Sarcoma
Conditions
Keywords
sarcoma, health information, artificial intelligence, ChatGPT, chatbot, unmeet needs, cancer
Brief summary
This study aims to develop and examine the feasibility and preliminary effectiveness of an AI-based sarcoma chatbot building on ChatGPT (ASCC) to address the information needs of sarcoma patients. We hypothesized: 1) the ASCC will demonstrate good usability; 2) the study will be feasible in terms of all feasibility indicators; 3) the experimental group will report improved satisfaction and self-efficacy, decreased anxiety and stress than the control groups upon completion of the intervention. The ASCC will be developed using a co-design approach. A pilot randomized controlled trial will then be conducted in the three oncology wards of collaborative hospitals. Seventy-eight sarcoma patients will be recruited and randomized to the experimental group (n=39) and the control group (n=39). The experimental group will use the ASCC available 24/7 via voice or text for disease-related questions for one month while the control group will receive usual care.
Interventions
OTHERASCC (sarcoma-specific AI chatbot)
The participants in the experimental group will access the ASCC via a webpage alongside standard information from health professionals for one month (15 minutes weekly).
Participants in the control group will receive routine clinical care, including standard information from doctor-patient consultations (e.g., diagnosis, treatment, medical tests) and hospital provided sarcoma and treatment leaflet.
Sponsors
The Hong Kong Polytechnic University
Queen Mary Hospital, Hong Kong
Prince of Wales Hospital, Shatin, Hong Kong
Queen Elizabeth Hospital, Hong Kong
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* (1) diagnosed with soft tissue sarcoma; * (2) receiving active treatment at any stage; * (3) aged≥18 years; * (4) able to speak Cantonese, Mandarin and English; * (5) access to an electronic device enabling ASCC use
Exclusion criteria
* (1) patients with unstable physical or mental conditions; * (2) inability to communicate logically; * (3) using chatbots (e.g., ChatGPT, Deepseek) for sarcoma information in the last month; * (4) mental health disorders documented by the medical system
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Screening rate | During the enrollment period of this project. | Calculated by dividing the number of sarcoma patients screened by the research team by the number of sarcoma patients. |
| Eligibility rate | During the enrollment period of this project. | Calculated by dividing the number of participants who are eligible by the number who are screened. |
| Consent rate | During the enrollment period of this project. | Calculated by dividing the number of participants who were randomized into intervention and control groups by those provide consent. |
| Randomization rate | During the enrollment period of this project. | Calculated by dividing the number of participants who consent to join the study by the number who are eligible. |
| Attendance rate | Baseline and immediately after intervention (T1) | Calculated by dividing the number of participants who complete the intervention by those who are randomised |
| Retention rate | Baseline and immediately after intervention (T1) | Calculated by dividing the number of participants who remain in the study by those who are randomized. This will be calculated by groups. |
| Completion rate | Baseline and immediately after intervention (T1) | Calculated by dividing the number of participants who returned questionnaires by the number of questionnaires distributed. Calculated by dividing the number of participants who remain in the study by those who are randomized. This will be calculated by groups. |
| Proportion of missing data | Baseline and immediately after intervention (T1) | Calculated as the percentage of missing values in the dataset. Unknown or blank values will be considered missing values. |
| Adverse events | immediately after intervention (T1) | Adverse events are defined as unfavourable and unintended events that are not present, or appear to have worsened during the study. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Information satisfaction | Baseline and immediately after intervention (T1) | The EORTC QLQ-INFO25 will be used to evaluate participants' satisfaction with the information received. A four-point Likert scale is used to score 25 items ('nothing' = 1 to 'a lot' = 4), with higher scores indicating greater information satisfaction. |
| Anxiety and stress | Baseline and immediately after intervention (T1) | The Depression Anxiety Stress Scale-21 (DASS-21) will be used to assess anxiety and stress.(36) 21 items are scored using a four-point Likert scale, with higher score indicating more severe symptoms. |
| Self-efficacy | Baseline and immediately after intervention (T1) | Self-efficacy will be assessed using the Strategies Used by People to Promote Health (SUPPH) with good reliability and validity. This 28-item likert scale measure self-care self-efficacy, with higher scores indicating more positive perceptions of self-efficacy. |
| System Usability Scale | immediately after intervention (T1) | System Usability Scale (SUS) will be used to measure the usability of digital health solutions by 10 items rated on a 5-point Likert scale from 0-100. The chatbot will be considered highly usable if the mean score exceeds 68. |
Contacts
CONTACTQi LIU, PhD
Outcome results
None listed