Hematopoietic NeoplasmWATCHNCT06587100
Wearable Activity Tracking to Curb Hospitalizations
An observational study of Hematopoietic Neoplasm and Malignant Solid Neoplasm, run by University of California, San Francisco at 1 site in United States.
Trial summary
This study is being done to collect patient generated health data to predict the risk of patients needing emergency department visits or hospitalization before, during. and after receiving radiation therapy. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hematopoietic Neoplasm, Malignant Solid Neoplasm, Lymphatic System Neoplasm
- What is being tested?
- Fitbit and Apple HealthKit-based devices
- Type of treatment
- Medical device
- How many people
- 260 participants
- Where
- 1 siteUnited States
- Study dates
- 7 Apr 2025to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients undergoing non-interventional, standard of care, radiotherapy (RT) at UCSF Department of Radiation Oncology
Who may be able to join
- Age >= 18.
- Eastern Cooperative Oncology Group (ECOG) performance status =< 2 or Karnofsky Performance Scale (KPS) >= 50%.
- Able to understand study procedures and to comply with them for the entire length of the study.
- Ability of individual or legal guardian/representative to understand a written informed consent document, and the willingness to sign it.
- Diagnosis of invasive malignancy.
- Able to ambulate independently (without the assistance of a cane or walker).
- Planned treatment with fractionated external beam radiotherapy over at least 5 days (no fractional requirement).
- Not a previous participant on this protocol for subsequent courses.
Who cannot join
- Participants bound to a wheelchair.
- Participants unable to ambulate independently (needing assistance of cane or walker).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of California, San Francisco
San Francisco, California, 94143
Imani Dunn, 877-827-3222, Imani.Dunn@ucsf.edu
cancertrials@ucsf.edu
About this study
Official title: Wearable Activity Tracking to Curb Hospitalizations (WATCH)
This study is being done to collect patient generated health data to predict the risk of patients needing emergency department visits or hospitalization before, during. and after receiving radiation therapy.
Read the detailed description
PRIMARY OBJECTIVE:
- Validate a previously developed step-count model for predicting all-cause acute care (pooled across all devices).
SECONDARY OBJECTIVES:
- Validate a previously developed model for predicting each ED visits or hospitalizations during external beam RT using continuous step counts before, during, and after treatment.
II. Validate the previously developed step-count model for predicting all-cause acute care for each of the two different device platforms.
III. Validate concordance of step counts across each of the device's platforms in the Apple group.
IV. Validate the previously developed SHIELD-RT Electronic health record (EHR)-based model for predicting unplanned acute care (ED visit or hospitalization).
EXPLORATORY OBJECTIVES:
- Refinement of the pre-existing models(step count and SHIELD-RT). II. Evaluate association between wearables collected parameters, EHR-based variables, and acute care events.
III. Develop and validate a multi-modal predictive model for predicting acute care.
OUTLINE: This is an observational study. Participants are assigned to 1 of 2 groups.
- GROUP I: Participants receive Fitbit device and undergo non-interventional, standard of care, radiation therapy.
- GROUP II: Participants receive Fitbit device and utilize their own personal Apple HealthKit-based device and undergo non-interventional, standard of care, radiation therapy.
- Being tested
- Fitbit, Apple HealthKit-based devices
- Keywords
- Activity tracking, Hospitalization prevention, Artificial Intelligence (AI) modelling
- Medical subject headings
- Hematologic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
FitbitDevice
Also called Wearable Activity Tracker
Participants will wear Fitbit device
Apple HealthKit-based devicesDevice
Also called iPhone, Apple watch
Participants will wear personal device and share data with study team.
| Study group | Role | What they receive |
|---|---|---|
| Observational Group I: Fitbit only | Participants receive Fitbit device while undergoing non-interventional, standard of care, radiation therapy. Receives: Device: Fitbit | |
| Observational Group II: Fitbit + Apple HealthKit | Participants receive Fitbit device and will utilize personal Apple HealthKit-based devices (iPhone, Apple Watch, etc.) to concurrently contribute Apple HealthKit-based data while undergoing non-interventional, standard of care, radiation therapy. Receives: Device: Fitbit; Device: Apple HealthKit-based devices |
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 step count model
The AUC-ROC of the step count model will measure the performance of a classification model by plotting the rate of true positives against false positives, and the score ranges from 0 - 1. The higher the AUC, the better the model's performance at distinguishing between the positive and negative classes. The AUC-ROC will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD). The performance metrics will only be calculated with respect to first acute care event.
