research studies on mental health

• Open access
• Published: 20 September 2022

Factors that influence mental health of university and college students in the UK: a systematic review

• Fiona Campbell 1 ,
• Lindsay Blank 1 ,
• Anna Cantrell 1 ,
• Susan Baxter 1 ,
• Christopher Blackmore 1 ,
• Jan Dixon 1 &
• Elizabeth Goyder 1  

BMC Public Health volume  22 , Article number:  1778 ( 2022 ) Cite this article

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Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students.

To identify factors associated with mental health of students in higher education.

We undertook a systematic review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010–2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.

We included 31 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy.

Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.

Peer Review reports

Poor mental health of students in further and higher education is an increasing concern for public health and policy [ 1 , 2 , 3 , 4 ]. A 2020 Insight Network survey of students from 10 universities suggests that “1 in 5 students has a current mental health diagnosis” and that “almost half have experienced a serious psychological issue for which they felt they needed professional help”—an increase from 1 in 3 in the same survey conducted in 2018 [ 5 ]. A review of 105 Further Education (FE) colleges in England found that over a three-year period, 85% of colleges reported an increase in mental health difficulties [ 1 ]. Depression and anxiety were both prevalent and widespread in students; all colleges reported students experiencing depression and 99% reported students experiencing severe anxiety [ 5 , 6 ]. A UK cohort study found that levels of psychological distress increase on entering university [ 7 ], and recent evidence suggests that the prevalence of mental health problems among university students, including self-harm and suicide, is rising, [ 3 , 4 ] with increases in demand for services to support student mental health and reports of some universities finding a doubling of the number of students accessing support [ 8 ]. These common mental health difficulties clearly present considerable threat to the mental health and wellbeing of students but their impact also has educational, social and economic consequences such as academic underperformance and increased risk of dropping out of university [ 9 , 10 ].

Policy changes may have had an influence on the student experience, and on the levels of mental health problems seen in the student population; the biggest change has arguably been the move to widen higher education participation and to enable a more diverse demographic to access University education. The trend for widening participation has been continually rising since the late 1960s [ 11 ] but gained impetus in the 2000s through the work of the Higher Education Funding Council for England (HEFCE). Macaskill (2013) [ 12 ] suggests that the increased access to higher education will have resulted in more students attending university from minority groups and less affluent backgrounds, meaning that more students may be vulnerable to mental health problems, and these students may also experience greater challenges in making the transition to higher education.

Another significant change has been the introduction of tuition fees in 1998, which required students to self fund up to £1,000 per academic year. Since then, tuition fees have increased significantly for many students. With the abolition of maintenance grants, around 96% of government support for students now comes in the form of student loans [ 13 ]. It is estimated that in 2017, UK students were graduating with average debts of £50,000, and this figure was even higher for the poorest students [ 13 ]. There is a clear association between a student’s mental health and financial well-being [ 14 ], with “increased financial concern being consistently associated with worse health” [ 15 ].

The extent to which the increase in poor mental health is also being seen amongst non-students of a similar age is not well understood and warrants further study. However, the increase in poor mental health specifically within students in higher education highlights a need to understand what the risk factors are and what might be done within these settings to ensure young people are learning and developing and transitioning into adulthood in environments that promote mental wellbeing.

Commencing higher education represents a key transition point in a young person’s life. It is a stage often accompanied by significant change combined with high expectations of high expectations from students of what university life will be like, and also high expectations from themselves and others around their own academic performance. Relevant factors include moving away from home, learning to live independently, developing new social networks, adjusting to new ways of learning, and now also dealing with the additional greater financial burdens that students now face.

The recent global COVID-19 pandemic has had considerable impact on mental health across society, and there is concern that younger people (ages 18–25) have been particularly affected. Data from Canada [ 16 ] indicate that among survey respondents, “almost two-thirds (64%) of those aged 15 to 24 reported a negative impact on their mental health, while just over one-third (35%) of those aged 65 and older reported a negative impact on their mental health since physical distancing began” (ibid, p.4). This suggests that older adults are more prepared for the kind of social isolation which has been brought about through the response to COVID-19, whereas young adults have found this more difficult to cope with. UK data from the National Union of Students reports that for over half of UK students, their mental health is worse than before the pandemic [ 17 ]. Before COVID-19, students were already reporting increasing levels of mental health problems [ 2 ], but the COVID-19 pandemic has added a layer of “chronic and unpredictable” stress, creating the perfect conditions for a mental health crisis [ 18 ]. An example of this is the referrals (both urgent and routine) of young people with eating disorders for treatment in the NHS which almost doubled in number from 2019 to 2020 [ 19 ]. The travel restrictions enforced during the pandemic have also impacted on student mental health, particularly for international students who may have been unable to commence studies or go home to see friends and family during holidays [ 20 ].

With the increasing awareness and concern in the higher education sector and national bodies regarding student mental health has come increasing focus on how to respond. Various guidelines and best practice have been developed, e.g. ‘Degrees of Disturbance’ [ 21 ], ‘Good Practice Guide on Responding to Student Mental Health Issues: Duty of Care Responsibilities for Student Services in Higher Education’ [ 22 ] and the recent ‘The University Mental Health Charter’ [ 2 ]. Universities UK produced a Good Practice Guide in 2015 called “Student mental wellbeing in higher education” [ 23 ]. An increasing number of initiatives have emerged that are either student-led or jointly developed with students, and which reflect the increasing emphasis students and student bodies place on mental health and well-being and the increased demand for mental health support: Examples include: Nightline— www.nightline.ac.uk , Students Against Depression— www.studentsagainstdepression.org , Student Minds— www.studentminds.org.uk/student-minds-and-mental-wealth.html and The Alliance for Student-Led Wellbeing— www.alliancestudentwellbeing.weebly.com/ .

Although requests for professional support have increased substantially [ 24 ] only a third of students with mental health problems seek support from counselling services in the UK [ 12 ]. Many students encounter barriers to seeking help such as stigma or lack of awareness of services [ 25 ], and without formal support or intervention, there is a risk of deterioration. FE colleges and universities have identified the need to move beyond traditional forms of support and provide alternative, more accessible interventions aimed at improving mental health and well-being. Higher education institutions have a unique opportunity to identify, prevent, and treat mental health problems because they provide support in multiple aspects of students’ lives including academic studies, recreational activities, pastoral and counselling services, and residential accommodation.

In order to develop services that better meet the needs of students and design environments that are supportive of developing mental wellbeing it is necessary to explore and better understand the factors that lead to poor mental health in students.

Research objectives

The overall aim of this review was to identify, appraise and synthesise existing research evidence that explores the aetiology of poor mental health and mental wellbeing amongst students in tertiary level education. We aimed to gain a better understanding of the mechanisms that lead to poor mental health amongst tertiary level students and, in so doing, make evidence-based recommendations for policy, practice and future research priorities. Specific objectives in line with the project brief were to:

To co-produce with stakeholders a conceptual framework for exploring the factors associated with poorer mental health in students in tertiary settings. The factors may be both predictive, identifying students at risk, or causal, explaining why they are at risk. They may also be protective, promoting mental wellbeing.

To conduct a review drawing on qualitative studies, observational studies and surveys to explore the aetiology of poor mental health in students in university and college settings and identify factors which promote mental wellbeing amongst students.

To identify evidence-based recommendations for policy, service provision and future research that focus on prevention and early identification of poor mental health

Methodology

Identification of relevant evidence.

The following inclusion criteria were used to guide the development of the search strategy and the selection of studies.

We included students from a variety of further education settings (16 yrs + or 18 yrs + , including mature students, international students, distance learning students, students at specific transition points).

Universities and colleges in the UK. We were also interested in the context prior to the beginning of tertiary education, including factors during transition from home and secondary education or existing employment to tertiary education.

Any factor shown to be associated with mental health of students in tertiary level education. This included clinical indicators such as diagnosis and treatment and/or referral for depression and anxiety. Self-reported measures of wellbeing, happiness, stress, anxiety and depression were included. We did not include measures of academic achievement or engagement with learning as indicators of mental wellbeing.

Study design

We included cross-sectional and longitudinal studies that looked at factors associated with mental health outcomes in Table 5 .

Data extraction and quality appraisal

We extracted and tabulated key data from the included papers. Data extraction was undertaken by one reviewer, with a 10% sample checked for accuracy and consistency The quality of the included studies were evaluated using the Newcastle-Ottawa Scale [ 26 ] and the findings of the quality appraisal used in weighting the strength of associations and also identifying gaps for future high quality research.

Involvement of stakeholders

We recruited students, ex-students and parents of students to a public involvement group which met on-line three times during the process of the review and following the completion of the review. During a workshop meeting we asked for members of the group to draw on their personal experiences to suggest factors which were not mentioned in the literature.

Methods of synthesis

We undertook a narrative synthesis [ 27 ] due to the heterogeneity in the exposures and outcomes that were measured across the studies. Data showing the direction of effects and the strength of the association (correlation coefficients) were recorded and tabulated to aid comparison between studies.

Search strategy

Searches were conducted in the following electronic databases: Medline, Applied Social Sciences Index and Abstracts (ASSIA), International Bibliography of Social Sciences (IBSS), Science,PsycINFO and Science and Social Sciences Ciatation Indexes. Additional searches of grey literature, and reference lists of included studies were also undertaken.

The search strategy combined a number of terms relating to students and mental health and risk factors. The search terms included both subject (MeSH) and free-text searches. The searches were limited to papers about humans in English, published from 2010 to June 2020. The flow of studies through the review process is summarised in Fig.  1 .

Flow diagram

The full search strategy for Medline is provided in Appendix 1 .

Thirty-one quantitative, observational studies (39 papers) met the inclusion criteria. The total number of students that participated in the quantitative studies was 17,476, with studies ranging in size from 57 to 3706. Eighteen studies recruited student participants from only one university; five studies (10 publications) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] included seven or more universities. Six studies (7 publications) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] only recruited first year students, while the majority of studies recruited students from a range of year groups. Five studies [ 39 , 42 , 43 , 44 , 45 ] recruited only, or mainly, psychology students which may impact on the generalisability of findings. A number of studies focused on students studying particular subjects including: nursing [ 46 ] medicine [ 47 ], business [ 48 ], sports science [ 49 ]. One study [ 50 ] recruited LGBTQ (lesbian, gay, bisexual, transgender, intersex, queer/questioning) students, and one [ 51 ] recruited students who had attended hospital having self-harmed. In 27 of the studies, there were more female than male participants. The mean age of the participants ranged from 19 to 28 years. Ethnicity was not reported in 19 of the studies. Where ethnicity was reported, the proportion that were ‘white British’ ranged from 71 – 90%. See Table 1 for a summary of the characteristics of the included studies and the participants.

Design and quality appraisal of the included studies

The majority of included studies ( n  = 22) were cross-sectional surveys. Nine studies (10 publications) [ 35 , 36 , 39 , 41 , 43 , 50 , 51 , 52 , 53 , 62 ] were longitudinal in design, recording survey data at different time points to explore changes in the variables being measured. The duration of time that these studies covered ranged from 19 weeks to 12 years. Most of the studies ( n  = 22) only recruited participants from a single university. The use of one university setting and the large number of studies that recruited only psychology students weakens the wider applicability of the included studies.

Quantitative variables

Included studies ( n  = 31) measured a wide range of variables and explored their association with poor mental health and wellbeing. These included individual level factors: age, gender, sexual orientation, ethnicity and a range of psychological variables. They also included factors that related to mental health variables (family history, personal history and mental health literacy), pre-university factors (childhood trauma and parenting behaviour. University level factors including social isolation, adjustment and engagement with learning. Their association was measured against different measures of positive mental health and poor mental health.

Measurement of association and the strength of that association has some limitations in addressing our research question. It cannot prove causality, and nor can it capture fully the complexity of the inter-relationship and compounding aspect of the variables. For example, the stress of adjustment may be manageable, until it is combined with feeling isolated and out of place. Measurement itself may also be misleading, only capturing what is measureable, and may miss variables that are important but not known. We included both qualitative and PPI input to identify missed but important variables.

The wide range of variables and different outcomes, with few studies measuring the same variable and outcomes, prevented meta-analyses of findings which are therefore described narratively.

The variables described were categorised during the analyses into the following categories:

Vulnerabilities – factors that are associated with poor mental health

Individual level factors including; age, ethnicity, gender and a range of psychological variables were all measured against different mental health outcomes including depression, anxiety, paranoia, and suicidal behaviour, self-harm, coping and emotional intelligence.

Six studies [ 40 , 42 , 47 , 50 , 60 , 63 ] examined a student’s ages and association with mental health. There was inconsistency in the study findings, with studies finding that age (21 or older) was associated with fewer depressive symptoms, lower likelihood of suicide ideation and attempt, self-harm, and positively associated with better coping skills and mental wellbeing. This finding was not however consistent across studies and the association was weak. Theoretical models that seek to explain this mechanism have suggested that older age groups may cope better due to emotion-regulation strategies improving with age [ 67 ]. However, those over 30 experienced greater financial stress than those aged 17-19 in another study [ 63 ].

Sexual orientation

Four studies [ 33 , 40 , 64 , 68 ] examined the association between poor mental health and sexual orientation status. In all of the studies LGBTQ students were at significantly greater risk of mental health problems including depression [ 40 ], anxiety [ 40 ], suicidal behaviour [ 33 , 40 , 64 ], self harm [ 33 , 40 , 64 ], use of mental health services [ 33 ] and low levels of wellbeing [ 68 ]. The risk of mental health problems in these students compared with heterosexual students, ranged from OR 1.4 to 4.5. This elevated risk may reflect the greater levels of isolation and discrimination commonly experienced by minority groups.

Nine studies [ 33 , 38 , 39 , 40 , 42 , 47 , 50 , 60 , 63 ] examined whether gender was associated mental health variables. Two studies [ 33 , 47 ] found that being female was statistically significantly associated with use of mental health services, having a current mental health problem, suicide risk, self harm [ 33 ] and depression [ 47 ]. The results were not consistent, with another study [ 60 ] finding the association was not significant. Three studies [ 39 , 40 , 42 ] that considered mediating variables such as adaptability and coping found no difference or very weak associations.

