Official Title
Personal Lifestyle Engine (PLX) is an Employee Wellness Platform and App Used at the Personal Lifestyle Medicine Center (PLMC). This Study Examines Correlations Between Lifestyle Factors, Genomic Data, Physical Exam Finding and Biomarkers
Brief Title
Personal Lifestyle Engine (PLX) - Personal Lifestyle Medicine Center (PLMC)
Protocol ID
NCT04007939
Lead Sponsor
Metagenics, Inc.
Brief Summary
It has been suggested that the best medicine should include four principles (4P) -
Medicine should be personalized, predictive, preventative and participatory. Technology
has provided the tools to collect data in ways not previously possible. Individuals can
now collect information on their genome (including their genetic predisposition to
tolerate medications and to respond to healthy lifestyle programs) that will modify their
lifestyle and therapeutic choices. Beyond spot checks of vital signs and weight,
individuals can now collect information on body composition, continuous monitoring of
heart rate, blood pressure, and even blood sugar. Data on food consumption at a caloric,
macronutrient and even micronutrient level can be collected. Standard medical histories
and detailed physical examination findings and laboratory biomarkers can be correlated
with this data.
Collections of individual patient data will need to be managed through computer programs
and smart phone applications that provide direct feedback about the influence of
lifestyle on health, wellness and biomarkers. To this end, Metagenics is designing and is
launching a smart phone application, Personal Lifestyle Engine (PLX), for individual use
by patients and their healthcare providers. The statistical analysis of these data is the
primary objective of this study.
Detailed Description
Technology has led to a significant revisioning and modification of the models of
medicine in practice today. It has been suggested that the best medicine should include
four principles - Medicine should be personalized, predictive, preventative and
participatory. This 4P medicine will thus be patient centered with a focus on the person
who has the disease and not the disease the person has. It will be predictive as it
identifies the preclinical trend/decline towards illness sooner than onset of symptoms
that herald the loss of function and health. It will be preventative as the information
gathered should offer opportunities to modify these trajectories towards illness and
finally it will be participatory as individuals will be intimately involved in the
gathering of data to identify trends and in the application of lifestyle measures to
improve the quality of their life.
Technology has provided the tools to collect data in ways not previously possible.
Individuals can now collect information on their genome (including their genetic
predisposition to tolerate medications and to respond to healthy lifestyle programs) that
will modify their lifestyle and therapeutic choices. Beyond spot checks of vital signs
and weight, individuals can now collect information on body composition, continuous
monitoring of heart rate, blood pressure, and even blood sugar. Data on food consumption
at a caloric, macronutrient and even micronutrient level can be collected. Standard
medical histories and detailed physical examination findings and laboratory biomarkers
can be correlated with this data.
As has been noted in the Nathan Price et al. article, "A wellness study of 108
individuals using personal, dense, dynamic data clouds" (PMID: 28714965), a significant
challenge to the effective use of these complex sets of individual patient data is how to
define the boundaries between disease, average health and optimal wellbeing. To meet this
challenge, compiling and analyzing collections of de-identified, detailed patient
histories, questionnaires regarding symptoms and general condition, and associated
objective findings (genomic data, vital signs, and physical exam and laboratory
biomarkers) will theoretically identify these boundaries and will facilitate the
deliverance of 4P Medicine. Comprehensive data collections on each subject evaluated in
aggregate provides a diversity of uniqueness markers that can be statistically probed to
identify patterns that predict wellbeing and perhaps individual response to lifestyle
interventions.
An additional challenge for both the patient and their health care provider in 2018 and
beyond is how to manage this data in an effective manner. Collections of individual
patient data will need to be managed through computer programs and smart phone
applications that provide direct feedback about the influence of lifestyle on health,
wellness and biomarkers. To this end, Metagenics is designing and is launching a smart
phone application, PLX, for individual use by patients and their healthcare providers.
After and while a statistical analysis of this data set has been/is being completed, the
data set will also be used in an initial beta test of the PLX operating system. The PLX
application will not be used to conduct the statistical analysis which is the primary
objective of this study."
Study Period
-
Enrollment Count
400 participants
Eligibility Criteria
Inclusion Criteria:
- Male or Female
- Ages 18-80, inclusive
- Willing to give written informed consent to participate in the study
Exclusion Criteria:
- A serious, unstable illness including cardiac, hepatic, renal, gastrointestinal,
respiratory, endocrinologic, neurologic, immunologic, or hematologic disease.
- Known infection with human immunodeficiency virus (HIV), tuberculosis (TB), or
hepatitis B or C.
- Inability to comply with study and/or follow-up visits.
- Any concurrent condition (including clinically significant abnormalities in medical
history, physical examination or laboratory evaluations) which, in the opinion of
the Principle Investigator (PI), would preclude safe participation in this study or
interfere with compliance.
- Any sound medical, psychiatric and/or social reason which, in the opinion of the PI,
would preclude safe participation in this study or interfere with compliance.
Filters
Health, Subjective
Gastrointestinal Dysfunction
Cardiovascular Risk Factor
Autoimmune Diseases
Dental Diseases
Hormone Disturbance
Neurocognitive Dysfunction
COMPLETED
ADULT
OLDER_ADULT