Development and Evaluation of an AI-Integrated Emotional Granularity Growth (AI-EGG) on Enhancing Resilience and Improving Quality of Life in Young and Middle-Aged Colorectal Cancer Survivors
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 54
- 试验地点
- 3
研究概览
简要总结
The goal of this clinical trial is to evaluate an AI-integrated Emotional Granularity Growth intervention (AI-EGG) designed to enhance resilience and improve quality of life in young and middle-aged colorectal cancer (CRC) survivors. Emotional granularity refers to the ability to clearly identify and differentiate subtle emotional experiences, which may help individuals regulate emotions more effectively and build resilience after cancer treatment.
The main questions it aims to answer are:
- Does the AI-EGG intervention improve resilience in CRC survivors compared with routine psychological care?
- Does the intervention improve emotional granularity, emotion regulation ability, and quality of life?
- Is the AI-EGG intervention feasible and acceptable for young and middle-aged CRC survivors?
Researchers will compare the AI-EGG intervention group to a control group receiving routine psychological care and standard educational materials to see whether the intervention leads to better psychological outcomes.
Participants will:
- Complete baseline assessments measuring emotional granularity, emotion regulation, resilience, and quality of life
- Be randomly assigned to either the intervention group or the control group
- In the intervention group, engage in a 4-week AI chatbot-based program focusing on emotional identification, differentiation, regulation, and reflective practice (at least two sessions per week)
- In the control group, receive routine psychological care and standard educational materials
- Complete post-intervention assessments immediately after the 4-week program and again at a 1-month follow-up
- Some participants in the intervention group will be invited to complete interviews about their experience of the program
详细描述
This study is a randomized controlled trial designed to evaluate the feasibility, acceptability, and preliminary effectiveness of an AI-integrated Emotional Granularity Growth intervention (AI-EGG) in improving resilience, quality of life emotional granularity and emotion regulation among young and middle-aged colorectal cancer (CRC) survivors.
CRC survivors in early and middle adulthood often experience persistent psychological adaptation difficulties after completion of primary treatment, even when clinical disease is stable. These difficulties include reduced resilience, impaired emotion regulation, and decreased quality of life. Emotional processes are considered central to post-treatment psychological adaptation, and resilience is conceptualized as a key psychosocial outcome reflecting individuals' ability to adapt to cancer-related adversity.
Emotion regulation is an important determinant of resilience; however, existing interventions typically focus on general emotion regulation strategies without directly targeting the individual's ability to differentiate and label emotional experiences. Emotional granularity, defined as the ability to distinguish and accurately label discrete emotional states, is proposed as a cognitive-affective mechanism that may enhance emotion regulation effectiveness and thereby support resilience.
The AI-EGG intervention is developed based on this theoretical framework and is delivered via an online chatbot platform. The intervention is structured as a 4-week program that provides guided, interactive training in emotional granularity. The chatbot system delivers standardized yet interactive modules focusing on emotional identification, emotional differentiation, emotion regulation, and reflective emotional processing. Participants are encouraged to engage with the system at least twice per week, with additional voluntary engagement supported by the platform.
The study adopts a parallel-group randomized controlled design. Participants are randomly assigned to either the AI-EGG intervention group or a control group receiving routine psychological care and standard educational materials. The intervention is delivered remotely through a mobile-based chatbot system, enabling flexible and repeated engagement in real-life contexts.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Supportive Care
- 盲法
- Single (Outcomes Assessor)
盲法说明
Outcome assessors are blinded to participants' group allocation to reduce assessment bias. Participants are assigned unique study identification codes, and outcome data are collected and analyzed using de-identified datasets to maintain blinding at the analysis stage. The research coordinator responsible for randomization and allocation is not involved in outcome assessment. Although participants are aware of their group assignment due to the nature of the behavioral intervention, access to the AI-EGG intervention is restricted through individual login credentials to minimize cross-group contamination. Backend usage logs are monitored for technical monitoring purposes only and are not accessible to outcome assessors.
入排标准
- 年龄范围
- 18 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •(1) young and middle-aged adult patients (age in the range of 18-60 years);
- •(2) patients diagnosed with CRC;
- •(3) patients have completed primary curative-intent treatment for colorectal cancer, and are currently in a stable post-treatment or maintenance phase of care without evidence of active disease progression or acute treatment-related instability;
- •(4) patients able to use a smartphone and agree to participate in the study.
排除标准
- •(1) patients who have been informed of their cancer diagnosis due to family decision to withhold information;
- •(2) patients suffering from severe comorbidities or conditions that may affect participation or assessment, such as significant cognitive impairment (e.g., dementia, severe memory loss), psychiatric disorders, or other serious medical complications;
- •(3) those who are participating in other psychological intervention studies.
研究者
Dr Joyce Chung
Associate Professor
The Hong Kong Polytechnic University