The UNIversity students’ LIFEstyle behaviors and Mental health cohort (UNILIFE-M): Study protocol of a multicenter, prospective cohort study

doi:10.21203/rs.3.rs-3794023/v1

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Study protocol

The UNIversity students’ LIFEstyle behaviors and Mental health cohort (UNILIFE-M): Study protocol of a multicenter, prospective cohort study

https://doi.org/10.21203/rs.3.rs-3794023/v1

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Background

Students enrolling in higher education often adopt lifestyles linked to worse mental health, potentially contributing to the peak age onset of mental health problems in early adulthood. However, extensive research is limited by focusing on single lifestyle behaviors, including single time points, within limited cultural contexts, and focusing on a limited set of mental health symptoms.

Methods

The UNIversity students’ LIFEstyle behaviors and Mental health cohort (UNILIFE-M) is a prospective worldwide cohort study aiming to investigate the associations between students’ lifestyle behaviors and mental health symptoms during their college years. The UNILIFE-M will gather self-reported data through an online survey on mental health symptoms (i.e., depression, anxiety, mania, sleep problems, substance abuse, inattention/hyperactivity, and obsessive/compulsive thoughts/behaviors) and lifestyle behaviors (i.e., diet, physical activity, substance use, stress management, social support, restorative sleep, environment, and sedentary behavior) over 3.5 years. Participants of 69 universities from 28 countries (300 per site) will be assessed at university admission in the 2023 and/or the 2024 academic year and followed up for 1, 2, and 3.5 years.

Discussion

The study portrays a unique opportunity to comprehensively understand how multiple lifestyle behavior trajectories relate to mental health symptoms in a large international cohort of university students.

mental illness

mental health

college

lifestyle

young adults

prospective

cohort study

university

health behaviors

The transition to university relates to marked shifts in social, academic, and financial demands [1, 2], and adjustments to these new demands may be linked to lifestyle behavior changes [3]. These changes are well documented, showing university students often present high levels of physical inactivity [4] and sedentary behavior [5], poor and unbalanced diet [3], high rates of alcohol and other substance misuse [6], poor sleep patterns [7], and longer screen time [8].

Lifestyle behaviors may constitute modifiable risk or protective factors for the emergence of mental health symptoms [9]. For example, high physical inactivity levels, poor diet, poor sleep quality, substance misuse, and long screen exposure increase the risk for incident mental health symptoms [10–18]. However, most studies to date have only evaluated the association between isolated lifestyle behaviors and mental health symptoms (e.g., sleep and depression) [10–18]. Lifestyle behaviors do not occur in isolation, but are often clustered [11–20]. Among university students, higher levels of physical activity cluster with better dietary habits and less smoking [21, 22]. On the other hand, higher levels of physical activity may cluster with increased alcohol consumption [22], while sedentary behavior is associated with poor sleep but not with smoking [23]. Considering the nature of cluster formations among multiple lifestyle behaviors, it is plausible that examining an individual's multiple lifestyle behaviors can provide a person-centered approach to explain the associations between lifestyle behaviors and mental health symptoms [24].

Some evidence suggests that clusters of lifestyle behaviors are associated with mental health in university students [25–33]. In Australian students [27], four distinct clusters were identified: “healthiest”, “healthy”, “mixed”, and “sedentary and distressed”. The "healthiest" cluster (higher levels of physical activity and fruit consumption, less binge drinking, and the least sedentary behavior) showed the lowest levels of depression, anxiety, and stress compared to the other clusters. Another study with Australian students found three clusters: “healthier”, “moderate”, and “unhealthier” lifestyles, with the last two being associated with a higher risk of psychological distress [25]. In Chinese students [31], four clusters were identified: 1) active pattern (those with higher levels of physical activity); 2) high sleep duration pattern; 3) high screen time pattern; and 4) low-physical activity-low-sleep duration pattern. The study found that those with low physical activity and low sleep duration were at increased risk for depressive symptoms. Notwithstanding the above, this evidence derives from cross-sectional studies, providing no evidence of the temporality of the association.

