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. Author manuscript; available in PMC: 2021 Dec 1.
Published in final edited form as: Depress Anxiety. 2020 Sep 16;37(11):1127–1136. doi: 10.1002/da.23094

Problematic Internet Use/Computer Gaming among US College Students: Prevalence and Correlates with Mental Health Symptoms

Courtney Stevens 1, Emily Zhang 2, Sara Cherkerzian 2,5, Justin A Chen 3,5, Cindy H Liu 4,5
PMCID: PMC8635392  NIHMSID: NIHMS1757799  PMID: 32939888

Abstract

Background:

Despite widespread internet use and engagement in computer gaming, as well as concerns about online addiction, little is known regarding the relationship between problematic internet use/computer gaming and mental health (MH) symptomatology among U.S. college students. To address this gap, the present study examines a large, nation-wide sample of U.S. college students to assess the rate of problematic internet use/computer gaming and its association with MH symptoms.

Methods:

Using data from 43,003 undergraduates participating in the 2017 American College Health Association-National College Health Assessment, we examined rates of problematic internet use/computer gaming, defined as self-reported internet use/computer gaming that negatively affected academic performance. Logistic regression using a generalized estimating equations approach to adjust for clustering by school examined whether rates of MH symptomatology differed among students who reported problematic vs. non-problematic internet use and computer gaming.

Results:

Ten percent of students reported problematic internet use/computer gaming that had negatively impacted academic performance. Adjusting for a range of covariates, students reporting problematic internet use/computer gaming had higher rates of all 11 MH indicators examined, with odds ratios ranging from 1.42 (‘ever attempted suicide’) to 3.90 (‘ever felt overwhelmed by all you had to do’).

Conclusions:

Problematic internet use/computer gaming is reported by 10% of undergraduate students and represents a significant correlate of MH symptomatology. These findings suggest that problematic internet use/computer gaming will be an important public health focus for college campuses.

Keywords: Depression, Internet, Child/Adolescent, Non-suicidal Self-injury

Introduction

The college years represent an important period of transition for students marked by increased autonomy and unstructured time (Cleary et al., 2011), accompanied by new academic and social stresses (Byrd & McKinney, 2012; Stevens et al., 2018). Mental health (MH) symptomatology, including mood symptoms and self-harm/suicidality, are especially common during the college years (Chen et al., 2019). Many students will also engage in maladaptive coping behaviors that can exacerbate the burden associated with MH or undermine academic performance (Rice & Van Arsdale, 2010). While prior research has focused on problematic drug and alcohol use among college students and its relation to MH symptomatology (Arria et al., 2017; Walters et al., 2018), emerging research points to the potential for internet use and/or computer/internet gaming to become problematic in similar ways (Feng et al., 2017; Ho et al., 2014; Kuss & Lopez-Fernandez, 2016). However, despite widespread internet use/computer gaming by college students, as well as high MH concerns within this population, little is known regarding the rates of problematic internet use/computer gaming among U.S. college students, or its relationship to mental health (MH) symptomatology.

Internet use and computer gaming are both ubiquitous (Müller et al., 2015; Perrin, 2018; Ryan, 2018), and neither activity is inherently problematic. However, emerging research points to the potential for problematic internet use (PIU) (Carbonell et al., 2009; Kuss et al., 2014; Kuss & Lopez-Fernandez, 2016), which can relate to any online activity including specifically to computer/internet gaming (American Psychiatric Association, 2013). While definitions of PIU vary and no gold-standard exists for measurement (Andreassen et al., 2016; Feng et al., 2017), one component of most definitions is a dysfunctional impact, such as negative consequences on job performance or productivity. For a college student, this dimension of PIU could manifest as negative impacts on academic coursework.

