Quantifying Nocturnal Rest-State Instability Using a Thermodynamic Potential Landscape: Evidence from Population-Scale Actigraphy

Quantifying Nocturnal Rest-State Instability Using a Thermodynamic Potential Landscape: Evidence from Population-Scale Actigraphy | 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 Research Article Quantifying Nocturnal Rest-State Instability Using a Thermodynamic Potential Landscape: Evidence from Population-Scale Actigraphy MINJUN PARK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9238576/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Traditional sleep medicine often relies on subjective questionnaires, whichprovidelimitedobjectivephysiologicalcharacterization. Whilewearableactig- raphyprovidescontinuousdata, standardmetricsfailtocapturetheunderlyingtran- sition dynamics of the sleep-wake cycle. We propose a non-equilibrium statistical physics framework to quantify nocturnal rest-state instability and its relationship with clinical confounders using hourly actigraphy. Methods: We analyzed hourly physical activity monitor data from 5,124 par- ticipants in the NHANES 2013-2014 cohort to derive physical metrics. The rest- activity sequence was modeled as a first-order discrete-time Markov model to cal- culate the nocturnal transition probability (P01) and transition entropy. Using an effective coarse-grained model, we reconstructed the empirical potential well (U (x) =−ln(P (x))) to visualize the “Rest Well” topology. A restricted cohort of 4,789 participants with complete clinical data was used for multivariate logistic regression to control for age, BMI, and PHQ-9 depression scores. Results: Our analysis revealed that subjects with sleep disorders exhibited a shal- lower nocturnal “rest well” (∆U ≈0.07), signifying a higher escape rate from the low-activity state. Global transition entropy showed a weak negative association with aging (r =−0.186, p < 0.001); in contrast, the directional transition probabil- ity P01 served as a more specific marker of sleep instability. Although the absolute shifts in these dynamical metrics were subtle, they revealed a statistically significant deviation in nocturnal rest-state stability, consistent with escape-rate theory derived from stochastic dynamics. In the full multivariate model, this physical instability was heavily mediated by depression (OR = 1.837, p < 0.001), suggesting it is a downstream physical manifestation of psychiatric burden rather than an indepen- dent etiology. Conclusion: By integrating Markovian dynamics with thermodynamic potential landscapes, we objectivelyquantifiedhow clinicaldisorderslike depressionphysically destabilize the circadian rest state. These findings support a scalable framework for digital phenotyping using wearable actigraphy, enabling objective assessment of circadian rest-state instability. This manuscript is a preprint and has not yet undergone peer review. The findings should therefore be interpreted as preliminary. Biophysics Systems Biology Physiology Actigraphy Sleep instability Markov dynamics Potential landscape Digital phenotyping Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version 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. 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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-9238576","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612946107,"identity":"594ced5c-0d82-44ce-ac96-f01b40a6f6b8","order_by":0,"name":"MINJUN PARK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYBACA3YGBmaGAgYQycDwwUBCDiR64AE+LcwgxQYQLYwzKmyMwVoSiNIC0sVzJi2xAcTCp8WcmcfwcYHBYTnzdvaHj3nbDqfPDzv8EGiLnZxuA3Ytls08xsYzDA4byxxmSDac23Y4d+PtNAOglmRjswM4HHaYx0yax+Bw4gxmhmMSb0FaZieAtBxI3IZbi/lvoJb6GcyM7T9ADjOcnf6BkBYzZqCWBAlmZjZGoPcT5KVz8Nti2cxWLD3DIN1wBjMbsyQwkA03SOcUHEgwwO0Xc/bmjZ8LKqzlJfiPP/wAjEp5+dnpmz98qLCTw6UFi1PBKg2IVQ4C8g2kqB4Fo2AUjIKRAAAVtlwGuRIihAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-5305-3491","institution":"Sapporo Medical University, Sapporo, Japan","correspondingAuthor":true,"prefix":"","firstName":"MINJUN","middleName":"","lastName":"PARK","suffix":""}],"badges":[],"createdAt":"2026-03-27 00:19:08","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9238576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9238576/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105723213,"identity":"755bcc41-117c-4e1b-9bd8-1b19a31f7713","added_by":"auto","created_at":"2026-03-30 09:57:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1540812,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMinjunPark.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9238576/v1_covered_8ab2f1f7-8ec5-41bd-a5f7-e3b27b3a6d54.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eQuantifying Nocturnal Rest-State Instability Using a Thermodynamic Potential Landscape: Evidence from Population-Scale Actigraphy\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sapporo Medical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Actigraphy, Sleep instability, Markov dynamics, Potential landscape, Digital phenotyping","lastPublishedDoi":"10.21203/rs.3.rs-9238576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9238576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTraditional sleep medicine often relies on subjective questionnaires, whichprovidelimitedobjectivephysiologicalcharacterization. Whilewearableactig- raphyprovidescontinuousdata, standardmetricsfailtocapturetheunderlyingtran- sition dynamics of the sleep-wake cycle. We propose a non-equilibrium statistical physics framework to quantify nocturnal rest-state instability and its relationship with clinical confounders using hourly actigraphy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed hourly physical activity monitor data from 5,124 par- ticipants in the NHANES 2013-2014 cohort to derive physical metrics. The rest- activity sequence was modeled as a first-order discrete-time Markov model to cal- culate the nocturnal transition probability (P01) and transition entropy. Using an effective coarse-grained model, we reconstructed the empirical potential well (U (x) =−ln(P (x))) to visualize the “Rest Well” topology. A restricted cohort of 4,789 participants with complete clinical data was used for multivariate logistic regression to control for age, BMI, and PHQ-9 depression scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur analysis revealed that subjects with sleep disorders exhibited a shal- lower nocturnal “rest well” (∆U ≈0.07), signifying a higher escape rate from the low-activity state. Global transition entropy showed a weak negative association with aging (r =−0.186, p \u0026lt; 0.001); in contrast, the directional transition probabil- ity P01 served as a more specific marker of sleep instability. Although the absolute shifts in these dynamical metrics were subtle, they revealed a statistically significant deviation in nocturnal rest-state stability, consistent with escape-rate theory derived from stochastic dynamics. In the full multivariate model, this physical instability was heavily mediated by depression (OR = 1.837, p \u0026lt; 0.001), suggesting it is a downstream physical manifestation of psychiatric burden rather than an indepen- dent etiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eBy integrating Markovian dynamics with thermodynamic potential landscapes, we objectivelyquantifiedhow clinicaldisorderslike depressionphysically destabilize the circadian rest state. These findings support a scalable framework for digital phenotyping using wearable actigraphy, enabling objective assessment of circadian rest-state instability.\u003c/p\u003e\n\u003cp\u003eThis manuscript is a preprint and has not yet undergone peer review. 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