The Seguimiento Universidad de Navarra (SUN) cohort is one of few prospective studies showing that students with ten healthy lifestyle behaviors, named as the "healthier lifestyle" cluster (i.e., classified as healthy across ten lifestyle behaviors: smoking, physical activity, diet, body mass index, alcohol consumption, television exposure, binge-drinking binge, afternoon nap, time spent with friends and working) had a 32% reduced risk of developing depression compared to those reporting three or less healthy behaviors [33]. Nonetheless, this evidence is limited to depression, hence preventing the generalization of results to other mental health symptoms.

Lifestyle behaviors and mental health symptoms are related to contextual factors such as cultural and social factors [34, 35]. There is literature suggesting that some lifestyle behaviors are universal protective factors for some mental health symptoms[15, 36], while others may be context-specific. For example, physical activity is a universal protective factor against depression and anxiety, with no significant variation in the magnitude of the effect size between geographical regions [15, 36]. On the other hand, alcohol consumption is prospectively associated with “positive mental health” and a lower frequency of depression, anxiety, and stress in German students, but with a higher frequency of depression, anxiety, and stress in Chinese students [36]. However, this evidence relies on data from two countries and a limited set of behaviors (physical and mental activity, alcohol consumption, smoking, circadian and social regularity). Further studies in multiple countries and evaluating a broader set of lifestyle behaviors are needed to understand the universal and country-specific associations.

Lifestyle behaviors and mental health vary during university years. Data from the Household, Income, and Labour Dynamics study (HILDA) demonstrated that mental health has a time-varying course in Australian students [37]. In this cohort, young university students experienced a reduction in mental health from 16 to 17 years, then an increase from age 17 to 18, then a reduction again from age 19 to 20, and then an increase from age 20 to 21. In a sample of UK students, four different trajectories were identified: persistent high-severity symptoms, varying opposed to national variation, varying consistently with national variation, and persistent to low severity [38]. Similarly, lifestyle behaviors seem to have varying trajectories over time [39, 40]. However, there is a paucity of prospective studies investigating the associations between lifestyle behavior and mental health trajectories in a large cohort of university students.

Given the aforementioned lack of prospective studies investigating the association of multiple lifestyle behaviors with multiple mental health symptoms in a diverse international cohort of universithy students, the UNIversity students’ LIFEstyle and Mental health (UNILIFE-M) was designed and conceived. The UNILIFE-M cohort is a large multicenter, international, prospective cohort study that aims to provide an intricated view of the dynamic associations between multiple lifestyle behaviors (physical activity, sedentary behavior, diet, stress management, sleep, substance consumption, exposure to green environment, and social support), either in an independent or clustering manner, and a broad scope of mental health symptoms (depressive, anxiety, manic, obsessive-compulsive, psychotic, or attention-deficit/hyperactivity symptoms, substance abuse, or suicidal ideation), in an international cohort of university students.

Study design

The UNILIFE-M is a multicenter, international, prospective cohort study. The study will be conducted in 69 universities in 28 countries. Of these, 48 already from 19 countries already started the data collection in 2023. The list of Participating universities can be seen in supplementary materials 1. The study report follows the Strengthening Observational Studies in Epidemiology (STROBE) guidelines [41]. The list of participating study sites and coordinators is included in supplementary materials 1.

Recruitment

The baseline recruitment and assessments will occur during the beginning of the academic calendar in 2023 and 2024, with three follow-ups: 1, 2, and 3.5 years following the baseline. The exact month of the study commencement will vary according to the beginning of the academic year in each specific study site. Through a convenience sampling procedure, students will be recruited using online resources such as advertising on social media (e.g., Twitter, Instagram, Facebook) and through university website/course-specific sites, and direct emails, following relevant data privacy laws and policies in the respective countries and institutions. Newsletters and posters can be fixed on the university walls with the link/QR code to the study survey, depending on ethics regulations. Lastly, face-to-face invitations through flyers distributed to students gathering in places such as university restaurants, parks, or university student services or during lectures.

Follow-ups

For the follow-up, participants will be directly contacted via email, and, whenever indicated by the participant and following ethics regulations, via text messages on social media. The use of participant compensation (e.g., going into a prize draw to receive an e-gift card upon completion of surveys at each time point) is permitted but not mandatory for all study sites. Given the breadth of countries and contexts and ethics regulations involved in UNILIFE-M, the use of participant compensation is to be determined as appropriate by each study site.

Participants

Inclusion criteria for the study are: 1) aged 16–35 years, 2) enrolled as a freshman (i.e., in the first semester of an undergraduate course) in the 2023 or 2024 academic year at a participating UNILIFE-M university. Participants of all courses and fields are eligible.

