Using Wearable Device to Improve Quality of Palliative Care

November 6, 2022 updated by: National Taiwan University Hospital

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

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.

Study Overview

Status

Recruiting

Conditions

Detailed 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.

Study Type

Observational

Enrollment (Anticipated)

75

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Taiwan
      • Taipei, Taiwan, 100
        • Completed
        • National Taiwan University Hospital
      • Taipei, Taiwan, 106
        • Recruiting
        • National Taiwan University, Cancer Center
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years to 105 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Terminal cancer patients are receiving palliative care in outpatient clinic, home care or ward admission and will receive regular follow-up in the future.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Wearable devices + Smartphone
The only arm in the study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment
Time Frame: 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.
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.
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
Time Frame: 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.

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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correlation between symptoms and wearable device parameters
Time Frame: 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.
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.
Correlation between Australia-modified Karnofsky Performance Status (AKPS) and wearable device parameters
Time Frame: 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.
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.
Correlation between palliative care phase and wearable device parameters
Time Frame: 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.
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.

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Performance Scale (PPS)
Time Frame: 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.
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.
Comparison of AUC-ROC in survival prediction between machine learning model and Glasgow Prognostic Score (GPS)
Time Frame: GPS assessed retrospectively if data available. Death or survival is recorded at the time the case closed.
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.
Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Index (PPI)
Time Frame: 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.
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.
Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Score (PaP)
Time Frame: 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.
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.
Time spent at medical service
Time Frame: Recorded when events happen or afterwards
If unexpected medical needs happen, investigators will record time spent at ER stay or hospital admission
Recorded when events happen or afterwards
Duration between events
Time Frame: From date of enrollment until the date of death, or assessed up to 26 weeks. Duration was calculated after cases closed.
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.
Overall survival and survival time
Time Frame: From date of enrollment until the date of death, or assessed up to 26 weeks. Calculated after all cases closed.
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.
Site of death
Time Frame: Assessed at the time the case closed, only if the patient died
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
Tolerability and user experience to wearable devices
Time Frame: Assessed at the time the case closed
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
Relation between personal background and user experience of wearable devices
Time Frame: Assessed at the time the case closed
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

National Taiwan University Hospital

Collaborators

National Taiwan University

Investigators

  • Principal Investigator: Jaw-Shiun Tsai, MDPHD, National Taiwan University Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 23, 2021

Primary Completion (Anticipated)

December 31, 2022

Study Completion (Anticipated)

April 30, 2023

Study Registration Dates

First Submitted

August 30, 2021

First Submitted That Met QC Criteria

September 17, 2021

First Posted (Actual)

September 23, 2021

Study Record Updates

Last Update Posted (Actual)

November 9, 2022

Last Update Submitted That Met QC Criteria

November 6, 2022

Last Verified

November 1, 2022

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 202105097RIND

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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