Background Cancer and its treatment can significantly impact the short- and long-term psychological well-being of patients and families. Emotional distress and depressive symptomatology are often associated with poor treatment adherence, reduced quality of life, and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective strategies to build resilience and enhance coping. However, in-person support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition (eg, fatigue, pain) is another. Emerging evidence supports the effectiveness of online support groups in reducing access barriers. Text-based and professional-led online support groups have been offered by Cancer Chat Canada. Participants join the group discussion using text in real time. However, therapist leaders report some challenges leading text-based online support groups in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or red flags for distress can sometimes be missed. Recent advances in artificial intelligence such as deep learning–based natural language processing offer potential solutions. This technology can be used to analyze online support group text data to track participants’ expressed emotional distress, including fear, sadness, and hopelessness. Artificial intelligence allows session activities to be monitored in real time and alerts the therapist to participant disengagement. Objective We aim to develop and evaluate an artificial intelligence–based cofacilitator prototype to track and monitor online support group participants’ distress through real-time analysis of text-based messages posted during synchronous sessions. Methods An artificial intelligence–based cofacilitator will be developed to identify participants who are at-risk for increased emotional distress and track participant engagement and in-session group cohesion levels, providing real-time alerts for therapist to follow-up; generate postsession participant profiles that contain discussion content keywords and emotion profiles for each session; and automatically suggest tailored resources to participants according to their needs. The study is designed to be conducted in 4 phases consisting of (1) development based on a subset of data and an existing natural language processing framework, (2) performance evaluation using human scoring, (3) beta testing, and (4) user experience evaluation. Results This study received ethics approval in August 2019. Phase 1, development of an artificial intelligence–based cofacilitator, was completed in January 2020. As of December 2020, phase 2 is underway. The study is expected to be completed by September 2021. Conclusions An artificial intelligence–based cofacilitator offers a promising new mode of delivery of person-centered online support groups tailored to individual needs. International Registered Report Identifier (IRRID) DERR1-10.2196/21453
BACKGROUND Commonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of non-verbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence-based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics. OBJECTIVE The aim of this study was to develop a method to train and evaluate AICF’s capacity to monitor group cohesion. METHODS Human scorers used a confusion matrix to evaluate the performance of AICF. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC). RESULTS AICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6,797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine-learning algorithms combined with human input can detect group cohesion, a clinically meaningful indicator of effective OSGs. After re-training with human input, AICF reached a F1-score of 0.82. AICF performed slightly better at identifying group cohesion compared to LIWC. CONCLUSIONS AICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in virtual settings by attending to individual needs. INTERNATIONAL REGISTERED REPORT RR2-10.2196/21453
BACKGROUND Cancer and its treatment can affect the short and long-term psychological well-being of patients and families in significant ways. Emotional distress, particularly symptoms of depression, are often associated with poor treatment adherence, reduced quality of life and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective coping strategies to build resilience and enhance coping. However, “in-person” support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition is another (e.g. fatigue, pain). Emerging evidence supports the effectiveness of online support groups (OSGs) to reduce access barriers. Text-based and professional-led OSGs have been offered by Cancer Chat Canada (CCC). Participants join the group discussion using text in real-time. Despite the reported benefits, therapist leaders report some challenges leading text-based OSGs in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or “red flags” for distress can sometimes be missed. Recent advances in artificial intelligence (AI) such as deep-learning based natural language processing (NLP) offer potential solutions. This technology can be used to analyze OSG text data to track participants’ expressed emotions and distress, including fear, sadness, and hopelessness. AI allows monitoring session activities in real-time and alerts the therapist of participant’s disengagement. OBJECTIVE This protocol outlines the development and evaluation of an Artificial Intelligence-based Co-facilitator (AICF) prototype to track and monitor OSG participants’ distress through real-time analysis of text-based messages posted during synchronous sessions. METHODS AICF will be developed to 1. Identify participant(s) who are at-risk for increased emotional distress, and track participant(s) engagement and in-session group cohesion levels, providing real-time alerts for therapist(s) to follow-up; 2. generate post-session participant profiles that contain discussion content keywords, and emotion profiles for each session; and 3. automatically suggests tailored resources to participants according to their needs. RESULTS The current protocol provides preliminary results for phase I development of AICF. CONCLUSIONS AICF offers a promising new mode of delivery of person-centred OSGs tailored to individual needs.
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