (CBT) is justified due to the good results in the treatment of other behavioral addictions/impulse-control disorders, such as pathological gambling, compulsive shopping, bulimia nervosa, and binge eating-disorders [ 61 ]. Wölfling [ 5 ] described a predominantly behavioral group treatment including identification of sustaining conditions, establishing of intrinsic motivation to reduce the amount of time being online, learning alternative behaviors, engagement in new social real-life contacts, psycho-education and exposure therapy, but unfortunately clinical evidence for the efficacy of these strategies is not mentioned. In her study, Young [ 62 ] used CBT to treat 114 clients suffering from IAD and found that participants were better able to manage their presenting problems post-treatment, showing improved motivation to stop abusing the Internet, improved ability to control their computer use, improved ability to function in offline relationships, improved ability to abstain from sexually explicit online material, improved ability to engage in offline activities, and improved ability to achieve sobriety from problematic applications. Cao, Su and Gao [ 63 ] investigated the effect of group CBT on 29 middle school students with IAD and found that IAD scores of the experimental group were lower than of the control group after treatment. The authors also reported improvement in psychological function. Thirty-eight adolescents with IAD were treated with CBT designed particularly for addicted adolescents by Li and Dai [ 64 ]. They found that CBT has good effects on the adolescents with IAD (CIAS scores in the therapy group were significant lower than that in the control group). In the experimental group the scores of depression, anxiety, compulsiveness, self-blame, illusion, and retreat were significantly decreased after treatment. Zhu, Jin, and Zhong [ 65 ] compared CBT and electro acupuncture (EA) plus CBT assigning forty-seven patients with IAD to one of the two groups respectively. The authors found that CBT alone or combined with EA can significantly reduce the score of IAD and anxiety on a self-rating scale and improve self-conscious health status in patients with IAD, but the effect obtained by the combined therapy was better.

Multimodal Treatments

A multimodal treatment approach is characterized by the implementation of several different types of treatment in some cases even from different disciplines such as pharmacology, psychotherapy and family counseling simultaneously or sequentially. Orzack and Orzack [ 66 ] mentioned that treatments for IAD need to be multidisciplinary including CBT, psychotropic medication, family therapy, and case managers, because of the complexity of these patients’ problems.

In their treatment study, Du, Jiang, and Vance [ 67 ] found that multimodal school-based group CBT (including parent training, teacher education, and group CBT) was effective for adolescents with IAD (n = 23), particularly in improving emotional state and regulation ability, behavioral and self-management style. The effect of another multimodal intervention consisting of solution-focused brief therapy (SFBT), family therapy, and CT was investigated among 52 adolescents with IAD in China. After three months of treatment, the scores on an IAD scale (IAD-DQ), the scores on the SCL-90, and the amount of time spent online decreased significantly [ 68 ]. Orzack et al . [ 69 ] used a psychoeducational program, which combines psychodynamic and cognitive-behavioral theoretical perspectives, using a combination of Readiness to Change (RtC), CBT and MI interventions to treat a group of 35 men involved in problematic Internet-enabled sexual behavior (IESB). In this group treatment, the quality of life increased and the level of depressive symptoms decreased after 16 (weekly) treatment sessions, but the level of problematic Internet use failed to decrease significantly [ 69 ]. Internet addiction related symptom scores significantly decreased after a group of 23 middle school students with IAD were treated with Behavioral Therapy (BT) or CT, detoxification treatment, psychosocial rehabilitation, personality modeling and parent training [ 70 ]. Therefore, the authors concluded that psychotherapy, in particular CT and BT were effective in treating middle school students with IAD. Shek, Tang, and Lo [ 71 ] described a multi-level counseling program designed for young people with IAD based on the responses of 59 clients. Findings of this study suggest this multi-level counseling program (including counseling, MI, family perspective, case work and group work) is promising to help young people with IAD. Internet addiction symptom scores significantly decreased, but the program failed to increase psychological well-being significantly. A six-week group counseling program (including CBT, social competence training, training of self-control strategies and training of communication skills) was shown to be effective on 24 Internet-addicted college students in China [ 72 ]. The authors reported that the adapted CIAS-R scores of the experimental group were significantly lower than those of the control group post-treatment.

The reSTART Program

The authors of this article are currently, or have been, affiliated with the reSTART: Internet Addiction Recovery Program [ 73 ] in Fall City, Washington. The reSTART program is an inpatient Internet addiction recovery program which integrates technology detoxification (no technology for 45 to 90 days), drug and alcohol treatment, 12 step work, cognitive behavioral therapy (CBT), experiential adventure based therapy, Acceptance and Commitment therapy (ACT), brain enhancing interventions, animal assisted therapy, motivational interviewing (MI), mindfulness based relapse prevention (MBRP), Mindfulness based stress reduction (MBSR), interpersonal group psychotherapy, individual psychotherapy, individualized treatments for co-occurring disorders, psycho- educational groups (life visioning, addiction education, communication and assertiveness training, social skills, life skills, Life balance plan), aftercare treatments (monitoring of technology use, ongoing psychotherapy and group work), and continuing care (outpatient treatment) in an individualized, holistic approach.

The first results from an ongoing OQ45.2 [ 74 ] study (a self-reported measurement of subjective discomfort, interpersonal relationships and social role performance assessed on a weekly basis) of the short-term impact on 19 adults who complete the 45+ days program showed an improved score after treatment. Seventy-four percent of participants showed significant clinical improvement, 21% of participants showed no reliable change, and 5% deteriorated. The results have to be regarded as preliminary due to the small study sample, the self-report measurement and the lack of a control group. Despite these limitations, there is evidence that the program is responsible for most of the improvements demonstrated.

As can be seen from this brief review, the field of Internet addiction is advancing rapidly even without its official recognition as a separate and distinct behavioral addiction and with continuing disagreement over diagnostic criteria. The ongoing debate whether IAD should be classified as an (behavioral) addiction, an impulse-control disorder or even an obsessive compulsive disorder cannot be satisfactorily resolved in this paper. But the symptoms we observed in clinical practice show a great deal of overlap with the symptoms commonly associated with (behavioral) addictions. Also it remains unclear to this day whether the underlying mechanisms responsible for the addictive behavior are the same in different types of IAD (e.g., online sexual addiction, online gaming, and excessive surfing). From our practical perspective the different shapes of IAD fit in one category, due to various Internet specific commonalities (e.g., anonymity, riskless interaction), commonalities in the underlying behavior (e.g., avoidance, fear, pleasure, entertainment) and overlapping symptoms (e.g., the increased amount of time spent online, preoccupation and other signs of addiction). Nevertheless more research has to be done to substantiate our clinical impression.

Despite several methodological limitations, the strength of this work in comparison to other reviews in the international body of literature addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [ 2 - 5 ], and to reviews [ 6 - 8 ] addressing the treatment of IAD, is that it connects theoretical considerations with the clinical practice of interdisciplinary mental health experts working for years in the field of Internet addiction. Furthermore, the current work gives a good overview of the current state of research in the field of internet addiction treatment. Despite the limitations stated above this work gives a brief overview of the current state of research on IAD from a practical perspective and can therefore be seen as an important and helpful paper for further research as well as for clinical practice in particular.

ACKNOWLEDGEMENTS

Declared none.

CONFLICT OF INTEREST

The authors confirm that this article content has no conflict of interest.

Internet Addiction: A Brief Summary of Research and Practice

Affiliation.

• 1 reSTART Internet Addiction Recovery Program, Fall City, WA 98024.
• PMID: 23125561
• PMCID: PMC3480687
• DOI: 10.2174/157340012803520513

Problematic computer use is a growing social issue which is being debated worldwide. Internet Addiction Disorder (IAD) ruins lives by causing neurological complications, psychological disturbances, and social problems. Surveys in the United States and Europe have indicated alarming prevalence rates between 1.5 and 8.2% [1]. There are several reviews addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [2-5], and some reviews [6-8] addressing the treatment of IAD. The aim of this paper is to give a preferably brief overview of research on IAD and theoretical considerations from a practical perspective based on years of daily work with clients suffering from Internet addiction. Furthermore, with this paper we intend to bring in practical experience in the debate about the eventual inclusion of IAD in the next version of the Diagnostic and Statistical Manual of Mental Disorders (DSM).

• Research article
• Open access
• Published: 06 January 2021

Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study

• Yosef Zenebe   ORCID: orcid.org/0000-0002-0138-6588 1 ,
• Kunuya Kunno 1 ,
• Meseret Mekonnen 1 ,
• Ajebush Bewuket 1 ,
• Mengesha Birkie 1 ,
• Mogesie Necho 1 ,
• Muhammed Seid 1 ,
• Million Tsegaw 1 &
• Baye Akele 2  

BMC Psychology volume  9 , Article number:  4 ( 2021 ) Cite this article

33k Accesses

39 Citations

280 Altmetric

Metrics details

Internet addiction is a common problem in university students and negatively affects cognitive functioning, leads to poor academic performance and engagement in hazardous activities, and may lead to anxiety and stress. Behavioral addictions operate on a modified principle of the classic addiction model. The problem is not well investigated in Ethiopia. So the present study aimed to assess the prevalence of internet addiction and associated factors among university students in Ethiopia.

Main objective of this study was to assess the prevalence and associated factors of internet addiction among University Students in Ethiopia.

A community-based cross-sectional study was conducted among Wollo University students from April 10 to May 10, 2019. A total of 603 students were participated in the study using a structured questionnaire. A multistage cluster sampling technique was used to recruit study participants. A binary logistic regression method was used to explore associated factors for internet addiction and variables with a p value < 0.25 in the bivariate analysis were fitted to the multi-variable logistic regression analysis. The strength of association between internet addiction and associated factors was assessed with odds ratio, 95% CI and p value < 0.05 in the final model was considered significant.

The prevalence of internet addiction (IA) among the current internet users was 85% (n = 466). Spending more time on the internet (adjusted odds ratio (AOR) = 10.13, 95% CI 1.33–77.00)), having mental distress (AOR = 2.69, 95% CI 1.02–7.06), playing online games (AOR = 2.40, 95% CI 1.38–4.18), current khat chewing (AOR = 3.34, 95% CI 1.14–9.83) and current alcohol use (AOR = 2.32, 95% CI 1.09–4.92) were associated with internet addiction.

Conclusions

The current study documents a high prevalence of internet addiction among Wollo University students. Factors associated with internet addiction were spending more time, having mental distress, playing online games, current khat chewing, and current alcohol use. As internet addiction becomes an evident public health problem, carrying out public awareness campaigns may be a fruitful strategy to decrease its prevalence and effect. Besides to this, a collaborative work among stakeholders is important to develop other trendy, adaptive, and sustainable countermeasures.

Peer Review reports

Globally, more than three billion people use the internet daily with young people being the most common users [ 1 ]. In the field of medicine and healthcare, it helps in the practice of evidence-based medicine, research and learning, access to medical and online databases, handling patients in remote areas, and academic and recreational purposes [ 2 , 3 ].

In terms of classical psychology and psychiatry, IA is a relatively new phenomenon. The literature uses interchangeable references such as “compulsive Internet use”, “problematic Internet use”, “pathological Internet use”, and “Internet addiction”. The Psychologist Mark Griffiths, one of the widely recognized authorities in the sphere of addictive behavior, is the author of the most frequently quoted definition: “Internet addiction is a non-chemical behavioral addiction, which involves human–machine (computer-Internet) interaction” [ 4 , 5 ]. Internet addiction is a behavioural problem that has gained increasing scientific recognition in the last decade, with some researchers claiming it is a "21st Century epidemic"[ 6 ]. The psychopathologic symptoms of internet addiction includes Salience(the respondent most likely feels preoccupied with the Internet, hides the behaviour from others, and may display a loss of interest in other activities and/or relationships only to prefer more solitary time online), Excessive Use (the respondent engages in excessive online behaviour and compulsive usage, and is intermittently unable to control time online that he or she hides from others), Neglect Work (Job or school performance and productivity are most likely compromised due to the amount of time spent online), Anticipation(the respondent most likely thinks about being online when not at the computer and feels compelled to use the Internet when offline), Lack of Control(the respondent has trouble managing his or her online time, frequently stays online longer than intended, and others may complain about the amount of time he or she spends online) and Neglect Social Life (the respondent frequently forms new relationships with fellow online users and uses the Internet to establish social connections that may be missing in his or her life) [ 7 , 8 , 9 , 10 ].

Events during the adolescence period greatly influence a person's development and can determine their attitudes and behavior in later life [ 11 ]. The teenagers are often in conflict with authority and cultural and moral norms of society, certain developmental effects can trigger a series of defense mechanisms [ 12 ]. During adolescence, there is an increased risk of emotional crises, often accompanied by mood changes and periods of anxiety and depressive behavior, which some adolescents attempt to fight through withdrawal, avoidance of any extensive social contact, aggressive reactions, and addictive behaviour [ 13 , 14 ]. Adolescents are exceptionally vulnerable and receptive during this period and can become drawn to the Internet as a form of release. Over time, this can lead to addiction [ 15 ].

Relaxed access and social networking are two of the several aspects of the Internet development of addictive behaviour [ 16 ]. Internet addiction is a newly emerged behavioral problem of adults which was reported after problem behavior theory was proposed [ 17 ]. Behavioral addictions operate on a modified principle of the classic addiction model [ 18 , 19 , 20 ]. Others have reported, that there is a tendency for individuals to be multiply ''addicted'' and to have overlapping addictions between common substances such as alcohol and cigarettes and ''addictions'' to activities such as internet use, gambling, exercising, and television [ 21 ]. A key factor to both models of substance and behavioral addictions is the concept of psychological dependence, in which no physiological exchange, such as ingestion of a substance, occurs [ 18 , 22 ]. Internet addiction in puberty and young adults can negatively impact life satisfaction and engagement [ 23 ], which may negatively affect cognitive functioning [ 24 ], lead to poor academic performance [ 25 , 26 ], and engagement in hazardous activities [ 27 ]. Internet addiction is also related to depression, somatization, and obsessive–compulsive disorder [ 28 ]. It has been found that paranoid ideation, hostility, anxiety, depression, interpersonal sensitivity, and obsessive–compulsive average scores are higher in people with high Internet Addiction scores than those without Internet addiction [ 29 , 30 ].

College students are especially susceptible to developing a dependence on the Internet, more than most other segments of society. This can be qualified to numerous factors including the following: Availability of time; ease of use; the psychological and developmental characteristics of young adulthood; limited or no parental supervision; an expectation of Internet/computer use covertly if not, as some courses are Internet-dependent, from assignments and projects to link with peers and mentors; the Internet offering a way of escape from exam anxiety [ 31 ].

Studies have indicated that IA is associated with different factors. Socio-demographic factors such as age (having lower age) [ 32 ] and male gender [ 33 , 34 , 35 , 36 , 37 ]. Reason for internet use related factors such as making new friendships online [ 33 ], getting into relationships online [ 33 ], using the internet less for coursework/assignments [ 33 ], visiting pornographic sites [ 34 ] and playing online games [ 31 , 34 , 38 ]. Time related and other factors such as higher internet usage time [ 37 , 39 ],continuous availability online [ 33 , 35 , 39 ] and mode of internet access [ 35 ]. Clinical and substance related factors such as insomnia [ 40 ], attention deficient disorder and hyperactivity symptoms [ 41 ], being sexual inactive [ 32 ], low self-esteem [ 40 ], failure in academic performance [ 32 ], smoking [ 41 ], and potential addictive personal habits of, drinking alcohol or coffee, and taking drugs [ 34 ]. Besides, mental illness like depression, anxiety and psychological distress [ 35 , 36 , 37 , 39 , 40 ] are associated with internet addiction. This could be based on the application of a general strain theory framework whereby negative emotions that are secondary to depression, anxiety, and psychological distress will be associated positively with internet addiction [ 42 ].

