Clinical trial NCT06587100: Wearable Activity Tracking to Curb Hospitalizations
Official title: Wearable Activity Tracking to Curb Hospitalizations (WATCH)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hematopoietic Neoplasm
Malignant Solid Neoplasm
Lymphatic System Neoplasm
Keywords: Activity tracking
Hospitalization prevention
Artificial Intelligence (AI) modelling
Lead sponsor: University of California, San Francisco
Sponsor class: OTHER
Collaborators: name: National Cancer Institute (NCI)
First posted: 2024-09-19
Enrollment: 260
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients undergoing non-interventional, standard of care, radiotherapy (RT) at UCSF Department of Radiation Oncology

Brief 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.

Detailed description: PRIMARY OBJECTIVE:

I. Validate a previously developed step-count model for predicting all-cause acute care (pooled across all devices).

SECONDARY OBJECTIVES:

I. 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:

I. 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.

Eligibility criteria: Inclusion Criteria:

* Age \>= 18.
* Eastern Cooperative Oncology Group (ECOG) performance status =\< 2.
* 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.

Exclusion Criteria:

* Participants bound to a wheelchair.
* Participants unable to ambulate independently (needing assistance of cane or walker).

Primary outcomes:
measure: Area under the receiver operating characteristic curve (AUC-ROC) of the step count model
timeFrame: Up to 3 years
measure: Calculation of a Brier Score
timeFrame: Up to 3 years
measure: Calculation of Log-Loss Score
timeFrame: Up to 3 years
measure: Area Under the Precision-Recall Curves (AUCPR)
timeFrame: Up to 3 years
Secondary outcomes:
measure: AUC-ROC for composite acute care
timeFrame: Up to 3 years
measure: Area under the receiver operating characteristic curve (AUC-ROC) for all cause acute care by group
timeFrame: Up to 3 years
measure: Mean squared error (MSE)
timeFrame: Up to 3 years
measure: Area under the receiver operating characteristic curve (AUC-ROC) for the composite acute care endpoint..
timeFrame: Up to 3 years
Other outcomes:
Locations:
city: San Francisco
country: United States
facility: University of California, San Francisco
state: California
Study officials:
affiliation: University of California, San Francisco
name: Julian Hong, MD, MS
role: PRINCIPAL_INVESTIGATOR
References: