research hypothesis on mental health

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• Published: 14 September 2023

Children and youth’s perceptions of mental health—a scoping review of qualitative studies

• Linda Beckman 1 , 2 ,
• Sven Hassler 1 &
• Lisa Hellström 3  

BMC Psychiatry volume  23 , Article number:  669 ( 2023 ) Cite this article

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Recent research indicates that understanding how children and youth perceive mental health, how it is manifests, and where the line between mental health issues and everyday challenges should be drawn, is complex and varied. Consequently, it is important to investigate how children and youth perceive and communicate about mental health. With this in mind, our goal is to synthesize the literature on how children and youth (ages 10—25) perceive and conceptualize mental health.

We conducted a preliminary search to identify the keywords, employing a search strategy across electronic databases including Medline, Scopus, CINAHL, PsychInfo, Sociological abstracts and Google Scholar. The search encompassed the period from September 20, 2021, to September 30, 2021. This effort yielded 11 eligible studies. Our scoping review was conducted in accordance with the PRISMA-ScR Checklist.

As various aspects of uncertainty in understanding of mental health have emerged, the results indicate the importance of establishing a shared language concerning mental health. This is essential for clarifying the distinctions between everyday challenges and issues that require treatment.

We require a language that can direct children, parents, school personnel and professionals toward appropriate support and aid in formulating health interventions. Additionally, it holds significance to promote an understanding of the positive aspects of mental health. This emphasis should extend to the competence development of school personnel, enabling them to integrate insights about mental well-being into routine interactions with young individuals. This approach could empower children and youth to acquire the understanding that mental health is not a static condition but rather something that can be enhanced or, at the very least, maintained.

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Introduction

In Western society, the prevalence of mental health issues, such as depression and anxiety [ 1 ], as well as recurring psychosomatic health complaints [ 2 ], has increased from the 1980s and 2000s. However, whether these changes in adolescent mental health are actual trends or influenced by alterations in how adolescents perceive, talk about, and report their mental well-being remains ambiguous [ 1 ]. Despite an increase in self-reported mental health problems, levels of mental well-being have remained stable, and severe psychiatric diagnoses have not significantly risen [ 3 , 4 ]. Recent research indicates that understanding how children and youth grasp mental health, its manifestations, and the demarcation between mental health issues and everyday challenges is intricate and diverse. Wickström and Kvist Lindholm [ 5 ] show that problems such as feeling low and nervous are considered deep-seated issues among some adolescents, while others refer to them as everyday challenges. Meanwhile, adolescents in Hellström and Beckman [ 6 ] describe mental health problems as something mainstream, experienced by everyone at some point. Furthermore, Hermann et al. [ 7 ] point out that adolescents can distinguish between positive health and mental health problems. This indicates their understanding of the complexity and holistic nature of mental health and mental health issues. It is plausible that misunderstandings and devaluations of mental health and illness concepts may increase self-reported mental health problems and provide contradictory results when the understanding of mental health is studied. In a previous review on how children and young people perceive the concept of “health,” four major themes have been suggested: health practices, not being sick, feeling good, and being able to do the desired and required activities [ 8 ]. In a study involving 8–11 year olds, children framed both biomedical and holistic perspectives of health [ 9 ]. Regarding the concept of “illness,” themes such as somatic feeling states, functional and affective states [ 10 , 11 ], as well as processes of contagion and contamination, have emerged [ 9 ]. Older age strongly predicts nuances in conceptualizations of health and illness [ 10 , 11 , 12 ].

As the current definitions of mental health and mental illness do not seem to have been successful in guiding how these concepts are perceived, literature has emphasized the importance of understanding individuals’ ideas of health and illness [ 9 , 13 ]. The World Health Organization (WHO) broadly defines mental health as a state of well-being in which the individual realizes his or her abilities, can cope with the normal stresses of life, work productively and fruitfully and make a contribution to his or her community [ 14 ] capturing only positive aspects. According to The American Psychology Association [ 15 ], mental illness includes several conditions with varying severity and duration, from milder and transient disorders to long-term conditions affecting daily function. The term can thus cover everything from mild anxiety or depression to severe psychiatric conditions that should be treated by healthcare professionals. As a guide for individual experience, such a definition becomes insufficient in distinguishing mental illness from ordinary emotional expressions. According to the Swedish National Board of Health and Welfare et al. [ 16 ], mental health works as an umbrella term for both mental well-being and mental illness : Mental well-being is about being able to handle life's difficulties, feeling satisfied with life, having good social relationships, as well as being able to feel pleasure, desire, and happiness. Mental illness includes both mild to moderate mental health problems and psychiatric conditions . Mild to moderate mental health problems are common and are often reactions to events or situations in life, e.g., worry, feeling low, and sleep difficulties.

It has been argued that increased knowledge of the nature of mental illness can help individuals to cope with the situation and improve their well-being. Increased knowledge about mental illness, how to prevent mental illness and help-seeking behavior has been conceptualized as “mental health literacy” (MHL) [ 17 ], a construct that has emerged from “health literacy” [ 18 ]. Previous literature supports the idea that positive MHL is associated with mental well-being among adolescents [ 19 ]. Conversely, studies point out that low levels of MHL are associated with depression [ 20 ]. Some gender differences have been acknowledged in adolescents, with boys scoring lower than girls on MHL measures [ 20 ] and a social gradient including a positive relationship between MHL and perceived good financial position [ 19 ] or a higher socio-economic status [ 21 ].

While MHL stresses knowledge about signs and treatment of mental illness [ 22 ], the concern from a social constructivist approach would be the conceptualization of mental illness and how it is shaped by society and the thoughts, feelings, and actions of its members [ 23 ]. Studies on the social construction of anxiety and depression through media discourses have shown that language is at the heart of these processes, and that language both constructs the world as people perceive it but also forms the conditions under which an experience is likely to be construed [ 24 , 25 ]. Considering experience as linguistically inflected, the constructionist approach offers an analytical tool to understand the conceptualization of mental illness and to distinguish mental illness from everyday challenges. The essence of mental health is therefore suggested to be psychological constructions identified through how adolescents and society at large perceive, talk about, and report mental health and how that, in turn, feeds a continuous process of conceptual re-construction or adaptation [ 26 ]. Considering experience as linguistically inflected, the constructionist approach could then offer an analytical tool to understand the potential influence of everyday challenges in the conceptualization of mental health.

Research investigating how children and youth perceive and communicate mental health is essential to understand the current rise of reported mental health problems [ 5 ]. Health promotion initiatives are more likely to be successful if they take people’s understanding, beliefs, and concerns into account [ 27 , 28 ]. As far as we know, no review has mapped the literature to explore children’s and youths’ perceptions of mental health and mental illness. Based on previous literature, age, gender, and socioeconomic status seem to influence children's and youths’ knowledge and experiences of mental health [ 10 , 11 , 12 ]; therefore, we aim to analyze these perspectives too. From a social constructivist perspective, experience is linguistically inflected [ 26 ]; hence illuminating the conditions under which a perception of health is formed is of interest.

Therefore, we aim to study the literature on how children and youth (ages 10—25) perceive and conceptualize mental health, and the specific research questions are:

What aspects are most salient in children’s and youths’ perceptions of mental health?

What concepts do children and youth associate with mental health?

In what way are children's and youth’s perceptions of mental health dependent on gender, age, and socioeconomic factors?

Literature search

A scoping review is a review that aims to provide a snapshot of the research that is published within a specific subject area. The purpose is to offer an overview and, on a more comprehensive level, to distinguish central themes compared to a systematic review. We chose to conduct a scoping review since our aim was to clarify the key concepts of mental health in the literature and to identify specific characteristics and concepts surrounding mental health [ 29 , 30 ]. Our scoping review was conducted following the PRISMA-ScR Checklist [ 31 ]. Two authors (L.B and L.H) searched and screened the eligible articles. In the first step, titles and abstracts were screened. If the study included relevant data, the full article was read to determine if it met the eligibility criteria. Articles were excluded if they did not fulfill all the eligibility criteria. Any uncertainties were discussed among L.B. and L.H., and the third author, S.H., and were carefully assessed before making an inclusion or exclusion decision. The software Picoportal was employed for data management. Figure  1 illustrates a flowchart of data inclusion.

PRISMA flow diagram outlining the search process

Eligibility criteria

We incorporated studies involving children and youth aged 10 to 25 years. This age range was chosen to encompass early puberty through young adulthood, a significant developmental period for young individuals in terms of comprehending mental health. Participants were required not to have undergone interviews due to chronic illness, learning disabilities (e.g., mental health linked to a cancer diagnosis), or immigrant status.

Studies conducted in clinical settings were excluded. For the purpose of comparing results under similar conditions, we specifically opted for studies carried out in Western countries .

Given that this review adopts a moderately constructionist approach, intentionally allowing for the exploration of how both young participants and society in general perceive and discuss mental health and how this process contributes to ongoing conceptual re-construction, the emphasis was placed on identifying articles in which participants themselves defined or attributed meaning to mental health and related concepts like mental illness. The criterion of selecting studies adopting an inductive approach to capture the perspectives of the young participants resulted in the exclusion of numerous studies that more overtly applied established concepts to young respondents [ 32 ].

Information sources

We utilized electronic databases and reached out to study authors if the article was not accessible online. Peer-reviewed articles were exclusively included, thereby excluding conference abstracts due to their perceived lack of relevance in addressing the review questions. Only research in English was taken into account. Publication years across all periods were encompassed in the search.

Search strategy

Studies concerning children’s and youths’ perceptions of mental health were published across a range of scientific journals, such as those within psychiatry, psychology, social work, education, and mental health. Therefore, several databases were taken into account, including Medline, Scopus, CINAHL, PsychInfo, Sociological abstracts, and Google Scholar, spanning from inception on September 20, 2021 to September 30, 2021. We involved a university librarian from the start in the search process. The combinations of search terms are displayed in Table 1 .

Quality assessment

We employed the Quality methods for the development of National Institute for Health Care Excellence (NICE) public health guidance [ 33 ] to evaluate the quality of the studies included. The checklist is based on checklists from Spencer et al. [ 34 ], Public Health Resource Unit (PHRU) [ 26 , 35 ], and the North Thames Research Appraisal Group (NTRAG) [ 36 ] (Refer to S2 for checklist). Eight studies were assigned two plusses, and three studies received one plus. The studies with lower grades generally lacked sufficient descriptions of the researcher’s role, context reporting, and ethical reporting. No study was excluded in this stage.

Data extraction and analysis

We employed a data extraction form that encompassed several key characteristics, including author(s), year, journal, country, details about method/design, participants and socioeconomics, aim, and main results (Table 2 ). The collected data were analyzed and synthesized using the thematic synthesis approach of Thomas and Harden [ 37 ]. This approach encompassed all text categorized as 'results' or 'findings' in study reports – which sometimes included abstracts, although the presentation wasn’t always consistent throughout the text. The size of the study reports ranged from a few sentences to a single page. The synthesis occurred through three interrelated stages that partially overlapped: coding of the findings from primary studies on a line-by-line basis, organization of these 'free codes' into interconnected areas to construct 'descriptive' themes, and the formation of 'analytical' themes.

The objective of this scoping review has been to investigate the literature concerning how children and youth (ages 10—25) conceptualize and perceive mental health. Based on the established inclusion- and exclusion criteria, a total of 11 articles were included representing the United Kingdom ( n  = 6), Australia ( n  = 3), and Sweden ( n  = 2) and were published between 2002 and 2020. Among these, two studies involved university students, while nine incorporated students from compulsory schools.

Salient aspects of children and youth’ perceptions of mental health

Based on the results of the included articles, salient aspects of children’s and youths’ understandings revealed uncertainties about mental health in various ways. This uncertainty emerged as conflicting perceptions, uncertainty about the concept of mental health, and uncertainty regarding where to distinguish between mild to moderate mental health problems and everyday stressors or challenges.

One uncertainty was associated with conflicting perceptions that mental health might be interpreted differently among children and youths, depending on whether it relates to their own mental health or someone else's mental health status. Chisholm et al. [ 42 ] presented this as distinctions being made between ‘them and us’ and between ‘being born with it’. Mental health and mental illness were perceived as a continuum that rather developed’, and distinctions were drawn between ‘crazy’ and ‘diagnosed.’ Participants established strong associations between the term mental illness and derogatory terms like ‘crazy,’ linking extreme symptoms of mental illness with others. However, their attitude was less stigmatizing when it came to individual diagnoses, reflecting a more insightful and empathetic understanding of the adverse impacts of stress based on their personal realities and experiences. Despite the initial reactions reflecting negative stereotypes, further discussion revealed that this did not accurately represent a deeper comprehension of mental health and mental illness.

There was also uncertainty about the concept of mental health , as it was not always clearly understood among the participating youth. Some participants were unable to define mental health, often confusing it with mental illness [ 28 ]. Others simply stated that they did not understand the term, as in O’Reilly [ 44 ]. Additionally, uncertainty was expressed regarding whether mental health was a positive or negative concept [ 27 , 28 , 40 , 44 ], and participants associated mental health with mental illness despite being asked about mental health [ 28 ]. One quote from a grade 9 student illustrates this: “ Interviewer: Can mental health be positive as well? Informant: No, it’s mental” [ 44 ]. In Laidlaw et al. [ 46 ], with participants ranging from 18—22 years of age, most considered mental health distinctly different from and more clinical than mental well-being. However, Roose et al. [ 38 ], for example, the authors discovered a more multifaceted understanding of mental health, encompassing emotions, thoughts, and behavior. In Molenaar et al.[ 45 ], mental health was highlighted as a crucial aspect of health overall. In Chisholm et al. [ 42 ], the older age groups discussed mental health in a more positive sense when they considered themselves or people they knew, relating mental health to emotional well-being. Connected to the uncertainty in defining the concept of mental health was the uncertainty in identifying those with good or poor mental health. Due to the lack of visible proof, children and youths might doubt their peers’ reports of mental illness, wondering if they were pretending or exaggerating their symptoms [ 27 ].

A final uncertainty that emerged was difficulties in drawing the line between psychiatric conditions and mild to moderate mental health problems and everyday stressors or challenges . Perre et al. [ 43 ] described how the participants in their study were uncertain about the meaning of mental illness and mental health issues. While some linked depression to psychosis, others related it to simply ‘feeling down.’ However, most participants indicated that, in contrast to transient feelings of sadness, depression is a recurring concern. Furthermore, the duration of feeling depressed and particularly a loss of interest in socializing was seen as appropriate criteria for distinguishing between ‘feeling down’ and ‘clinical depression.’ Since feelings of anxiety, nervousness, and apprehension are common experiences among children and youth, defining anxiety as an illness as opposed to an everyday stressor was more challenging [ 43 ].

Terms used to conceptualize mental health

When children and youth were asked about mental health, they sometimes used neutral terms such as thoughts and emotions or a general ‘vibe’ [ 27 ], and some described it as ‘peace of mind’ and being able to balance your emotions [ 38 ]. The notion of mental health was also found to be closely linked with rationality and the idea of normality, although, according to the young people, Armstrong et al. [ 28 ], there was no consensus about what ‘normal’ meant. Positive aspects of mental health were described by the participants as good self-esteem, confidence [ 40 ], happiness [ 39 , 43 ], optimism, resilience, extraversion and intelligence [ 27 ], energy [ 43 ], balance, harmony [ 39 , 43 ], good brain, emotional and physical functioning and development, and a clear idea of who they are [ 27 , 41 ]. It also included a feeling of being a good person, feeling liked and loved by your parents, social support, and having people to talk with [ 27 , 39 ], as well as being able to fit in with the world socially and positive peer relationships [ 41 ], according to the children and youths, mental health includes aspects related to individuals (individual factors) as well as to people in their surroundings (relationships). Regarding mental illness, participants defined it as stress and humiliation [ 40 ], psychological distress, traumatic experiences, mental disorders, pessimism, and learning disabilities [ 27 ]. Also, in contrast to the normality concept describing mental health, mental illness was described as somehow ‘not normal’ or ‘different’ in Chisholm et al. [ 42 ].

Depression and bipolar disorder were the most often mentioned mental illnesses [ 27 ]. The inability to balance emotions was seen as negative for mental health, for example, not being able to set aside unhappiness, lying to cover up sadness, and being unable to concentrate on schoolwork [ 38 ]. The understanding of mental illness also included feelings of fear and anxiety [ 42 ]. Other participants [ 46 ] indicated that mental health is distinctly different from, and more clinical than, mental well-being. In that sense, mental health was described using reinforcing terms such as ‘serious’ and ‘clinical,’ being more closely connected to mental illness, whereas mental well-being was described as the absence of illness, feeling happy, confident, being able to function and cope with life’s demands and feeling secure. Among younger participants, a more varied and vague understanding of mental health was shown, framing it as things happening in the brain or in terms of specific conditions like schizophrenia [ 44 ].

Gender, age, socioeconomic status

Only one study had a gender theoretical perspective [ 40 ], but the focus of this perspective concerned gender differences in what influences mental health more than the conceptualization of mental health. According to Johansson et al.[ 39 ], older girls expressed deeper negative emotions (e.g., described feelings of lack of meaning and hope in various ways) than older boys and younger children.

Several of the included studies noticed differences in age, where younger participants had difficulty understanding the concept of mental health [ 39 , 44 ], while older participants used more words to explain it [ 39 ]. Furthermore, older participants seemed to view mental health and mental illness as a continuum, with mental illness at one end of the continuum and mental well-being at the other end [ 42 , 46 ].

Socioeconomic status

The role of socioeconomic status was only discussed by Armstrong et al. [ 28 ], finding that young people from schools in the most deprived and rural areas experienced more difficulties defining the term mental health compared to those from a less deprived area.

This scoping review aimed to map children's and youth’s perceptions and conceptualizations of mental health. Our main findings indicate that the concept of mental health is surrounded by uncertainty. This raises the question of where this uncertainty stems from and what it symbolizes. From our perspective, this uncertainty can be understood from two angles. Firstly, the young participants in the different studies show no clear and common understanding of mental health; they express uncertainty about the meaning of the concept and where to draw the line between life experiences and psychiatric conditions. Secondly, uncertainty exists regarding how to apply these concepts in research, making it challenging to interpret and compare research results. The shift from a positivistic understanding of mental health as an objective condition to a more subjective inner experience has left the conceptualization open ranging from a pathological phenomenon to a normal and common human experience [ 47 ]. A dilemma that results in a lack of reliability that mirrors the elusive nature of the concept of mental health from both a respondent and a scientific perspective.

“Happy” was commonly used to describe mental health, whereas "unhappy" was used to describe mental illness. The meaning of happiness for mental health has been acknowledged in the literature, and according to Layard et al. [ 48 ], mental illness is one of the main causes of unhappiness, and happiness is the ultimate goal in human life. Layard et al. [ 48 ] suggest that schools and workplaces need to raise more awareness of mental health and strive to improve happiness to promote mental health and prevent mental illness. On the other hand, being able to experience and express different emotions could also be considered a part of mental health. The notion of normality also surfaced in some studies [ 38 ], understanding mental health as being emotionally balanced or normal or that mental illness was not normal [ 42 ]. To consider mental illness in terms of social norms and behavior followed with the sociological alternative to the medical model that was introduced in the sixties portraying mental illness more as socially unacceptable behavior that is successfully labeled by others as being deviant. Although our results did not indicate any perceptions of what ‘normal’ meant [ 28 ], one crucial starting point to the understanding of mental health among adolescents should be to delineate what constitutes normal functioning [ 23 ]. Children and youths’ understanding of mental illness seems to a large extent, to be on the same continuum as a normality rather than representing a medicalization of deviant behavior and a disjuncture with normality [ 49 ].

Concerning gender, it seemed that girls had an easier time conceptualizing mental health than boys. This could be due to the fact that girls mature verbally faster than boys [ 50 ], but also that girls, to a larger extent, share feelings and problems together compared to boys [ 51 ]. However, according to Johansson et al. [ 39 ], the differences in conceptualizations of mental health seem to be more age-related than gender-related. This could be due to the fact that older children have a more complex view of mental health compared to younger children.. Not surprisingly, the older the children and youth were, the more complex the ability to conceptualize mental health becomes. Only one study reported socioeconomic differences in conceptualizations of mental health [ 28 ]. This could be linked to mental health literacy (MHL) [ 18 ], i.e., knowledge about mental illness, how to prevent mental illness, and help-seeking behavior. Research has shown that disadvantaged social and socioeconomic conditions are associated with low MHL, that is, people with low SES tends to know less about symptoms and prevalence of different mental health problems [ 19 , 21 ]. The perception and conceptualizations of mental health are, as we consider, strongly related to knowledge and beliefs about mental health, and according to von dem Knesebeck et al. [ 52 ] linked primarily to SES through level of education.