Internet Addiction is now becoming a serious mental health problem among Chinese adolescents. The researchers identified 10.6% to 13.6% of Chinese college students as Internet addicts [ 43 , 44 ]. A study conducted among Taiwan college students reported that the prevalence of Internet Addiction was 15.3% [ 37 ].

The prevalence of Problematic Internet Use (PIU) was greater among university students. For instance, the prevalence was 36.9 to 81% in Malaysian medical students by using the internet addiction questionnaire and Internet Addiction Diagnostic Questionnaire study instrument with a cut-offs point of ≥ 43 and 31to 79 respectively [ 45 , 46 ], 25.1% in American community university students by using the YIATstudy instrument with a cut-offs point of ≥ 40 [ 47 ], 40.7% in Iranian university students by utilizing the YIAT study instrument with a cut-offs point of ≥ 40 [ 48 ], 38.2–63.5% IA in Japanese university students as measured with the YIAT study instrument with a cut-offs point of ≥ 40 and ≥ 40 respectively [ 36 , 49 ], 16.8% IA in Lebanon University students by utilizing the YIAT study instrument with a cut-offs point of ≥ 50 [ 40 ], 35.4% IA in Nepal undergraduate students as measured with the YIAT study instrument with a cut-offs point of ≥ 40 [ 32 ], 40% IA in Jordan University students by utilizing the YIAT study instrument with a cut-offs point of ≥ 50 [ 50 ],19.85% to 42.9% IA in various parts of India as measured with the YIAT study instrument with a cut-offs point of 31to79, ≥ 50 and ≥ 50 respectively [ 33 , 35 , 39 ], 12% IA to 34.7% (PIU) in Greek University students by utilizing the Problematic Internet Use Diagnostic Test study instrument with no stated cut-offs point [ 34 ], 1.6% IA in Turkey students by using the Young’s Internet Addiction Scale study instrument with a cut-offs point of 70–100 [ 41 ].

In general, the main reason why youths are at particular risk of internet addiction is that they spend most of their time on online gaming and social applications like online social networking such as Twitter, Facebook, and telegrams [ 51 ].

Even though developing countries shares for a large magnitude of internet addiction, indicating the public health impact of the problem in the region, much is not known about the occurrence rate of the problem in these regions in general and Ethiopia in particular. As a result, trustworthy assessments of internet addiction in university students in these circumstances are required for delivering a focused intervention geared towards addressing the associated factors.

Moreover, it will be a ground for the expansion of national and international plans, procedures, and policy. At last but not least, the findings from this study will provide significant implications for counsellors and policymakers to prevent students' Internet addiction. Hence, this a community university-based cross-sectional study aimed and assessed the prevalence and associated factors of internet addiction among Wollo university students.

Research questions

The purpose of this study was to measure prevalence and associated factors of IA among undergraduate university students in Ethiopia. The specific research questions that guided the present study were:

What is the prevalence of IA among undergraduate university students in Ethiopia?

What are the associated factors of IA?

Methods and materials

Study area and period.

The study was done at Wollo University, Dessie campus that is found in South Wollo Zone, Amhara Regional State which is 401 kms far from Addis Ababa, Northeastern Ethiopia. It had 5 colleges and 2 schools and a total of 62 departments. The number of regular students in 2018/2019 is 7248; among these 4009 are males and 3239 are females. The study was conducted from April 10 to May 10/ 2019. The sample size was determined using single population proportion formula, taking a 50% prevalence of Internet Addiction with the following assumption: 95% CI, 5% margin of error, 10% non-response rate, and a design effect of 1.5. So, the final sample size was 603.

Sampling technique and procedure

A multistage cluster sampling technique was used to recruit study participants. In the first stage, by the use of the lottery method, two colleges (College of medicine and health sciences, and College of natural sciences, and one school (school of law)) were selected. In the second stage, 18 departments (9 from the college of medicine and health science, 8 from the college of natural science and 1 from the school of law) were selected. Students were selected proportionally from the given departments based on the number of students of a particular.

Study design

A community university-based cross-sectional study was carried out to assess the prevalence and associated factors of Internet Addiction among undergraduate students at Wollo University, Amhara Region, Ethiopia.

Inclusion and exclusion criteria

All generic regular undergraduate adult students whose ages were 18 years and above, and who were present at the time of data collection. Students who gave consent to the study were recruited. The study participants who are blind and severely ill were excluded from the study.

Study instruments

Self-administered, well-structured, and organized English version questionnaire was disseminated to students, and data were collected from the individual student. The questionnaires comprised six parts. The first part consisted of socio-demographic details; a structured questionnaire was used to assess sociodemographic characteristics. The second part consists of Young’s Internet Addiction Test (YIAT); a structured, self-administered questionnaire was used to assess Internet Addiction. The YIAT [ 7 ] is the most commonly used measure of Internet Addiction among adults [ 52 ]. It includes 20 questions with a scoring of 1–5 for each question and a total maximum score of 100. Based on scoring subjects would be classified into normal users (0–30), mild (31–49), moderate (50–79), and severe (80–100) Internet Addiction groups. Mild Internet addiction, moderate Internet addiction, and severe Internet Addiction were considered as having an Internet Addiction [ 53 , 54 , 55 ]. YIAT-20 showed that it is more reliable in University students. The Cronbach α in the present study was 0.89. The third part time-associated factors; a self-report structured questionnaire was prepared from different kinds of literature to assess time-associated factors (such as Internet use experience in months and Internet use per day in hours). The fourth part reasons for internet use; a structured questionnaire was used to assess the reasons for internet use. The fifth part psychoactive substance use-associated factors; a self-report questionnaire was used to assess the current use of psychoactive substances (Khat, Cigarette, Alcohol, and Cannabis), and the last part mental health problem-associated factors and it was assessed by Kessler10 (K10). The K10 scale [ 56 ] is a simple measure of mental distress. The K10-item scale, which has been translated into Amharic and validated in Ethiopia [ 57 ], was used to measure mental distress (depressive, anxiety, and somatic symptoms). The internal consistency of the K10 psychological distress scale in the present study was checked with a reliability assessment and was found to be 0.86 [ 58 , 59 , 60 ]. Scores will range from 10 to 50. A score under 20 is likely to be well, a score of 20–24 is likely to have mild mental distress, a score of 25–29 is likely to have moderate mental distress and a score of 30 and over are likely to have severe mental distress. Study participants with a score of 20 or more points on the K10 Likert scale were considered as having mental distress [ 61 ].

Data quality control

A structured self-administered questionnaire was developed in English and would be translated to Amharic language and again translated back into English to ensure consistency. Data collectors and supervisors would be trained for two days on the objective of the study, the content of the questionnaire, and the data collection procedure. Data would be pilot tested on 5% of the total sample size outside the study area and based on feedback obtained from the pilot test; the necessary modification would be done. During the study period, the collected data would be checked continuously daily for completeness by principal investigator and supervisor in the respective departments.

Data processing and analysis

Quantitative data would be cleaned, coded, and entered into Epi-data 3.1 and exported to SPSS version 25 for analysis. Descriptive data would be presented by a table, graphs, charts, and means. Multicollinearity test was checked by using standard error and there was no correlation between independent variables. The association between independent variables and Internet Addiction would be made using a binary logistic regression model and all independent variables having p value ≤ 0.25 would be included in multiple logistic regression models. A p value less than 0.05 and Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) not inclusive of one would be considered as statically significant and would be used to determine predictors of Internet Addiction in the final model. Hosmer–Lemeshow test was done to check model fitness and the model was fit.

Socio-demographic characteristics of study participants

A total of 603 participants were involved with a response rate of 90.9% (n = 548). However, the rest 9.1% (n = 55) participants were excluded due to incomplete responses. The mean age of the respondents was 21.4 (SD 1.8) years, the minimum and maximum age of the participants was 18 years and 30 years respectively. More than half of, 291 (53.1%) of respondents were males. Many of the study participants had a practice of using the internet for more than twelve months, 321 (58.6%). About 501 (91.4%), 268 (48.9%), 433 (79%) were using the internet less than five hours per day, most common mode of internet access Wi-Fi, and log in and off occasionally during the day respectively. The study participants with current khat use, current cigarette smoking, current alcohol use, and current cannabis use were 19.0%, 11.3%, 25.4%; and 4.0% respectively. About 19.3% of the participants had mental distress (Table 1 ).

Prevalence of Internet addiction

The prevalence of IA was 466 (85%) of the 305(55.6%), 153(27.9%), 8(1.5%) mild, moderate, and severe Internet Addiction respectively. Nevertheless, the remaining 82 (15%) are free from Internet Addiction (Fig.  1 ). Participants who login permanently had a greater figure of IA than those who log in and off occasionally during the day (92.2% versus 83.1%). Those who used the internet for about six months had a greater prevalence of Internet Addiction than those who used greater than twelve months (91.6% versus 84.1%) (Table 2 ).

Internet Addiction by severity among undergraduate university students in Ethiopia, 2019 (n = 548)

Reasons for internet use among Wollo University students

The furthermost frequent reasons for internet use among Wollo University undergraduate students were using the internet for courses / assignments (93.6%), for social networks (Facebook, etc.) (85.6%), for reading / posting news (76.6%), for getting into relationships online (66.6%),for playing mobile games (44.5%), for downloading music or videos (65.7%), for watching videos (57.8%),for retrieving sexual information (22.8%), for chat rooms (47.6%) and for e-mail ( reading, writing) (49.8%) (Fig.  2 ).

Reasons for internet use among undergraduate university students in Ethiopia, 2019 (n = 548)

Factors associated with internet addiction in the univariate analysis

Time related factors.

Duration of using the internet was associated with Internet Addiction i.e. students who used the internet for more than a year was 51% lower risks of having internet addiction than their counterparts (OR=0.49; CI 0.24–0.96). Respondents who were spending more time on the internet were more likely to develop Internet Addiction than their counterparts (OR=8.87; CI 1.21–65.25).

Mode of internet access was related to Internet Addiction i.e. those who used mobile internet were 45% lower risks of having Internet Addiction than those who used data cards (OR = 0.55; 95% CI 0.28–1.07).Participants who were permanently online were most likely to have Internet Addiction than those who were not (OR=2.39; 95% CI 1.16–4.93).

Reasons for internet use related factors

Study participants who played mobile games online were more likely to develop Internet Addiction than those who were not played mobile games (OR = 2.67; 95% CI 1.57–4.52). Those who downloaded music or videos were higher risks of having Internet Addiction than those who didn’t (OR = 1.62; 95% CI 1.00–2.61). Study participants who watched the video online were most likely to have Internet Addiction than those who didn’t watch (OR=1.94; 95% CI 1.21–3.12).

Psychoactive substance use related factors

Those who chewed khat currently were higher odds of having Internet Addiction than those who were not (OR = 5.33; 95% CI 1.90–14.91). Respondents who smoked cigarettes currently were more likely to have Internet Addiction than their counterparts (OR = 12.20; 95% CI 1.67–89.28).

Those who used alcohol currently were greater risks of having Internet Addiction than those who hadn't (OR = 2.76; 95% CI 1.38–5.51).

Mental health problem related factors

Study participants who had mental distress were four times more likely to develop Internet Addiction than those who didn't have mental distress (OR = 4.26; 95% CI 1.68–10.81) (Table 2 ).

Factors associated with internet addiction in the multivariate analysis

In the final model, spending more time on the internet, having mental distress and playing online games were the factors associated with Internet Addiction. Moreover, current khat chewing and current alcohol use were the independent predictors for Internet Addiction. Using the internet for more than twelve months and using the internet by mobile internet were negatively associated with Internet Addiction (Table 2 ).

Discussions

The present study aims to assess the prevalence and associated factors of Internet Addiction among undergraduate university students in Ethiopia. The prevalence of IA was 85% (n = 466). In the final model; spending more time on the internet, having mental distress and playing online games were the factors associated with Internet Addiction. Moreover, current khat chewing and current alcohol use were the independent predictors for Internet Addiction. Using the internet for more than twelve months and using the internet by mobile internet were negatively associated with Internet Addiction.

The prevalence of Internet Addiction in the present study was higher than the prevalence of Internet Addiction that was done in different universities such as three medical schools across three countries ( Croatia, India, and Nigeria) 49.7% [ 55 ], Malaysian 36.9% to 81% [ 45 , 46 ], American community 25.1% [ 47 ], Iran 12.5 to 40.7% [ 48 , 62 , 63 ], Japan 38.2% to 63.5% [ 36 , 49 ], Greek 12% to 30.1% [ 54 , 64 ], Jordan 40% [ 50 ], Lebanon 16.8% [ 40 ], Nepal 35.4% [ 32 ] and in different parts of India 19.85% to 42.9% [ 33 , 35 , 39 ]. The discrepancy might be due to the cut-off point of YIAT-20, instrument difference, mental health policy, a cultural difference like time utilization, the difference in study participants such as in our study the participants were from two colleges and one school, and all participants were internet users, sample size and the time difference between the studies. The study in Malaysian University was conducted among medical students only and focusing on mild Internet Addiction and moderate Internet Addiction and not on severe Internet addiction.

In our study spending more time on the internet was 10 times more likely to develop Internet Addiction than those who are spending less time. The finding of this study is in line with similar studies done on college students in Taiwan and three medical schools across three countries (Croatia, India, and Nigeria) [ 37 , 55 ]. The possible explanation for the association between Internet usage time and Internet Addiction is that it might be as much a symptom as it is a cause. However, this study design was cross-sectional and no causal relationship can be clarified, further studies ought to examine whether Internet usage time is an essential factor for determining Internet addiction.

Likewise, students who had mental distress were 2.7 times more likely to develop Internet Addiction as compared to their counterparts. Study findings in these areas showed that students who had mental distress were related to higher levels of Internet Addiction than students who hadn’t mental distress [ 35 , 36 , 39 , 40 , 41 , 50 ]. This could be due to the Khantzian’s [ 65 ] self-medication hypothesis, indicating that mentally distressed university students might come to rely on the Internet as a method for coping with their mental distress. Hence, they will devote more and more time on the Internet and headway toward addiction if their mental distress symptoms are not cured [ 66 ].Students who had playing online games were 2.4 times higher to have Internet Addiction than their counterparts. A similar finding was also reported in Greek University and others [ 34 , 38 , 54 , 67 ].

Furthermore, students who chewed khat currently were three times most likely to develop Internet Addiction than students who reported no current khat chewing which is in line with the study finding in Greek University students [ 34 ]. In this study, students who drank alcohol currently were 2.3 times most likely to have Internet Addiction as compared with students who didn’t drink alcohol. Other studies reported a similar finding [ 17 , 34 , 68 , 69 ]. Probable reasons involve; based on the problem behavior theory, the problem behaviors (Internet Addiction and substance abuse) are inter-related.

Students who used the internet by mobile internet were 60% of lower risks of having Internet Addiction as compared to those students who used data cards. This might be due to inadequate finance to use the internet on mobile internet. So, the students may refrain from using the internet through mobile internet. Students who used the internet for more than 12 months were 52% less likely to have Internet Addiction than their counterparts. The current finding is not supported by other studies in the world. The present study has limitations such as alpha inflation from multiple testing and the analysis did not account for the complex sampling strategy in adjusting the standard errors.

The current study documents a high prevalence of Internet Addiction among Wollo University students. The factors associated with Internet Addiction were spending more time on the internet, having mental distress, playing online games, current khat chewing, and current alcohol use. As internet addiction becomes an evident public health problem, carrying out public awareness campaigns on its severity and negative consequences of excruciating agonies may be a fruitful strategy to decrease its prevalence and effect. Campaign programs may aim at informing the adults on the phenomenon of internet addiction, knowing the possible risks and symptoms. Besides to this, a collaborative work among all stakeholders is important to develop other trendy, adaptive, ethical and sustainable countermeasures.