Two studies [ 47 , 60 ] examined the extent to which ethnicity was associated with mental health One study [ 47 ] reported that the risks of depression were significantly greater for those who categorised themselves as non-white (OR 8.36 p = 0.004). Non-white ethnicity was also associated with poorer mental health in another cross-sectional study [ 63 ]. There was no significant difference in the McIntyre et al. (2018) study [ 60 ]. The small number of participants from ethnic minority groups represented across the studies means that this data is very limited.

Family factors

Six studies [ 33 , 40 , 42 , 50 , 60 ] explored the association of a concept that related to a student’s experiences in childhood and before going to university. Three studies [ 40 , 50 , 60 ] explored the impact of ACEs (Adverse Childhood Experiences) assessed using the same scale by Feletti (2009) [ 69 ] and another explored the impact of abuse in childhood [ 46 ]. Two studies examined the impact of attachment anxiety and avoidance [ 42 ], and parental acceptance [ 46 , 59 ]. The studies measured different mental health outcomes including; positive and negative affect, coping, suicide risk, suicide attempt, current mental health problem, use of mental health services, psychological adjustment, depression and anxiety.

The three studies that explored the impact of ACE’s all found a significant and positive relationship with poor mental health amongst university students. O’Neill et al. (2018) [ 50 ] in a longitudinal study ( n  = 739) showed that there was in increased likelihood in self-harm and suicidal behaviours in those with either moderate or high levels of childhood adversities (OR:5.5 to 8.6) [ 50 ]. McIntyre et al. (2018) [ 60 ] ( n  = 1135) also explored other dimensions of adversity including childhood trauma through multiple regression analysis with other predictive variables. They found that childhood trauma was significantly positively correlated with anxiety, depression and paranoia (ß = 0.18, 0.09, 0.18) though the association was not as strong as the correlation seen for loneliness (ß = 0.40) [ 60 ]. McLafferty et al. (2019) [ 40 ] explored the compounding impact of childhood adversity and negative parenting practices (over-control, overprotection and overindulgence) on poor mental health (depression OR 1.8, anxiety OR 2.1 suicidal behaviour OR 2.3, self-harm OR 2.0).

Gaan et al.’s (2019) survey of LGBTQ students ( n  = 1567) found in a multivariate analyses that sexual abuse, other abuse from violence from someone close, and being female had the highest odds ratios for poor mental health and were significantly associated with all poor mental health outcomes [ 33 ].

While childhood trauma and past abuse poses a risk to mental health for all young people it may place additional stresses for students at university. Entry to university represents life stage where there is potential exposure to new and additional stressors, and the possibility that these students may become more isolated and find it more difficult to develop a sense of belonging. Students may be separated for the first time from protective friendships. However, the mechanisms that link childhood adversities and negative psychopathology, self-harm and suicidal behaviour are not clear [ 40 ]. McLafferty et al. (2019) also measured the ability to cope and these are not always impacted by childhood adversities [ 40 ]. They suggest that some children learn to cope and build resilience that may be beneficial.

McLafferty et al. (2019) [ 40 ] also studied parenting practices. Parental over-control and over-indulgence was also related to significantly poorer coping (OR -0.075 p  < 0.05) and this was related to developing poorer coping scores (OR -0.21 p  < 0.001) [ 40 ]. These parenting factors only became risk factors when stress levels were high for students at university. It should be noted that these studies used self-report, and responses regarding views of parenting may be subjective and open to interpretation. Lloyd et al.’s (2014) survey found significant positive correlations between perceived parental acceptance and students’ psychological adjustment, with paternal acceptance being the stronger predictor of adjustment.

Autistic students may display social communication and interaction deficits that can have negative emotional impacts. This may be particularly true during young adulthood, a period of increased social demands and expectations. Two studies [ 56 ] found that those with autism had a low but statistically significant association with poor social problem-solving skills and depression.

Mental health history

Three studies [ 47 , 51 , 68 ] investigated mental health variables and their impact on mental health of students in higher education. These included; a family history of mental illness and a personal history of mental illness.

Students with a family history or a personal history of mental illness appear to have a significantly greater risk of developing problems with mental health at university [ 47 ]. Mahadevan et al. (2010) [ 51 ] found that university students who self-harm have a significantly greater risk (OR 5.33) of having an eating disorder than a comparison group of young adults who self-harm but are not students.

Buffers – factors that are protective of mental wellbeing

Psychological factors.

Twelve studies [ 29 , 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 , 64 ] assessed the association of a range of psychological variables and different aspects of mental wellbeing and poor mental health. We categorised these into the following two categories: firstly, psychological variables measuring an individual’s response to change and stressors including adaptability, resilience, grit and emotional regulation [ 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 ] and secondly, those that measure self-esteem and body image [ 29 , 64 ].

The evidence from the eight included quantitative studies suggests that students with psychological strengths including; optimism, self-efficacy [ 70 ], resilience, grit [ 58 ], use of positive reappraisal [ 49 ], helpful coping strategies [ 42 ] and emotional intelligence [ 41 , 46 ] are more likely to experience greater mental wellbeing (see Table 2 for a description of the psychological variables measured). The positive association between these psychological strengths and mental well-being had a positive affect with associations ranging from r  = 0.2–0.5 and OR1.27 [ 41 , 43 , 46 , 49 , 54 ] (low to moderate strength of association). The negative associations with depressive symptoms are also statistically significant but with a weaker association ( r  = -0.2—0.3) [ 43 , 49 , 54 ].

Denovan (2017a) [ 43 ] in a longitudinal study found that the association between psychological strengths and positive mental wellbeing was not static and that not all the strengths remained statistically significant over time. The only factors that remained significant during the transition period were self-efficacy and optimism, remaining statistically significant as they started university and 6 months later.

Parental factors

Only one study [ 59 ] explored family factors associated with the development of psychological strengths that would equip young people as they managed the challenges and stressors encountered during the transition to higher education. Lloyd et al. (2014) [ 59 ] found that perceived maternal and paternal acceptance made significant and unique contributions to students’ psychological adjustment. Their research methods are limited by their reliance on retrospective measures and self-report measures of variables, and these results could be influenced by recall bias.

Two studies [ 29 , 64 ] considered the impact of how individuals view themselves on poor mental health. One study considered the impact of self-esteem and the association with non-accidental self-injury (NSSI) and suicide attempt amongst 734 university students. As rates of suicide and NSSI are higher amongst LGBT (lesbian, gay, bisexual, transgender) students, the prevalence of low self-esteem was compared. There was a low but statistically significant association between low self-esteem and NSSI, though not for suicide attempt. A large survey, including participants from seven universities [ 42 ] compared depressive symptoms in students with marked body image concerns, reporting that the risk of depressive symptoms was greater (OR 2.93) than for those with lower levels of body image concerns.

Mental health literacy and help seeking behaviour

Two studies [ 48 , 68 ] investigated attitudes to mental illness, mental health literacy and help seeking for mental health problems.

University students who lack sufficient mental health literacy skills to be able to recognise problems or where there are attitudes that foster shame at admitting to having mental health problems can result in students not recognising problems and/or failing to seek professional help [ 48 , 68 ]. Gorcyznski et al. (2017) [ 68 ] found that women and those who had a history of previous mental health problems exhibited significantly higher levels of mental health literacy. Greater mental health literacy was associated with an increased likelihood that individuals would seek help for mental health problems. They found that many students find it hard to identify symptoms of mental health problems and that 42% of students are unaware of where to access available resources. Of those who expressed an intention to seek help for mental health problems, most expressed a preference for online resources, and seeking help from family and friends, rather than medical professionals such as GPs.

Kotera et al. (2019) [ 48 ] identified self-compassion as an explanatory variable, reducing social comparison, promoting self-acceptance and recognition that discomfort is an inevitable human experience. The study found a strong, significant correlation between self-compassion and mental health symptoms ( r  = -0.6. p  < 0.01).

There again appears to be a cycle of reinforcement, where poor mental health symptoms are felt to be a source of shame and become hidden, help is not sought, and further isolation ensues, leading to further deterioration in mental health. Factors that can interrupt the cycle are self-compassion, leading to more readiness to seek help (see Fig.  2 ).

Poor mental health – cycles of reinforcement

Social networks

Nine studies [ 33 , 38 , 41 , 46 , 51 , 54 , 60 , 64 , 65 ] examined the concepts of loneliness and social support and its association with mental health in university students. One study also included students at other Higher Education Institutions [ 46 ]. Eight of the studies were surveys, and one was a retrospective case control study to examine the differences between university students and age-matched young people (non-university students) who attended hospital following deliberate self-harm [ 51 ].

Included studies demonstrated considerable variation in how they measured the concepts of social isolation, loneliness, social support and a sense of belonging. There were also differences in the types of outcomes measured to assess mental wellbeing and poor mental health. Grouping the studies within a broad category of ‘social factors’ therefore represents a limitation of this review given that different aspects of the phenomena may have been being measured. The tools used to measure these variables also differed. Only one scale (The UCLA loneliness scale) was used across multiple studies [ 41 , 60 , 65 ]. Diverse mental health outcomes were measured across the studies including positive affect, flourishing, self-harm, suicide risk, depression, anxiety and paranoia.

Three studies [ 41 , 60 , 62 ] measuring loneliness, two longitudinally [ 41 , 62 ], found a consistently positive association between loneliness and poor mental health in university students. Greater loneliness was linked to greater anxiety, stress, depression, poor general mental health, paranoia, alcohol abuse and eating disorder problems. The strength of the correlations ranged from 0–3-0.4 and were all statistically significant (see Tables 3 and 4 ). Loneliness was the strongest overall predictor of mental distress, of those measured. A strong identification with university friendship groups was most protective against distress relative to other social identities [ 60 ]. Whether poor mental health is the cause, or the result of loneliness was explored further in the studies. The results suggest that for general mental health, stress, depression and anxiety, loneliness induces or exacerbates symptoms of poor mental health over time [ 60 , 62 ]. The feedback cycle is evident, with loneliness leading to poor mental health which leads to withdrawal from social contacts and further exacerbation of loneliness.

Factors associated with protecting against loneliness by fostering supportive friendships and promoting mental wellbeing were also identified. Beliefs about the value of ‘leisure coping’, and attributes of resilience and emotional intelligence had a moderate, positive and significant association with developing mental wellbeing and were explored in three studies [ 46 , 54 , 66 ].

The transition to and first year at university represent critical times when friendships are developed. Thomas et al. (2020) [ 65 ] explored the factors that predict loneliness in the first year of university. A sense of community and higher levels of ‘social capital’ were significantly associated with lower levels of loneliness. ‘Social capital’ scales measure the development of emotionally supportive friendships and the ability to adjust to the disruption of old friendships as students transition to university. Students able to form close relationships within their first year at university are less likely to experience loneliness (r-0.09, r- 0.36, r- 0.34). One study [ 38 ] investigating the relationship between student experience and being the first in the family to attend university found that these students had lower ratings for peer group interactions.

Young adults at university and in higher education are facing multiple adjustments. Their ability to cope with these is influenced by many factors. Supportive friendships and a sense of belonging are factors that strengthen coping. Nightingale et al. (2012) undertook a longitudinal study to explore what factors were associated with university adjustment in a sample of first year students ( n  = 331) [ 41 ]. They found that higher skills of emotion management and emotional self-efficacy were predictive of stable adjustment. These students also reported the lowest levels of loneliness and depression. This group had the skills to recognise their emotions and cope with stressors and were confident to access support. Students with poor emotion management and low levels of emotional self-efficacy may benefit from intervention to support the development of adaptive coping strategies and seeking support.

The positive and negative feedback loops

The relationship between the variables described appeared to work in positive and negative feedback loops with high levels of social capital easing the formation of a social network which acts as a critical buffer to stressors (see Fig.  3 ). Social networks and support give further strengthening and reinforcement, stimulating positive affect, engagement and flourishing. These, in turn, widen and deepen social networks for support and enhance a sense of wellbeing. Conversely young people who enter the transition to university/higher education with less social capital are less likely to identify with and locate a social network; isolation may follow, along with loneliness, anxiety, further withdrawal from contact with social networks and learning, and depression.

Triggers – factors that may act in combination with other factors to lead to poor mental health

Stress is seen as playing a key role in the development of poor mental health for students in higher education. Theoretical models and empirical studies have suggested that increases in stress are associated with decreases in student mental health [ 12 , 43 ]. Students at university experience the well-recognised stressors associated with academic study such as exams and course work. However, perhaps less well recognised are the processes of transition, requiring adapting to a new social and academic environment (Fisher 1994 cited by Denovan 2017a) [ 43 ]. Por et al. (2011) [ 46 ] in a small ( n  = 130 prospective survey found a statistically significant correlation between higher levels of emotional intelligence and lower levels of perceived stress ( r  = 0.40). Higher perceived stress was also associated with negative affect in two studies [ 43 , 46 ], and strongly negatively associated with positive affect (correlation -0.62) [ 54 ].

University variables

Eleven studies [ 35 , 39 , 47 , 51 , 52 , 54 , 60 , 63 , 65 , 83 , 84 ] explored university variables, and their association with mental health outcomes. The range of factors and their impact on mental health variables is limited, and there is little overlap. Knowledge gaps are shown by factors highlighted by our PPI group as potentially important but not identified in the literature (see Table 5 ). It should be noted that these may reflect the focus of our review, and our exclusion of intervention studies which may evaluate university factors.

High levels of perceived stress caused by exam and course work pressure was positively associated with poor mental health and lack of wellbeing [ 51 , 52 , 54 ]. Other potential stressors including financial anxieties and accommodation factors appeared to be less consistently associated with mental health outcomes [ 35 , 38 , 47 , 51 , 60 , 62 ]. Important mediators and buffers to these stressors are coping strategies and supportive networks (see conceptual model Appendix 2 ). One impact of financial pressures was that students who worked longer hours had less interaction with their peers, limiting the opportunities for these students to benefit from the protective effects of social support.

Red flags – behaviours associated with poor mental health and/or wellbeing

Engagement with learning and leisure activities.