Within the empirical literature, PIU is associated with higher rates of MH symptoms and disorders in adolescents, including depression, anxiety, psychological distress, and low selfesteem (Hoare et al., 2016; Kumar & Mondal, 2018; Kuss et al., 2014; Maras et al., 2015). As with the literature on drug/alcohol use and MH symptomatology (Pacek et al., 2013), research on PIU also suggests bidirectional associations, with PIU both serving as a possible strategy for coping with stressors and also exacerbating underlying or comorbid psychiatric conditions (Andreassen et al., 2016; Hsieh et al., 2018). Models of internet use/gaming highlight both the self-reactive functions of these technologies that encourage use (e.g., helping to relax, relieving boredom, escaping other tasks), as well as how self-regulation difficulties can make it difficult to restrict use (e.g., struggling to limit time use, feeling that play is out of control) (Haagsma et al., 2013; Lee & LaRose, 2007). Developmental considerations should also be made when examining internet/gaming as a problem for college students. College students may have particular issues with self-regulating/coping, exacerbated by greater exposure to stressors including the sudden lack of structure and greater responsibilities from the transition to college (Cleary et al., 2011; Park et al., 2012). The large majority of MH problems also emerge during adolescence and early adulthood (10-24 years) ((De Girolamo et al., 2012) due to biological and psychosocial maturational changes (Lockhart et al., 2018), suggesting this is a high risk developmental period. As well, it may be difficult for college students to assess if their internet use/gaming has become problematic, because these technologies are used normatively among their peers (Haagsma et al., 2013).

While most studies of PIU have focused on younger, pre-college populations (Andreassen et al., 2016), a few recent studies have examined PIU specifically in college populations, though with smaller sample sizes. One study of 200 college students in India indicated that those scoring higher on an internet addiction measure also had increased MH symptoms across a range of domains, including depression and anxiety (Kumar & Mondal, 2018). Another study of 500 college students in Taiwan also found associations between internet addiction and depression, as well as self-harm and suicidality (Hsieh et al., 2018). However, less is known about the general prevalence of problematic internet use/computer gaming that negatively impacts academic functioning or the relationship between problematic use and MH symptomatology in the U.S. college student population. To address this gap, the present study examines a large, nation-wide sample of U.S. college students to assess the rate of problematic internet/gaming use that negatively impacts academic performance and its association with MH symptomatology.

Methods

Data Source and Sample

Data were drawn from the Spring 2017 American Health Association-National College Health Assessment (ACHA-NCHA IIB) Reference Group (American College Health Association, 2017). The ACHA-NCHA is a national research survey that can be purchased by any U.S. postsecondary institution and used to collect information on students’ health behaviors and perceptions. Details on the survey instrument are available from ACHA (American College Health Association, 2013; Jackson, 2008). The reference group data set compiles anonymized data from all participating institutions that used a random or census sampling procedure. The full reference dataset included 63,497 respondents from 92 institutions. Respondents were roughly equally represented across public (47.8%) and private (52.2%) institutions and across regions of the U.S.: northeast (29.9%), midwest (20.3%), south (23.5%), and west (26.3%). Most respondents attended institutions classified as Carnegie Research (65.6%) or Masters (23.4%) institutions, with smaller numbers at Baccalaureate (6.7%) or Associates (1.6%) institutions.

Each participating institution invited students 18 years of age or older to complete the ~30-minute, voluntary survey. The survey covered a comprehensive set of health-related topics including substance use/abuse, sexual health, personal safety and violence, and physical and mental health. The majority of institutions (n = 89) used web-based surveys with a 19% average response rate. The remaining three institutions used paper surveys with an 81% average response rate. Secondary data analysis of this de-identified dataset was approved as exempt from human subjects review according to the Partners Healthcare Institutional Review Board, Brigham and Women’s Hospital.

From the total sample of respondents, the current study included degree-seeking undergraduates with available data on all measures described below. To be consistent with previous studies (Chen et al., 2019; C. H. Liu et al., 2019), we excluded respondents who reported implausible height and weight data (BMI >65 or <16; height >210 cm or <120 cm; weight>180 kg or <35 kg). The number of respondents meeting study criteria and included in the analytic dataset was n=43,003. Demographic information on the analytic sample is available in Table 1.

Table 1.

Distribution of Descriptive Characteristics of ACHA-NCHA IIB Participants, Spring 2017 (N=43,003).

