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Montanari A, Wang L, Birenboim A, Chaix B. Urban environment influences on stress, autonomic reactivity and circadian rhythm: protocol for an ambulatory study of mental health and sleep. Front Public Health 2024; 12:1175109. [PMID: 38375340 PMCID: PMC10875008 DOI: 10.3389/fpubh.2024.1175109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024] Open
Abstract
Introduction Converging evidence suggests that urban living is associated with an increased likelihood of developing mental health and sleep problems. Although these aspects have been investigated in separate streams of research, stress, autonomic reactivity and circadian misalignment can be hypothesized to play a prominent role in the causal pathways underlining the complex relationship between the urban environment and these two health dimensions. This study aims at quantifying the momentary impact of environmental stressors on increased autonomic reactivity and circadian rhythm, and thereby on mood and anxiety symptoms and sleep quality in the context of everyday urban living. Method The present article reports the protocol for a feasibility study that aims at assessing the daily environmental and mobility exposures of 40 participants from the urban area of Jerusalem over 7 days. Every participant will carry a set of wearable sensors while being tracked through space and time with GPS receivers. Skin conductance and heart rate variability will be tracked to monitor participants' stress responses and autonomic reactivity, whereas electroencephalographic signal will be used for sleep quality tracking. Light exposure, actigraphy and skin temperature will be used for ambulatory circadian monitoring. Geographically explicit ecological momentary assessment (GEMA) will be used to assess participants' perception of the environment, mood and anxiety symptoms, sleep quality and vitality. For each outcome variable (sleep quality and mental health), hierarchical mixed models including random effects at the individual level will be used. In a separate analysis, to control for potential unobserved individual-level confounders, a fixed effect at the individual level will be specified for case-crossover analyses (comparing each participant to oneself). Conclusion Recent developments in wearable sensing methods, as employed in our study or with even more advanced methods reviewed in the Discussion, make it possible to gather information on the functioning of neuro-endocrine and circadian systems in a real-world context as a way to investigate the complex interactions between environmental exposures, behavior and health. Our work aims to provide evidence on the health effects of urban stressors and circadian disruptors to inspire potential interventions, municipal policies and urban planning schemes aimed at addressing those factors.
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Affiliation(s)
- Andrea Montanari
- Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Sorbonne Universités, Paris, France
- Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
| | - Limin Wang
- Department of Geography, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amit Birenboim
- Department of Geography, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Basile Chaix
- Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Sorbonne Universités, Paris, France
- Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
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Ike CO, Wen JT, Oishi MMK, Brown LK, Julius AA. Efficient Estimation of the Human Circadian Phase via Kalman Filtering. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083233 DOI: 10.1109/embc40787.2023.10340241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Circadian rhythms play a vital role in maintaining a person's well-being but remain difficult to quantify accurately. Numerous approaches exist to measure these rhythms, but they often suffer from performance issues on the individual level. This work implements a Steady-State Kalman Filter as a method for estimating the circadian phase shifts from biometric signals. Our framework can automatically fit the filter's parameters to biometric data obtained for each individual, and we were able to consistently estimate the phase shift within 1 hour of melatonin estimates on 100% of all subjects in this study. The estimation method opens up the possibility of real-time control and assessment of the circadian system, as well as chronotherapeutic intervention.Clinical relevance- This establishes a near real-time alternative to melatonin measurements for the estimation of circadian phase shifts, with potential applications in feedback circadian control and chronotherapeutics.
