How It Works
Biomarkers simplify and clarify health data, making it accessible and actionable:
- Health data is gathered from various sources, such as wearables, apps, and medical devices, capturing a wide array of health-related information.
- This raw data undergoes a meticulous process of cleaning, deduplication, and analysis. The transformation turns complex datasets into clean, reliable metrics.
- Through advanced analytics, these processed data points are then converted into biomarkers, which are concise indicators of different health aspects like activities, body composition, sleep patterns, and vital signs.
- These metrics offer a granular view of health and are instrumental in tracking changes, identifying trends, and guiding health-related decisions.
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Learn how intelligence layers transform biomarkers into actionable scores, behavioral archetypes, and predictive insights.
🧠 Explore Intelligence Layers & Scores →Biomarker Categories Overview
🏃
Activity
10 biomarkers
Track movement, exercise intensity, energy expenditure, and daily activity patterns. All activity biomarkers can be collected from smartphones without wearables.
Use Cases
- • Fitness apps & gym chains
- • Corporate wellness programs
- • Health insurance incentives
- • Churn prediction models
⚖️
Body
8 biomarkers
Monitor body composition, weight changes, BMI, fat mass, lean mass, and metabolic rate. Most require manual input or smart scales.
Use Cases
- • Weight management apps
- • Nutrition & supplement platforms
- • Clinical research
- • Insurance underwriting
❤️
Vitals
13 biomarkers
Measure cardiovascular health, respiratory function, blood metrics, and physiological indicators. Most require wearable sensors.
Use Cases
- • Clinical monitoring
- • Fitness performance tracking
- • Stress & recovery assessment
- • Health screening programs
😴
Sleep
13 biomarkers
Analyze sleep duration, quality, stages (light/deep/REM), interruptions, and circadian patterns. Basic metrics available from smartphones.
Use Cases
- • Sleep therapy (CBT-I)
- • Mental health monitoring
- • Performance optimization
- • Shift work management
🩺
Reproductive
10 biomarkers
Track menstrual cycles, phases (follicular, ovulation, luteal), fertile windows, and hormonal patterns. No wearable required.
Use Cases
- • Fertility tracking
- • Women's health apps
- • Hormonal health monitoring
- • Family planning
Complete Biomarker Reference
🏃 Activity Biomarkers (10)
| Biomarker | Units | Description | Significance | Wearable? |
|---|---|---|---|---|
| steps | count | The total number of steps taken | Tracking steps is pivotal for daily physical activity, associated with lower risks of cardiovascular diseases, obesity, and diabetes | 🟢 No |
| floors_climbed | count | The total number of floors climbed, reflecting vertical movement | Enhances cardiovascular fitness and leg strength, contributing to a decreased risk of heart disease and obesity | 🟢 No |
| active_hours | hour | Number of hours in the day during which any physical activity occurs | Critical for reducing sedentary lifestyle risks, including obesity and metabolic syndrome | 🟢 No |
| active_duration | minute | Total duration of all physical activities, including walking and exercises | Assesses overall activity levels, crucial for cardiovascular health and chronic disease prevention | 🟢 No |
| activity_low_intensity_duration | minute | Duration in low-intensity activities (1.5-2.9 METs), like slow walking or light tasks | Aids in reducing sedentary behavior, linked with decreased risk of chronic diseases and mental health improvement | 🟢 No |
| activity_medium_intensity_duration | minute | Duration in moderate-intensity activities (3-5.9 METs), such as brisk walking | Key for cardiovascular benefits, reducing risks of heart disease, stroke, and hypertension | 🟢 No |
| activity_high_intensity_duration | minute | Time in high-intensity activities (>6 METs), likely intense exercises beyond walking | Boosts cardiovascular and metabolic health, significantly reducing various disease risks | 🟢 No |
| activity_sedentary_duration | minute | Time spent inactive, highlighting minimal movement phases | Crucial for identifying and minimizing sedentary time, reducing risks of metabolic syndrome and obesity | 🟢 No |
| active_energy_burned | kcal | Energy expended during active phases, including walking and exercise | Key for weight management, obesity prevention, and promoting metabolic health | 🟢 No |
| total_energy_burned | kcal | Overall energy expenditure, combining resting and active states | Offers a holistic view of energy expenditure, aiding in informed health and weight management decisions | 🟢 No |
⚖️ Body Biomarkers (8)
| Biomarker | Units | Description | Significance | Wearable? |
|---|---|---|---|---|
| height | meter | The measure of the individual's stature from base to top | Crucial for clinical assessments like BMI calculation, nutritional status evaluation, and growth tracking in children and adolescents | 🟢 No |
| weight | kilogram | The total body mass of the individual | Fundamental for health assessment, nutritional evaluation, and BMI calculation, aiding in the identification of potential health risks associated with underweight or overweight conditions | 🟢 No |
