Feasibility, Usability, and Acceptability of an AI-Powered MASCC Supportive Care Platform Among Young Adults With Cancer
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 30
- 试验地点
- 1
- 主要终点
- Acceptability of AI-powered supportive care chatbot
研究概览
简要总结
This clinical trial studies whether an artificial intelligence (AI)-powered supportive care chatbot is helpful for addressing the supportive care needs of young adult cancer survivors. Young adult cancer survivors often experience ongoing and distressing symptoms following treatment, including extreme tiredness and lack of energy, anxiety, and difficulty sleeping. Young adult cancer survivors report a variety of strategies to self-manage these symptoms; however, there remains a gap in targeted interventions focused on the needs in young adult survivors. The AI-powered supportive care chatbot is designed to provide evidence-based information on supportive care for young adult cancer survivors. Users interact with the chatbot by entering free-text questions or selecting from predefined topics to receive tailored educational responses related to supportive care across the cancer continuum, including treatment effects, symptom management, care transitions, and life after cancer. The AI-powered supportive care chatbot may be an effective way to help address the supportive care needs of young adult cancer survivors.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Supportive Care
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 39 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •18 - 39 years old
- •Able to speak/read English
- •Completed primary cancer treatment (e.g., surgery, radiation, chemotherapy, immunotherapy) at least one month prior to the time of consent. Although, participants will be eligible if they are receiving maintenance treatments
- •Report at least one moderate to severe symptom, side effect, or supportive care concern from cancer or its treatment
- •Able to access Wi-Fi/internet
- •Willing to complete surveys electronically
排除标准
- •Completed cancer treatment more than three years ago
结局指标
主要结局
Acceptability of AI-powered supportive care chatbot
时间窗: At end of intervention, assessed up to 12 weeks
Acceptability will be supported if mean scores on the Acceptability E-Scale are ≥ 4 (on a 5-point scale). Will be described (i.e., means, medians, standard deviations, and ranges) at the post-intervention time point.
Demand of AI-powered supportive care chatbot
时间窗: Up to 12 months
Demand will be demonstrated by successful recruitment of the target sample (N=30) within 12 months.
Implementation of AI-powered supportive care chatbot
时间窗: During intervention use, assessed up to 12 weeks
Implementation will be assessed by engagement with the chatbot, defined as ≥ 70% of participants reporting at least one use per week during the initial 4-week period, rather than a fixed duration of use, given the self-directed nature of the intervention. Will be described (i.e., means, medians, standard deviations, and ranges) weekly. Given the pilot nature of the study, no hypothesis testing or formal comparisons will be conducted.
Retention
时间窗: Up to 12 weeks
Retention will be considered feasible if ≥ 80% of participants complete 4-week assessments, and ≥ 50% elect to continue to the optional extended use period.
Usability of AI-powered supportive care chatbot
时间窗: At end of intervention, assessed up to 12 weeks
Usability will be supported if mean System Usability Scale scores are ≥ 70, indicating acceptable usability. Will be described (i.e., means, medians, standard deviations, and ranges) at the post-intervention time point.
Patient Reported Outcomes Measurement Information System measure
时间窗: At baseline, 4 weeks, and/or 12 weeks
Will be summarized using descriptive statistics (e.g., means, medians, standard deviations, and ranges) at each time point. Changes over time (baseline, post-intervention, as applicable) will be examined descriptively.
Digital Health Literacy Scale
时间窗: At baseline
Will be summarized using descriptive statistics (e.g., means, medians, standard deviations, and ranges) at the baseline time point. The Digital Health Literacy Scale is a 0 to 12 point score (based on 3 items), with higher scores indicating greater digital health care literacy.
Interview themes and subthemes
时间窗: At end of intervention, assessed up to 12 weeks
The audio-recorded interviews will be transcribed verbatim by a professional transcription company and verified for accuracy by another study team member. The finalized transcripts will be imported into NVivo 12 (QSR International Pty Ltd). Inductive content analysis will be used to analyze the interview transcripts. Two study team members will review the transcripts and the interview guide to create an initial list of codes. Three transcripts will be independently coded using the initial codebook. After three interviews are coded, two study team members will meet to resolve any coding discrepancies and to revise the codebook further. The same process will be repeated after three more interviews are coded. After the codebook is finalized, one study team member will code the remaining interviews. Subsequently, the study team will meet as a group to review the transcripts in their entirety, making sense of the data and generating potential major themes and subthemes.
次要结局
未报告次要终点