Clinical trial · Observational
Using Wearable Device to Improve Quality of Palliative Care
Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care
NCT05054907CI-TRIAL-00062080unknownClinicalTrials.gov clinicaltrialsProvenance
- 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)
This study is going to use wearable devices and smartphones to collect physical data from terminal patients and build a survival predicting model for terminal patients with machine learning. Investigators hypothesize that continuous physical data monitoring could offer a hint to better predictability in end-of-life care.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| End Stage Cancer | — | UNRESOLVED | — |
| Terminal Cancer | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (1)
- label
- Wearable devices + Smartphone
- description
- The only arm in the study.
Primary outcomes (2)
- measure
- Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment
- timeFrame
- From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Other clinical assessments are performed every week. Death or survival is recorded at the time the case closed.
- description
- Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patients' death or survival within specific time range. The primary outcome is to evaluate the Area Under the Receiver Operating Characteristic curve (AUC - ROC) of the machine-learning model in predicting patients' survival.
- measure
- Area Under the Receiver Operating Characteristic curve (AUC-ROC) of machine-learning model to predict unexpected medical needs using wearable device parameters and clinical assessment
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
- Maximum age
- 105 Years
Show eligibility criteria text
Inclusion criteria * Age: 20 years old or older * Clinical diagnosis: cancer in terminal stage. Exclusion criteria \- Cannot cooperate with use of wearable devices or smartphones.
References
Publications (2)
- BACKGROUNDPavic M, Klaas V, Theile G, Kraft J, Troster G, Blum D, Guckenberger M. Mobile Health Technologies for Continuous Monitoring of Cancer Patients in Palliative Care Aiming to Predict Health Status Deterioration: A Feasibility Study. J Palliat Med. 2020 May;23(5):678-685. doi: 10.1089/jpm.2019.0342. Epub 2019 Dec 23. PMID 31873052
- DERIVEDLiu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res. 2023 Aug 18;25:e47366. doi: 10.2196/47366. PMID 37594793