Longevity Metrics AI/ML Development Study

NCT07808619 | Recruiting

• 1 other identifier: AIML
• Study Type: observational
• Target: 1,000,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life. The sit-to-rise test and the timed walk are scored by hand today, from a person's count. A model scores the same test from video instead. It also measures what no one can count by eye - speed, asymmetry, steadiness - so one capture yields both the original score and additional measurements, intended to enrich the model and strengthen what it predicts. Every test in a participant's record measures the same body, so the tests are correlated: a test that was performed carries information about one that was not. A model trained across the library learns those relationships and estimates a missing result from the results that are present. Each estimate is checked against records where that part was actually measured, and over decades against death and disease through linkage to the 100-Year Human Aging Study (NCT07563777). The hypothesis is that the full battery can eventually be predicted across modalities with high accuracy using a few short video clips, replacing most in-person screening. That would let preventive screening reach people and places a physical laboratory cannot. How far the input can be reduced is the question this study exists to answer. Every model is a physician-reviewed clinical decision aid until it is cleared by the FDA.

Conditions

Aging; Mortality; Cardiovascular Disease Prevention; Metabolic Syndrome; Cognitive Dysfunction; Frailty; Musculoskeletal Diseases; Activities of Daily Living; Physical Disability; Preventive Health Services; Diagnostic Techniques and Procedures; Health Services Accessibility

Keywords

Longevity; Preventive Health; Health Screening; Biological Age; Artificial Intelligence; Machine Learning; Computer Vision; Sit to Rise; Gait Speed; Pose Estimation; All-Cause Mortality; Life Expectancy Prediction; Healthspan; Preventive Medicine; Functional Decline; Human Performance

Outcome Measures

Primary Outcomes (5)

• Measurement Accuracy, Non-Inferiority to Human Scoring.

  Agreement between the model's value and the reference standard, established as non-inferiority to a qualified human scorer where a human reference exists. Gates deployment as a clinical decision aid.

  At each model freeze, through study completion, up to 100 years.

• Cross-Prediction of Non-Performed Results and Derived Scores

  Accuracy of predicting, from video or from the performed part of a record, both the screening results the participant did not obtain, against the measured value; and the derived scores computed from a complete record, including the Longevity Score, biological age, and estimated age at death, against the value the scoring engine produces from the full measured record.

  At each model freeze, through study completion, up to 100 years.

• Prediction of Remaining Life Expectancy

  Accuracy of predicting remaining life expectancy from video and library data, against observed mortality through linkage to the 100-Year Human Aging Study.

  Through study completion, up to 100 years

• Prediction of Physician Clinical Judgment

  Accuracy of predicting the physician's clinical judgment where no determining measurement exists, against the sealed record. Applies to models that predict a clinical determination rather than a measured value. Where a determining test exists but was not performed, the outcome falls under Primary Outcome 2.

  At each model freeze, through study completion, up to 100 years

• Screening-Related Injuries and Adverse Events

  Participant injuries or adverse events attributed to measurements added under this study, such as the sit-to-rise test. Ascertained from two independent sources so that an event missed by one is still captured: the tester logs any fall or injury at the time of screening, and the participant reports separately on the post-screening form.

  Continuously from first screening through study completion, up to 100 years

Secondary Outcomes (13)

• Measurement Accuracy, Superiority to Human Scoring

  At each model freeze, through study completion, up to 100 years

• Uncertainty Calibration

  At each model freeze, through study completion, up to 100 years

• Within-Session Repeatability

  At each model freeze, through study completion, up to 100 years.

• Missing-Data and Completeness Robustness

  At each model freeze, through study completion, up to 100 years

• Physician Assessment of Model Output

  Continuously from first model read through study completion, up to 100 years

Interventions

AIML scored health screening battery DIAGNOSTIC_TEST

Video and audio-captured physical, cognitive, and observational screening tests scored by frozen, versioned AI models; models automatically score standard tests (sit-to-rise, timed walk, chair stand), predict non-performed results from performed ones, and predict clinical judgment; all outputs are physician-reviewed clinical decision aids, and no model output reaches a participant report without provider confirmation.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Participants enrolled in the 100-Year Human Aging Study (NCT07563777) through the unified consent, receiving any Longevity Metrics health screening service at any fixed, mobile, or remote (online) site, current and future. Participants are self-selected community members of all health statuses; no exclusions based on health status, diagnosis, or population group. This study adds no separate enrollment: all records analyzed and all prospective measurements collected under this protocol arise from participants consented under the associated protocols. Participants are followed for life, with mortality, incident disease, and functional-status outcomes ascertained through linkage to the 100-Year Human Aging Study.

You may qualify if:

• Age \>= 18
• willing to participate in the study

You may not qualify if:

• Age \< 18 years

Sponsors & Collaborators

• Longevity Metrics, Inc.: lead

Study Sites (1)

Longevity Metrics

Boulder, Colorado, 80301, United States

RECRUITING

Related Links

• Longevity Metrics Research

MeSH Terms

Conditions

Metabolic Syndrome; Cognitive Dysfunction; Frailty; Musculoskeletal Diseases

Condition Hierarchy (Ancestors)

Insulin Resistance; Hyperinsulinism; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Cognition Disorders; Neurocognitive Disorders; Mental Disorders; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• William E Brandenburg, MD

  Longevity Metrics, Inc.

  PRINCIPAL INVESTIGATOR

Central Study Contacts

William E Brandenburg, MD

CONTACT

3035010016; info@longevitymetrics.org

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 2, 2026
• First Posted: September 9, 2026
• Study Start: August 8, 2026
• Primary Completion (Estimated): December 31, 2099
• Study Completion (Estimated): December 31, 2099
• Last Updated: September 9, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)