Wearable Activity Tracking to Curb Hospitalizations (WATCH)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 260
- 试验地点
- 1
- 主要终点
- Area under the receiver operating characteristic curve (AUC-ROC) of the step count model
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •Participants bound to a wheelchair.
- •Participants unable to ambulate independently (needing assistance of cane or walker).
研究组 & 干预措施
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.
干预措施: Apple HealthKit-based devices (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.
干预措施: Fitbit (Device)
Observational Group I: Fitbit only
Participants receive Fitbit device while undergoing non-interventional, standard of care, radiation therapy.
干预措施: Fitbit (Device)
结局指标
主要结局
Area under the receiver operating characteristic curve (AUC-ROC) of the step count model
时间窗: 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
时间窗: 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
时间窗: 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)
时间窗: 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.
次要结局
- 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)