In cancer patients, the integration between anticancer therapies and palliative care is of fundamental importance. In this context, telemedicine can improve the quality of life (QoL) of chronic patients through self-management and remote monitoring solutions. This approach can favor the effectiveness of the treatment and therapeutic adherence. Of note, telemedicine can also be applied to the management of cancer pain. In the advanced stages of cancer disease, pain is one of the most obvious and most disabling symptoms. Consequently, proper pain management has a significant impact on the QoL, the ability to withstand treatment, and the recovery of patients. On the other hand, given the complexity of cancer pain, the main obstacle to its proper management is the lack of adequate measurement methods. Although in recent years a great deal of effort has been made in the direction of automatic pain assessment, both concerning the creation of datasets and the development of classification algorithms, the literature is lacking regarding the automatic measurement of pain in the setting of cancer patients. Observation by experienced clinical staff and self-assessment by patients could be useful for obtaining the ground truth and, in turn, for training automatic pain recognition systems.
For the entire duration of the study, patients will remain under the care of the Early Palliative Care and Simultaneous Care Outpatient team of the Istituto Nazionale Tumori, Fondazione Pascale, at home. Pain and other symptoms will be managed according to the good clinical practice and patients will receive assistance in agreement to the routine medical care. The following devices will be used: 1. Software 2. Instrumentation 3. Clinical Assessment Tools: European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30), Daily Pain Diary, 0-10 numeric rating scale (NRS). The project will be divided into three main Work Packages (WPs), dedicated respectively to the creation of the IT infrastructure to support acquisitions (WP1), the patient data collection campaign (WP2), and the development of machine learning algorithms for automatic pain recognition (WP3). The application of the devices and verification of correct functioning will be carried out at the patient's home by the IT staff involved in the study. WP1 - The system consists of three main components: the server, with the attached database, the application for mobile devices, also responsible for managing data acquisition from physiological signal acquisition devices, and the desktop application, used by the clinical staff to monitor the progress of data collection. The mobile application will have the role of interfacing directly with the patient and acquiring biometric data from wearable devices. Specifically, the following signals will be acquired: heart rate, body temperature, non-invasive blood pressure, and galvanic skin response (GSR). The heart rate will be obtained through a wearable device (Garmin Vivosmart 4) while the body temperature, the non-invasive blood pressure, and the GSR will be acquired by an external device (a BITalino platform).To further validate the accuracy of the algorithm that will deal with pain detection, patients will also be given a QoL questionnaire (EORTC QLQ-C30). In order to acquire the ground truth of the data, the patient will be asked to provide feedback on the level of pain, both at certain intervals of time during the day, and in case of acute pain episodes. This feedback can be based on NRS and multimedia strategies (e.g., videos). Patients will fill out a daily pain diary. WP2 - The campaign will include a preliminary acquisition phase aimed at testing the IT infrastructure. For obtaining an adequate inter-subject and intra-subject variability, it will be necessary to enroll at least 40 patients, acquiring data for 10-14 days. Thus, the data collection campaign will be conducted for about 6 months. Each subject will use the mobile application and sensors for 2 weeks. Data will be acquired using simultaneously data collection bundles (application, sensors, and any mobile device). Upon enrolment and at the end, EORTC QLQ-C30 will be administered. WP3 - The objective is the development of algorithms able to predict the level of pain perceived by the patient. Having a considerable amount of labelled data available, the system will learn from the examples.
Study Type
OBSERVATIONAL
Enrollment
40
National Cancer Institute of Naples
Naples, Campania, Italy
RECRUITINGA.O.U. Federico II
Napoli, Campania, Italy
NOT_YET_RECRUITINGTo develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Heart rate (beats per minute, bpm)
Time frame: Up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Non-invasive Blood Pressure (mmHg). The patient will use the device provided (BITalino).
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: The Galvanic Skin Response (GSR) refers to changes in sweat gland activity that are reflective of the intensity of the emotional state. The patient will use the device provided (BITalino).
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features: A daily Pain Diary will be used. Type: how pain is felt (e.g., sharp, ache, shooting, tingling).
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features: A daily Pain Diary will be used. Degree: 0-10 numeric rating scale (NRS) where 0 is no pain and 10 is the worst pain imaginable.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features: A daily Pain Diary will be used. Duration (minutes, hours, days).
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features: A daily Pain Diary will be used. Precipitating factors.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features: A daily Pain Diary will be used. Name and amount of drug used and time it was taken.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Patients' quality of life assessed by the EORTC QLQ-C30 questionnaire.
Quality of life (QoL) of patients according to European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. It is scored on a metric from 0 to 100. Higher scores mean better outcome.
Time frame: At the beginning and at the end of the observation, up to 2 weeks
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