Predicting circadian phase in community-dwelling later-life adults using actigraphy data
Caleb Mayer
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Department of Genetics, Stanford University, Stanford, California, USA
Contribution: Conceptualization, Investigation, Writing - original draft, Methodology, Writing - review & editing, Visualization, Software, Data curation, Formal analysis
Search for more papers by this authorDae Wook Kim
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Department of Brain and Cognitive Sciences, KAIST, Daejeon, Republic of Korea
Department of Mathematics, Sogang University, Seoul, Republic of Korea
Contribution: Conceptualization, Investigation, Writing - original draft, Methodology, Writing - review & editing, Formal analysis, Supervision, Visualization
Search for more papers by this authorMeina Zhang
College of Nursing, University of Iowa, Iowa City, Iowa, USA
Contribution: Writing - review & editing, Data curation, Resources, Project administration, Formal analysis
Search for more papers by this authorMinki P. Lee
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Contribution: Writing - review & editing, Formal analysis
Search for more papers by this authorDaniel B. Forger
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
Michigan Center for Interdisciplinary and Applied Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Contribution: Writing - review & editing, Methodology, Project administration, Supervision
Search for more papers by this authorHelen J. Burgess
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
Contribution: Writing - review & editing, Funding acquisition, Conceptualization, Supervision
Search for more papers by this authorCorresponding Author
Chooza Moon
College of Nursing, University of Iowa, Iowa City, Iowa, USA
Correspondence
Chooza Moon, College of Nursing, University of Iowa, Iowa City, Iowa, 52242, USA.
Email: [email protected]
Contribution: Writing - review & editing, Conceptualization, Investigation, Funding acquisition, Data curation, Supervision
Search for more papers by this authorCaleb Mayer
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Department of Genetics, Stanford University, Stanford, California, USA
Contribution: Conceptualization, Investigation, Writing - original draft, Methodology, Writing - review & editing, Visualization, Software, Data curation, Formal analysis
Search for more papers by this authorDae Wook Kim
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Department of Brain and Cognitive Sciences, KAIST, Daejeon, Republic of Korea
Department of Mathematics, Sogang University, Seoul, Republic of Korea
Contribution: Conceptualization, Investigation, Writing - original draft, Methodology, Writing - review & editing, Formal analysis, Supervision, Visualization
Search for more papers by this authorMeina Zhang
College of Nursing, University of Iowa, Iowa City, Iowa, USA
Contribution: Writing - review & editing, Data curation, Resources, Project administration, Formal analysis
Search for more papers by this authorMinki P. Lee
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Contribution: Writing - review & editing, Formal analysis
Search for more papers by this authorDaniel B. Forger
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
Michigan Center for Interdisciplinary and Applied Mathematics, University of Michigan, Ann Arbor, Michigan, USA
Contribution: Writing - review & editing, Methodology, Project administration, Supervision
Search for more papers by this authorHelen J. Burgess
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
Contribution: Writing - review & editing, Funding acquisition, Conceptualization, Supervision
Search for more papers by this authorCorresponding Author
Chooza Moon
College of Nursing, University of Iowa, Iowa City, Iowa, USA
Correspondence
Chooza Moon, College of Nursing, University of Iowa, Iowa City, Iowa, 52242, USA.
Email: [email protected]
Contribution: Writing - review & editing, Conceptualization, Investigation, Funding acquisition, Data curation, Supervision
Search for more papers by this authorCaleb Mayer and Dae Wook Kim these authors contributed equally.
Summary
The accurate estimation of circadian phase in the real-world has a variety of applications, including chronotherapeutic drug delivery, reduction of fatigue, and optimal jet lag or shift work scheduling. Recent work has developed and adapted algorithms to predict time-consuming and costly laboratory circadian phase measurements using mathematical models with actigraphy or other wearable data. Here, we validate and extend these results in a home-based cohort of later-life adults, ranging in age from 58 to 86 years. Analysis of this population serves as a valuable extension to our understanding of phase prediction, since key features of circadian timekeeping (including circadian amplitude, response to light stimuli, and susceptibility to circadian misalignment) may become altered in older populations and when observed in real-life settings. We assessed the ability of four models to predict ground truth dim light melatonin onset, and found that all the models could generate predictions with mean absolute errors of approximately 1.4 h or below using actigraph activity data. Simulations of the model with activity performed as well or better than the light-based modelling predictions, validating previous findings in this novel cohort. Interestingly, the models performed comparably to actigraph-derived sleep metrics, with the higher-order and nonphotic activity-based models in particular demonstrating superior performance. This work provides evidence that circadian rhythms can be reasonably estimated in later-life adults living in home settings through mathematical modelling of data from wearable devices.
CONFLICT OF INTEREST STATEMENT
Financial disclosure: Daniel B Forger (DBF) is the CSO of Arcascope, a company that makes circadian rhythms software. Both he and the University of Michigan own equity in Arcascope. HJB serves on the scientific advisory board for Natrol, LLC and is a consultant for F. Hoffmann-La Roche Ltd.
Open Research
DATA AVAILABILITY STATEMENT
The computer codes used in this study are accessible from the GitHub repository: https://github.com/ojwalch/predicting_dlmo. Further information and requests for resources should be directed to and will be fulfilled when possible by the lead contact, Chooza Moon ([email protected]).
Supporting Information
| Filename | Description |
|---|---|
| jsr14425-sup-0001-supinfo.docxWord 2007 document , 354.2 KB | Data S1. Supporting Information. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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