Ananya Bhattacharjee

Harsh Kumar, Tong Li, Jiakai Shi et al.

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.

Zahra Hassanzadeh, Anne Hsu, Rachel Kornfield et al.

This paper explores the design space for one-minute digital interventions that prompt immediate action without onboarding or sensing. By embracing Fogg's Behavior Model and four design principles informed by literature, the goal of these interventions was to provide triggers that encourage actions so simple that even people with low motivation would be willing to complete them. We examined the utility of these prompts by conducting a 14-day study with 22 participants interested in making small lifestyle improvements in at least one of three domains: physical activity, healthy eating, and mental well-being. When combined with insights drawn from participants' rewrites of our prompts, our findings suggest that intentional personalization through co-authorship could be a lightweight personalization mechanism that balances relevance with low friction.

Ananya Bhattacharjee, Michael Liut, Matthew Jörke et al.

Digital mental health (DMH) tools have extensively explored personalization of interventions to users' needs and contexts. However, this personalization often targets what support is provided, not how it is experienced. Even well-matched content can fail when the interaction format misaligns with how someone can engage. We introduce generative experience as a paradigm for DMH support, where the intervention experience is composed at runtime. We instantiate this in GUIDE, a system that generates personalized intervention content and multimodal interaction structure through rubric-guided generation of modular components. In a preregistered study with N = 237 participants, GUIDE significantly reduced stress (p = .02) and improved the user experience (p = .04) compared to an LLM-based cognitive restructuring control. GUIDE also supported diverse forms of reflection and action through varied interaction flows, while revealing tensions around personalization across the interaction sequence. This work lays the foundation for interventions that dynamically shape how support is experienced and enacted in digital settings.

Mohit Chandra, Suchismita Naik, Denae Ford et al. · gatech

Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences of individuals. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through the lived experiences of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 people with lived mental health experience and workshops involving experts with lived experience to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette-based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers.

Dominik P. Hofer, Haochen Song, Rania Islambouli et al.

Behaviour Change Techniques (BCTs) are central to digital health interventions, yet selecting and delivering effective techniques remains challenging. Contextual bandits enable statistically grounded optimisation of BCT selection, while Large Language Models (LLMs) offer flexible, context-sensitive message generation. We conducted a 4-week study on physical activity motivation (N=54; 9 post-study interviews) that compared five daily messaging approaches: random templates, contextual bandit with templates, LLM generation, hybrid bandit+LLM, and LLM with interaction history. LLM-based approaches were rated substantially more helpful than templates, but no significant differences emerged among LLM conditions. Unexpectedly, bandit optimisation for BCTs selection yielded no additional perceived helpfulness compared with LLM-only approaches. Unconstrained LLMs focused heavily on a single BCT, whereas bandit systems enforced systematic exploration-exploitation across techniques. Quantitative and qualitative findings suggest contextual acknowledgement of user input drove perceived helpfulness. We contribute design suggestions for reflective AI health behaviour change systems that address a trade-off between structured exploration and generative autonomy.

Haochen Song, Dominik Hofer, Rania Islambouli et al.

Machine learning approaches, such as contextual multi-armed bandit (cMAB) algorithms, offer a promising strategy to reduce sedentary behavior by delivering personalized interventions to encourage physical activity. However, cMAB algorithms typically require large participant samples to learn effectively and may overlook key psychological factors that are not explicitly encoded in the model. In this study, we propose a hybrid approach that combines cMAB for selecting intervention types with large language models (LLMs) to personalize message content. We evaluate four intervention types: behavioral self-monitoring, gain-framed, loss-framed, and social comparison, each delivered as a motivational message aimed at increasing motivation for physical activity and daily step count. Message content is further personalized using dynamic contextual factors including daily fluctuations in self-efficacy, social influence, and regulatory focus. Over a seven-day trial, participants receive daily messages assigned by one of four models: cMAB alone, LLM alone, combined cMAB with LLM personalization (cMABxLLM), or equal randomization (RCT). Outcomes include daily step count and message acceptance, assessed via ecological momentary assessments (EMAs). We apply a causal inference framework to evaluate the effects of each model. Our findings offer new insights into the complementary roles of LLM-based personalization and cMAB adaptation in promoting physical activity through personalized behavioral messaging.

Ananya Bhattacharjee, Pan Chen, Linjia Zhou et al.

We investigate users' perspectives on an online reflective question activity (RQA) that prompts people to externalize their underlying emotions on a troubling situation. Inspired by principles of cognitive behavioral therapy, our 15-minute activity encourages self-reflection without a human or automated conversational partner. A deployment of our RQA on Amazon Mechanical Turk suggests that people perceive several benefits from our RQA, including structured awareness of their thoughts and problem-solving around managing their emotions. Quantitative evidence from a randomized experiment suggests people find that our RQA makes them feel less worried by their selected situation and worth the minimal time investment. A further two-week technology probe deployment with 11 participants indicates that people see benefits to doing this activity repeatedly, although the activity may get monotonous over time. In summary, this work demonstrates the promise of online reflection activities that carefully leverage principles of psychology in their design.

Ananya Bhattacharjee, S M Taiabul Haque, Abdul Hady et al.

Objective: This study aims to identify the social determinants of mental health among undergraduate students in Bangladesh, a developing nation in South Asia. Our goal is to identify the broader social determinants of mental health among this population, study the manifestation of these determinants in their day-to-day life, and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that impact their mental health. Methods: We conducted a 21-day study with 38 undergraduate students from seven universities in Bangladesh. We conducted two semi-structured interviews: one pre-study and one post-study. During the 21-day study, participants used an Android application to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that participants could independently review their mood and conversation patterns. Results: Our results show that academics, family, job and economic condition, romantic relationships, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors as participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, participants took certain steps on their own to improve their mental health (e.g., reduced the frequency of communication with certain persons). Conclusions: Overall, the findings from this study would provide better insights for the researchers to design better solutions to help the younger population from this part of the world.