Wearable Activity Tracking to Curb Hospitalizations

NCT06587100 | Recruiting

• 3 other identifiers: 23722NCI-2024-06762R01CA277782
• Study Type: observational
• Target: 260
• Locations: 1 country
• Sites: 1

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.

Conditions

Hematopoietic Neoplasm; Malignant Solid Neoplasm; Lymphatic System Neoplasm

Keywords

Activity tracking; Hospitalization prevention; Artificial Intelligence (AI) modelling

Outcome Measures

Primary Outcomes (4)

• 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.

  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.

  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.

  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.

  Up to 3 years

Secondary Outcomes (4)

• AUC-ROC for composite acute care

  Up to 3 years

• Area under the receiver operating characteristic curve (AUC-ROC) for all cause acute care by group

  Up to 3 years

• Mean squared error (MSE)

  Up to 3 years

• Area under the receiver operating characteristic curve (AUC-ROC) for the composite acute care endpoint..

  Up to 3 years

Study Arms (2)

Observational Group I: Fitbit only

Participants receive Fitbit device while undergoing non-interventional, standard of care, radiation therapy.

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.

Device: Fitbit; Device: Apple HealthKit-based devices

Interventions

Fitbit DEVICE

Participants will wear Fitbit device

Also known as: Wearable Activity Tracker

Observational Group I: Fitbit only; Observational Group II: Fitbit + Apple HealthKit

Apple HealthKit-based devices DEVICE

Participants will wear personal device and share data with study team.

Also known as: iPhone, Apple watch

Observational Group II: Fitbit + Apple HealthKit

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients undergoing non-interventional, standard of care, radiotherapy (RT) at UCSF Department of Radiation Oncology

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• University of California, San Francisco: lead
• National Cancer Institute (NCI): collaborator

Study Sites (1)

University of California, San Francisco

San Francisco, California, 94143, United States

RECRUITING

MeSH Terms

Conditions

Hematologic Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Hematologic Diseases; Hemic and Lymphatic Diseases

Study Officials

• Julian Hong, MD, MS

  University of California, San Francisco

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Imani Dunn

CONTACT

877-827-3222; Imani.Dunn@ucsf.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 4, 2024
• First Posted: September 19, 2024
• Study Start: April 7, 2025
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: April 17, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)