Suicide is the leading cause of non-accidental death for Native American young people ages 15-24 years old. Concerningly, suicide rates have continued to rise over the past decade despite a myriad of prevention efforts. This shortcoming has urged some scholars to (re)examine key theoretical constructs to better direct suicide prevention efforts in tribal communities. Using Indigenous Wholistic Theory, an algorithmic approach was employed to identify a broader set of factors that may influence suicidal ideation among Native American high schoolers in California (n = 2,609). Lasso penalized regression was used to select the most accurate predictors of suicidal ideation. Ten out of the 17 input predictors were significant including: depressive symptoms; school-based victimization; sexual and gender minority status; lifetime use of alcohol, vapes, and cannabis; breakfast consumption; access to alcohol and other drugs; and parent education level. The study found that a combination of factors across individual, emotional-social, mental-political, and physical-economic domains could be used to predict the individualized risk of experiencing suicidal ideation. I argue that this multi-level wholistic model is more appropriate and useful, especially for Native American youth. The study highlights the need for a more comprehensive understanding of suicide-related behavior among Native American youth and points to new directions in suicide screening.

Background

Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic.

Objective

In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows.

Methods

The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion.

Results

The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings.

Conclusions

Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions.

Trial registration

ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599.

International registered report identifier (irrid)

DERR1-10.2196/23592.