BACKGROUND
Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression.
OBJECTIVE
This study aims to identify links between productivity and biobehavioral rhythms modeled from passively collected mobile data streams.
METHODS
In this study, we used a multimodal mobile sensing data set consisting of data collected from smartphones and Fitbits worn by 188 college students over a continuous period of 16 weeks. The participants reported their self-evaluated daily productivity score (ranging from 0 to 4) during weeks 1, 6, and 15. To analyze the data, we modeled cyclic human behavior patterns based on multimodal mobile sensing data gathered during weeks 1, 6, 15, and the adjacent weeks. Our methodology resulted in the creation of a rhythm model for each sensor feature. Additionally, we developed a correlation-based approach to identify connections between rhythm stability and high or low productivity levels.
RESULTS
Differences exist in the biobehavioral rhythms of high- and low-productivity students, with those demonstrating greater rhythm stability also exhibiting higher productivity levels. Notably, a negative correlation (C=–0.16) was observed between productivity and the SE of the phase for the 24-hour period during week 1, with a higher SE indicative of lower rhythm stability.
CONCLUSIONS
Modeling biobehavioral rhythms has the potential to quantify and forecast productivity. The findings have implications for building novel cyber-human systems that align with human beings’ biobehavioral rhythms to improve health, well-being, and work performance.
CLINICALTRIAL