The Remote Emotional Support and Symptom Self-Management Intervention (REMI): A pilot study.

e24070 Background: Symptom burdens related to advanced cancer remain high. The Remote Emotional Support and Symptom Self-Management Intervention (REMI) incorporates palliative care and Acceptance and Commitment Therapy principles to encourage patients living with metastatic disease to engage in symptom self-management activities. REMI is an AI-driven, supportive care program that presents empathic messaging, utilizes the conversational aspects of Amazon ALEXA, and offers symptom management recommendations. The purpose of this study is to assess the acceptability of generated messages that would be part of the REMI device through participant evaluation. Methods: Patients with metastatic solid tumors (n = 20 participants) were recruited from the UPMC Hillman Cancer Center network, having at least 20% of the patient sample categorized as rural. Empathic messages generated by the ChatGPT-4o model were evaluated by participants through both quantitative evaluation packet surveys and semi-structured interviews. Evaluation questions were created by our expert team, pulling from multiple validated surveys exploring symptom management and technology acceptance. Each quantitative question was scored on a 1-10 scale with higher scores translating to higher acceptability. Results: There was overall acceptability of these messages, as participants reported high average scores for all quantitative evaluation questions. Table 1 describes the summary statistics for the quantitative portion of this study. There were varying responses regarding language use; some participants enjoyed personable language while others found it condescending. The majority of participants (n = 16, 80%) expressed interest in using a conversational device based on the information given during this pilot. Conclusions: This pilot project demonstrates early acceptability of the use of AI-generated messages as a part of the REMI program. Future work will focus on usability testing of the REMI device with these patient-informed, empathic messages to help create an effective symptom management program for those living with metastatic disease. Summary statistics for quantitative questions. Average (sd) Message clarity 8.65 (1.79) Tone of message 8.41 (1.88) Message relatability 7.45 (2.50) Message believability 7.82 (2.23) To what extent do you feel this message can help patients living with cancer manage their symptoms? 7.72 (2.36) How likely would you be to change your behavior based on this message? 6.72 (2.88)

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Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-28
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.e24070
Primary Topic
Digital Mental Health Interventions
Type
article
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article

The Remote Emotional Support and Symptom Self-Management Intervention (REMI): A pilot study.

Yael Schenker, Kathryn H. Schmitz, Jane Schell, William Calo et al.
Journal of Clinical Oncology
Digital Mental Health Interventions
article

The Remote Emotional Support and Symptom Self-Management Intervention (REMI): A pilot study.

Yael Schenker, Kathryn H. Schmitz, Jane Schell, William Calo, Saeed Abdullah, Shawna Doerksen, Megan Hamm, Shane Belin, Hannah Kwiecinski, Robert Arnold
article en

Abstract

e24070 Background: Symptom burdens related to advanced cancer remain high. The Remote Emotional Support and Symptom Self-Management Intervention (REMI) incorporates palliative care and Acceptance and Commitment Therapy principles to encourage patients living with metastatic disease to engage in symptom self-management activities. REMI is an AI-driven, supportive care program that presents empathic messaging, utilizes the conversational aspects of Amazon ALEXA, and offers symptom management recommendations. The purpose of this study is to assess the acceptability of generated messages that would be part of the REMI device through participant evaluation. Methods: Patients with metastatic solid tumors (n = 20 participants) were recruited from the UPMC Hillman Cancer Center network, having at least 20% of the patient sample categorized as rural. Empathic messages generated by the ChatGPT-4o model were evaluated by participants through both quantitative evaluation packet surveys and semi-structured interviews. Evaluation questions were created by our expert team, pulling from multiple validated surveys exploring symptom management and technology acceptance. Each quantitative question was scored on a 1-10 scale with higher scores translating to higher acceptability. Results: There was overall acceptability of these messages, as participants reported high average scores for all quantitative evaluation questions. Table 1 describes the summary statistics for the quantitative portion of this study. There were varying responses regarding language use; some participants enjoyed personable language while others found it condescending. The majority of participants (n = 16, 80%) expressed interest in using a conversational device based on the information given during this pilot. Conclusions: This pilot project demonstrates early acceptability of the use of AI-generated messages as a part of the REMI program. Future work will focus on usability testing of the REMI device with these patient-informed, empathic messages to help create an effective symptom management program for those living with metastatic disease. Summary statistics for quantitative questions. Average (sd) Message clarity 8.65 (1.79) Tone of message 8.41 (1.88) Message relatability 7.45 (2.50) Message believability 7.82 (2.23) To what extent do you feel this message can help patients living with cancer manage their symptoms? 7.72 (2.36) How likely would you be to change your behavior based on this message? 6.72 (2.88)

Journal of Clinical OncologyVol. 44(16_suppl)
Pennsylvania State University (US), University of Pittsburgh (US), Denver Health Medical Center (US), Penn State Milton S. Hershey Medical Center (US), University of Colorado Denver (US)
McElhattan Foundation
Quality Education
Openalex Percentile: Top 5%
Digital Mental Health Interventions
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