Availability of data and materials

The datasets supporting the conclusions of this article are not publicly available due to ethics regulations but may be available from the corresponding author upon reasonable request.

Abbreviations

Adjusted odds ratio

Confidence interval

Crude odds ratio

• Internet addiction

Statistical package for social science

Young’s internet addiction test

Bremer J. The internet and children: advantages and disadvantages. Child Adolesc Psychiat Clin. 2005;14(3):405–28.

Article   Google Scholar  

Swaminath G. Internet addiction disorder: fact or fad? Nosing into nosology. Indian J Psychiat. 2008;50(3):158.

Dargahi H, Razavi S. Internet addiction and its related factors: a study of an Iranian population. 2007.

Tereshchenko S, Kasparov E. Neurobiological risk factors for the development of internet addiction in adolescents. Behav Sci. 2019;9(6):62.

Article   PubMed Central   Google Scholar  

Griffiths M. Does internet and computer" addiction" exist? Some case study evidence. CyberPsychol Behav. 2000;3(2):211–8.

Kuss D, Griffiths M. Internet addiction in psychotherapy. Berlin: Springer; 2014.

Google Scholar  

Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav. 1998;1(3):237–44.

Morahan-Martin J, Schumacher P, editors. Incidence and correlates of pathological internet usage. Paper presented art the 105th annual convention of the American Psychological Association, Chicago, Illinois; 1997.

Young KS, De Abreu CN. Internet addiction. A handbook and guide to evaluation. 2011.

Scherer K. College life online of college life and development. Healthy Unhealthy Internet Use J. 1997;38(6):655–65.

Blos P. On adolescence: psychoanalytic interpretation. Reissue. New York, NY: Free Press; 1966.

Karacic S, Oreskovic S. Internet addiction through the phase of adolescence: a questionnaire study. JMIR Mental Health. 2017;4(2):e11.

Article   PubMed   PubMed Central   Google Scholar  

Achenbach TM, Becker A, Döpfner M, Heiervang E, Roessner V, Steinhausen HC, et al. Multicultural assessment of child and adolescent psychopathology with ASEBA and SDQ instruments: research findings, applications, and future directions. J Child Psychol Psychiat. 2008;49(3):251–75.

Article   PubMed   Google Scholar  

Graovac M. Adolescent u obitelji. Medicina Fluminensis: Medicina Fluminensis. 2010;46(3):261–6.

ZboralskiACD K, OrzechowskaDEF A, TalarowskaDE M, DarmoszAB A, JaniakGF A, JaniakGF M, et al. The prevalence of computer and Internet addiction among pupils Rozpowszechnienie uzależnienia od komputera i Internetu. Postepy Hig Med Dosw(online). 2009;63:8–12.

Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health. 2011;8(9):3528–52.

Ko CH, Yen J-Y, Yen CF, Chen CS, Weng CC, Chen CC. The association between Internet addiction and problematic alcohol use in adolescents: the problem behavior model. Cyberpsychol Behav. 2008;11(5):571–6.

Bradley BP. Behavioural addictions: common features and treatment implications. Br J Addict. 1990;85(11):1417–9.

Holden C. 'Behavioral'addictions: do they exist?: American Association for the Advancement of Science; 2001.

Miele GM, Tilly SM, First M, Frances A. The definition of dependence and behavioural addictions. Br J Addict. 1990;85(11):1421–3.

Greenberg JL, Lewis SE, Dodd DK. Overlapping addictions and self-esteem among college men and women. Addict Behav. 1999;24(4):565–71.

Buckley RC. Adventure thrills are addictive. Front Psychol. 2015;6:1915.

PubMed   PubMed Central   Google Scholar  

Shahnaz I, Karim A. The impact of internet addiction on life satisfaction and life engagement in young adults. Univ J Psychol. 2014;2(9):273–84.

Park M-H, Park E-J, Choi J, Chai S, Lee J-H, Lee C, et al. Preliminary study of Internet addiction and cognitive function in adolescents based on IQ tests. Psychiatry Res. 2011;190(2–3):275–81.

Akhter N. Relationship between internet addiction and academic performance among university undergraduates. Educ Res Rev. 2013;8(19):1793–6.

Usman N, Alavi M, Shafeq SM. Relationship between internet addiction and academic performance among foreign undergraduate students. Proced Soc Behav Sci. 2014;114:845–51.

Pfaff DW, Volkow ND. Neuroscience in the 21st century: from basic to clinical: Springer, Berlin, 2016.

Alavi SS, Alaghemandan H, Maracy MR, Jannatifard F, Eslami M, Ferdosi M. Impact of addiction to internet on a number of psychiatric symptoms in students of isfahan universities, iran, 2010. Int J Prevent Med. 2012;3(2):122.

Xiuqin HHZ, Mengchen L, Jinan W, Ying Z, Ran T. Mental health, personality, and parental rearing styles of adolescents with Internet addiction disorder. Cyberpsychol Behav Soc Netw. 2010;13(4):401–6.

Alavi SSAH, Maracy MR, Jannatifard F, Eslami M, Ferdosi M. Impact of addiction to internet on a number of psychiatric symptoms in students of Isfahan universities. Iran Int J Prev Med. 2012;3(2):122–7.

PubMed   Google Scholar  

Kandell JJ. Internet addiction on campus: The vulnerability of college students. Cyberpsychol Behav. 1998;1(1):11–7.

Bhandari PM, Neupane D, Rijal S, Thapa K, Mishra SR, Poudyal AK. Sleep quality, internet addiction and depressive symptoms among undergraduate students in Nepal. BMC Psychiat. 2017;17(1):106.

Krishnamurthy S, Chetlapalli SK. Internet addiction: prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India. Indian J Public Health. 2015;59(2):115.

Frangos CC, Frangos CC, Sotiropoulos I. Problematic internet use among Greek university students: an ordinal logistic regression with risk factors of negative psychological beliefs, pornographic sites, and online games. Cyberpsychol Behav Soc Netw. 2011;14(1–2):51–8.

Gedam SR, Ghosh S, Modi L, Goyal A, Mansharamani H. Study of internet addiction: Prevalence, pattern, and psychopathology among health professional undergraduates. Indian J Soc Psychiat. 2017;33(4):305.

Kitazawa M, Yoshimura M, Murata M, Sato-Fujimoto Y, Hitokoto H, Mimura M, et al. Associations between problematic internet use and psychiatric symptoms among university students in Japan. Psychiat Clin Neurosci. 2018;72(7):531–9.

Lin M-P, Ko H-C, Wu JY-W. Prevalence and psychosocial risk factors associated with Internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol Behav Soc Netw. 2011;14(12):741–746.

Petry NM, O'Brien CP. Internet gaming disorder and the DSM‐5. 2013.

Gupta A, Khan AM, Rajoura O, Srivastava S. Internet addiction and its mental health correlates among undergraduate college students of a university in North India. J Family Med Primary Care. 2018;7(4):721.

Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al. Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: a cross-sectional designed study. PLoS ONE. 2016;11(9):e0161126.

Seyrek S, Cop E, Sinir H, Ugurlu M, Şenel S. Factors associated with Internet addiction: Cross-sectional study of Turkish adolescents. Pediatr Int. 2017;59(2):218–22.

Jun S, Choi E. Academic stress and Internet addiction from general strain theory framework. Comput Hum Behav. 2015;49:282–7.

Chou C, Hsiao M-C. Internet addiction, usage, gratification, and pleasure experience: the Taiwan college students’ case. Comput Educ. 2000;35(1):65–80.

Yang T, Yu L, Oliffe JL, Jiang S, Si Q. Regional contextual determinants of internet addiction among college students: a representative nationwide study of China. Eur J Public Health. 2017;27(6):1032–7.

Ching SM, Awang H, Ramachandran V, Lim S, Sulaiman W, Foo Y, et al. Prevalence and factors associated with internet addiction among medical students-A cross-sectional study in Malaysia. Med J Malaysia. 2017;72(1):7–11.

Haque M, Rahman NAA, Majumder MAA, Haque SZ, Kamal ZM, Islam Z, et al. Internet use and addiction among medical students of Universiti Sultan Zainal Abidin. Malaysia Psychol Res Behav Manag. 2016;9:297.

Jelenchick LA, Becker T, Moreno MA. Assessing the psychometric properties of the internet addiction test (IAT) in US college students. Psychiat Res. 2012;196(2–3):296–301.

Bahrainian SA, Alizadeh KH, Raeisoon M, Gorji OH, Khazaee A. Relationship of Internet addiction with self-esteem and depression in university students. J Prevent Med Hygiene. 2014;55(3):86.

Tateno M, Teo AR, Shirasaka T, Tayama M, Watabe M, Kato TA. Internet addiction and self-evaluated attention-deficit hyperactivity disorder traits among Japanese college students. Psychiatry Clin Neurosci. 2016;70(12):567–72.

Al-Gamal E, Alzayyat A, Ahmad MM. Prevalence of internet addiction and its association with psychological distress and coping strategies among university students in Jordan. Perspect Psychiat Care. 2016;52(1):49–61.

Kuss DJ, Van Rooij AJ, Shorter GW, Griffiths MD, van de Mheen D. Internet addiction in adolescents: prevalence and risk factors. Comput Hum Behav. 2013;29(5):1987–96.

Frangos CC, Frangos CC, Sotiropoulos I, editors. A meta-analysis of the reliabilty of young’s internet addiction test. Proceedings of the World Congress on Engineering; 2012: World Congress on Engineering London, United Kingdom.

Young KS. Internet addiction test. Center for on-line addictions. 2009.

Tsimtsiou Z, Haidich A-B, Spachos D, Kokkali S, Bamidis P, Dardavesis T, et al. Internet addiction in Greek medical students: an online survey. Acad Psychiatry. 2015;39(3):300–4.

Balhara YPS, Gupta R, Atilola O, Knez R, Mohorović T, Gajdhar W, et al. Problematic internet use and its correlates among students from three medical schools across three countries. Acad Psychiatry. 2015;39(6):634–8.

Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, et al. Screening for serious mental illness in the general population. Arch Gen Psychiat. 2003;60(2):184–9.

Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand S-L, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–76.

Tadegge AD. The mental health consequences of intimate partner violence against women in Agaro Town, southwest Ethiopia. Trop Doct. 2008;38(4):228–9.

Hanlon C, Medhin G, Selamu M, Breuer E, Worku B, Hailemariam M, et al. Validity of brief screening questionnaires to detect depression in primary care in Ethiopia. J Affect Disord. 2015;186:32–9.

Fekadu A, Medhin G, Selamu M, Hailemariam M, Alem A, Giorgis TW, et al. Population level mental distress in rural Ethiopia. BMC Psychiat. 2014;14(1):194.

Andrews G, Slade T. Interpreting scores on the Kessler psychological distress scale (K10). Aust N Z J Public Health. 2001;25(6):494–7.

Arbabisarjou A, Gorgich EAC, Barfroshan S, Ghoreishinia G. The Association of internet addiction with academic achievement, emotional intelligence and strategies to prevention of them from student’s perspectives. Int J Hum Cult Stud (IJHCS). 2016;3(1):1656–66.

Gorgich EAC, Moftakhar L, Barfroshan S, Arbabisarjou A. Evaluation of internet addiction and mental health among medical sciences students in the southeast of Iran. Shiraz E Med J. 2018;19(1).

Frangos CCFC, Sotiropoulos I. A meta-analysis of the reliability of young’s internet addiction test. A meta-analysis of the reliability of young’s internet addiction test. WCE. 2012;1:368–71.

Khantzian EJ. The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Rev Psychiat. 1997;4(5):231–44.

Yen J-Y, Ko C-H, Yen C-F, Wu H-Y, Yang M-J. The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health. 2007;41(1):93–8.

Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®): American Psychiatric Pub; 2013.

Sajeev Kumar P, Prasad N, Raj Z, Abraham A. Internet addiction and substance use disorders in adolescent students-a cross sectional study. J Int Med Dent. 2015;2:172–9.

Ko C-H, Yen J-Y, Chen C-C, Chen S-H, Wu K, Yen C-F. Tridimensional personality of adolescents with internet addiction and substance use experience. Can J Psychiat. 2006;51(14):887–94.

Download references

Acknowledgements

We thank the Department of Psychiatry, College of Medicine and Health Sciences, Wollo University for supporting the research in different ways. We extend our heartfelt thanks to the student service directorate office for providing us the necessary information. We are grateful to all the students who participated in the study.

Not applicable.

Author information

Authors and affiliations.

Department of Psychiatry, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

Yosef Zenebe, Kunuya Kunno, Meseret Mekonnen, Ajebush Bewuket, Mengesha Birkie, Mogesie Necho, Muhammed Seid & Million Tsegaw

Department of Pharmacy, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

You can also search for this author in PubMed   Google Scholar

Contributions

YZ and KK designed and supervised the study, carried out the analysis, and interpreted the data; MM, AB, MB, MNA, MS, MT, and BA assisted in the design, analysis, and interpretation of the data; and YZ wrote the manuscript. All authors contributed toward data analysis, drafting, and critically revising the paper and agree to be accountable for all aspects of the work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yosef Zenebe .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted after getting ethical clearance from Wollo University College of medicine and health science institutional review board with a certificate of approval number: CMHS/508/2019. A formal letter of permission was obtained from the student service directorate of Wollo University. The respondents were informed about the aim of the study. Confidentiality was maintained by giving codes for respondents rather than recording their names. The privacy of the respondents was also assured since the anonymous data collection procedure was followed. The data collectors have informed the clients that they had the full right to discontinue or refuse to participate in the study. Written consent was obtained from each participant before administering the questionnaire.

Consent for publication

Competing interests.

The authors report no conflicts of interest in this work.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Zenebe, Y., Kunno, K., Mekonnen, M. et al. Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: a community university-based cross-sectional study. BMC Psychol 9 , 4 (2021). https://doi.org/10.1186/s40359-020-00508-z

Download citation

Received : 21 May 2020

Accepted : 21 December 2020

Published : 06 January 2021

DOI : https://doi.org/10.1186/s40359-020-00508-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Advertisement

“Internet Addiction”: a Conceptual Minefield

• Open access
• Published: 19 September 2017
• Volume 16 , pages 225–232, ( 2018 )

Cite this article

You have full access to this open access article

• Francesca C. Ryding 1 &
• Linda K. Kaye 1  

17k Accesses

55 Citations

19 Altmetric

Explore all metrics

With Internet connectivity and technological advancement increasing dramatically in recent years, “Internet addiction” (IA) is emerging as a global concern. However, the use of the term ‘addiction’ has been considered controversial, with debate surfacing as to whether IA merits classification as a psychiatric disorder as its own entity, or whether IA occurs in relation to specific online activities through manifestation of other underlying disorders. Additionally, the changing landscape of Internet mobility and the contextual variations Internet access can hold has further implications towards its conceptualisation and measurement. Without official recognition and agreement on the concept of IA, this can lead to difficulties in efficacy of diagnosis and treatment. This paper therefore provides a critical commentary on the numerous issues of the concept of “Internet addiction”, with implications for the efficacy of its measurement and diagnosticity.

Similar content being viewed by others

Internet Addiction

Avoid common mistakes on your manuscript.

What Is Internet Addiction (IA)?

Traditionally, the term addiction has been associated with psychoactive substances such as alcohol and tobacco; however, behaviours including the use of the Internet have more recently been identified as being addictive (Sim et al. 2012 ). The concept of IA is generally characterised as an impulse disorder by which an individual experiences intense preoccupation with using the Internet, difficulty managing time on the Internet, becoming irritated if disturbed whilst online, and decreased social interaction in the real world (Tikhonov and Bogoslovskii 2015 ). These features were initially proposed by Young ( 1998 ) based on the criteria for pathological gambling (Yellowlees and Marks 2007 ), and have since been adapted for consideration within the DSM-5. This has been well received by many working in the field of addiction (Király et al. 2015 ; Petry et al. 2014 ), and has been suggested to enable a degree of standardisation in the assessment and identification of IA (King and Delfabbro 2014 ). However, there is still debate and controversy surrounding this concept, in which researchers acknowledge much conceptual disparity and the need for further work to fully understand IA and its constituent disorders (Griffiths et al. 2014 ).