Chisholm et al. [ 42 ] found that the initial reactions from participants related to negative stereotypes, but further discussion revealed that the participants had more refined knowledge than at first glance. This illuminates the importance of talking to children and helping them verbalize their feelings, in many respects complex and diversified understanding of mental health. It is plausible that misunderstandings and devaluations of mental health and mental illness may increase self-reported mental health problems [ 5 ], as well as decrease them, preventing children and youth from seeking help. Therefore, increased knowledge of the nature of mental health can help individual cope with the situations and improve their mental well-being. Finding ways to incorporate discussions about mental well-being, mental health, and mental illness in schools could be the first step to decreasing the existing uncertainties about mental health. Experiencing feelings of sadness, anger, or upset from time to time is a natural part of life, and these emotions are not harmful and do not necessarily indicate mental illness [ 5 , 6 ]. Adolescents may have an understanding of the complexity of mental health despite using simplified language but may need guidance on how to communicate their feelings and how to manage everyday challenges and normal strains in life [ 7 ].

With the aim of gaining a better understanding of how mental health is perceived among children and youth, this study has highlighted the concept’s uncertainty. Children and youth reveal a variety of understandings, from diagnoses of serious mental illnesses such as schizophrenia to moods and different types of behaviors. Is there only one way of understanding mental health, and is it reasonable to believe that we can reach a consensus? Judging by the questions asked, researchers also seem to have different ideas on what to incorporate into the concept of mental health — the researchers behind the present study included. The difficulties in differentiating challenges being part of everyday life with mental health issues need to be paid closer attention to and seems to be symptomatic with the lack of clarity of the concepts.

A constructivist approach would argue that the language of mental health has changed over time and thus influence how adolescents, as well as society at large, perceive, talk about, and report their mental health [ 26 ]. The re-construction or adaptation of concepts could explain why children and youth re struggling with the meaning of mental health and that mental health often is used interchangeably with mental illness. Mental health, rather than being an umbrella term, then represents a continuum with a positive and a negative end, at least among older adolescents. But as mental health according to this review also incorporates subjective expressions of moods and feelings, the reconstruction seems to have shaped it into a multidimensional concept, representing a horizontal continuum of positive and negative mental health and a vertical continuum of positive and negative well-being, similar to the health cross by Tudor [ 53 ] referred to in Laidlaw et al. [ 46 ] A multidimensional understanding of mental health constructs also incorporates evidence from interventions aimed at reducing mental health stigma among adolescents, where attitudes and beliefs as well as emotional responses towards mental health are targeted [ 54 ].

The contextual understanding of mental health, whether it is perceived in positive terms or negative, started with doctors and psychiatrists viewing it as representing a deviation from the normal. A perspective that has long been challenged by health workers, academics and professionals wanting to communicate mental health as a positive concept, as a resource to be promoted and supported. In order to find a common ground for communicating all aspects and dimensions of mental health and its conceptual constituents, it is suggested that we first must understand the subjective meaning ascribed to the use of the term [ 26 ]. This line of thought follows a social-constructionist approach viewing mental health as a concept that has transitioned from representing objective mental descriptions of conditions to personal subjective experiences. Shifting from being conceptualized as a pathological phenomenon to a normal and common human experience [ 47 ]. That a common understanding of mental health can be challenged by the healthcare services tradition and regulation for using diagnosis has been shown in a study of adolescents’ perspectives on shared decision-making in mental healthcare [ 55 ]. A practice perceived as labeling by the adolescents, indicating that steps towards a common understanding of mental health needs to be taken from several directions [ 55 ]. In a constructionist investigation to distinguish everyday challenges from mental health problems, instead of asking the question, “What is mental health?” we should perhaps ask, “How is the word ‘mental health’ used, and in what context and type of mental health episode?” [ 26 ]. This is an area for future studies to explore.

Methodological considerations

The first limitation we want to acknowledge, as for any scoping review, is that the results are limited by the search terms included in the database searches. However, by conducting the searches with the help of an experienced librarian we have taken precautions to make the searches as inclusive as possible. The second limitation concerns the lack of homogeneous, or any results at all, according to different age groups, gender, socioeconomic status, and year when the study was conducted. It is well understood that age is a significant determinant in an individual’s conceptualization of more abstract phenomena such as mental health. Some of the studies approached only one age group but most included a wide age range, making it difficult to say anything specific about a particular age. Similar concerns are valid for gender. Regarding socioeconomic status, only one study reported this as a finding. However, this could be an outcome of the choice of methods we had — i.e., qualitative methods, where the aim seldom is to investigate differences between groups and the sample is often supposed to be a variety. It could also depend on the relatively small number of participants that are often used in focus groups of individual interviews- there are not enough participants to compare groups based on gender or socioeconomic status. Finally, we chose studies from countries that could be viewed as having similar development and perspective on mental health among adolescents. Despite this, cultural differences likely account for many youths’ conceptualizations of mental health. According to Meldahl et al. [ 56 ], adolescents’ perspectives on mental health are affected by a range of factors related to cultural identity, such as ethnicity, race, peer and family influence, religious and political views, for example. We would also like to add organizational cultures, such as the culture of the school and how schools work with mental health and related concepts [ 56 ].

Conclusions and implications

Based on our results, we argue that there is a need to establish a common language for discussing mental health. This common language would enable better communication between adults and children and youth, ensuring that the content of the words used to describe mental health is unambiguous and clear. In this endeavor, it is essential to actively listen to the voices of children and youth, as their perspectives will provide us with clearer understanding of the experiences of being young in today’s world. Another way to develop a common language around mental health is through mental health education. A common language based on children’s and youth’s perspectives can guide school personnel, professionals, and parents when discussing and planning health interventions and mental health education. Achieving a common understanding through mental health education of adults and youth could also help clarify the boundaries between everyday challenges and problems needing treatment. It is further important to raise awareness of the positive aspect of mental health—that is, knowledge of what makes us flourish mentally should be more clearly emphasized in teaching our children and youth about life. It should also be emphasized in competence development for school personnel so that we can incorporate knowledge about mental well-being in everyday meetings with children and youth. In that way, we could help children and youth develop knowledge that mental health could be improved or at least maintained and not a static condition.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Beckman, L., Hassler, S. & Hellström, L. Children and youth’s perceptions of mental health—a scoping review of qualitative studies. BMC Psychiatry 23 , 669 (2023). https://doi.org/10.1186/s12888-023-05169-x

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• Published: 19 April 2021

A systematic review and meta-analysis of psychological interventions to improve mental wellbeing

• Joep van Agteren   ORCID: orcid.org/0000-0002-7347-7649 1 , 2 ,
• Matthew Iasiello   ORCID: orcid.org/0000-0003-1449-602X 1 , 2 , 3 ,
• Laura Lo 1 ,
• Jonathan Bartholomaeus 1 , 4 , 5 ,
• Zoe Kopsaftis   ORCID: orcid.org/0000-0002-9189-1405 6 , 7 , 8 ,
• Marissa Carey 1 &
• Michael Kyrios   ORCID: orcid.org/0000-0001-9438-9616 1 , 2 , 9  

Nature Human Behaviour volume  5 ,  pages 631–652 ( 2021 ) Cite this article

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Our current understanding of the efficacy of psychological interventions in improving mental states of wellbeing is incomplete. This study aimed to overcome limitations of previous reviews by examining the efficacy of distinct types of psychological interventions, irrespective of their theoretical underpinning, and the impact of various moderators, in a unified systematic review and meta-analysis. Four-hundred-and-nineteen randomized controlled trials from clinical and non-clinical populations ( n  = 53,288) were identified for inclusion. Mindfulness-based and multi-component positive psychological interventions demonstrated the greatest efficacy in both clinical and non-clinical populations. Meta-analyses also found that singular positive psychological interventions, cognitive and behavioural therapy-based, acceptance and commitment therapy-based, and reminiscence interventions were impactful. Effect sizes were moderate at best, but differed according to target population and moderator, most notably intervention intensity. The evidence quality was generally low to moderate. While the evidence requires further advancement, the review provides insight into how psychological interventions can be designed to improve mental wellbeing.

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Acknowledgements

The authors thank colleagues at the South Australian Health and Medical Research Institute, Wellbeing and Resilience Centre, for their support during the creation of this review, S. Brown and N. May, for their help in crafting the search strategy. This work was supported by a grant by the James and Diana Ramsay Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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van Agteren, J., Iasiello, M., Lo, L. et al. A systematic review and meta-analysis of psychological interventions to improve mental wellbeing. Nat Hum Behav 5 , 631–652 (2021). https://doi.org/10.1038/s41562-021-01093-w

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ORIGINAL RESEARCH article

The anxiety-buffer hypothesis in the time of covid-19: when self-esteem protects from the impact of loneliness and fear on anxiety and depression.

• 1 Section of Applied Psychology, Department of Philosophy, Sociology, Education, and Applied Psychology, University of Padua, Padua, Italy
• 2 Interdepartmental Center for Family Research, University of Padua, Padua, Italy
• 3 Unit of Psychology and Neuropsychology, Maugeri Scientific Institutes IRCCS, Novara, Italy
• 4 Psychology Research Laboratory, Ospedale San Giuseppe, IRCCS, Istituto Auxologico Italiano, Verbania, Italy
• 5 Department of Psychology, Catholic University of Milan, Milan, Italy
• 6 Department of Psychology, eCampus University, Novedrate, Italy

Introduction: The coronavirus (COVID-19) disease has spread worldwide, generating intense fear of infection and death that may lead to enduring anxiety. At the same time, quarantine and physical isolation can intensify feelings of dispositional loneliness that, by focusing on thoughts of disconnection from others, can trigger intense anxiety. Anxiety, generated by both fear of COVID-19 and dispositional loneliness, can activate negative expectations and thoughts of death, potentially generating alarming depressive symptoms. However, the anxiety-buffer hypothesis suggests that self-esteem acts as a shield (buffer) against mental health threats – fear and loneliness – thus hampering anxiety and depressive symptoms.

Objective: This study aims to test the process – triggered by COVID-19 fear and loneliness – in which self-esteem should buffer the path leading to anxiety symptoms, then to depression.

Methods: An observational research design with structural equation models was used. A sample of 1200 participants enrolled from the general population answered an online survey comprising: the fear of COVID-19 scale, the UCLA loneliness scale, the Rosenberg self-esteem scale, and the anxiety and depression scales of the Symptom Checklist-90-Revised.

Results: Structural equation models showed the link between anxiety symptoms ( mediator ) with both the fear of COVID-19 and dispositional loneliness ( predictors ), as well as its association with consequent depressive symptomatology ( outcome ). In line with the anxiety-buffer hypothesis, self-esteem mediated the relationship between the predictors and their adverse psychological consequences.

Conclusion: Self-esteem represents a protective factor from the antecedents of depression. Targeted psychological interventions should be implemented to minimize the psychological burden of the disease whilst promoting adaptation and positive psychological health outcomes.

Introduction

The novel coronavirus (COVID-19) is a new severe and potentially mortal disease threatening to infect the entire human population given that there is no prior immunity and not even a well-established cure or vaccine yet ( Baud et al., 2020 ).

COVID-19 displays a variety of clinical features ranging from asymptomatic presentations (20–50%), fever (>90%), cough (75%), shortness of breath (50%), up to acute respiratory distress syndrome, and death ( Byambasuren et al., 2020 ; Center for Disease Control and Prevention, 2020 ; Jiang et al., 2020 ). Categories of people at higher risk of developing severe complications of COVID-19 are older adults and people with previous underlying medical conditions, such as hypertension, cardiovascular disease, respiratory disease, and cancer ( Liu et al., 2020 ; Armitage and Nellums, 2020 ; Zheng et al., 2020 ). The contagion occurs from an infected person, even without obvious symptom manifestation, via respiratory droplets that can be inhaled or can land on surfaces which are later in contact with other people.

Due to its high transmissibility, since December 2019 COVID-19 has been rapidly spreading worldwide causing the current pandemic ( World Health Organization [WHO], 2020 ). Across the world, strict preventive policies were adopted to contain the outbreak of COVID-19 – including social distancing and social isolation. Nevertheless, the magnitude of this pandemic has generated serious concerns about its social and economic consequences both in the short and long-term ( Cerami et al., 2020 ). Thus, COVID-19 represents an epochal economic, physical, and biological threat to everyone’s lives.

Therefore, beyond threatening people’s physical conditions, COVID-19 is accompanied by remarkable psychological burdens heavily affecting people’s mental health ( Brooks et al., 2020 ; Torales et al., 2020 ; Wang et al., 2020 ). Similar to other physical diseases, COVID-19 represents a specific dangerous trigger activating a “fight or flight” reaction of (functional) fear focused on illness and death ( Schaller et al., 2015 ; Harper et al., 2020 ). The COVID-19 pandemic-related fear also led to counterproductive and detrimental behaviors for the whole society (i.e., demanding unnecessary medical care, excessively protecting against the virus, and overstocking certain supplies) ( Lin, 2020 ).

Moreover, fear of illness and death commonly lead to chronic vigilance for potential threats, thus contributing to the development of anxiety (i.e., the anticipation of a feared threat without a stimulus) that is future-oriented, unfocused, diffused, and extended to non-threatening situations ( Barlow, 2002 ; Harding et al., 2008 ).

In turn, anxiety might trigger and catalyze depressive symptoms via the activation of processes including persistent preoccupations, negative expectations, thoughts about death (of themselves or significant others), and pervasive pessimism ( Thompson et al., 2005 ; Starr and Davila, 2012 ). Depressive symptoms include feelings of sadness and loss, a negative view of the self, of the world, and of the future, thought and behavior are slowed down, and positive emotions are absent ( Beck, 1979 ). Noteworthy, depressive symptoms spread widely during the COVID-19 pandemic, representing an alarming predictor of suicide-behaviors ( McIntyre and Lee, 2020 ; Thakur and Jain, 2020 ).

At the same time, quarantine and physical distancing generated widespread feelings of isolation and loneliness – despite that fact that human connections were facilitated and granted by the use of communication technology ( Russell, 1996 ; Usher et al., 2020 ). Indeed, the dispositional trait of loneliness may have a crucial role in perceiving and amplifying feelings of isolation, thus exacerbating the adverse psychological impact of the outbreak ( Boffo et al., 2012 ). Indeed, dispositional loneliness is characterized by perceived disconnection from others and unpleasant feelings of isolation. Dispositional loneliness activates distressing thinking processes focusing on comparisons between the actual and the desired socio-relational situation. This contributes to the increase of unpleasant feelings and leads to the development of symptoms of anxiety that – in turn – lead to depressive symptomatology ( Cacioppo et al., 2006 , 2014 ; Santini et al., 2020 ). In other words, by activating (maladaptive) mechanisms and by influencing the brain and behavior, loneliness makes people more susceptible to the onset of anxious and depressive symptoms – thus representing an important risk factor for poor mental health ( Fiese et al., 2002 ; Heinrich and Gullone, 2006 ; Hossain et al., 2020 ; Lunn et al., 2020 ; Zhou et al., 2020 ), long-term morbidity (i.e., cardiovascular), and mortality ( Cacioppo et al., 2014 ; Leigh-Hunt et al., 2017 ; Rico-Uribe et al., 2018 ).

Consequently, both a fear of COVID-19 and dispositional loneliness could be considered as predictors of severe psychological symptoms of anxiety and depression, potentially leading to dismal effects, including extreme life-threatening behaviors ( Santini et al., 2020 ; Thakur and Jain, 2020 ).

However, self-esteem – that is the individuals’ attitudes, beliefs, and evaluations toward the self – may buffer these adverse patterns. According to Becker (1971 , 1973) , self-esteem is built on deep-rooted personal values derived from a given social, relational, and cultural context, and it is reinforced by social validation and the feeling of being a valuable human being with a meaningful role in society given by meeting the standards of a given culture and worldview ( Pyszczynski et al., 2004 ). More recently, the terror management theory (TMT) ( Greenberg et al., 1986 ) postulated that individuals’ awareness of mortality – in this case elicited by COVID-19 – conflicts with the human intrinsic desire for life and tendency to survive, thus generating terrifying fears of death and then anxiety. In this framework, the anxiety-buffer hypothesis (ABH; Greenberg et al., 1992 ) theorizes that, by reconnecting the individual with an enlarged universe of meanings and values, self-esteem could act as a protecting shield (buffer) against the detrimental psychological effects of life-threats and stressors.

Aims and Hypotheses

Considering this background, the present study aimed at testing the anxiety-buffer hypothesis during the COVID-19 pandemic. More in detail, self-esteem should buffer the relationships from both a fear of COVID-19 and dispositional loneliness to anxiety symptoms – that in turn lead to depressive symptoms. Moreover, specific hypotheses about each path (relationship) between variables were formulated:

H1: fear of COVID-19 and dispositional loneliness are positively associated with depressive symptomatology;

H2a: fear of COVID-19 predicts depressive symptomatology through anxiety symptoms (simple mediation) – without considering the buffering effect of self-esteem;

H2b: dispositional loneliness predicts depressive symptomatology through anxiety (simple mediation) – without considering the buffering effect of self-esteem;

H3: fear of COVID-19 and dispositional loneliness predict depressive symptomatology through anxiety symptoms (mediation) – without considering the buffering effect of self-esteem;

H4: fear of COVID-19 and dispositional loneliness predict depressive symptoms through self-esteem (buffering effect) and anxiety symptoms (multiple mediation).

In other words, it was hypothesized that a fear of COVID-19 and loneliness are associated with depressive symptomatology, but this relationship should be mediated by both anxiety and self-esteem. In particular, self-esteem should play a buffering role.

Materials and Methods

An online survey was developed and disseminated using the Qualtrics software for data collection.

Firstly, the survey was administered to 20 participants – not included into analysis (A) to ensure whether the items were understandable by the general population and (B) to estimate an acceptable time for its fulfillment (8’–20’), so as to deal adequately with potentially biased responses: too fast – random answers – or too slow –in which the subject could have been interrupted during the completion.

Then, the snowball sampling method ( Fricker, 2008 ) was used to recruit participants from the general population through personal invitations or materials advertised via social media platforms (i.e., Facebook, Twitter).

The recruitment materials provided details of what was required for participation in the study and a weblink to access the online questionnaire. The weblink directed potential participants first to further information on the research project in order to make an informed decision about study participation. Participants were informed that their responses were anonymous as well as that no economic payment was offered for their voluntary participation. Those who provided their consent online proceeded to the online questionnaire.

Inclusion criteria for the participants into the study were: (A) being a native Italian speaker; (B) being over 18 years old; and (C) providing informed consent. We excluded participants from the study who: (D) did not answer all the questions in the survey and (E) spent less than 8 min or more than 20 min completing the survey.

Data were collected in their entirety in a single week interval during the Italian quarantine to avoid confounding effects due to pandemic fluctuations. The study was approved by the Ethic Committee of the University of Padua in accordance with the Ethical standards of the Declaration of Helsinki.

Sample Size Determination

Considering the statistical analyses used in this study (see designated section), the sample size was calculated a priori according to the “ n:q criterion”: where n is the number of participants and q is the number of (free) model parameters to be estimated ( Hu and Bentler, 1999 ; Muthén and Asparouhov, 2002 ; Yu, 2002 ). Consequently, ten subject per free parameter (10:73; n minimum = 730) were guaranteed ( Bentler and Chou, 1987 ; Marsh et al., 1988 ; Hu and Bentler, 1999 ; Boomsma and Hoogland, 2001 ; Muthén and Asparouhov, 2002 ; Yu, 2002 ; Flora and Curran, 2004 ; Tomarken and Waller, 2005 ).

Participants

According to the inclusion criteria, 62 respondents were excluded from the study due to incomplete surveys ( n = 35) and inappropriate completion times ( n = 27).

The final sample was composed by 1200 participants [217 males (23.3%) and 713 females (76.7%), aged from 18 to 81 years ( mean = 39.59, SD = 12.334)], the average time competing the survey was 11’0.27” (SD = 3’0.02”). A total of 965 respondents were from Northern Italy (80.4%), 165 were from central Italy (13.8%), and 70 participants were from Southern Italy and the islands (5.8%). Descriptive statistics of this sample are reported in Table 1 .

Table 1. Socio-demographic characteristics of the sample.

Socio-demographic information included sex, age, education, employment, Italian region of residence, number of persons living with, and confirmed positive COVID-19 diagnosis of the respondent and of his/her significant others. Table 1 reports the sample characteristics.

In addition, the following self-report measures were administered.

Fear of COVID-19 Scale – (FCV-19S)

The FCV-19S ( Ahorsu et al., 2020 ; Soraci et al., 2020 ) is a 7-item self-report questionnaire aimed at assessing emotional, cognitive, physiological, and behavioral manifestations of COVID-19-related fear in the general population. Respondents are asked to indicate their degree of agreement to each statement on a 5-point Likert-type scale (ranging from 1 = “strongly disagree” to 5 = “strongly agree” ) that provides a single-factor structure. Higher values indicate greater fear of COVID-19. In this study, the FCV-19S showed a high internal consistency (Cronbach’s alpha = 0.881).