Outcomes and assessments

The prospective study will evaluate lifestyle behaviors, mental health symptoms, and demographics. Questionnaires not available in the language of the participating study sites will be translated and back-translated according to previous guidelines for translating questionnaires [42].

Lifestyle

The lifestyle assessments include the domains proposed by the American College of Lifestyle Medicine [43]: diet and nutrition, substance misuse, physical activity, stress management, restorative sleep, social support, and environmental exposures and will be assessed by two instruments: the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) [21] and two questions on sedentary behavior [49].

The Short Multidimensional Inventory Lifestyle Evaluation for University Students (U-SMILE)

The U-SMILE is a variation of the self-reported SMILE questionnaire [21], developed with pilot data of this cohort, collected between June-December 2022. The original SMILE scale has 43 items, while the U-SMILE comprises 24 items evaluating seven domains (diet and nutrition, substance use, physical activity, stress management, restorative sleep, social support, and environmental exposures). The items ask about the frequency of lifestyle behaviors in a regular week over the last month (always, often, seldom, or never). The original U-SMILE is a shortened version of the SMILE, as we observed that a significant proportion of participants dropped out while responding to the survey in a pilot sample of the present study. Scores of the U-SMILE range from 0 to 96; higher scores mean a healthier lifestyle for that domain [26]. The U-SMILE is currently available in Brazilian Portuguese, Spanish, and English, and will be translated into all languages of the participating study sites. The scale has demonstrated acceptable internal consistency (Cronbach’s α = 0.73, McDonald’s ω = 0.79) in the pilot study sample [44].

Sedentary behavior

The sedentary behavior assessment includes two questions, one measuring the average amount of time spent sitting and another measuring the time spent lying down or in a reclined posture. The scale response was provided in five categories: Less than 3h/day, from 3h to less than 6h/day; from 6h to less than 8h/day; from 8h to less than 12h/day; more than 12 h/day. The question on sitting time has demonstrated acceptable validity and reliability [45].

Mental health symptoms

Mental health symptoms in the cohort study will be assessed at two levels. At the first level, the Level 1 Cross-Cutting Symptom Measure for Adults Diagnostic [46], proposed by the Diagnostic and Statistical Manual of Mental Disorders − 5 (DSM-5), will be answered by all participants. Respondents with a positive screening at the level 1 instrument for depression, mania, anxiety, sleep, obsessive and compulsive behaviors, or alcohol/substance abuse will be directed to a level 2 instrument for that problem. There will be no level 2 instrument for anger, somatic symptoms, psychosis, memory problems, dissociation, or personality, as suggested by the cross-sectional DSM-5 tool. Attention deficit and hyperactivity symptoms are not assessed in the level 1 cross-cutting tool, so all participants will respond to the Adult Self-Report Scale (ASRS) – Shortened version at level 1 [47]

The DSM-5 Level 1 Cross-Cutting Symptom Measure for Adults

The DSM-5 Level 1 Cross-Cutting Symptom Measure for Adults is a self-reported questionnaire that assesses important domains across most psychiatric diagnoses [48]. The adult version comprises 23 questions that assess 13 psychiatric domains: depression, anger, mania, anxiety, somatic symptoms, suicidal ideation, psychosis, sleep problems, memory problems, repetitive thoughts and behaviors, dissociation, personality functioning, and substance use. The frequency of experiencing associated symptoms is assessed using a 5-point Likert scale in which participants will respond about how much (or how often) they have been bothered by a given problem during the past two weeks. Responses range from 0 (None / not at all) to 4 (Severe / nearly every day). A score of 2 (mild or several days) or greater on any item within a domain is considered a positive screen, except for substance use, suicidal ideation, and psychosis, for which a positive screen is considered when a participant scores 1 (slightly or rare, less than a day or two). Subjects with a positive screening at level 1 will answer the level 2 instruments. The DSM-5 Level 1 Cross-Cutting Symptom Measure for Adults has been shown to be a valid and reliable tool for evaluating mental health symptoms in university students [49]. The cutoff per domain and the subsequent level 2 instrument are seen in Table 2.