Engagement with learning activities was strongly and positively associated with characteristics of adaptability [ 39 ] and also happiness and wellbeing [ 52 ] (see Fig.  4 ). Boulton et al. (2019) [ 52 ] undertook a longitudinal survey of undergraduate students at a campus-based university. They found that engagement and wellbeing varied during the term but were strongly correlated.

Engagement and wellbeing

Engagement occurred in a wide range of activities and behaviours. The authors suggest that the strong correlation between all forms of engagement with learning has possible instrumental value for the design of systems to monitor student engagement. Monitoring engagement might be used to identify changes in the behaviour of individuals to assist tutors in providing support and pastoral care. Students also were found to benefit from good induction activities provided by the university. Greater induction satisfaction was positively and strongly associated with a sense of community at university and with lower levels of loneliness [ 65 ].

The inte r- related nature of these variables is depicted in Fig.  4 . Greater adaptability is strongly associated with more positive engagement in learning and university life. More engagement is associated with higher mental wellbeing.

Denovan et al. (2017b) [ 54 ] explored leisure coping, its psychosocial functions and its relationship with mental wellbeing. An individual’s beliefs about the benefits of leisure activities to manage stress, facilitate the development of companionship and enhance mood were positively associated with flourishing and were negatively associated with perceived stress. Resilience was also measured. Resilience was strongly and positively associated with leisure coping beliefs and with indicators of mental wellbeing. The authors conclude that resilient individuals are more likely to use constructive means of coping (such as leisure coping) to proactively cultivate positive emotions which counteract the experience of stress and promote wellbeing. Leisure coping is predictive of positive affect which provides a strategy to reduce stress and sustain coping. The belief that friendships acquired through leisure provide social support is an example of leisure coping belief. Strong emotionally attached friendships that develop through participation in shared leisure pursuits are predictive of higher levels of well-being. Friendship bonds formed with fellow students at university are particularly important for maintaining mental health, and opportunities need to be developed and supported to ensure that meaningful social connections are made.

The ‘broaden-and-build theory’ (Fredickson 2004 [ 85 ] cited by [ 54 ]) may offer an explanation for the association seen between resilience, leisure coping and psychological wellbeing. The theory is based upon the role that positive and negative emotions have in shaping human adaptation. Positive emotions broaden thinking, enabling the individual to consider a range of ways of dealing with and adapting to their environment. Conversely, negative emotions narrow thinking and limit options for adapting. The former facilitates flourishing, facilitating future wellbeing. Resilient individuals are more likely to use constructive means of coping which generate positive emotion (Tugade & Fredrickson 2004 [ 86 ], cited by [ 54 ]). Positive emotions therefore lead to growth in coping resources, leading to greater well-being.

Health behaviours at university

Seven studies [ 29 , 31 , 38 , 45 , 51 , 54 , 66 ] examined how lifestyle behaviours might be linked with mental health outcomes. The studies looked at leisure activities [ 63 , 80 ], diet [ 29 ], alcohol use [ 29 , 31 , 38 , 51 ] and sleep [ 45 ].

Depressive symptoms were independently associated with problem drinking and possible alcohol dependence for both genders but were not associated with frequency of drinking and heavy episodic drinking. Students with higher levels of depressive symptoms reported significantly more problem drinking and possible alcohol dependence [ 31 ]. Mahadevan et al. (2010) [ 51 ] compared students and non-students seen in hospital for self-harm and found no difference in harmful use of alcohol and illicit drugs.

Poor sleep quality and increased consumption of unhealthy foods were also positively associated with depressive symptoms and perceived stress [ 29 ]. The correlation with dietary behaviours and poor mental health outcomes was low, but also confirmed by the negative correlation between less perceived stress and depressive symptoms and consumption of a healthier diet.

Physical activity and participation in leisure pursuits were both strongly correlated with mental wellbeing ( r  = 0.4) [ 54 ], and negatively correlated with depressive symptoms and anxiety ( r  = -0.6, -0.7) [ 66 ].

Thirty studies measuring the association between a wide range of factors and poor mental health and mental wellbeing in university and college students were identified and included in this review. Our purpose was to identify the factors that contribute to the growing prevalence of poor mental health amongst students in tertiary level education within the UK. We also aimed to identify factors that promote mental wellbeing and protect against deteriorating poor mental health.

Loneliness and social isolation were strongly associated with poor mental health and a sense of belonging and a strong support network were strongly associated with mental wellbeing and happiness. These associations were strongly positive in the eight studies that explored them and are consistent with other meta-analyses exploring the link between social support and mental health [ 87 ].

Another factor that appeared to be protective was older age when starting university. A wide range of personal traits and characteristics were also explored. Those associated with resilience, ability to adjust and better coping led to improved mental wellbeing. Better engagement appeared as an important mediator to potentially explain the relationship between these two variables. Engagement led to students being able to then tap into those features that are protective and promoting of mental wellbeing.

Other important risk factors for poor mental wellbeing that emerged were those students with existing or previous mental illness. Students on the autism spectrum and those with poor social problem-solving also were more likely to suffer from poor mental health. Negative self-image was also associated with poor mental health at university. Eating disorders were strongly associated with poor mental wellbeing and were found to be far more of a risk in students at university than in a comparative group of young people not in higher education. Other studies of university students also found that pre-existing poor mental health was a strong predictor of poor mental health in university students [ 88 ].

At a family level, the experience of childhood trauma and adverse experiences including, for example, neglect, household dysfunction or abuse, were strongly associated with poor mental health in young people at university. Students with a greater number of ‘adverse childhood experiences’ were at significantly greater risk of poor mental health than those students without experience of childhood trauma. This was also identified in a review of factors associated with depression and suicide related outcomes amongst university undergraduate students [ 88 ].

Our findings, in contrast to findings from other studies of university students, did not find that female gender associated with poor mental health and wellbeing, and it also found that being a mature student was protective of mental wellbeing.

Exam and course work pressure was associated with perceived stress and poor mental health. A lack of engagement with learning activities was also associated with poor mental health. A number of variables were not consistently shown to be associated with poor mental health including financial concerns and accommodation factors. Very little evidence related to university organisation or support structures was assessed in the evidence. One study found that a good induction programme had benefits for student mental wellbeing and may be a factor that enables students to become a part of a social network positive reinforcement cycle. Involvement in leisure activities was also found to be associated with improved coping strategies and better mental wellbeing. Students with poorer mental health tended to also eat in a less healthy manner, consume more harmful levels of alcohol, and experience poorer sleep.

This evidence review of the factors that influence mental health and wellbeing indicate areas where universities and higher education settings could develop and evaluate innovations in practice. These include:

Interventions before university to improve preparation of young people and their families for the transition to university.

Exploratory work to identify the acceptability and feasibility of identifying students at risk or who many be exhibiting indications of deteriorating mental health

Interventions that set out to foster a sense of belonging and identify

Creating environments that are helpful for building social networks

Improving mental health literacy and access to high quality support services

This review has a number of limitations. Most of the included studies were cross-sectional in design, with a small number being longitudinal ( n  = 7), following students over a period of time to observe changes in the outcomes being measured. Two limitations of these sources of data is that they help to understand associations but do not reveal causality; secondly, we can only report the findings for those variables that were measured, and we therefore have to support causation in assuming these are the only factors that are related to mental health.

Furthermore, our approach has segregated and categorised variables in order to better understand the extent to which they impact mental health. This approach does not sufficiently explore or reveal the extent to which variables may compound one another, for example, feeling the stress of new ways of learning may not be a factor that influences mental health until it is combined with a sense of loneliness, anxiety about financial debt and a lack of parental support. We have used our PPI group and the development of vignettes of their experiences to seek to illustrate the compounding nature of the variables identified.

We limited our inclusion criteria to studies undertaken in the UK and published within the last decade (2009–2020), again meaning we may have limited our inclusion of relevant data. We also undertook single data extraction of data which may increase the risk of error in our data.

Understanding factors that influence students’ mental health and wellbeing offers the potential to find ways to identify strategies that enhance the students’ abilities to cope with the challenges of higher education. This review revealed a wide range of variables and the mechanisms that may explain how they impact upon mental wellbeing and increase the risk of poor mental health amongst students. It also identified a need for interventions that are implemented before young people make the transition to higher education. We both identified young people who are particularly vulnerable and the factors that arise that exacerbate poor mental health. We highlight that a sense of belonging and supportive networks are important buffers and that there are indicators including lack of engagement that may enable early intervention to provide targeted and appropriate support.

Availability of data and materials

Further details of the study and the findings can be provided on request to the lead author ([email protected]).

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Acknowledgements

We acknowledge the input from our public advisory group which included current and former students, and family members of students who have struggled with their mental health. The group gave us their extremely valuable insights to assist our understanding of the evidence.

This project was supported by funding from the National Institute for Health Research as part of the NIHR Public Health Research  Programme (fuding reference 127659 Public Health Review Team). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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All of the included authors designed the project methods and prepared a protocol. A.C. designed the search strategy. F.C, L.B and C.B screened the identified citations and undertook data extraction. S.B. led the PPI involvement. JD participated as a member of the PPI group. F.C and L.B undertook the analysis. F.C. and L.B wrote the main manuscript text. All authors reviewed the manuscript. F.C designed Figs. 2 , 3 and 4 . The author(s) read and approved the final manuscript.

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Campbell, F., Blank, L., Cantrell, A. et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health 22 , 1778 (2022). https://doi.org/10.1186/s12889-022-13943-x

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Student mental health is in crisis. Campuses are rethinking their approach

Amid massive increases in demand for care, psychologists are helping colleges and universities embrace a broader culture of well-being and better equipping faculty to support students in need

Vol. 53 No. 7 Print version: page 60

• Mental Health

By nearly every metric, student mental health is worsening. During the 2020–2021 school year, more than 60% of college students met the criteria for at least one mental health problem, according to the Healthy Minds Study, which collects data from 373 campuses nationwide ( Lipson, S. K., et al., Journal of Affective Disorders , Vol. 306, 2022 ). In another national survey, almost three quarters of students reported moderate or severe psychological distress ( National College Health Assessment , American College Health Association, 2021).

Even before the pandemic, schools were facing a surge in demand for care that far outpaced capacity, and it has become increasingly clear that the traditional counseling center model is ill-equipped to solve the problem.

“Counseling centers have seen extraordinary increases in demand over the past decade,” said Michael Gerard Mason, PhD, associate dean of African American Affairs at the University of Virginia (UVA) and a longtime college counselor. “[At UVA], our counseling staff has almost tripled in size, but even if we continue hiring, I don’t think we could ever staff our way out of this challenge.”

Some of the reasons for that increase are positive. Compared with past generations, more students on campus today have accessed mental health treatment before college, suggesting that higher education is now an option for a larger segment of society, said Micky Sharma, PsyD, who directs student life’s counseling and consultation service at The Ohio State University (OSU). Stigma around mental health issues also continues to drop, leading more people to seek help instead of suffering in silence.

But college students today are also juggling a dizzying array of challenges, from coursework, relationships, and adjustment to campus life to economic strain, social injustice, mass violence, and various forms of loss related to Covid -19.

As a result, school leaders are starting to think outside the box about how to help. Institutions across the country are embracing approaches such as group therapy, peer counseling, and telehealth. They’re also better equipping faculty and staff to spot—and support—students in distress, and rethinking how to respond when a crisis occurs. And many schools are finding ways to incorporate a broader culture of wellness into their policies, systems, and day-to-day campus life.

“This increase in demand has challenged institutions to think holistically and take a multifaceted approach to supporting students,” said Kevin Shollenberger, the vice provost for student health and well-being at Johns Hopkins University. “It really has to be everyone’s responsibility at the university to create a culture of well-being.”

Higher caseloads, creative solutions

The number of students seeking help at campus counseling centers increased almost 40% between 2009 and 2015 and continued to rise until the pandemic began, according to data from Penn State University’s Center for Collegiate Mental Health (CCMH), a research-practice network of more than 700 college and university counseling centers ( CCMH Annual Report , 2015 ).

That rising demand hasn’t been matched by a corresponding rise in funding, which has led to higher caseloads. Nationwide, the average annual caseload for a typical full-time college counselor is about 120 students, with some centers averaging more than 300 students per counselor ( CCMH Annual Report , 2021 ).

“We find that high-caseload centers tend to provide less care to students experiencing a wide range of problems, including those with safety concerns and critical issues—such as suicidality and trauma—that are often prioritized by institutions,” said psychologist Brett Scofield, PhD, executive director of CCMH.

To minimize students slipping through the cracks, schools are dedicating more resources to rapid access and assessment, where students can walk in for a same-day intake or single counseling session, rather than languishing on a waitlist for weeks or months. Following an evaluation, many schools employ a stepped-care model, where the students who are most in need receive the most intensive care.

Given the wide range of concerns students are facing, experts say this approach makes more sense than offering traditional therapy to everyone.

“Early on, it was just about more, more, more clinicians,” said counseling psychologist Carla McCowan, PhD, director of the counseling center at the University of Illinois at Urbana-Champaign. “In the past few years, more centers are thinking creatively about how to meet the demand. Not every student needs individual therapy, but many need opportunities to increase their resilience, build new skills, and connect with one another.”

Students who are struggling with academic demands, for instance, may benefit from workshops on stress, sleep, time management, and goal-setting. Those who are mourning the loss of a typical college experience because of the pandemic—or facing adjustment issues such as loneliness, low self-esteem, or interpersonal conflict—are good candidates for peer counseling. Meanwhile, students with more acute concerns, including disordered eating, trauma following a sexual assault, or depression, can still access one-on-one sessions with professional counselors.

As they move away from a sole reliance on individual therapy, schools are also working to shift the narrative about what mental health care on campus looks like. Scofield said it’s crucial to manage expectations among students and their families, ideally shortly after (or even before) enrollment. For example, most counseling centers won’t be able to offer unlimited weekly sessions throughout a student’s college career—and those who require that level of support will likely be better served with a referral to a community provider.

“We really want to encourage institutions to be transparent about the services they can realistically provide based on the current staffing levels at a counseling center,” Scofield said.

