Defining the Habitome: Phenotypes of Routine and Their Relationship to Health Outcomes

Defining the Habitome: Phenotypes of Routine and Their Relationship to Health Outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Defining the Habitome: Phenotypes of Routine and Their Relationship to Health Outcomes Jessilyn Dunn, Leeor Hershkovich, Hayoung Jeong, Shun Sakai, Harrison Kane, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5861743/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Human health is deeply interconnected with behaviors that are dictated by daily routines, including activities such as sleep, physical activity, and dietary habits. Wearable technologies like smartwatches provide continuous, real-world insights into these behaviors and have presented a new opportunity to study habits and their relationship to health. Leveraging data from the NIH All of Us Research Program, we analyzed over 13.9 million days of Fitbit data from 15,389 participants spanning 5 years to define "routine-otypes"—quantifiable phenotypes of behavioral routines. Unsupervised learning identified distinct patterns of device wear across seasonal, weekly, and daily dimensions, uncovering seven long-term wear patterns, six circadian wear patterns including behaviors like daytime-only wear, nighttime-only wear, and precise charging times, and fourteen weekly wear patterns. Ultimately, we reveal five dimensions of routine-otypes, including circadian, weekly, and seasonal patterns, as well as overall wear levels and habituality. Routine-otypes were significantly associated with demographic factors such as age, sex, and income. Importantly, routine-otypes showed strong associations with health outcomes like depression and chronic disease, opening the possibility of developing new digital biomarkers for these conditions. This study introduces routine-otypes as objective, scalable, and modifiable human characteristics and highlights their potential to act as digital biomarkers of disease. By proposing the "habitome" framework, this work lays the foundation for developing deeper insights into routines and their role in health. Health sciences/Biomarkers/Predictive markers Biological sciences/Computational biology and bioinformatics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files HabitomeV2ExtendedData.pdf Tables 1-6 and Figures 1-6 Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5861743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441879447,"identity":"11c1bb2a-fdc1-45f6-b026-de918c70bab0","order_by":0,"name":"Jessilyn 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Wearable technologies like smartwatches provide continuous, real-world insights into these behaviors and have presented a new opportunity to study habits and their relationship to health. Leveraging data from the NIH All of Us Research Program, we analyzed over 13.9 million days of Fitbit data from 15,389 participants spanning 5 years to define \"routine-otypes\"—quantifiable phenotypes of behavioral routines. Unsupervised learning identified distinct patterns of device wear across seasonal, weekly, and daily dimensions, uncovering seven long-term wear patterns, six circadian wear patterns including behaviors like daytime-only wear, nighttime-only wear, and precise charging times, and fourteen weekly wear patterns. Ultimately, we reveal five dimensions of routine-otypes, including circadian, weekly, and seasonal patterns, as well as overall wear levels and habituality. Routine-otypes were significantly associated with demographic factors such as age, sex, and income. Importantly, routine-otypes showed strong associations with health outcomes like depression and chronic disease, opening the possibility of developing new digital biomarkers for these conditions. This study introduces routine-otypes as objective, scalable, and modifiable human characteristics and highlights their potential to act as digital biomarkers of disease. 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