Total
(N=43,003)		 Internet use / computer game
concerns over the past 12 months
No / Harmless
(N = 38,387)		 Yes
(N = 4,616)
Characteristic n % n % n % χ2 p-value
Race - - - - - - 200.3 <.001
 White 29,460 68.5 26,680 69.5 2,780 60.2
 Hispanic 2,926 6.8 2,534 6.6 392 8.5
 Black 2,003 4.7 1,766 4.6 237 5.1
 Asian 4,399 10.2 3,707 9.7 692 15.0
 Multiracial 4,215 9.8 3,700 9.6 515 11.2
Age (years) - - - - - - 24.7 <.001
 18-24 40,191 93.5 35,798 93.3 4,393 95.2
 25+ 2,812 6.5 2,589 6.7 223 4.8
Gender - - - - - - 236.6 <.001
 Male 12,752 29.7 10,960 28.6 1,792 38.8
 Female 29,187 67.9 26,515 69.1 2,672 57.9
 Another 1,064 2.5 912 2.4 152 3.3
Gender
Identity
Sexual - - - - - - 121.0 <.001
Orientation
 Heterosexual 34,433 80.1 30,929 80.6 3,504 75.9
 Gay/Lesbian 1,355 3.2 1,185 3.1 170 3.7
 Bisexual 2,801 6.5 2,435 6.3 366 7.9
 Asexual 2,181 5.1 1,979 5.2 202 4.4
 Other 2,233 5.2 1,859 4.8 374 8.1
Year in school - - - - - - 19.5 .001
 1st 12,354 28.7 11,048 28.8 1,306 28.3
 2nd 10,173 23.7 9,030 23.5 1,143 24.8
 3rd 10,374 24.1 9,261 24.1 1,113 24.1
 4th 8,465 19.7 7,627 19.9 838 18.2
 ≥5th 1,637 3.8 1,421 3.7 216 4.7
Transfer - - - - - - 1.7 .190
 No 37,489 87.2 33,493 87.3 3,996 86.6
 Yes 5,514 12.8 4,894 12.7 620 13.4
International - - - - - - 3.9 <.05
 No 40,768 94.8 36,420 94.9 4,348 94.2
 Yes 2,235 5.2 1,967 5.1 268 5.8
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Measures

Mental Health Symptoms

Eleven MH-related feelings and behaviors related to mood, including three specific to self-harm and suicidality, were assessed by participant self-report: “Felt things were hopeless”; “Felt overwhelmed by all you had to do”; “Felt exhausted (not from physical activity)”; “Felt very lonely”; “Felt very sad”; “Felt so depressed that it was difficult to function”; “Felt overwhelming anxiety”; “Felt overwhelming anger”; “Intentionally cut, burned, bruised, or otherwise injured yourself”; “Seriously considered suicide”; and “Attempted suicide.”

Participants indicated the frequency of each symptom by selecting one of five options: “No, never”; “No, not in the last 12 months”; “Yes, in the last two weeks”; “Yes, in the last 30 days”; or “Yes, in the last 12 months.” Responses were re-categorized to create 12-month prevalence indicators for each MH symptom: No (combining “No, never” and “No, not in the past 12 months”) and Yes (combining all three ‘yes’ responses, which all fell within the past 12 months).

Internet Use/Computer Gaming

Internet use and computer gaming was queried as part of a larger list of potential issues that could affect students’ academic performance (e.g., alcohol use, roommate difficulties, allergies). Students were asked to indicate “Within the last 12 months, have any of the following affected your academic performance? (Please select the most serious outcome for each item below).” We focus here on responses to the item “Internet use/computer games.” Options included “This did not happen to me/not applicable”; “I have experienced this issue but my academics have not been affected”; “Received a lower grade on an exam or important project”; “Received a lower grade in the course”; “Received an incomplete or dropped the course”; or “Significant disruption in thesis, dissertation, research, or practicum work.” Participant responses were recoded into a binary outcome variable: Students who reported problematic internet use/gaming, defined as reported use having a negative effect on academic performance (regardless of severity of effect) versus those who reported no effect of their internet use/gaming (students who chose “This did not happen to me/not applicable” or “I have experienced this issue but my academics have not been affected”).

Sociodemographic Characteristics

Potential confounding of the association between problematic internet use/computer gaming and MH symptomatology by sociodemographic characteristics was addressed by adjusting for these measures in the model. The sociodemographic characteristics included have been previously associated with MH outcomes and/or internet/computer game use in college samples: participant age (Ketchen Lipson et al., 2018), year in school (Bewick et al., 2010), transfer and international student status (Beiter et al., 2015), race/ethnicity (Chen et al., 2019), gender identity (Eisenberg et al., 2013), and sexual orientation (Kerr et al., 2013).

Participant age was coded using two categories: “18-24 years” (traditional-aged undergraduate students) and “25+ years” (Brittain & Dinger, 2015). Participant years in school included 1, 2, 3, 4, and 5+ years. Transfer and international student status (yes/no) were dichotomously coded.