University of California, Los Angeles, Loneliness Scale-Version 3 (UCLA-LS3)

The UCLA-LS3 ( Russell, 1996 ; Boffo et al., 2012 ) is a 20-item self-report scale that evaluates the individuals’ global and prolonged (dispositional) perceived sense of loneliness through three dimensions: (A) sense “habitual” isolation, (B) perception of being socially isolated, and (C) “traits” and dispositional factors of loneliness ( Boffo et al., 2012 ). In addition, a general dimension of “dispositional” loneliness is assumed. Respondents are asked to rate how often they feel the way described by each sentence on a 4-point Likert-type scale (ranging from 1 = “never” to 4 = “always” ). Higher values indicate the presence of a greater feeling of loneliness. In this study, the UCLA-LS3 showed a high internal consistency for each dimension (A – Isolation: Cronbach’s alpha = 0.805; B – Relational connectedness: Cronbach’s alpha = 0.822; C – Trait loneliness: Cronbach’s alpha = 0.869) and for the general dimension (Cronbach’s alpha = 0.913).

Rosenberg Self-Esteem Scale (RSE)

The RSE ( Rosenberg, 1965 ; Prezza et al., 1997 ) is one the most widely used self-report scales assessing global self-esteem in both clinical settings and in the general population. It consists of 10 positively and negatively worded statements evaluating feelings about one’s self. Respondents are asked to express their degree of agreement to each statement on a 4-point Likert-type scale (ranging from 1 = “not at all” to 4 = “always” ), and it provides a single-factor structure. Higher values indicate a greater sense of global self-esteem. In the present sample, the RSE showed a high internal consistency (Cronbach’s alpha = 0.869).

Anxiety Subscale of the Symptom Checklist-90 Revised (SCL-90R – ANX)

The SCL-90R ANX subscale ( Derogatis and Unger, 2010 ) is a 10-item self-report tool evaluating psychological, cognitive, and physical manifestations of anxiety during the previous week. For each statement, respondents are asked to rate the severity of their symptoms on a 5-point Likert-type scale (ranging from 1 = “not at all” to 5 = “always” ). The ANX subscale provides a single factor structure. Higher values indicate a greater anxiety symptomatology. In this study, the ANX subscale showed a high internal consistency (Cronbach’s alpha = 0.932).

Depression Subscale of the Symptom Checklist-90 Revised (SCL-90R – DEP)

The SCL-90R DEP scale of Derogatis and Unger (2010) is a 13-item self-report tool evaluating emotive, cognitive, and somatic manifestations of depression during the previous week. Respondents are asked to rate the severity of their symptoms on a 5-point Likert-type scale (ranging from 1 = “not at all” to 5 = “always” ). Also the DEP subscale provides a single factor structure. Higher values indicate a greater depressive symptomatology. In the present sample, the DEP subscale showed a high internal consistency (Cronbach’s alpha = 0.907).

Statistical Analyses

All analyses were performed with the R statistical software system (v. 3.5.3) [R-core project ( R Core Team, 2014 , 2017 )]. The following packages were used: psych (v. 1.8.12; Revelle, 2018 ), lavaan (v. 0.6-6; Rosseel, 2012 ; Rosseel et al., 2015 ), and semTools (v. 0.5-2; Jorgensen et al., 2019 ). Graphical representations were performed with graphViz in DiagrammeR (v.1.0.6.1; Iannone, 2018 ).

Preliminarily, a multivariate multiple regression analysis was performed to exclude the potential confounding effects of the following variables (covariates) on the aforementioned psychological constructs: (A) Italian region where respondents lived – as COVID-19 played out differently in Italy, (B) number of persons respondents lived with, (C) confirmed positive COVID-19 diagnosis of the respondents, and (D) confirmed positive COVID-19 diagnosis of the respondents’ significant other. Thus, external variables were simultaneously regressed on all the psychological constructs.

A Pearson correlation coefficient ( r ) was computed to evaluate the relationships between variables ( Tabachnick and Fidell, 2014 ).

A structural equation modeling (SEM) approach with latent variables was followed ( McDonald and Ho, 2002 ; Frazier et al., 2004 ; Weston and Gore, 2006 ; Iacobucci, 2008 ; Wiedermann and von Eye, 2015 ). A two-related separated predictors with a sequential multiple mediation model was specified ( MacKinnon, 2012 ; VanderWeele and Vansteelandt, 2014 ; Daniel et al., 2015 ; Hayes, 2017 ). The following procedure was performed.

Before examining the hypothesized model, the structural validity of each scale used in this study was tested by means of confirmatory factor analysis (CFAs). Considering the response scale of each of the questions administered in the study, the diagonally weighted least square (DWLS) estimator was used to perform each CFA separately ( Hoyle, 2012 ; Brown, 2015 ; Kline, 2016 ; Lionetti et al., 2016 ). Model fit was assessed by means of the following fit indices – and recommended cutoff values ( Bollen, 1989 ; Yu, 2002 ; Iacobucci, 2009 ; Hoyle, 2012 ; van de Schoot et al., 2012 ; Kline, 2016 ): (A) the Chi-square statistics (χ 2 ), preferably non-statistically significant ( p > 0.05) ( Bentler and Bonett, 1980 ; Muthén and Muthén, 1998-2017 ; Barrett, 2007 ); (B) the root-mean-square error of approximation (RMSEA), with values below 0.08 indicating an “acceptable” model fit and values below 0.05 indicating a “good” model fit ( Steiger and Lind, 1980 ; Steiger, 1990 ; Hu and Bentler, 1999 ; Barrett, 2007 ; van de Schoot et al., 2012 ); (C) the comparative fit index (CFI), with values between 0.90 and 0.95 for an “acceptable” fit ( Browne and Cudeck, 1989 ; Bentler, 1990 ; van de Schoot et al., 2012 ; Brown, 2015 ) and higher than 0.95 to indicate a “good” fit ( Bentler, 1990 ; Browne and Cudeck, 1992 ; Hu and Bentler, 1999 ), and (D) the standard root mean square residual (SRMR), with values lower than 0.08 considered a good model fit ( Hu and Bentler, 1999 ; Hoyle, 2012 ).

The Harman’s single-factor test was performed to check the potential “common method bias” ( Harman, 1976 ; Podsakoff et al., 2003 ; Brown, 2015 ). Firstly, a correlated factors model was specified: according to the measurement model, seven correlated factors (FCV19 – single factor, UCLA-LS3– three factors, RSE – single factor, ANX – single factor, and DEP – single factor) were specified – each item was specified to load onto its specific factor. Secondly, an alternative model was specified: a first-order single factor model was specified – all the items of the abovementioned scales were loaded onto a single latent dimension. Models were sequentially specified and compared using the test differences in goodness-of-fit indices (Δχ 2 : p > 0.050; ΔCFI: >0.010; ΔRMSEA: >0.015). Model comparisons were based on typical interpretation guidelines: for example, a statistically significant chi-square difference (Δχ 2 ; p < 0.050) and a ΔCFI greater than 0.010 suggest the absence of the bias ( Meredith, 1993 ; Vandenberg and Lance, 2000 ; Cheung and Rensvold, 2002 ; Chen, 2007 ; Millsap, 2012 ; Brown, 2015 ).

Latent factors were defined by using a partially disaggregated parcel approach in which latent constructs were defined by using parcels as indicators ( Bandalos and Finney, 2001 ; Coffman and MacCallum, 2005 ; Little et al., 2013 ). More in detail, since four scales were unidimensional (FCV-19S, RSE, ANX, and the DEP), item parcels were created using the “item-to-construct balance strategy” ( Bandalos and Finney, 2001 ; Little et al., 2002 ; Yang et al., 2010 ) – by inspecting factor loadings resulting from each measurement model ( Little et al., 2002 , 2013 ). However, since the UCLA-LS3 showed a hierarchical second-order structure, item parcels were created by using the “domain-representative strategy” ( Kishton and Widaman, 1994 ; Graham et al., 2000 ; Little et al., 2002 , 2013 ; Graham, 2004 ) – for each dimension, items were aggregated together. For each scale, at least a 3-item-parcel per latent variable were created – allowing each factor to be at least “just identified” – with factor loadings higher than |0.5| on the related construct ( Hoyle, 2012 ; Little et al., 2013 ; Brown, 2015 ; Kline, 2016 ). Once item parcels were created, descriptive statistics were examined: an almost normal distribution was found for the large majority of parcels. Thus, the maximum likelihood (ML) estimator was used for each SEM described in the following step (“Step 4”) ( Muthén and Muthén, 1998-2017 ; Hoyle, 2012 ; Kline, 2016 ). In addition, a 10,000 bootstrap resampling procedure was applied to each tested model ( MacKinnon, 2012 ).

The two-related separated predictors multiple mediation model was tested using a four-step approach ( MacKinnon et al., 2007 ; Iacobucci, 2009 ; Rucker et al., 2011 ; MacKinnon, 2012 ). Firstly , a predictors-only model was specified: fear of COVID-19 (X 1 ) and dispositional loneliness (X 2 ) predict depressive symptomatology (Y) – Figure 1 , Model 1. Secondly , a model was specified by excluding the effect of self-esteem (buffering variable) and dispositional loneliness: fear of COVID-19 (X 1 ) predict depressive symptomatology (Y) through anxiety symptoms (M) – Figure 1 , Model 2a. Thirdly , a parallel model was specified by excluding the effect of self-esteem (buffering variable) and fear of COVID-19: dispositional loneliness (X 2 ) predicts depressive symptomatology (Y) through anxiety symptoms (M) – Figure 1 , Model 2b. Fourthly , a semi-completed model was specified by only excluding the effect of self-esteem (buffering variable): fear of COVID-19 (X 1 ) and dispositional loneliness (X 2 ) predicts depressive symptomatology (Y) through anxiety symptoms (M) – Figure 1 , Model 3. Fifthly , the final model was specified by including the buffering effect of self-esteem: fear of COVID-19 (X 1 ) and dispositional loneliness (X 2 ) predict depressive symptoms (Y) through self-esteem (M 1 ) and anxiety symptoms (M 2 ) – Model 4, Figure 2 .

Figure 1. Graphical representation of the several mediation models tested.

Figure 2. Graphical representation of the full sequential multiple mediation model with two-related different predictors.

Each of the five models described in “Step 4” was evaluated using the abovementioned “goodness-of-fit” indices (χ 2 , RMSEA, CFI, SRMR) and their cutoffs values – and each tested model had to provide good fit indices ( Frazier et al., 2004 ; Iacobucci, 2010 ). In addition, to avoid possible biases related to the scaling method (by default, the first factor loading of each latent variable was fixed to 1), an alternative model was specified by fixing the variance of each latent variable to unity ( Gonzalez and Griffin, 2001 ). This procedure was repeated for each of the five models described above. Finally, all regression coefficients (β) reported in the text were unstandardized.

Preliminary Analysis

The multivariate multiple regression analysis showed no statistically significant effects of external variables on psychological constructs. More in detail, controlling for other external variables, no statistically significant effect of (A) Italian region of residence was found on FCV-19S (β = 0.515, SE = 0.289, z = 1.786, p = 0.074), UCLA-LS3 (β = 0.290, SE = 0.518, z = 0.561, p = 0.575), RSE (β = −0.191, SE = 0.255, z = −0.751, p = 0.453), and DEP (β = 0.073, SE = 0.041, z = 1.800, p = 0.072). A negligible effect was found on ANX (β = 0.095, SE = 0.043, z = 2.207, p = 0.027). Moreover, controlling for other external variables, no statistically significant effect of the (B) number of persons living with was found on UCLA-LS3 (β = −0.377, SE = 0.236, z = −1.599, p = 0.110), RSE (β = 0.089, SE = 0.119, z = 0.747, p = 0.455), ANX (β = 0.029, SE = 0.020, z = 1.444, p = 0.149), and DEP (β = −0.017, SE = 0.019, z = −0.910, p = 0.363). A small effect was found on FCV-19S (β = 0.483, SE = 0.140, z = 3.443, p = 0.001). Moreover, controlling for other external variables, no statistically significant effect of (C) confirmed positive COVID-19 diagnosis of the respondent was found on FCV-19S (β = 0.556, SE = 0.544, z = 1.022, p = 0.307), UCLA-LS3 (β = −0.067, SE = 0.863, z = −0.077, p = 0.939), RSE (β = 0.508, SE = 0.394, z = 1.290, p = 0.197), ANX (β = 0.026, SE = 0.087, z = 0.302, p = 0.763), and DEP (β = −0.059, SE = 0.074, z = −0.800, p = 0.424). Finally, controlling for other external variables, no statistically significant effect of the presence of (D) confirmed positive COVID-19 diagnosis of the respondents’ significant other was found on FCV-19S (β = 0.100, SE = 0.268, z = 0.372, p = 0.710), UCLA-LS3 (β = 0.502, SE = 0.413, z = 1.217, p = 0.223), RSE (β = 0.086, SE = 0.205, z = 0.419, p = 0.675), ANX (β = −0.021, SE = 0.040, z = −0.511, p = 0.609), and DEP (β = −0.022, SE = 0.034, z = −0.667, p = 0.505).

In addition, correlation analyses suggested small-to-large associations between the variables involved in the multiple mediation model ( Table 2 ).

Table 2. Mean, standard deviation, and correlations between observed variables.

Structural Models

The FCV19S showed adequate goodness-of-fit indices: χ 2 (14) = 88.338; p < 0.001; RMSEA = 0.067; 90%CI 0.054–0.080; p (RMSEA < 0.05) = 0.018, CFI = 0.996, SRMR = 0.038. Factor loadings of the items ranged from 0.705 (item#2) to 0.872 (item#5) ( mean = 0.778; SD = 0.065).

The UCLA-LS3 showed adequate goodness-of-fit indices: χ 2 (167) = 1261.908; p < 0.001; RMSEA = 0.074; 90%CI 0.070–0.078; p (RMSEA < 0.05) < 0.001, CFI = 0.985, SRMR = 0.059. Factor loadings of the first-order items ranged from 0.555 (item#7 – Relational connectedness) to 0.892 (item#14 – Relational connectedness) ( mean = 0.719; SD = 0.157). Factor loadings of the second-order items ranged from 0.785 (Isolation) to 0.939 (Relational connectedness) ( mean = 0.851; SD = 0.079).

Also the RSE revealed good results: χ 2 (35) = 249.239; p < 0.001; RMSEA = 0.071; 90%CI 0.063–0.080; p (RMSEA < 0.05) < 0.001, CFI = 0.990, SRMR = 0.052. Factor loadings of the items ranged from 0.541 (item#4) to 0.817 (item#2) ( mean = 0.704; SD = 0.105).

Even the ANX showed good fit indices: χ 2 (35) = 208.462; p < 0.001; RMSEA = 0.064; 90%CI 0.056–0.073; p (RMSEA < 0.05) = 0.003, CFI = 0.997, SRMR = 0.036. Factor loadings of the items ranged from 0.768 (item#2) to 0.887 (item#3) ( mean = 0.830; SD = 0.043).

Finally, also the DEP revealed good fit indices: χ 2 (65) = 310.064; p < 0.001; RMSEA = 0.056; 90%CI 0.050–0.062; p (RMSEA < 0.05) = 0.053, CFI = 0.994, SRMR = 0.046. Factor loadings of the items ranged from 0.448 (item#1) to 0.896 (item#8) ( mean = 0.724; SD = 0.110).

Harman’s Single-Factor Test

The Harman’s single-factor test showed the absence of the “common method bias.” Indeed, the CFA with seven correlated factors (FCV19 – single factor, UCLA-LS3– three factors, RSE – single factor, ANX – single factor, and DEP – single factor) provided good fit indices [χ 2 (1689) = 8434.991; p < 0.001; RMSEA = 0.058; 90%CI 0.056–0.059; p (RMSEA < 0.05) < 0.001, CFI = 0.983, SRMR = 0.060]. Contrarily, the CFA with a single latent factor provided poor fit indices [χ 2 (1710) = 54429.649; p < 0.001; RMSEA = 0.160; 90%CI 0.159–0.162; p (RMSEA < 0.05) < 0.001, CFI = 0.866, SRMR = 0.147]. Model comparison suggested the absence of the “common method bias”: Δχ 2 (21) = 45995, p < 0.001; |ΔRMSEA| = 0.103, and |ΔCFI| = 0.117.

Multiple Mediation Model

The first model ( Figure 1 , model 1) provided adequate goodness-of-fit indices: χ 2 (51) = 377.938; p < 0.001; RMSEA = 0.073; 90%CI 0.066–0.080; p (RMSEA < 0.05) < 0.001, CFI = 0.964, SRMR = 0.043 ( Table 3 ). The fear of COVID-19 (X 1 ) was positively associated with depressive symptomatology (Y): β = 0.537 (SE = 0.047) [95%CI: 0.452; 0.632], z = 11.551, p < 0.001. At the same time, the dispositional loneliness (X 2 ) was positively associated with depressive symptomatology (Y): β = 0.932 (SE = 0.060) [95%CI: 0.822; 1.057], z = 15.484, p < 0.001. Moreover, fear of COVID-19 and loneliness were statistically significantly associated: β = 0.199 (SE = 0.035) [95%CI: 0.129; 0.267], z = 5.601, p < 0.001.

Table 3. Parcel descriptive statistics and standardized factor loadings (λ).

The second model ( Figure 1 , model 2a) provided adequate goodness-of-fit indices: χ 2 (74) = 505.982; p < 0.001; RMSEA = 0.070; 90%CI 0.064–0.076; p (RMSEA < 0.05) < 0.001, CFI = 0.968, SRMR = 0.039 ( Table 3 ). The fear of COVID-19 (X 1 ) was positively associated with anxiety symptomatology (M): β = 1.257 (SE = 0.064) [95%CI: 1.140; 1.390], z = 19.566, p < 0.001. Moreover, anxiety symptomatology (M) predicted depressive symptoms (Y): β = 0.827 (SE = 0.054) [95%CI: 0.724; 0.937], z = 15.321, p < 0.001. Also, fear of COVID-19 was negatively associated with depressive symptomatology: β = −0.338 (SE = 0.069) [95%CI: −0.476; −0.205], z = −4.865, p < 0.001. Furthermore, the total indirect effect was statistically significant [β = 1.039 (SE = 0.072) [95%CI: 0.908; 1.188], z = 14.372, p < 0.001] as well as the total model effect [β = 0.701 (SE = 0.058) [95%CI: 0.590; 0.821], z = 11.986, p < 0.001] – thus suggesting a partially mediated path.

The third model ( Figure 1 , model 2b) provided adequate goodness-of-fit indices: χ 2 (74) = 583.259; p < 0.001; RMSEA = 0.076; 90%CI 0.070–0.082; p (RMSEA < 0.05) < 0.001, CFI = 0.958, SRMR = 0.043 ( Table 3 ). Dispositional loneliness (X 2 ) was positively associated with anxiety symptomatology (M): β = 0.366 (SE = 0.038) [95%CI: 0.293; 0.442], z = 9.631, p < 0.001. Moreover, anxiety symptomatology (M) predicted depressive symptomatology (Y): β = 0.988 (SE = 0.063) [95%CI: 0.874; 1.121], z = 15.752, p < 0.001. In this case, dispositional loneliness was positively associated with depressive symptomatology: β = 0.931 (SE = 0.066) [95%CI: 0.806; 1.065], z = 14.025, p < 0.001. The total indirect effect was statistically significant [β = 0.361 (SE = 0.042) [95%CI: 0.285; 0.449], z = 8.660, p < 0.001] as well as the total model effect [β = 1.292 (SE = 0.080) [95%CI: 1.147; 1.459], z = 16.074, p < 0.001] – thus suggesting, a partially meditated model.

The fourth model ( Figure 1 , model 3) provided adequate goodness-of-fit indices: χ 2 (113) = 703.306; p < 0.001; RMSEA = 0.066; 90%CI 0.061–0.071; p (RMSEA < 0.05) < 0.001, CFI = 0.962, SRMR = 0.043 ( Table 3 ). As shown for “Model 1,” fear of COVID-19 (X 1 ) and dispositional loneliness (X 2 ) were positively associated: β = 0.199 (SE = 0.036) [95%CI: 0.128; 0.270], z = 5.523, p < 0.001. Fear of COVID-19 (X 1 ) was also positively associated with anxiety symptomatology (M): β = 1.256 (SE = 0.064) [95%CI: 1.136; 1.389], z = 19.713, p < 0.001. At the same time, dispositional loneliness (X 2 ) was positively associated with anxiety symptomatology (M): β = 0.330 (SE = 0.040) [95%CI: 0.251; 0.410], z = 8.179, p < 0.001. Moreover, anxiety symptomatology (M) predicted depressive symptomatology (Y): β = 0.722 (SE = 0.060) [95%CI: 0.661; 0.896], z = 12.938, p < 0.001. Also, as shown in “Model 2a” fear of COVID-19 was negatively associated with depressive symptomatology [β = −0.288 (SE = 0.079) [95%CI: −0.451; −0.138], z = −3.639, p < 0.001] and, as for “Model 2b,” dispositional loneliness was positively associated with depressive symptomatology: β = 0.924 (SE = 0.067) [95%CI: 0.801; 1.064], z = 13.852, p < 0.001.

