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A Mechanism Randomised Controlled Trial of a Three-Agent LLM-Augmented mHealth Intervention for Late-Life Loneliness in Older Adults

2026年9月10日 更新者:The University of Hong Kong
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.

研究概览

详细说明

Loneliness in later life is associated with adverse physical and mental health outcomes. Conversational applications based on large language models have been proposed as a scalable form of support, but most published evaluations remain at the pilot feasibility stage, and few have used a comparator that holds interface richness, attention, and novelty constant. Without such a comparator, any observed benefit cannot be attributed to the language model rather than to the surrounding experience.

This trial addresses that gap using an architecturally matched comparator. Both arms deliver the identical mobile application: the same visual interface, navigation, three named conversational companions, tool layer, and in-app assessment prompts. The sole difference is the backend that generates companion response content. In the Hybrid arm, responses are generated by a large language model using each companion's system prompt, conversation history, and user input, and the arm exhibits language-model-distinctive conversational behaviours including anchoring on specific content, cross-session memory, explicit admission of unfamiliarity, mixed-content routing, and generative summarisation. In the Rule-based arm, responses are produced by a template system driven by keyword matching and conversational state, without cross-session memory and without those behaviours; conversational content in this arm is not transmitted to any external service.

Seventy-two community-dwelling Cantonese-speaking adults aged 60 or above who score at least 2 on the De Jong Gierveld Emotional and Social Loneliness Scale are allocated 1:1, stratified by baseline emotional loneliness using permuted blocks within stratum, with allocation concealment. A designated staff member performs randomisation and onboarding and does not collect outcome data. The Principal Investigator conducts all in-person assessments while unaware of allocation. Participants are not informed which version they use and are debriefed at study completion.

The intervention period is four weeks, followed by four weeks of follow-up with continued application access and continuous usage logging. The primary analysis is a longitudinal 1-1-1 multilevel mediation model with arm as the between-person predictor, session-level perceived responsiveness as the within-person mediator, and emotional loneliness as the outcome; the indirect effect is tested using Monte Carlo confidence intervals. The study is powered for the indirect effect rather than for confirmatory testing of the between-arm main effect, which is reported as an effect estimate with a 95% confidence interval. A falsifiable specification of the proposed mechanism is that an arm advantage appears on emotional loneliness but not on social loneliness.

A three-layer safety architecture operates in both arms: a Cantonese-calibrated distress detector that surfaces Hong Kong crisis resources in real time and alerts the research team; a daily 100% audit of Thought Exercise events against pre-specified scope criteria; and weekly Principal Investigator review of all flags and audits. Pre-specified pause criteria are an intervention-related serious adverse event, boundary-crossed events exceeding 10%, or distress detector recall below 0.90 on a real Cantonese corpus.

研究类型

介入性

注册 (估计的)

72

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习联系方式

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

是的

描述

Inclusion Criteria:

  • Aged 60 years or above
  • Self-identified Cantonese as primary language of communication
  • Community-dwelling in Hong Kong (not in residential care)
  • De Jong Gierveld Emotional and Social Loneliness Scale total score of 2 or above at screening, indicating at least moderate loneliness
  • Owns a smartphone, or willing to use a study-provided device
  • Able to demonstrate understanding of the study using the teach-back method
  • Willing to provide written informed consent

Exclusion Criteria:

  • Acute suicidality, defined as a PHQ-9 item 9 score above 1 with reported active intent
  • Currently receiving formal psychiatric treatment
  • Severe hearing or visual impairment precluding application use even with accommodation
  • Self-reported diagnosis of dementia or significant cognitive impairment
  • Unable to demonstrate understanding of the study using the teach-back method
  • Prior participation in the feasibility phase of this research programme

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

  • 主要用途:基础科学
  • 分配:随机化
  • 介入模型:并行分配
  • 屏蔽:双倍的

武器和干预

参与者组/臂
干预/治疗
实验性的:Hybrid arm (LLM-driven)
Participants receive the mobile application with companion responses generated by a large language model backend, using each companion's system prompt, conversation history, and user input. Language-model-distinctive conversational behaviours (content anchoring, cross-session memory, admission of unfamiliarity, mixed-content routing, generative summarisation) are operationally present. Recommended use is at least three sessions per week over four weeks; actual use is at the participant's discretion and is logged.
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
有源比较器:Rule-based arm (template-driven)
Participants receive an application identical in interface, navigation, companion personae, tool layer, and in-app assessment prompts, with companion responses produced by a template system driven by keyword matching and conversational state. Cross-session memory and the language-model-distinctive behaviours are absent. Conversational content is not transmitted externally. Recommended use and logging are identical to the Hybrid arm.
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.

研究衡量的是什么?

主要结果指标

结果测量
措施说明
大体时间
Session-level perceived responsiveness (primary mediator)
大体时间:After each conversation session, Weeks 1 through 4
Brief perceived-responsiveness measure administered in-app immediately after each companion conversation. Single-item sliders scored 1-7 covering Understanding, Validation, Caring and Insensitivity. Higher scores indicate greater perceived responsiveness. This is the within-person mediator in the pre-specified primary mediation model, not an efficacy endpoint.
After each conversation session, Weeks 1 through 4
Change in emotional loneliness (De Jong Gierveld emotional subscale)
大体时间:Baseline (Week 0) and Week 4
De Jong Gierveld Emotional and Social Loneliness Scale, 3-item emotional subscale. Score range 0-3; higher scores indicate greater emotional loneliness. Change from baseline to Week 4. This is the outcome variable in the pre-specified primary mediation model. The trial is powered for the indirect effect via session-level perceived responsiveness and is not powered for confirmatory testing of the between-arm difference, which is reported as an effect estimate with a 95% confidence interval.
Baseline (Week 0) and Week 4

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年9月21日

初级完成 (估计的)

2026年12月31日

研究完成 (估计的)

2026年12月31日

研究注册日期

首次提交

2026年9月10日

首先提交符合 QC 标准的

2026年9月10日

首次发布 (实际的)

2026年9月16日

研究记录更新

最后更新发布 (实际的)

2026年9月16日

上次提交的符合 QC 标准的更新

2026年9月10日

最后验证

2026年9月1日

更多信息

与本研究相关的术语

其他相关的 MeSH 术语

其他研究编号

  • EA260523

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

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