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A Mechanism Randomised Controlled Trial of a Three-Agent LLM-Augmented mHealth Intervention for Late-Life Loneliness in Older Adults
Sponsor: The University of Hong Kong
Summary
This study examines how a smartphone conversational application affects feelings of loneliness in Cantonese-speaking older adults living in Hong Kong. Seventy-two adults aged 60 or above who report at least moderate loneliness will be randomly assigned to one of two versions of the same application. Both versions look and work the same way, offer the same three conversational companions, and provide the same set of in-app tools. The two versions differ only in how the companions' replies are produced: in one version replies are generated by a large language model, and in the other they are assembled from pre-written templates selected by keyword and conversation state. Participants use the application for four weeks and are then followed for a further four weeks with continued access. The main question is whether any difference between the two versions in emotional loneliness operates through how understood, validated and cared for participants feel during individual conversations. Participants are not told which version they are using, and the researcher who carries out the assessments is also unaware of the assignment.
Key Details
Gender
All
Age Range
60 Years - Any
Study Type
INTERVENTIONAL
Enrollment
72
Start Date
2026-09-21
Completion Date
2026-12-31
Last Updated
2026-09-16
Healthy Volunteers
Yes
Interventions
Hybrid conversational companion application
Mobile application providing three named Cantonese-language conversational companions plus four in-app tools (Action Loop, Thought Exercise, Education, Progress). Companion replies are generated turn-by-turn by a large language model (DeepSeek-V3, accessed via Firebase Cloud Functions), conditioned on the companion's system prompt, retrieved conversation history, and current user input. This arm can therefore produce five conversational behaviours the comparator cannot: anchoring on specific content the participant has just said, memory carried across separate sessions, explicit admission of unfamiliarity, routing of single messages containing mixed emotional and informational content, and generative summarisation. Occurrences are tagged in system logs and analysed as a cumulative within-arm exposure variable. Conversational text is transmitted to the model provider for response generation; participants are advised at consent not to include identifying information. Recommended use is a
Rule-based conversational companion application
Mobile application identical to the experimental arm in interface, navigation, companion names and personae, tool layer (Action Loop, Thought Exercise, Education, Progress), notification schedule, and all in-app assessment prompts. The sole difference is the response-generation backend: companion replies are assembled from pre-written templates selected by keyword matching and conversational state, with no language model involved. The system retains no memory across sessions, cannot anchor on unanticipated content, cannot admit unfamiliarity outside scripted cases, and cannot generate novel summaries. Conversational content is not transmitted to any external service. This is an attention- and interface-matched active comparator rather than a waitlist or usual-care control: participants receive equivalent contact time, interface richness, tool access, and prompting schedule. Recommended use, expected daily duration, and logging are identical to the experimental arm.