Table 4.

Summary of Features and Functions of Smartphone Sensing Study Design

Design feature	Function	Examples
Sensing device and sensing application software	Front-end of the sensing system The smartphone user-interface that people use to respond to surveys and participate in the study Determines the types of sensor data collected and the sampling rate	Sensing apps can be: Commercial (e.g., Easy M, MetricWire) Open-source (e.g., AWARE, Emotion Sense, Funf, Purple Robot; Sensus) Prototypes (e.g., BeWell, StudentLife, StressSense)
Server storage space	Back-end of the sensing system Communicates with the front-end to run the sensing software Can be either physical servers (hardware) or virtual servers (cloud-based) Stores the data in databases, in various file formats (e.g., CSV, JSON)	Servers can be hosted by: Commercial platforms (e.g., Amazon Web Services) University or company-based computing and information technology services Databases: MongoDB MySQL
Data management	Data processing component of sensing system Monitor data collection to identify potential problems Extract behavioral inferences from the smartphone data (e.g., applying classifiers, algorithms, combining data)	Programming languages R Python
Data analyses	Aggregate the sensor data to appropriate units of analysis (e.g., hourly, daily, weekly units) Run more formal analyses of the given research questions of interest	Analytic software MATLAB Mplus R SPSS