PERsonal ContExtual Precision healTh
| Status: | Completed |
|---|---|
| Conditions: | Depression, High Blood Pressure (Hypertension) |
| Therapuetic Areas: | Cardiology / Vascular Diseases, Psychiatry / Psychology |
| Healthy: | No |
| Age Range: | 18 - 80 |
| Updated: | 3/7/2019 |
| Start Date: | November 6, 2017 |
| End Date: | December 31, 2018 |
Personal Mobile and Contextual Precision Health
The exponential growth of physiological, behavioral and environmental data generated through
consumer mobile health (mHealth) devices and Internet of Things (IoT) technology provide
unprecedented sources of personalized and contextual health information. If linked to
clinical health data from the Electronic Health Record (EHR), these data can provide dynamic
and individualized views of patient health states and trajectories that can greatly inform
clinical care and health-related research. The investigators propose to advance precision
health through the development and evaluation of a mobile application and data platform that
collects, harmonizes and integrates mHealth and environmental data from patients' daily lives
with their clinical histories and electronic health record data.
The investigators propose a participatory design approach to implement and evaluate a
precision health platform through the study and modeling of hypertension (HTN) and depression
in patient communities of UC Davis (UCD) and UC San Francisco (UCSF). These chronic diseases
have high prevalence across geography, socioeconomic status, and race/ethnicity, and have
significant economic, societal and personal costs. They are considerably challenging to
manage due to difficulties in acquiring high-quality and consistent data from patients
outside of their clinical care appointments that is so needed for a full view of the
patient's disease state. Despite a broad array of self-monitoring devices and consumer
applications, mHealth data are not getting into the clinical care process, and patients do
not regularly monitor their own health states, particularly during periods of medication
change, when frequent assessments are especially important.
The investigators propose to conduct a 6-month single arm feasibility study of 200 ambulatory
men and women (100 each at UCSF and UCD) with either hypertension or depression to implement
an open, web-accessible, standards driven and patient-centric data platform for the
integration of patient-reported and clinical data.
consumer mobile health (mHealth) devices and Internet of Things (IoT) technology provide
unprecedented sources of personalized and contextual health information. If linked to
clinical health data from the Electronic Health Record (EHR), these data can provide dynamic
and individualized views of patient health states and trajectories that can greatly inform
clinical care and health-related research. The investigators propose to advance precision
health through the development and evaluation of a mobile application and data platform that
collects, harmonizes and integrates mHealth and environmental data from patients' daily lives
with their clinical histories and electronic health record data.
The investigators propose a participatory design approach to implement and evaluate a
precision health platform through the study and modeling of hypertension (HTN) and depression
in patient communities of UC Davis (UCD) and UC San Francisco (UCSF). These chronic diseases
have high prevalence across geography, socioeconomic status, and race/ethnicity, and have
significant economic, societal and personal costs. They are considerably challenging to
manage due to difficulties in acquiring high-quality and consistent data from patients
outside of their clinical care appointments that is so needed for a full view of the
patient's disease state. Despite a broad array of self-monitoring devices and consumer
applications, mHealth data are not getting into the clinical care process, and patients do
not regularly monitor their own health states, particularly during periods of medication
change, when frequent assessments are especially important.
The investigators propose to conduct a 6-month single arm feasibility study of 200 ambulatory
men and women (100 each at UCSF and UCD) with either hypertension or depression to implement
an open, web-accessible, standards driven and patient-centric data platform for the
integration of patient-reported and clinical data.
Inclusion Criteria:
1. Primary care patient at UCD or UCSF
2. Able to speak and read English
3. Male or female 18-80 years of age at Telephone screening
4. Documentation of a diagnosis of hypertension (defined as SBP >= 140 mmHg or DBP >= 90
mmHg on anti-hypertensive medication including beta-blockers, ACE-I, ARB,
alpha-blockers, calcium-channel blockers) OR depression (PHQ-8 > 5) on an
antidepressant medication
5. Written informed consent (and assent when applicable) obtained from subject or
subject's legal representative and ability for subject to comply with the requirements
of the study
6. Have an Android or Apple iOS smartphone
7. Willing to install the PERCEPT, iHealth (for hypertension cohort) and Moves mobile
applications
8. Willing to self-report blood pressure (for those with hypertension and with provided
iHealth and/or standard blood pressure cuff) or mood data (for those with depression)
at specified frequency
9. Willing to be have your location and activity tracked
10. Have downloaded a mobile application from the appropriate mobile app store (App store
for iPhones or Google Play for Android) within the past 1 year
11. Have home Wifi access.
Exclusion Criteria:
1. High blood pressure or depression being managed by a physician outside of UCD or UCSF
2. Current participation in any other mobile app-based clinical study
3. A diagnosis of both hypertension and depression
4. A diagnosis of depression with psychosis (ICD-9: 296.24, 296.34) bipolar disorder
(ICD-9: 296.0, 296.1, 296.4, 296.5, 296.6, 296.7, 296.8, 296.9) schizophrenia (ICD-9:
295.x), schizoaffective disorder (ICD-9 295.70)
5. Planning to relocate from area within the study duration
6. Impaired vision that could limit the use of the mobile apps (participant-reported)
7. Primary care patient of the Investigator, Dr. Meghana Gadgil
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