Quantifying time spent outdoors: A versatile method using any type of global positioning system (GPS) and...

Area of Science:

• Environmental Health
• Biomedical Engineering
• Human Movement Science

Background:

• Objective quantification of outdoor time is crucial for understanding its health benefits.
• Self-reported measures of outdoor time are limited by recall bias.
• Existing objective methods often require specialized equipment, limiting widespread application.

Purpose of the Study:

• To develop a versatile, rule-based method for classifying indoor and outdoor (I/O) GPS data.
• To enable objective quantification of time spent outdoors using common GPS and accelerometer devices.
• To validate the developed method against participant-logged activity diaries.

Main Methods:

• Utilized a rule-based, point cluster algorithm for I/O GPS data classification.
• Integrated accelerometer (lux) and GPS data with building footprint and park location data.
• Employed the GPSAS_Destinations application for point cluster detection and R for classification.
• Collected data from five university students wearing GPS and accelerometer devices.

Main Results:

• Achieved an overall I/O GPS classification accuracy rate of 89.58% (Kappa = 0.78).
• Demonstrated good classification accuracy, indicating reliability of the developed method.
• Validated classification results against participant activity diaries.

Conclusions:

• The developed method offers a reliable and versatile approach to objectively classify indoor and outdoor GPS data.
• This technique can be widely applied across various GPS and accelerometer devices.
• Facilitates more accurate research into the health impacts of spending time outdoors.