Clinical trial · Observational
Pain ASsessment in CAncer Patients by Machine LEarning (PASCALE)
Home-Based Telemedicine for Automatic Pain Assessment in Cancer Patients: Dataset Creation and Development of Machine Learning Algorithms
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer Pain | — | UNRESOLVED | — |
| Oncology | — | UNRESOLVED | — |
| Quality of Life | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (9)
- measure
- 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.
- timeFrame
- Up to 2 weeks
- description
- Clinical data: Heart rate (beats per minute, bpm)
- measure
- 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.
- timeFrame
- Whenever the patient has a worsening of his/her pain, up to 2 weeks
- description
- Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
- measure
- 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.
- timeFrame
- Whenever the patient has a worsening of his/her pain, up to 2 weeks
- description
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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)
References
Publications (11)
- BACKGROUNDReis-Pina P, Lawlor PG, Barbosa A. Adequacy of cancer-related pain management and predictors of undertreatment at referral to a pain clinic. J Pain Res. 2017 Aug 31;10:2097-2107. doi: 10.2147/JPR.S139715. eCollection 2017. PMID 28919809
- BACKGROUNDAdamse C, Dekker-Van Weering MG, van Etten-Jamaludin FS, Stuiver MM. The effectiveness of exercise-based telemedicine on pain, physical activity and quality of life in the treatment of chronic pain: A systematic review. J Telemed Telecare. 2018 Sep;24(8):511-526. doi: 10.1177/1357633X17716576. Epub 2017 Jul 11. PMID 28696152
- BACKGROUNDSirintrapun SJ, Lopez AM. Telemedicine in Cancer Care. Am Soc Clin Oncol Educ Book. 2018 May 23;38:540-545. doi: 10.1200/EDBK_200141. PMID 30231354
- BACKGROUNDAung MSH, Kaltwang S, Romera-Paredes B, Martinez B, Singh A, Cella M, Valstar M, Meng H, Kemp A, Shafizadeh M, Elkins AC, Kanakam N, de Rothschild A, Tyler N, Watson PJ, de C Williams AC, Pantic M, Bianchi-Berthouze N. The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset. IEEE Trans Affect Comput. 2016 Oct-Dec;7(4):435-451. doi: 10.1109/TAFFC.2015.2462830. Epub 2015 Jul 30. PMID 30906508
- BACKGROUNDGruss S, Geiger M, Werner P, Wilhelm O, Traue HC, Al-Hamadi A, Walter S. Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli. J Vis Exp. 2019 Apr 5;(146). doi: 10.3791/59057. PMID 31009005
- BACKGROUNDPfeifer AC, Uddin R, Schroder-Pfeifer P, Holl F, Swoboda W, Schiltenwolf M. Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J Clin Med. 2020 Nov 5;9(11):3557. doi: 10.3390/jcm9113557. PMID 33167300
- BACKGROUNDCuomo A, Bimonte S, Forte CA, Botti G, Cascella M. Multimodal approaches and tailored therapies for pain management: the trolley analgesic model. J Pain Res. 2019 Feb 19;12:711-714. doi: 10.2147/JPR.S178910. eCollection 2019.