Behavioral
Short-horizon exposures
· Active energy · Dietary energy · Carbohydrate · Sodium · Alcohol · Daylight · Mindfulness
Hours → days
A laboratory for self-experimentation
A scientific workstation for exploring your health. Take charge of your health. See today’s snapshot, navigate your health history, and trace what’s shaping your trajectory. BioMIR brings wearable signals, daily habits, home measurements, and clinical results into one interactive view.
to open the ranked list. Before check-in, tap
to plan or to consider an action; use
to undo a selection or
to reset. At check-in, record
for a completed action or
and
; afterward, record
or
. Use the panel to set when the review takes place and whether a reminder should prompt it, while separate alert controls let you choose which recurring patterns to follow over time.
and 3
outcomes; the REC counter updates alongside recorded follow-through. Use the totals to see which commitments were completed, set aside, or remain unresolved, then compare the day’s action record with its eligible opportunities and running credit in the same view.
Inspired by the spirit of a tricorder and the familiar logic of a market index, BioMIR makes your trajectory visible—and the contributors behind its movement open to investigation. BioMIR brings that idea to selected, high-value health signals through an engaging glass-like interface—inviting you to navigate the whole and investigate what shapes your vitality and longevity.
Preventive health, made engaging
through data visualization.
A biological time machine—an interactive window into your health history. Scrub to revisit a moment; swipe to follow a trajectory. Explore the signals, habits and patterns in your record—and investigate what changed, what preceded it and what happened next.
Read the direction in color, hear your daily brief, or move through your history with as much analytic and scientific depth as you prefer—then focus on what deserves attention.
Put persistence in perspective. Follow individual contributors across days, months, and a year to see how often elevated model contributions recur. Bring sustained patterns into your decisions—and keep the supporting data and their dates within reach.
No dedicated BioMIR wearable required. Apple Health integration.
Core health calculations on device. Contributor-level interpretation.
Read the direction. Negative Δ-years indicate favorable model contributions; positive values indicate adverse ones. Blue and red contribution colors reinforce that direction, while chart hues distinguish domains. These are model estimates, not years of lifespan gained or lost.
BioMIR analytical architecture
Behavioral, Functional, and Cardiometabolic inputs form Adaptive BioAge. KDM and Levine PhenoAge provide separate context from episodic clinical panels.
Short-horizon exposures
· Active energy · Dietary energy · Carbohydrate · Sodium · Alcohol · Daylight · Mindfulness
Hours → days
Responsive physiologic capacity
· HRV · Resting HR · VO₂max · Total sleep · Deep sleep
Days → months
Slow-moving baseline state
· BMI · SBP · FBG
Weeks → months
Episodic reference state
· Alb · ALP · BUN · Cr · CRP · HbA1c · SBP · TC · Glu · Lymph% · MCV · RDW · WBC
Episodic · dated panels
Integrated longitudinal model
Hierarchical composite with an age-calibrated cardiometabolic baseline and bounded, asymmetric Functional and Behavioral Δ-year contributions. The output is expressed in age-equivalent Δ-years relative to chronological age, with negative values representing age-equivalent reward and positive values age-equivalent penalty, while retaining contributor attribution over time.
Multivariate biological-age estimator combining age-calibrated biomarker regressions with chronological age.
Mortality-trained multivariable biomarker score mapped through a Gompertz function to an age-equivalent phenotype.
Cadence depicts characteristic observation and interpretation timescale—not a causal sequence, fixed biological deadline, or prescribed testing interval. Cardiometabolic state is interpreted longitudinally; KDM and Levine PhenoAge use separate clinical-panel context.
Evidence to action · a separate record
REC turns prioritized signals into a short action list and records whether eligible actions are reported complete. Its Adherence view summarizes those reports and can be reviewed beside Adaptive BioAge and later observations, while each remains a separate record.
Conceptual sequence, not measured data; timing alone does not establish cause or response.
