Ambient Temperature
Supported on Android.
Measures the ambient room temperature in degrees Celsius.
Use Cases
- Sleep research: Studying how room temperature affects sleep quality and duration
- Physical health studies: Examining how temperature impacts physiological and cardiovascular responses
- Mood and behavior research: Investigating the relationship between temperature and mental well-being
Data Structure
Besides the common data fields, each record contains:
- Temperature: Ambient air temperature. Internally stored as
temp_c. - Accuracy: Same as Accuracy in Accelerometer.
Light
Supported on Android.
Measures the ambient light level (illumination) in lx.
Use Cases
- Sleep research: Analyzing how light exposure affects sleep-wake cycles and melatonin regulation
- Mood and mental health studies: Investigating links between light levels, seasonal affective disorder, and mood
- Physical activity and lifestyle studies: Using light patterns to infer indoor/outdoor time, routines, and activity levels
Data Structure
Besides the common data fields, each record contains:
- Illuminance: Illuminance, in lx. Internally stored as
illuminance_lx. - Accuracy: Same as Accuracy in Accelerometer.
Pressure
Supported on Android & iOS.
Measures the ambient air pressure in hectopascals (hPa) or millibars (mbar). Usually used for monitoring air pressure changes.
Use Cases
- Environmental health studies: Analyzing how changes in barometric pressure relate to headaches, migraines, joint pain, and respiratory illnesses such as COPD
- Physical activity research: Using pressure changes to detect altitude shifts, such as climbing stairs or hiking, and to enhance geospatial tracking with GPS
- Mood and behavior studies: Investigating how pressure fluctuations, often tied to weather changes, may influence mood, seasonal affective disorder, or general well-being
Data Structure
Besides the common data fields, each record contains:
- Pressure: Ambient air pressure, in hPa or mbar. Internally stored as
pressure_hpa. - Accuracy: Same as Accuracy in Accelerometer.
Proximity
Supported on Android.
Measures the proximity of an object in centimeters relative to the view screen of a device. It is typically used to determine whether a handset is being held up to a person’s ear.
Use Cases
- Sleep and well-being studies: Analyzing how device usage, particularly before sleep, impacts well-being
- Screen time and digital health research: Investigating the relationship between screen distance and the risk of developing myopia or other visual health issues.
- Communication behavior studies: Using proximity to a person’s ear to understand phone call habits
Data Structure
Besides the common data fields, each record contains:
- Distance: Distance from an object, in centimeters. Internally stored as
dist_cm. - Accuracy: Same as Accuracy in Accelerometer.
Relative Humidity
Supported on Android.
Measures the relative ambient humidity in percent.
Use Cases
- Respiratory health studies: Analyzing how humidity levels impact respiratory conditions such as asthma, bronchitis, and COPD
- Skin health and dermatology research: Investigating the relationship between humidity and skin conditions like eczema, psoriasis, and dry skin
- Environmental and sleep research: Examining the effect of humidity on sleep quality
Data Structure
Besides the common data fields, each record contains:
- Humidity: Ambient relative humidity, in percent. Internally stored as
humidity_percent. - Accuracy: Same as Accuracy in Accelerometer.
Additional Information for Environmental Sensors
The following information applies to all the environmental sensor data sources listed above.
Adding Data Sources
See Adding Data Sources.
Data Collection Behavior
See Data Collection Behavior of Avicenna.
Monitoring and Exporting Data
You can monitor and export environmental sensors data using either the Data Export page or Kibana.
Environmental Sensors in the Participant App
Participants do not need to do anything (e.g., grant permissions) for these data sources, and the data collection will start immediately.