Breast Carcinoma, Head and Neck Carcinoma, Lung Carcinoma, Malignant Solid Neoplasm
Conditions
Brief summary
This study explores the use of the Hugo platform to involve study participants in their healthcare and treatments. The Hugo platform may allow researchers to collect better and more accurate real-time health data, from other clinics/hospitals, pharmacies and other electronic devices. The purpose of this research is to see if the use of the Hugo platform is consistent, accurate, cost effective, and time efficient, as it collects and transmits important real-time health information from other clinics/hospitals, pharmacies, and other electronic devices.
Detailed description
PRIMARY OBJECTIVES: I. Evaluate the data aggregation from a patient centered platform over a follow up period for completeness and quality, including externally linked electronic health record (EHR) information, toxicity completion, and the patient reported outcomes (PRO) response rates for study patients, compared with historical pre-coronavirus disease (COVID) cohort of registry patients. II. To explore biometric information obtained from wearables as it pertains to the toxicity of treatment for patients using a wearable device. III. To estimate app-based survey completion rates using Hugo-based survey incentive completion (gift cards). IV. To explore the cost data available on the platform. OUTLINE: Patients complete surveys via the Hugo Platform at baseline (before treatment), after treatment, and at 3 and 6 months after treatment. Patients' medical records and Fitbit information are also collected into the Hugo Platform.
Interventions
OTHERElectronic Health Record Review
Medical records are collected
OTHERMedical Device Usage and Evaluation
Fitbit information is collected
OTHERSurvey Administration
Complete surveys
Sponsors
Mayo Clinic
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age 18+ * English speaking * Consented for Outcomes Registry Study (15-000136) * Undergoing radiotherapy for cancer treatment with curative intent * Willing to and able to give consent and participate in study * Willing to complete all surveys * Able to access a device (computer, smartphone, or tablet) with web access every day to complete study surveys * Willing to connect to a device (i.e., a smartphone, Fitbit, or tablet) that can regularly link to Hugo for data transfer * Willing to share personal information to Tremendous to be able to obtain the Visa e-cards for time and effort put into this study * Willing to use the Hugo health data sharing platform * Willing to create a Mayo Clinic Patient Portal (if not already created)
Exclusion criteria
* Unable to give consent and enroll prior to administration of baseline survey * Partial breast radiation therapy (RT) (3 fraction), due to overlap in cadence used for all disease sites * Stereotactic body radiation therapy (SBRT) to the lung, due to overlap in cadence used for all disease sites * Co-enrollment on another PRO related study (soft rule) * Coordinator would need to get source data from Adam via Hugo * Response data will only be accessible by select people
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Survey completion rate | Baseline up to 6 months after completion of radiation therapy | The difference in rates will be estimated. A chi square, or Fisher's exact test as appropriate, will be used to compare the rates between cohorts. A logistic regression model will be used to understand the association of patient and disease variables with the odds of completion (at least one of the surveys at the treated site). |
| Percentage of patients with access to wearable device | Up to 6 months after completion of radiation therapy | A logistic regression model will be used to understand the association of patient and disease variables with the odds of access to a wearable device. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of adverse events | Up to 6 months after completion of radiation therapy | Logistic regression will be used to predict presence of a patient toxicity. Baseline patient and disease characteristics, along with biometric information will be used to assess associations with toxicity. Multiple variable models will be considered depending on the number of patients identified with a toxicity. |
Countries
United States
Outcome results
None listed