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Moreno JP, Hannay KM, Walch O, Dadabhoy H, Christian J, Puyau M, El-Mubasher A, Bacha F, Grant SR, Park RJ, Cheng P. Estimating circadian phase in elementary school children: leveraging advances in physiologically informed models of circadian entrainment and wearable devices. Sleep 2022; 45:6547079. [PMID: 35275213 PMCID: PMC9189953 DOI: 10.1093/sleep/zsac061] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES Examine the ability of a physiologically based mathematical model of human circadian rhythms to predict circadian phase, as measured by salivary dim light melatonin onset (DLMO), in children compared to other proxy measurements of circadian phase (bedtime, sleep midpoint, and wake time). METHODS As part of an ongoing clinical trial, a sample of 29 elementary school children (mean age: 7.4 ± .97 years) completed 7 days of wrist actigraphy before a lab visit to assess DLMO. Hourly salivary melatonin samples were collected under dim light conditions (<5 lx). Data from actigraphy were used to generate predictions of circadian phase using both a physiologically based circadian limit cycle oscillator mathematical model (Hannay model), and published regression equations that utilize average sleep onset, midpoint, and offset to predict DLMO. Agreement of proxy predictions with measured DLMO were assessed and compared. RESULTS DLMO predictions using the Hannay model outperformed DLMO predictions based on children's sleep/wake parameters with a Lin's Concordance Correlation Coefficient (LinCCC) of 0.79 compared to 0.41-0.59 for sleep/wake parameters. The mean absolute error was 31 min for the Hannay model compared to 35-38 min for the sleep/wake variables. CONCLUSION Our findings suggest that sleep/wake behaviors were weak proxies of DLMO phase in children, but mathematical models using data collected from wearable data can be used to improve the accuracy of those predictions. Additional research is needed to better adapt these adult models for use in children. CLINICAL TRIAL The i Heart Rhythm Project: Healthy Sleep and Behavioral Rhythms for Obesity Prevention https://clinicaltrials.gov/ct2/show/NCT04445740.
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Affiliation(s)
- Jennette P Moreno
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Kevin M Hannay
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA.,Arcascope Inc., Chantilly, VA, USA
| | - Olivia Walch
- Arcascope Inc., Chantilly, VA, USA.,Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Hafza Dadabhoy
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Jessica Christian
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Maurice Puyau
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Abeer El-Mubasher
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Fida Bacha
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Sarah R Grant
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Rebekah Julie Park
- Department of Pediatrics, USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Philip Cheng
- Thomas Roth Sleep Disorders and Research Center, Henry Ford Health System, Detroit, MI, USA
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Huang Y, Mayer C, Walch OJ, Bowman C, Sen S, Goldstein C, Tyler J, Forger DB. Distinct Circadian Assessments From Wearable Data Reveal Social Distancing Promoted Internal Desynchrony Between Circadian Markers. Front Digit Health 2021; 3:727504. [PMID: 34870267 PMCID: PMC8634937 DOI: 10.3389/fdgth.2021.727504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/05/2021] [Indexed: 11/22/2022] Open
Abstract
Mobile measures of human circadian rhythms (CR) are needed in the age of chronotherapy. Two wearable measures of CR have recently been validated: one that uses heart rate to extract circadian rhythms that originate in the sinoatrial node of the heart, and another that uses activity to predict the laboratory gold standard and central circadian pacemaker marker, dim light melatonin onset (DLMO). We first find that the heart rate markers of normal real-world individuals align with laboratory DLMO measurements when we account for heart rate phase error. Next, we expand upon previous work that has examined sleep patterns or chronotypes during the COVID-19 lockdown by studying the effects of social distancing on circadian rhythms. In particular, using data collected from the Social Rhythms app, a mobile application where individuals upload their wearable data and receive reports on their circadian rhythms, we compared the two circadian phase estimates before and after social distancing. Interestingly, we found that the lockdown had different effects on the two ambulatory measurements. Before the lockdown, the two measures aligned, as predicted by laboratory data. After the lockdown, when circadian timekeeping signals were blunted, these measures diverged in 70% of subjects (with circadian rhythms in heart rate, or CRHR, becoming delayed). Thus, while either approach can measure circadian rhythms, both are needed to understand internal desynchrony. We also argue that interventions may be needed in future lockdowns to better align separate circadian rhythms in the body.
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Affiliation(s)
- Yitong Huang
- Department of Mathematics, Dartmouth College, Hanover, NH, United States
| | - Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States
| | - Olivia J. Walch
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Clark Bowman
- Department of Mathematics and Statistics, Hamilton College, Clinton, NY, United States
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Jonathan Tyler
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
| | - Daniel B. Forger
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States
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