Measured: Up to 3 years
Calculation of a Brier Score
The Brier Score is a strictly proper score function or strictly proper scoring rule that measures the accuracy of probabilistic predictions. A Brier Score can take on any value between 0 and 1, with 0 being the best score achievable and 1 being the worst score achievable. The lower the Brier Score, the more accurate the prediction(s). The score will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as MI-CLAIM and TRIPOD. The performance metrics will only be calculated with respect to first acute care event.
Measured: Up to 3 years
Calculation of Log-Loss Score
Logarithmic loss indicates how close a prediction probability comes to the actual/corresponding true value. The Log-Loss Score can take on any value between 0 and 1. The more the predicted probability diverges from the actual value, the higher is the log-loss value. The log-loss value will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as MI-CLAIM and TRIPOD. The performance metrics will only be calculated with respect to first acute care event.
Measured: Up to 3 years
Area Under the Precision-Recall Curves (AUCPR)
The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. It represents the tradeoff between precision and recall for different thresholds, where high AUCPR indicates both high recall and high precision. The AUCPR will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as MI-CLAIM and TRIPOD. The performance metrics will only be calculated with respect to first acute care event.
Measured: Up to 3 years
Secondary outcomes (4)
AUC-ROC for composite acute care
The AUC-ROC will be used to validate a previously developed model in the primary endpoint for predicting each ED visits or hospitalizations during external beam RT using continuous step counts before, during, and after treatment.
Measured: Up to 3 years
Area under the receiver operating characteristic curve (AUC-ROC) for all cause acute care by group
The AUC-ROC will be used to validate the previously developed step-count model in the primary endpoint for predicting all-cause acute care for each of the two different device platforms.
Measured: Up to 3 years
Mean squared error (MSE)
The MSE will be used to validate concordance of step counts across each of the device's platforms in the Apple group. Mean Squared Error (MSE) is a fundamental concept in statistics and machine learning in assessing the accuracy of the predictive models which measures the average squared difference between predicted values and the actual values in the dataset.
Measured: Up to 3 years
Area under the receiver operating characteristic curve (AUC-ROC) for the composite acute care endpoint..
Validate the previously developed SHIELD-RT EHR-based model for predicting unplanned acute care (ED visit or hospitalization) to discover additional variables which may be predictors not previously included.
Measured: Up to 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start7 Apr 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hematopoietic NeoplasmStudies of the same condition, open and under way.
- More trials run by University of California, San FranciscoThe 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
- 260 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, San Francisco
- Sponsor type
- University, hospital or other
- Collaborators
- National Cancer Institute (NCI)
- Responsible party
- Sponsor
- Investigators
- Julian Hong, MD, MS, Principal investigator, University of California, San Francisco
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06587100
- Sponsor's protocol ID
- 23722
- Other IDs
- NCI-2024-06762, R01CA277782
- Acronym
- WATCH
- Status last verified
- Sep 2026
- First submitted
- 4 Sep 2024
- Last updated
- 23 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Wearable Activity Tracking to Curb Hospitalizations (WATCH)
- ClinicalTrials.gov ID
- NCT06587100
- Acronym / sponsor ID
- WATCH / 23722
- Keywords
- Activity tracking, Hospitalization prevention, Artificial Intelligence (AI) modelling
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 260 participants (estimated)
- Lead sponsor
- University of California, San FranciscoUniversity, hospital or other
- Collaborators
- National Cancer Institute (NCI)
- Study start
- 7 Apr 2025 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 19 Sep 2024 (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 being done to collect patient generated health data to predict the risk of patients needing emergency department visits or hospitalization before, during. and after receiving radiation therapy.
Detailed description
PRIMARY OBJECTIVE:
- Validate a previously developed step-count model for predicting all-cause acute care (pooled across all devices).
SECONDARY OBJECTIVES:
- Validate a previously developed model for predicting each ED visits or hospitalizations during external beam RT using continuous step counts before, during, and after treatment.
II. Validate the previously developed step-count model for predicting all-cause acute care for each of the two different device platforms.
III. Validate concordance of step counts across each of the device's platforms in the Apple group.
IV. Validate the previously developed SHIELD-RT Electronic health record (EHR)-based model for predicting unplanned acute care (ED visit or hospitalization).
EXPLORATORY OBJECTIVES:
- Refinement of the pre-existing models(step count and SHIELD-RT). II. Evaluate association between wearables collected parameters, EHR-based variables, and acute care events.
