Effectiveness of the Minder Mobile Mental Health and Substance Use Intervention for University Students: Randomized Controlled Trial.

BACKGROUND: University attendance represents a transition period for students that often coincides with the emergence of mental health and substance use challenges. Digital interventions have been identified as a promising means of supporting students due to their scalability, adaptability, and acceptability. Minder is a mental health and substance use mobile app that was codeveloped with university students. OBJECTIVE: This study aims to examine the effectiveness of the Minder mobile app in improving mental health and substance use outcomes in a general population of university students. METHODS: A 2-arm, parallel-assignment, single-blinded, 30-day randomized controlled trial was used to evaluate Minder using intention-to-treat analysis. In total, 1489 participants were recruited and randomly assigned to the intervention (n=743, 49.9%) or waitlist control (n=746, 50.1%) condition. The Minder app delivers evidence-based content through an automated chatbot and connects participants with services and university social groups. Participants are also assigned a trained peer coach to support them. The primary outcomes were measured through in-app self-assessments and included changes in general anxiety symptomology, depressive symptomology, and alcohol consumption risk measured using the 7-item General Anxiety Disorder scale, 9-item Patient Health Questionnaire, and US Alcohol Use Disorders Identification Test-Consumption Scale, respectively, from baseline to 30-day follow-up. Secondary outcomes included measures related to changes in the frequency of substance use (cannabis, alcohol, opioids, and nonmedical stimulants) and mental well-being. Generalized linear mixed-effects models were used to examine each outcome. RESULTS: In total, 79.3% (589/743) of participants in the intervention group and 83% (619/746) of participants in the control group completed the follow-up survey. The intervention group had significantly greater average reductions in anxiety symptoms measured using the 7-item General Anxiety Disorder scale (adjusted group mean difference=-0.85, 95% CI -1.27 to -0.42; P<.001; Cohen d=-0.17) and depressive symptoms measured using the 9-item Patient Health Questionnaire (adjusted group mean difference=-0.63, 95% CI -1.08 to -0.17; P=.007; Cohen d=-0.11). A reduction in the US Alcohol Use Disorders Identification Test-Consumption Scale score among intervention participants was also observed, but it was not significant (P=.23). Statistically significant differences in favor of the intervention group were found for mental well-being and reductions in the frequency of cannabis use and typical number of drinks consumed. A total of 77.1% (573/743) of participants in the intervention group accessed at least 1 app component during the study period. CONCLUSIONS: In a general population sample of university students, the Minder app was effective in reducing symptoms of anxiety and depression, with provisional support for increasing mental well-being and reducing the frequency of cannabis and alcohol use. These findings highlight the potential ability of e-tools focused on prevention and early intervention to be integrated into existing university systems to support students' needs. TRIAL REGISTRATION: ClinicalTrials.gov NCT05606601; https://clinicaltrials.gov/ct2/show/NCT05606601. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/49364.

Suicidal Ideation Amongst University Students During the COVID-19 Pandemic: Time Trends and Risk Factors.

OBJECTIVE: Examine time trends in suicidal ideation in post-secondary students over the first three waves of the COVID-19 pandemic in Canada and identify subpopulations of students with increased risk. METHOD: We analysed 14 months of data collected through repeated cross-sectional deployment of the World Health Organization (WHO) World Mental Health-International College Student (WMH-ICS) survey at the University of British Columbia. Estimated log odds weekly trends of 30-day suicidal ideation (yes/no) were plotted against time with adjustments for demographics using binary logistic generalized additive model (GAM). Risk factors for 30-day suicidal ideation frequency (four categories) were examined using the ordered logistic GAM, with a cubic smoothing spline for modelling time trend in obervation weeks and accounting for demographics. RESULTS: Nearly one-fifth (18.9%) of students experienced suicidal ideation in the previous 30 days. While the estimated log odds suggested that binary suicidal ideation was relatively stable across the course of the pandemic, an initial drop followed by an increasing trend was observed. Risk factors for suicidal ideation frequency during the pandemic included identifying as Chinese or as another non-Indigenous ethnic minority; experiencing current symptoms of depression or anxiety; having a history of suicidal planning or attempts; and feeling overwhelmed but unable to get help as a result of COVID-19. Older age was identified as a protective factor. CONCLUSIONS: The general university student population in our study was relatively resilient with respect to suicidal ideation during the first three waves of the pandemic, but trends indicate the possibility of delayed impact. Specific sub-populations were found to be at increased risk and should be considered for targeted support. Further analyses should be undertaken to continue monitoring suicidality trends throughout the remainder of the pandemic and beyond.