Much of the debate relates to the issue that IA is conceptualised as addiction to the Internet as a singular entity, although it incorporates an array of potential activities (Van Rooij and Prause 2014 ). That is, the Internet, in all its formats, whether accessed via PC, console, laptop or mobile device, is fundamentally a portal through which we access activities and services. Internet connectivity thus provides us with ways of accessing the following types of activities; play (e.g. online forms of gaming, gambling), work (accessing online resources, downloading software, emailing, website hosting), socialising (social networking sites, group chats, online dating), entertainment (film databases, porn, music), consumables (groceries, clothes), as well as many other activities and services. In this way, the Internet is a highly multidimensional and diverse environment which affords a multitude of experiences as a product of the specific virtual domain. Thus, it is questionable as to whether there is any degree of consistency in the concept of IA, in light of these diverse and specific affordances which may relate to Internet engagement. Indeed, it has been indicated that there are several distinct types of IA, including online gaming, social media, and online shopping (Kuss et al. 2013 ), and it has been claimed that through engagement in these behaviours, individuals may become addicted to these experiences, as opposed to the medium itself (Widyanto et al. 2011 ). Thus, IA is arguably too generalised as a concept to adequately capture these nuances. That is, an individual who spends excessive time online for shopping is qualitatively different from someone who watches or downloads porn excessively. These represent distinct behaviours which are arguably underpinned by different gratifications. Thus, the functionality of aspects of the Internet is a key consideration for research in this area (Tokunaga 2016 ). This is perhaps best approached from a uses and gratifications perspective (LaRose et al. 2003 ; Larose et al. 2001a ; Wegmann et al. 2015 ), to more fully understand the aetiology of IA (discussed subsequently). This is often best underpinned by the uses and gratifications theory (Larose et al. 2001a , 2003 ), which seeks to explain (media) behaviours by understanding their specific functions and how they gratify certain needs. Indeed, in the context of IA, this may be particularly useful to establish the extent to which certain Internet-based behaviours may be more or less functional in need gratification than others, and the extent to which it is Internet platform itself which is driving usage or indeed the constituent domains which it affords. If the former, then controlling Internet-based usage behaviour more generically is perhaps appropriate, however, a more specified approach may often be required given the diverse needs the online environment can afford users.

IA from a Gratifications Perspective

It is questionable on the extent to which IA is itself the “addiction” or whether its aetiology relates to other pre-existing conditions, which may be gratified through Internet domains (Caplan 2002 ). One particular theory that has been referenced throughout much developing research (King et al. 2012 ; Laier and Brand 2014 ) is the cognitive-behavioural model, proposed by Davis ( 2001 ). This model suggests that maladaptive cognitions precede the behavioural symptoms of IA (Davis 2001 ; Taymur et al. 2016 ). Since much research focuses on the comorbidity between IA and psychopathology (Orsal et al. 2013 ), this is particularly useful in underpinning the concept of IA, and perhaps provides support that IA is a manifestation of underlying disorders, due to its psychopathological aetiology (Taymur et al. 2016 ). Additionally, the cognitive-behavioural model also distinguishes between both specific and generalised pathological Internet use, in comparison to global Internet behaviours that would not otherwise exist outside of the Internet, such as surfing the web (Shaw and Black 2008 ). As such, it would assume those individuals who spend excessive time playing poker online, for example, are perhaps better categorised as problematic gamblers rather than as Internet addicts (Griffiths 1996 ). This has been particularly advantageous in the contribution to defining IA, as earlier literature tended to focus solely on either content-specific IA, or the amount of time spent online, rather than focussing as to why individuals are actually online (Caplan 2002 ). Indeed, this shows promise in resolving some of the aforementioned issues in the specificity of IA, as well as the likelihood of pre-existing conditions underpinning problematic behaviours on the Internet.

Much of the recent literature in the realm of IA has focused upon Internet Gaming Disorder (IGD) which has recently been included as an appendix as “a condition for further study” in the DSM-5 (American Psychiatric Association 2013 ). This has driven a wide range of research which has sought to establish the validity of IGD as an independent clinical condition (Kuss et al. 2017 ). Among the wealth of research papers surrounding this phenomenon, there remains large disparity within the academic community. Although some researchers claim there is consensus on IGD as a valid clinical disorder (Petry et al. 2014 ), others do not support this (e.g. Griffiths et al. 2016 ). As such, the academic literature has some way to go before more established claims can be made towards IGD as a valid construct, and indeed how this impacts upon clinical treatment.

One means by which researchers could move forward in this regard is to establish the validity of IGD to a wider range of gaming formats. That is, IGD research has predominantly defined the reference point in studies as “online games” or in some cases, is has been even less specific (Lemmens et al. 2015 ; Rehbein et al. 2015 ; Thomas and Martin 2010 ). Arguably, there are a range of forms of “online” gaming, including social networking site (SNS) games which are Internet-mediated and thus by definition, would appear under the remit of IGD. Indeed, links between SNS and gaming have been previously noted (Kuss and Griffiths 2017 ), although this has not specifically been empirically explored in the context of IGD symptomology. For example, causal form of gaming as is typically the case for SNS gaming have their own affordances in respect of where and how they are played, given these are often played on mobile devices rather than on more traditional PC or console platforms. Further, the demographics of who are most likely to play these games can vary from others forms of gaming which have predominated the IGD literature (Hull et al. 2013 ; Leaver and Wilson 2016 ). Accordingly, these affordances present additional nuances, which the literature has not yet fully accounted for in its exploration of IGD. Clearly, IGD relates to a specific form of Internet behaviour which may be conceptualised within IA, yet is paramount to understand it as a separate entity to ensure the conceptualisation and any associated treatment provision is sufficiently nuanced. Likewise, the same case can be made for many other Internet-based behaviours which may be best being established in respect of their functionality and gratification purposes for users.

IA as a Contextual Phenomenon

There is growing evidence suggesting that context is key towards the processes and cognitions associated with consumption of substances such as alcohol (Monk and Heim 2013 , 2014 ; Monk et al. 2016 ), highlighting some important implications towards understanding IA, as a form of behavioural addiction. That is, the study of IA has rarely been studied in respect of its contextual affordances, even though the combination of Internet connectivity (WiFi) and mobility (smartphones) means that the Internet may be accessed in many ways and in multiple contexts. It has been indicated by Griffiths ( 2000 ) that few studies consider the context of Internet use, despite many users spending a substantial amount of time on the Internet via the use of different platforms, such as mobile devices, as opposed to a computer (Hadlington 2015 ). It has been highlighted by Kawabe et al. ( 2016 ) that smartphone ownership in particular is rapidly increasing, and for some, smartphone devices have become a substitute for the computer (Aljomaa et al. 2016 ). It has also been suggested that the duration of usage on smartphones have been significantly associated with IA (Kawabe et al. 2016 ). This can largely be attributed to the advancement of smartphone technology, which permit them to function as a “one-stop-shop” for a variety of our everyday needs (checking the time, replying to emails, listening to music, interacting with others, playing games), and thus it is understandable that we are spending more of our time in using these devices. This further implicates research in IA, as this has often focussed on users’ Internet engagement through computers as opposed to mobile devices, albeit the numerous Internet subtypes accessible through mobile devices (Sinkkonen et al. 2014 ). One Internet subtype in particular which may facilitate addictive behaviours are social networking sites such as Facebook (Wu et al. 2013 ). Particularly, research has identified a positive relationship between daily usage of smartphones and addictive symptoms towards Facebook (Wu et al. 2013 ). This may also be the case for behaviours such as gaming through SNS which are typically accessed on mobile devices rather than computers. However, of critical interest here, is that addiction to these games has been argued to fall under the classification of IGD, despite being online via Facebook (Ryan et al. 2014 ). This indicates that the platform of Internet access is important in online behaviours, as well as implicating that further distinction between Internet subtypes should be made (particularly within SNS), to establish the different features of these, and how these affordances may be related to excessive usage. This issue is particularly pertinent given the increased interest in “smartphone addiction” (Kwon et al. 2013 ) in which the name assumes we are simply studying addiction to our smartphones themselves, not necessarily the functions they are affording to us. Research such as this is assuming the “problem” is the interaction with the technology (e.g. specific device) itself, when this is most likely not the case. Indeed, recent evidence highlights that different uses/functions of smartphones may be more likely to prompt users to feel more “attached” to the device than others, and that usage is often framed by one’s current context (Fullwood et al. 2017 ).

In addition to being able to access the Internet through multiple platforms, we are often reliant on the Internet for many everyday tasks, which poses a further issue in conceptualising what is “problematic” compared to “required” usage. The increased exposure to the Internet in both work and education make it difficult to avoid usage in such environments (Kiliҫer and Ҫoklar 2015 ; Uçak 2007 ), and it could be argued that the amount of time spent on the Internet for such contexts cannot be reflected as an addiction (LaRose et al. 2003 ). This is pertinent in light of much research, which tends to rely on metrics such as time spent online (e.g. average hours per week) as a variable in research paradigms. Particularly, this tends to be used to correlate against other psychological factors, such as depression or well-being, to indicate how “internet use” may be a problematic predictor of these outcomes (e.g. Sanders et al. 2000 ). In light of the aforementioned issues, this does not offer any degree of specificity in how time spent online is theoretically related to the outcomes variables of interest (Kardefelt-Winther 2014 ). Other studies have approached this with greater nuance by considering specific activities, such as number of emails sent and received in a given time period (Ford and Ford 2009 ; LaRose et al. 2001b ), or studied Internet use for a variety of different purposes, such as for health purposes and communication (Bessière et al. 2010 ). Further, other researchers have highlighted the distinction between behaviours such as smartphone “usage” versus “checking” (Andrews et al. 2015 ), whereby the latter may represent a more compulsive and less consciously driven and potentially more addictive form of behaviour than actual “usage”. These more nuanced approaches provide a more useful and theoretically insightful means of establishing how time spent online may be psychologically relevant as a concept. This suggests that future research which theorises on the impacts of “time spent online” (or “screen-time use”) should provide distinction between usage for work/education and leisure, and the gratification this engagement affords, to obtain greater nuance beyond the typical flawed metrics such as general time spent online.

A further compliment to the existing IA literature would be greater use of behavioural measures which garner users’ actual Internet-based behaviours. This is particularly relevant when considering that almost all existing research on smartphone addiction or problematic use, for example has been based on users’ self-reported usage, with no psychometric measure being validated against behavioural metrics. Worryingly, it has been noted that smartphone users grossly underestimate the amount of times they check their smartphone on a daily basis, with digital traces of their smartphone behaviours illuminating largely disparate findings (Andrews et al. 2015 ). Clearly, there is much opportunity to establish forms of Internet usage by capitalising on behavioural metrics and digital traces rather than relying on self-report which may not always be entirely accurate.

The concept of IA is more complex than it often theorised. Although there have been multiple attempts to define the characteristics of IA, there a numerous factors which require greater clarity in the theoretical underpinnings of this concept. Specifically, IA is often considered from the perspective that the Internet itself (and indeed the technology through which we access it) is harmful, with little specificity in how this functions in different ways for individual users, as well as the varying affordances which can be gained through it. Unfortunately, this aligns somewhat with typical societal conceptions of “technology is harmful” perspective, rather than considering the technology itself is simply a portal through which a psychological need is being served. This perspective is not a new phenomenon. Most new media has been subject to such moral panic and thus this serves a historical tradition within societal conception of new media. Indeed, this has been particularly relevant to violent videogames which scholars have discussed in respect of this issue (Ferguson 2008 ). Whilst many scholars recognise this notion through the application of a user and gratifications perspective, stereotypical conceptions of “technology is harmful” still remain. This raises the question about how we as psychologists can enable a cultural shift in these conceptions, to provide a more critical perspective on such issues. The pertinence of this surrounds two key issues; firstly that moving beyond a “technology is harmful” perspective, particularly for concerns over “Internet addiction” as one example, can enable a more critical insight into the antecedents of problematic behaviour to aid treatment, rather than simply revoking access from the Internet for such individuals. Arguably, this latter strategy would not always address the route of the issue and raises implications about the extent to which recidivism would occur upon reinstating Internet access. Secondly, on a more general level, diverging from an “anti-technology” perspective can enable researchers to draw out the nuances of specific Internet environments and their psychological impacts rather than battling with more blanket assumptions that “technology” (as a unitary concept) is presenting all individuals with the same issues and affordances, regardless of the specific virtual platform or context. In this way, we may be presented with more plentiful opportunities to more critically explore individuals and their interactions across many Internet-mediated domains and contexts.

Aljomaa, S. S., Al Qudah, M. F., Albursan, I. S., Bakhiet, S. F., & Abduljabbar, A. S. (2016). Smartphone addiction among university students in the light of some variables. Computers in Human Behavior, 61 , 155–164. https://doi.org/10.1016/j.chb.2016.03.041 .

Article   Google Scholar  

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.

Book   Google Scholar  

Andrews, S., Ellis, D. A., Shaw, H., & Piwek, L. (2015). Beyond self-report: tools to compare estimated and real-world smartphone use. PloS One, 10 , e0139004.

Article   PubMed   PubMed Central   Google Scholar  

Bessière, K., Pressman, S., Kiesler, S., & Kraut, R. (2010). Effects of Internet use on health and depression: a longitudinal study. Journal of Medical Internet Research, 12 (1), e6. https://doi.org/10.2196/jmir.1149 .

Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: development of a theory-based cognitive–behavioral measurement instrument. Computers in Human Behavior, 18 (5), 553–575. https://doi.org/10.1016/s0747-5632(02)00004-3 .

Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17 (2), 187–195. https://doi.org/10.1016/s0747-5632(00)00041-8 .

Ferguson, C. J. (2008). The school shooting/violent video game link: casual relationship or moral panic? Journal of Investigative Psychology and Offender Profiling, 5 , 25–37.

Ford, G. S., & Ford, S. G. (2009). Internet use and depression among the elderly . Phoenix Center Policy Paper Series. Retrieved April 26, 2016, from http://www.phoenix-center.org/pcpp/PCPP38Final.pdf .

Fullwood, C., Quinn, S., Kaye, L. K., & Redding, C. (2017). My Virtual friend: a qualitative analysis of the attitudes and experiences of Smartphone users: implications for Smartphone attachment. Computers in Human Behavior, 75 , 347–355. https://doi.org/10.1016/j.chb.2017.05.029 .

Griffiths, M. D. (1996). Gambling on the Internet: a brief note. Journal of Gambling Studies, 12 (4), 471–473.

Article   CAS   PubMed   Google Scholar  

Griffiths, M. (2000). Internet addiction - time to be taken seriously? Addiction Research & Theory, 8 (5), 413–418. https://doi.org/10.3109/16066350009005587 .

Griffiths, M. D., King, D. L., & Demetrovics, Z. (2014). DSM-5 Internet Gaming Disorder needs a unified approach to assessment. Neuropsychiatry, 4 (1), 1–4.

Griffiths, M. D., Van Rooij, A. J., Kardefelt-Winther, D., et al. (2016). Working towards an international consensus on criteria for assessing internet gaming disorder: a critical commentary on Petry et al. (2014). Addiction, 111 , 167–175.

Hadlington, L. J. (2015). Cognitive failures in daily life: exploring the link with Internet addiction and problematic mobile phone use. Computers in Human Behavior, 51 , 75–81. https://doi.org/10.1016/j.chb.2015.04.036 .

