Clinical trial NCT05054907: Using Wearable Device to Improve Quality of Palliative Care
Official title: Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Terminal Cancer
End Stage Cancer
Keywords: palliative
cancer
survival prediction
artificial intelligence
wearable devices
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
Collaborators: name: National Taiwan University
First posted: 2021-09-23
Enrollment: 75
Sex: ALL
Minimum age (years): 20
Maximum age (years): 105
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

Brief 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.

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.

Eligibility criteria: 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.

Primary outcomes:
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.
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: 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:
measure: Correlation between symptoms and wearable device parameters
timeFrame: 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.
measure: Correlation between Australia-modified Karnofsky Performance Status (AKPS) and wearable device parameters
timeFrame: 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.
measure: Correlation between palliative care phase and wearable device parameters
timeFrame: 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 outcomes:
measure: Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Performance Scale (PPS)
timeFrame: 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.
measure: Comparison of AUC-ROC in survival prediction between machine learning model and Glasgow Prognostic Score (GPS)
timeFrame: GPS assessed retrospectively if data available. Death or survival is recorded at the time the case closed.
measure: Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Index (PPI)
timeFrame: 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.
measure: Comparison of AUC-ROC in survival prediction between machine learning model and Palliative Prognostic Score (PaP)
timeFrame: 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.
measure: Time spent at medical service
timeFrame: Recorded when events happen or afterwards
measure: Duration between events
timeFrame: From date of enrollment until the date of death, or assessed up to 26 weeks. Duration was calculated after cases closed.
measure: Overall survival and survival time
timeFrame: From date of enrollment until the date of death, or assessed up to 26 weeks. Calculated after all cases closed.
measure: Site of death
timeFrame: Assessed at the time the case closed, only if the patient died
measure: Tolerability and user experience to wearable devices
timeFrame: Assessed at the time the case closed
measure: Relation between personal background and user experience of wearable devices
timeFrame: Assessed at the time the case closed
Locations:
city: Taipei
country: Taiwan
facility: National Taiwan University Hospital
city: Taipei
country: Taiwan
facility: National Taiwan University, Cancer Center
Study officials:
affiliation: National Taiwan University Hospital
name: Jaw-Shiun Tsai, MDPHD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.