Table 2

Level 1 cutoffs per domain and subsequent level 2 instruments
Domain	Cutoff	Level 2 Scale
Depressive symptoms	≥ 2	Patient Health Questionnaire (PHQ-9)
Anger	≥ 2	-
Hypomanic/manic symptoms	≥ 2	Hypomania CheckList-16 (HCL-16)
Anxiety symptoms	≥ 2	Generalized Anxiety Disorder (GAD-7)
Somatic symptoms	≥ 2	-
Suicidal ideation	≥ 1	-
Psychosis	≥ 1	-
Sleep problems	≥ 2	Pittsburgh Sleep Quality Index (PSQI)
Memory	≥ 2	-
Obsessive-and compulsive symptoms	≥ 2	Obsessive-Compulsive Inventory (OCI-R)
Dissociation	≥ 2	-
Personality	≥ 2	-
Substance use	≥ 1	Alcohol Smoking and Substance Involvement Screening Test (ASSIST) – Frequency of use items
Inattention/hyperactivity*	≥ 4	Adult Self-Report Scale (ASRS) – Long form

Slight and mild symptoms correspond to 1 and 2 points on the scales, respectively. *Level 1 instrument for Inattention/hyperactivity is the Adult Self-Report Scale (ASRS) shortened version, with 6 items.

Depressive symptoms

Depressive symptoms will be assessed using the Patient Health Questionnaire (PHQ-9). The PHQ-9 is a 9-item self-reported questionnaire that evaluates the presence of depressive symptoms, defined according to the DSM criteria over the last 2 weeks [50]. The PHQ-9 scale showed excellent internal reliability (Cronbach’s α = 0.89 − 0.86) [50]. The scoring ranges from 0 to 27, and higher scores indicate higher symptom severity. Participants scoring 10 or higher will be considered caseness for depression [50, 51].

Hypomanic/manic symptoms

Hypomanic/manic symptoms will be assessed with the hypomania checklist (HCL-16). The HCL-16 is a 16-item self-report questionnaire that evaluates hypomanic and manic symptoms, with acceptable reliability (Cronbach’s α = 0.77) [52]. Scores range from 0 to 16, with higher scores indicating higher severity of manic symptoms, and scores higher than 7 will be considered caseness for mania symptoms [53].

Anxiety symptoms

Anxiety symptoms will be assessed with the generalized anxiety disorder questionnaire (GAD-7) [54], a 7-item self-report questionnaire evaluating the presence of the core symptoms of generalized anxiety, with excellent reliability (Cronbach’s α = 0.92) [54]. Scores range from 0 to 21; higher scores indicate increased anxiety symptoms and scores equal to or greater than 10 indicate caseness for anxiety [54].

Sleep disturbances

The Pittsburgh Sleep Quality Index (PSQI) comprises 19 self-reported items assessing sleep quality and disturbances, with good reliability (Cronbach’s α = 0.83) [55]. In the current project, we are using the ten items of the sleep disturbances domain. The sum of scores generates a global score ranging from 0 to 21. Higher scores indicate sleep disturbances and scores greater than 5 are caseness for sleep problems [55].

Obsessive and compulsive symptoms

The Obsessive-Compulsive Inventory (OCI-R) comprises 18 self-reported items that evaluate the impact of obsessive and compulsive symptoms on daily life, with good reliability (Cronbach’s α = 0.81) [56]. The scores range from 0 to 72, and higher scores reflect higher symptom severity, with a score of 21 or higher indicating caseness for obsessive-compulsive problems [56].

Alcohol and substance use

The use of alcohol and other substances will be evaluated using the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) [57]. The ASSIST has eight items assessing the frequency, related problems, stop attempts, and risk and compulsive behaviors associated with the use of tobacco, alcohol, cocaine, marijuana, amphetamine stimulants, sedatives, inhalants, hallucinogens, opioids, and other drugs. The current study will assess the frequency of used substances [57]. The instrument showed acceptable to excellent reliability for each substance (Cronbach's α ranging from 0.73 for tobacco use to 0.92 for alcohol use) [57].

Attention deficit and hyperactivity symptoms

Attention deficit and hyperactivity symptoms will be assessed using the Adult Self-Report Scale (ASRS) for screening for attention deficit and hyperactivity disorder [47], the instrument presents acceptable reliability (Cronbach's α: 0.63–0.72) [58]. The shortened version with 6 items will be used for initial screening at level 1, and those above the cutoff (scoring ≥ 4) will answer the full scale (level 2) composed of 12 additional items (i.e., 18 items in total) [59]. The cutoff for caseness for the full scale is 16 or higher.