The first total indirect effect (fear of COVID-19 → anxiety symptomatology → depressive symptomatology) was statistically significant [β = 0.970 (SE = 0.082) [95%CI: 0.822; 1.145], z = 11.785, p < 0.001] as well as the total model effect [β = 0.682 (SE = 0.060) [95%CI: 0.579; 0.806], z = 11.306, p < 0.001] – thus suggesting a partially mediated model. In addition, the second total indirect effect (dispositional loneliness → anxiety symptomatology → depressive symptomatology) was statistically significant [β = 0.255 (SE = 0.034) [95%CI: 0.191; 0.326], z = 7.427, p < 0.001] as well as the total model effect [β = 1.179 (SE = 0.078) [95%CI: 1.187; 1.714], z = 15.102, p < 0.001] – thus suggesting a partially mediated model.

The final model ( Figure 2 ) provided satisfying goodness-of-fit indices: χ 2 (199) = 918.943; p < 0.001; RMSEA = 0.055; 90%CI 0.051–0.059; p (RMSEA < 0.05) = 0.012, CFI = 0.962, SRMR = 0.039 ( Table 3 ). As shown for “Model 1,” fear of COVID-19 (X 1 ) and dispositional loneliness (X 2 ) were positively associated: β = 0.199 (SE = 0.036) [95%CI: 0.126; 0.269], z = 5.484, p < 0.001. According to the ABH, fear of COVID-19 (X 1 ) was negatively associated with self-esteem (M 1 ): β = −0.160 (SE = 0.040) [95%CI: −0.237; −0.082], z = −4.015, p < 0.001, and self-esteem – in turn – negatively predicted anxiety symptomatology (M 2 ): β = −0.127 (SE = 0.045) [95%CI: −0.216; −0.039], z = −2.797, p = 0.005 – thus revealing the buffering effect of self-esteem. Finally, anxiety symptomatology (M 2 ) positively predicted depressive symptomatology (Y): β = 0.769 (SE = 0.060) [95%CI: 0.657; 0.894], z = 12.775, p < 0.001. In addition, in line with the ABH, self-esteem (M 1 ) was negatively associated with depressive symptomatology (Y): β = −0.371 (SE = 0.052) [95%CI: −0.474; −0.269], z = −7.095, p < 0.001 – further suggesting the buffering effect of self-esteem. Furthermore, fear of COVID-19 (X 1 ) was positively associated with anxiety symptomatology (M 2 ) [β = 1.245 (SE = 0.065) [95%CI: 1.128; 1.380], z = 19.283, p < 0.001] and in line with “Model 2a” and “Model 3” fear of COVID-19 (X 1 ) was negatively associated with depressive symptomatology (Y) [β = −0.309 (SE = 0.079) [95%CI: −0.471; −0.159], z = −3.924, p < 0.001].

At the same time, according to the ABH, dispositional loneliness (X 2 ) was negatively associated with self-esteem (M 1 ): β = −0.798 (SE = 0.055) [95%CI: −0.913; −0.695], z = −14.403, p < 0.001 – revealing the buffering effect of self-esteem. Furthermore, dispositional loneliness (X 2 ) was positively associated with anxiety symptomatology (M 2 ) [β = 0.231 (SE = 0.055) [95%CI: 0.125; 0.341], z = 4.211, p < 0.001] and, in line with “Model 2b” and “Model 3,” also positively associated with depressive symptomatology (Y) [β = 0.703 (SE = 0.072) [95%CI: 0.570; 0.854], z = 9.700, p < 0.001].

The first total indirect effect (fear of COVID-19 → self-esteem → anxiety symptomatology → depressive symptomatology) was statistically significant [β = 0.016 (SE = 0.007) [95%CI: 0.004; 0.030], z = 2.324, p = 0.020] as well as the total model effect [β = 0.724 (SE = 0.064) [95%CI: 0.604; 0.858], z = 11.252, p < 0.001] – suggesting a partially mediated model that highlighted the buffering effect of self-esteem.

In addition, the second total indirect effect (dispositional loneliness → self-esteem → anxiety symptomatology → depressive symptomatology) was statistically significant [β = 0.078 (SE = 0.030) [95%CI: 0.023; 0.140], z = 2.634, p = 0.008] as well as the total model effect [β = 1.154 (SE = 0.083) [95%CI: 1.008; 1.332], z = 13.967, p < 0.001] – thus suggesting a partially mediated model with the buffering effect of self-esteem ( Table 4 ).

Table 4. Summary of parameter estimates (beta) with 95% confidence intervals for key pathways tested full model, Model 4 – Figure 2 .

Recently, the potential negative impact that the adverse psychological consequences of COVID-19 further had on the disease itself have been highlighted in the literature ( Center for Disease Control and Prevention, 2020 ; Lima et al., 2020 ; Parola, 2020 ; Parola et al., 2020 ; Thakur and Jain, 2020 ; Wind et al., 2020 ). Indeed, the advent of COVID-19 generated intense fear and anxiety about contagion, disease, and thoughts of death in the general population. At the same time, the sense of isolation was amplified by dispositional loneliness during the COVID-19 lockdown, with a consequent increase of anxiety symptoms. Therefore, both a fear of COVID-19 and dispositional loneliness represent major risk factors for the development of symptoms of anxiety and following symptoms of depression.

This study highlighted the buffering-effect of self-esteem on the relationships between negative psychological constructs, such as a fear of COVID-19 and dispositional loneliness feelings (predictors), and their consequent adverse psychological correlates – anxiety and depression (outcomes) during the COVID-19 pandemic.

In line with the scientific literature showing that (prolonged) fear can lead to depression ( Bowman, 2001 ), this study revealed that a fear of COVID-19 and loneliness might lead to depressive symptoms ( Santini et al., 2020 ; Thakur and Jain, 2020 ). Indeed, the first model that has been tested (Model 1 – predictors only) showed a positive relationship between a fear of COVID-19 and depressive symptomatology, with higher fear predicting higher depressive symptomatology. Indeed, when controlling for loneliness, an increase of 1 point in fear of COVID-19 was associated with an increase of 0.537 points in depression. At the same time, loneliness was positively associated with depressive symptoms: an increase in 1 point in dispositional loneliness was associated with an increase of 0.932 points in depression. These results suggest that a prolonged state of fear and dispositional feelings of loneliness might lead to the development of adverse psychological symptoms – thus representing major risk factors for the onset of symptoms of depression.

However, when controlling for anxiety activation (Model 3), fear (of COVID-19) and depression showed a negative association, probably due to the different nature of these emotional states. Indeed, on one hand, fear represents an activating emotion prompting the organism to react with the well-known “fight or flight” response. On the other hand, depression is characterized by a generalized de-activation, reflected in slowed-down behavior and thinking as well as flattened affectivity and pleasure ( Beck, 1979 ; Harper et al., 2020 ). At the same time, fear was positively strongly associated with anxiety symptoms ( Barlow, 2002 ; Harding et al., 2008 ), which might lead to depression ( Bowman, 2001 ) – thus suggesting a partially mediated model starting from fear up to depression through anxiety.

Simultaneously – when controlling for anxiety – dispositional loneliness was positively associated with depressive symptoms, further highlighting the existence of a strong relationship between these two constructs ( Cacioppo et al., 2014 ; Santini et al., 2020 ). Also, dispositional loneliness was positively associated with anxiety symptoms leading to depression ( Thompson et al., 2005 ; Starr and Davila, 2012 ) – suggesting, a partially mediated model (Model 3).

However, in line with the hypotheses, the final model (Model 4) highlighted the buffering role of self-esteem: despite positive associations held between fear of COVID-19, dispositional loneliness, and anxiety, the effect of self-esteem slowed down these negative adverse paths. Indeed – in line with the ABH and the TMT ( Greenberg et al., 1986 , 1992 ) – self-esteem had negative relationships with all the other psychological constructs (negative β values) due to its buffering effect hampering the relationships between adverse psychological variables. A partial mediation model was, therefore, suggested given that the relationship between fear of COVID-19, dispositional loneliness, anxiety, and depression held even when their paths were buffered by self-esteem.

Summarizing, results showed that self-esteem had a buffer effect protecting against anxiety symptoms triggered by a fear of COVID-19 and dispositional loneliness. Thus, these findings confirmed the validity of the ABH in the context of the COVID-19 pandemic.

Results also highlighted that both a fear of COVID-19 and dispositional loneliness were able to trigger unbearable feelings of anxiety that, in turn, were strongly linked to depressive symptomatology.

The strict interconnection between self-esteem and loneliness was probably due to the fact that loneliness is often related to negative self-evaluations, and feelings of being worthless, inferior, or unlovable ( Heinrich and Gullone, 2006 ). Previous studies suggested that self-esteem may impact on loneliness as a reinforcer or a buffer, as instances of influencing the relational competences ( Olmstead et al., 1991 ; Brage and Meredith, 1994 ; Heinrich and Gullone, 2006 ).

These results are in line with previous scientific literature highlighting that self-esteem can be a mediator in the relationship between loneliness, anxiety, and depression ( Brage and Meredith, 1994 ; Heinrich and Gullone, 2006 ; Çivitci and Çivitci, 2009 ).

Regarding the clinical implications of this study, its findings suggest a possible intervention strategy to provide psychological support to people suffering from the emotional consequences of COVID-19 and other COVID-19-related issues in order to alleviate the psychological outbreak of the pandemic. Indeed, according to the ABH, if self-esteem provides protection against stressors, such sources of stress should increase the need for self-esteem to relieve psychological burden ( Harmon-Jones et al., 1997 ). Consequently, increased self-esteem should function as a buffer toward anxiety, reducing the adverse psychological issues in response to threats or stressors. Thus, psychological interventions targeting self-esteem can represent an effective strategy to attenuate the distressing psychological responses to COVID-19 fear and dispositional feelings of loneliness – particularly among populations most susceptible and vulnerable to the negative psychological effects of the COVID-19 pandemic, including people with psychiatric disorders, those at risk of domestic violence, elderly people, and health-care practitioners ( Lai et al., 2020 ; Armitage and Nellums, 2020 ; Yao et al., 2020 ).

Moreover, given that loneliness derives from the perceived discrepancy between the actual and desired quality of relationships ( Peplau and Perlman, 1982 ), these results highlight the importance of perceived social support and positive relationships for people ( Ratti et al., 2017 ; Panzeri et al., 2019 ; Duan and Zhu, 2020 ). Individuals should, therefore, be guided and educated in strengthening their relationships and social support resources when physical contact is not possible (i.e., quarantine, hospitalization) by adopting tele-communication tools, such as smartphones. In line with the debated internet-paradox, proper technology use should be promoted to prevent distressing feelings ( Moody, 2001 ; Enez Darcin et al., 2016 ; Király et al., 2020 ).

Some noteworthy limitations of this study need to be acknowledged. Due to the observational/correlational nature of the research design, it was not possible to establish a causal relationship among variables, but only predictive relationships – still in line with the study purpose ( Fiedler et al., 2011 ). Moreover, the self-report nature of the online survey may convey intrinsic biases related to social desirability and other well-known issues ( Vidotto et al., 2018 ). Other limitations of this study were the prevalence of females in the sample and that the fact that geographical areas in Italy were not equally represented – although preliminary analyses showed no associated effects. Likewise, no differences emerged from sociodemographic characteristics, but future studies should deepen their possible role as protective/risk factors (i.e., presence vs. absence of social support) ( Mannarini et al., 2017a ). In addition, multi-group analyses assessing tested models across sex (males vs. females) were not performed. However, due to the small male sample size, multi-group mediation analyses would not be able to provide an accurate estimation of model parameters ( Hoyle, 2012 ; Kline, 2016 ). Future studies should, therefore, further test potential effects of sex on the suggested models. Moreover, all participants were Italian and possible effects of cross-cultural differences cannot be considered. Even though the ABH was successfully replicated in various countries as well as in different contexts ( Pyszczynski et al., 2004 ), future studies specifically examining the impact of COVID-19 on people’s lives should compare these results among different countries thus increasing the generalizability of these findings.

Finally, a mediation model was preferred to a moderation one for both theoretical and statistical reasons. Indeed, from a theoretical perspective a mediation-based approach is closer and more related to the original ABH and the TMT ( Greenberg et al., 1986 , 1992 ), conceptualizing self-esteem as an intermediating buffer between life-threatening stressors and anxiety ( Pyszczynski et al., 2004 ). In fact, self-esteem not only is able to influence individuals’ levels of anxiety and depression, but it is itself influenced by negative psychological experiences – such as fear and loneliness – activating negative cognitions and emotions that significantly affect the idea of oneself ( Greenberg et al., 1986 ; Heinrich and Gullone, 2006 ; Sowislo and Orth, 2013 ). Research show that fear can threaten self-evaluation ( Greenberg et al., 1986 ), and that people experiencing higher feelings of loneliness also have a worse self-evaluation ( Heinrich and Gullone, 2006 ). More in detail, negative experiences can activate both negative cognitions and emotions that significantly affect the idea of oneself (i.e., “I am a failure”, “I am worthless”) ( Beck, 1979 ) – thus, leaving scars in the self-concept, as well as persistently threatening and reducing self-esteem and self-efficacy ( Mannarini, 2010 ; Sowislo and Orth, 2013 ). Thus, a moderation approach would not suit the theoretical background of this study, and would not allow us to properly take into account the complexity of relationships among the considered psychological constructs. Regarding the strengths of the present study, it relies on a well-grounded theoretical basis supported by several experimental and longitudinal studies ( Greenberg et al., 1992 ; Brage and Meredith, 1994 ; Pyszczynski et al., 2004 ; Heinrich and Gullone, 2006 ). A wide sample of individuals from the general population was analyzed with strong statistical methodologies ( Iacobucci et al., 2007 ; MacKinnon, 2012 ; MacKinnon et al., 2012 , 2013 ) providing good results ( McDonald and Ho, 2002 ; Hayes, 2009 ; Iacobucci, 2010 ; Preacher, 2015 ). Moreover, the hypothesized models resulted in having a good fit, even if other solutions would have been possible but with lower fit indexes.

Given that individuals faced similar problems during past epidemics, findings from this study could also be generalized and applied to support people still coping with the negative consequences that previous disease outbreaks had on their mood (i.e., Ebola, SARS, MERS, and tuberculosis) ( Brown and Lees-Haley, 1992 ; Betancourt et al., 2016 ; Huremović, 2019 ; Chew et al., 2020 ). In a broader sense, these results could be extended to relieve the psychological burden of dysfunctional psychological reactions in response to physical and/or psychological illnesses ( Rossi Ferrario and Panzeri, 2020 ).

Overall, this study contributes to the current debate about the psychological implications of the COVID-19 pandemic, a prolonged and distressing situation triggering or worsening psychological issues. These findings may also be useful to help clinicians develop efficient and tailored interventions for increasing individuals’ mental health – with particular attention to the more fragile categories, such as young people and elderly people ( Parola and Donsì, 2018 , 2019 ; Balestroni et al., 2020 ).

Although, a considerable number of individuals may avoid seeking professional psychological help ( Rossi and Mannarini, 2019 ) due to the associated stigma ( Mannarini et al., 2017b , 2018 , 2020 ; Faccio et al., 2019 ; Mannarini and Rossi, 2019 ) or because of defensive denial reactions toward their psychological difficulties ( Sareen et al., 2007 ; Rossi Ferrario et al., 2019 ), thus choosing to manage the psychological issues on their own ( Wilson and Deane, 2012 ).

Future research will provide further insight about the evolution over time of the psychological issues related to COVID-19. Future studies might examine the role of social support as well as the changes in the dynamics of social and family relationships ( Mannarini et al., 2013 , 2017a ; Balottin et al., 2017 ).

Still, the role of other psychological constructs that may act as protective or risk factors, such as anger, post-traumatic symptoms, hopelessness, and denial should be further explored in future research in order to find effective treatment strategies to adopt in order to deal with consequences of both the COVID-19 and future pandemics.

The present research offers further support for the anxiety-buffer role of self-esteem, confirming TMT to be a well-grounded theoretical framework offering interesting and useful clinical insights in the context of the COVID-19 pandemic. Targeted psychological interventions should be implemented to properly support individuals suffering from COVID-19-related issues in order to minimize the psychological burden of the disease whilst promoting adaptation and positive psychological health outcomes.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, on reasonable requests.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethic Committee of the University of Padua. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AR conceived the study, performed the statistical analyses, and wrote the first draft. AP wrote a large part of the first draft and collected the data. GP helped with data collection and wrote part of the first draft. GM, GC, and SM provided important intellectual revisions. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : COVID-19, anxiety buffer hypothesis, terror management theory, anxiety, depression, self-esteem, fear, loneliness

Citation: Rossi A, Panzeri A, Pietrabissa G, Manzoni GM, Castelnuovo G and Mannarini S (2020) The Anxiety-Buffer Hypothesis in the Time of COVID-19: When Self-Esteem Protects From the Impact of Loneliness and Fear on Anxiety and Depression. Front. Psychol. 11:2177. doi: 10.3389/fpsyg.2020.02177

Received: 01 June 2020; Accepted: 03 August 2020; Published: 10 November 2020.

Reviewed by:

Copyright © 2020 Rossi, Panzeri, Pietrabissa, Manzoni, Castelnuovo and Mannarini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alessandro Rossi, [email protected] ; [email protected]

† ORCID: Alessandro Rossi, orcid.org/0000-0001-7000-5999 ; Anna Panzeri, orcid.org/0000-0001-5999-858X ; Giada Pietrabissa, orcid.org/0000-0002-5911-5748 ; Gian Mauro Manzoni, orcid.org/0000-0003-3384-0359 ; Gianluca Castelnuovo, orcid.org/0000-0003-2633-9822 ; Stefania Mannarini, orcid.org/0000-0002-8446-785X

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Mental health and the pandemic: What U.S. surveys have found

The coronavirus pandemic has been associated with worsening mental health among people in the United States and around the world . In the U.S, the COVID-19 outbreak in early 2020 caused widespread lockdowns and disruptions in daily life while triggering a short but severe economic recession that resulted in widespread unemployment. Three years later, Americans have largely returned to normal activities, but challenges with mental health remain.

Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic. These findings reflect a snapshot in time, and it’s possible that attitudes and experiences may have changed since these surveys were fielded. It’s also important to note that concerns about mental health were common in the U.S. long before the arrival of COVID-19 .

Three years into the COVID-19 outbreak in the United States , Pew Research Center published this collection of survey findings about Americans’ challenges with mental health during the pandemic. All findings are previously published. Methodological information about each survey cited here, including the sample sizes and field dates, can be found by following the links in the text.

The research behind the first item in this analysis, examining Americans’ experiences with psychological distress, benefited from the advice and counsel of the COVID-19 and mental health measurement group at Johns Hopkins Bloomberg School of Public Health.

At least four-in-ten U.S. adults (41%) have experienced high levels of psychological distress at some point during the pandemic, according to four Pew Research Center surveys conducted between March 2020 and September 2022.

Young adults are especially likely to have faced high levels of psychological distress since the COVID-19 outbreak began: 58% of Americans ages 18 to 29 fall into this category, based on their answers in at least one of these four surveys.

Women are much more likely than men to have experienced high psychological distress (48% vs. 32%), as are people in lower-income households (53%) when compared with those in middle-income (38%) or upper-income (30%) households.

In addition, roughly two-thirds (66%) of adults who have a disability or health condition that prevents them from participating fully in work, school, housework or other activities have experienced a high level of distress during the pandemic.

The Center measured Americans’ psychological distress by asking them a series of five questions on subjects including loneliness, anxiety and trouble sleeping in the past week. The questions are not a clinical measure, nor a diagnostic tool. Instead, they describe people’s emotional experiences during the week before being surveyed.

While these questions did not ask specifically about the pandemic, a sixth question did, inquiring whether respondents had “had physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart” when thinking about their experience with the coronavirus outbreak. In September 2022, the most recent time this question was asked, 14% of Americans said they’d experienced this at least some or a little of the time in the past seven days.

More than a third of high school students have reported mental health challenges during the pandemic. In a survey conducted by the Centers for Disease Control and Prevention from January to June 2021, 37% of students at public and private high schools said their mental health was not good most or all of the time during the pandemic. That included roughly half of girls (49%) and about a quarter of boys (24%).

In the same survey, an even larger share of high school students (44%) said that at some point during the previous 12 months, they had felt sad or hopeless almost every day for two or more weeks in a row – to the point where they had stopped doing some usual activities. Roughly six-in-ten high school girls (57%) said this, as did 31% of boys.

On both questions, high school students who identify as lesbian, gay, bisexual, other or questioning were far more likely than heterosexual students to report negative experiences related to their mental health.

Mental health tops the list of worries that U.S. parents express about their kids’ well-being, according to a fall 2022 Pew Research Center survey of parents with children younger than 18. In that survey, four-in-ten U.S. parents said they’re extremely or very worried about their children struggling with anxiety or depression. That was greater than the share of parents who expressed high levels of concern over seven other dangers asked about.

While the fall 2022 survey was fielded amid the coronavirus outbreak, it did not ask about parental worries in the specific context of the pandemic. It’s also important to note that parental concerns about their kids struggling with anxiety and depression were common long before the pandemic, too . (Due to changes in question wording, the results from the fall 2022 survey of parents are not directly comparable with those from an earlier Center survey of parents, conducted in 2015.)

