Sustainable UNiversity Life (SUN) Study — SUN  

Musculoskeletal Pain Clinical Trial

Official title: Sustainable UNiversity Life (SUN): Mental Illness and Pain Conditions Among Students - a Cohort Study to Identify Modifiable Risk and Prognostic Factors

Status Active, not recruiting

Clinical Trial Summary

In 2017, Socialstyrelsen reported that mental ill-health in young adults had increased by almost 70% in the previous10 years. A 2014 report showed that 5% of men and 11% of women 18-24 years were diagnosed with depression or anxiety in Stockholm County. Furthermore, 41% of women 21-24 years have self reported psychological distress. Regarding pain, 28% of men and 36% of women 16-24 years have disabling neck pain. Little is known about the etiology and prognosis of poor mental health in university students.

The aim is to advance knowledge about the etiology of depression, anxiety, stress and pain in undergraduate university students. The investigators will study a cohort of students at full-year programs at universities in the Stockholm area. Primary research questions are whether modifiable factors such as sleep quality, lifestyle, screen time and work environment are independent risk factors for incident episodes or unfavorable trajectories of depression, anxiety and pain in men and women? To be able to answer these research questions we designed a prospective cohort study targeting 5000 university students.

Clinical Trial Description

Study objectives:

The overall study aim is to advance the knowledge about the etiology and prognosis of common mental health problems (depression, anxiety and stress) and musculoskeletal pain experienced by undergraduate university students in Sweden.

Specific research questions:

Are modifiable factors as bad sleep quality, meal patterns, low physical activity/sedentary lifestyle, pain conditions and bad study environment independent risk factors of incident episodes of troublesome depressive and anxiety symptomatology and musculoskeletal pain and of unfavorable trajectories of depressive, anxiety, stress and pain symptoms? Are modifiable factors as bad sleep quality, meal patterns, low physical activity/sedentary lifestyle, pain conditions and bad study environment independent prognostic factors for recovery from troublesome depressive and anxiety symptomatology and musculoskeletal pain? Do such potential risk factors and trajectories vary between men and women? What are the trajectories of sleep quality, meal patterns, physical activity/sedentary lifestyle, pain and substance use during one academic year (describe the fluctuations of risk factors)?

Study design:

Cohort study of undergraduate or graduate (up to master level) full time program students, 18 years of age or older, with at least one academic year before graduation, followed-up four times during an academic year.

Source populations:

The source populations for this project are undergraduate or graduate (up to master level) full-time program students, 18 years of age or older, with at least one academic year before graduation at universities in the Stockholm area.

Data collection:

Data collection will be performed with questionnaires through a link sent to students e-mail address, provided by the participating universities. The students will be able to access the questionnaire for a four-week period at baseline and each follow-up.

Exposures:

Sleep quality, physical activity and sedentary behavior, substance use in the past three months (non-medical use), study environment, body image, perfectionism, gambling, compulsive exercise, social media use, cyberbullying, procrastination and loneliness.

Outcomes:

Outcomes will be measured with the Depression Anxiety Stress Scales-21 (DASS-21). The DASS-21 includes three subscales designed to measure depression, anxiety and stress symptoms in non-clinical populations with response alternatives on a Likert scale ranging from 0-3. Higher scores indicate more severe symptoms. Musculoskeletal pain will be measured with the The Nordic Musculoskeletal pain Questionnaire (NMQ) that cover most potential musculoskeletal pain sights. The questionnaire measure musculoskeletal symptoms, and pain intensity in nine body areas: the neck, shoulder, elbow, wrists/hands, upper back, lower back, hips/thighs/buttocks, knees and ankle/feet. The questions were modified to ask about the previous month rather than the previous 12 months as per the original NMQ. Those reporting no symptoms were assigned a pain score of zero, while those who reported having musculoskeletal symptoms were also asked to report how intense pain was on average over the last month, on a scale from zero, which refers to no complaints, to nine, which refers to the pain 'as bad as it can be'.

Potential confounders:

The investigators will collect the following variables as potential confounders of the association between the exposures and the outcomes. These confounders were selected based on our review of the literature.

