Assessing the Effectiveness of an N-of-1 Platform Using Study of Cognitive Enhancers

NCT ID: NCT04056650

Last Updated: 2021-03-12

Basic Information

• Recruitment Status: TERMINATED
• Clinical Phase: PHASE4
• Total Enrollment: 57 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-10-18
• Study Completion Date: 2020-12-22

Brief Summary

The growing consumer-grade molecular and digital wellness market is generating unprecedented volumes of information to support decision-making around individual health. Current trends suggest the demand for personalized health information, tools, and services will continue to rise in the next decade. What is missing is a reliable, individualized way to turn this data into action. Dialogue around consumer health often ignores the disconnect between measurements and goals. For example, monitoring one's weight is not the same as losing weight, and counting steps is not the same as lowering blood pressure. If individuals are to benefit from data, they must be able to relate changes in their personal data to targeted changes in actions and outcomes. There is a great need and opportunity to adapt the tools and capabilities of modern computer science, statistics, and clinical trial design to the needs of individual patients and consumers. The team at the Institute for Next Generation Healthcare (INGH) has created a smartphone-based app ("N1 app") and study platform that together allow individuals to design, implement, and analyze methodologically sound, statistically robust studies of their personal health data. The focus of the platform will be the creation of single-participant randomized crossover studies, known as n-of-1 trials. The platform employs informatics-based intelligence that automates study design and analysis while simultaneously maintaining high standards of statistical rigor and reproducibility.

These novel methods and tools are designed to empower individuals to make rational, data-driven choices about their own health, maximizing the benefit all will receive from new and existing sources of personal health data.

Detailed Description

The growing burden of chronic disease in the U.S. and the economics of accountable care are driving a shift toward proactive approaches to disease prevention and health maintenance. At the same time, precision medicine studies continue to reveal substantial heterogeneity in the manifestations of even the most common chronic diseases. The bulk of morbidity and mortality in the U.S. arises from conditions with a significant lifestyle component (e.g. type II diabetes), and responsibility for monitoring and maintaining health largely falls on individuals.

Recent advances in molecular biology, sensors, and digital health technology underlie rapidly growing market availability of products and devices for measuring and monitoring individual health. A vast array of wearable devices, smart home monitors, and health tracking apps provide an unprecedented view of individuals "in the wild" and provide customers with health information once accessible only to researchers. The growing digital health market is generating unprecedented volumes of information to support decision making around individual health, and current trends suggest the demand for personalized health information, tools, and services will continue to grow in the next decade.

What is missing from this technological and scientific growth is a reliable, individualized way to translate data into action. If society wants to prevent diabetes, heart disease, and other chronic illnesses that kill millions of Americans each year, individuals must be empowered to address precursor conditions like obesity, hypertension, and depression. Dialogue around consumer health often fails to address the profound disconnect between measurements and outcomes/goals; e.g. monitoring one's weight is not the same as losing weight, and counting steps is not the same as lowering blood pressure. Data are only useful if they can help individuals identify interventions that work for them. The combination of diet, exercise, drugs/supplements, activities, and lifestyle changes that targets an individual's particular set of health problems is unique to him or her, and it is dependent on a complex web of factors including genetics, environment, and personal lifestyle. If individuals are to benefit from data, they must be able to relate changes in their personal data to targeted adjustments in actions and outcomes. This effectively necessitates conducting a robust trial at the level of the individual to determine the most promising recipe of personal lifestyle adjustments to effect change.

To address these challenges, the researchers have developed a unified statistical framework for producing consistent, interpretable study results from diverse n-of-1 study designs. The analysis framework is the backbone of the initial software platform, which includes modules for study design, e-consent, data ingestion, data analysis, and visualization of results.

To test this platform, the researchers plan to deploy a prototype study that allows individuals to test the cognitive effects of two commonly consumed substances: caffeine and caffeine in combination with a safe, prevalent compound, L-theanine. Each enrolled individual will participate in his/her own n-of-1 trial. After a baseline period where neither treatment is taken, participants will alternate between the two treatments ("caffeine alone" and "caffeine + L-theanine") according to a predefined schedule. Participants will complete a daily cognitive assessment composed of 3 validated cognitive tests administered via the N1 app. The platform will analyze the cognitive assessment data and determine whether there is a statistically meaningful treatment effect for either treatment compared to baseline for any of the 3 cognitive tests for each individual that completes the study.

It is important to state explicitly that the research objectives for this protocol are not related to the efficacy of L-theanine and caffeine. This specific study is designed to allow the researchers to efficiently recruit and enroll subjects so that the underlying statistical methods and software platform for executing n-of-1 trials may be evaluated.

Conditions

Cognitive Performance

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: CROSSOVER
• Primary Study Purpose: DEVICE_FEASIBILITY
• Blinding Strategy: NONE

Study Groups

Single caffeinated beverage/supplement

Group Type: ACTIVE_COMPARATOR

Caffeine supplement

Intervention Type: DIETARY_SUPPLEMENT

50-400mg

N1 app

Intervention Type: DEVICE

N1 App on mobile device

Combination caffeine and L-theanine

Group Type: ACTIVE_COMPARATOR

L-theanine

Intervention Type: DRUG

up to 250 mg

Caffeine supplement

Intervention Type: DIETARY_SUPPLEMENT

50-400mg

N1 app

Intervention Type: DEVICE

N1 App on mobile device

Interventions

L-theanine

up to 250 mg

Intervention Type: DRUG

Caffeine supplement

50-400mg

Intervention Type: DIETARY_SUPPLEMENT

N1 app

N1 App on mobile device

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• US resident
• 18 years old or over
• Has an iPhone
• Regular caffeine drinker

Exclusion Criteria

• Pregnant/breastfeeding
• Any contraindication/health issue in which risk is added by consumption of caffeine

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Icahn School of Medicine at Mount Sinai

OTHER

Sponsor Role: lead

Responsible Party

Jason Bobe

Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Jason Bobe

Role: PRINCIPAL_INVESTIGATOR

Icahn School of Medicine at Mount Sinai

Locations

Icahn School of Medicine at Mount Sinai

New York, New York, United States

Countries

United States

References

Bobe JR, Buros J, Golden E, Johnson M, Jones M, Percha B, Viglizzo R, Zimmerman N. Factors Associated With Trial Completion and Adherence in App-Based N-of-1 Trials: Protocol for a Randomized Trial Evaluating Study Duration, Notification Level, and Meaningful Engagement in the Brain Boost Study. JMIR Res Protoc. 2020 Jan 8;9(1):e16362. doi: 10.2196/16362.

Reference Type: DERIVED

PMID: 31913135 (View on PubMed)

Other Identifiers

GCO 18-0756

Identifier Type: -

Identifier Source: org_study_id