ORCID Profile
0000-0002-7402-3171
Current Organisation
Massachusetts General Hospital
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Neurosciences not elsewhere classified | Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology) | Psychology | Developmental Psychology and Ageing
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 06-2021
DOI: 10.1161/STROKEAHA.120.031742
Abstract: Circadian biology modulates almost all aspects of mammalian physiology, disease, and response to therapies. Emerging data suggest that circadian biology may significantly affect the mechanisms of susceptibility, injury, recovery, and the response to therapy in stroke. In this review erspective, we survey the accumulating literature and attempt to connect molecular, cellular, and physiological pathways in circadian biology to clinical consequences in stroke. Accounting for the complex and multifactorial effects of circadian rhythm may improve translational opportunities for stroke diagnostics and therapeutics.
Publisher: Wiley
Date: 20-06-2021
DOI: 10.1111/JPI.12745
Abstract: The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of s les for DLMO is time and resource‐intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved in iduals on controlled and stable sleep‐wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5‐1.6 hours. We reframed the problem as a classification problem and estimated whether an in idual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation—identifying the time at which the switch in classification occurs—to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%‐65% with a range of 20%‐80% for a given day this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included when developing and testing actigraphy‐based approaches to circadian phase estimation. Our novel algorithm may be a promising approach for estimating the onset of melatonin in some conditions and could be generalized to other hormones.
Publisher: JMIR Publications Inc.
Date: 13-11-2017
Abstract: earable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. e developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. e designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the in idual to improve these measures. e identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. ew semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.
Publisher: Wiley
Date: 03-08-2021
DOI: 10.1111/JPI.12757
Abstract: During the COVID‐19 pandemic, schools around the world rapidly transitioned from in‐person to remote learning, providing an opportunity to examine the impact of in‐person vs remote learning on sleep, circadian timing, and mood. We assessed sleep‐wake timing using wrist actigraphy and sleep diaries over 1‐2 weeks during in‐person learning (n = 28) and remote learning (n = 58, where n = 27 were repeat assessments) in adolescents (age M ± SD = 12.79 ± 0.42 years). Circadian timing was measured under a single condition in each in idual using salivary melatonin (Dim Light Melatonin Onset DLMO). Online surveys assessed mood (PROMIS Pediatric Anxiety and Depressive Symptoms) and sleepiness (Epworth Sleepiness Scale – Child and Adolescent) in each condition. During remote (vs in‐person) learning: (i) on school days, students went to sleep 26 minutes later and woke 49 minutes later, resulting in 22 minutes longer sleep duration (all P .0001) (ii) DLMO time did not differ significantly between conditions, although participants woke at a later circadian phase (43 minutes, P = .03) during remote learning and (iii) participants reported significantly lower sleepiness ( P = .048) and lower anxiety symptoms ( P = .006). Depressive symptoms did not differ between conditions. Changes in mood symptoms were not mediated by sleep. Although remote learning continued to have fixed school start times, removing morning commutes likely enabled adolescents to sleep longer, wake later, and to wake at a later circadian phase. These results indicate that remote learning, or later school start times, may extend sleep and improve some subjective symptoms in adolescents.
Publisher: JMIR Publications Inc.
Date: 08-06-2018
DOI: 10.2196/JMIR.9410
Publisher: Elsevier BV
Date: 06-2021
DOI: 10.1016/J.SLEH.2021.02.009
Abstract: Polyphasic sleep is the practice of distributing multiple short sleep episodes across the 24-hour day rather than having one major and possibly a minor ("nap") sleep episode each day. While the prevalence of polyphasic sleep is unknown, anecdotal reports suggest attempts to follow this practice are common, particularly among young adults. Polyphasic-sleep advocates claim to thrive on as little as 2 hours of total sleep per day. However, significant concerns have been raised that polyphasic sleep schedules can result in health and safety consequences. We reviewed the literature to identify the impact of polyphasic sleep schedules (excluding nap or siesta schedules) on health, safety, and performance outcomes. Of 40,672 potentially relevant publications, with 2,023 selected for full-text review, 22 relevant papers were retained. We found no evidence supporting benefits from following polyphasic sleep schedules. Based on the current evidence, the consensus opinion is that polyphasic sleep schedules, and the sleep deficiency inherent in those schedules, are associated with a variety of adverse physical health, mental health, and performance outcomes. Striving to adopt a schedule that significantly reduces the amount of sleep per 24 hours and/or fragments sleep into multiple episodes throughout the 24-hour day is not recommended.
Publisher: Oxford University Press (OUP)
Date: 17-04-2021
Abstract: Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an in idual’s average these traditional metrics include intra-in idual standard deviation (StDev), interdaily stability (IS), and social jet lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: composite phase deviation (CPD) and sleep regularity index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Multiple sleep–wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect the measurement of sleep regularity: “scrambling” the order of days daily vs. weekly variation naps awakenings “all-nighters” and length of study. SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep–wake data for unbiased estimates, whereas CPD and SRI required larger s le sizes to detect group differences. Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and s le size, and which aspects of sleep regularity are most pertinent to the research question.
Publisher: Wiley
Date: 29-06-2022
DOI: 10.1002/OBY.23451
Abstract: Later circadian timing of energy intake is associated with higher body fat percentage. Current methods for obtaining accurate circadian timing are labor‐ and cost‐intensive, limiting practical application of this relationship. This study investigated whether the timing of energy intake relative to a mathematically modeled circadian time, derived from easily collected ambulatory data, would differ between participants with a lean or overweight/obesity body fat percentage. Participants ( N = 87) wore a light‐ and activity‐measuring device (actigraph) throughout a cross‐sectional 30‐day study. For 7 consecutive days within these 30 days, participants used a time‐st ed‐picture phone application to record energy intake. Body fat percentage was recorded. Circadian time was defined using melatonin onset from in‐laboratory collected repeat saliva s ling or using light and activity or activity data alone entered into a mathematical model. Participants with overweight/obesity body fat percentages ate 50% of their daily calories significantly closer to model‐predicted melatonin onset from light and activity data (0.61 hours closer) or activity data alone (0.86 hours closer both log‐rank p 0.05). Use of mathematically modeled circadian timing resulted in similar relationships between the timing of energy intake and body composition as that observed using in‐laboratory collected metrics. These findings may facilitate use of circadian timing in time‐based interventions.
Publisher: Public Library of Science (PLoS)
Date: 26-10-2017
Start Date: 06-2019
End Date: 12-2024
Amount: $558,824.00
Funder: Australian Research Council
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