Participant race/ethnicity was coded using the item “How do you usually describe yourself?” which included response options of “White”; “Black”; “Hispanic or Latino/a”; “Asian or Pacific Islander”; “American Indian, Alaska Native, or Native Hawaiian (AI/AN/NH)”; “Biracial or Multiracial”; and “Other.” Since participants were able to select multiple options, responses were recoded into mutually exclusive categories. Respondents selecting only one option were coded using the selected identity. Those who selected more than one option were combined with those who selected only “Biracial or Multiracial” and coded as “Multiracial.” Due to low sample sizes for the “Other” and “AI/AN/NH” groups, students selecting only those options were excluded from analysis.

Participants’ gender identity was categorized into “Male”, “Female”, and “Another gender identity.” This recoded variable was created based on student responses to three variables: sex at birth (Female/Male), transgender (Yes/No), and gender identity (Woman, Man, Trans woman, Trans man, Genderqueer, and Another Identity). If the respondent’s sex at birth aligned with their gender identity and the respondent selected “No” for transgender, then the respondent was sorted as “Male” or “Female,” as appropriate. Respondents selecting “Yes” for transgender or whose sex at birth was not consistent with their gender identity were categorized as “Another gender identity.”

Participants’ sexual orientation was recoded into a variable with five levels: “Heterosexual,” “Gay/Lesbian,” “Bisexual,” “Asexual,” and “Other” (collapsing the options “Pansexual,” “Queer,” “Questioning,” “Same Gender Loving,” and “Another identity.”)

Data Analysis

The associations between problematic internet use/computer gaming and binary measures of MH outcomes (n = 11) were modelled using logistic regression with a generalized estimating equations (GEE) approach to take into account the correlated nature of the responses arising from the relatedness of individuals within the same cluster (i.e., school). These associations were evaluated in models both unadjusted and adjusted for potential confounding by the set of covariates specified above. We set a conservative level of significance at p<.01 and report 99% confidence intervals to account for the large sample size and number of comparisons in this analysis.

Results

Table 1 presents the distribution of demographic characteristics for the analytic sample, as well as separately for those who reported problematic internet use/computer gaming. Across the sample, over one in 10 students reported problematic internet use/computer gaming that had interfered with their academic performance. Chi-square analyses indicated that problematic internet use/computer gaming was relatively more common among students who were male, in their first or second year of college, of traditional undergraduate age (<25 years), or from a racial-, gender-, or sexual-minority background. As shown in Table 2, most students reporting problematic internet/use gaming indicated their most serious academic consequence was receiving a lower grade on an exam or important project (75.2%) or a lower grade in the course (21.1%).

Table 2.

Distribution of Most Severe Academic Impacts from Internet Use/Computer Gaming.

Total
(N = 4,616)
Type of Academic Impact n %
 Received a lower grade on an exam or important project 3,473 75.2%
 Received a lower grade in the course 975 21.1%
 Received an incomplete or dropped the course 65 1.4%
 Significant disruption in thesis, dissertation, research, or practicum work 103 2.2%
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Table 3 presents rates of MH symptoms overall and separately for students reporting problematic internet use/computer gaming. In general, a high percentage of students reported experiencing MH symptoms during the past 12 months, with 93% reporting at least one of the 11 indicators. As shown in Table 4, chi-square comparisons by gaming status indicated that students reporting problematic internet use/computer gaming exhibited consistently elevated rates across all 11 MH indicators relative to students not reporting problematic use (all p < .001). Tables 4 and 5 present results from the GEE analyses. As the results from the adjusted and unadjusted model provide similar conclusions, here we detail the results from the adjusted model, which controlled for potential confounding by the set of sociodemographic covariates. As shown in Table 5, students reporting problematic internet use/gaming had significantly elevated odds of endorsing each of the 11 MH indicators (OR range 1.42 (‘ever attempted suicide’) – 3.90) (‘ever felt overwhelmed by all you had to do’)). Students who reported problematic internet use/computer gaming were more than twice as likely to report feeling ‘hopeless’ (OR 2.32, 99% CI 2.12–2.54), ‘overwhelmed’ (OR 3.90, 99% CI 3.18–4.79), ‘exhausted’ (OR 2.81, 99% CI 2.40–3.29), ‘very lonely’ (OR 2.29, 99% CI 2.07–2.53), or ‘very sad’ (OR 2.32, 99% CI 2.09–2.58) in the past year. Similarly, students who reported problematic internet use/gaming were also more likely to report feeling ‘so depressed it was difficult to function’ (OR 2.00, 99% CI 1.84–2.18), ‘overwhelming anxiety’ (OR 1.96, 99% CI 1.78–2.15), or ‘overwhelming anger’ (OR 1.83, 99% CI 1.68–1.98) in the past year. Measures of self-harm and suicidality followed a similar pattern, with students who reported problematic internet use/computer gaming more likely to report that in the past year they had engaged in intentional self-injurious behavior (1.51, 99% CI 1.32–1.73), had seriously considered suicide (OR 1.72, 99% CI 1.54–1.92), or attempted suicide (OR 1.42, 99% CI 1.08–1.87).

