Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study Article Swipe
Runze Yan
,
Xinwen Liu
,
Janine M. Dutcher
,
Michael Tumminia
,
Daniella K. Villalba
,
Sheldon Cohen
,
J. David Creswell
,
Kasey G. Creswell
,
Jennifer Mankoff
,
Anind K. Dey
,
Afsaneh Doryab
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/47194
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/47194
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/47194
- OA Status
- diamond
- Cited By
- 1
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391858735
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391858735Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/47194Digital Object Identifier
- Title
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Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative StudyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-15Full publication date if available
- Authors
-
Runze Yan, Xinwen Liu, Janine M. Dutcher, Michael Tumminia, Daniella K. Villalba, Sheldon Cohen, J. David Creswell, Kasey G. Creswell, Jennifer Mankoff, Anind K. Dey, Afsaneh DoryabList of authors in order
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https://doi.org/10.2196/47194Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2196/47194Direct OA link when available
- Concepts
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Rhythm, STREAMS, Productivity, Computer science, Exploratory research, Psychology, Artificial intelligence, Neuroscience, Economics, Medicine, Sociology, Computer network, Social science, Macroeconomics, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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