Using Wearable Device to Improve Quality of Palliative Care

End Stage Cancer Clinical Trial

Official title:

Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05054907
• Other study ID #: 202105097RIND
• Status: Recruiting
• Start date: September 23, 2021
• Est. completion date: April 30, 2023

Study information

• Verified date: November 2022
• Source: National Taiwan University Hospital
• Contact: Jen-Hsuan Liu, MD
• Phone: +886922068868
• Email: b98401001[@]ntu.edu.tw
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

The study aim to examine the feasibility of utilizing wearable devices and smartphones in palliative patients in Taiwan. In addition, investigators try to identify the relationship between mobile health data and disease progression and establish a predicting model to the emergent medical need and death of patients, via machine learning. This is a single-arm observational study using wearable devices and smartphones in terminal cancer patients. Investigators planned to enroll 75 patients who receive palliative care. After obtaining consent from the patients or their legally authorized surrogate decision-makers, a baseline assessment will be conducted, with a guide to use wearable devices and phone apps. Investigators will keep regular follow-up for 52 weeks or until the participants' death. Assessment will be conducted every week, face-to-face or by telephone contact. A routine assessment includes symptoms and functionality in the past week, and vital signs and facial photograph will be recorded if possible. Physical data measured from wearable devices would be recorded continuously. The emergent medical needs of patient, including emergency department visit, unplanned admission and death of participants will be recorded if happen. The primary outcome is the predictive performance (sensitivity and specificity) of the machine-learning model using wearable device data and symptoms assessment. The secondary outcomes are symptoms, including pain, dyspnea, diarrhea, constipation, nausea, vomiting, insomnia, depression, anxiety and fatigue. Users' opinion and comment to using experience will also be recorded.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 75
• Est. completion date: April 30, 2023
• Est. primary completion date: December 31, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years to 105 Years

Eligibility

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.

Related Conditions & MeSH terms

• End Stage Cancer
• Terminal Cancer

Locations

Country	Name	City	State
Taiwan	National Taiwan University Hospital	Taipei
Taiwan	National Taiwan University, Cancer Center	Taipei

Sponsors (2)

Lead Sponsor	Collaborator
National Taiwan University Hospital	National Taiwan University

Country where clinical trial is conducted

Taiwan, 

References & Publications (1)

Pavic M, Klaas V, Theile G, Kraft J, Tröster 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Performance Scale (PPS)	Palliative performance scale (PPS) will be regularly assessed during the follow-up. The AUC-ROC of using PPS for survival prediction will be calculated and compared with the machine-learning model.	From date of enrollment until the date of death, or assessed up to 26 weeks. PPS are assessed every week. Death or survival is recorded at the time the case closed.
Other	Comparison of AUC-ROC in survival prediction between machine learning model and Glasgow Prognostic Score (GPS)	Glasgow Prognostic Score (GPS) will be assessed if C-reactive protein (CRP) and albumin are examined during the follow-up. The AUC-ROC using GPS for survival prediction will be calculated and compared with the machine-learning model.	GPS assessed retrospectively if data available. Death or survival is recorded at the time the case closed.
Other	Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Index (PPI)	Palliative Prognostic Index(PPI) will be regularly assessed during the follow-up. The AUC-ROC of using PPI for survival prediction will be calculated and compared with the machine-learning model.	From date of enrollment until the date of death, or assessed up to 26 weeks. PPI are assessed every week. Death or survival is recorded at the time the case closed.
Other	Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Score (PaP)	Palliative Prognostic Score (PaP) will be assessed if laboratory data available during the follow-up. The AUC-ROC of using PaP for survival prediction will be compared with the machine-learning model.	From date of enrollment until the date of death, or assessed up to 26 weeks. PaP assessed every week only if the laboratory data available.
Other	Time spent at medical service	If unexpected medical needs happen, investigators will record time spent at ER stay or hospital admission	Recorded when events happen or afterwards
Other	Duration between events	Investigators will record duration between events (death, unexpected medical needs, admission and discharge) or duration from enrollment to events, if they happen	From date of enrollment until the date of death, or assessed up to 26 weeks. Duration was calculated after cases closed.
Other	Overall survival and survival time	Investigators will record the overall survival and survival time from enrollment.	From date of enrollment until the date of death, or assessed up to 26 weeks. Calculated after all cases closed.
Other	Site of death	If patient died during the follow-up, investigator will record the site of death (at home or any other chosen place, in the hospital or ER). Other details will be recorded if the family or caregivers are willing to provide.	Assessed at the time the case closed, only if the patient died
Other	Tolerability and user experience to wearable devices	Investigator will ask and record any discomfort or side effect noted during the follow-up and at the end of the study. Investigator will survey for user experience of patients or caregivers at the end of the study.	Assessed at the time the case closed
Other	Relation between personal background and user experience of wearable devices	Personal background such as educational level, age, and previous use of technological product will be recorded. Investigator will explore the relation between these factors and the user experience.	Assessed at the time the case closed
Primary	Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment	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.	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.
Primary	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	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 patient's unexpected medical needs (which is defined as emergency department visit or unplanned admission to hospital).; The primary outcome is to evaluate Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model in predicting unexpected medical needs.	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. Events are recorded upon happening or afterwards.
Secondary	Correlation between symptoms and wearable device parameters	The severity of symptoms will be recorded by symptoms assessment scale (SAS). Investigators will explore the correlation between the wearable device parameters and symptoms.	From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Symptoms assessed every week.
Secondary	Correlation between Australia-modified Karnofsky Performance Status (AKPS) and wearable device parameters	The functional status will be assessed by Australia-modified Karnofsky Performance Status (AKPS) during the follow-up. Investigators will explore the correlation between AKPS and wearable device parameters	From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Functional status assessed every week.
Secondary	Correlation between palliative care phase and wearable device parameters	Evaluation of palliative care phases from the Palliative Care Outcomes Collaboration (PCOC) system will be assessed regularly. Investigators will explore the correlation between the palliative care phases and other parameters (wearable device parameters, symptoms, medical condition).	From date of enrollment until the date of death, or assessed up to 26 weeks. Wearable device parameters are collected continuously. Palliative care phase assessed every week.