The first line of defense

Faculty may be hired to teach, but schools are also starting to rely on them as “first responders” who can help identify students in distress, said psychologist Hideko Sera, PsyD, director of the Office of Equity, Inclusion, and Belonging at Morehouse College, a historically Black men’s college in Atlanta. During the pandemic, that trend accelerated.

“Throughout the remote learning phase of the pandemic, faculty really became students’ main points of contact with the university,” said Bridgette Hard, PhD, an associate professor and director of undergraduate studies in psychology and neuroscience at Duke University. “It became more important than ever for faculty to be able to detect when a student might be struggling.”

Many felt ill-equipped to do so, though, with some wondering if it was even in their scope of practice to approach students about their mental health without specialized training, Mason said.

Schools are using several approaches to clarify expectations of faculty and give them tools to help. About 900 faculty and staff at the University of North Carolina have received training in Mental Health First Aid , which provides basic skills for supporting people with mental health and substance use issues. Other institutions are offering workshops and materials that teach faculty to “recognize, respond, and refer,” including Penn State’s Red Folder campaign .

Faculty are taught that a sudden change in behavior—including a drop in attendance, failure to submit assignments, or a disheveled appearance—may indicate that a student is struggling. Staff across campus, including athletic coaches and academic advisers, can also monitor students for signs of distress. (At Penn State, eating disorder referrals can even come from staff working in food service, said counseling psychologist Natalie Hernandez DePalma, PhD, senior director of the school’s counseling and psychological services.) Responding can be as simple as reaching out and asking if everything is going OK.

Referral options vary but may include directing a student to a wellness seminar or calling the counseling center to make an appointment, which can help students access services that they may be less likely to seek on their own, Hernandez DePalma said. Many schools also offer reporting systems, such as DukeReach at Duke University , that allow anyone on campus to express concern about a student if they are unsure how to respond. Trained care providers can then follow up with a welfare check or offer other forms of support.

“Faculty aren’t expected to be counselors, just to show a sense of care that they notice something might be going on, and to know where to refer students,” Shollenberger said.

At Johns Hopkins, he and his team have also worked with faculty on ways to discuss difficult world events during class after hearing from students that it felt jarring when major incidents such as George Floyd’s murder or the war in Ukraine went unacknowledged during class.

Many schools also support faculty by embedding counselors within academic units, where they are more visible to students and can develop cultural expertise (the needs of students studying engineering may differ somewhat from those in fine arts, for instance).

When it comes to course policy, even small changes can make a big difference for students, said Diana Brecher, PhD, a clinical psychologist and scholar-in-residence for positive psychology at Toronto Metropolitan University (TMU), formerly Ryerson University. For example, instructors might allow students a 7-day window to submit assignments, giving them agency to coordinate with other coursework and obligations. Setting deadlines in the late afternoon or early evening, as opposed to at midnight, can also help promote student wellness.

At Moraine Valley Community College (MVCC) near Chicago, Shelita Shaw, an assistant professor of communications, devised new class policies and assignments when she noticed students struggling with mental health and motivation. Those included mental health days, mindful journaling, and a trip with family and friends to a Chicago landmark, such as Millennium Park or Navy Pier—where many MVCC students had never been.

Faculty in the psychology department may have a unique opportunity to leverage insights from their own discipline to improve student well-being. Hard, who teaches introductory psychology at Duke, weaves in messages about how students can apply research insights on emotion regulation, learning and memory, and a positive “stress mindset” to their lives ( Crum, A. J., et al., Anxiety, Stress, & Coping , Vol. 30, No. 4, 2017 ).

Along with her colleague Deena Kara Shaffer, PhD, Brecher cocreated TMU’s Thriving in Action curriculum, which is delivered through a 10-week in-person workshop series and via a for-credit elective course. The material is also freely available for students to explore online . The for-credit course includes lectures on gratitude, attention, healthy habits, and other topics informed by psychological research that are intended to set students up for success in studying, relationships, and campus life.

“We try to embed a healthy approach to studying in the way we teach the class,” Brecher said. “For example, we shift activities every 20 minutes or so to help students sustain attention and stamina throughout the lesson.”

Creative approaches to support

Given the crucial role of social connection in maintaining and restoring mental health, many schools have invested in group therapy. Groups can help students work through challenges such as social anxiety, eating disorders, sexual assault, racial trauma, grief and loss, chronic illness, and more—with the support of professional counselors and peers. Some cater to specific populations, including those who tend to engage less with traditional counseling services. At Florida Gulf Coast University (FGCU), for example, the “Bold Eagles” support group welcomes men who are exploring their emotions and gender roles.

The widespread popularity of group therapy highlights the decrease in stigma around mental health services on college campuses, said Jon Brunner, PhD, the senior director of counseling and wellness services at FGCU. At smaller schools, creating peer support groups that feel anonymous may be more challenging, but providing clear guidelines about group participation, including confidentiality, can help put students at ease, Brunner said.

Less formal groups, sometimes called “counselor chats,” meet in public spaces around campus and can be especially helpful for reaching underserved groups—such as international students, first-generation college students, and students of color—who may be less likely to seek services at a counseling center. At Johns Hopkins, a thriving international student support group holds weekly meetings in a café next to the library. Counselors typically facilitate such meetings, often through partnerships with campus centers or groups that support specific populations, such as LGBTQ students or student athletes.

“It’s important for students to see counselors out and about, engaging with the campus community,” McCowan said. “Otherwise, you’re only seeing the students who are comfortable coming in the door.”

Peer counseling is another means of leveraging social connectedness to help students stay well. At UVA, Mason and his colleagues found that about 75% of students reached out to a peer first when they were in distress, while only about 11% contacted faculty, staff, or administrators.

“What we started to understand was that in many ways, the people who had the least capacity to provide a professional level of help were the ones most likely to provide it,” he said.

Project Rise , a peer counseling service created by and for Black students at UVA, was one antidote to this. Mason also helped launch a two-part course, “Hoos Helping Hoos,” (a nod to UVA’s unofficial nickname, the Wahoos) to train students across the university on empathy, mentoring, and active listening skills.

At Washington University in St. Louis, Uncle Joe’s Peer Counseling and Resource Center offers confidential one-on-one sessions, in person and over the phone, to help fellow students manage anxiety, depression, academic stress, and other campus-life issues. Their peer counselors each receive more than 100 hours of training, including everything from basic counseling skills to handling suicidality.

Uncle Joe’s codirectors, Colleen Avila and Ruchika Kamojjala, say the service is popular because it’s run by students and doesn’t require a long-term investment the way traditional psychotherapy does.

“We can form a connection, but it doesn’t have to feel like a commitment,” said Avila, a senior studying studio art and philosophy-neuroscience-psychology. “It’s completely anonymous, one time per issue, and it’s there whenever you feel like you need it.”

As part of the shift toward rapid access, many schools also offer “Let’s Talk” programs , which allow students to drop in for an informal one-on-one session with a counselor. Some also contract with telehealth platforms, such as WellTrack and SilverCloud, to ensure that services are available whenever students need them. A range of additional resources—including sleep seminars, stress management workshops, wellness coaching, and free subscriptions to Calm, Headspace, and other apps—are also becoming increasingly available to students.

Those approaches can address many student concerns, but institutions also need to be prepared to aid students during a mental health crisis, and some are rethinking how best to do so. Penn State offers a crisis line, available anytime, staffed with counselors ready to talk or deploy on an active rescue. Johns Hopkins is piloting a behavioral health crisis support program, similar to one used by the New York City Police Department, that dispatches trained crisis clinicians alongside public safety officers to conduct wellness checks.

A culture of wellness

With mental health resources no longer confined to the counseling center, schools need a way to connect students to a range of available services. At OSU, Sharma was part of a group of students, staff, and administrators who visited Apple Park in Cupertino, California, to develop the Ohio State: Wellness App .

Students can use the app to create their own “wellness plan” and access timely content, such as advice for managing stress during final exams. They can also connect with friends to share articles and set goals—for instance, challenging a friend to attend two yoga classes every week for a month. OSU’s apps had more than 240,000 users last year.

At Johns Hopkins, administrators are exploring how to adapt school policies and procedures to better support student wellness, Shollenberger said. For example, they adapted their leave policy—including how refunds, grades, and health insurance are handled—so that students can take time off with fewer barriers. The university also launched an educational campaign this fall to help international students navigate student health insurance plans after noticing below average use by that group.

Students are a key part of the effort to improve mental health care, including at the systemic level. At Morehouse College, Sera serves as the adviser for Chill , a student-led advocacy and allyship organization that includes members from Spelman College and Clark Atlanta University, two other HBCUs in the area. The group, which received training on federal advocacy from APA’s Advocacy Office earlier this year, aims to lobby public officials—including U.S. Senator Raphael Warnock, a Morehouse College alumnus—to increase mental health resources for students of color.

“This work is very aligned with the spirit of HBCUs, which are often the ones raising voices at the national level to advocate for the betterment of Black and Brown communities,” Sera said.

Despite the creative approaches that students, faculty, staff, and administrators are employing, students continue to struggle, and most of those doing this work agree that more support is still urgently needed.

“The work we do is important, but it can also be exhausting,” said Kamojjala, of Uncle Joe’s peer counseling, which operates on a volunteer basis. “Students just need more support, and this work won’t be sustainable in the long run if that doesn’t arrive.”

Further reading

Overwhelmed: The real campus mental-health crisis and new models for well-being The Chronicle of Higher Education, 2022

Mental health in college populations: A multidisciplinary review of what works, evidence gaps, and paths forward Abelson, S., et al., Higher Education: Handbook of Theory and Research, 2022

Student mental health status report: Struggles, stressors, supports Ezarik, M., Inside Higher Ed, 2022

Before heading to college, make a mental health checklist Caron, C., The New York Times, 2022

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Open Access

Peer-reviewed

Research Article

Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Child and Adolescent Mental Health, Department of Brain Sciences, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

Roles Conceptualization, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation Behavioural Brain Sciences Unit, Population Policy Practice Programme, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

• Max L. Y. Chang, 
• Irene O. Lee

• Published: June 4, 2024
• https://doi.org/10.1371/journal.pmen.0000022
• Peer Review
• Reader Comments

Internet usage has seen a stark global rise over the last few decades, particularly among adolescents and young people, who have also been diagnosed increasingly with internet addiction (IA). IA impacts several neural networks that influence an adolescent’s behaviour and development. This article issued a literature review on the resting-state and task-based functional magnetic resonance imaging (fMRI) studies to inspect the consequences of IA on the functional connectivity (FC) in the adolescent brain and its subsequent effects on their behaviour and development. A systematic search was conducted from two databases, PubMed and PsycINFO, to select eligible articles according to the inclusion and exclusion criteria. Eligibility criteria was especially stringent regarding the adolescent age range (10–19) and formal diagnosis of IA. Bias and quality of individual studies were evaluated. The fMRI results from 12 articles demonstrated that the effects of IA were seen throughout multiple neural networks: a mix of increases/decreases in FC in the default mode network; an overall decrease in FC in the executive control network; and no clear increase or decrease in FC within the salience network and reward pathway. The FC changes led to addictive behaviour and tendencies in adolescents. The subsequent behavioural changes are associated with the mechanisms relating to the areas of cognitive control, reward valuation, motor coordination, and the developing adolescent brain. Our results presented the FC alterations in numerous brain regions of adolescents with IA leading to the behavioural and developmental changes. Research on this topic had a low frequency with adolescent samples and were primarily produced in Asian countries. Future research studies of comparing results from Western adolescent samples provide more insight on therapeutic intervention.

Citation: Chang MLY, Lee IO (2024) Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies. PLOS Ment Health 1(1): e0000022. https://doi.org/10.1371/journal.pmen.0000022

Editor: Kizito Omona, Uganda Martyrs University, UGANDA

Received: December 29, 2023; Accepted: March 18, 2024; Published: June 4, 2024

Copyright: © 2024 Chang, Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The behavioural addiction brought on by excessive internet use has become a rising source of concern [ 1 ] since the last decade. According to clinical studies, individuals with Internet Addiction (IA) or Internet Gaming Disorder (IGD) may have a range of biopsychosocial effects and is classified as an impulse-control disorder owing to its resemblance to pathological gambling and substance addiction [ 2 , 3 ]. IA has been defined by researchers as a person’s inability to resist the urge to use the internet, which has negative effects on their psychological well-being as well as their social, academic, and professional lives [ 4 ]. The symptoms can have serious physical and interpersonal repercussions and are linked to mood modification, salience, tolerance, impulsivity, and conflict [ 5 ]. In severe circumstances, people may experience severe pain in their bodies or health issues like carpal tunnel syndrome, dry eyes, irregular eating and disrupted sleep [ 6 ]. Additionally, IA is significantly linked to comorbidities with other psychiatric disorders [ 7 ].

Stevens et al (2021) reviewed 53 studies including 17 countries and reported the global prevalence of IA was 3.05% [ 8 ]. Asian countries had a higher prevalence (5.1%) than European countries (2.7%) [ 8 ]. Strikingly, adolescents and young adults had a global IGD prevalence rate of 9.9% which matches previous literature that reported historically higher prevalence among adolescent populations compared to adults [ 8 , 9 ]. Over 80% of adolescent population in the UK, the USA, and Asia have direct access to the internet [ 10 ]. Children and adolescents frequently spend more time on media (possibly 7 hours and 22 minutes per day) than at school or sleeping [ 11 ]. Developing nations have also shown a sharp rise in teenage internet usage despite having lower internet penetration rates [ 10 ]. Concerns regarding the possible harms that overt internet use could do to adolescents and their development have arisen because of this surge, especially the significant impacts by the COVID-19 pandemic [ 12 ]. The growing prevalence and neurocognitive consequences of IA among adolescents makes this population a vital area of study [ 13 ].

Adolescence is a crucial developmental stage during which people go through significant changes in their biology, cognition, and personalities [ 14 ]. Adolescents’ emotional-behavioural functioning is hyperactivated, which creates risk of psychopathological vulnerability [ 15 ]. In accordance with clinical study results [ 16 ], this emotional hyperactivity is supported by a high level of neuronal plasticity. This plasticity enables teenagers to adapt to the numerous physical and emotional changes that occur during puberty as well as develop communication techniques and gain independence [ 16 ]. However, the strong neuronal plasticity is also associated with risk-taking and sensation seeking [ 17 ] which may lead to IA.