III. Develop and validate a multi-modal predictive model for predicting acute care.
OUTLINE: This is an observational study. Participants are assigned to 1 of 2 groups.
- GROUP I: Participants receive Fitbit device and undergo non-interventional, standard of care, radiation therapy.
- GROUP II: Participants receive Fitbit device and utilize their own personal Apple HealthKit-based device and undergo non-interventional, standard of care, radiation therapy.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Observational Group I: Fitbit only | – | Device: FitbitParticipants receive Fitbit device while undergoing non-interventional, standard of care, radiation therapy. |
| Observational Group II: Fitbit + Apple HealthKit | – | Device: Fitbit, Device: Apple HealthKit-based devicesParticipants receive Fitbit device and will utilize personal Apple HealthKit-based devices (iPhone, Apple Watch, etc.) to concurrently contribute Apple HealthKit-based data while undergoing non-interventional, standard of care, radiation therapy. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients undergoing non-interventional, standard of care, radiotherapy (RT) at UCSF Department of Radiation Oncology
Key inclusion criteria (8 listed)
- Age >= 18.
- Eastern Cooperative Oncology Group (ECOG) performance status =< 2 or Karnofsky Performance Scale (KPS) >= 50%.
- Able to understand study procedures and to comply with them for the entire length of the study.
- Ability of individual or legal guardian/representative to understand a written informed consent document, and the willingness to sign it.
- Diagnosis of invasive malignancy.
Key exclusion criteria (2 listed)
- Participants bound to a wheelchair.
- Participants unable to ambulate independently (needing assistance of cane or walker).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| FitbitAlso: Wearable Activity TrackerParticipants will wear Fitbit device | Device | Observational Group I: Fitbit only, Observational Group II: Fitbit + Apple HealthKit |
| Apple HealthKit-based devicesAlso: iPhone, Apple watchParticipants will wear personal device and share data with study team. | Device | Observational Group II: Fitbit + Apple HealthKit |
Primary outcome
Area under the receiver operating characteristic curve (AUC-ROC) of the step count model
Time frame: Up to 3 years
The AUC-ROC of the step count model will measure the performance of a classification model by plotting the rate of true positives against false positives, and the score ranges from 0 - 1. The higher the AUC, the better the model's performance at distinguishing between the positive and negative classes. The AUC-ROC will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD). The performance metrics will only be calculated with respect to first acute care event.
Calculation of a Brier Score
Time frame: Up to 3 years
The Brier Score is a strictly proper score function or strictly proper scoring rule that measures the accuracy of probabilistic predictions. A Brier Score can take on any value between 0 and 1, with 0 being the best score achievable and 1 being the worst score achievable. The lower the Brier Score, the more accurate the prediction(s). The score will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as MI-CLAIM and TRIPOD. The performance metrics will only be calculated with respect to first acute care event.
Calculation of Log-Loss Score
Time frame: Up to 3 years
Logarithmic loss indicates how close a prediction probability comes to the actual/corresponding true value. The Log-Loss Score can take on any value between 0 and 1. The more the predicted probability diverges from the actual value, the higher is the log-loss value. The log-loss value will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as MI-CLAIM and TRIPOD. The performance metrics will only be calculated with respect to first acute care event.
Area Under the Precision-Recall Curves (AUCPR)
Time frame: Up to 3 years
The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. It represents the tradeoff between precision and recall for different thresholds, where high AUCPR indicates both high recall and high precision. The AUCPR will be reported including both estimates and confidence intervals. All models will be reported per up-to-date guidelines, such as MI-CLAIM and TRIPOD. The performance metrics will only be calculated with respect to first acute care event.
4 secondary outcomes
AUC-ROC for composite acute care
Time frame: Up to 3 years
Area under the receiver operating characteristic curve (AUC-ROC) for all cause acute care by group
Time frame: Up to 3 years
Mean squared error (MSE)
Time frame: Up to 3 years
Area under the receiver operating characteristic curve (AUC-ROC) for the composite acute care endpoint..
Time frame: Up to 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2026.
- 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.
Julian Hong, MD, MS principal investigator
University of California, San Francisco
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hematopoietic Neoplasm and Malignant Solid Neoplasm, run by University of California, San Francisco at 1 site in United States.
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 8 inclusion and 2 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: looking for participants now, as of the sponsor's last update on 23 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Imani Dunn (877-827-3222, Imani.Dunn@ucsf.edu), 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 step count model (time frame: Up to 3 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 23 Sep 2026.