pyActigraphy: Open-source python package for actigraphy data visualization and analysis

CircadiPy: An open-source toolkit for analyzing chronobiology time series

BACKGROUND

Chronobiology is the scientific field focused on studying periodicity in biological processes. In mammals, most physiological variables exhibit circadian rhythmicity, such as metabolism, body temperature, locomotor activity, and sleep. The biological rhythmicity can be statistically evaluated by examining the time series and extracting parameters that correlate to the period of oscillation, its amplitude, phase displacement, and overall variability.

NEW METHOD

We have developed a library called CircadiPy, which encapsulates methods for chronobiological analysis and data inspection, serving as an open-access toolkit for the analysis and interpretation of chronobiological data. The package was designed to be flexible, comprehensive and scalable in order to assist research dealing with processes affected or influenced by rhythmicity.

RESULTS

The results demonstrate the toolkit's capability to guide users in analyzing chronobiological data collected from various recording sources, while also providing precise parameters related to the circadian rhythmicity.

COMPARISON WITH EXISTING METHODS

The analysis methodology from this proposed library offers an opportunity to inspect and obtain chronobiological parameters in a straightforward and cost-free manner, in contrast to commercial tools.

CONCLUSIONS

Moreover, being an open-source tool, it empowers the community with the opportunity to contribute with new functions, analysis methods, and graphical visualizations given the simplified computational method of time series data analysis using an easy and comprehensive pipeline within a single Python object.

Open-Source Python Module for the Analysis of Personalized Light Exposure Data from Wearable Light Loggers and Dosimeters

Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to various scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time series of an individual's light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight. This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data.

Hyperarousal dynamics reveal an overnight increase boosted by insomnia

Hyperarousal is a key symptom of anxiety, stress-related disorders, and insomnia. However, it has been conceptualized in many different ways, ranging from various physiological markers (e.g. cortisol levels, high-frequency EEG activity) to personality traits, or state assessments of subjective anxiety and tension. This approach resulted in partly inconsistent evidence, complicating unified interpretations. Crucially, no previous studies addressed the likely variability of hyperarousal within and across days, nor the relationship of such variability in hyperarousal with the night-by-night variability in sleep quality characteristic of insomnia. Here, we present a novel data-driven approach to understanding dynamics of state hyperarousal in insomnia. Using ecological momentary assessment, we tracked fluctuations in a wide range of emotions across 9 days in 169 people with insomnia disorders and 38 controls without sleep problems. Exploratory factor analysis identified a hyperarousal factor, comprised of items describing tension and distress. People with insomnia scored significantly higher on this factor than controls at all timepoints. In both groups, the hyperarousal factor score peaked in the morning and waned throughout the day, pointing to a potential contributing role of sleep or other circadian processes. Importantly, the overnight increase in hyperarousal was stronger in people with in insomnia than in controls. Subsequent adaptive LASSO regression analysis revealed a stronger overnight increase in hyperarousal across nights of worse subjective sleep quality. These findings demonstrate the relationship between subjective sleep quality and overnight modulations of hyperarousal. Disorders in which hyperarousal is a predominant complaint might therefore benefit from interventions focused on improving sleep quality.

The Ecology of Human Sleep (EcoSleep) Cohort Study: Protocol for a longitudinal repeated measurement burst design study to assess the relationship between sleep determinants and outcomes under real-world conditions across time of year

The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. The Ecology of Human Sleep (EcoSleep) cohort study will investigate the combined impact of sleep determinants on individuals' daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and inter-individual variability. This study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 years (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (six paticipants). Every 4 weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ('bursts') to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts in the laboratory. The bursts will be separated by two at-home electroencephalogram recordings each night. Circadian phase and amplitude will be estimated during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously.

Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features

Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.

Napping and circadian sleep-wake regulation during healthy aging

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.

Exploring an objective measure of overactivity in children with rare genetic syndromes

BACKGROUND

Overactivity is prevalent in several rare genetic neurodevelopmental syndromes, including Smith-Magenis syndrome, Angelman syndrome, and tuberous sclerosis complex, although has been predominantly assessed using questionnaire techniques. Threats to the precision and validity of questionnaire data may undermine existing insights into this behaviour. Previous research indicates objective measures, namely actigraphy, can effectively differentiate non-overactive children from those with attention-deficit hyperactivity disorder. This study is the first to examine the sensitivity of actigraphy to overactivity across rare genetic syndromes associated with intellectual disability, through comparisons with typically-developing peers and questionnaire overactivity estimates.

METHODS

A secondary analysis of actigraphy data and overactivity estimates from The Activity Questionnaire (TAQ) was conducted for children aged 4-15 years with Smith-Magenis syndrome (N=20), Angelman syndrome (N=26), tuberous sclerosis complex (N=16), and typically-developing children (N=61). Actigraphy data were summarized using the M10 non-parametric circadian rhythm variable, and 24-hour activity profiles were modelled via functional linear modelling. Associations between actigraphy data and TAQ overactivity estimates were explored. Differences in actigraphy-defined activity were also examined between syndrome and typically-developing groups, and between children with high and low TAQ overactivity scores within syndromes.