Despite the fact that the precise neuronal mechanisms underlying IA are still largely unclear, functional magnetic resonance imaging (fMRI) method has been used by scientists as an important framework to examine the neuropathological changes occurring in IA, particularly in the form of functional connectivity (FC) [ 18 ]. fMRI research study has shown that IA alters both the functional and structural makeup of the brain [ 3 ].

We hypothesise that IA has widespread neurological alteration effects rather than being limited to a few specific brain regions. Further hypothesis holds that according to these alterations of FC between the brain regions or certain neural networks, adolescents with IA would experience behavioural changes. An investigation of these domains could be useful for creating better procedures and standards as well as minimising the negative effects of overt internet use. This literature review aims to summarise and analyse the evidence of various imaging studies that have investigated the effects of IA on the FC in adolescents. This will be addressed through two research questions:

• How does internet addiction affect the functional connectivity in the adolescent brain?
• How is adolescent behaviour and development impacted by functional connectivity changes due to internet addiction?

The review protocol was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 Checklist ).

Search strategy and selection process

A systematic search was conducted up until April 2023 from two sources of database, PubMed and PsycINFO, using a range of terms relevant to the title and research questions (see full list of search terms in S1 Appendix ). All the searched articles can be accessed in the S1 Data . The eligible articles were selected according to the inclusion and exclusion criteria. Inclusion criteria used for the present review were: (i) participants in the studies with clinical diagnosis of IA; (ii) participants between the ages of 10 and 19; (iii) imaging research investigations; (iv) works published between January 2013 and April 2023; (v) written in English language; (vi) peer-reviewed papers and (vii) full text. The numbers of articles excluded due to not meeting the inclusion criteria are shown in Fig 1 . Each study’s title and abstract were screened for eligibility.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pmen.0000022.g001

Quality appraisal

Full texts of all potentially relevant studies were then retrieved and further appraised for eligibility. Furthermore, articles were critically appraised based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework to evaluate the individual study for both quality and bias. The subsequent quality levels were then appraised to each article and listed as either low, moderate, or high.

Data collection process

Data that satisfied the inclusion requirements was entered into an excel sheet for data extraction and further selection. An article’s author, publication year, country, age range, participant sample size, sex, area of interest, measures, outcome and article quality were all included in the data extraction spreadsheet. Studies looking at FC, for instance, were grouped, while studies looking at FC in specific area were further divided into sub-groups.

Data synthesis and analysis

Articles were classified according to their location in the brain as well as the network or pathway they were a part of to create a coherent narrative between the selected studies. Conclusions concerning various research trends relevant to particular groupings were drawn from these groupings and subgroupings. To maintain the offered information in a prominent manner, these assertions were entered into the data extraction excel spreadsheet.

With the search performed on the selected databases, 238 articles in total were identified (see Fig 1 ). 15 duplicated articles were eliminated, and another 6 items were removed for various other reasons. Title and abstract screening eliminated 184 articles because they were not in English (number of article, n, = 7), did not include imaging components (n = 47), had adult participants (n = 53), did not have a clinical diagnosis of IA (n = 19), did not address FC in the brain (n = 20), and were published outside the desired timeframe (n = 38). A further 21 papers were eliminated for failing to meet inclusion requirements after the remaining 33 articles underwent full-text eligibility screening. A total of 12 papers were deemed eligible for this review analysis.

Characteristics of the included studies, as depicted in the data extraction sheet in Table 1 provide information of the author(s), publication year, sample size, study location, age range, gender, area of interest, outcome, measures used and quality appraisal. Most of the studies in this review utilised resting state functional magnetic resonance imaging techniques (n = 7), with several studies demonstrating task-based fMRI procedures (n = 3), and the remaining studies utilising whole-brain imaging measures (n = 2). The studies were all conducted in Asiatic countries, specifically coming from China (8), Korea (3), and Indonesia (1). Sample sizes ranged from 12 to 31 participants with most of the imaging studies having comparable sample sizes. Majority of the studies included a mix of male and female participants (n = 8) with several studies having a male only participant pool (n = 3). All except one of the mixed gender studies had a majority male participant pool. One study did not disclose their data on the gender demographics of their experiment. Study years ranged from 2013–2022, with 2 studies in 2013, 3 studies in 2014, 3 studies in 2015, 1 study in 2017, 1 study in 2020, 1 study in 2021, and 1 study in 2022.

https://doi.org/10.1371/journal.pmen.0000022.t001

(1) How does internet addiction affect the functional connectivity in the adolescent brain?

The included studies were organised according to the brain region or network that they were observing. The specific networks affected by IA were the default mode network, executive control system, salience network and reward pathway. These networks are vital components of adolescent behaviour and development [ 31 ]. The studies in each section were then grouped into subsections according to their specific brain regions within their network.

Default mode network (DMN)/reward network.

Out of the 12 studies, 3 have specifically studied the default mode network (DMN), and 3 observed whole-brain FC that partially included components of the DMN. The effect of IA on the various centres of the DMN was not unilaterally the same. The findings illustrate a complex mix of increases and decreases in FC depending on the specific region in the DMN (see Table 2 and Fig 2 ). The alteration of FC in posterior cingulate cortex (PCC) in the DMN was the most frequently reported area in adolescents with IA, which involved in attentional processes [ 32 ], but Lee et al. (2020) additionally found alterations of FC in other brain regions, such as anterior insula cortex, a node in the DMN that controls the integration of motivational and cognitive processes [ 20 ].

https://doi.org/10.1371/journal.pmen.0000022.g002

The overall changes of functional connectivity in the brain network including default mode network (DMN), executive control network (ECN), salience network (SN) and reward network. IA = Internet Addiction, FC = Functional Connectivity.

https://doi.org/10.1371/journal.pmen.0000022.t002

Ding et al. (2013) revealed altered FC in the cerebellum, the middle temporal gyrus, and the medial prefrontal cortex (mPFC) [ 22 ]. They found that the bilateral inferior parietal lobule, left superior parietal lobule, and right inferior temporal gyrus had decreased FC, while the bilateral posterior lobe of the cerebellum and the medial temporal gyrus had increased FC [ 22 ]. The right middle temporal gyrus was found to have 111 cluster voxels (t = 3.52, p<0.05) and the right inferior parietal lobule was found to have 324 cluster voxels (t = -4.07, p<0.05) with an extent threshold of 54 voxels (figures above this threshold are deemed significant) [ 22 ]. Additionally, there was a negative correlation, with 95 cluster voxels (p<0.05) between the FC of the left superior parietal lobule and the PCC with the Chen Internet Addiction Scores (CIAS) which are used to determine the severity of IA [ 22 ]. On the other hand, in regions of the reward system, connection with the PCC was positively connected with CIAS scores [ 22 ]. The most significant was the right praecuneus with 219 cluster voxels (p<0.05) [ 22 ]. Wang et al. (2017) also discovered that adolescents with IA had 33% less FC in the left inferior parietal lobule and 20% less FC in the dorsal mPFC [ 24 ]. A potential connection between the effects of substance use and overt internet use is revealed by the generally decreased FC in these areas of the DMN of teenagers with drug addiction and IA [ 35 ].

The putamen was one of the main regions of reduced FC in adolescents with IA [ 19 ]. The putamen and the insula-operculum demonstrated significant group differences regarding functional connectivity with a cluster size of 251 and an extent threshold of 250 (Z = 3.40, p<0.05) [ 19 ]. The molecular mechanisms behind addiction disorders have been intimately connected to decreased striatal dopaminergic function [ 19 ], making this function crucial.

Executive Control Network (ECN).

5 studies out of 12 have specifically viewed parts of the executive control network (ECN) and 3 studies observed whole-brain FC. The effects of IA on the ECN’s constituent parts were consistent across all the studies examined for this analysis (see Table 2 and Fig 3 ). The results showed a notable decline in all the ECN’s major centres. Li et al. (2014) used fMRI imaging and a behavioural task to study response inhibition in adolescents with IA [ 25 ] and found decreased activation at the striatum and frontal gyrus, particularly a reduction in FC at inferior frontal gyrus, in the IA group compared to controls [ 25 ]. The inferior frontal gyrus showed a reduction in FC in comparison to the controls with a cluster size of 71 (t = 4.18, p<0.05) [ 25 ]. In addition, the frontal-basal ganglia pathways in the adolescents with IA showed little effective connection between areas and increased degrees of response inhibition [ 25 ].

https://doi.org/10.1371/journal.pmen.0000022.g003

Lin et al. (2015) found that adolescents with IA demonstrated disrupted corticostriatal FC compared to controls [ 33 ]. The corticostriatal circuitry experienced decreased connectivity with the caudate, bilateral anterior cingulate cortex (ACC), as well as the striatum and frontal gyrus [ 33 ]. The inferior ventral striatum showed significantly reduced FC with the subcallosal ACC and caudate head with cluster size of 101 (t = -4.64, p<0.05) [ 33 ]. Decreased FC in the caudate implies dysfunction of the corticostriatal-limbic circuitry involved in cognitive and emotional control [ 36 ]. The decrease in FC in both the striatum and frontal gyrus is related to inhibitory control, a common deficit seen with disruptions with the ECN [ 33 ].

The dorsolateral prefrontal cortex (DLPFC), ACC, and right supplementary motor area (SMA) of the prefrontal cortex were all found to have significantly decreased grey matter volume [ 29 ]. In addition, the DLPFC, insula, temporal cortices, as well as significant subcortical regions like the striatum and thalamus, showed decreased FC [ 29 ]. According to Tremblay (2009), the striatum plays a significant role in the processing of rewards, decision-making, and motivation [ 37 ]. Chen et al. (2020) reported that the IA group demonstrated increased impulsivity as well as decreased reaction inhibition using a Stroop colour-word task [ 26 ]. Furthermore, Chen et al. (2020) observed that the left DLPFC and dorsal striatum experienced a negative connection efficiency value, specifically demonstrating that the dorsal striatum activity suppressed the left DLPFC [ 27 ].

Salience network (SN).

Out of the 12 chosen studies, 3 studies specifically looked at the salience network (SN) and 3 studies have observed whole-brain FC. Relative to the DMN and ECN, the findings on the SN were slightly sparser. Despite this, adolescents with IA demonstrated a moderate decrease in FC, as well as other measures like fibre connectivity and cognitive control, when compared to healthy control (see Table 2 and Fig 4 ).

https://doi.org/10.1371/journal.pmen.0000022.g004

Xing et al. (2014) used both dorsal anterior cingulate cortex (dACC) and insula to test FC changes in the SN of adolescents with IA and found decreased structural connectivity in the SN as well as decreased fractional anisotropy (FA) that correlated to behaviour performance in the Stroop colour word-task [ 21 ]. They examined the dACC and insula to determine whether the SN’s disrupted connectivity may be linked to the SN’s disruption of regulation, which would explain the impaired cognitive control seen in adolescents with IA. However, researchers did not find significant FC differences in the SN when compared to the controls [ 21 ]. These results provided evidence for the structural changes in the interconnectivity within SN in adolescents with IA.

Wang et al. (2017) investigated network interactions between the DMN, ECN, SN and reward pathway in IA subjects [ 24 ] (see Fig 5 ), and found 40% reduction of FC between the DMN and specific regions of the SN, such as the insula, in comparison to the controls (p = 0.008) [ 24 ]. The anterior insula and dACC are two areas that are impacted by this altered FC [ 24 ]. This finding supports the idea that IA has similar neurobiological abnormalities with other addictive illnesses, which is in line with a study that discovered disruptive changes in the SN and DMN’s interaction in cocaine addiction [ 38 ]. The insula has also been linked to the intensity of symptoms and has been implicated in the development of IA [ 39 ].

“+” indicates an increase in behaivour; “-”indicates a decrease in behaviour; solid arrows indicate a direct network interaction; and the dotted arrows indicates a reduction in network interaction. This diagram depicts network interactions juxtaposed with engaging in internet related behaviours. Through the neural interactions, the diagram illustrates how the networks inhibit or amplify internet usage and vice versa. Furthermore, it demonstrates how the SN mediates both the DMN and ECN.

https://doi.org/10.1371/journal.pmen.0000022.g005

(2) How is adolescent behaviour and development impacted by functional connectivity changes due to internet addiction?

The findings that IA individuals demonstrate an overall decrease in FC in the DMN is supported by numerous research [ 24 ]. Drug addict populations also exhibited similar decline in FC in the DMN [ 40 ]. The disruption of attentional orientation and self-referential processing for both substance and behavioural addiction was then hypothesised to be caused by DMN anomalies in FC [ 41 ].

In adolescents with IA, decline of FC in the parietal lobule affects visuospatial task-related behaviour [ 22 ], short-term memory [ 42 ], and the ability of controlling attention or restraining motor responses during response inhibition tests [ 42 ]. Cue-induced gaming cravings are influenced by the DMN [ 43 ]. A visual processing area called the praecuneus links gaming cues to internal information [ 22 ]. A meta-analysis found that the posterior cingulate cortex activity of individuals with IA during cue-reactivity tasks was connected with their gaming time [ 44 ], suggesting that excessive gaming may impair DMN function and that individuals with IA exert more cognitive effort to control it. Findings for the behavioural consequences of FC changes in the DMN illustrate its underlying role in regulating impulsivity, self-monitoring, and cognitive control.

Furthermore, Ding et al. (2013) reported an activation of components of the reward pathway, including areas like the nucleus accumbens, praecuneus, SMA, caudate, and thalamus, in connection to the DMN [ 22 ]. The increased FC of the limbic and reward networks have been confirmed to be a major biomarker for IA [ 45 , 46 ]. The increased reinforcement in these networks increases the strength of reward stimuli and makes it more difficult for other networks, namely the ECN, to down-regulate the increased attention [ 29 ] (See Fig 5 ).

Executive control network (ECN).