Among parents of teenagers, roughly three-in-ten (28%) are extremely or very worried that their teen’s use of social media could lead to problems with anxiety or depression, according to a spring 2022 survey of parents with children ages 13 to 17 . Parents of teen girls were more likely than parents of teen boys to be extremely or very worried on this front (32% vs. 24%). And Hispanic parents (37%) were more likely than those who are Black or White (26% each) to express a great deal of concern about this. (There were not enough Asian American parents in the sample to analyze separately. This survey also did not ask about parental concerns specifically in the context of the pandemic.)

Looking back, many K-12 parents say the first year of the coronavirus pandemic had a negative effect on their children’s emotional health. In a fall 2022 survey of parents with K-12 children , 48% said the first year of the pandemic had a very or somewhat negative impact on their children’s emotional well-being, while 39% said it had neither a positive nor negative effect. A small share of parents (7%) said the first year of the pandemic had a very or somewhat positive effect in this regard.

White parents and those from upper-income households were especially likely to say the first year of the pandemic had a negative emotional impact on their K-12 children.

While around half of K-12 parents said the first year of the pandemic had a negative emotional impact on their kids, a larger share (61%) said it had a negative effect on their children’s education.

• Coronavirus (COVID-19)
• Happiness & Life Satisfaction
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John Gramlich is an associate director at Pew Research Center .

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, economy remains the public’s top policy priority; covid-19 concerns decline again, most popular.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• Mental Health

The prevalence inflation hypothesis: Are mental health awareness efforts contributing to the rise in mental health problems?

• October 2022

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Do you have a mental illness? Why some people answer ‘yes’, even if they haven’t been diagnosed

PhD Candidate at Melbourne School of Psychological Sciences, The University of Melbourne

Professor of Psychology, The University of Melbourne

Disclosure statement

Nick Haslam receives funding from the Australian Research Council.

Jesse Tse does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Mental illnesses such as depression and anxiety disorders have become more prevalent, especially among young people . Demand for treatment is surging and prescriptions of some psychiatric medications have climbed.

These upswinging prevalence trends are paralleled by rising public attention to mental illness. Mental health messages saturate traditional and social media. Organisations and governments are developing awareness, prevention and treatment initiatives with growing urgency.

The mounting cultural focus on mental health has obvious benefits. It increases awareness, reduces stigma and promotes help-seeking.

However, it may also have costs. Critics worry social media sites are incubating mental illness and that ordinary unhappiness is being pathologised by the overuse of diagnostic concepts and “ therapy speak ”.

British psychologist Lucy Foulkes argues the trends for rising attention and prevalence are linked. Her “ prevalence inflation hypothesis ” proposes that increasing awareness of mental illness may lead some people to diagnose themselves inaccurately when they are experiencing relatively mild or transient problems.

Foulkes’ hypothesis implies that some people develop overly broad concepts of mental illness. Our research supports this view. In a new study, we show that concepts of mental illness have broadened in recent years – a phenomenon we call “ concept creep ” – and that people differ in the breadth of their concepts of mental illness.

Why do people self-diagnose mental illnesses?

In our new study , we examined whether people with broad concepts of mental illness are, in fact, more likely to self-diagnose.

We defined self-diagnosis as a person’s belief they have an illness, whether or not they have received the diagnosis from a professional. We assessed people as having a “broad concept of mental illness” if they judged a wide variety of experiences and behaviours to be disorders, including relatively mild conditions.

We asked a nationally representative sample of 474 American adults if they believed they had a mental disorder and if they had received a diagnosis from a health professional. We also asked about other possible contributing factors and demographics.

Mental illness was common in our sample: 42% reported they had a current self-diagnosed condition, a majority of whom had received it from a health professional.

Unsurprisingly, the strongest predictor of reporting a diagnosis was experiencing relatively severe distress.

The second most important factor after distress was having a broad concept of mental illness. When their levels of distress were the same, people with broad concepts were substantially more likely to report a current diagnosis.

The graph below illustrates this effect. It divides the sample by levels of distress and shows the proportion of people at each level who report a current diagnosis. People with broad concepts of mental illness (the highest quarter of the sample) are represented by the dark blue line. People with narrow concepts of mental illness (the lowest quarter of the sample) are represented by the light blue line. People with broad concepts were much more likely to report having a mental illness, especially when their distress was relatively high.

People with greater mental health literacy and less stigmatising attitudes were also more likely to report a diagnosis.

Two interesting further findings emerged from our study. People who self-diagnosed but had not received a professional diagnosis tended to have broader illness concepts than those who had.

In addition, younger and politically progressive people were more likely to report a diagnosis, consistent with some previous research , and held broader concepts of mental illness. Their tendency to hold these more expansive concepts partially explained their higher rates of diagnosis.

Why does it matter?

Our findings support the idea that expansive concepts of mental illness promote self-diagnosis and may thereby increase the apparent prevalence of mental ill health. People who have a lower threshold for defining distress as a disorder are more likely to identify themselves as having a mental illness.

Our findings do not directly show that people with broad concepts over-diagnose or those with narrow concepts under-diagnose. Nor do they prove that having broad concepts causes self-diagnosis or results in actual increases in mental illness. Nevertheless, the findings raise important concerns.

First, they suggest that rising mental health awareness may come at a cost . In addition to boosting mental health literacy it may increase the likelihood of people incorrectly identifying their problems as pathologies.

Inappropriate self-diagnosis can have adverse effects. Diagnostic labels may become identity-defining and self-limiting, as people come to believe their problems are enduring, hard-to-control aspects of who they are.

Second, unwarranted self-diagnosis may lead people experiencing relatively mild levels of distress to seek help that is unnecessary, inappropriate and ineffective. Recent Australian research found people with relatively mild distress who received psychotherapy worsened more often than they improved.

Third, these effects may be particularly problematic for young people. They are most liable to hold broad concepts of mental illness, in part due to social media consumption , and they experience mental ill health at relatively high and rising rates. Whether expansive concepts of illness play a role in the youth mental health crisis remains to be seen.

Ongoing cultural shifts are fostering increasingly expansive definitions of mental illness. These shifts are likely to have mixed blessings. By normalising mental illness they may help to remove its stigma. However, by pathologising some forms of everyday distress, they may have an unintended downside.

As we wrestle with the mental health crisis, it is crucial we find ways to increase awareness of mental ill health without inadvertently inflating it.

• Mental health
• Mental illness
• Overdiagnosis
• Health research
• New research, Australia New Zealand

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• Research article
• Open access
• Published: 21 March 2017

Anxiety and depression symptoms and migraine: a symptom-based approach research

• Mario Fernando Prieto Peres 1 , 4 ,
• Juliane P. P. Mercante 1 ,
• Patricia R. Tobo 2 ,
• Helder Kamei 2 &
• Marcelo Eduardo Bigal 3  

The Journal of Headache and Pain volume  18 , Article number:  37 ( 2017 ) Cite this article

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Anxiety and mood disorders have been shown to be the most relevant psychiatric comorbidities associated with migraine, influencing its clinical course, treatment response, and clinical outcomes. Limited information is available on how specific anxiety and depression symptoms are related to migraine. Symptoms-based approach, a current trend in mental health research, may improve our understanding in migraine comorbidity. The purpose of this study was to analyze how anxiety and depression aspects are related to migraine through a symptom-based approach.

We studied 782 patients from the general population who completed a self-administered questionnaire assessing demographics, headache features, anxiety and depression symptoms. A binary logistic regression analyses were conducted to test the association between all four ratings in GAD-7 (anxiety) and PHQ-9 (depression) scales subitems as covariates, and migraine vs no headache as the outcome.

The leading Odd Ratios (OR) observed in individuals with migraine relative to those without migraine were anxiety related, “Not being able to stop or control worrying” on a daily basis [OR (CI 95%)] 49.2 (13.6–178.2), “trouble relaxing” 25.7 (7.1–92.6), “Feeling nervous, anxious or on edge” on a daily basis 25.4 (6.9–93.8), and “worrying too much about different things” 24.4 (7.7–77.6). Although the hallmark symptoms of depression are emotional (hopelessness and sadness), the highest scores found were physical: apetite, fatigue, and poor sleep. Irritability had a significant increase in migraine risk [OR 3.8 (1.9–7.8) if experienced some days, 7.5 (2.7–20.7) more than half the days, and 22.0 (5.7–84.9) when experienced nearly every day].

Conclusions

Anxiety was more robustly associated with increase in migraine risk than depression. Lack of ability to properly control worrying and to relax are the most prominent issues in migraine psychiatric comorbidity. Physical symptoms in depression are more linked to migraine than emotional symptoms. A symptom-based approach helps clarifying migraine comorbidity and should be replicated in other studies.

Migraine is known to be a multifactorial disorder, with genetic, hormonal, environmental, dietary, sleep and psychological aspects playing a different role in each individual [ 1 ], it is considered a bio-behavioural disorder [ 2 ].

Anxiety and mood disorders have been shown to be the most relevant psychiatric comorbidities associated with migraine, influencing disease prevalence, prognosis, treatment and clinical outcomes [ 3 ]. Studies reported that mood and anxiety disorders are two to ten times more common in migraineurs than in the general population [ 4 ]. Furthermore, they are related, both at the clinical settings [ 5 ] and general population [ 6 ], to poorer quality of life [ 7 ] and increased suicide risk [ 8 ]. In addition, psychiatric comorbidities are risk factors for the progression from episodic to chronic migraine [ 9 ]. Patients with tension type headache and comorbid psychiatric disorders often exhibit affective temperament dysregulation and suicidal behaviors [ 10 , 11 ].

Anxiety and mood disorders coexist in the general population at large and also in headache patients [ 5 ]. The hallmark of anxiety is excessive of worry, while in depression lack of energy, motivation and sadness prevail. Both disorders display significant physical symptoms. In anxiety, irritability, concentration problems, and agitation are common, whereas in depression, fatigue, concentration, sleep and appetite are included in the diagnostic definition [ 12 ]. Although anxiety and mood disorders are consolidated as the two main psychological issues related to migraine, limited information is available on what symptoms or aspects are more relevant. A more detailed study looking into these variables is necessary.

Our objective was to test the hypothesis that migraine might be not equally related to anxiety and mood symptoms, so we designed a symptom-based approach study, a current trend in mental health research, to answer this question.

This was a population-based, cross-sectional study conducted from August to October 2014 in a representative sample of the Brazilian population and drawn from a panel maintained by Qualtrics. Participants were invited by e-mail to complete a self-administered, online survey, in return for incentives/cash honorarium (as established a priori within the panels’ agreements). As surveys were being completed, response patterns were monitored against established quotas and made decisions about sampling in order to meet them. Quotas have been set in order to limit the respondents according to social class distribution, age, gender, geographic location so the population surveyed could meet the same profile of the general adult population in Brazil, according to the 2010 census.

Security checks and quality verifications were used on all sources before respondents had begun the survey. Participants were included if they had online access to the Internet and were excluded if their email was no longer valid, if they did not answered or incomplete answered the questionnaire or if they did not accept the consent term. Quality checks questions and attention filters were added. Questions were divided into five blocks randomized so the impact of tiredness of respondents affected equally all questions. Force response validation was included in all questions. The length of interview was less than 30 min.

In order to evaluate how much the online population would differ from a general population, the same questionnaire was completed by a door-to-door population of 201 randomly assigned individuals in the city of São Paulo (Brazil). No differences between groups were found in education ( p  = 0.793), income ( p  = 0.078), BMI ( p  = 0.662) and gender ( p  = 0.313), but age in the online sample was younger (34.5 vs. 39.8, p  < 0.001). All participants provided written consent. The protocol was approved by the Albert Einstein Hospital ethics committee.

Instruments

Respondents were asked to complete a self-administered questionnaire assessing the following sociodemographics: gender, age, family income, self-reported weight and height, marital status and education. They were also asked about symptoms necessary to assign a migraine diagnoses as per the International Classification of Headache Disorders, second edition (ICHD-2) criteria [ 13 ].

Anxiety was assessed with the “General Anxiety Disorder-7” (GAD-7) scale; while depression was estimated with the “Patient Health Questionnaire-9” (PHQ-9) scale. Data on religiousness, optimism, life satisfaction, and happiness were analyzed elsewhere.

GAD-7 [ 14 ] is a questionnaire designed to assess anxiety symptoms, patients are invited to respond to 7 questions assessing a two-weeks period. Questions are : A) Feeling nervous, anxious or on edge; B) Not being able to stop or control worrying; worrying too much about different things; C) Trouble relaxing; D) Being so restless that is hard to sit still, E) becoming easily annoyed or irritable, F) Feeling afraid as if something awful might happen. Four alternatives are offered: 1. Not at all, 2. Several days, 3. More than half the days, and 4. Nearly every day. Scores can range from 0 to 21. The scale has been validated to portuguese [ 15 ].

The PHQ-9 is a 9-item scoring scale designed and validated for diagnosis and grading depression based on DSM-IV criteria, including the following aspects : (1) anhedonia; (2) depressed mood; (3) trouble sleeping; (4) feeling tired; (5) change in appetite; (6) guilt, self-blame, or worthlessness; (7) trouble concentrating; (8) feeling slowed down or restless; and (9) thoughts of being better off dead or hurting oneself [ 16 ]. Symptoms are rated using a 4-point scale (0 - never; 1 – several days; 2 - more than half the time; and 3 - nearly every day) regarding the past two weeks experienced. The overall scores ranged from 0 to 27. Validation to portugueses is available [ 17 ].

Participants were not tested for psychiatric conditions and data regarding psychoactive substances were not collected.

Statistical analysis

Sample size calculation was done based on previous studies, we estimated a difference in 10% between migraine and controls in anxiety and depression symptoms, with a 5% significancy and power of 80%, a target of 209 patients were necessary.

Binary logistic regression was applied [ 18 ]. Covariates were items in GAD-7 and PHQ-9 scales, and outcome was migraine vs no headache in the past year, never reporting migraine in life. Control group had no symptoms of headache, and was free from any reported complicating medical or psychiatric conditions. Only the online population was used in the analysis.

Univariate binary logistic regression analyses were conducted to test the association between each covariate and the outcome [ 19 ]. All four ratings in GAD-7 and PHQ-9 subitems were analyzed as covariates. Sociodemographic variables were included and the results adjusted accordingly. The assumption of linearity in the logit scale (log-odds) between each quantitative covariate and the outcome in binary logistic regression analysis was assessed by examination of smoothed scatter plots. A multiple test correction with Bonferroni procedure was performed. The software R (R Foundation for Statistical Computing, Vienna, Austria) [ 20 ] was used for statistical analyses. A p -value of <0.05 was considered to be statistically significant, and all reported p-values were two-sided.

From 967 participants reached, 782 (80.8%) completed the questionnaire. The main reason for uncompleted responses was availability of less than 70% of valid or finished answers. Participants were women in 51.0%, mean age of 34.4years (SD: 11.3), married (51.5%), with university level education – completed or in course (50.6%) and with an income of more than US$400.00 (65.8%). 213 patients fit diagnostic criteria for migraine. Table  1 describes migraine characteristics. Tables  2 and 3 shows odds ratios and confidence intervals for GAD-7 and PHQ-9 subitems as covariates in univariate logistic regression 1 is the compared aspect wich is none in the aspect, 2 is some, 3 is moderate, 4 is severe. Binary outcomes migraine versus no pain controls. Figure  1 shows GAD-7 and PHQ-9 subitens ORs with ratings 4, 3, 2 versus 1 (none).

Odds ratios for migraine risk according to anxiety ( left ) and depression ( right ) symptoms

All anxiety and depression items were significantly related to migraine compared to non-headache controls ( p  < 0.05), with the exception of death thoughts at the two higher ratings.

When looking at anxiety and depression symptoms and their relation to migraine, odds ratio are much higher for anxiety symptoms than for depressive symptoms (Table  2 ). Anxiety items had increasingly higher odds, meanignn that the higher the score, the higher the odds for migraine. This was not seen for the majority of depression symptoms. The leading ORs observed were anxiety related, “Not being able to stop or control worrying” on a daily basis [OR (CI 95%)] 49.2 (13.6–178.2), “trouble relaxing” 25.7 (7.1–92.6), “Feeling nervous, anxious or on edge” on a daily basis 25.4 (6.8–93.8), and “worrying too much about different things” 24.4 (7.6–77.6).

Depressive symptoms scored the highest odds were physical symptoms, first “poor appetite or overeating” [OR (CI 95%)] 6.74 (3.2–14.3) for some days, 9.6 (3.8–24.4) when experienced more than half the days, and 23.3 (6.8–79.2) when experienced nearly every day. “Feeling tired or having little energy was 9.30 (3.8–22.6), 19.7 (6.6–59.0), and 20.8 (6.2–70.1); “trouble falling or staying asleep or sleeping too much” 5.9 (2.9–11.9), 3.5 (1.3–9.4), 16.2 (5.4–48.8).

The results presented in this paper provide information on the relation between anxiety, depression and migraine. It shows higher odds in anxiety than depression, and found some inner aspects of anxiety and depression more important than others. Previously published articles showed significant implication of depression and anxiety in migraine, but which aspects, domains or symptoms are more relevant have not been studied in great detail. A recent study focused on affective disorders symptoms in migraineurs [ 3 ] analysing patients from different Dutch databases, LUMINA (migraine) and NESDA (depression and anxiety), using the Mood and Anxiety Questionnaire (MASQ-30) to assess three dimensions —lack of positive affect (depression specific); negative affect (nonspecific); and somatic arousal (anxiety specific), the later being the most important.

Specific aspects or symptoms in depression and/or anxiety may be more relevant for migraineurs, being more sensitive than non-migraine headache sufferers, or non-headache pain, or non-pain individuals in developing a certain clinical psychopathological presentation. Our data supports the idea of anxiety being more implicated to migraine than depression.

When analyzing sub-items utilizing the first rating (not at all) versus the second possible rating or the first positive rating (some days) as covariables, (Tables  2 and 3 ), even mild symptomatology were significantly related to migraine with the exception of thoughts about dying.

One may consider not only a GAD DSM diagnosis, but a trait, or a minimal amount of worry, inability to control anxiety symptoms, feeling afraid, nervous, or anxious can play a critical role in migraine, by triggering an attack, making it lasting longer, affecting headache frequency and duration [ 21 , 22 ] quality of life, health care expenditures [ 23 ] and chronification [ 9 ]. In a Brazilian population, we found anxiety subthreshold and full DSM diagnosis (GAD) predominantly affecting migraine risk, generalized anxiety disorder (GAD) with [OR (CI 95%)] 7.0 (4.2–11.7) for all primary headaches, 7.8 (4.3–14.1) for migraine, 12.8 (4.5–36.3) for chronic migraine, and 3.9 (1.3–11.9) for tension-type headache. Subthreshold anxiety showed significantly higher ORs; whereas depression disclosed lower ORs, 2.5 (1.5–3.9) for all headaches; 3.4 (2.0–5.7) migraine; 3.8 (1.8–8.3) chronic migraine, and 1.1 (0.4–3.7) for tension-type headache. These numbers supported the ideia that anxiety plays a more important role than depression in migraine risk. Moreover, the concept of a subthreshold diagnosis, (where patients fit all but one item for the full diagnostic criteria), being more linked to migraine than GAD diagnosis and depression, are reinforced by the findings in this study [ 23 ].

When we compare severe symptoms (e.g. when the aspect or symptom is felt every day), much higher odds are observed, particularly in anxiety, ranging from 24.4 to 49.2. “Not being able to stop or control worrying” (OR 49.2), “trouble relaxing” (25.9), “Feeling nervous, anxious or on edge” (25.4), “worrying too much about different things” (24.4) (Table  2 ). This “dose-response” observed by us is unique and displays that some subsets of patients with anxiety and depression display a much higher risk than what has been reported by other populational studies [ 24 , 25 ]. It is noteworthy that not only excessive worry or tension, but the inability to control them was found to be more important. Sheftell and Atlas [ 26 ] mentioned losing the locus of control was a tipping point in migraine chronification.

The implication of not controling anxiety on a daily basis, as well as not easily relaxing, feeling anxious and experience daily excess in worrying should be better evaluated in migraineurs in both population and clinic settings. Not only in patients with a subthreshold, subclinical, partial, or subsyndromal psychiatric diagnosis, terms used synonymously to refer a clinical syndrome that does not fully meet the DSM or ICD diagnosis; but also in patients where those aspects can be found isolated, and should be taken in consideration in migraine management. The higher patients scored in the GAD-7 scale, the higher the severity of anxiety, and patients scoring higher than 7 (0–21) have shown a high sensitivity and specificity [ 27 ] for the GAD diagnosis. The item “Not being able to stop or control worrying” might be a good screening question to assess anxiety implication in migraine.

