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Digital Biomarkers
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Digital Biomarkers provide a comprehensive suite of metrics derived from various health and activity data sources. These biomarkers translate raw data into meaningful measures, enabling a deeper understanding of an individual's health status and lifestyle patterns.
Note
Interested in discovering patterns or trends within biomarkers? Explore User Insights .
Key Features
- Clarity from Complexity : Digital Biomarkers distill vast amounts of raw health data into clear, straightforward metrics, making it easy for anyone to understand their health status.
- Multidimensional Analysis : Capture a broad spectrum of health indicators, from physical activity to sleep patterns, offering a comprehensive health overview.
- Proactive Monitoring : By converting raw health data into meaningful metrics, Digital Biomarkers empower to make informed decisions and take proactive steps toward better health.
- Evolving with Innovation : Constantly refined with the latest technological advancements and health research.
How It Works
Digital Biomarkers simplify and clarify health data, making it accessible and actionable:
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Health data is gathered from various sources, such as wearables, apps, and medical devices, capturing a wide array of health-related information.
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This raw data undergoes a meticulous process of cleaning, deduplication, and analysis. The transformation turns complex datasets into clean, reliable metrics.
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Through advanced analytics, these processed data points are then converted into Digital Biomarkers, which are concise indicators of different health aspects like activities, body composition, sleep patterns, and vital signs.
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These metrics offer a granular view of health and are instrumental in tracking changes, identifying trends, and guiding health-related decisions.
List of Biomarkers
Activity
| Biomarker | Units | Description | Significance | Requires 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
| Biomarker | Units | Description | Significance | Requires 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/m^2 | 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
| Biomarker | Units | Description | Significance | Requires 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_cycle_day_number | day | The current day number within the menstrual cycle. | Useful for tracking cycle progression and identifying patterns for reproductive health monitoring. | 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_start_date | date | The date when the current phase of the menstrual cycle started. | Useful for tracking phase duration and understanding individual cycle patterns. | No |
| menstrual_phase_end_date | date | The date when the current phase of the menstrual cycle ended. | Useful for tracking phase duration and understanding individual cycle patterns. | 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 |
| menstrual_phase_day_number | day | The current day number within the menstrual phase. | Useful for understanding phase progression and hormonal changes. | 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 |
| 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 |
Sleep
| Biomarker | Units | Description | Significance | Requires 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, which are vital for cognitive function and mood regulation | 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, cognitive performance, and overall physical health | 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, including heart disease and obesity | 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, mood instability, and increased susceptibility to illness | 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, mood stability, 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, aiding in the management of sleep health | 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 or environmental issues impacting sleep | 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 and contributing to the sleep cycle's effectiveness | 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, playing a crucial role in overall health maintenance | 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, whereas shorter latency indicates healthy sleep initiation | 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, whereas low efficiency may point to sleep disturbances or inefficiency in sleep initiation or maintenance | Yes |
Vitals
| Biomarker | Units | Description | Significance | Requires 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 and health | 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 |
Output Schema
Understand the structure of Digital Biomarker data with the following schema:
{ "id": "unique_identifier", "type": "biomarker_type", "category": "biomarker_category", "value": numerical_value, "unit": "measurement_unit", "periodicity": "data_periodicity", "aggregation": "data_aggregation", "startDateTime": "start_timestamp", "endDateTime": "end_timestamp"}- id : A unique identifier for each biomarker reading.
- type : Specifies the biomarker type (e.g., steps, heart_rate).
- category : Classifies the biomarker into a category (e.g., activity, sleep).
- value : The numerical value of the biomarker.
- unit : The unit of measurement for the value.
- periodicity : The frequency at which the biomarker data is generated (e.g., daily, weekly, intraday).
- aggregation : The method of data aggregation used to calculate the value (e.g., total, average, maximum).
- startDateTime : The starting timestamp for the period over which the biomarker was calculated.
- endDateTime : The ending timestamp for that period.
FAQs
Q: How frequently are Digital Biomarkers updated?
A: Digital Biomarkers are updated based on their periodicity settings, which can be daily, weekly, or intraday.
Q: Are wearable devices required to capture all Digital Biomarkers?
A: While some biomarkers require wearable devices, others can be derived from different health data sources.
Getting Started
To begin using Digital Biomarkers, integrate the Sahha SDK and subscribe to the biomarker webhooks.
Support
For additional assistance or more detailed information on Digital Biomarkers, please contact support@sahha.ai or reach out in the slack community .