Home-Based Telemedicine Study Using Machine Learning to Automatically Assess Pain in Adults with Advanced Cancer

What this Trial Is About

Researchers are studying how telemedicine can improve the quality of life for cancer patients by helping manage symptoms like pain through remote monitoring and self-assessment. The focus is on developing machine learning algorithms to automatically assess pain levels in advanced cancer patients, as pain is a common and disabling symptom that affects treatment and recovery. This observational study aims to create a database combining clinical data and patient-reported pain information to support this technology. Participants will remain under home care by a specialized outpatient palliative care team while using wearable devices and mobile applications to collect physiological signals such as heart rate, body temperature, blood pressure, and skin response. They will also complete quality-of-life questionnaires and daily pain diaries over about two weeks. The study includes building the IT system to support data collection, conducting a data campaign with at least 40 patients over 6 months, and developing algorithms to predict pain levels from collected data. During the study, patients will be monitored remotely with devices and applications that gather biometric and symptom data. They will provide feedback on pain at scheduled times and during acute episodes using numeric rating scales and multimedia inputs. Researchers will evaluate data quality, patient symptoms, and quality of life through questionnaires. The primary outcome is the development of machine learning tools for pain prediction, with secondary outcomes assessing quality of life. The total data collection period per patient is approximately 10 to 14 days, with ongoing clinical care throughout.

Pain ASsessment in CAncer Patients by Machine LEarning (PASCALE)