Hematopoietic and Lymphatic System Neoplasm, Malignant Solid Neoplasm
Conditions
Brief summary
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.
Interventions
Interact with AI-powered supportive care chatbot
OTHERInterview
Ancillary studies
OTHERSurvey Administration
Ancillary studies
Sponsors
University of Michigan Rogel Cancer Center
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 39 Years
Healthy volunteers
No
Inclusion criteria
* 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
Exclusion criteria
* Completed cancer treatment more than three years ago
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Acceptability of AI-powered supportive care chatbot | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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. |
Countries
United States
Contacts
CONTACTRobert Knoerl
PRINCIPAL_INVESTIGATORRobert Knoerl
University of Michigan Rogel Cancer Center
Outcome results
None listed