Detecting anxiety symptoms and implementing pharmacological and non-pharmacological treatments targeting those patterns could improve headache control and patients’ quality of life. Psychoterapies with cognitive-behavioural approach as well as physical and mental relaxation techniques may be usefull add on therapeutic strategies for migraine prevention. Although this has to be specifically tested in clinical trials, improvement in anxiety control is possibly the mechanism why behavioural treatments are efficacious in migraine treatment. The mechanisms behind the effect of preventive medications, antidepressants and/or neuromodulators, could be by improving anxiety-related abnormal physiological responses.

It is important to further understand the mechanisms behind the lack of control in anxiety, excess worry, and fear. Anxiety and fear-avoidance mechanisms have been linked to several pain disorders including primary headaches [ 22 ]. One may hypothesize a probable role of genetics, life events, psychological trauma, sleep, and cultural aspects.

Excess worry, fear, and other anxiety symptoms can be part of the migraine clinical spectrum. Irritability has been recognized as part of the prodrome [ 28 ], muscle tension a common finding in migraine and other headaches [ 29 ]. On the other hand, head pain may also be part of an anxiety clinical spectrum. A relevant discussion is whethere anxiety symptoms are part of the migraine spectrum, or vice-versa, and if their comorbidity is uni or bi-directional. As Merikangas first showed in 1990 and others confirmed [ 25 ], anxiety preceded migraine diagnosis, therefore early recognizing and treatment of anxiety symptoms in children and adolescentes may reduce the appearance of migraine in the future.

The hallmark features of depression are emotional symptoms, hopelessness and sadness; nevertheless, the three highest scores found in our data were appetite, fatigue, and sleep disturbances, all physical symptoms.

Although not possible to define whether increase or decrease in appetite was occurring, we can suppose it was increase, based on epidemiological data in depressive disorders [ 30 ], and literature studying migraine and obesity [ 31 ]. Decrease in appetite may also be present, since nausea or vomiting are migraine symptoms. Our findings support a common biological mechanism between migraine and depression, through the hypothalamus, since it plays an important role in apetite, fatigue and sleep function. A hypothalamic dysfunction can be hypothesized as a link between both conditions.

The most related depression symptoms found: appetite, fatigue, and poor sleep may be overemphasized in migraine patients because they can be part of the migraine clinical picture [ 32 , 33 ]. In addition, lack of concentration is commonly part of the migraine attack and is found interictally [ 34 ]. It is unlikely a migraine patient, at least near an attack, present without fatigue, apetite change, sleep problems, lack of concentration, or lack of pleasure. Feeling sad, although obviously expected during pain, was found to be less common, as low self-esteem and death thinking. One may speculate how valid and specific is depression diagnosis in migraine patients, simply applying DSM criteria and scales may not define the disorder, a reappraisal of both epidemiological and clinical data regarding depression and migraine comorbidity is necessary, previously published numbers could be affected by depression physical symptoms and migraine features, as well as case definitions, since studies varied accross DSM III, IV and V.

A diary study could answer how these overlapping symptoms are linked in headache disorders and psychiatric comorbidity, moreover, looking back into databases and subtracting anxiety and depression symptoms may help understanding the role of psychiatric symptoms in migraine.

Another key aspect is irritability, a symptom of the anxiety GAD-7 scale, but eventually more linked to depression than anxiety, particularly if we consider it as part of the bipolar spectrum. Becoming easily annoyed or irritable had an OR 3.8 (1.9–7.8) if experienced some days, 7.5 (2.7–20.7) more than half the days, and 22.0 (5.7–84.9) when experienced nearly every day. Irritability is also part of the Attention Deficit Hyperactivity Disorder clinical spectrum. ADHD overlap with anxiety clinical aspects, and can be a differential diagnosis. Four out of seven GAD items may be related to ADHD: trouble relaxing; feeling nervous, anxious or on edge; becoming easily annoyed or irritable; being so restless that is hard to sit still. ADHD is linked to the inability or lack of control [ 35 ].

Our paper supports the validity symptom-based concept, a relatively recent trend in mental health research and medicine [ 36 ]. By dissecting different elements in the two main disorder groups, anxiety and depression, and looking at their ranges, from mild to extreme manifestation, resulting in a more detailed, deeper understanding of psychiatric comorbidity with migraine.

Although inverting the traditional paradigm of starting with symptoms and moving towards a diagnosis, ask investigators to step back from pre-defined syndromes and focus on basic dimensions of functioning could be an important move in migraine comorbity research.

Strengths of our study are the robust sample size, comprehensive general population studied, not only a small set of young adults as seen in some seminal studies in the area (breslau and merikangas), migraine diagnosis were not self reported. Anxiety and depression were studied using detailed questionnaires, where sensitivity and specificity for a DSM diagnosis has been validated [ 27 ]. Limitations include its cross-sectional study design, where results are correlative and cannot address causality; absence of a full psychiatric diagnostic interview, where other mental health issues relevant to migraine comorbidity could be explore such as psychological trauma, ADHD, and the bipolar spectrum. The self-report nature of responses could be a limitation but most of the data on comorbidity was based on self-administered questionnaires.

Analyzing individual items from scales may be not considered as good practice because it lacks the reliability of the full scale, but this is the very concept we are challenging here. Anxiety and depression symptoms are overlapping and should be considered as a continuum spectrum of affective symptoms, especially in presence of pain disorders. Our objective was not to detect a psychiatric disease but understand within the aspects raised in each scale’s question, which are the most related to migraine, we think this is what our paper adds to the literature.

For further research, other psychiatric disorders, psychological and cultural aspects, beliefs, personality, and biological variables, from genetics to neuroimaging should be studied, as implications in public health and treatment outcomes. Studying other pain syndromes, other headache disorders, migraine subtypes, migraine chronicity, other chronic conditions, and their relation to anxiety and depression symptoms would improve our understanding in the field.

Anxiety overall and its symptoms had a stronger association with migraine than depression. Hability to control worrying and relaxing are the most prominent issues in migraine psychiatric comorbidity. Physical symptoms in depression are more linked to migraine than emotional. A symptom-based approach helps clarifying migraine comorbidity and should be replicated in other studies.

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Study Funding: this study was supported by a grant from Natura Campus, Brazil.

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Peres, M.F.P., Mercante, J.P.P., Tobo, P.R. et al. Anxiety and depression symptoms and migraine: a symptom-based approach research. J Headache Pain 18 , 37 (2017). https://doi.org/10.1186/s10194-017-0742-1

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Adolescence

More research questions the “social media hypothesis” of mental health, a new study shows that social media does not lead to anxiety or depression..

Posted August 10, 2023 | Reviewed by Gary Drevitch

• What Changes During Adolescence?
• Find a therapist to support kids and teens
• Many believe that social media causes teens to experience depression and anxiety, despite lacking evidence.
• A new study found that when teenagers used social media more, their mental health did not change over time.
• Mainstream media should devote more coverage to studies like this one.

As I’ve discussed previously , conventional wisdom suggests that using social media promotes poor mental health, especially in teenagers . But there is good reason to question this idea. As more high-quality research becomes available, we can see room for nuance and see that social media is not consistently detrimental to everyone’s well-being.

A critical limitation in many existing studies on this topic is that they are cross-sectional. This means all variables are assessed only once, and at the same time. This isn’t necessarily a bad thing; it just means we don’t know how behavioral changes over time might be associated with changes in emotional variables. Longitudinal research helps us to better understand how change happens by measuring these variables repeatedly over a period of months or even years.

Longitudinal research is especially valuable in this case because some young people may use social media to alleviate distress , so we might observe that increases in depression or anxiety will predict increases in social media use , rather than the reverse. On the other hand, if the social media hypothesis is correct, then as teenagers spend more and more time online, this should be followed by decreased mental health (i.e., greater anxiety/depression). But that’s not what the data reveal.

What Researchers Found

A research team in Norway recently published a study in which they tracked young people aged 10-16, and assessed them every 2 years. Each time, the researchers interviewed participants about their behaviors online (e.g., posting photos, “liking,” or commenting on others' posts), and they conducted clinical assessments of depression and anxiety with standardized psychiatric measures. The researchers found no evidence that increased social media use was followed by elevated anxiety or depression. This means that as these teenagers used more social media, their mental health did not change. These findings directly contradict the idea that social media use leads to poor psychological well-being.

The authors are careful to note that even though social media did not make teenagers feel worse, on average, it also did not make them feel better. So, social media use may not have an overall negative or positive effect for the average teenager. This idea is consistent with what I have argued previously , which is that social media use may have differential effects depending on the user’s initial motivations. When people are motivated to use social media because they find it interesting or rewarding, then it’s likelier to make them happy, whereas when they feel compelled or obligated to use it, then it’s likelier to make them feel worse. Motivations matter more than the technology itself.

The researchers also suggest that perhaps subgroups of teenagers may experience different outcomes following social media use, such as those who are bullied or have low self-esteem . The specific content that people view on social media may also play a role. It is also true that digital technologies change rapidly and we cannot assume that all future forms of social media will operate the same way psychologically. New applications have the potential to be better or worse than what people currently use.

Time Trend Data Are Inconclusive

Those who hold with the “social media hypothesis” of mental health will often point to time trend data as evidence. They argue that because social media use has risen in teenagers over the past 15 years, and that teen depression and anxiety has also risen over the same period of time, then those two trends are likely connected.

But if that were true, we ought to be able to observe this trend happening during teenagers’ lives. The fact is, we do not observe this pattern, and these null findings should make us skeptical about such claims. When researchers track teenagers’ mental health over a span of years, there is no link between their social media use and their experiences of depression or anxiety. In the words of the authors , “ the frequency with which adolescents engage in behaviors like posting, liking, and commenting on others’ posts does not influence their risk for symptoms of depression and anxiety .”

It would be great to see more mainstream media coverage of studies like this, especially considering the widespread belief that if young people are permitted to use social media, their mental health will deteriorate. Perhaps parents of teenagers can take some comfort in the fact that for the average user, there is little risk of this.

Cauberghe, V., Van Wesenbeeck, I., De Jans, S., Hudders, L., & Ponnet, K. (2021). How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety During COVID-19 Lockdown. Cyberpsychology, behavior and social networking , 24 (4), 250–257. https://doi.org/10.1089/cyber.2020.0478

Puukko, K., Hietajärvi, L., Maksniemi, E., Alho, K., & Salmela-Aro, K. (2020). Social Media Use and Depressive Symptoms—A Longitudinal Study from Early to Late Adolescence. International Journal of Environmental Research and Public Health , 17 (16), 5921. MDPI AG. Retrieved from http://dx.doi.org/10.3390/ijerph17165921

Steinsbekk, S., Nesi, J., & Wichstrøm, L. (2023). Social media behaviors and symptoms of anxiety and depression. A four-wave cohort study from age 10–16 years. Computers in Human Behavior , 147 , 107859.

Dylan Selterman, Ph.D., is an Associate Teaching Professor at Johns Hopkins University in the Department of Psychological and Brain Sciences. He teaches courses and conducts research on personality traits, happiness, relationships, morality/ethics, game theory, political psychology, and more.

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Understanding mental health in the research environment: A Rapid Evidence Assessment

• PMID: 29607246
• PMCID: PMC5873519

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.

Keywords: Depression; Scientific Professions; Workforce Management; Workplace Wellness Programs.

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June 25, 2024

Teens’ Mental Health May Improve When They Help Others

Volunteering in community programs can reduce youth depression and anxiety, researchers are beginning to learn

By Lydia Denworth

In college my oldest son volunteered as a Big Brother and taught computer science at local elementary and middle schools. After graduating, he said his time with those young students was one of the most rewarding parts of his college experience. According to emerging research, it might also have improved his mental health. There is already considerable evidence from studies with adults that volunteering—doing something for someone else or for one’s community—benefits a person’s physical and mental health and improves overall well-­being. Researchers have found that the sense of mattering to those around you that volunteering provides is one important reason it is as­sociated with psychological well-being.

Now scientists are finding similar links to both physical and mental health in children and adolescents. An early experiment found that 10th graders who volunteered in an elementary school for two months showed fewer signs of harmful inflammation and lower levels of obesity compared with students who didn’t volunteer. A 2023 analysis found that among more than 50,000 children and adolescents in the National Survey of Children’s Health, young people who had participated in community service or had volunteered over the previous 12 months were more likely to be in very good or excellent health and stayed calm and in control when faced with challenges, and the adolescents were less likely to be anxious, among other benefits. This improvement was in comparison with young people who did not volunteer.

Granted, those findings are only correlations. “It could be that the children who were volunteering were already in great health,” says study co-­leader Kevin Lanza, who is an environmental health scientist at UTHealth Houston School of Public Health.

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But because of an alarming rise in mental health issues among young people, Lanza and others believe this early evidence is promising enough to pursue. A 2021 advisory from U.S. Surgeon General Vivek Murthy warned that the proportion of young people reporting persistent feelings of sadness or hopelessness had increased by 40 percent over the previous decade, starting even before the pandemic. The number of high school students seriously considering a suicide attempt rose by 36 percent. In the first years of the pandemic, the percentage of young people with depressive and anxiety symptoms doubled. There are multiple possible causes in addition to the pandemic, experts say, including the polarized political environment, anxiety over climate change, the effects of social media use and adverse personal circumstances.

When looking for ways to counter these problems, researchers point to the importance of “contribution”—providing support or resources to others or helping to achieve a shared goal—as an essential piece of social and emotional development for adolescents. Young people have a developmental need to connect and belong. “Part of the exploration of adolescence and young adulthood is figuring out where you can be needed and useful—arguably core aspects of our mental health,” says developmental psychologist Andrew Fuligni, co-­executive director of the Center for the Developing Adolescent at the University of California, Los Angeles.

Volunteering is one good way young people can contribute. The importance of mattering to others and to the larger world “translates really well to the needs of adolescents to have a meaningful role to play in their community,” says developmental psychologist Parissa Ballard of the Wake Forest University School of Medicine. In a small 2022 pilot study, Ballard and her colleagues tested volunteering as an intervention for nine 14- to 20-year-olds who had been recently diagnosed with mild to moderate depression or anxiety and were recruited through their clinicians. After 30 hours of volunteer work at animal shelters, food banks, and other community organizations, the average reduction in de­pressive symptoms among participants was 19 percent.

Everyone in the study enjoyed the work and reported a sense of pride and accomplishment. “Young people who were struggling with anxiety said that they were pretty anxious before doing it but then felt so much better after,” Ballard says. Although volunteering should not replace mental health treatment, she says, it could help in conjunction with other forms of therapy. She is pursuing that hypothesis in a larger study.

What accounts for the benefits? Helping others improves mood and raises self-esteem. It provides fertile ground for building social connections. It also shifts people’s focus away from negative things and can change how they see themselves. Many teens say they don’t feel important, Ballard says. “Volunteering can give people a different sense of themselves, a sense of confidence and efficacy.” Lanza thinks of it as “a health pipeline.” He adds that “it equips you with certain types of skills that better control anxiety.”

There may be a potential downside to volunteering, however. Fuligni and his colleagues have found that young people’s mental health can suffer if they feel their contributions are devalued because of their gender, racial or ethnic identity. And if they feel like they are being forced to participate or are not doing much, the experience can be harmful, Ballard says. One report found that people who were required to volunteer when they were young were less likely to do such work when they were older. “Young people have to choose something that feels meaningful to them,” Ballard says. Adults can help by offering choices and by vetting volunteer opportunities to be sure that organizations are well run and equipped to offer a good experience.

When these situations are carefully thought out, volunteering doesn’t just help the volunteers. It also helps the people and communities on the receiving end. “Volunteering could be a win-win,” Lanza says.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

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Hypotheses for the Rise of Recognized Mental Disorders

The New York Times recently published an article on the evolving Diagnostic and Statistical Manual of Mental Disorders (DSM).  The DSM is the official source for psychologists who are diagnosing patients with mental disorders.  The article points out that the number of disorders in the manual has more than doubled since the 1950s:

Hypothesis One:  The DSM reflects an increasingly sophisticated and exhuastive compendium of all possible mental disorders.

Hypothesis Two:  More psychological disorders = more people diagnosed with mental disorders = more money is siphoned off to hospitals, treatment centers, drug companies, mental health professionals, social workers, school counselors, etc.  (Scientists who are currently working on the next version of the DSM have agreed to restrict their income from drug makes to $10,000 a year or less.)

Hypothesis Three:  We are an increasingly rationalized society and all things are becoming increasingly listed, compiled, organized, and annotated.

Hypothesis Four:  What is considered a “problem” depends on the social context.  (“Homosexuality” used to be in the DSM, but it isn’t any longer.)  Perhaps a shift in the last 50 years has created a social context that is less tolerant of difference, more insistent upon happiness, or requires a more compliant citizen.

Hypothesis Five:  Grassroots activists get together and lobby scientists to include disorders in the DSM so that they can raise awareness and money for research.

What do you think?

Thanks to Francisco for pointing me to this article!

Comments 18

Elena — february 1, 2009.

I'm for 1 with a dash of 4 -- the current consensus is that it's an illness when it impairs the individual's life, and that may change with societal changes.

I'd also like to remark that psychology and psychiatry have changed a lot from the 50s, when lobotomies and electroshock were standard treatment. And if we go back 50 years more... yeah . And then you go further back, and mental illnesses were all demonic possessions that should be treated by an exorcist. Psychology and psychiatry are some of the areas of medicine that have changed the most in the recent past.

Laura Agustin — February 1, 2009

For me it's about rich western societies selling the idea that everyone is supposed to be happy, calm, comfortable and problem-free. And if they are not, then something is individually wrong, and needs to be fixed on the personal level, by interactions with professionals. Then it can be called a disorder or illness. This trend works against any sort of political analysis of social problems.

Keith — February 1, 2009

I thought it was largely known that a lot of 'disorders' come AFTER drug companies invent a solution. Disorders in this book are decided by a committee. The committee members sometimes get sponsorship or funding from drug companies.

Also, there are some 'disorders' that are nothing more than some of the stages normal people go through growing up. Until there's a solution (drugs), there's no use mentioning it as a real problem.

I guess I'm a bit pessimistic on the view, but my first sociology teacher explained it pretty well, at least well enough for me to buy into it.

Trish — February 1, 2009

I hate to rain on anyone's cynical parade, but mental illness is real and much of what we know about the brain and its disorders has been learned since 1980. However, I will concede that the rise of the Drug Giants is a contributing factor to what is considered a "brain disorder" and our consumer driven insistence on eternal happiness as the norm does cast suspicion.

Eh. Today's depression was yesterday's spleen, ennui, Weltschmerz or melancholia (which was produced by an excess of black bile, of course). It's definitely not new.

And as someone who couldn't get out of a years-long depression until after adding SSRIs to therapy, I'm glad that Big Pharma is invested in fixing alterations of brain biochemistry.

Chris — February 1, 2009

There's a six possibility, that psychiatry/psychology has matured to the point where it's no longer an question of whether a person is completely healthy or completely incapacitated to the point of requiring hospitalization. It can see nuances.

I am a classic example of this. Asperger's Syndrome wasn't recognized in the US until recently, and it's still seems to be mostly a boutique self-diagnosis by jerks. But talking to an autism specialist and taking the information to a LCSW has been a godsend. It's been a lot of work, but at least we know that there's something real going on and can get insight from others.

Thirty years ago nobody would have seen this as a real issue. How could they -- aspies are usually extremely high functioning in the academic and professional spheres, and their interpersonal problems were dismissed as being just a sign of genius. It's not like we require hospitalization. Now we have the maturity to see that the interpersonal problems aren't just due to poor socialization and a more medical approach can help. Healthy relationships, who would have thunk it?

BTW, the main thing with AS is that we can't pick up emotions or show our own, there's a disconnect in the part of the brain that handles that. The answer is to learn to read subtle clues (think of that new series "Lie to Me") and to "act". But that feels like it's manipulative until you learn that you really have a form of high-functioning autism and it's no more manipulative than somebody who's nearsighted wearing glasses.

L. G. — February 1, 2009

I'm an Aspie as well, and I'm afraid my experiences with Psychiatric professionals weren't so very smooth. I was obsessed with books and dragons when I was younger, to the exclusion of interaction with other children. For a long time, they tried treating me as if I were depressed (my mother has a history of manic depression) and attempted to literally force drugs on me. I refused, and it became a loop of attempts to push medication on me, and then 'punishments' for refusal. Finally, someone thought to test me for Asperger's, and after my diagnosis they stopped trying the drugs and only forced me through some very uncomfortable group therapy sessions with other kids who had problems like depression, who cut themselves for attention, or who were considered "hyper sexual" for their ages.

Basically, I'd rather they'd never treated me as if I were a problem. I don't think any of the psychological treatments were of any help to me (rather making me feel as if I was damaged in some way) and what eventually helped socialize me was locating other kids who had the patience to allow me to develop by myself, without immediately judging me. Really, I think the problem with Aspies is how non-Aspies have been taught to view those who don't socialize as effectively as they do. I.e., society in general needs treatment, not us.

This from someone born in 1985, so we're not talking about ancient Psyche practices or societal attitudes.