Sociodemographic characteristics

Participant sociodemographic characteristics will be assessed, including age, sex, pregnancy (for women), gender identity, sexual orientation, height, weight, marital status, residency, occupation, household income, previous mental health diagnosis and treatments, physical conditions, COVID-19 infections, the university enrolled at, and study subject.

University-specific (study-site) information

We will collect details, for each study site, on the strategies used for participants recruitment (e.g., in-person, online, or both), the use of (yes or no) and type (financial, scholar credits, vouchers, or others) of participant compensation, the total number of students entering the university at that period, as well as the age and gender distribution of enrolled students.

Sample size estimation

For a latent growth model, a sample size of N = 150 is sufficient to reliably detect lifestyle behaviors and mental health trajectory associations (i.e., among slopes and/or intercepts with a moderate effect size of d = 0.5), with a power = 80% in four-time points, and to test associations between trajectories, for an alpha level of 0.05. According to the current literature [60], we expect a 50% attrition rate for the 3.5-year follow-up, so the sample size, accounting for attrition, is estimated at N = 300. The sample size corresponds to the minimum per study site.

Statistical analyses

Statistical analyses include descriptive measures such as mean and standard deviation and relative and absolute frequencies. Each and any mental health problem's prevalence and incidence of each mental health symptoms will be calculated. The associations between trajectories will be tested using latent growth curve models (LGCMs). Multilevel models may be adopted to control the cluster effect of countries and/or universities. All models will include mental health problems as categorical and lifestyle behaviors as continuous variables. First, univariate LGCMs will be fitted independently to each mental health problem and lifestyle behavior as a function of time. Univariate LGCMs allow for an examination of (1) the initial level of a target outcome (i.e., intercept), (2) its rate of change (i.e., slope), and the form of this change (i.e., linear or nonlinear latent growth trajectory), and (3) the association between the outcome at baseline and its rate of change over time. The intercept of each target outcome (mental health symptoms and lifestyle behaviors) will be centered at baseline, and a latent growth trajectory (i.e., linear or nonlinear latent growth trajectory) will be tested. Intercepts and slopes of all target outcomes will be adjusted for baseline age (in years), gender, and country (for the cross-country analysis). After establishing the latent growth trajectories and ensuring a good model fit in univariate LGCMs, cross-domain LGCMs will be performed to examine the association between each mental health symptom trajectory with lifestyle behaviors. To understand the relationship between specific lifestyle measured continuously and psychopathology measured at 3-time points, random intercept cross-lagged panel models (RI-CLPM) will be used. This model can differentiate the trait-level relationship between lifestyle and psychopathology and the relationship between changes in their levels [61].

On top of the parameters estimated in univariate LGCMs, cross-domain LGCMs will assess associations between (1) baseline mental health symptoms and baseline lifestyle behaviors, (2) baseline lifestyle behaviors and the rate of change in mental health symptoms, and (3) the rate of change in mental health symptoms and the rate of change in lifestyle behaviors. These models will be adjusted for baseline age (in years), sex, and country (for the cross-country analyses). Sensitivity LGCM analyses will investigate the association between mental health symptoms and lifestyle in the context of adjustment for potential confounders such as age and gender [58]. Models including cross-country data may be weighted for the sample size in each country to control for the unbalanced number of study sites per country. Models including data from a single country will be weighted for the achieved sample size in each study site. Missing data will be tested for randomness. To understand the pattern of possible missing data (missing completely at random, missing at random, missing not at random), and the possibility of biased estimates, Little's MCAR is to be performed. If significant, the assumption that data are missing completely at random should be rejected. multivariate imputation by chained equation will be used for multivariate imputations.

The prevalence of mental health diagnoses will be corrected by considering the sensitivity and specificity of the screening test properties by applying Bayesian adjustment to correct apparent prevalence estimates [62, 63].