| body_mass_index | kg/m² | A numerical computation of body fat, derived from the individual's weight and height | Serves as a standard metric for categorizing weight status, helping to identify risks for conditions such as obesity, cardiovascular disease, and diabetes | 🟢 No |
| body_fat | percentage | The proportion of total body weight that is composed of fat for the individual | Essential for determining body composition, assessing obesity-related disease risks, and guiding dietary and exercise interventions | 🟡 Yes |
| fat_mass | kilogram | The total weight of fat in the individual's body | Provides insight into body composition, crucial for evaluating obesity risk and designing targeted weight management programs | 🟡 Yes |
| lean_mass | kilogram | The total weight of non-fat body components, including muscle, bone, and water | Indicates overall muscle and organ mass, important for assessing nutritional status, physical fitness, and metabolic health | 🟡 Yes |
| waist_circumference | meter | The circumference measurement around the individual's waist | A key indicator of visceral adiposity, predictive of metabolic syndrome, cardiovascular risk, and insulin resistance | 🟢 No |
| resting_energy_burned | kcal | The amount of energy expended by the individual's body at rest to maintain vital functions | Reflects basal metabolic rate, providing insights into metabolic efficiency and health status; important for nutrition and weight management planning | 🟢 No |
🩺 Reproductive Biomarkers (10)
| Biomarker | Units | Description | Significance | Wearable? |
|---|---|---|---|---|
| menstrual_cycle_start_date | date | The date when the current menstrual cycle started | Useful for tracking current menstrual cycle and predicting future cycles | 🟢 No |
| menstrual_cycle_end_date | date | The date when the current menstrual cycle ended | Useful for tracking current menstrual cycle and predicting future cycles | 🟢 No |
| menstrual_cycle_length | day | The length of the current menstrual cycle, calculated from the start date to the end date | Useful for tracking current menstrual cycle and predicting future cycles | 🟢 No |
| menstrual_phase | none | The current phase of the menstrual cycle, categorized as menstruation, follicular, ovulation, or luteal | Provides insights into hormonal fluctuations, which can affect mood, energy levels, and overall health | 🟢 No |
| menstrual_phase_length | day | The length of the current menstrual phase, calculated from the start date to the end date | Useful for tracking phase duration and understanding individual cycle patterns | 🟢 No |
| fertile_window_start_date | date | The start date of the fertile window within the current menstrual cycle | Important for understanding the optimal time for conception, aiding in reproductive planning | 🟢 No |
| fertile_window_end_date | date | The end date of the fertile window within the current menstrual cycle | Important for understanding the optimal time for conception, aiding in reproductive planning | 🟢 No |
| menstruation_period_start_date | date | The start date of the menstruation period within the current menstrual cycle | Useful for tracking menstrual health and predicting future periods | 🟢 No |
| menstruation_period_end_date | date | The end date of the menstruation period within the current menstrual cycle | Useful for tracking menstrual health and predicting future periods | 🟢 No |
| menstrual_phase_days_to_next_phase | day | The number of days remaining until the next menstrual phase | Helps in predicting phase transitions, which can be useful for planning and managing health and lifestyle activities | 🟢 No |
😴 Sleep Biomarkers (13)
| Biomarker | Units | Description | Significance | Wearable? |
|---|---|---|---|---|
| sleep_start_time | datetime | The exact time when the individual falls asleep | Understanding sleep onset helps in analyzing sleep patterns and consistency, crucial for maintaining circadian rhythm and promoting mental health | 🟢 No |
| sleep_mid_time | datetime | The midpoint time in the sleep cycle, equidistant between falling asleep and waking up | Reflects the balance and structure of the sleep cycle, aiding in the assessment of sleep quality and circadian rhythm alignment | 🟢 No |
| sleep_end_time | datetime | The time when the individual wakes up from sleep | Tracking waking time is essential for evaluating sleep regularity and duration, impacting alertness and cognitive performance | 🟢 No |
| sleep_duration | minute | The total time spent sleeping | Critical for physical and mental recovery, supports memory consolidation, and is essential in reducing risks of various chronic conditions | 🟢 No |
| sleep_debt | hour | The discrepancy between the amount of sleep an individual requires and the actual amount obtained | Monitoring sleep debt is fundamental for understanding and mitigating long-term health impacts such as cognitive decline and mood instability | 🟢 No |
| sleep_interruptions | count | The count of awakenings or breaks in sleep throughout the night | High interruption frequency can significantly deteriorate sleep quality, affecting next-day functioning and long-term health | 🟡 Yes |
