Input
Read authorized health samples, recent training, and subjective context.
Transparency
Health Insight Methodology
This page explains the data sources, calculation approach, missing-data behavior, and limitations behind SuperStrive's fitness and general-wellness estimates.
Last updated: July 31, 2026
From body signals to everyday guidance
Start with the calculation path, then state the boundary. Every output should show what was used, how it was handled, what it produced, and what remains uncertain.
Calculation
Organize usable signals by time window, completeness, and product rules.
Output
Produce trends, estimates, and practical prompts for everyday use.
Uncertainty
Show missingness, noise, and limits instead of presenting an estimate as diagnosis.
Data provenance
SuperStrive does not independently measure heart rate, HRV, temperature, sleep, or workout history. It reads only the data types you authorize in Apple Health through HealthKit.
Samples may originate from Apple Watch, iPhone, manual entries, or authorized third-party apps and compatible devices. Apple Health source settings, corrections, and duplicate handling can affect the samples returned to the app.
Wrist temperature is a skin-temperature trend from compatible Apple Health sources. It is not core body temperature and is not used to detect fever, infection, inflammation, or any disease.
Shared limitations
Every estimate depends on available sample quality, timing, device fit, sensor availability, source configuration, and personal history. Missing or unusual data can change a result.
Illness, medication, travel, alcohol, environment, and unusual activity may affect underlying signals. Read results as trends and planning context, not clinical findings.
Readiness
Inputs: available recovery, recent activity, sleep, heart-rate, and other wellness signals shown in the detail screen.
Method: available signals are combined into a 0–100 planning score. Missing contributors are excluded, so the score may use fewer inputs. A higher score reflects more favorable app-defined planning conditions; it does not establish medical fitness to exercise.
Body readiness
Inputs: resting heart rate, HRV balance, wrist temperature when available, sleep duration and balance, sleep regularity, recovery index, and recent activity.
Method: each available contributor is compared with an app-defined reference range or personal history. Fixed weights are applied and available weights are normalized into a 0–100 estimate. Unavailable contributors are omitted.
Daytime physiological load
Inputs: HRV, resting heart rate, and recent training-load signals.
Method: available signals are normalized to fixed ranges and combined into a 0–100 estimate. This is a physiological workload heuristic and does not directly measure mood, anxiety, or psychological stress.
Training load ratio
Inputs: duration of authorized workouts in the most recent 7 days and the comparison 28-day period.
Method: 7-day workout duration is divided by average weekly duration across 28 days. The ratio is a training-planning heuristic, not an injury probability. Duration alone does not capture every aspect of intensity or individual tolerance.
Fitness age estimate
Inputs: chronological age, resting heart rate, HRV, sleep duration, and readiness.
Method: chronological age is adjusted using fixed app-defined wellness factors and limited to ages 18–80. Missing resting-heart-rate, HRV, or sleep signals use neutral reference values.
This is an app-generated fitness estimate, not a clinical assessment, validated biological-age test, or statement about lifespan.