Publication and dissemination

Results from the UNILIFE-M project will be disseminated through scientific publications, presentations at scientific meetings, press releases, the general media, and social media. The project aims to generate at least two main papers merging data from all the included countries/territories/regions, one with cross-sectional data just after the 2024 baseline assessments and one after study completion, with data from all follow-ups. Additional study dataset analyses (focusing on one or two specific disorders, lifestyle behaviors, or countries/regions) may be performed under preregistration with the study coordinators. These analyses will be registered at the osf.io.

Social and scientific strengths

The UNILIFE-M cohort study is a unique opportunity to understand the relations between multiple lifestyle behaviors and mental health problem trajectories among university students in a large international cohort, including low, middle and high income countries. The main strengths of this study are (1) a comprehensive assessment of lifestyle behaviors, focusing on seven behaviors according to the current, official definitions of lifestyle [48], (2) the evaluation of time trajectories of both lifestyle and mental health issues at multiple time points throughout the college years, (3) the assessment of a wide range of mental health symptoms that are highly prevalent in this age group, and (4) a large sample size cohort with students from 69 universities located in 28 different countries worldwide including diverse cultures and socio-economic contexts. With the UNILIFE-M, we can provide insights on the universal and country/regional-specific lifestyle and mental health trajectories in university students and how these outcomes are associated.

Limitations

It should be acknowledged that some degree of heterogeneity in recruitment processes is expected, given the cultural and organizational differences between universities worldwide and local ethical regulations. For example, in some cultures, compensation for research participation is usually expected, but in other countries, such as Brazil, compensation for research participants is not allowed by law. The use and type of compensation, and the convenience sampling utilized may make a study site more prone to sampling and response bias, and self-selection bias, which are common limitations of most studies based on web surveys. Second, the study primarily focuses on self-reported measures, which are more likely to suffer from recall problems and social desirability biases. Despite this, we selected instruments broadly used and validated in multiple countries and languages. Further, the use of self-reported questionnaires may be prone to measurement errors and cannot replace clinical interviews to diagnose mental disorders. However, online surveys further enable data to be collected from a broader range of countries, particularly those with limited resources, which is imperative to ensuring inclusiveness. There might be further issues with low response rates, loss of participants, and high dropout rates. Furthermore, it can be that some students finish their university career after three years (involving a transition to working life). In contrast, the last follow-up in our study is after 3.5 years.

Given that transitioning to higher education is generally characterized by both the onset of mental health symptoms and engaging in poorer health/lifestyle behaviors, global efforts must be made to comprehensively understand how multiple lifestyle behavior trajectories relate to mental health symptoms in university students worldwide. The findings of UNILIFE-M will be used to help inform the development of policies and programs to better support university students' mental health and lifestyle behaviors internationally.

Ethics approval and consent to participate

The UNILIFE-M study procedures are in line with the Declaration of Helsinki. Participants provided electronically will sign the consent before completing the survey. The data will be pseudomized to secure participants' privacy. The data will be managed according to data privacy regulations as the Lei Geral de Proteção de Dados (LGPD) as the data is to be stored in Brazil. Participants are free to withdraw at any time. To comply with the ethics regulations of some study sites, students with mental health symptoms may be referred to specialized mental health services provided by the institutions. The study is approved in ethics and registered (CAE:63025822.8.1001.5346) at the Brazilian ethics committee. Study sites outside Brazil obtained their local approvals.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This study is supported by the Conselho Nacional de pesquisa (CNPq) concerted actions on gambling (grant#444435/2023-9) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior CAPES (grant#001). These funding agencies had no role in the design and conduct of the study. The research team has full autonomy in all aspects of the study, including analyzing the data, interpreting the results as well as writing the draft and final manuscript.

Author Contribution

All authors are responsible for this reported research. Prof. Schuch, Prof. Deslandes, and Prof. Wolf conceptualized the study. Prof. Schuch, and Prof. Deslandes, together with Brazilian collaborators designed the study. All authors critically reviewed and revised the manuscript.

Acknowledgements

The authors wanted to acknowledge Prof. Dr. Philip Bentley Ward, Luiz Eduardo Wearick and Victor Callegaro for the support in the development of this study.

Availability of data and materials

Not applicable.

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The UNIversity students’ LIFEstyle behaviors and Mental health cohort (UNILIFE-M): Study protocol of a multicenter, prospective cohort study

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Abstract

Background

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Background

Methods

Lifestyle

Mental health symptoms

Sociodemographic characteristics

Discussion

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Competing interests

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Acknowledgements

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References

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