A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.

Objectives: To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI). Materials and Methods: Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI. Results: Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data. Discussion and Conclusion: Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.

Evaluating the Effectiveness of a Codeveloped e-Mental Health Intervention for University Students: Protocol for a Randomized Controlled Trial.

BACKGROUND: University life typically occurs during a period of life transition, where the incidence of mental health and substance use problems and disorders peaks. However, relatively few students obtain effective treatment and support. e-Interventions have proven effective in improving the psychological outcomes of university students and have the potential to provide scalable services that can easily integrate into existing models of care. Minder is a mobile app codeveloped with university students that offers users a collection of evidence-based interventions tailored to help university students maintain their mental health and well-being and manage their substance use. OBJECTIVE: This paper describes the protocol for a randomized controlled trial (RCT) that aims to assess the effectiveness of the Minder app in improving the mental health and substance use outcomes of university students. METHODS: This study is a 2-arm, parallel assignment, single-blinded, 30-day RCT with 1 intervention group and 1 waitlist control group. Overall, 1496 (748 per trial arm) university students from the University of British Columbia Vancouver Campus (N=54,000) who are aged ≥17 years, have a smartphone with Wi-Fi or cellular data, and speak English will be recruited via a variety of web-based and offline strategies. Participants will be randomized into the intervention or control group after completing a baseline survey. Those randomized into the intervention group will gain immediate access to the Minder app and will be assessed at 2 weeks and 30 days. Those randomized into the control group will be given access to the app content after their follow-up assessment at 30 days. The primary outcomes are measured from baseline to follow-up at 30 days and include changes in general anxiety symptomology, depressive symptomology, and alcohol consumption risk measured by the General Anxiety Disorder 7-Item scale, Patient Health Questionnaire 9-Item scale, and US Alcohol Use Disorders Identification Test-Consumption Scale, respectively. Secondary outcomes include measures related to changes in the frequency of substance use, mental well-being, self-efficacy in managing mental health and substance use, readiness to change, and self-reported use of mental health services and supports (including referral) from baseline to follow-up at 30 days. RESULTS: Trial recruitment and data collection began in September 2022, and the completion of data collection for the trial is anticipated by June 2023. As of May 10, 2023, a total of 1425 participants have been enrolled. CONCLUSIONS: The RCT described in this protocol paper will assess whether the Minder app is effective in improving the mental health and substance use outcomes of a general population of Canadian university students. Additional secondary outcome research aims to explore additional outcomes of interest for further research and better understand how to support students' general mental well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT05606601; https://clinicaltrials.gov/ct2/show/NCT05606601. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49364.

Patterns of anxiety, depression, and substance use risk behaviors among university students in Canada.

Objective: To identify subgroups of students with distinct profiles of mental health symptoms (MH) and substance use risk (SU) and the extent to which MH history and socio-demographics predict subgroup membership. Participants: University students (N = 10,935: 63% female). Methods: Repeated cross-sectional survey administered weekly to stratified random samples. Latent class analysis (LCA) was used to identify subgroups and multinomial regression was used to examine associations with variables of interest. Results: LCA identified an optimal 4-latent class solution: High MH-Low SU (47%), Low MH-Low SU (22%), High MH-High SU (19%), and Low MH-High SU (12%). MH history, gender, and ethnicity were associated with membership in the classes with high risk of MH, SU, or both. Conclusion: A substantial proportion of students presented with MH, SU, or both. Gender, ethnicity and MH history is associated with specific patterns of MH and SU, offering potentially useful information to tailor early interventions.