What sits beneath the model
The architecture is intentionally compact: selected signals are grouped by how the model interprets them—not as 15 equal votes.
Modifiable routines and exposures enter Adaptive BioAge as bounded contributors rather than direct measurements of physiological state.
Autonomic regulation, restorative physiology, and cardiorespiratory capacity provide responsive physiological context across different measurement and adaptation timescales.
A slower-moving age-equivalent baseline anchors the daily composite, with original observation dates preserved when eligible prior values are carried forward.
Adaptive BioAge uses all three domains. KDM and Levine PhenoAge remain separate in Clinical Long View. Reclaimed BioAge (REC) connects prioritized signals with practical actions and recorded follow-through. See how the models fit together ↗
A controlled scientific core
Current core. Adaptive BioAge is computed from prespecified, version-controlled model rules rather than a self-training model. For the same eligible inputs evaluated under the same model version, the scientific calculation remains reproducible and the result can be traced back through domains and contributors.
Future personalization. BioMIR’s roadmap keeps adaptive intelligence downstream of that authoritative calculation. On-device learning may eventually characterize individual baseline, variability, temporal response, and action-response patterns without silently rewriting the equations that produced the underlying BioMIR result.
See the architecture and roadmapA model output expressed in age-equivalent units. A way to put signals into context.
Adaptive BioAge is a model output expressed in age-equivalent units relative to chronological age. Δ-years provides a common signed scale: negative values represent an age-equivalent reward and positive values an age-equivalent penalty. It is not a direct measurement of years gained or lost.
Interpret the result alongside its contributors, Data Confidence, Data Freshness, and trends—not as a verdict on your health.
From signals to understanding
Follow a change over time. Inspect its contributors. Understand the measurements behind it. Move from Trends to domain and biomarker detail with Dashboard and Trends sharing period and statistic selections.
Take a closer lookExplore biomarker-level contributions instead of stopping at a single summary estimate.
Read trends with Data Confidence and Data Freshness. Data Confidence summarizes daily input support; Data Freshness keeps the age of carried cardiometabolic observations visible.
Review prioritized actions as
, , or
, then record
or
at check-in.
Set an evening check-in time and choose whether to receive a daily completion reminder at that time in
Action Check-in settings.
An open, careful approach
Understand reference context, age-equivalent outputs, assumptions, data support, and the distinction between a model output and a clinical conclusion.
Explore the scienceExplore scientific reading and discussion, keeping population evidence separate from individual observations.
Browse resourcesEvidence-grounded explainers
Short, referenced guides on the scientific ideas behind age-like health scores, wearables, functional physiology, cardiometabolic context, and longitudinal interpretation.
Browse all InsightsBiological age fundamentals
Age-like health scores can be useful summaries, but the number only makes sense in the context of the inputs, reference population, outcome, and model assumptions behind it.
Read the explainerLongitudinal interpretation
High-frequency age estimates are dynamic summaries. Short-term movement can be informative without being interpreted as irreversible aging or lifespan change.
Read the explainerDigital health landscape
Apple has announced Health Age for a forthcoming Health app update, making model transparency and interpretation an increasingly important comparison.
Read the explainerChoose your path
Download the current public release and explore Adaptive BioAge, contributors, trends, and clinical long-view models.
App Store ↗ 02Review the architecture, model assumptions, evidence boundaries, missing-data logic, and validation status.
Methods ↗ 03Explore collaboration priorities for longitudinal convergence, preventive engagement, reliability, interpretability, and prospective validation.
Research & collaboration ↗Help shape BioMIR
Explore the beta and share your experience. Thoughtful feedback helps inform what comes next.
BioMIR helps adults inspect habits and physiology alongside slower cardiometabolic measurements and separate clinical age models. Trace contributors, supporting dates and persistence across a personal timeline. Independent validation of the proprietary composite remains pending.
Check whether BioMIR fits your data and goals · Compare approaches · See what software controls establish