Hull, D. C., Williams, G. A., & Griffiths, M. D. (2013). Video game characteristics, happiness and flow as predictors of addiction among video game players: a pilot study. Journal of Behavioral Addictions, 2 (3), 145–152. https://doi.org/10.1556/JBA.2.2013.005 .

Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Computers in Human Behavior, 31 , 351–354.

Kawabe, K., Horiuchi, F., Ochi, M., Oka, Y., & Ueno, S. (2016). Internet addiction: prevalence and relation with mental states in adolescents. Psychiatry and Clinical Neurosciences, 70 (9), 405–412. https://doi.org/10.1111/pcn.12402 .

Article   PubMed   Google Scholar  

Kiliҫer, K., & Ҫoklar, A. N. (2015). Examining human value development of children with different habits of Internet usage. Hacettepe University of Education, 30 (1), 163–177.

Google Scholar  

King, D. L., & Delfabbro, P. H. (2014). The cognitive psychology of Internet gaming disorder. Clinical Psychology Review, 34 (4), 298–308. https://doi.org/10.1016/j.cpr.2014.03.006 .

King, D. L., Delfabbro, P. H., Griffiths, M. D., & Gradisar, M. (2012). Cognitive-behavioral approaches to outpatient treatment of Internet addiction in children and adolescents. Journal of Clinical Psychology, 68 (11), 1185–1195. https://doi.org/10.1002/jclp.21918 .

Király, O., Griffiths, M. D., & Demetrovics, Z. (2015). Internet gaming disorder and the DSM-5: conceptualization, debates, and controversies. Current Addiction Reports, 2 (3), 254–262. https://doi.org/10.1007/s40429-015-0066-7 .

Kuss, D. J., & Griffiths, M. D. (2017). Social networking networking sites and addiction: ten lessons learned. International Journal of Environmental Research and Public Health, 14 , 311. https://doi.org/10.3390/ijerph14030311 .

Article   PubMed Central   Google Scholar  

Kuss, D. J., van Rooij, A. J., Shorter, G. W., Griffiths, M. D., & van de Mheen, D. (2013). Internet addiction in adolescents: prevalence and risk factors. Computers in Human Behavior, 29 (5), 1987–1996. https://doi.org/10.1016/j.chb.2013.04.002 .

Kuss, D. J., Grittihs, M. D., & Pontes, H. M. (2017). DSM-5 diagnosis of Internet Gaming Disorder: some ways forward in overcoming issues and concerns in the gaming studies field. Journal of Behavioral Addictions. https://doi.org/10.1556/2006.6.2017.032 .

Kwon, M., Lee, J., Won, W., Park, J., Min, J., Hahn, C., Gu, X., Choi, J., & Kim, D. (2013). Development and validation of a smartphone addiction scale. PloS One, 8 (2), e56936. https://doi.org/10.1371/journal.pone.0056936 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Laier, C., & Brand, M. (2014). Empirical evidence and theoretical considerations on factors contributing to cybersex addiction from a cognitive-behavioral view. Sexual Addiction & Compulsivity, 21 (4), 305–321. https://doi.org/10.1080/10720162.2014.970722 .

Larose, R., Mastro, D., & Eastin, M. S. (2001a). Understanding Internet usage: a social-cognitive approach to uses and gratifications. Social Science Computer Review, 19 (4), 395–413. https://doi.org/10.1177/089443930101900401 .

LaRose, R., Eastin, M. S., & Gregg, J. (2001b). Reformulating the Internet paradox: social cognitive explanations of Internet use and depression. Journal of Online Behavior, 1 (2). Retrieved Setpember 12, 2017 from http://psycnet.apa.org/record/2002-14047-001 .

LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction, habit, or deficient self-regulation? Media Psychology, 5 (3), 225–253. https://doi.org/10.1207/s1532785xmep0503_01 .

Leaver, T., & Wilson, M. (2016). Social networks, casual games and mobile devices: the shifting contexts of gamers and gaming . London: Bloomsbury Publishing Inc..

Lemmens, J. S., Valkenburg, P. M., & Gentile, D. A. (2015). The internet gaming disorder scale. Psychological Assessment, 27 (2), 567–582.

Monk, R. L., & Heim, D. (2013). Environmental context effects on alcohol-related outcome expectancies, efficacy and norms: a field study. Psychology of Addictive Behaviors, 27 , 814–818.

Monk, R. L., & Heim, D. (2014). A real-time examination of context effects on alcohol cognitions. Alcoholism: Clinical and Experimental Research, 38 , 2452–2459.

Monk, R. L., Pennington, C. R., Campbell, C., Price, A., & Heim, D. (2016). Implicit alcohol-related expectancies and the effect of context. Journal of Studies on Alcohol and Drugs, 77 , 819–827.

Orsal, O., Unsal, A., & Ozalp, S. S. (2013). Evaluation of Internet addiction and depression among university students. Procedia - Social and Behavioral Sciences, 82 , 445–454. https://doi.org/10.1016/j.sbspro.2013.06.291 .

Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H., Mӧßle, T., et al. (2014). An international consensus for assessing Internet Gaming Disorder using the new DSM-5 approach. Addiction, 109 (9), 1399–1406. https://doi.org/10.1111/add.12457 .

Rehbein, F., Kliem, S., Baier, D., Mossle, T., & Petry, N. M. (2015). Prevalence of internet gaming disorder in German adolescents: diagnostic contribution of the nine DSM-5 criteria in a state-wide representative sample. Addiction, 110 (5), 842–851.

Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: a review of Facebook addiction. Journal of Behavioral Addictions, 3 (3), 133–148. https://doi.org/10.1556/jba.3.2014.016 .

Sanders, C. E., Field, T. M., Diego, M., & Kaplan, M. (2000). The relationship between Internet use to depression and social isolation among adolescents. Adolescence, 35 (138), 237–242.

CAS   PubMed   Google Scholar  

Shaw, M., & Black, D. W. (2008). Internet addiction. CNS Drugs, 22 (5), 353–365. https://doi.org/10.2165/00023210-200822050-00001 .

Sim, T., Gentile, D. A., Bricolo, F., Serpelloni, G., & Gulamoydeen, F. (2012). A conceptual review of research on the pathological use of computers, video games, and the Internet. International Journal of Mental Health and Addiction, 10 (5), 748–769. https://doi.org/10.1007/s11469-011-9369-7 .

Sinkkonen, H.-M., Puhakka, H., & Meriläinen, M. (2014). Internet use and addiction among Finnish adolescents (15–19 years). Journal of Adolescence, 37 (2), 123–131. https://doi.org/10.1016/j.adolescence.2013.11.008 .

Taymur, I., Budak, E., Demirci, H., Akdağ, H. A., Güngör, B. B., & Özdel, K. (2016). A study of the relationship between internet addiction, psychopathology and dysfunctional beliefs. Computers in Human Behavior, 61 , 532–536. https://doi.org/10.1016/j.chb.2016.03.043 .

Thomas, N. J., & Martin, F. H. (2010). Video-arcade game, computer game and Internet activities of Australian students: participation participation habits and prevalence of addiction. Australian Journal of Psychology, 62 (2), 59–66.

Tikhonov, M. N., & Bogoslovskii, M. M. (2015). Internet addiction factors. Automatic Documentation and Mathematical Linguistics, 49 (3), 96–102. https://doi.org/10.3103/s0005105515030073 .

Tokunaga, R. S. (2016). An examination of functional difficulties from Internet use: Media habit and displacement theory explanations. Human Communication Research, 42 (3), 339–370. https://doi.org/10.1111/hcre.12081 .

Uçak, N. Ö. (2007). Internet use habits of students of the department of information management, Hacettepe University, Ankara. The Journal of Academic Librarianship, 33 (6), 697–707. https://doi.org/10.1016/j.acalib.2007.09.004 .

Van Rooij, A., & Prause, N. (2014). A critical review of “Internet addiction” criteria with suggestions for the future. Journal of Behavioral Addictions, 3 (4), 203–213. https://doi.org/10.1556/jba.3.2014.4.1 .

Wegmann, E., Stodt, B., & Brand, M. (2015). Addictive use of social networking sites can be explained by the interaction of Internet use expectancies, Internet literacy, and psychopathological symptoms. Journal of Behavioral Addictions, 4 (3), 155–162. https://doi.org/10.1556/2006.4.2015.021 .

Widyanto, L., Griffiths, M. D., & Brunsden, V. (2011). A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis. Cyberpsychology, Behavior, and Social Networking, 14 (3), 141–149. https://doi.org/10.1089/cyber.2010.0151 .

Wu, A. M. S., Cheung, V. I., Ku, L., & Hung, E. P. W. (2013). Psychological risk factors of addiction to social networking sites among Chinese smartphone users. Journal of Behavioral Addictions, 2 (3), 160–166. https://doi.org/10.1556/jba.2.2013.006 .

Yellowlees, P. M., & Marks, S. (2007). Problematic Internet use or Internet addiction? Computers in Human Behavior, 23 (3), 1447–1453. https://doi.org/10.1016/j.chb.2005.05.004 .

Young, K. S. (1998). Internet addiction: the emergence of a new clinical disorder. Cyberpsychology & Behavior, 1 (3), 237–244. https://doi.org/10.1089/cpb.1998.1.237 .

Download references

Author information

Authors and affiliations.

Department of Psychology, Edge Hill University, St Helens Road, Ormskirk, Lancashire, L39 4QP, UK

Francesca C. Ryding & Linda K. Kaye

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Linda K. Kaye .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflicts of interest.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Ryding, F.C., Kaye, L.K. “Internet Addiction”: a Conceptual Minefield. Int J Ment Health Addiction 16 , 225–232 (2018). https://doi.org/10.1007/s11469-017-9811-6

Download citation

Published : 19 September 2017

Issue Date : February 2018

DOI : https://doi.org/10.1007/s11469-017-9811-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Internet addiction
• Gratifications
• Find a journal
• Publish with us
• Track your research

This paper is in the following e-collection/theme issue:

Published on 29.3.2024 in Vol 26 (2024)

Telehealth Care Through Internet Hospitals in China: Qualitative Interview Study of Physicians’ Views on Access, Expectations, and Communication

Authors of this article:

Original Paper

• Yuqiong Zhong 1, 2 , Mphil   ; 
• Jessica Hahne 3 , MA, MPH   ; 
• Xiaomin Wang 4, 5 , PhD   ; 
• Xuxi Wang 1 , Bphil   ; 
• Ying Wu 1 , MPhil   ; 
• Xin Zhang 2, 6 * , MD, PhD   ; 
• Xing Liu 6, 7 * , PhD  

1 School of Humanities, Central South University, Changsha, China

2 Xiangya Hospital, Central South University, Changsha, China

3 Department of Psychological & Brain Sciences, Washington University in St Louis, St Louis, MO, United States

4 Center for Clinical Pharmacology, The Third Xiangya Hospital of Central South University, Changsha, China

5 Center for Medical Ethics, Central South University, Changsha, China

6 Medical Humanities Research Center, Central South University, Changsha, China

7 Office of International Cooperation and Exchanges, Xiangya Hospital, Central South University, Changsha, China

*these authors contributed equally

Corresponding Author:

Xing Liu, PhD

Office of International Cooperation and Exchanges

Xiangya Hospital, Central South University

No 87 Xiangya Road, Kaifu District

Changsha, 410008

Phone: 86 18229765509

Email: [email protected]

Background: Internet hospitals in China are an emerging medical service model similar to other telehealth models used worldwide. Internet hospitals are currently in a stage of rapid development, giving rise to a series of new opportunities and challenges for patient care. Little research has examined the views of chronic disease physicians regarding internet hospitals in China.

Objective: We aimed to explore the experience and views of chronic disease physicians at 3 tertiary hospitals in Changsha, China, regarding opportunities and challenges in internet hospital care.

Methods: We conducted semistructured qualitative interviews with physicians (n=26) who had experience working in internet hospitals affiliated with chronic disease departments in 3 tertiary hospitals in Changsha, Hunan province, south central China. Interviews were transcribed verbatim and analyzed by content analysis using NVivo software (version 11; Lumivero).

Results: Physicians emphasized that internet hospitals expand opportunities to conduct follow-up care and health education for patients with chronic illnesses. However, physicians described disparities in access for particular groups of patients, such as patients who are older, patients with lower education levels, patients with limited internet or technology access, and rural patients. Physicians also perceived a gap between patients’ expectations and the reality of limitations regarding both physicians’ availability and the scope of services offered by internet hospitals, which raised challenges for doctor-patient boundaries and trust. Physicians noted challenges in doctor-patient communication related to comprehension and informed consent in internet hospital care.

Conclusions: This study explored the experience and views of physicians in 3 tertiary hospitals in Changsha, China, regarding access to care, patients’ expectations versus the reality of services, and doctor-patient communication in internet hospital care. Findings from this study highlight the need for physician training in telehealth communication skills, legislation regulating informed consent in telehealth care, public education clarifying the scope of internet hospital services, and design of internet hospitals that is informed by the needs of patient groups with barriers to access, such as older adults.

Introduction

As information technology develops rapidly in the current era, telehealth is growing exponentially in use [ 1 - 3 ]. Particularly in the wake of the COVID-19 pandemic, the use of telehealth for both primary and specialist care has accelerated around the globe [ 4 ]. In particular, telehealth is being implemented at an increasing scale in various countries with aging populations to improve health care access and quality for growing numbers of patients with chronic diseases [ 5 - 9 ].

The internet hospital is 1 major emerging telehealth model that is distinct to China, a country with a particularly large aging population and a high chronic disease burden [ 10 - 12 ]. Designed to make health care services more available, convenient, affordable, and efficient, internet hospitals are a type of online platform through which certain health care services can be conducted remotely. There are 3 main types of internet hospitals—those initiated by physical hospitals, those jointly established by physical hospitals and business enterprises, and those initiated by business enterprises relying on physical medical institutions. Research suggests that internet hospitals initiated solely by physical hospitals are the most widespread type [ 13 ]. In terms of the target patient population, internet hospitals primarily aim to facilitate services for patients with common illnesses requiring relatively simple treatment [ 14 ], patients with chronic diseases (diabetes, hypertension, and cancer) [ 15 ], and patients in remote and rural areas [ 16 ].

However, the scope of internet hospitals goes beyond telemedicine services for patients. Services provided by internet hospitals can be classified into three categories, (1) “core medical services,” which mainly include follow-up care for in-person medical services, telemedicine consultations, guidance on chronic disease management, and guidance on medication use; (2) “non-core medical services,” which mainly include medical consultations between health care providers and remote education for health care providers; and (3) “convenience services,” which mainly include health care appointment scheduling, mobile payment for health services, remote examination of medical test results, and dispensation and distribution of some medications [ 10 , 13 , 17 , 18 ]. Thus, the internet hospital model has the potential to increase access to health care for patients and training for providers, and to decrease costs across the health care system.

A number of recent policies by the Chinese national government have promoted rapid development and uptake of the internet hospital model. In 2015 [ 19 ] and 2018 [ 20 ], the State Council issued guidelines promoting “‘Internet +’ Healthcare,” which emphasized the development of internet hospitals as part of the “Health China” strategy for health care reform. Concurrently in 2018, the National Health Commission formulated specific regulations on internet hospital management, which officially authorized internet hospitals to facilitate a range of telehealth services and marked the start of their standardized development [ 21 - 23 ]. In 2020, the National Health Commission issued the “Notice on Strengthening Informatization to Support the Prevention and Control of the Novel Coronavirus Pneumonia Epidemic,” emphasizing the advantages of internet hospitals in controlling the spread of the COVID-19 pandemic [ 24 ]. In the wake of these policies, by June 2023, the number of internet hospitals had reached more than 3000, and 364 million of China’s 1.079 billion internet users were using online medical services [ 25 , 26 ]. However, research suggests that most internet hospitals are not yet fully developed or providing the full scope of services intended to achieve these goals [ 27 - 29 ].

