Digital Interventions for Adolescents and Young Adults Experiencing Self-Injurious Thoughts and Behaviors.

Purpose Of Review: To summarize literature on digital mental health interventions (DMHIs) for self-injurious thoughts and behaviors (SITBs) among adolescents and young adults. This includes studies evaluating DMHI efficacy in reducing SITBs, exploring the quality of these interventions, and describing the features, functionality, and psychological strategies of these interventions.

Recent Findings: Evidence for the efficacy of DMHIs for SITBs is limited but growing.

Cognitive Vulnerability to Mood Deterioration in an Exercise Cessation Paradigm.

Mood deterioration in response to exercise cessation is well-documented, but moderators of this effect remain unknown. This study tested the hypothesis that physically active individuals with higher levels of cognitive vulnerability (i.e.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups.

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups.

Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety.

Background: Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety.

Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms.

Background: Digital mental health interventions (DMHIs) offer potential solutions for addressing mental health care gaps, but often suffer from low engagement. Text messaging is one promising medium for increasing access and sustaining user engagement with DMHIs. This paper examines the Small Steps SMS program, an 8-week, automated, adaptive text message-based intervention for depression and anxiety.

From formative design to service-ready therapeutic: A pragmatic approach to designing digital mental health interventions across domains.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service.

Mental Health Self-Tracking Preferences of Young Adults With Depression and Anxiety Not Engaged in Treatment: Qualitative Analysis.

Background: Despite the high prevalence of anxiety and depression among young adults, many do not seek formal treatment. Some may turn to digital mental health tools for support instead, including to self-track moods, behaviors, and other variables related to mental health. Researchers have sought to understand processes and motivations involved in self-tracking, but few have considered the specific needs and preferences of young adults who are not engaged in treatment and who seek to use self-tracking to support mental health.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype.

Background: Young adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.

Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?

Background: Relatively little is known about how communication changes as a function of depression severity and interpersonal closeness. We examined the linguistic features of outgoing text messages among individuals with depression and their close- and non-close contacts.

Methods: 419 participants were included in this 16-week-long observational study.

Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing.

Without a nuanced understanding of users' perspectives and contexts, text messaging tools for supporting psychological wellbeing risk delivering interventions that are mismatched to users' dynamic needs. We investigated the contextual factors that influence young adults' day-to-day experiences when interacting with such tools. Through interviews and focus group discussions with 36 participants, we identified that people's daily schedules and affective states were dominant factors that shape their messaging preferences.

Preparing a Workforce of Care Coordinators to Address Patient Mental Health Needs in the Digital Age: Training and Needs Identification.

Introduction: Care coordinators (CCs) are specialized healthcare providers and often the primary point of contact for patients with multiple medical and mental health comorbidities in integrated healthcare settings. Prior work shows CCs have lower comfort addressing mental health than physical health concerns. Digital mental health interventions can support CCs' management of patient mental health needs, but training gaps must be addressed prior to a digital mental health intervention's implementation.

: Translating Mental Health Principles into Real Life Through Story-Based Text Messages.

Adopting new psychological strategies to improve mental wellness can be challenging since people are often unable to anticipate how new habits are applicable to their circumstances. Narrative-based interventions have the potential to alleviate this burden by illustrating psychological principles in an applied context. In this work, we explore how stories can be delivered via the ubiquitous and scalable medium of text messaging.

The association of language style matching in text messages with mood and anxiety symptoms.

Context: Impairment in social functioning is a feature and consequence of depression and anxiety disorders. For example, in depression, anhedonia and negative feelings about the self may impact relationships; in anxiety, fear of negative evaluation may interfere with getting close to others. It is unknown whether social impairment associated with depression and anxiety symptoms is reflected in day-to-day language exchanges with others, such as through reduced language style matching (LSM).

System design of a text messaging program to support the mental health needs of non-treatment seeking young adults.

Young adults (ages 18-25) experience the highest levels of mental health problems of any adult age group, but have the lowest mental health treatment rates. Text messages are the most used feature on the mobile phone and provide an opportunity to reach non-treatment engaged users throughout the day in a conversational manner. We present the design of an automated text message-based intervention for symptom self-management.

Meeting Young Adults' Social Support Needs across the Health Behavior Change Journey: Implications for Digital Mental Health Tools.

In pursuit of mental wellness, many find that behavioral change is necessary. This process can often be difficult but is facilitated by strong social support. This paper explores the role of social support across behavioral change journeys among young adults, a group at high risk for mental health challenges, but with the lowest rates of mental health treatment utilization.

Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety.

Objective: Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety.

Methods: Over 16 weeks, we collected outgoing text messages from 335 adults.

Social media-based interventions for adolescent and young adult mental health: A scoping review.

Background: Mental health conditions are common among adolescents and young adults, yet few receive adequate mental health treatment. Many young people seek support and information online through social media, and report preferences for digital interventions. Thus, digital interventions deployed through social media have promise to reach a population not yet engaged in treatment, and at risk of worsening symptoms.

Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults.

Young adults have high rates of mental health conditions, but most do not want or cannot access formal treatment. We therefore recruited young adults with depression or anxiety symptoms to co-design a digital tool for self-managing their mental health concerns. Through study activities-consisting of an online discussion group and a series of design workshops-participants highlighted the importance of easy-to-use digital tools that allow them to exercise independence in their self-management.

"I Wanted to See How Bad it Was": Online Self-screening as a Critical Transition Point Among Young Adults with Common Mental Health Conditions.

Young adults have high rates of mental health conditions, yet they are the age group least likely to seek traditional treatment. They do, however, seek information about their mental health online, including by filling out online mental health screeners. To better understand online self-screening, and its role in help-seeking, we conducted focus groups with 50 young adults who voluntarily completed a mental health screener hosted on an advocacy website.

Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages.

Digital tools can support individuals managing mental health concerns, but delivering sufficiently engaging content is challenging. This paper seeks to clarify how individuals with mental health concerns can contribute content to improve push-based mental health messaging tools. We recruited crowdworkers with mental health symptoms to evaluate and revise expert-composed content for an automated messaging tool, and to generate new topics and messages.