Sociodemographic variables: age, gender, citizenship, marital status, number of dependents, parents' marital status, parents' employment status, annual personal and household income, hours of work per week, living arrangement, and commute time.

Academic variables: Overall average in the past year (first year students will provide the average for the final year of high school), program of study, year of study.

General health and medically diagnosed comorbidities: The investigators will ask participants to rate their general health and comorbidities diagnosed by a health care provider in the past year. We will selected comorbidities that are reported by > 5% of the sample in our pilot study. In the Canadian pilot study, these include: allergies, arthritis, asthma, attention disorder/learning disability, eating disorder, hypertension, intestinal or stomach ulcers, migraine headaches, mood disorder, scoliosis, and sexually transmitted infections.

Data analysis and statistics:

Definition of trajectories: Trajectories of the outcomes depression, anxiety, stress symptoms and pain will emerge by latent class growth mixture models (LCGMM) that allows for the identification of multiple underlying trajectories within a defined population. In LCGMM, each trajectory is defined by its own growth parameters (intercept, linear slope), which are assumed to be latent. We will use the Baysian Information Criteria (BIC), Bootstrap Likelihood Ratio Test (BLRT) and entropy (measure of uncertainty) to determine whether our 4-trajactory hypothesis offers the best fitting solution for the data. Also trajectories of sleep quality, food insecurity, physical activity/sedentary lifestyle, and substance use will be identified with this method.

Associations between exposures and trajectories: The investigators will use multinomial logistic regression analyses to determine the associations between each of the exposures and trajectories of outcomes. The investigators will report the associations as odds ratios (OR) and 95% confidence intervals (95% CI). The investigators will first build bivariate models to measure the crude associations between the exposures and trajectories. To identify confounders, the investigators will build models to test whether the inclusion of each potential confounder produced a ≥ 10% change in any of the associations.

Associations between exposures and incident cases of depression, anxiety, stress symptoms and pain: At baseline, the investigators will identify a sub-cohort of students at risk of developing troublesome depressive and anxiety symptoms and pain respectively. The investigators will use Kaplan-Meier estimates to describe the incidence and discrete time survival analysis to measure the associations between the exposures and the outcome. In all models, the reference category will be the level of exposure hypothesized to be associated with the lowest risk of the outcome. ORs and 95% CIs will be used to describe the strength and direction of association. The same approach as described above will be used to identify prognostic factors (starting with a sub-cohort under "risk" of recovery) and for control for confounding.

Sample size: The investigators estimated the number of parameters that could be included in the multinomial logistic regression models based on distributions of participants across the four hypothesized trajectories (no symptoms; improvement; worsening; and persistent) using two diverse distribution scenarios. In scenario 1, we hypothesized that 70% have no symptoms, 13% experience worsening, 10% improve and 7% have persistent symptoms. In scenario 2, the investigator hypothesized equal distribution (25%) across trajectories. Based on these assumptions, the investigators estimated that the models could accommodate between 32 parameters if the investigators recruit 1000 students (scenario 1) to as many as 234 parameters if the investigators recruit 5000 participants (scenario 2).

Covid-19 sub-study:

The recruitment and data collection for the cohort study took place before and during the Covid-19 pandemic.Therefore, the investigators started a more fequent recording of levels of pain, axiety and depression from May 2020 with weekly text messages. The data Collection before and during the pandemic provide a chance to investigate the change in symptoms of depression, anxiety and stress and lifestyle behaviors in relation to the pandemic. The specific research questions with regards to this Covid-19-related sub-study are: 1) Are there any changes regarding symptoms of mental health problems during the first month of the pandemic? 2) Will students display changes in healthy lifestyle behaviors such as sleep patterns and quality, meal pattern, exercise and substance use during the first month of the pandemic and 3) Different trajectories of symptoms of low mood, worry and pain during the course of the pandemic will be measured weekly with the aim of identifying factors related to unfavorable trajectories. ;

Related Conditions & MeSH terms

• Mental Health Issue
• Musculoskeletal Pain

• NCT number: NCT04465435
• Study type: Observational
• Source: Sophiahemmet University
• Status: Active, not recruiting
• Start date: August 19, 2019
• Completion date: January 31, 2032