Table 3.

Rates of Mental Health Symptoms in the Past 12 Months by Internet Use / Computer Game Concerns status over Past 12 Months. Chi-square df = 1 for all tests.

Total
(N = 43,003)		 Internet use / computer game concerns
over the past 12 months
No
(N = 38,387)
89.3% of sample		 Yes
(N = 4,616)
10.7% of sample		 χ2 p-value
Symptom n % n % n %
 Ever felt things were hopeless 23,009 53.5 19,729 51.4 3,280 71.1 640 <.001
 Ever felt overwhelmed by all you had to do 38,334 89.1 33,894 88.3 4,440 96.2 265 <.001
 Ever felt exhausted (not from physical activity) 36,795 85.6 32,486 84.6 4,309 93.3 254 <.001
 Ever felt very lonely 27,952 65.0 24,285 63.3 3,667 79.4 474 <.001
 Ever felt very sad 29,892 69.5 26,085 68.0 3,807 82.5 410 <.001
 Ever felt so depressed that it was difficult to function 17,340 40.3 14,768 38.5 2,572 55.7 509 <.001
 Ever felt ovewhelming anxiety 26,841 62.4 23,435 61.0 3,406 73.8 285 <.001
 Ever felt overwhelming anger 17,679 41.1 15,150 39.5 2,529 54.8 400 <.001
 Ever intentionally cut, burned, bruised, or otherwise injured yourself 3,440 8.0 2,917 7.6 523 11.3 78 <.001
 Ever seriously considered suicide 4,919 11.4 4,090 10.7 829 18.0 217 <.001
 Ever attempted suicide 711 1.7 599 1.6 112 2.4 19 <.001
Yes to any of the 11 MH symptoms 40,041 93.1 35,496 92.5 4,545 98.5 231 <.001
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Table 4.

Odds of Mental Health Symptoms among Students Reporting Internet Use / Computer Game Concerns over Past 12 Months Relative to Those Who Did Not, Spring 2017 (unadjusted)

Students Reporting Internet Use /
Computer Game Concerns
Mental Health Symptoms OR 99% CI
 Ever felt things were hopeless 2.32** 2.13-2.53
 Ever felt overwhelmed by all you had to do 3.34** 2.73-4.09
 Ever felt exhausted (not from physical activity) 2.55** 2.18-2.98
 Ever felt very lonely 2.24** 2.04-2.47
 Ever felt very sad 2.22** 2.00-2.46
 Ever felt so depressed that it was difficult to function 2.01** 1.86-2.18
 Ever felt overwhelming anxiety 1.80** 1.64-1.97
 Ever felt overwhelming anger 1.86** 1.71-2.02
 Ever intentionally cut, burned, bruised, or otherwise injured yourself 1.55** 1.37-1.77
 Ever seriously considered suicide 1.84** 1.65-2.04
 Ever attempted suicide 1.57** 1.20-2.05
 Yes to any of the 11 MH symptoms 3.06** 2.66-3.53
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**

p<.001

Table 5.

