Digital peer-to-peer mental health tools have shown promise in supporting the well-being of those receiving help and giving it (i.e. helper therapy), but promoting engagement remains a challenge. We examine whether the framing of helper therapy exercises motivates active user participation and how user characteristics shape differential effects of the framings in a publicly deployed interactive text messaging-based mental health program. Among 3,817 users randomized to different helper therapy framings, we find causal evidence that framings which emphasize helpng oneself increase written engagement rates as much as 4.6% over other framings, with even larger effects seen among minoritized identities. These self-focused framings also elicited messages with more positive, trust, and anticipation-related words and fewer fear, anger, disgust, and sadness words. Our findings highlight the importance of centering the user in the framing of digital intervention content, and personalizing digital mental health tools to align with a diversity of user identities.

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. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

Self-guided mental health interventions, such as “do-it-yourself” tools to learn and practice coping strategies, show great promise to improve access to mental health care. However, these interventions are often cognitively demanding and emotionally triggering, creating accessibility barriers that limit their wide-scale implementation and adoption. In this paper, we study how human-language model interaction can support self-guided mental health interventions. We take cognitive restructuring, an evidence-based therapeutic technique to overcome negative thinking, as a case study. In an IRB-approved randomized field study on a large mental health website with 15,531 participants, we design and evaluate a system that uses language models to support people through various steps of cognitive restructuring. Our findings reveal that our system positively impacts emotional intensity for 67% of participants and helps 65% overcome negative thoughts. Although adolescents report relatively worse outcomes, we find that tailored interventions that simplify language model generations improve overall effectiveness and equity.

Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, Tim Althoff. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

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. We explored (1) catalysts for taking the screener, (2) anticipated outcomes, (3) reactions to the results, and (4) desired next steps. For many participants, the screener results validated their lived experiences of symptoms, but they were nevertheless unsure how to use the information to improve their mental health moving forward. Our findings suggest that online screeners can serve as a transition point in young people's mental health journeys. We discuss design implications for online screeners, post-screener feedback, and digital interventions broadly.