Tim M — February 1, 2009

I think the DSM series is more about how to make sure that people with specific or unique needs can be helped within the system of US healthcare. Insurance companies won't cover a problem that isn't considered a disease, so the APA categorizes characteristics and the best course of treatment to make it easier for policy to be written up at the HMOs and other healthcare providers. Plus, medications cannot be made for symptoms or traits, but disorders or diseases, so we'd be stuck pretending that Prozac is a heart medication or some nonsense. I guess this is mostly Hypothesis 3.

The problem is that not everyone with a condition in the DSM feels like they're diseased or have a disorder, and they're likely right. Asperger's Syndrome is a condition that can make life difficult, but it doesn't mean it should be cured or always even treated, as many people with AS have lead happy, albeit different, lives. What matters is that the diagnosis makes it possible for people with such conditions to be understood by medical professionals, specifically psychologists/psychiatrists, teachers, professors, and employers.

About depression, I think many people write it off as being a side-effect of our expectations of our consumer culture. However, I think it's legitimate (I've had my bouts with it), and people are entitled to be able to enjoy life. It's not western selfishness, as happiness comes from within. I'd call it the emotional equivalent of hemophilia. The fact not everyone can afford psychiatric care is a big concern that I think makes depression seem like a classist disease, but if healthcare were to be nationalized in the United States, that concern would be erased.

Then again, my definition of depression is the inability to feel happy (not the ability to feel happy all or most of the time), feel entirely, motivate oneself, or otherwise fail to function well enough due to mood issues. Some people may feel that they need to be happier than is natural, but a good psychiatrist or therapist will deal with that unrealistic belief, as well. I don't think people should just be in pure bliss all the time, as that would just make joy and happiness pointless. People need to be able to enjoy the good things in life.

Fernando — February 1, 2009

I'd say a mix of everything in there, with emphasis to one and two, plus people's acceptance of all of that because they like to feel victimized, with the proper exceptions of people that really do have something.

amandaw — February 1, 2009

I vote for "Wow, people really can't stand the idea of difference, can they?"

I have no idea whether each and every disorder listed is a valid one, but I do know that middle class white America's knee-jerk reaction when faced with any of them is "God, what a bunch of sissies."

Vidya — February 1, 2009

About a year back, an editorial article appeared in the APA's flagship publication, advocating that 'obesity' be added to the next edition of the DSM. Now, bracketing off the false conflation of weight with dietary habits, the idea that a *body type* in and of itself could be grounds for a psychiatric diagnosis is shocking (though not unknown in psychiatric history). As a fat person, I still find myself emotionally overcome when I think about that piece -- fatphobic hysteria (i.e., the latest moral panic) used as a pretense for potentially forced medication and institutionalization.

hypatia — February 4, 2009

I think it's interesting that Chris and L.G. brought up the Asperger's as it was the first thing I thought of when it comes down to how diagnosing mental illness.

Autism was first "discovered" in the 1930's and now there are five separate disorders with related symptoms.

There seems to be an ever branching tree of psychology disorders.

I also think there is an element of the hyper-aware.

My cousin and I were both quiet kids, kept to ourselves, didn't like and found it difficult to interact with others, did not have many emotional reactions and were slow to start talking. (I even had to go through speech therapy.) I'll admit I had a horrible time trying to socialize with others in my own age group and it was a skill I had to learn with time. We were considered a little odd but still far within the realm of normal.

Now my niece, at 18 months, is exhibiting similar symptoms and her doctor has her on "autism watch" as I like to call it. Unfortunately it seems like an attempt to pigeon-hole the poor child. And of course it makes us wonder. If we had been born twenty years later would we have been going through the same are they or aren't they autistic? And how this perception have changed us or perhaps even an official diagnosis?

Ryan, Sociological Images, and Trans Narratives « genderkid — May 2, 2009

[...] Tools of Economic Development, On Being Genderqueer, Gender-Ambiguous People as Incomplete, the new Diagnostic and Statistical Manual of Mental Disorders, and One Laptop Per [...]

andboo — May 10, 2009

If someone has to count to fifteen every time they open the fridge, and it is becoming a serious detriment to their life - they can go to a psychiatrist. The psychiatrist can use the book to find the name and criteria for the issue, and can use this information to help with treatment. They can research it and find out what has helped other people. Like any science, it is just naming and describing things that already exist. FYI: If you don't think you have a problem, and you aren't dangerous to yourself or society - then technically, you don't have a mental illness, even if it's in the book. Part of having a mental problem is it being a problem.

As we learn more about these "diseases" the categories get more specific. There isn't just one kind of depression that everyone has. So more categories are put in the book.

Once upon a time, women depended on husbands or fathers for everything and for the most part - did what they were told to do without getting a say in it. That was normal. Now, it would be called something like "Dependent Personality Disorder".

Leeda Copley — September 19, 2011

medicalization of society, folks...

kirim barang — May 8, 2020

HeHe hope it helps https://www.instagram.com/mapsnesia/

Nydia Deso — November 26, 2023

Undeniably consider that which you stated. Your favorite justification seemed to be on the net the easiest thing to have in mind of. I say to you, I definitely get annoyed even as people think about worries that they just don't understand about. You controlled to hit the nail upon the highest and defined out the entire thing with no need side effect , other people can take a signal. Will likely be back to get more. Thank you

Von Kotson — April 2, 2024

Es ist wirklich eine großartige und nützliche Information. Ich bin froh, dass Sie diese nützliche Info mit uns geteilt haben. Bitte halten Sie uns auf diese Weise auf dem Laufenden. Danke fürs Teilen.

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Your gut microbes may influence how you handle stress.

An illustration of the human microbiome. The bacteria in our gut may influence our mental health, research finds. MEHAU KULYK/Getty Images/Science Photo Library RF hide caption

The gut microbiome — the ecosystem of tiny organisms inside us all — has emerged as fertile new territory for studying a range of psychiatric conditions and neurological diseases .

Research has demonstrated the brain and gut are in constant communication and that changes in the microbiome are linked to mood and mental health. Now a study published this month in Nature Mental Health finds distinct biological signatures in the microbiomes of people who are highly resilient in the face of stressful events.

“The accuracy with which these patterns emerged was really amazing,” says Arpana Church , a neuroscientist at UCLA’s Goodman-Luskin Microbiome Center who led the new study.

The research is a jumping off point for future human studies that some researchers believe could ultimately lead to treatments. It may also point the way to biomarkers in the microbiome that can help tailor decisions on how to use existing therapies in mental health.

Studying the link between the gut and mental health is personal for this scientist

Resilience linked to anti-inflammatory microbes.

For their analysis, Church and her team separated 116 adults without a mental health diagnosis into two groups based on how they scored on a scale of psychological resilience.

Next, they sifted through a huge amount of data gathered from brain imaging, stool samples and psychological questionnaires and fed that into a machine-learning model to find patterns.

This analysis of gene activity, metabolites and other information came up with several key associations in the high resilience group. In the brain, there were increased features related to improved emotion regulation and cognition.

“Think about the cognitive part, or the frontal part, of your brain being like the brakes,” says Church. “The highly resilient individuals had really efficient brakes, and less of this hyper-stressed response. ” 

Then they delved into the microbiome, looking not only at the abundance of different microorganisms, but also at their genetic activity to see what they were actually doing.

Two major patterns emerged in people who were more resilient to stress: The activity in their microbiome was linked to reduced inflammation and to improved gut barrier integrity.

This tracks with previous research that has shown patients with a variety of psychiatric conditions have a balance of gut bacteria that includes more of certain pro-inflammatory bacteria and less of those with anti-inflammatory effects.

Patients say keto helps with their mental illness. Science is racing to understand why

Church notes the gut barrier absorbs nutrients and keeps toxins and pathogens from entering the bloodstream. When that becomes more permeable, or “leaky,” the resulting inflammation acts as a stress signal to the brain that all is not well.

Microbes that ‘talk’ to our nervous system

The new study fits into a quickly-expanding body of work on the brain-gut connection.

“I was really excited to see this being done in quite a big human cohort,” says Thomaz Bastiaanssen , a bioinformatician who studies the gut microbiome and mental health at Amsterdam University Medical Center.

In recent years, he says scientists have established that there’s a strong “bi-directional relationship” between the gut and the brain. Much of that is based on preclinical lab studies using animal models, as well as some human observational studies and in vitro work.

“All of this points towards roughly four ways that the microbiome communicates with the host,” says Bastiaanssen.

Along with the immune system, there’s the vagus nerve that functions like a superhighway, running from the brain to the gut and directly interfacing with the microbiome.

These gut microbiota also talk with the central nervous system by secreting neurotransmitters, like serotonin and dopamine (about 90% of serotonin is produced in the gut and about 50% of dopamine).

In addition, the microbiome can produce short-chain fatty acids that help maintain the gut barrier and exert an anti-inflammatory effect on the brain, among other things.

Just last year, Jane Foster , a neuroscientist at UT Southwestern Medical Center, found that a community of bacteria related to the production of these short-chain fatty acids was reduced in people with depression who had elevated anxiety.

In recent years, other observational studies have strengthened the evidence linking gut microbiome and mental health in humans, although there are still many unanswered questions because this research is finding correlations.

For example, large studies from scientists in the Netherlands and elsewhere have found microbiomes with less diversity of bacteria can be predictive of depression, and that having more or less of certain bacteria linked to the synthesis of neurotransmitters and short chain fatty acids may be key .

Foster praised the UCLA study as “novel” because it took a full-body view of the brain-gut-microbiome and its potential role in resilience.

She notes the analysis turned up a link between anxiety and the microbiome, which is already a well-established area of research . More than a decade ago, Foster and others showed this link in lab experiments with “germ-free” mice and anxiety.

In the context of stress, scientists have found even short term exposure to stress can lead to alterations in the microbiome, and that changing the composition of the microbiome could make some mice more resilient to stress.

Probiotic treatments for stress? Not yet

There are growing efforts to move this research into actionable treatments, using diets, prebiotic and probiotic supplements. But Bastiaanssen says the complexity of the microbiome calls for a different approach than what’s typically used in pharmaceutical development, which tends to focus on finding a single molecule or drug.

He says that would be like trying to grow a forest in a desert by planting a few seeds.

“Obviously it’s not going to work,” he says, “because there is no supporting ecosystem.”

He says the microbiome field is still coming out of its infancy stage.

“We've established a link in the microbiome, gut-brain axis. We’ve got really robust evidence,” he says. “The next question we need to understand is, how exactly it works?”

He notes there is some promising evidence from small human studies that have shown targeting the microbiome with certain diets ( one rich in fermented foods ) can reduce inflammation.

Another trial, this one from Bastiaanssen and a team at the University College Cork, found that a diet focused on vegetables and foods known to influence the microbiota, could reduce perceived stress .

While these efforts are completely “valid,” Foster argues the power of these studies is they can lead to the discovery of biomarkers that can help steer decisions about how to use existing treatments and who will be the best candidate.

“ Can I measure something in your microbiome, maybe in your blood and maybe in your brain to determine if you're depressed, should I give you an antidepressant? ... or neurostimulation? Shall I do cognitive behavior therapy or tell you to exercise?”

That could be the value of a holistic marker that can be measured in your microbiome, she says. And she thinks it could become an effective tool for clinical care within the next decade.

For her part, Church envisions, hypothetically, one day leveraging this field of research to “engineer a probiotic blend that could help mitigate stress” and prevent the onset of some diseases.

“The biggest problem is that we need more studies that are actually going to test these in human trials,” she says. She acknowledges there are all sorts of unsubstantiated claims out there when it comes to improving the microbiome. For now she tells people the data isn’t strong enough yet to know which treatment to try.

“There isn't really one out there that's been really tested,” she says, “I say come back to me in a year or more and I'll let you know.”

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An Experimental Investigation into Promoting Mental Health Service Use on Social Media: Effects of Source and Comments

Zhaomeng niu.

1 Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA

2 Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China

David C. Jeong

3 Department of Communication, Santa Clara University, Santa Clara, CA 95053, USA; ude.ucs@gnoejcd

Jared Brickman

4 Carnegie Dartlet, Portland, OR 97201, USA; ude.usw@namkcirbsj

Jerod L. Stapleton

5 College of Public Health, University of Kentucky, Lexington, KY 40506, USA; [email protected]

Mental health is an increasingly prevalent topic of public interest, but remains a complex area requiring focused research that must account for negative perceptions surrounding mental health issues. The current work explores the roles of social media information source credibility and valence of social media comments on health outcomes in such a mental health context. We used a 2 (message source: professional vs. layperson) × 3 (valence of comments: positive vs. negative vs. mixed) online experiment to examine the effects of source and valence of comments on trust, attitudes and intentions related to mental health information and services among 422 undergraduate students. Results supported the hypothesized model in which source influenced cognitive trust while comments influenced affective trust. Cognitive and affective trust both impacted attitudes towards mental health information which encourages the intention to share such information on social media. Additionally, affective trust impacted attitudes towards mental services which influenced intentions to seek them out. Source and valence of comments on social media impact different behavioral intentions regarding the use of mental health services. This study provides insights for future social media campaigns promoting mental health service use.

1. Introduction

Nearly 1 in 5 of all adults in the United States, approximately 46.6 million people, experienced mental illness in 2017 [ 1 ]. Young adults have the highest prevalence of any mental illness (25.8%) [ 1 ], making it one of the greatest health concerns for this population. While mental health or mental illness may refer to a range of disorders and conditions (e.g., anxiety, depression, schizophrenia), it is often incorrectly perceived as less important than physical health due to its lack of visual symptoms [ 2 ]. The consequences of lack of treatment of mental illness include serious health concerns, such as high risk for chronic medical conditions and suicide. However, only 41% of those with a mental illness have sought treatment [ 3 ]. Low rates of utilization of mental health services may be attributed to an unwillingness to seek treatment due to distrust or negative attitudes towards such services.

Promisingly, positive information about mental health delivered on social media has been shown to reduce negative perceptions and change unfavorable attitudes towards mental health counseling [ 4 ]. Engaging social media users and sharing personal stories about mental health could help spread information and change attitudes about seeking treatment [ 5 ]. However, health communication on social media may also come from sources perceived to have questionable credibility and may be accompanied with negative comments from other social media users [ 6 ]. Therefore, it is important to understand how these different sources and the valence of comments influence how users process mental health information online and how such information impacts their users’ perceptions related to health information.

One critical concern in the current social media landscape is the credibility of online information [ 7 , 8 ]. People tend to judge credibility based on the perceived information source and prior research on source credibility has primarily focused on the impact of cognitive trust (perceived credibility of the information) on attitudes and intentions [ 9 , 10 ]. Affective trust, the emotional trust placed in information, can also impact audience attitudes and intentions [ 11 ]. This study is unique in its investigation of the differences between the effects of cognitive and affective trust on attitudes and behavioral intentions related to mental health services.

Perceptions of mental health issues may also be influenced by comments shared on social media. A key deterrent to seeking treatment for mental health issues is negative perception [ 6 , 12 ], which may stem in part from negative social commentary [ 13 , 14 ]. However, the additivity hypothesis [ 15 ] posits that comments that are consistent with a message could boost its persuasive effects, indicating that comments that support using mental health services could reinforce the message. No empirical study has investigated how the valence of user comments to social media messages, defined as the positive or negative emotional tone of a comment, impacts the persuasive effect of social media content regarding mental health services.

Given that cues on social media such as sources of content and user comments on such content may together impact viewers’ trust and perceptions of social media messages, the current study aimed to investigate the impact of sources (i.e., layperson and professional sources), and the valence of user comments on individuals’ trust (i.e., cognitive trust and affective trust) and health-related perceptions (i.e., attitudes and behavioral intentions) in the context of mental health as a communication process.

1.1. Mental Health and Social Media

While mental health or mental illness may refer to a range of disorders and conditions (e.g., anxiety, depression, schizophrenia), it is often incorrectly perceived as less severe than physical illness due to its lack of visual symptoms [ 2 ]. In fact, 18% [ 3 ] to 25% [ 16 ] percent of Americans suffer from a mental illness, making it the most prevalent national health issue. Perhaps even more alarmingly, only 41% of those with a mental illness have sought treatment [ 3 ], which may be attributed to a lack of awareness or an unwillingness to seek treatment. Specifically, reasons for not seeking mental health treatment are often tied to negative social commentary, which may stem from inaccurate public knowledge [ 13 ] and is often experienced by youth [ 14 ].

Promisingly, there are signs that mental health interventions on social media have reduced negative perceptions and are changing unfavorable attitudes toward mental health counseling [ 4 ] in a general audience, which suggests that engagement with user-generated content and shared articles about mental health could help spread information and change attitudes about seeking treatment [ 5 ]. The current study attempts to contribute to the ongoing mental health discussion on social media with an added emphasis on information source credibility and the shaping influence of others’ opinions.

1.2. The MAIN Model and Cues

Given the ease with which users may edit and publish information online without a rigorous gatekeeping process, concerns about the credibility of such information have been well-documented [ 17 , 18 ]. Online users typically need to utilize cues that aid their evaluation of the credibility of the content before taking actions based on the information (e.g., to form attitudes). The Modality Agency Interactivity Navigability (MAIN) model of digital media [ 19 ] explores the impact of technologically afforded heuristics on users’ judgment of the credibility of online information.

Social media platforms such as Facebook, and e-commerce sites such as Amazon have digital features and cues (e.g., “likes,” comments, ratings) indicating endorsement or disagreement with a message or a product. Such comments and “likes” comprise a critical feature in modern communication technology research as they have the potential to trigger a heuristic, where users tend to construct opinions regarding online content based on other users’ opinions (e.g., “ If others think that something is good, then I should, too ” [ 19 ]). Thus, the valence of user comments plays an important role in constructing perceptions toward social media content.

Closely related to endorsement, online users also often rely on authority cues about an information source to make judgments on online information [ 19 ]. This is particularly significant in the case of health information, which may come from many different sources that range in level of expertise and reliability. For instance, participants have reported different levels of perceived credibility and of behavioral intention of health information, depending on whether they were told the information was from a layperson source or a professional source [ 10 ]. In sum, social media cues such as the valence of user comments and the perceived authority of an information source are critical factors in understanding how people process health information regarding information credibility, attitudes, and intentions on social media.

Although source credibility and message valence on social media have been explored in previous health communication research, less is known about the impact of the valence of user comments on social media on shaping individuals’ opinions. Further, relatively little research has focused on how different types of cues in a social media environment work together in influencing individuals’ perceptions, as a communication process. The current study aimed to evaluate the impact of two types of cues, valence of comments and source type, on health information credibility, attitudes, and intentions as a communication process on social media.

1.3. Dimensions of Trust, Cues and Cognitive Perceptions

Based on prior work in psychology, e-commerce, advertising, and marketing, the concept of information trust may be split into two dimensions, cognitive trust and affective trust [ 20 , 21 ]. Although different items might be used to measure these two types of trust across different researchers [ 22 ], it is acknowledged that cognitive trust is logic-driven and affective trust is emotion-driven [ 11 ]. Cognitive trust is grounded in individuals’ rational assessment based on “evidence of trustworthiness” and is usually measured with objective adjectives such as “reliable” and “accurate” [ 23 ] and is thus similar to the concept of “perceived credibility”. In contrast, affective trust refers to how one feels about such trust attributes as warmth and affection when interacting with an objective, instead of reasoning and logic [ 24 , 25 ]. Affective trust is generated based on feelings [ 11 ], and commonly reflects positive and likable feelings [ 26 ]. Thus, it is usually measured via feeling and affections. Previous research [ 23 , 27 ] has operationalized “affective trust” from an emotional perspective with adjectives that describe feelings such as “likable” and “warm”.

Source can impact users’ cognitive trust of online information. Prior work on source credibility of health information has suggested that professional sources (i.e., authority cues) are perceived to be more credible than layperson sources [ 10 ]. Due to the user-generated nature of social media, which is different from traditional media, and the fact that anyone could be an information source, it is important to investigate the effects of source types of social media message on individuals’ perceptions of mental health, which is certainly a sensitive but critical issue to address in contemporary health communication.

In addition, there is evidence that suggests user comments may be more influential than message source in forming cognitive perceptions towards health information, such as attitudes and behavioral intentions [ 28 , 29 ]. For example, Ahn [ 30 ] found that positive comments that support the content yielded more favorable feelings toward the content than negative comments. Further, online news with comments supporting one opinion/direction have been observed to influence perceptions more strongly than those with no comments or mixed comments [ 31 ]. More empirical research is needed to examine the relationship between the valence of comments and cognitive perceptions regarding health content.

Though prior research mainly focuses on how the above two types of trust are formed through different types of information process [ 23 ], less is known about how these two types of trust influence attitudes and behavioral intentions under different health contexts. Individuals’ cognitive trust is positively related to health perceptions [ 32 ]. Marton and Choo [ 33 ] indicated that cognitive trust positively influences participants’ attitudes toward online health information, which is a positive and substantial predictor of intention to use the health information online [ 34 ]. Affective trust was found to account for variances in persuasion and influenced behavioral beliefs and attitudes toward health messages [ 23 ]. Therefore, affective trust may influence attitudes about sharing the social media messages with others, and/or lead to a greater intention to use mental health services.

