Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study
- URL: http://arxiv.org/abs/2604.07558v1
- Date: Wed, 08 Apr 2026 19:59:28 GMT
- Title: Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study
- Abstract summary: GUIDE is a system that generates personalized intervention content and multimodal interaction structure.<n>In a preregistered study with N = 237 participants, GUIDE significantly reduced stress and improved the user experience.<n>This work lays the foundation for interventions that dynamically shape how support is experienced and enacted in digital settings.
- Score: 56.17549485306432
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions [51.74800196847922]
Ambivalence and hesitancy (A/H) play a primary role for individuals to delay, avoid, or abandon health interventions.<n>While experts can be trained to recognize A/H, integrating them into digital health interventions is costly and less effective.<n>Here, we explore the application of deep learning models for A/H recognition in videos, a multi-modal task by nature.
arXiv Detail & Related papers (2026-04-13T17:05:38Z) - MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
arXiv Detail & Related papers (2026-02-03T09:47:49Z) - Diffusion Forcing for Multi-Agent Interaction Sequence Modeling [52.769202433667125]
MAGNet is a unified autoregressive diffusion framework for multi-agent motion generation.<n>It supports a wide range of interaction tasks through flexible conditioning and sampling.<n>It captures both tightly synchronized activities and loosely structured social interactions.
arXiv Detail & Related papers (2025-12-19T18:59:02Z) - Foundation Model for Skeleton-Based Human Action Understanding [56.89025287217221]
This paper presents a Unified Skeleton-based Dense Representation Learning framework.<n>USDRL consists of a Transformer-based Dense Spatio-Temporal (DSTE), Multi-Grained Feature Decorrelation (MG-FD), and Multi-Perspective Consistency Training (MPCT)
arXiv Detail & Related papers (2025-08-18T02:42:16Z) - A Novel Dataset for Video-Based Neurodivergent Classification Leveraging Extra-Stimulatory Behavior [15.88235629194724]
We introduce the Video ASD dataset-a dataset that contains video frame convolutional and attention map feature data.<n>Results show that our model effectively generalizes and understands key differences in the distinct movements of the children.
arXiv Detail & Related papers (2024-09-06T20:11:02Z) - PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurements [4.215144489966779]
We introduce PsyDI, a personalized and progressively in-depth chatbots for psychological measurements.<n>PsyDI uses user-related multi-modal information and engages in customized, multi-turn interactions to provide personalized, easily accessible measurements.<n>We introduce a novel training paradigm that involves learning the ranking of proxy variables associated with psychological traits.<n>The score model enables PsyDI to conduct comprehensive and precise measurements through multi-turn interactions.
arXiv Detail & Related papers (2024-07-22T07:19:12Z) - PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
arXiv Detail & Related papers (2024-07-17T08:13:22Z) - Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
ConCue is a novel approach for improving visual feature extraction in HOI detection.
We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
arXiv Detail & Related papers (2023-11-26T09:11:32Z) - Hybrid Handcrafted and Learnable Audio Representation for Analysis of
Speech Under Cognitive and Physical Load [17.394964035035866]
We introduce a set of five datasets for task load detection in speech.
The voice recordings were collected as either cognitive or physical stress was induced in the cohort of volunteers.
We used the datasets to design and evaluate a novel self-supervised audio representation.
arXiv Detail & Related papers (2022-03-30T19:43:21Z) - Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic
Interactions [33.67477398036821]
We present Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions.
Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations.
This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes.
arXiv Detail & Related papers (2021-09-20T12:45:04Z) - Multi-Agent Imitation Learning with Copulas [102.27052968901894]
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions.
In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems.
Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
arXiv Detail & Related papers (2021-07-10T03:49:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.