At this early stage of the model’s development, little research to date has evaluated the views of Chinese medical professionals and patients regarding internet hospitals. However, research on telehealth in other countries reveals that telehealth raises many new concerns and challenges alongside the aforementioned opportunities [ 30 - 32 ]. One of the most common concerns raised by patients is the potential for misdiagnosis due to the inability to conduct physical examinations through telehealth [ 33 , 34 ]. Particular groups such as lower-income older adults also commonly report barriers to the use of telehealth such as lack of familiarity with technology or limited access to technological devices or internet connections [ 35 ]. Smartphone data or internet connection problems can also lower patient satisfaction and limit access among rural patient populations [ 36 , 37 ]. Various groups of patients also commonly report feeling concerned about patient privacy and the security and protection of medical data when using telehealth [ 38 ].

In order to guide the direction of internet hospital development in China, further research is needed to examine the emerging challenges and opportunities to patient care presented by this country-specific telehealth platform. The aim of this study was to explore the experience and views of chronic disease physicians at 3 tertiary hospitals in Changsha, China, regarding opportunities and challenges presented by internet hospital care.

The methodology whereby this study was designed and conducted is reported following the items in the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist [ 39 ]. See Multimedia Appendix 1 [ 39 ] for more information.

Setting, Participant Recruitment, and Eligibility Criteria

We conducted in-depth, semistructured interviews with physicians at 3 tertiary hospitals in Changsha, Hunan Province, south central China. Inclusion criteria for participants were 18 years of age or older, experience working in internet hospitals, and employment in a chronic disease department at one of the study hospitals. Our rationale for these inclusion criteria was to select doctors who had work experience relevant to the research questions. Enrollment occurred over a 2-month period from April to May 2022. Using a purposive sampling approach, we obtained a list of doctors who had previous experience working in internet hospitals. We then messaged or called the doctors on the list to briefly explain the primary and secondary objectives of the study, invited them to share their perspectives related to the study, and asked them to be available for interviews. Out of the 28 doctors contacted, 26 agreed to participate, while 2 physicians declined due to a lack of time. Those who responded positively to the invitation were subsequently contacted by the author, YZ, either via WeChat (Tencent Holdings Limited) or telephone to schedule an interview.

We recruited participants until reaching data saturation, at which point no new information about the meaning of codes or themes and the relationship between them continued to appear [ 40 ].

In order to allow spontaneous answers and mitigate bias, participants were given minimum information in advance about specific interview topics.

Ethical Considerations

In April 2022, the research protocol was approved by the institutional review board of Xiangya Hospital, Central South University (#202204092). No prior relationships existed between study participants and members of the research team. Verbal informed consent was recorded via an audio recorder for each participant before participation. Participants were informed in advance that their interviews would be recorded, with the assurance that these recordings would be subject to encryption for security purposes, and they provided their verbal consent accordingly. All participants received a compensation of 200 RMB (1 CNY=US $0.15 on May 2022) for their participation, which was disbursed through a WeChat transfer. To protect the information of the interviewees, the interview data were deidentified in the process of transcription from audio recordings.

Data Collection

The interview guide was collaboratively developed and then subjected to pilot-testing by the research team. Throughout the concurrent phases of data collection and analysis, the interview guide underwent iterative refinement in response to emerging insights and participant responses. This adaptive approach is considered vital to the robustness of qualitative research [ 41 ]. Revisions were implemented subsequent to discussions involving YZ, JH, and Xiaomin Wang, aiming to clarify, define, and critically examine emerging content from interviews as relevant to the research questions. All questions from the finalized interview guide are listed in Textbox 1 .

Q1: What are your views on internet hospitals?

Q2: Could you tell me about your experience working in internet hospitals?

Q3: What do you think are the biggest advantages of internet hospitals? Can you give some examples?

Q4: What do you think are the most troubling or difficult aspects of internet hospitals? Can you give some examples?

Q5: How do you inform the patients who come to the internet hospitals before treatment?

Q6: Do you think there are any differences between doctor-patient communication in internet hospitals and physical hospitals?

Q7: What impact do you think internet hospitals have on doctor-patient communication? Can you give some examples?

Q8: What training have you participated in regarding internet hospitals? What do you think of this training?

Q9: What do you think about the current status of the development of internet hospital laws and regulations in China? How could they be improved? What other aspects can promote the further development of internet hospitals?

Q10: Is there anything else you would like to add about doctor-patient communication in internet hospitals?

Data for this study were collected from April to May 2022. We carried out interviews through a combination of online and offline modalities, depending on each participant’s preference and availability. Online interviews were conducted remotely by video call, via the mobile app WeChat. Offline interviews were conducted in private rooms at the study hospitals. The interviews were conducted in Mandarin Chinese by the authors, YZ (a postgraduate student) and Xiaomin Wang (an associate professor). Both interviewers have received professional training in qualitative interviewing and had extensive experience conducting qualitative research prior to this study.

The research team discussed possible probes and follow-up questions before beginning interviews, and interviewers used them when necessary to draw out more information relevant to the main research question. Concurrently, a second interviewer assumed the role of an observer to ensure the standardization of interview methods and to mitigate potential biases.

Interviews were audio recorded, transcribed, and uploaded into qualitative data management NVivo software (version 11; Lumivero) on password-protected computers to facilitate the analysis. Field notes made by interviewers during the interview process were also stored on password-protected computers, to be used for reference by the research team during analysis. Interviews ranged from 20 to 50 minutes long. Transcripts were sent to participants upon request, but no corrections, comments, or notes were made to transcripts.

Data Analysis

Analysis of the data was performed through conventional content analysis, using guidelines described by Hsieh and Shannon [ 42 ]. An advantage of conventional content analysis is that it avoids using preconceived categories, to generate codes inductively from the data. This modality is considered appropriate when current knowledge of the phenomenon being researched is limited [ 42 ].

Authors YZ and Xuxi Wang transcribed all interviews verbatim and reviewed transcripts several times to acquire a thorough understanding of the whole data set. They then read transcripts line-by-line and highlighted keywords and sentences from a set of initial transcripts, to generate primary codes that captured key concepts. Primary codes were repeatedly reviewed and revised through discussion among the authors and comparison across the transcripts. A finalized codebook including 17 codes and 61 subcodes was used to code all interviews, using NVivo software (version 11). Data saturation was reached after 26 interviews, once the research team determined by the consensus that we had interviewed a sufficiently varied sample of physicians from the 3 study hospitals, while also having obtained sufficiently content-rich data.

Following the coding of all transcripts, all coded segments of the interview data were translated into English by authors YZ and Xuxi Wang, native Mandarin speakers, and double-checked for accuracy by author JH, a native English speaker. Codes and subcodes were repeatedly reviewed and were grouped into clusters according to similarities and differences. Clusters of codes were then treated as subcategories and aggregated into the main categories that were representative of the key findings. These categories were repeatedly reviewed until fully developed, through a process of identifying and comparing exemplary excerpts for each code, category, and subcategory.

This process of analysis culminated in three main categories describing the experiences and views of chronic disease physicians regarding the opportunities and challenges presented by internet hospital care, (1) advancements and shortcomings in care access due to internet hospitals, (2) patients’ expectations versus limitations on doctors’ availability and the scope of services—implications for doctor-patient boundaries and trust, and (3) advantages and downsides of online communication for comprehension and informed consent. These main categories are shown in Textbox 2 , alongside the subcategories from which they were aggregated, and further explained in the results narrative below.

Advancements and shortcomings in care access due to internet hospitals

• Enhanced ability to conduct follow-up care for patients with chronic illness
• More efficient channels for health education
• Disparities in access (ie, for older adults, patients with lower education levels, patients with limited internet or technology access, rural patients)

Patients’ expectations versus limitations on doctors’ availability and the scope of services

• Patients’ expectations of doctors’ availability create unclear professional boundaries.
• Patients’ expectations of the service scope of internet hospitals affect doctor-patient trust.

Advantages and downsides of online communication for comprehension and informed consent:

• Doctors value having extra time to think carefully about replies to patients’ messages, compared to in-person communication.
• Internet hospitals’ restrictions on consultation times, procedures, and arbitrary rules or schedules can hinder effective patient communication.
• Doctors have concerns about the quality of online diagnoses and advice, as well as patient accuracy and comprehension, due to the limitations of online care.
• Doctors have concerns about the completeness and uniformity of clinical informed consent in internet hospitals.

Description of Study Participants

The 26 participants came from 3 different affiliated hospitals with 10, 5, and 11 participants interviewed from each hospital, respectively. Participants ranged in age from 29 to 49 years, and all 26 participants had PhD degrees. Only 5 participants stated that they had received specific training for working in internet hospitals, and 1 of them stated that training included discussion of clinical ethics in internet hospital care. We interviewed doctors from several departments involved in care for patients with chronic disease—oncology, cardiovasology, hematology, endocrinology, gastroenterology, nephrology, and infection departments. Aggregated participant characteristics are presented in Table 1 .

a Some percentages may not add up to 100 due to rounding.

Advancements and Shortcomings in Care Access Due to Internet Hospitals: Follow-Up Care, Health Education, and Disparities

Most doctors stated that internet hospitals affiliated with their physical hospitals of employment were still in the early stages of development and that their internet hospital work experience mainly took place in enterprise-initiated internet hospitals. Doctors stated that internet hospitals initiated by physical hospitals were “not fully operational yet,” (Dr B) and “the consultation volume of patients is relatively small” (Dr C). They also suggested that there was currently a “lack of incentives” (Dr D) to work in internet hospitals initiated by physical hospitals, whereas enterprise-initiated internet hospitals offered “higher income” (Dr B) and a setting where “doctors set their own prices” (Dr C).

In both internet hospitals initiated by physical hospitals and enterprise-initiated internet hospitals, doctors stated that the majority of their work consisted of online consultation for common or easily diagnosable diseases, and follow-up services for patients with chronic diseases who had previously received care at physical hospitals, such as adjusting medications and ordering medical tests to be scheduled in person. Most doctors were motivated to work for internet hospitals particularly because of the opportunity to be part of expanding follow-up care for patients with chronic diseases.

Most of the patients who come to the internet hospitals are chronic disease patients, with conditions such as hypertension, diabetes, coronary heart disease, etc. These patients have been clearly diagnosed in our hospital, and some of them need to be guided or communicated with about what needs to be paid attention to in the process of home-based management. For example, patients' blood pressure might fluctuate, or they can consult online if they have any uncomfortable symptoms, which is quite common. [Dr X]

Some doctors also believed based on experience that internet hospitals could serve as a more efficient channel to provide health education for patients, particularly for the management of chronic diseases.

We also feel that doctors in tertiary hospitals do not have much time to do health education with patients, but through the internet hospitals platforms, we can explain to patients the concept of health or a healthy way of life. For example, for a patient with heart failure, he isn’t expected to come back to the hospital again and again, because I have instilled him with an understanding of healthy lifestyle and diet, and the workload of the doctor will be reduced in the long run. [Dr C]

Despite the ways in which doctors felt internet hospitals expanded access to care and services, they had also observed disparities in access to internet hospitals across several groups, including older adults and patients with lower levels of education or technological literacy: “Older patients may not use smartphones or might need assistance to do so from family members” (Dr E).

Relatively speaking, if the patients come to the internet hospital for consultation, the education level of these patients will be higher, otherwise they will not be able to fully communicate with their doctor. [Dr O]

Most doctors mentioned that internet hospitals are especially suitable for patients with chronic diseases. Doctors also stated that while older adults are one of the most common groups of patients with chronic diseases, many older adults have difficulties in using or accessing internet hospitals (Dr D and Dr X). Some doctors mentioned that internet hospitals currently have limited connections and overlap with local health services in rural areas. They believed moving toward more connection with local services was an important goal—particularly because the demand at tertiary hospitals frequently outstrips resources (Dr P and Dr Q), and because many rural patients travel long distances to receive care at tertiary hospitals.

Even for follow-up visits for chronic diseases or common diseases, many patients will still go to tertiary hospitals. Instead, the patient can go to a qualified local hospital and send us the results of the test, and then [through internet hospital care,] we can advise the patient or tell him how to adjust the medication, or refer the patient to a tertiary hospital for testing. But at present, internet hospitals have not played a big enough role in these aspects. [Dr E]

Patients’ Expectations Versus Limitations on Doctors’ Availability and the Scope of Services: Implications for Doctor-Patient Boundaries and Trust

Patients’ expectations of doctors’ availability create unclear professional boundaries.

When asked about new challenges in patient care posed by internet hospitals, only 1 doctor mentioned risks related to patient privacy and data security.

The internet hospital platform where I am located requires patients to provide their name, gender, age, ID number, and other information, which can be seen on the doctor's portal, but as a doctor I will definitely not disclose the patient's private information but just give him diagnostic advice according to the necessary information provided by the patient. [Dr G]

By contrast, many doctors expressed concerns about their own privacy. Some doctors shared stories from their work in physical hospitals of willingly sharing their personal WeChat with patients in case patients had questions after discharge (Dr D, Dr M, and Dr R). While some doctors did not seem to mind-bending this boundary with patients, others remembered negative experiences when patients had sent messages making demands of doctors’ time at all hours (Dr E, Dr M, Dr P, and Dr Z). They also recalled times when patients obtained the doctor’s personal contact information through their own means and contacted them after leaving the hospital without the doctor’s consent (Dr V and Dr Z). As a result, some doctors had positive views of internet hospitals because they can serve as a means for online communication with patients without requiring the doctor to disclose their own personal contact information.

I prefer to use the official platform to communicate with patients, rather than through private WeChat or phone, because I really don't want to receive phone calls or text messages from patients after I work. But if the phone does ring, I will take into account that he is an old patient of mine, and I will still answer it, because I am not sure if there is any emergency. But for patients on such online hospital platforms, I rarely give them my phone number and personal WeChat. [Dr P]

Several doctors were also uncomfortable that they were required to post information about themselves when working on internet hospital platforms, such as their name, location, and credentials (Dr P and Dr X). Because the audience of patients in internet hospitals is wider, they worried that patients who were dissatisfied with care may have the ability to post negative information about them in public forums online, citing their personal information. As a result, doctors stated that they would be more cautious in diagnosis and giving advice when dealing with patients in internet hospitals with whom they were less familiar (Dr J, Dr O, and Dr V).

Patients’ Expectations of the Service Scope of Internet Hospitals Affect Doctor-Patient Trust

Another concern expressed by many doctors was that patients held unrealistic expectations of the scope of services that internet hospital doctors provide. Some doctors mentioned that some patients feel that just because they spend money in an internet hospital, they should be able to get all their problems solved at once or get immediate treatment (Dr E and Dr G)—when in reality in many cases, the doctor might need to conduct tests over multiple online consultations or might recommend that patients seek further medical services in offline, physical settings. Doctors were concerned that patients’ dissatisfaction with unmet expectations might generate distrust toward the doctor.

What patients don't know is that in fact, most of the time, seeing a doctor is a step-by-step process, and it is necessary to do examinations step-by-step to exclude diseases or diagnose diseases. They often have high expectations for the effect of consultation in internet hospitals, and they think that doctors should be able to diagnose their diseases at one time; and not only to diagnose them, but also to propose a treatment plan. [Dr E]

Some doctors suggested that this gap between expectations and reality could be especially strong for new patients whom the doctor had not seen previously for in-person care. They described that they often recommend for new internet hospital patients to go to physical hospitals to be examined before receiving further internet hospital care or advice, and that patients who are expecting immediate solutions can find this disappointing (Dr U and Dr X). One doctor suggested the need to educate patients and the general public on the scope of internet hospital care, in light of this mismatch in patients’ and doctors’ expectations (Dr Y).

However, some doctors raised concerns about issues related to doctor-patient trust that had less to do with adjusting patients’ expectations and more to do with the format of online communication itself.

