Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory...

Area of Science:

• Human-computer interaction
• Chronobiology
• Digital phenotyping

Background:

• Biobehavioral rhythms, encompassing biological, behavioral, and psychosocial cycles, are crucial for health.
• Disruptions in these rhythms are associated with health issues like sleep disorders, obesity, and depression.

Purpose of the Study:

• To investigate the relationship between productivity and biobehavioral rhythms.
• To model these rhythms using passively collected mobile data streams.

Main Methods:

• Utilized a multimodal mobile sensing dataset from 188 college students over 16 weeks.
• Collected self-evaluated productivity scores and sensor data from smartphones and Fitbits.
• Modeled cyclic behavior patterns and analyzed rhythm stability in relation to productivity.

Main Results:

• Students with greater biobehavioral rhythm stability reported higher productivity.
• A negative correlation was found between productivity and the standard error of the phase (SE) for the 24-hour period, indicating lower stability with higher SE.

Conclusions:

• Biobehavioral rhythm modeling can quantify and predict productivity.
• Findings support the development of cyber-human systems that synchronize with human rhythms to enhance well-being and performance.