The numerous IA-affected components in the ECN have a role in a variety of behaviours that are connected to both response inhibition and emotional regulation [ 47 ]. For instance, brain regions like the striatum, which are linked to impulsivity and the reward system, are heavily involved in the act of playing online games [ 47 ]. Online game play activates the striatum, which suppresses the left DLPFC in ECN [ 48 ]. As a result, people with IA may find it difficult to control their want to play online games [ 48 ]. This system thus causes impulsive and protracted gaming conduct, lack of inhibitory control leading to the continued use of internet in an overt manner despite a variety of negative effects, personal distress, and signs of psychological dependence [ 33 ] (See Fig 5 ).

Wang et al. (2017) report that disruptions in cognitive control networks within the ECN are frequently linked to characteristics of substance addiction [ 24 ]. With samples that were addicted to heroin and cocaine, previous studies discovered abnormal FC in the ECN and the PFC [ 49 ]. Electronic gaming is known to promote striatal dopamine release, similar to drug addiction [ 50 ]. According to Drgonova and Walther (2016), it is hypothesised that dopamine could stimulate the reward system of the striatum in the brain, leading to a loss of impulse control and a failure of prefrontal lobe executive inhibitory control [ 51 ]. In the end, IA’s resemblance to drug use disorders may point to vital biomarkers or underlying mechanisms that explain how cognitive control and impulsive behaviour are related.

A task-related fMRI study found that the decrease in FC between the left DLPFC and dorsal striatum was congruent with an increase in impulsivity in adolescents with IA [ 26 ]. The lack of response inhibition from the ECN results in a loss of control over internet usage and a reduced capacity to display goal-directed behaviour [ 33 ]. Previous studies have linked the alteration of the ECN in IA with higher cue reactivity and impaired ability to self-regulate internet specific stimuli [ 52 ].

Salience network (SN)/ other networks.

Xing et al. (2014) investigated the significance of the SN regarding cognitive control in teenagers with IA [ 21 ]. The SN, which is composed of the ACC and insula, has been demonstrated to control dynamic changes in other networks to modify cognitive performance [ 21 ]. The ACC is engaged in conflict monitoring and cognitive control, according to previous neuroimaging research [ 53 ]. The insula is a region that integrates interoceptive states into conscious feelings [ 54 ]. The results from Xing et al. (2014) showed declines in the SN regarding its structural connectivity and fractional anisotropy, even though they did not observe any appreciable change in FC in the IA participants [ 21 ]. Due to the small sample size, the results may have indicated that FC methods are not sensitive enough to detect the significant functional changes [ 21 ]. However, task performance behaviours associated with impaired cognitive control in adolescents with IA were correlated with these findings [ 21 ]. Our comprehension of the SN’s broader function in IA can be enhanced by this relationship.

Research study supports the idea that different psychological issues are caused by the functional reorganisation of expansive brain networks, such that strong association between SN and DMN may provide neurological underpinnings at the system level for the uncontrollable character of internet-using behaviours [ 24 ]. In the study by Wang et al. (2017), the decreased interconnectivity between the SN and DMN, comprising regions such the DLPFC and the insula, suggests that adolescents with IA may struggle to effectively inhibit DMN activity during internally focused processing, leading to poorly managed desires or preoccupations to use the internet [ 24 ] (See Fig 5 ). Subsequently, this may cause a failure to inhibit DMN activity as well as a restriction of ECN functionality [ 55 ]. As a result, the adolescent experiences an increased salience and sensitivity towards internet addicting cues making it difficult to avoid these triggers [ 56 ].

The primary aim of this review was to present a summary of how internet addiction impacts on the functional connectivity of adolescent brain. Subsequently, the influence of IA on the adolescent brain was compartmentalised into three sections: alterations of FC at various brain regions, specific FC relationships, and behavioural/developmental changes. Overall, the specific effects of IA on the adolescent brain were not completely clear, given the variety of FC changes. However, there were overarching behavioural, network and developmental trends that were supported that provided insight on adolescent development.

The first hypothesis that was held about this question was that IA was widespread and would be regionally similar to substance-use and gambling addiction. After conducting a review of the information in the chosen articles, the hypothesis was predictably supported. The regions of the brain affected by IA are widespread and influence multiple networks, mainly DMN, ECN, SN and reward pathway. In the DMN, there was a complex mix of increases and decreases within the network. However, in the ECN, the alterations of FC were more unilaterally decreased, but the findings of SN and reward pathway were not quite clear. Overall, the FC changes within adolescents with IA are very much network specific and lay a solid foundation from which to understand the subsequent behaviour changes that arise from the disorder.

The second hypothesis placed emphasis on the importance of between network interactions and within network interactions in the continuation of IA and the development of its behavioural symptoms. The results from the findings involving the networks, DMN, SN, ECN and reward system, support this hypothesis (see Fig 5 ). Studies confirm the influence of all these neural networks on reward valuation, impulsivity, salience to stimuli, cue reactivity and other changes that alter behaviour towards the internet use. Many of these changes are connected to the inherent nature of the adolescent brain.

There are multiple explanations that underlie the vulnerability of the adolescent brain towards IA related urges. Several of them have to do with the inherent nature and underlying mechanisms of the adolescent brain. Children’s emotional, social, and cognitive capacities grow exponentially during childhood and adolescence [ 57 ]. Early teenagers go through a process called “social reorientation” that is characterised by heightened sensitivity to social cues and peer connections [ 58 ]. Adolescents’ improvements in their social skills coincide with changes in their brains’ anatomical and functional organisation [ 59 ]. Functional hubs exhibit growing connectivity strength [ 60 ], suggesting increased functional integration during development. During this time, the brain’s functional networks change from an anatomically dominant structure to a scattered architecture [ 60 ].

The adolescent brain is very responsive to synaptic reorganisation and experience cues [ 61 ]. As a result, one of the distinguishing traits of the maturation of adolescent brains is the variation in neural network trajectory [ 62 ]. Important weaknesses of the adolescent brain that may explain the neurobiological change brought on by external stimuli are illustrated by features like the functional gaps between networks and the inadequate segregation of networks [ 62 ].

The implications of these findings towards adolescent behaviour are significant. Although the exact changes and mechanisms are not fully clear, the observed changes in functional connectivity have the capacity of influencing several aspects of adolescent development. For example, functional connectivity has been utilised to investigate attachment styles in adolescents [ 63 ]. It was observed that adolescent attachment styles were negatively associated with caudate-prefrontal connectivity, but positively with the putamen-visual area connectivity [ 63 ]. Both named areas were also influenced by the onset of internet addiction, possibly providing a connection between the two. Another study associated neighbourhood/socioeconomic disadvantage with functional connectivity alterations in the DMN and dorsal attention network [ 64 ]. The study also found multivariate brain behaviour relationships between the altered/disadvantaged functional connectivity and mental health and cognition [ 64 ]. This conclusion supports the notion that the functional connectivity alterations observed in IA are associated with specific adolescent behaviours as well as the fact that functional connectivity can be utilised as a platform onto which to compare various neurologic conditions.

Limitations/strengths

There were several limitations that were related to the conduction of the review as well as the data extracted from the articles. Firstly, the study followed a systematic literature review design when analysing the fMRI studies. The data pulled from these imaging studies were namely qualitative and were subject to bias contrasting the quantitative nature of statistical analysis. Components of the study, such as sample sizes, effect sizes, and demographics were not weighted or controlled. The second limitation brought up by a similar review was the lack of a universal consensus of terminology given IA [ 47 ]. Globally, authors writing about this topic use an array of terminology including online gaming addiction, internet addiction, internet gaming disorder, and problematic internet use. Often, authors use multiple terms interchangeably which makes it difficult to depict the subtle similarities and differences between the terms.

Reviewing the explicit limitations in each of the included studies, two major limitations were brought up in many of the articles. One was relating to the cross-sectional nature of the included studies. Due to the inherent qualities of a cross-sectional study, the studies did not provide clear evidence that IA played a causal role towards the development of the adolescent brain. While several biopsychosocial factors mediate these interactions, task-based measures that combine executive functions with imaging results reinforce the assumed connection between the two that is utilised by the papers studying IA. Another limitation regarded the small sample size of the included studies, which averaged to around 20 participants. The small sample size can influence the generalisation of the results as well as the effectiveness of statistical analyses. Ultimately, both included study specific limitations illustrate the need for future studies to clarify the causal relationship between the alterations of FC and the development of IA.

Another vital limitation was the limited number of studies applying imaging techniques for investigations on IA in adolescents were a uniformly Far East collection of studies. The reason for this was because the studies included in this review were the only fMRI studies that were found that adhered to the strict adolescent age restriction. The adolescent age range given by the WHO (10–19 years old) [ 65 ] was strictly followed. It is important to note that a multitude of studies found in the initial search utilised an older adolescent demographic that was slightly higher than the WHO age range and had a mean age that was outside of the limitations. As a result, the results of this review are biased and based on the 12 studies that met the inclusion and exclusion criteria.

Regarding the global nature of the research, although the journals that the studies were published in were all established western journals, the collection of studies were found to all originate from Asian countries, namely China and Korea. Subsequently, it pulls into question if the results and measures from these studies are generalisable towards a western population. As stated previously, Asian countries have a higher prevalence of IA, which may be the reasoning to why the majority of studies are from there [ 8 ]. However, in an additional search including other age groups, it was found that a high majority of all FC studies on IA were done in Asian countries. Interestingly, western papers studying fMRI FC were primarily focused on gambling and substance-use addiction disorders. The western papers on IA were less focused on fMRI FC but more on other components of IA such as sleep, game-genre, and other non-imaging related factors. This demonstrated an overall lack of western fMRI studies on IA. It is important to note that both western and eastern fMRI studies on IA presented an overall lack on children and adolescents in general.

Despite the several limitations, this review provided a clear reflection on the state of the data. The strengths of the review include the strict inclusion/exclusion criteria that filtered through studies and only included ones that contained a purely adolescent sample. As a result, the information presented in this review was specific to the review’s aims. Given the sparse nature of adolescent specific fMRI studies on the FC changes in IA, this review successfully provided a much-needed niche representation of adolescent specific results. Furthermore, the review provided a thorough functional explanation of the DMN, ECN, SN and reward pathway making it accessible to readers new to the topic.

Future directions and implications

Through the search process of the review, there were more imaging studies focused on older adolescence and adulthood. Furthermore, finding a review that covered a strictly adolescent population, focused on FC changes, and was specifically depicting IA, was proven difficult. Many related reviews, such as Tereshchenko and Kasparov (2019), looked at risk factors related to the biopsychosocial model, but did not tackle specific alterations in specific structural or functional changes in the brain [ 66 ]. Weinstein (2017) found similar structural and functional results as well as the role IA has in altering response inhibition and reward valuation in adolescents with IA [ 47 ]. Overall, the accumulated findings only paint an emerging pattern which aligns with similar substance-use and gambling disorders. Future studies require more specificity in depicting the interactions between neural networks, as well as more literature on adolescent and comorbid populations. One future field of interest is the incorporation of more task-based fMRI data. Advances in resting-state fMRI methods have yet to be reflected or confirmed in task-based fMRI methods [ 62 ]. Due to the fact that network connectivity is shaped by different tasks, it is critical to confirm that the findings of the resting state fMRI studies also apply to the task based ones [ 62 ]. Subsequently, work in this area will confirm if intrinsic connectivity networks function in resting state will function similarly during goal directed behaviour [ 62 ]. An elevated focus on adolescent populations as well as task-based fMRI methodology will help uncover to what extent adolescent network connectivity maturation facilitates behavioural and cognitive development [ 62 ].

A treatment implication is the potential usage of bupropion for the treatment of IA. Bupropion has been previously used to treat patients with gambling disorder and has been effective in decreasing overall gambling behaviour as well as money spent while gambling [ 67 ]. Bae et al. (2018) found a decrease in clinical symptoms of IA in line with a 12-week bupropion treatment [ 31 ]. The study found that bupropion altered the FC of both the DMN and ECN which in turn decreased impulsivity and attentional deficits for the individuals with IA [ 31 ]. Interventions like bupropion illustrate the importance of understanding the fundamental mechanisms that underlie disorders like IA.

The goal for this review was to summarise the current literature on functional connectivity changes in adolescents with internet addiction. The findings answered the primary research questions that were directed at FC alterations within several networks of the adolescent brain and how that influenced their behaviour and development. Overall, the research demonstrated several wide-ranging effects that influenced the DMN, SN, ECN, and reward centres. Additionally, the findings gave ground to important details such as the maturation of the adolescent brain, the high prevalence of Asian originated studies, and the importance of task-based studies in this field. The process of making this review allowed for a thorough understanding IA and adolescent brain interactions.

Given the influx of technology and media in the lives and education of children and adolescents, an increase in prevalence and focus on internet related behavioural changes is imperative towards future children/adolescent mental health. Events such as COVID-19 act to expose the consequences of extended internet usage on the development and lifestyle of specifically young people. While it is important for parents and older generations to be wary of these changes, it is important for them to develop a base understanding of the issue and not dismiss it as an all-bad or all-good scenario. Future research on IA will aim to better understand the causal relationship between IA and psychological symptoms that coincide with it. The current literature regarding functional connectivity changes in adolescents is limited and requires future studies to test with larger sample sizes, comorbid populations, and populations outside Far East Asia.

This review aimed to demonstrate the inner workings of how IA alters the connection between the primary behavioural networks in the adolescent brain. Predictably, the present answers merely paint an unfinished picture that does not necessarily depict internet usage as overwhelmingly positive or negative. Alternatively, the research points towards emerging patterns that can direct individuals on the consequences of certain variables or risk factors. A clearer depiction of the mechanisms of IA would allow physicians to screen and treat the onset of IA more effectively. Clinically, this could be in the form of more streamlined and accurate sessions of CBT or family therapy, targeting key symptoms of IA. Alternatively clinicians could potentially prescribe treatment such as bupropion to target FC in certain regions of the brain. Furthermore, parental education on IA is another possible avenue of prevention from a public health standpoint. Parents who are aware of the early signs and onset of IA will more effectively handle screen time, impulsivity, and minimize the risk factors surrounding IA.