The current study aims to measure both attitudes toward health information (Facebook posts) and attitudes toward mental health services along with intentions to share health information on social media and intention to use mental health services. Based on previous evidence, different types of trust may influence attitudes toward information or attitudes towards the object or behavior described in the information [ 35 ] and the Theory of Planned Behavior [ 36 ] posits that attitudes will influence relevant behavioral intentions. Therefore, trust in health information could impact different health attitudes and intentions.

1.4. Research Questions and Hypotheses

Based on the literature reviewed, the following hypotheses and research questions were developed:

Posts from a professional source will be perceived with greater cognitive trust than posts from a layperson source.

Posts with positive comments will be perceived with greater affective trust than those with mixed or negative comments.

(a) Cognitive trust and (b) affective trust in a Facebook post will be positively associated with attitudes towards Facebook posts about mental health information.

(a) Cognitive trust and (b) affective trust in a Facebook post will be positively associated with attitudes towards mental health services.

Attitudes towards Facebook posts about mental health information will be positively associated with intentions to (a) use mental health services, and (b) share the Facebook posts about mental health information.

Attitudes towards mental health services will be positively associated with intentions to (a) use mental health services, and (b) share the Facebook posts about mental health information.

A conceptual model was proposed based on the theoretical relationships in Figure 1 . Structural equation modeling (SEM) was used to examine the study hypotheses, as it is useful to understand health communication as a complex multivariate phenomenon rather than an isolated incidence [ 37 , 38 ].

Conceptual Model of Source and Valence of Comments on Behavioral Intentions.

2.1. Design Overview

Our experiment employed a between-subjects 2 (source: professional vs. layperson) × 3 (comments: positive, mixed or negative) factorial design in an online experiment. We evaluated the effects of different social media sources and the valence of comments on cognitive trust, affective trust, attitudes toward social media posts on health information, attitudes toward mental health services, behavioral intention to share mental health information on social media, and behavioral intention to use mental health services in the future.

2.2. Participants and Procedure

A total of 425 undergraduate students were recruited to participate in the online experiment on a participatory online system called Sona, incentivized by extra credit. We used a general student sample rather than limiting the study to students who were undergoing serious mental health issues. Since college students are heavy social media users and tend to experience a variety of mental issues such as depression and stress [ 39 ], a student sample is appropriate for the current study in order for researchers to design social media campaigns and preventions which promote mental health service use among those who may potentially suffer or will suffer from mental health problems in the future. Participants who signed up received a link to an online study and were randomly assigned to one of six experimental conditions. Subjects in each condition viewed three mock Facebook posts (and attached comments) and were given at least 60 s to view each post. After viewing all the mock Facebook posts, the participants completed a questionnaire about what they had viewed, as well as demographic questions. The study was approved by the university’s Institutional Review Board (WSU IRB # 15136).

2.3. Experimental Treatment Conditions

A total of 18 mock Facebook posts were constructed through Facebook post generating tools by the researchers (3 per condition) and the users in the screenshots were not real users. While each of the six conditions varied according to source, comments and the number of likes on the post, the content of the posts remained the same. The sources for the three layperson conditions were named “Amy Jones,” “Peter Brown,” and “Elena Gale” (e.g., Figure 2 and Figure 3 ), and the source for the 3 professional conditions were named “Mental Health Professional,” “Mental Health America,” and “Mental Health NIMH (National Institute of Mental Health” (e.g., Figure 2 and Figure 3 ). In addition to names, professional and layperson sources also varied in profile pictures (See Figure 2 ). We also included two comments on each post from both a male and a female “user” to reduce potential gender biases in the mixed comments condition (e.g., Figure 2 ). The positive comments and negative conditions had the same content as the mixed comments condition except that the comments are purely positive or negative (e.g., Figure 3 ). The Facebook post content was derived from the website of the NIMH (National Institute of Mental Health). Both negative and positive comments were taken from actual online comments on mental health services.

Professional Source 1 and Mixed Comments 1 a vs. Layperson Source 1 and Mixed Comments 1. a Each condition has three sets of screenshots. Professional source 1 and layperson source 1 have the same content except for account name and profile picture.

Professional Source 2 and Mixed Comments 2 vs. Layperson Source 2 a and Positive Comments 2 b . a Layperson source 2 and professional source 2 have the same content except for account name and profile picture. b Layperson source 2 × negative comment 2 and Layperson source 2 × positive comment 2 only differ in the comment section.

2.4. Measurements

2.4.1. manipulation checks.

At the end of the survey, we conducted two manipulation checks: (1) We conducted a manipulation check for the source of the Facebook posts by asking participants to indicate the extent to which they deemed the sources to be professional on a 5-point Likert scale ranging from 1 (“ very unprofessional ”) to 5 (“ very professional ”); (2) We conducted a manipulation check on the positive and negative valence of the comments by asking participants to indicate how positive or negative they felt about the comments on a scale from 1 (“ very negative ”) to 5 (“ very positive ”). Two independent-sample t-tests were used to compare the means of different groups of sources and comments. For both tests, the assumption of homogeneity of variance was not violated. Participants who viewed positive comments ( M = 3.84, SD = 0.78, n = 148) indicated that the Facebook comments were significantly more positive than those who viewed negative comments ( M = 2.24, SD = 0.75), n = 141), t (287) = 17.74, p < 0.001. Further, participants in the professional source group ( M = 3.48, SD = 0.90) indicated that the source was significantly more professional than those in the layperson source group ( M = 2.91, SD = 0.88), t (422) = −6.65, p < 0.001. As such, both manipulations (positive, negative; professional, layperson) were confirmed.

2.4.2. Mediating Variables

Perceived credibility (cognitive trust) was measured using four items adapted from prior work [ 27 , 40 ] indicating whether the Facebook posts were “ accurate ”, “ reliable ”, “ credible ”, and “ believable ” on a 10-point Likert scale ranging from 1 (“ not at all ”) to 10 (“ extremely ”) (α = 0.88, M = 5.48, SD = 1.72).

Affective trust was measured by four items obtained from Koh and Sundar’s [ 27 ] and Kim and Sundar. Four 10-point Likert items ranging from 1 (“ not at all ”) to 10 (“ extremely ”) were used to measure whether the Facebook posts were “ likable ”, “ interested in my well-being ”, “ empathetic”, and “ warm ” ( α = 0.87, M = 5.49, SD = 1.75).

Attitude toward Facebook posts containing mental health information [ 41 ] was measured by seven items ( α = 0.94, M = 4.57, SD = 1.19) asking whether the Facebook posts about mental health information were “ bad ” or “ good ,” “ unhelpful ” or “ helpful ,” “ unenjoyable ” or “ enjoyable ,” “ harmful ” or “ beneficial ,” “ worthless ” or “ valuable ,” “ foolish ” or “ wise ,” and “ not useful ” or “ useful ” on a 7-point Likert scale items, ranging from 1 to 7.

Similarly, attitude toward mental health services was measured using seven items as above ( α = 0.97, M = 5.60, SD = 1.21).

2.4.3. Dependent Variables

Intention to use mental health services was adapted from prior work on behavioral intention [ 41 , 42 ]. Three 5-point Likert-type items were used to measure respondents’ degree of agreement related to intentions to use mental health services if they had the need ( α = 0.93, M = 3.79, SD = 0.85).

Intention to share mental health information was based on Hu and Sundar’s [ 10 ] adapted multidimensional scale measuring behavioral intention. Respondents indicated their degree of agreement regarding three 5-point Likert-type items about their behavioral intention to recommend the information to others such as “ I will forward this Facebook message to my online acquaintances ” ( α = 0.85, M = 2.45, SD = 1.00).

2.5. Data Analysis

Since it is useful to understand health communication as a complex multivariate phenomenon rather than an isolated incidence [ 37 , 38 ], SEM was used to examine the mediating relationships as a communication process with Mplus Version 7.11 (Muthén & Muthén, Los Angeles, CA, USA) [ 43 ]. The analysis controlled for gender and race due to their potential influences on college students’ mental health. Female students may experience higher level of depression [ 39 ] and ethnic minority students are sometimes more likely to experience stress on campus [ 44 ]. ANOVA analysis was used in SPSS 24.0 (IBM Corp., Armonk, NY, USA) [ 45 ] for post-hoc comparison.

3.1. Sample

In total, 425 undergraduate students were recruited and three incomplete responses were dropped. The final analytic sample was 422. Participants age ranged from 18 to 26 years old ( M = 19.83, SD = 1.42), two-thirds of the sample was female (62.2%), and a majority identified as White (70.4%). Participants also reported being Hispanic (7.5%), African American (5.9%), and Asian/Pacific Islander and other (14.2%). Descriptive characters of the variables under study are shown in Table 1 .

Descriptive Statistics of Main Variables under Study.

3.2. Results of SEM

The values of root mean squared error of approximation (RMSEA) and Comparative Fit Index (CFI) indicated a good model fit ( Table 2 ) and the results of each path are shown in Figure 4 . According to the results of the SEM analysis, race/ethnicity was associated with sharing intention and white participants were more likely to share mental health posts on Facebook ( p < 0.05).

Model of Source and Valence of Comments on Behavioral Intentions. The model includes effects of control variables, which are not displayed. Indicators of each latent variable are not displayed. Dashed lines indicate non-significant paths. ** p < 0.01. *** p < 0.001.

Summary of Model Fit.

df = degree of freedom; RMSEA = root mean squared error of approximation; CFI = Comparative Fit Index; TLI = Tucker–Lewis index

Posts from a professional source had a significant positive influence on cognitive trust ( β = 0.46, p < 0.001), thus supporting H1.

The valence of comments had a significant effect on affective trust ( β = 0. 33, p < 0.01). Since there were three levels of comments, we conducted an additional one-way ANOVA test to further examine the associations between the valence of comments and affective trust. Based on the results of the post-hoc test of the one-way ANOVA, the respondents who viewed positive comments ( M = 6.08, SD = 1.86) reported higher affective trust about mental health services information than those who viewed mixed ( M = 5.34, SD = 1.55, p < 0.001) or negative comments ( M = 5.02, SD = 1.63, p < 0.001), thus H2 was supported.

Furthermore, higher cognitive trust ( β = 0.26, p < 0.001) and affective trust ( β = 0.41, p < 0.001) in a Facebook post were significantly associated with more positive attitudes towards Facebook posts, thus supporting H3a and H3b.

Only affective trust had a significant effect on attitude towards mental health services ( β = 0.28, p < 0.001), thus H4b was supported while H4a was not.

Attitude towards Facebook posts led to greater intention to share the health information ( β = 0.49, p < 0.001), but had no effect on intention to use mental health services. Therefore, H5a was not supported while H5b was supported.

Attitudes towards mental health services had a significant effect on intention to use mental health services ( β = 0.41, p < 0.001), but did not have a significant effect on intention to share a post about mental health services; thus, H6a was supported while H6b was not.

4. Discussion

The present study examined the effects of two types of interface cues on social media, different types of sources and valence of comments, on behavioral intentions about using mental health services and sharing mental health related information on media. The results of our analysis indicate three sets of primary findings: (1) first, mental health posts delivered by professional sources were deemed as credible sources of information, while comments demonstrating positive or negative valence were associated with emotion-driven affective trust; (2) both cognitive and affective forms of trust were positively associated with one’s attitudes towards the mental health information delivered in the Facebook posts, which in turn promoted in individuals a greater intention to share the mental health information on social media; (3) only elevated affective trust positively impacted one’s attitudes towards mental health services, which in turn promoted in individuals a greater intention to actively use mental health services.

Elaborating on the above, we found that source cues affect users’ attitudes through cognitive trust in posts containing health information. If the source was perceived as more professional, respondents were more likely to have higher cognitive trust in the social media post that is shown to them. The effect of cognitive trust on behavioral intention to share Facebook posts about mental health was fully mediated by the level of attitude toward mental health posts. The professional source cues led to a rational and logical judgment of the health information on social media sites, which could result in a rational evaluation and favorable attitude toward information about mental health posts in the future. Moreover, the more favorable attitude the individual has, the more likely he or she will engage in sharing mental health counseling related information on his or her online social networks.

Additionally, we found strong evidence supporting that different types of social media comments significantly impact affective trust among viewers. More specifically, positively-valenced comments indicating support or agreement were associated with greater affective trust compared to negatively- or mixed-valenced comments. Further, we found that affective trust in posts about health information directly influenced attitudes towards mental health posts as well as attitudes toward mental health services, and influenced different intentions. More favorable attitudes towards mental health services were associated with greater intentions to use mental health services in the future. This finding suggests that positive comments towards mental health services on social media can help combat the negative perceptions around mental health on social media through their influences on affective trust [ 12 , 46 ].

The current work presents three primary contributions. First, as mental health is a topic not yet covered in the credibility and message valence literature, the present work offers new insights into this topic. Second, the current study sheds light on understanding the theoretical relationships between cues on social media and cognitive perceptions as a complex communication process. Finally, healthcare professionals can learn the importance of comments on social media in the comment section, in addition to credibly-sourced posts, in social media interventions.

4.1. Health Applications

Social media platforms are ubiquitous and offer channels for advertising, marketing and information seeking. It is critical to understand which technological features (e.g., comments) on social media sites have effects on health related cognitive, attitudinal, and behavioral outcomes [ 47 ]. The current study identified the mechanisms of health information processing on social media influenced by information source and valence of comments and suggests that different types of sources on social media sites may directly influence perceived credibility of health information, which in turn impacts attitudes toward the information as well as behavioral intention to share the mental health information with their social network.

We found a link between valence of comments and intention to use mental health services through affective trust and attitudes towards mental health services, which is critical for mental health research. Compared to sharing information online, seeking mental health services is arguably a more crucial behavior to directly impact positive outcomes on individuals’ mental health. The current work found a potential way to boost such behavior through positive comments. Positive comments affirming the information contained in the mental health message may contribute to reducing negative perceptions surrounding mental health, which is a key deterrent to seeking mental health treatment [ 12 ]. In addition using mental health services, sharing mental health information in social networks could help maximize the effects of social media mental health interventions and reach a larger audience, which in turn leads to positive and actionable health behaviors [ 35 ]. Future studies should explore the benefits of sharing health information on social media.

4.2. Design Applications

The current work provides empirical evidence supporting the notion that cues on social media significantly impact one’s trust regarding attitudes and behavioral intentions towards mental health, suggesting that source cue in health message persuasion is critical from a design perspective. As observed in the current work, health practitioners and researchers who aim to promote health messages should create authority-based source cues to signal their expertise to their target audience.

Further, health messages on social media is also a critical component for promoting effective and actionable health information since they can be shared online and reach a broader population. The findings of the current study are also consistent with prior work suggesting that different comments boost involvement with online content [ 35 , 48 ], indicating that health practitioners and researchers promoting health information using social media [ 49 ] should address negative user comments in a timely and professional matter, such as replying to user comments with accurate health information and contact information for local mental health services.

Summing up, this study has both theoretical and practical implications. This work provided empirical evidence for understanding the theoretical relationships between cues on social media and cognitive perceptions as a complex communication process. The proposed model extended the previous theoretical framework by building associations between cues and different types of cognitive perception. The current study also has practical implications which could provide insights for health researchers into understanding and designing social media mental health campaigns for college students in the US. However, individuals from different cultures or countries may have distinct reactions to mental health-related messages [ 50 ]. Therefore, more empirical studies in different cultures are needed for understanding mental health campaigns and prevention on social media.

4.3. Limitations and Future Directions

The current study has certain limitations. First of all, the Facebook posts did not employ a real Facebook page or group in an interactive setting. The screenshots of Facebook posts are different from the interactive and dynamic social media environment, and this may have influenced the results. Future studies should examine conditions in a real social media setting and evaluate the engagement of the participants on the social media platform. Second, the current study used cross-sectional data which only measures variables from a single time point and did not track longitudinal effects of the experiment. Future studies should use longitudinal design to collect data over time. Finally, the present study used an undergraduate student sample, which is appropriate since college students are a population that have been observed to be both heavy social media users and be vulnerable to different mental issues, such as depression [ 39 ]. However, we did not measure the mental health status of the study sample, warranting examination of these effects in an actual mental health intervention using a sample of individuals diagnosed with mental health disorders in the future. Future social mental health interventions could target individuals with mental health issues to increase their use of mental health services, and also target the general public to raise positive perceptions and increase their intentions towards using mental health services when they have the need. Future studies should also investigate the effects of source and comments in different cultures or countries and further explore the benefits of sharing health information on social media, which could expand the effects of social media campaigns and interventions.

5. Conclusions

The current work contributes to the growing issue of information source credibility in modern health communication, particularly in a mental health context. The present study explores the effects of cues on mental health information perceptions on social media and suggests that source and valence of comments are critical for promoting the seeking of mental health counseling. Specifically, we found that information source could influence cognitive trust, which ultimately impacts intentions to share mental health information on social media. We also found that positively-valenced comments significantly impact affective trust in the message, which ultimately impacts both intentions to share mental health information and to use mental health services, via its previous effects on attitudes.

Author Contributions

Conceptualization, Z.N.; methodology, Z.N.; formal analysis, Z.N; writing—original draft preparation, Z.N.; writing—review and editing, L.H., D.C.J., J.B. and J.L.S.; supervision, J.L.S.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Pioneer Hundred Talents Program of Chinese Academy of Sciences.

Conflicts of Interest

The authors declare no conflict of interest.

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Neuroprotective and mental health benefits of salt-tolerant plants: a comprehensive review of traditional uses and biological properties.

1. Introduction

1.1. biochemical targets in mental health disorders, 1.2. role of natural remedies in promoting mental wellness, 1.3. importance of salt-tolerant plants, 2. methodology, 3. traditional uses of salt-tolerant plants as neuroprotective and mental health commodities, 4. salt-tolerant plants as sources of neuroprotective and mental health commodities, 4.1. in vitro assays, 4.2. in vivo studies, 5. conclusions and future research directions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Rodrigues, M.J.; Pereira, C.G.; Custódio, L. Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties. Appl. Sci. 2024 , 14 , 5534. https://doi.org/10.3390/app14135534

Rodrigues MJ, Pereira CG, Custódio L. Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties. Applied Sciences . 2024; 14(13):5534. https://doi.org/10.3390/app14135534

Rodrigues, Maria João, Catarina Guerreiro Pereira, and Luísa Custódio. 2024. "Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties" Applied Sciences 14, no. 13: 5534. https://doi.org/10.3390/app14135534

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Understanding the Availability of Mental Telehealth Services

June 26, 2024 • Research Highlight

During the coronavirus pandemic, public health measures and restrictions impacted in-person health care visits, leading to a surge in telehealth services as a way of accessing assessment and treatment. Particularly in mental health care, telehealth saw a significant rise, and usage remains high even post-pandemic. However, despite the increased utilization of telehealth services, there's a limited understanding of the availability and structure of these services.

What did the researchers do?

In an NIMH-funded study, researchers led by Jonathan Cantor, Ph.D.   , of the RAND Corporation investigated the availability of different types of telehealth services and the time it took patients to access telehealth care.

Between December 2022 and March 2023, researchers contacted more than 1,900 outpatient mental health care facilities to ask about telehealth services. The underlying sample came from outpatient mental health treatment facilities, not individual practitioners.

The researchers used a secret shopper approach, using a script that mirrored information a prospective patient might ask when inquiring about telehealth services. The secret shoppers asked about the availability of telehealth services for treating major depressive disorder, generalized anxiety disorder, or schizophrenia. They also asked about the specific services offered via telehealth (behavioral therapy, medication management, diagnostic services) and the number of days they would have to wait before having their first telehealth appointment. Both men and women served as secret shoppers, and the names used by the shoppers were chosen to reflect a variety of racial and ethnic backgrounds.

What did the researchers find?

Out of the more than 1,900 facilities contacted, the researchers received replies from 1,404. Among these, 1,221 were accepting new patients. Of those 1,221 facilities, 80% (980) offered telehealth services. Out of the 980 treatment facilities that offered telehealth services:

• 97% provided counseling services
• 77% provided medication management
• 96% provided diagnostic services

Among the facilities that responded to the telehealth question, the researchers found:

• Not-for-profit and for-profit private treatment facilities were more likely to offer telehealth services than public treatment facilities.
• Treatment facilities in metropolitan areas were more likely than non-urban areas to offer medication management but less likely to offer diagnostic services.
• The average wait time for a telehealth appointment was 14 days (ranging from 4 to 75 days, depending on the facility contacted).

What do the findings mean?

The researchers found that some of the facilities they initially reached out to for information did not respond, suggesting that people looking for any type of mental health care may experience barriers to accessing it.

Of the facilities that did respond, most were accepting new patients, and most provided telehealth services; however, the availability of those services and the type of care offered varied by location and state. This suggests there may be disparities in access to telehealth services across the United States.

The researchers note that telehealth services and availability may differ at health centers not included in this study and that the availability of technology that makes telehealth possible—such as broadband services—was not examined in this analysis.

Cantor, J., Schuler, M. S., Matthews, S., Kofner, A., Breslau, J., & McBain, R. K. (2024). Availability of mental telehealth services in the US. JAMA Health Forum , 5 (2), Article e235142. https://doi.org/10.1001/jamahealthforum.2023.5142  

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