Doctor-patient trust will be better in physical hospitals. Because the doctor-patient relationship is a very special relationship, offline communication can observe the patient's expression, speed of speech, action, etc, and is more suitable for empathy with patients. When it comes to the doctor-patient relationship and trust, I think face-to-face consultation is still necessary. [Dr J]

Face-to-face communication in physical hospitals may be more detailed, because if it is through text messages or phone calls, I may be able to talk to the patient in a few words, but if the patient is communicating in our hospital, it may take me half an hour. Because there is unequal information in medicine itself, the patient himself is not very clear about medicine, and without adequate communication, there is no trust between doctors and patients. [Dr W]

Advantages and Downsides of Online Communication for Comprehension and Informed Consent

Doctors working in internet hospitals mainly used pictures and texts, and rarely video calls, to communicate with patients. Some doctors valued the extra time gained by this format to think carefully about their replies to patients’ messages (Dr E, Dr U, and Dr X). However, most doctors pointed out how the lack of nonverbal communication could increase miscommunication and misunderstandings.

Online communication is through typing, and some doctors can't see the facial expressions of patients, which is very inconvenient. The communication between doctors and patients may need body language, facial expressions and other aspects.... I want the patient to really understand me in terms of attitude or tone or feeling or whatever. [Dr G]

Doctors also expressed dissatisfaction with limitations on the time and procedures for consultation through various internet hospitals, and how sometimes arbitrary rules or schedules hindered communication with patients.

The doctors in our department need to be on duty every month in the internet hospitals. When it is my turn to be on duty, a patient will send his questions to me through the platform, but I think this mode is not good. For example, the patient might leave a message for me, but I am busy and don’t reply to him in time, and he may not see my reply in time when I reply. If I go back and forth with him several times, this problem will not be solved until I come back on duty next month, and then the patient's problem will not be solved at all. [Dr C]

The internet hospital at our hospital stipulates that patients can ask five questions at a time.... Sometimes doctors are not able to inquire in detail in order to understand the condition. [Dr G]

Doctors had concerns about the quality of diagnoses and advice that they provided online, due to the inability to do direct physical examination. These concerns were intensified by their perception that many patients could not describe their symptoms clearly and accurately.

Currently, a lot of people still lack of basic medical knowledge, which will lead to ineffective or inefficient consultation on the internet, because they cannot describe their own symptoms, or cannot collect their own data and then summarize it. Patients cannot provide information about their condition sufficiently and accurately, which will seriously affect the efficiency of consultation. [Dr E]

Two doctors also specifically mentioned the difficulty in internet hospital care of not being able to use the “four-diagnosis method”—a method used by doctors in traditional Chinese medicine for diagnosing illness, including diagnosis through observation, diagnosis through auscultation and olfaction, diagnosis through inquiry, and diagnosis through pulse feeling [ 43 ]. Although doctors in this study practiced mainly “Western” medicine, they described integrating certain traditional practices such as this method into their care at physical hospitals (Dr D and Dr H).

In light of concerns about the potential for miscommunication with patients, a few doctors also expressed uncertainty about the completeness and uniformity of clinical informed consent as it is currently practiced in internet hospitals. While they believed that a standardized process of online informed consent for medical advice and treatment was needed, they did not know of any relevant laws or procedures.

Because we have so little time to communicate online, and such a narrow scope of care services, we don't usually obtain informed consent online. I might listen to the patient explain his symptoms. I might tell him what tests he needs before I give my advice, or if I'm dealing with a familiar patient, I might just prescribe his medication, so there's no need for informed consent. However, I think how to issue online informed consent, whether online informed consent is legally effective, and how to sign online informed consent all need to be considered. This is also for the protection of medical staff. [Dr U]

Principal Findings

This study sheds light on previously underresearched aspects of internet hospitals in China, as both the first interview study to examine physicians’ perceptions of internet hospitals and one of the few studies on internet hospitals conducted in China outside of its most major cities. Our research revealed that physicians see enhanced opportunities in internet hospitals to conduct follow-up care for patients with chronic illnesses and to provide health education. However, physicians noted disparities in access for different groups, such as older adults, patients with lower education levels, patients with limited internet or technology access, and rural patients. One particularly novel finding was the conflict between patients’ expectations and the reality of limitations on doctors’ availability and the scope of services available through internet hospitals. Physicians perceived that this gap affected both boundaries and trust in the doctor-patient relationship. Physicians also discussed opportunities and challenges in doctor-patient communication, including issues of comprehension and informed consent. Considering that the development of internet hospitals involves multiple industries, including medical institutions, national policymaking departments, and technology providers, we raise several suggestions below on physician training, patient education, regulations, and design, as well as directions for future research.

Training for Doctors

Internet hospital care involves real-time online sharing of medical data. Information about both doctors and patients is centralized and easily accessible to authorized users on the internet hospital platform. Some doctors in our study were uncomfortable when required to publicly post their names, basic personal information, and credentials on internet hospital platforms because it might make them more vulnerable to public criticism. Doctors’ reasons for being concerned about this were in line with previous research showing a high degree of conflict in the doctor-patient relationship in China [ 44 , 45 ]. This underscores the importance of current efforts both locally and internationally that aim to rebuild trust in the doctor-patient relationship [ 46 - 48 ]. Considering doctors’ concerns about patients requesting for them to disclose their WeChat in both physical and internet hospital work, communication skills training for doctors should prepare doctors for how to interact with patients with empathy and care, while also maintaining their preferred professional boundaries.

It was also notable that only 1 doctor who was interviewed discussed concerns related to patient privacy and data security when asked about challenges presented by internet hospitals. By contrast, Li et al’s [ 49 ] study on the determinants of patients’ use of internet hospitals in China showed that while patients generally desire to use internet hospitals, they are apprehensive about the associated risk of their personal information being leaked. Due to the heightened potential for data leaks and breaches of patient health information associated with the use of internet hospitals, it is imperative that health care professionals undergo training to raise their awareness of data security precautions. For instance, physicians should be trained on proactive measures that they can take to guarantee that the internet hospital services they are affiliated with implement adequate security protocols around patient information. Furthermore, physicians should be trained to communicate with internet hospital patients or their legal proxies about potential risks related to data security and to apprise them of protective measures enacted to safeguard information. Future research should also evaluate the frequency with which data leaks and breaches in internet hospitals actually occur.

Findings from our study also suggest that internet hospitals have led to changes in doctor-patient communication. Doctors in our study considered it to be an advantage of internet hospital care that they generally had more time to communicate with patients compared to in-person care. However, a previous study on internet hospitals suggested that while doctors can obtain key information from patients within a few minutes through in-person communication and examination, information received in the same amount of time online tends to be more limited [ 43 ]. Research conducted by Deng et al [ 50 ] also highlights that engaging in online consultation work while simultaneously engaging in a main career providing in-person medical consultation may place excessive demands on doctors’ time and energy. This phenomenon of work overload could potentially impede the widespread adoption of internet hospitals and introduce added risk to medical practice.

Relatedly, doctors in our study mentioned that when working in internet hospitals, they could only communicate with patients in the form of text, pictures, or video-based consultations, and that they had to rely largely on patient self-report. Both of these factors caused doctors to worry about the accuracy of their diagnoses. This aligns with recent research showing that about 70% of surveyed health care providers believe communication difficulties between patients and health care providers result in online consultations being insufficient [ 51 ], and about 70% of providers report feeling apprehensive about the possibility of misdiagnosis when providing care through internet hospitals [ 51 ]. Recent research has also found that patients who use internet hospitals have more negative views on the doctor-patient relationship than nonusers—including both interpersonal factors such as the degree to which patients trust doctors and practical factors such as the degree to which patients agree with their doctors’ medical opinions. Studies from other countries have similarly shown that telehealth can present new challenges or deficiencies in communication [ 52 - 55 ].

To address such challenges, telehealth communication competencies need to become a core component of both future research and physician training for internet hospitals in China—just as similar competencies are emerging as a priority for telehealth enhancement around the world [ 56 ]. Physicians providing care through internet hospitals should undergo standardized training for web-based communication skills, as research from other countries suggests such training can adapt interpersonal skills to the telehealth environment [ 57 ] and enhance empathic expression. More training for physicians on this skill set might reduce their apprehension about communicating through internet hospitals and assist them in communicating in a manner that improves outcomes for patients. Considering that doctors in our study expressed concern about patient comprehension and diagnostic accuracy, further research is also needed to evaluate and establish methods for measuring patient satisfaction, patient comprehension of information communicated by doctors, and diagnostic accuracy in internet hospital care. Future research should also examine the feasibility of integrating traditional Chinese medical practices such as the four-diagnosis method into telehealth care in China.

Education for Patients or the Public

Findings from our study highlight new challenges in the doctor-patient relationship posed by internet hospital care. One especially novel finding in our study was doctors’ perception that patients subconsciously expected them to be online 24 hours a day, while doctors actually had limited hours working in the internet hospital and could not meet this expectation. Particularly when patients still needed to ask questions after the end of the physician’s available time for consultation, doctors described the risk of conflicts with patients. These findings suggest many patients may be unaware when message-based interactions with physicians in internet hospitals are discontinuous or asynchronous. Therefore, public information about internet hospitals should specify the boundaries of physicians’ availability for internet hospital consultations. While the scope of services may expand as internet hospitals continue to develop, information disseminated to the public should make it clear that internet hospital care is currently only intended for either follow-up care for previously diagnosed patients with chronic diseases, or for new patients with common and more easily diagnosable conditions. Finally, public education should equip patients or their proxies for distinct ways in which they might self-advocate for optimal care in the context of internet hospitals compared to in-person care. This may involve the development of interventions such as question-prompt lists that are specific to equipping patients for internet hospital consultations.

Regulations and Laws

Doctors in our study believed that difficulties with nonverbal communication in internet hospitals often led to miscommunication and misunderstanding, and many raised concerns that there were no specific laws regulating online doctor-patient communication. As a result, most doctors in our study expressed that they felt they were walking on eggshells concerning possible conflicts with patients. This builds on findings from the “2022 China E-hospital development research report” [ 51 ], in which one of the most common suggestions made by health care providers for the further development of internet hospitals was to standardize legal protection for doctors practicing in internet hospitals. Gaps in relevant laws and regulations may reduce the willingness of risk-conscious clinicians to provide medical services through internet hospitals.

Existing internet hospital laws and regulations in China are still mainly in the trial stage [ 51 , 58 ], and are being outpaced by evolving challenges in internet hospital care. The doctors in our study believed that internet hospitals may increase the difficulty of diagnosis and treatment, increase medical safety risks related to miscommunication, and increase the risk of medical malpractice liability. Research by Zhi et al describes how the inability of doctors to perform physical examinations or certain laboratory or imaging examinations through internet hospitals may compromise the accuracy of doctors’ judgments [ 59 ]. However, the legal responsibilities of physical medical institutions, internet hospitals, and doctors regarding issues such as these have not been fully clarified. We suggest that further refinement and clarification of these and other aspects of internet hospital law will help doctors feel more protected in their work and increase the motivation of doctors to work in internet hospitals.

Doctors in our study mentioned that China also lacks detailed legal provisions on the implementation of online informed consent. Internationally recognized ethics standards highlight 4 core elements of informed consent—capacity to consent, information disclosure, comprehension, and voluntary authorization [ 60 ]. Informed consent issues involved in telehealth in other countries are similar to those described by doctors working in internet hospitals in this study, namely, the degree of discernment required from providers to ensure that patients are sufficiently informed to provide consent increases dramatically in telehealth [ 61 ]. In the United States, different states have different regulations on remote informed consent, and no federal policy has been formed at present. Some states require patients to fill out and sign written consent forms, while others do not [ 62 ]. In China, the Administrative Measures for internet hospitals stipulate that “internet hospitals must warn patients of risks and obtain informed consent from patients” [ 22 ]. However, current laws in China do not provide clear rules regarding the validity of electronic signatures for informed consent in internet hospital care. Informed consent in internet hospital care also involves unique information security issues due to the use of electronic health records, but there is currently no specific legal guidance for internet hospital platform developers or doctors concerning data security protection of informed consent in internet hospital care.

Tackling Disparities

Our research revealed that while older adults are at higher risk for chronic illness and are the main target population for internet hospitals, they are also reported by doctors to experience a number of barriers to internet hospital use. This finding aligns with research from various countries showing that older adults are less likely than younger patients to express positive attitudes toward using telehealth [ 63 , 64 ]. Health care providers in China and other countries have observed that older adults may be apprehensive about telehealth due to difficulty in operating computers or smartphones [ 65 ], may need the help of care partners to log into telehealth accounts successfully, and may need more time on average to download and set up applications [ 43 , 66 ]. Previous surveys have also shown that medical personnel believe that the low efficiency of online communication between doctors and patients and the low internet use rate of some patient groups (such as older adults) are the main factors hindering the development of internet hospitals [ 51 ].

Previous research in various countries has shown that the ease of use and perceived usefulness of telehealth systems have a positive impact on the acceptance of telehealth in patients who are older [ 67 , 68 ]. However, to date, China has not established an effective quality control system for internet hospitals [ 27 , 69 ], and the aforementioned ways in which internet hospitals currently pose increased risks for patient safety may affect general patients’, let alone older adults’ willingness to use them [ 49 ]. We recommend that the needs of older adults be considered in the design and development of internet hospital platforms and that older adults participate in the system design process [ 70 , 71 ]. Community health workers may be a workforce that could be mobilized to support telehealth training efforts among patients who are older, assist individuals with limited telehealth literacy in attending online appointments, and provide culturally and linguistically appropriate information about telehealth to rural patients and communities [ 64 , 72 , 73 ]. In general, health care organizations should invest in developing internet hospitals that are functional and easy to use. Drawing from research on telehealth design improvement in other countries, internet hospitals could be designed with features in mind to help physicians communicate more clearly with patients, such as providing notifications to physicians when patients read messages [ 74 ]. In addition, it may be beneficial for platforms to provide training materials to patients when patients register and log into internet hospitals for the first time [ 75 ]. Considering that a major goal of internet hospital development is to expand health care access, it will be crucial to address disparities in internet hospital use through these and other educational and design considerations.

Limitations

This study should be interpreted in the light of certain limitations. As most participants interviewed were attending physicians, findings may not be generalizable to the perspective of other health care workers or patients. The generalizability of our study findings from a single region and time point may also be limited, as there may be variations in internet hospital features and practice in other regions in China, and over time as internet hospitals continue to develop rapidly.

Conclusions

This study explored the experience and views of physicians in 3 tertiary hospitals in Changsha, China regarding access to care, patients’ expectations versus the reality of services, and doctor-patient communication in internet hospital care. Findings from this study indicate that there is a need to train physicians in telehealth-specific communication skills. National policymaking departments should also further refine laws and regulations concerning internet hospitals, particularly those related to online informed consent. Technology developers should take the needs of older adults into particular account in the design of internet hospital platforms.

Acknowledgments

This study was supported by Major Program of National Social Science Fund of China, Research on Moral Issues in the Field of Contemporary Science and Technology (22&ZD044), the Xiangya Medical Humanities Series: Principles of Biomedical Ethics (monograph Award) Fund (KTZZPT019), Hunan Provincial Innovation Foundation for Postgraduate (CX20220133), the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0041), the China Scholarship Council (CSC,202206370069), and the Natural Science of Changsha City (kq2202362).

Data Availability

The data generated and analyzed during this study are available from the corresponding author upon reasonable request.

Authors' Contributions

YZ and XL conceptualized this study and designed the methodology. YZ and Xiaomin Wang conducted the interviews for data collection. Xuxi Wang and YZ transcribed the interviews. YZ, Xuxi Wang, YW, and XZ conducted and provided resources for preliminary analysis of the data. YZ and JH wrote and edited the paper. JH, XL, and XZ oversaw the implementation of all study activities. All authors read and approved the final paper.