Additionally, an increased attention towards internet related fMRI research is needed in the West, as mentioned previously. Despite cultural differences, Western countries may hold similarities to the eastern countries with a high prevalence of IA, like China and Korea, regarding the implications of the internet and IA. The increasing influence of the internet on the world may contribute to an overall increase in the global prevalence of IA. Nonetheless, the high saturation of eastern studies in this field should be replicated with a Western sample to determine if the same FC alterations occur. A growing interest in internet related research and education within the West will hopefully lead to the knowledge of healthier internet habits and coping strategies among parents with children and adolescents. Furthermore, IA research has the potential to become a crucial proxy for which to study adolescent brain maturation and development.

Supporting information

S1 checklist. prisma checklist..

https://doi.org/10.1371/journal.pmen.0000022.s001

S1 Appendix. Search strategies with all the terms.

https://doi.org/10.1371/journal.pmen.0000022.s002

S1 Data. Article screening records with details of categorized content.

https://doi.org/10.1371/journal.pmen.0000022.s003

Acknowledgments

The authors thank https://www.stockio.com/free-clipart/brain-01 (with attribution to Stockio.com); and https://www.rawpixel.com/image/6442258/png-sticker-vintage for the free images used to create Figs 2 – 4 .

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Mental Health

Poor Mental Health Impacts Adolescent Well-being

Key Takeaways

• The number of adolescents reporting poor mental health is increasing.
• Building strong bonds and connecting to youth can protect their mental health.
• School staff and families can create protective relationships with students and help them grow into healthy adulthood.

Promoting Mental Health and Well-Being in Schools: An Action Guide for School Administrators and Leaders

Learn about school-based strategies and approaches to support student mental health.

Adolescent Mental Health Continues to Worsen

CDC’s Youth Risk Behavior Surveillance Data Summary & Trends Report: 2011-2021 [PDF – 10 MB]  highlights concerning trends about the mental health of U.S. high school students.

• In 2021, more than 4 in 10 (42%) students felt persistently sad or hopeless and nearly one-third (29%) experienced poor mental health.
• In 2021, more than 1 in 5 (22%) students seriously considered attempting suicide and 1 in 10 (10%) attempted suicide.

These data bring into focus the level of distress many students are experiencing.

Some groups are more affected than others.

These feelings of distress were found to be more common among LGBQ+ students, female students, and students across racial and ethnic groups.

• Nearly half (45%) of LGBQ+ students in 2021 seriously considered attempting suicide—far more than heterosexual students.
• Black students were more likely to attempt suicide than students of other races and ethnicities.
• Youth Mental Health: The Numbers

Why Is This a Big Deal?

Poor mental health in adolescence is more than feeling blue. It can impact many areas of a teen’s life.

Youth with poor mental health may struggle with school and grades , decision making, and their health.

Mental health problems in youth often go hand-in-hand with other health and behavioral risks like increased risk of drug use , experiencing violence , and higher risk sexual behaviors  that can lead to HIV, STDs, and unintended pregnancy.

Because many health behaviors and habits are established in adolescence that will carry over into adult years, it is very important to help youth develop good mental health.

Need Support Now?

• Call or text 988
• Chat with Lifeline

The Good News

The good news is that teens are resilient, and we know what works to support their mental health:  feeling  connected  to school and family .

• Fortunately, the same prevention strategies that promote mental health—like helping students feel connected to school/family—help prevent a range of negative experiences, like drug use and violence.
• Building strong bonds and relationships with adults and friends at school, at home and in the community provides youth with a sense of connectedness.
• This feeling of connectedness is important and can protect adolescents from poor mental health, and other risks like drug use and violence.
• Youth need to know someone cares about them. Connections can be made virtually or in person.

There is a Role for Everyone in Supporting Teen Mental Health

As we’ve learned nationally during the COVID-19 pandemic , schools are critical in our communities to supporting children and families. While the expectation is that schools provide education, they also provide opportunities for youth to engage in physical activity and academic, social, mental health, and physical health services, all of which can relieve stress and help protect against negative outcomes.

However, the pandemic disrupted many school-based services, increasing the burden on parents, increasing stress on families, and potentially affecting long-term health outcomes for parents and children alike, especially among families already at risk for negative health outcomes from social and environmental factors.

Support is needed to mitigate these negative outcomes and lessen educational and health disparities.

Critical supports and services need to be comprehensive and community wide and should include:

What schools can do:.

• Implement strategies and approaches that can help prevent mental health problems and promote positive behavioral and mental health of students.
• Help students cope with emergencies and their aftermath.
• Linking students to mental health services.
• Integrating social emotional learning.
• Training staff.
• Supporting staff mental health.
• Reviewing discipline policies to ensure equity.
• Building safe and supportive environments.

What parents and families can do:

• Communicate openly and honestly, including about their values.
• Supervise their adolescent to facilitate healthy decision-making.
• Spend time with their adolescent enjoying shared activities.
• Become engaged in school activities and help with homework.
• Volunteer at their adolescent’s school.
• Communicate regularly with teachers and administrators.

What healthcare providers can do:

• Ask adolescents about family relationships and school experiences as a part of routine health screenings.
• Encourage positive parenting practices .
• Engage parents in discussions about how to connect with their adolescents, communicate effectively, and monitor activities and health behaviors.
• Educate parents and youth about adolescent development and health risks.

More Information

Parents and families may find the following resources helpful to support the mental and emotional well-being of their adolescents:

• CDC Children’s Mental Health
• CDC Mental Health
• School Connectedness
• Social Connection
• Teen Mental Health
• Resources for Coping After Emergencies
• School-Based Physical Activity Improves the Social and Emotional Climate for Learning
• School Nutrition and the Social and Emotional Climate and Learning

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• Open access
• Published: 27 February 2024

Adolescent mental health and academic performance: determining evidence-based associations and informing approaches to support in educational settings

• Xzania Lee 1 ,
• Anya Griffin 1 , 2 ,
• Maya I. Ragavan 3 &
• Mona Patel 1 , 2  

Pediatric Research volume  95 ,  pages 1395–1397 ( 2024 ) Cite this article

1823 Accesses

Metrics details

In 2021, the American Academy of Pediatrics, the American Academy of Child and Adolescent Psychiatry and the Children’s Hospital Association (CHA) declared a “National State of Emergency in Children’s Mental Health.” 1 The statement identified how the pandemic exacerbated the already worsening mental health problem among US youth due to the compounding challenges faced by youth and acknowledged the significant impact of this mental health crisis on youth. This declaration made a call for schools, policymakers, and advocates for children and adolescents to prioritize and focus on pediatric mental health.

Adolescent mental health and academic performance are intricately linked aspects of development, each influencing and being influenced by the other. The recognition of this bidirectional association has sparked considerable interest within the research community, prompting an investigation into the nuanced dynamics between mental health and educational outcomes during the formative adolescent years. Numerous studies have explored the connection and influence between mental health and academic performance, and further acknowledge that the multifaceted interconnectedness of mental health and academic performance require a holistic view. 2 , 3 , 4 Researchers have identified that higher academic aspirations are associated with better mental health outcomes and that socioemotional well-being is needed for academic thriving. 5 , 6 Furthermore, Yu and associates described how interpersonal relationships are positively correlated with academic performance, especially student-peer relationships, which had more influence than the parent-student or teacher-student relationship on academic achievement. 7 , 8 Finally, impacts of social determinants of health have been shown to exert profound influences on both mental health and academic outcomes further emphasizing the need to consider the broader ecological context in which adolescents develop and the importance of considering a socioecological model, suggesting that factors such as family, school, and community environments play pivotal roles in shaping both mental health and academic outcomes. 6 , 9

In this article by Monzonis-Carda and associates, the authors explore the bidirectional longitudinal association between the dual-factor model of mental health and academic performance in adolescents. The dual-factor model of mental health, in contrast to traditional models of mental health which focus on psychopathological symptoms, integrates mental health wellbeing and psychopathology into a mental health continuum. 10 The authors hypothesize that a bidirectional association between academic performance and adolescent mental health would be present in their sample of 266 secondary school students from Spain. They assessed mental health through the Spanish language Behavior Assessment System for Children and Adolescents (BASC-S3) and examined grade point average, and academic performance based on the Test of Educational Abilities. They then employed a cross-lagged modeling approach to analyze the bidirectional association over 2 years. The key findings suggested that higher academic performance at baseline was associated with better mental health over time, but better mental health was not associated with academic performance. Therefore, the association was not bidirectional as expected. Based on these findings, the authors posit academic performance may be a predictor of adolescents’ mental health status; and conversely, mental health may not be a predictor of adolescents’ academic performance. They offered school-based recommendations for the promotion of good mental health practices for students with low academic performance and supported future policy and health and educational professionals to promote adolescent mental health wellbeing. Overall, the article underscores the importance of considering academic performance as a target for interventions to promote adolescents’ mental health. It suggests that focusing on reducing school pressure and establishing personalized academic goals could contribute to better psychological well-being.

While the article provides some important insights into the association between mental health and academic performance in adolescents, some limitations were noted. While there is some limited adjustment for socioeconomic status, the article lacks a comprehensive exploration of social determinants of health and impacts of adverse childhood events (ACES), such as cultural background, and other important social and familial dynamics. These factors play a pivotal role in shaping an adolescent’s mental health and academic performance and may result in an oversimplified understanding of the complex interplay between mental health and academic outcomes. The study further focuses on academic grades and “abilities” as indicators of academic performance. Academic success is multifaceted and includes factors like motivation, engagement, and teacher-student relationships, and a more nuanced exploration of these components could provide a richer understanding of the relationship between mental health and academic outcomes. The study authors reviewed limitations that require further investigation including the use of BASC-S3 as the primary self-reported measure of adolescent mental health. Depending on individual developmental level of insight and situational context, adolescents are often unreliable and inaccurate reporters of their functioning, and adolescents in clinical populations tend to overreport symptoms and provide inaccurate information regarding their functioning on the BASC-S3. 11 , 12 Incorporating objective measures or multi-method assessments and the inclusion of multi-rater methods (i.e., teachers, caregivers, etc.) may provide a more detailed picture of the student’s true socioemotional functioning through the provision of differing perspectives of each student’s functioning. 13 The study’s authors also acknowledge a relatively small sample size, homogeneity of the study population, and short study length to determine longitudinal outcomes may further limit generalizability to other populations. Lack of testing for sex assigned at birth and self-identified gender effects, and not integrating broader social determinant impact upon adolescent mental health may result in misguided or ineffective approaches to promoting mental health in adolescents. Further, previous research and psychological assessment literature have indicated the significant impact of social determinants of health and ACES on youth academic achievement and behavioral health outcomes. Students with elevated social risk, including ACES, are often at increased risk for mental health and academic achievement deterioration. 14 This supports the need for school leaders and policymakers to continue to focus efforts on maximizing the recognition of these factors for youth and promote the implementation of programs to address roots of social risk and integration of socioemotional mental health supports in academic institutions. 15 Due to the interconnectedness of mental wellness and academic success, addressing aspects of mental health functioning within the school setting will equip students with the essential skills to navigate challenges, manage stress, and build resilience. By bolstering emotional, behavioral, and social skills, students are primed to engage in learning, establish positive relationships with peers and teachers, and cope with the pressures of academic stress and daily life hassles. 16 A structured educational tier one (i.e., general education curriculum) mental health intervention will assist students with stress reduction, and behavior management, improve executive functioning skills, and establish a scholastic environment conducive to effective knowledge consumption and academic performance. 17 Incorporating evidence-based practices to support student emotional wellness holistically nurtures the development of students and provides a foundation for lifelong well-being and academic excellence. While this article contributes to the understanding of the association between mental health and academic performance, it also highlights the need for future exploration of factors that influence the causality between adolescent mental health and academic performance and further informs the recommendation to have mental health interventions and social-emotional learning curriculums in educational settings.

The 2021 joint declaration of the “National State of Emergency in Children’s Mental Health” catalyzed federal, state, and local awareness of evolving needs in pediatric mental health in the United States of America. While there has been increasing bipartisan support and focus for mental health funding at all levels of government, appropriate allocation of such funding to support identifying factors that impact pediatric mental health and using a data-driven approach to effective programming is critical. An example of more recent federally supported funding programs for child mental health includes the Health Resources & Services Administration (HRSA) funded Pediatric Mental Health Care Access (PMHCA) program, which has seen increased funding from 2018 through 2022, with an additional 80 million dollars added by the Bipartisan Safer Communities Act. ( https://mchb.hrsa.gov/programs-impact/programs/pediatric-mental-health-care-access ) Currently, under-resourced and under-reimbursed health systems fraught with post-pandemic short staffing and pre-pandemic existing behavioral health access challenges pose continued roadblocks to access. Pediatric policy recommendations to aid with improving meaningful pediatric mental health access include:

Increased funding and support for access to meaningful mental health resources in the community and schools

Integrated behavioral health delivery models within primary care and specialty care will be critical in enhancing access to care.

Increase the behavioral health workforce, training programs for primary care pediatricians and pediatric psychologists are needed, as the number of child psychiatrists and pediatric psychologists is currently not sufficient to meet demand.

Innovative and integrative team-based models including non-traditional licensed and non-licensed behavioral health support teams, including community health work may allow further access and a more impactful peer-to-peer support structure.

Behavioral health reimbursement shifts may ultimately be required to build infrastructure to address the current critical socio-emotional needs of our youth. Ultimately, research informing a more comprehensive perspective, including health-related social needs and ACES will be essential for advancing the field with evidence-based mental health interventions for youth.

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Xzania Lee, Anya Griffin & Mona Patel

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Lee, X., Griffin, A., Ragavan, M.I. et al. Adolescent mental health and academic performance: determining evidence-based associations and informing approaches to support in educational settings. Pediatr Res 95 , 1395–1397 (2024). https://doi.org/10.1038/s41390-024-03098-3

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Understanding mental health in the research environment

Short abstract.

This study aimed to establish what is known about the mental health of researchers based on the existing literature. The literature identified focuses mainly on stress in the academic workforce and contributory factors in the academic workplace.

This study aimed to establish what is known about the mental health of researchers based on the existing literature. There is limited published evidence on the prevalence of specific mental health conditions among researchers. The majority of the identified literature on prevalence relates to work-related stress among academic staff and postgraduate students in university settings.

