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.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
Inclusion Criteria:
* Patients aged \> 18 years
* Home care patients diagnosed with advanced cancer disease and life expectancy ≤ 1 year
* Patients receiving treatment for cancer pain
* Patients who have given their consent
Exclusion Criteria:
* Patients aged \< 18 years
* Willingness to sign the informed consent form (unable to read or write)
* Cognitive deficit (e.g. Alzheimer disease or senile dementia)
Primary outcome measure(s)
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. — Up to 2 weeks Clinical data: Heart rate (beats per minute, bpm)
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks Clinical data: Non-invasive Blood Pressure (mmHg). The patient will use the device provided (BITalino).
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks 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).
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks Pain features:
A daily Pain Diary will be used. Type: how pain is felt (e.g., sharp, ache, shooting, tingling).
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks 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.
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks Pain features:
A daily Pain Diary will be used. Duration (minutes, hours, days).
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks Pain features:
A daily Pain Diary will be used. Precipitating factors.
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. — Whenever the patient has a worsening of his/her pain, up to 2 weeks Pain features:
A daily Pain Diary will be used. Name and amount of drug used and time it was taken.
Trial sites (2)
Facility
City
Region
Status
National Cancer Institute of Naples
Naples
Campania
Recruiting
A.O.U. Federico II
Naples
Campania
Not Yet Recruiting
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This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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