| sleep_in_bed_duration | minute | Total time spent in bed, not necessarily sleeping | This metric helps assess sleep efficiency and identify patterns related to sleep disorders or insomnia | 🟢 No |
| sleep_awake_duration | minute | The time spent being awake after initially falling asleep and before finally waking up | Crucial for understanding sleep disturbances; prolonged awake durations can signal underlying sleep disorders | 🟡 Yes |
| sleep_light_duration | minute | The time spent in the light sleep phase | Light sleep is essential for memory processing and overall recovery, acting as a bridge to deeper sleep stages | 🟡 Yes |
| sleep_rem_duration | minute | The time spent in REM (Rapid Eye Movement) sleep phase | REM sleep supports brain health, including memory and learning, emotional processing, and is closely linked with dreaming | 🟡 Yes |
| sleep_deep_duration | minute | The time spent in deep (slow-wave) sleep phase | Deep sleep is fundamental for physical restoration, cell regeneration, and bolstering the immune system | 🟡 Yes |
| sleep_regularity | index | A measure of how consistent sleep patterns are over time | Regular sleep patterns are associated with better overall health, reduced risk of chronic diseases, improved mood, and cognitive function | 🟢 No |
| sleep_latency | minute | Time it takes to fall asleep after going to bed | Sleep latency is an indicator of sleep initiation difficulty, where prolonged latency can be a marker of stress, anxiety, or sleep disorders | 🟡 Yes |
| sleep_efficiency | percentage | The ratio of total sleep time to the total time spent in bed | An essential marker of sleep quality; high sleep efficiency is indicative of sound sleep health | 🟡 Yes |
❤️ Vitals Biomarkers (13)
| Biomarker | Units | Description | Significance | Wearable? |
|---|---|---|---|---|
| heart_rate_resting | bpm | The heart rate when the individual is at rest | Indicates cardiovascular health and efficiency; lower resting heart rates are linked to better heart function and fitness | 🟡 Yes |
| heart_rate_sleep | bpm | The average heart rate during a sleep session | Offers insights into sleep quality and the balance of the autonomic nervous system during rest, which is crucial for recovery | 🟡 Yes |
| heart_rate_variability_sdnn | millisecond | The standard deviation of NN intervals, representing variability in heartbeats | Higher HRV values suggest better cardiovascular fitness and resilience to stress, while lower values can signal potential health issues | 🟡 Yes |
| heart_rate_variability_rmssd | millisecond | The root mean square of successive differences between heartbeats | A key measure of parasympathetic nervous system activity, crucial for evaluating stress response, recovery, and cardiovascular health | 🟡 Yes |
| respiratory_rate | count/minute | The frequency of breaths per minute while at rest | An important indicator of respiratory and overall health, with significant implications for detecting various health conditions | 🟡 Yes |
| respiratory_rate_sleep | count/minute | The average respiratory rate during sleep | Changes or abnormalities can signal sleep-related or respiratory conditions, affecting overall health quality | 🟡 Yes |
| oxygen_saturation | percentage | The proportion of oxygen-saturated hemoglobin in the blood | Critical for evaluating cardiovascular and respiratory function, with low levels indicating potential health concerns | 🟡 Yes |
| oxygen_saturation_sleep | percentage | Average oxygen saturation levels during sleep | Important for assessing nighttime respiratory and cardiovascular efficiency, with deviations indicating potential health issues | 🟡 Yes |
| vo2_max | mL/kg/min | The maximum volume of oxygen an individual can utilize during intense exercise | A strong indicator of cardiovascular fitness and aerobic capacity, with higher levels signifying better health and endurance | 🟡 Yes |
| blood_glucose | mg/dL | The level of glucose present in the blood | Essential for metabolic health monitoring, with implications for energy management, mood regulation, and diabetes control | 🟡 Yes |
| blood_pressure_systolic | mmHg | The peak arterial pressure during heart beats | Elevated systolic pressure can signify cardiovascular risk, making its monitoring vital for hypertension management | 🟡 Yes |
| blood_pressure_diastolic | mmHg | The lowest arterial pressure during heart relaxation | Critical for cardiovascular health assessment, with its management being key in hypertension and related health risks | 🟡 Yes |
| body_temperature_basal | celsius | The body's temperature at rest | Provides baseline for metabolic and overall health, with deviations indicating potential medical concerns | 🟡 Yes |
| skin_temperature_sleep | celsius | The skin temperature during sleep | Offers insights into circulatory and environmental adaptation of the body during sleep, affecting sleep quality and health | 🟡 Yes |
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Raw biomarkers are just the beginning. Learn how Sahha's intelligence layers transform this data into wellness scores, behavioral archetypes, and predictive trends.
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All biomarker information on this page is sourced from Sahha Biomarkers Documentation. Data is accurate as of 2025.