Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings

Loock, Ann-Sophie; Sullivan, Ameena Khan; Reis, Cátia; Paiva, Teresa; Ghotbi, Neda; Pilz, Luísa K.; Biller, Anna M; Molenda, Carmen; Vuori-Brodowski, Maria T; Roenneberg, Till; Winnebeck, Eva C.

doi:10.1111/jsr.13371

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…So far, among studies that validate SW cycle algorithms, our study stands out in terms of sample size ( N = 1857) and bias (underestimating SOTs and WOTs by 3.6 min and 0.8 min, respectively). Loock et al [ 20 ] compared Munich Actimetry Sleep Detection Algorithm against event markers from data collected in 34 adolescents and 28 young adults, and their algorithm underestimated both SOT and WOT for 21 min. Werner et al [ 34 ] compared actigraphy S/WOTs estimated by Actiwatch-4 against sleep diary collected in 50 kindergarten children, and found actigraphy underestimated SOTs by 5 min and WOTs by 6 min.…”

Section: Discussionmentioning

confidence: 99%

Validating CircaCP: a generic sleep–wake cycle detection algorithm for unlabelled actigraphy data

Chen,

Sun

2024

R. Soc. Open Sci.

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Sleep–wake (SW) cycle detection is a key step for extracting temporal sleep metrics from actigraphy. Various supervised learning algorithms have been developed, yet their generalizability from sensor to sensor or study to study is questionable. In this paper, we detail and validate an unsupervised algorithm—CircaCP—for detecting SW cycles from actigraphy. It first uses a robust cosinor model to estimate circadian rhythm, then searches for a single change point (CP) within each circadian cycle. Using CircaCP, we estimated sleep/wake onset times (S/WOTs) from 2125 individuals’ data in the MESA sleep study and compared the estimated S/WOTs against self-reported S/WOT event markers, using Bland–Altman analysis as well as variance component analysis. On average, SOTs estimated by CircaCP were 3.6 min behind those reported by event markers, and WOTs by CircaCP were less than 1 min behind those reported by markers. These differences accounted for less than 0.2% variability in S/WOTs, considering other sources of between-subject variations. Rooted in first principles of human circadian rhythms, our algorithm transferred seamlessly from children’s hip-worn ActiGraph data to ageing adults’ wrist-worn Actiwatch data. The generalizability of our algorithm suggests that it can be widely applied to actigraphy collected by other sensors and studies.

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Section: Discussionmentioning

confidence: 99%

Validating CircaCP: a generic sleep–wake cycle detection algorithm for unlabelled actigraphy data

Chen,

Sun

2024

R. Soc. Open Sci.

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“…The automatic scoring of the Munich Actimetry Sleep Detection Algorithm (MASDA [43,44]) was used to detect consolidated rest periods over daytime with the following settings: at least 15 minutes with activity counts below 15% of the 24-hour centered moving average (see Supplemental Material for further specifications of the algorithm; see also [29] for sensitivity and specificity analyses when comparing the output using these settings to visual scoring of actimetry-derived rest periods in a cohort with similar demographic characteristics). We also compared actimetry-derived DTR characteristics scored by the MASDA algorithm and self-reported napping as derived from the sleep diaries of our participants.…”

Section: Daytime Rest Assessment: Actimetrymentioning

confidence: 99%

Napping and circadian sleep–wake regulation during healthy aging

Deantoni,

Reyt,

Baillet

et al. 2023

Sleep

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Study Objectives Daytime napping is frequently reported among the older population and has attracted increasing attention due to its association with multiple health conditions. Here, we tested whether napping in the aged is associated with altered circadian regulation of sleep, sleepiness, and vigilance performance. Methods Sixty healthy older individuals (mean age: 69 years, 39 women) were recruited with respect to their napping habits (30 nappers, 30 non-nappers). All participants underwent an in-lab 40-hour multiple nap protocol (10 cycles of 80 minutes of sleep opportunity alternating with 160 minutes of wakefulness), preceded and followed by a baseline and recovery sleep period. Saliva samples for melatonin assessment, sleepiness, and vigilance performance were collected during wakefulness and electrophysiological data were recorded to derive sleep parameters during scheduled sleep opportunities. Results The circadian amplitude of melatonin secretion was reduced in nappers, compared to non-nappers. Furthermore, nappers were characterized by higher sleep efficiencies and REM sleep proportion during day- compared to nighttime naps. The nap group also presented altered modulation in sleepiness and vigilance performance at specific circadian phases. Discussion Our data indicate that napping is associated with an altered circadian sleep–wake propensity rhythm. They thereby contribute to the understanding of the biological correlates underlying napping and/or sleep–wake cycle fragmentation during healthy aging. Altered circadian sleep–wake promotion can lead to a less distinct allocation of sleep into nighttime and/or a reduced wakefulness drive during the day, thereby potentially triggering the need to sleep at adverse circadian phase.

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“…The disruption of complex interacting metabolic processes, for example through shift work, results in an increased risk of metabolic syndrome, obesity and consequently diabetes [53], amongst others. Corresponding algorithms allow for analysis of sleep-wake patterns [54].…”

Section: Insufficient Sleep Duration and Circadian Rhythm Disruptionmentioning

confidence: 99%

Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine

et al. 2021

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Sleep quality and duration as well as activity-rest-cycles at individual level are crucial for maintaining physical and mental health. Although several methods do exist to monitor these parameters, optimal approaches are still under consideration and technological development. Wrist actigraphy is a non-invasive electro-physical method validated in the field of chronobiology to record movements and to allow for monitoring human activity-rest-cycles. Based on the continuous recording of motor activity and light exposure, actigraphy provides valuable information about the quality and quantity of the sleep–wake rhythm and about the amount of motor activity at day and night that is highly relevant for predicting a potential disease and its targeted prevention as well as personalisation of medical services provided to individuals in suboptimal health conditions and patients. Being generally used in the field of sleep medicine, actigraphy demonstrates a great potential to be successfully implemented in primary, secondary and tertiary care, psychiatry, oncology, and intensive care, military and sports medicines as well as epidemiological monitoring of behavioural habits as well as well-being medical support, amongst others. Prediction of disease development and individual outcomes Activity-rest-cycles have been demonstrated to be an important predictor for many diseases including but not restricted to the development of metabolic, psychiatric and malignant pathologies. Moreover, activity-rest-cycles directly impact individual outcomes in corresponding patient cohorts. Targeted prevention Data acquired by actigraphy are instrumental for the evidence-based targeted prevention by analysing individualised patient profiles including light exposure, sleep duration and quality, activity-rest-cycles, intensity and structure of motion pattern. Personalised therapy Wrist actigraphic approach is increasingly used in clinical care. Personalised measurements of sedation/agitation rhythms are useful for ICU patients, for evaluation of motor fatigue in oncologic patients, for an individual enhancement of performance in military and sport medicine. In the framework of personalised therapy intervention, patients can be encouraged to optimise their behavioural habits improving recovery and activity patterns. This opens excellent perspectives for the sleep-inducing medication and stimulants replacement as well as for increasing the role of participatory medicine by visualising and encouraging optimal behavioural patterns of the individual.

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Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings

Cited by 12 publications

References 38 publications

Validating CircaCP: a generic sleep–wake cycle detection algorithm for unlabelled actigraphy data

Validating CircaCP: a generic sleep–wake cycle detection algorithm for unlabelled actigraphy data

Napping and circadian sleep–wake regulation during healthy aging

Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine

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