Odds of Mental Health Symptoms for Students Reporting Internet Use / Computer Game Concerns over Past 12 Months Relative to Those Who Did Not, Spring 2017 (adjusted)

Students Reporting Internet Use /
Computer Game Concerns
Mental Health Symptoms OR 99% CI
 Ever felt things were hopeless 2.32** 2.12-2.54
 Ever felt overwhelmed by all you had to do 3.90** 3.18-4.79
 Ever felt exhausted (not from physical activity) 2.81** 2.40-3.29
 Ever felt very lonely 2.29** 2.07-2.53
 Ever felt very sad 2.32** 2.09-2.58
 Ever felt so depressed that it was difficult to function 2.00** 1.84-2.18
 Ever felt overwhelming anxiety 1.96** 1.78-2.15
 Ever felt overwhelming anger 1.83** 1.68-1.98
 Ever intentionally cut, burned, bruised, or otherwise injured yourself 1.51** 1.32-1.73
 Ever seriously considered suicide 1.72** 1.54-1.92
 Ever attempted suicide 1.42* 1.08-1.87
 Yes to any of the 11 MH symptoms 3.53** 3.05-4.08
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*

p<.01

**

p<.001

Adjusted for potential confounding by the following: race/ethnicity, age, gender, sexual orientation, year in school, transfer student status, and international student status

Discussion

Main Findings

In this large, nation-wide sample of U.S. undergraduate college students, over 10% of students reported that their academic performance had been negatively impacted by internet use/computer gaming. Our definition of problematic internet use/computer gaming focused only on negative impacts on academic performance and did not incorporate other dimensions of problematic or addictive use (e.g., withdrawal, tolerance, lack of control) or potential negative impacts on social functioning. Thus, it is difficult to compare our prevalence rate with those of other studies. However, one prior review of the literature reported prevalence rates of internet addiction in larger-samples (over 1000 participants) ranging from 1-27%, depending upon the definitions, assessment tools, and cutoffs used (Kuss et al., 2014). As literature accrues in this area, it will be important to attend to how PIU is defined (Andreassen et al., 2016; Van Rooij et al., 2017) as well as whether internet use is considered in the aggregate or separated by usage type (e.g., gaming, social media, etc.).

While rates of past-year MH symptoms were high overall, these rates were even higher among those 10% of students who reported internet use/computer gaming that had been problematic for their academic functioning. Significantly elevated rates were observed for all MH indicators examined, including those related to mood and anxiety (e.g., felt overwhelming anxiety, felt so depressed it was difficult to function) as well as suicidality (e.g., self-harm, suicidal ideation, and suicide attempts). These findings are consistent with prior research linking problematic video game use to more severe levels of depression, anxiety, and other MH outcomes (Kim et al., 2016; Maras et al., 2015; Mentzoni et al., 2011; Yang, 2001) but highlight this relationship specifically in a U.S. undergraduate college sample and using a large data set that allowed controlling for a range of demographic covariates.

It will be important for future research to identify whether certain groups of students are more at-risk for problematic internet use/computer gaming. Our data suggest that negative academic effects from internet use/computer gaming may be relatively more common among some demographic groups, including those who are in their earlier years of college, of younger, traditional undergraduate age, or male, as well as among those from racial-, gender-, or sexual-minority backgrounds. Future research can examine these associations systematically, also in the context of stressors that might unique to some demographics of students. Such work may also help focus and guide intervention efforts.

Public Health Implications

The findings reported here have implications for college campus MH outreach and advocacy, as well as efforts to support student academic success. More than one in 10 undergraduate students report problematic internet use/computer gaming, and this problematic usage co-occurs with a host of MH indicators over a range of severity. While these data do not allow causal conclusions to be drawn regarding the direction of these associations, based on prior literature in this area, the relationship is likely to be bidirectional, with internet use serving as both a coping behavior among those with MH symptoms as well as an exacerbating factor for MH burden (Lau et al., 2018). Similar to the patterns seen with substance use disorders, the underlying MH problems we examined may render some students especially vulnerable to using the internet and computer gaming as a coping mechanism. Internet use and computer gaming also has the potential to be addictive (American Psychiatric Association, 2013; Feng et al., 2017), and some evidence suggests that PIU can produce a cascade of effects that impact academic functioning and also produce MH symptoms (Maras et al., 2015; Twenge et al., 2018). Regardless of the direction of the association, these findings suggest that querying students about whether their internet use or computer gaming has negatively affected their academic functioning might be a strategy for identifying students at risk for MH problems. This approach may be particularly useful as internet use is likely less stigmatized than other MH symptoms or diagnoses and because students and universities tend to gauge outcomes not by MH status but through students’ ability to function academically.

