Wearable Activity Tracking to Curb Hospitalizations (WATCH)
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Enrollment
- 260
- Locations
- 1
- Primary Endpoint
- Area under the receiver operating characteristic curve (AUC-ROC) of the step count model
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •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.
Exclusion Criteria
- •Participants bound to a wheelchair.
- •Participants unable to ambulate independently (needing assistance of cane or walker).
Arms & Interventions
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.
Intervention: Fitbit (Device)
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.
Intervention: Apple HealthKit-based devices (Device)
Observational Group I: Fitbit only
Participants receive Fitbit device while undergoing non-interventional, standard of care, radiation therapy.
Intervention: Fitbit (Device)
Outcomes
Primary Outcomes
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.
Secondary Outcomes
- 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)