Conflicts of Interest

None declared.

COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist.

• Barbosa W, Zhou K, Waddell E, Myers T, Dorsey ER. Improving access to care: telemedicine across medical domains. Annu Rev Public Health. Apr 01, 2021;42:463-481. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kirkland EB, DuBose-Morris R, Duckett A. Telehealth for the internal medicine resident: a 3-year longitudinal curriculum. J Telemed Telecare. Oct 2021;27(9):599-605. [ CrossRef ] [ Medline ]
• Losorelli SD, Vendra V, Hildrew DM, Woodson EA, Brenner MJ, Sirjani DB. The future of telemedicine: revolutionizing health care or flash in the pan? Otolaryngol Head Neck Surg. Aug 2021;165(2):239-243. [ CrossRef ] [ Medline ]
• Hafner M, Yerushalmi E, Dufresne E, Gkousis E. The potential socio-economic impact of telemedicine in Canada. Rand Health Q. Jun 2022;9(3):6. [ FREE Full text ] [ Medline ]
• Alhamam NM, Buhalim RA, Almakhayitah IH, AlBahr AW, AlYaeesh IA. Telemedicine for musculoskeletal care during the COVID-19 pandemic: evaluating readiness of Saudi citizens. Cureus. Feb 16, 2021;13(2):e13380. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bhaskar S, Bradley S, Chattu VK, Adisesh A, Nurtazina A, Kyrykbayeva S, et al. Telemedicine across the globe-position paper from the COVID-19 pandemic health system resilience PROGRAM (REPROGRAM) international consortium (part 1). Front Public Health. 2020;8:556720. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cioti AM, Stanescu AMA, Grajdeanu IV, Serban B, Popescu E, Bratu OG, et al. Telemedicine in Europe—current status and future perspectives. Rev Med Mod. 2019;26(4):165-168. [ FREE Full text ] [ CrossRef ]
• Raja M, Bjerkan J, Kymre IG, Galvin KT, Uhrenfeldt L. Telehealth and digital developments in society that persons 75 years and older in European countries have been part of: a scoping review. BMC Health Serv Res. Oct 26, 2021;21(1):1157. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brucksch S. Japan and its rapid ageing society: does e-health technology provide a solution? J Aging Sci. 2018;6(2):1-4. [ FREE Full text ] [ CrossRef ]
• Han Y, Lie RK, Guo R. The internet hospital as a telehealth model in China: systematic search and content analysis. J Med Internet Res. Jul 29, 2020;22(7):e17995. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Scott R, Mars M. Telehealth in the developing world: current status and future prospects. SHTT. Feb 2015;2015(3):25-37. [ FREE Full text ] [ CrossRef ]
• Yip W, Fu H, Chen AT, Zhai T, Jian W, Xu R, et al. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. Lancet. Sep 28, 2019;394(10204):1192-1204. [ CrossRef ] [ Medline ]
• 2021 China E-hospital development report. National Telemedicine and Connected Health Center and CN-Healthcare. 2021. URL: https://zk.cn-healthcare.com/doc-show-53644.html [accessed 2023-09-17]
• Chen X, Wu X, Zhang Q, Jing R, Cheng W, Tian J, et al. The construction and operational models of internet hospitals in China: a hospital-based survey study. BMC Health Serv Res. Jun 21, 2023;23(1):669. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Houston TK, Allison JJ. Users of Internet health information: differences by health status. J Med Internet Res. 2002;4(2):E7. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lai Y, Chen S, Li M, Ung COL, Hu H. Policy interventions, development trends, and service innovations of internet hospitals in China: documentary analysis and qualitative interview study. J Med Internet Res. Jul 20, 2021;23(7):e22330. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yu T, Jin C, Wu X, Yue D. Implementation of shared decision-making within internet hospitals in China based on patients' needs: feasibility study and content analysis. JMIR Form Res. Jan 06, 2023;7:e39965. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tu J, Wang C, Wu S. The internet hospital: an emerging innovation in China. Lancet Glob Health. Aug 2015;3(8):e445-e446. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guiding opinions of the State Council on actively promoting the "Internet +" action. The State Council of the People's Republic of China. 2015. URL: https://www.gov.cn/zhengce/content/2015-07/04/content_10002.htm [accessed 2023-09-16]
• Opinions on promoting the development of "Internet + medical health". General Office of the State Council. 2018. URL: https://www.gov.cn/zhengce/content/2018-04/28/content_5286645.htm [accessed 2023-09-16]
• Internet diagnosis and treatment management measures (trial implementation). National Health Commission of the People's Republic of China. 2018. URL: https://www.gov.cn/gongbao/content/2019/content_5358684.htm [accessed 2023-09-16]
• Internet hospital management measures (trial implementation). National Health Commission of the People's Republic of China. 2018. URL: https://www.gov.cn/gongbao/content/2019/content_5358684.htm [accessed 2023-09-16]
• Telemedicine service management standards (trial implementation). National Health Commission of the People's Republic of China. 2018. URL: https://www.gov.cn/gongbao/content/2019/content_5358684.htm [accessed 2023-09-16]
• Notice of the general office of the NHC on strengthening informatization to support the prevention and control of the novel coronavirus pneumonia epidemic. National Health Commission of China. 2020. URL: https://www.gov.cn/zhengce/zhengceku/2020-02/05/content_5474692.htm [accessed 2023-09-16]
• The 52nd statistical report on China's internet development. China Internet Network Information Center. 2023. URL: https://www.cnnic.cn/NMediaFile/2023/0908/MAIN1694151810549M3LV0UWOAV.pdf [accessed 2024-02-28]
• There are more than 3,000 internet hospitals, and it is necessary to explore a better development path. Health Times. 2023. URL: http://www.jksb.com.cn/index.php?m=content&c=index&a=show&catid=788&id=200853 [accessed 2024-02-28]
• Qiu Y, Liu Y, Ren W, Qiu Y, Ren J. Internet-based and mobile-based general practice: cross-sectional survey. J Med Internet Res. Sep 25, 2018;20(9):e266. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Xie X, Zhou W, Lin L, Fan S, Lin F, Wang L, et al. Internet hospitals in China: cross-sectional survey. J Med Internet Res. Jul 04, 2017;19(7):e239. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yuan J, He F. Status quo and problems of the internet hospitals in China. Chin Rural Health Serv Adm. 2023;43(02):126-128+140. [ CrossRef ]
• Pogorzelska K, Chlabicz S. Patient satisfaction with telemedicine during the COVID-19 pandemic-a systematic review. Int J Environ Res Public Health. 2022;19(10):6113. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sagaro GG, Battineni G, Amenta F. Barriers to sustainable telemedicine implementation in Ethiopia: a systematic review. Telemed Rep. 2020;1(1):8-15. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Şahin E, Yavuz Veizi BG, Naharci MI. Telemedicine interventions for older adults: a systematic review. J Telemed Telecare. Feb 2024;30(2):305-319. [ CrossRef ] [ Medline ]
• Imlach F, McKinlay E, Middleton L, Kennedy J, Pledger M, Russell L, et al. Telehealth consultations in general practice during a pandemic lockdown: survey and interviews on patient experiences and preferences. BMC Fam Pract. Dec 13, 2020;21(1):269. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kumar S, Kumar A, Kumar M, Kumar A, Arora R, Sehrawat R. Feasibility of telemedicine in maintaining follow-up of orthopaedic patients and their satisfaction: a preliminary study. J Clin Orthop Trauma. Oct 2020;11(Suppl 5):S704-S710. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Latulipe C, Gatto A, Nguyen HT, Miller DP, Quandt SA, Bertoni AG, et al. Design considerations for patient portal adoption by low-income, older adults. Proc SIGCHI Conf Hum Factor Comput Syst. Apr 2015;2015:3859-3868. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Greenberg AJ, Haney D, Blake KD, Moser RP, Hesse BW. Differences in access to and use of electronic personal health information between rural and urban residents in the United States. J Rural Health. 2018;34(Suppl 1):s30-s38. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Johnson BA, Lindgren BR, Blaes AH, Parsons HM, LaRocca CJ, Farah R, et al. The new normal? Patient satisfaction and usability of telemedicine in breast cancer care. Ann Surg Oncol. 2021;28(10):5668-5676. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Solimini R, Busardò FP, Gibelli F, Sirignano A, Ricci G. Ethical and legal challenges of telemedicine in the era of the COVID-19 pandemic. Medicina (Kaunas). 2021;57(12):1314. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tong A, Sainsbury P, Craig J. Consolidated Criteria for Reporting Qualitative Research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349-357. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are enough? Qual Health Res. 2017;27(4):591-608. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Klem NR, Bunzli S, Smith A, Shields N. Demystifying qualitative research for musculoskeletal practitioners part 5: rigor in qualitative research. J Orthop Sports Phys Ther. 2022;52(2):60-62. [ CrossRef ] [ Medline ]
• Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277-1288. [ CrossRef ] [ Medline ]
• Li Y, Hu H, Rozanova L, Fabre G. COVID-19 and internet hospital development in China. Epidemiologia (Basel). 2022;3(2):269-284. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jiang MM, Wu ZY, Tu AX. Research on the cooperative governance path of multiple stakeholders in doctor-patient disputes under the environment of information asymmetry. Int J Environ Res Public Health. 2023;20(2):1597. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhao D, Zhang Z. Changes in public trust in physicians: empirical evidence from China. Front Med. 2019;13(4):504-510. [ CrossRef ] [ Medline ]
• Du L, Xu J, Chen X, Zhu X, Zhang Y, Wu R, et al. Rebuild doctor-patient trust in medical service delivery in China. Sci Rep. 2020;10(1):21956. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fu Y, Tang T, Long J, Lin B, Li J, Quan G, et al. Factors associated with using the internet for medical information based on the doctor-patient trust model: a cross-sectional study. BMC Health Serv Res. 2021;21(1):1268. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tucker JD, Wong B, Nie JB, Kleinman A, Patient-Physician Trust Team. Rebuilding patient-physician trust in China. Lancet. 2016;388(10046):755. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li D, Hu Y, Pfaff H, Wang L, Deng L, Lu C, et al. Determinants of patients' intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. J Med Internet Res. 2020;22(10):e22716. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Deng W, Yang T, Deng J, Liu R, Sun X, Li G, et al. Investigating factors influencing medical practitioners' resistance to and adoption of internet hospitals in China: mixed methods study. J Med Internet Res. 2023;25:e46621. [ FREE Full text ] [ CrossRef ] [ Medline ]
• 2022 China E-hospital development research report. Shanghai Jiao Tong University. 2022. URL: https://www.100ec.cn/detail--6620464.html [accessed 2023-01-08]
• Alpert JM, Dyer KE, Lafata JE. Patient-centered communication in digital medical encounters. Patient Educ Couns. 2017;100(10):1852-1858. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Alpert JM, Krist AH, Aycock RA, Kreps GL. Applying multiple methods to comprehensively evaluate a patient portal's effectiveness to convey information to patients. J Med Internet Res. May 17, 2016;18(5):e112. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gerber DE, Beg MS, Duncan T, Gill M, Craddock Lee SJ. Oncology nursing perceptions of patient electronic portal use: a qualitative analysis. Oncol Nurs Forum. Mar 01, 2017;44(2):165-170. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sieck CJ, Hefner JL, Schnierle J, Florian H, Agarwal A, Rundell K, et al. The rules of engagement: perspectives on secure messaging from experienced ambulatory patient portal users. JMIR Med Inform. 2017;5(3):e13. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van Galen LS, Wang CJ, Nanayakkara PWB, Paranjape K, Kramer MHH, Car J. Telehealth requires expansion of physicians' communication competencies training. Med Teach. 2019;41(6):714-715. [ CrossRef ] [ Medline ]
• Wright HH, O'Shea MC, Sekula J, Mitchell LJ. Assessment of communication skills using telehealth: considerations for educators. Front Med (Lausanne). 2022;9:841309. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, status quo, and challenges to China's health informatization during COVID-19: evaluation and recommendations. J Med Internet Res. 2021;23(6):e27345. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhi L, Yin P, Ren J, Wei G, Zhou J, Wu J, et al. Running an internet hospital in China: perspective based on a case study. J Med Internet Res. 2021;23(9):e18307. [ FREE Full text ] [ CrossRef ] [ Medline ]
• World Medical Association. World Medical Association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Blake JH, Schwemmer MK, Sade RM. The patient-surgeon relationship in the cyber era: communication and information. Thorac Surg Clin. 2012;22(4):531-538. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fields BG. Regulatory, legal, and ethical considerations of telemedicine. Sleep Med Clin. 2020;15(3):409-416. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chiang KF, Wang HH, Chien IK, Liou JK, Hung CL, Huang CM, et al. Healthcare providers' perceptions of barriers in implementing of home telecare in Taiwan: a qualitative study. Int J Med Inform. 2015;84(4):277-287. [ CrossRef ] [ Medline ]
• Gray DM, Joseph JJ, Olayiwola JN. Strategies for digital care of vulnerable patients in a COVID-19 world-keeping in touch. JAMA Health Forum. 2020;1(6):e200734. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Miller DP, Latulipe C, Melius KA, Quandt SA, Arcury TA. Primary care providers' views of patient portals: interview study of perceived benefits and consequences. J Med Internet Res. 2016;18(1):e8. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Varsi C, Gammon D, Wibe T, Ruland CM. Patients' reported reasons for non-use of an internet-based patient-provider communication service: qualitative interview study. J Med Internet Res. 2013;15(11):e246. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cimperman M, Brenčič MM, Trkman P. Analyzing older users' home telehealth services acceptance behavior-applying an extended UTAUT model. Int J Med Inform. 2016;90:22-31. [ CrossRef ] [ Medline ]
• Zhou M, Zhao L, Kong N, Campy KS, Qu S, Wang S. Factors influencing behavior intentions to telehealth by Chinese elderly: an extended TAM model. Int J Med Inform. 2019;126:118-127. [ CrossRef ] [ Medline ]
• 2020 research report on the development of internet hospitals in China. Institute of Healthcare. 2020. URL: http://zk.cn-healthcare.com/doc-show-39773.html [accessed 2023-09-17]
• Bhattarai P, Phillips JL. The role of digital health technologies in management of pain in older people: an integrative review. Arch Gerontol Geriatr. 2017;68:14-24. [ CrossRef ] [ Medline ]
• Cosco TD, Fortuna K, Wister A, Riadi I, Wagner K, Sixsmith A. COVID-19, social isolation, and mental health among older adults: a digital catch-22. J Med Internet Res. 2021;23(5):e21864. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Huang W, Long H, Li J, Tao S, Zheng P, Tang S, et al. Delivery of public health services by Community Health Workers (CHWs) in primary health care settings in China: a systematic review (1996-2016). Glob Health Res Policy. 2018;3:18. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lohr AM, Ingram M, Nuñez AV, Reinschmidt KM, Carvajal SC. Community-clinical linkages with community health workers in the United States: a scoping review. Health Promot Pract. 2018;19(3):349-360. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Laukka E, Huhtakangas M, Heponiemi T, Kujala S, Kaihlanen AM, Gluschkoff K, et al. Health care professionals' experiences of patient-professional communication over patient portals: systematic review of qualitative studies. J Med Internet Res. 2020;22(12):e21623. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hefner JL, Sieck CJ, Walker DM. Patient and physician perspectives on training to improve communication through secure messaging: clarifying the rules of engagement. Health Care Manage Rev. 2022;47(1):3-11. [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso; submitted 22.03.23; peer-reviewed by Y Cao, N Mungoli, A Gangadhara Rao; comments to author 06.09.23; revised version received 27.09.23; accepted 26.02.24; published 29.03.24.

©Yuqiong Zhong, Jessica Hahne, Xiaomin Wang, Xuxi Wang, Ying Wu, Xin Zhang, Xing Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.