These data suggest that internet use/gaming will interfere with academic performance in roughly one in 10 undergraduates. Though this risk may be greater among students with MH symptomatology, internet use/computer gaming has the potential to negatively impact all students’ academic functioning, regardless of MH vulnerability. This risk poses a particular challenge because internet and computer use are integral to many academic activities (e.g., typing assignments, conducting research, or using online learning management systems). Thus, such use is impossible to avoid altogether, and this challenge may be further exacerbated by the current context of COVID-19, in which much of college students’ academic coursework and social lives have shifted online. Coupled with the high rates of MH symptomatology among young adults reported during the COVID-19 pandemic (Liu et al., 2020a, 2020b), current college students may find regulating problematic internet use/computer gaming all the more vexing.

Whereas screen time monitoring has been discussed extensively for young children (American Academy of Pediatrics, 2018), there is little guidance for older adolescents or college students where screen time and internet use are normalized and generally unscrutinized. Colleges may wish to consider offering workshops or other educational strategies to help students develop self-awareness and regulatory strategies for engaging with internet use in a healthy way. To increase the likelihood of programming uptake and impact for college students, such workshops or educational outreach may want to anchor the intervention to outcomes that are considered priorities by students themselves (e.g., academic performance, social opportunities, Farrer et al., 2015)

Strengths and Limitations

The present study has several strengths. The focus on undergraduate students has been relatively less explored and shines new light on a vulnerable group experiencing a transition toward increased unstructured time, high levels of stress, and high rates of MH concerns (Cleary et al., 2011; Eisenberg, 2019). The use of a large dataset collected from college campuses across the U.S. increases generalizability of findings and allowed analyses that control for a range of background characteristics. The findings highlight the importance of internet use/computer gaming for college MH outreach and advocacy, representing a relatively new potential coping strategy or indicator of broader MH concerns.

The study also has several limitations. The dataset is based on student self-report of both problematic internet use/computer gaming as well as MH symptomatology, which can be subject to reporter bias or error. The measure of internet use/computer gaming is also broad. By combining internet use and computer gaming into a single, double-barreled indictor, the measure does not distinguish different types of use (e.g., social networking vs. gaming), which may obscure more nuanced relationships specific certain activities (Andreassen et al., 2016). This issue is particularly relevant given recent emphases on computer/internet gaming as a specific concern (American Psychiatric Association, 2013; Feng et al., 2017). The measure also does not address other dimensions of PIU (e.g., withdrawal symptoms). The MH symptoms used as outcomes do not represent the gold standard of a structured clinical interview or diagnostic screener and therefore provide more general information on MH symptomatology, as indicated in prior research (Chen et al., 2019; C. H. Liu et al., 2019). The cross-sectional nature of the dataset prevents drawing causal conclusions, and, as noted, the relationship between MH symptomatology and problematic internet use/computer gaming is likely bidirectional (Lau et al., 2018). Future research could address these limitations by using longitudinal designs and/or more nuanced indicators of internet use/computer gaming or MH indicators, including the possible role internet use/computer gaming might serve as a coping mechanism.

Conclusion

Internet use and computer gaming represent a relatively new focus for college MH advocacy and student success efforts. However, given the prevalence of students for whom internet use/computer gaming negatively affects academic functioning, as well as the increased MH burden among these students, college campuses should consider how to integrate this information with efforts to support undergraduate student success.

Acknowledgements

We are grateful to the American College Health Association for providing and approving the use of this this dataset: American College Health Association-National College Health Assessment, Spring 2017. Hanover, MD: American College Health Association [producer and distributor]; (2017-04-17 of distribution). The opinions, findings, and conclusions presented/reported in this article/presentation are those of the author(s) and are in no way meant to represent the corporate opinions, views, or policies of the American College Health Association (ACHA). The ACHA does not warrant nor assume any liability or responsibility for the accuracy, completeness, or usefulness of any information presented in this article/presentation. Support for preparing this manuscript was provided through the Mary A. Tynan Faculty Fellowship and a NIMH K23 MH 107714-01 A1 award (to C.H.L.) and a Willamette University Atkinson Research Award (to C.S.). We are grateful to Fifi Wong for assistance with manuscript preparation.

Footnotes

Conflict of Interest Statement

No conflict of interest has been declared by the authors.

Data Availability Statement

The data that support the findings of this study are available from the American College Health Association – National College Health Assessment. Restrictions apply to the availability of these data, which were used under license for this study. Data are available at acha.org/ncha with the permission of the American College Health Association.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the American College Health Association – National College Health Assessment. Restrictions apply to the availability of these data, which were used under license for this study. Data are available at acha.org/ncha with the permission of the American College Health Association.

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