An interventional study of Artificial Intelligence-based Intervention and Interview in Hematopoietic and Lymphatic System Neoplasm and Malignant Solid Neoplasm, sponsored by University of Michigan Rogel Cancer Center. Not yet recruiting at 1 site in United States. Open to participants aged 18 Years to 39 Years. Per ClinicalTrials.gov, last updated 2026-08-19.
Sponsored by University of Michigan Rogel Cancer Center · Not applicable, Interventional, and Supportive care
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.
Exclusion Criteria:
Patients interact with AI-powered supportive care chatbot in a self-directed manner for 4 weeks. Following the initial 4-week use period, patients choose to either continue to use the chatbot for an additional 8 weeks or conclude study participation.
Other: Artificial Intelligence-based Intervention · Other: Interview · Other: Survey Administration
Interact with AI-powered supportive care chatbot
Also known as: AI Intervention, AI-based Intervention
Ancillary studies
Ancillary studies
Acceptability of AI-powered supportive care chatbot
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.
Time frame: At end of intervention, assessed up to 12 weeks
Demand of AI-powered supportive care chatbot
Demand will be demonstrated by successful recruitment of the target sample (N=30) within 12 months.
Time frame: Up to 12 months
Implementation of AI-powered supportive care chatbot
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.
Time frame: During intervention use, assessed up to 12 weeks
Retention
Retention will be considered feasible if ≥ 80% of participants complete 4-week assessments, and ≥ 50% elect to continue to the optional extended use period.
Time frame: Up to 12 weeks
Usability of AI-powered supportive care chatbot
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.
Time frame: At end of intervention, assessed up to 12 weeks
Patient Reported Outcomes Measurement Information System measure
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.
Time frame: At baseline, 4 weeks, and/or 12 weeks
Digital Health Literacy Scale
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.
Time frame: At baseline
Interview themes and subthemes
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.
Time frame: At end of intervention, assessed up to 12 weeks
Plan to share: Yes — De-identified participant data will be shared with other researchers upon reasonable request and execution of a data transfer agreement.
Supporting information: Study protocol, Sap, Icf, Csr, Analytic code
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This study is not yet recruiting, as verified in Jul 2026. You cannot join it, but the record below documents what was studied.
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University of Michigan Rogel Cancer Center