Survey data indicate that the majority of university staff find their job stressful. Levels of burnout appear higher among university staff than in general working populations and are comparable to “high-risk” groups such as healthcare workers. The proportions of both university staff and postgraduate students with a risk of having or developing a mental health problem, based on self-reported evidence, were generally higher than for other working populations. Large proportions (>40 per cent) of postgraduate students report symptoms of depression, emotion or stress-related problems, or high levels of stress.

Factors including increased job autonomy, involvement in decision making and supportive management were linked to greater job satisfaction among academics, as was the amount of time spent on research. Opportunities for professional development were also associated with reduced stress. UK higher education (HE) and research staff report worse wellbeing, as compared to staff in other sectors, in most aspects of work that can affect workers' stress levels.

The evidence around the effectiveness of interventions to support the mental health of researchers specifically is thin. Few interventions are described in the literature and even fewer of those have been evaluated.

The Royal Society and Wellcome Trust are interested in better understanding the mental health needs of researchers, and what interventions could be used to support them. This reflects the recent focus on mental health among undergraduate students in the UK, and the concern that others in the academic and wider research environment may have mental health needs that have not been as well explored and considered. This study aims to establish what is currently known about the mental health of researchers based on the existing literature.

Over 6 million working-age people in England have a mental health condition at a given time. The most common diagnosable difficulties among working age adults are anxiety and depression, each of which includes a number of different conditions. Less common but still widespread mental health diagnoses include personality disorders and psychoses such as bipolar disorder and schizophrenia. Many people will have more than one diagnosis at a time, or receive different diagnoses over time.

The causes and triggers of poor mental health are complex and not fully understood. There is evidence that the vast majority of people who experience poor mental health in adulthood first experienced difficulties as children, often from a young age. Risk factors for poor mental health include having a parent with mental health difficulties, growing up in prolonged poverty and housing insecurity, experiences of abuse, neglect and bullying, and traumatic experiences during childhood. Some groups of people have a heightened risk of poor mental health, including some black and ethnic minority communities, people with long-term physical conditions, lesbian, gay, bisexual and transgender people, and people with disabilities.

There is mixed evidence about the extent to which a person's experiences of work contribute to their having a mental health difficulty. Survey evidence suggests that workplace factors such as bullying, insecurity and a lack of control are major causes of mental ill health among staff. On the other hand, there is also evidence that work helps many people to recover from an episode of poor mental health, and there is clear evidence that unemployment is a major risk factor for mental and physical ill health.

Mental ill health and work-related stress are key issues for the labour market as they affect productivity through absenteeism and presenteeism, and are associated with high economic costs for individuals, employers and the economy at large. It has been estimated that poor mental health costs employers in the UK £26 billion nationwide each year, equivalent to £1,035 for every employee in the workforce ( Centre for Mental Health, 2007 ).

Little is known about how mental health needs vary across working environments, or about how to tailor interventions to address different working populations.

The aim of this study was to assess what is known about mental health in research environments through a literature review, and it focused on the UK and comparable research systems. A better understanding of researchers' mental health needs will enable the design of more effective interventions to address them, while a better understanding of evidence gaps can also help guide future research efforts in this area. The following research questions guided the study:

• How are “mental health” and “wellbeing” understood in the context of research environments?
• What is currently known about researchers' mental health and wellbeing, and does it differ from that of other populations?
• What interventions are used to support researchers, and what evidence is there of their effectiveness?
• What are the strengths and limitations of the evidence base in this area?

How Are “Mental Health” and “Wellbeing” Understood in Research Environments?

Overall, the existing literature offers little insight into what sets the research environment apart from other workplaces, or into how mental health, stress, and wellbeing are defined in these contexts. Rather, the majority of the literature identified focuses on describing the levels of stress amongst the academic workforce and, in particular, identifying contributory factors within the workplace. There is little available evidence based on objective clinical assessment about the prevalence of clinically defined mental health conditions and their treatment in this context. The focus on wellbeing raises the issue that although the presence of common mental health conditions does correlate with some of the wellbeing scales used commonly in the literature, more serious (e.g. psychotic) mental illnesses are not necessarily aligned with measurement of wellbeing.

The literature is also almost exclusively focused on universities, with many studies covering all university staff, which will include both researchers and non-research staff. Some studies focused more specifically on researchers, and a more limited group within that looked at particular groups of researchers—most commonly PhD students, reflecting the wider focus on (typically undergraduate) students in the literature around this topic. The majority of the existing research is based on survey data, which is subject to sampling biases, relies on self-reporting, and was not triangulated with other objective indicators, such as absence data.

What Is Currently Known About Researchers' Mental Health and Wellbeing, and How It Differs from Other Populations?

Evidence on the prevalence of work-related stress and mental health problems.

Despite widely reported anecdotal evidence and press coverage of a “mental health crisis” in academia, there is limited published evidence regarding the prevalence of specific mental health conditions among researchers. The majority of the literature on prevalence identified through this review relates to the experience of work-related stress (and arguably the risk of developing a mental health condition as a result of exposure to identified stressors) among academic staff and postgraduate students in university settings.

• Survey data indicate that the majority of university staff find their job stressful. Levels of burnout appear higher among university staff than in general working populations and are comparable to “high-risk” groups such as healthcare workers.
• The proportions of both university staff and postgraduate students with a risk of having or developing a mental health problem, based on self-reported evidence, were generally higher than for other working populations.
• Large proportions (>40 per cent) of postgraduate students report symptoms of depression, emotion or stress-related problems, or high levels of stress.

UK national statistics indicate that only 6.2 per cent of staff disclosed a mental health condition to their university, though academics have been found to be among the occupational groups with the highest levels of common mental disorders with prevalence around 37 per cent. It should be noted, however, that prevalence may generally be over-reported in surveys of occupational groups.

Personal Factors That Contribute to Mental Health Outcomes in the Research Workplace

Gender was the key personal factor that emerged as a determinant for mental health (or its reporting), with women reporting more exposure to stress than men, as well as greater challenges around work-life balance. There was also evidence that personality and perceived competence affect mental health as self-critical personalities are more susceptible to stress, though it is also possible that they are more aware of it or more willing to report it. However, it was unclear whether stress was a result of working conditions in the research environment, or whether research settings attracted particular types of individuals. The results on whether age affects mental health were inconclusive, partly as age is often difficult to disentangle from discussions about rank and seniority. Other factors such as disability, sexuality and minority status were mentioned in a small number of articles in the sample, and these articles indicated that these personal factors generally increase stress.

Environmental Factors Commonly Considered in Surveys of Mental Health and Wellbeing in Workplaces

Based on the Health and Safety Executive's framework, and evidence from the wider literature, we identify six key aspects of work that can affect workers' stress levels: work demands, job control, change management, work relationships, support provided by managers and colleagues, and clarity about one's role.

• These aspects of the work environment can be sources of stress or they can help counteract it.
• Findings from studies of university staff and researchers were consistent with the wider understanding of factors that contribute to stress in workplaces.
• Factors including increased job autonomy, involvement in decision making and supportive management were linked to greater job satisfaction among academics, as was the amount of time spent on research. Opportunities for professional development were also associated with reduced stress.

UK higher education (HE) and research staff report worse wellbeing in most of the six aspects, as compared to staff in other sectors.

• In large-scale surveys, UK higher education staff have reported worse wellbeing than staff in other types of employment (including education, and health and social work) in the areas of work demands, change management, support provided by managers and clarity about one's role.
• The only area where higher education staff have reported higher wellbeing in large-scale surveys is in job control, though even here results are mixed across studies. Wide variability was seen among respondents in relation to the level of support provided by managers and colleagues.
• Job insecurity (real and perceived) appears to be an important issue for those working in the research environment, and particularly for early-career researchers, who are often employed on successive short-term contracts.

PhD students face similar challenges to other researchers and higher education staff.

• The main factors associated with development of depression and other common mental health problems in PhD students are high levels of work demands and work-life conflict, low job control, poor support from the supervisor and exclusion from decision making.
• Believing that PhD work is valuable for one's future career helps reduce stress, as does confidence in one's own research abilities.

Some studies suggested that changes to the UK higher education system had brought increased job stress.

• These studies discussed changes that had occurred in the UK higher education system from the 1990s onwards, and had resulted in increased emphasis on accountability, efficiency and performance management. Study authors suggested that these changes could have brought about increases in job stress for staff working in this system.
• However, data explicitly linking the changes to an increase in stress are limited, partly due to a lack of comparable data from before the 1990s.

Staff who can devote a large proportion of their working time to research have better wellbeing.

• Studies found that spending a larger percentage of one's time on research was associated with reduced stress, and that research-only staff reported lower levels of work-life conflict and had better wellbeing than other higher education institution (HEI) staff. However, this may be to some extent confounded by other characteristics of such researchers (e.g. they may be more senior).

Research on emotionally challenging topics can put staff wellbeing at risk.

• Studies showed that staff involved in research on sensitive topics, such as trauma or abuse, may be emotionally affected by the material they encounter in their work and should receive greater support to mitigate the negative impacts of this work.

Outcomes Related to Poor Mental Health and Wellbeing

In addition to considering the extent to which individuals in research environments suffer from mental health issues, it is important for employers and institutions to recognise that these issues have further implications:

• Job stress and poor workplace wellbeing can contribute to reduced productivity—both through absence and, more importantly, through presenteeism, where researchers attend work and are less productive.
• They can also lead to lower levels of commitment to their research and to institutions—which can be seen in high levels of turnover and through negative attitudes in the workplace.
• Effects on job satisfaction are less clear because of the satisfaction researchers gain from intrinsic factors such as the intellectual stimulation of their work. Several studies note that high levels of job-related stress can coexist with high levels of job satisfaction.
• Effects can also spill over into personal and family life.

The overall effects of these negative outcomes on the sector have not been fully quantified, but estimates drawing on broader experience suggest that the costs could be high. An estimate from Shutler-Jones et al (2008) which has several caveats and assumptions, suggests that the costs to the UK HE sector could be more than £500 million per year (c. 5 per cent of the sector's total annual income). Costs to the economy and the country more widely could also be significant due to the lost potential for scientific advances and due to impacts on the availability of research talent if PhD students fail to complete their studies or choose to leave research subsequently.

What Interventions Are Used to Support Researchers, and What Evidence Is There of Their Effectiveness?

Though poor mental health at work is often related to difficulties that are not caused by work (e.g. childhood adversity, family life and other stressors), support in the workplace can offer benefits. However, the evidence around the effectiveness of interventions to support the mental health of researchers specifically is thin. Few interventions are described in the literature and even fewer of those have been evaluated. Where evaluations have been conducted, they are often of limited utility, either because of the evaluation design or the length of follow-up.

Interventions typically focus on stress and wellbeing rather than clinical mental health conditions, reflecting the wider focus in the literature as described above. In addition, the majority of interventions identified aim to support researchers to deal with workplace stress, but they may not be effective in addressing the root causes of that stress or stresses relating to life outside work. The interventions identified can be broadly classified into four groups: policy changes, communication activities, training, and health-promotion activities.

Focusing specifically on the UK, a range of interventions were piloted and evaluated (to a limited extent) as part of a wellbeing initiative by the Higher Education Funding Council for England (HEFCE) around 2009–2011. These offer scope for further investigation and potentially evaluation now that more time has elapsed. Additionally, the project, though completed in 2011, has spawned a network that is now managed by the Universities and Colleges Employers Association (UCEA), which may offer a route to identify further ongoing initiatives and potentially a space to pursue and evaluate efforts to address these issues in the HE sector.

What Are the Strengths and Limitations of the Evidence Base in This Area?

The existing evidence base is limited, meaning it is not possible to draw robust conclusions about the mental health status and needs of researchers, and how researchers may differ from other populations in this regard. More work is needed to understand both the mental health needs of researchers and how they can be addressed. Particular gaps include the effectiveness of interventions, prevalence of specific mental health needs (rather than stress) among researchers, and any evidence about researchers outside the academic setting. There are also limitations to the quality and design of many of the studies conducted, such as lack of long-term follow-up and absence of control groups.

Based on the evidence gaps identified and the information available, we suggest the following avenues for further research on this topic:

• Study the prevalence of mental health conditions amongst postdoctoral researchers: Further work on prevalence could use a targeted approach building on the recent work by Levecque et al. (2017) , who used a survey to assess the presence of psychological distress and potential psychiatric disorders in a sample of PhD students and compared the results to those of three other sample populations, and Eisenberg et al. (2007) , who surveyed a sample of undergraduate and postgraduate university students to assess prevalence of depressive and anxiety disorders and took steps to address the issue of non-response bias. In particular, we suggest a similar study focusing on postdoctoral researchers, a group that is particularly poorly addressed in the existing literature.
• Map mental health policies and procedures at UK HEIs: The current standard of mental health policies and procedures in UK research institutions is not well understood. We suggest that a mapping of the current policies in place across institutions could be valuable, and could build on standards such as those set out in the Mindful Employer Charter ( Mindful Employer, 2017 ).
• Evaluate the interventions introduced through the HEFCE wellbeing and engagement initiative: The wellbeing initiative established by the HEFCE and subsequently maintained as a network by UCEA offers a range of interventions for evaluation. In the project reporting in 2011, many of the institutions noted that it was too soon to tell whether their interventions had been effective. Though these initiatives generally focus on wellbeing rather than clinical mental health conditions, there is scope to explore with the relevant institutions whether those interventions have developed over the years, and whether data are now available (or could be collected) to provide more useful evaluation of the interventions introduced.
• Investigate and develop the HSE management standards as a framework for workplace mental health management in research environments: As well as providing a framework for workplace stress used in several important surveys, the Health and Safety Executive (HSE) have also set out management standards that describe an approach to identifying sources of workplace stress and addressing them at an organisational level. It could be useful to work through that approach with a university or a research organisation to identify the mechanisms at play in those environments. Doing so could establish the relevance of the approach in this context, and potentially provide a model that could be used more widely in the sector.
• Conduct more and higher-quality evaluations of mental health interventions and publish their results: Broadly, better-quality evaluations are needed to identify what works in this area. There is a need for high-quality studies to test the effectiveness of interventions.

The research described in this article was prepared for the Royal Society and the Wellcome Trust and conducted by RAND Europe.

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