Terminal CancerNCT05054907
Using Wearable Device to Improve Quality of Palliative Care
An observational study of Terminal Cancer and End Stage Cancer, run by National Taiwan University Hospital at 2 sites in Taiwan.
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. Read more about this study
- Who is it for?
- Ages 20 to 105 years
- Conditions the study is for
- Terminal Cancer, End Stage Cancer
- How many people
- 75 participants
- Where
- 2 sitesTaiwan
- Study dates
- 23 Sep 2021to 30 Apr 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 105 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Age: 20 years old or older
- Clinical diagnosis: cancer in terminal stage.
Who cannot join
- Cannot cooperate with use of wearable devices or smartphones.
Trial locations and contacts
2 sites in 1 country.
Jaw-Shiun Tsai, MDPHD contact
Taiwan2 sites, 1 recruiting
- Completed
National Taiwan University Hospital
Taipei, 100
- Recruiting
National Taiwan University, Cancer Center
Taipei, 106
Jenhsuan Liu, +886972654705, G01740@hch.gov.tw
About this study
Official title: 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.
Read the 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.
- Conditions
- Terminal Cancer, End Stage Cancer
- Keywords
- palliative, cancer, survival prediction, artificial intelligence, wearable devices
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Wearable devices + Smartphone | The only arm in the study. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
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.
Measured: 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 outcomes (3)
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.
Measured: 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
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
Measured: 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
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).
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Sep 2021
- Study start23 Sep 2021
- Primary completion31 Dec 2022 (estimated)
- Study completion30 Apr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Terminal CancerStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 75 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Taiwan University
- Responsible party
- Sponsor
- Investigators
- Jaw-Shiun Tsai, MDPHD, Principal investigator, National Taiwan University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05054907
- Sponsor's protocol ID
- 202105097RIND
- Status last verified
- Nov 2022
- First submitted
- 30 Aug 2021
- Last updated
- 9 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- No
Publications
Pavic 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.
PubMed 31873052Liu 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.
PubMed 37594793
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care
- ClinicalTrials.gov ID
- NCT05054907
- Acronym / sponsor ID
- 202105097RIND
- Conditions
- Terminal Cancer, End Stage Cancer
- Keywords
- palliative, cancer, survival prediction, artificial intelligence, wearable devices
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 75 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- National Taiwan University
- Study start
- 23 Sep 2021 (actual)
- Primary completion
- 31 Dec 2022 (estimated)
- Study completion
- 30 Apr 2023 (estimated)
- First posted
- 23 Sep 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Wearable devices + Smartphone | – | –The only arm in the study. |
Who may qualify
- Ages
- 20 to 105 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Age: 20 years old or older
- Clinical diagnosis: cancer in terminal stage.
Key exclusion criteria (1 listed)
- Cannot cooperate with use of wearable devices or smartphones.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
3 secondary outcomes
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.
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.
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2022.
- Background: PubMed 31873052Pavic 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.
- Derived from this study: PubMed 37594793Liu 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jaw-Shiun Tsai, MDPHD contact
Jaw-Shiun Tsai, MDPHD principal investigator
National Taiwan University Hospital
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Terminal Cancer and End Stage Cancer, run by National Taiwan University Hospital at 2 sites in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jen-Hsuan Liu, MD (+886922068868, b98401001@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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 (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.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05054907, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Nov 2022.