RESULTS

M10 and TAQ overactivity scores were strongly positively correlated for children with Angelman syndrome and Smith-Magenis syndrome. M10 did not substantially differ between the syndrome and typically-developing groups. Higher early morning activity and lower evening activity was observed across all syndrome groups relative to typically-developing peers. High and low TAQ group comparisons revealed syndrome-specific profiles of overactivity, persisting throughout the day in Angelman syndrome, occurring during the early morning and early afternoon in Smith-Magenis syndrome, and manifesting briefly in the evening in tuberous sclerosis complex.

DISCUSSION

These findings provide some support for the sensitivity of actigraphy to overactivity in children with rare genetic syndromes, and offer syndrome-specific temporal descriptions of overactivity. The findings advance existing descriptions of overactivity, provided by questionnaire techniques, in children with rare genetic syndromes and have implications for the measurement of overactivity. Future studies should examine the impact of syndrome-related characteristics on actigraphy-defined activity and overactivity estimates from actigraphy and questionnaire techniques.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Overview of methods and available tools used in complex brain disorders

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.

Quantitative measurement of motor activity during sleep in isolated REM sleep behavior disorder patients using actigraphy before and after treatment with clonazepam

STUDY OBJECTIVES

We conducted a prospective study to quantify motor activity during sleep measured by actigraphy before and after 3 months of treatment with clonazepam in patients with video-polysomnography (vPSG) confirmed isolated rapid eye movement (REM) sleep behavior disorder (iRBD).

METHODS

The motor activity amount (MAA) and the motor activity block (MAB) during sleep were obtained from actigraphy. Then, we compared quantitative actigraphic measures with the results of the REM sleep behavior disorder questionnaire for the previous 3-month period (RBDQ-3M) and of the Clinical Global Impression-Improvement scale (CGI-I), and analyzed correlations between baseline vPSG measures and actigraphic measures.

RESULTS

Twenty-three iRBD patients were included in the study. After medication treatment, large activity MAA dropped in 39% of patients, and the number of MABs decreased in 30% of patients when applying 50% reduction criteria. 52% of patients showed more than 50% improvement in either one. On the other hand, 43% of patients answered "much or very much improved" on the CGI-I, and RBDQ-3M was reduced by more than half in 35% of patients. However, there was no significant association between the subjective and objective measures. Phasic submental muscle activity during REM sleep was highly correlated with small activity MAA (Spearman's rho = 0.78, p < .001) while proximal and axial movements during REM sleep correlated with large activity MAA (rho = 0.47, p = .030 for proximal movements, rho = 0.47, p = .032 for axial movements).

CONCLUSIONS

Our findings imply that quantifying motor activity during sleep using actigraphy can objectively assess therapeutic response in drug trials in patients with iRBD.

An exploration of actigraphy in the context of ketamine and treatment-resistant depression

OBJECTIVES

This study explored the potential of non-parametric and complexity analysis metrics to detect changes in activity post-ketamine and their association with depressive symptomatology.

METHODS

Individuals with treatment-resistant depression (TRD: n = 27, 16F, 35.9 ± 10.8 years) and healthy volunteers (HVs: n = 9, 4F, 36.4 ± 9.59 years) had their activity monitored during an inpatient, double-blind, crossover study where they received an infusion of ketamine or saline placebo. All participants were 18-65 years old, medication-free, and had a MADRS score ≥20. Non-parametric metrics averaged over each study day, metrics derived from complexity analysis, and traditionally calculated non-parametric metrics averaged over two weeks were calculated from the actigraphy time series. A separate analysis was conducted for a subsample (n = 17) to assess the utility of these metrics in a hospital setting.

RESULTS

In HVs, lower intradaily variability was observed within daily rest/activity patterns post-ketamine versus post-placebo (F = 5.16(1,15), p = 0.04). No other significant effects of drug or drug-by-time or correlations between depressive symptomatology and activity were detected.

CONCLUSIONS

Weak associations between non-parametric variables and ketamine were found but were not consistent across actigraphy measures.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT00088699.

Actigraphy in studies on insomnia: Worth the effort?

In the past decades, actigraphy has emerged as a promising, cost-effective, and easy-to-use tool for ambulatory sleep recording. Polysomnography (PSG) validation studies showed that actigraphic sleep estimates fare relatively well in healthy sleepers. Additionally, round-the-clock actigraphy recording has been used to study circadian rhythms in various populations. To this date, however, there is little evidence that the diagnosis, monitoring, or treatment of insomnia can significantly benefit from actigraphy recordings. Using a case-control design, we therefore critically examined whether mean or within-subject variability of actigraphy sleep estimates or circadian patterns add to the understanding of sleep complaints in insomnia. We acquired actigraphy recordings and sleep diaries of 37 controls and 167 patients with varying degrees of insomnia severity for up to 9 consecutive days in their home environment. Additionally, the participants spent one night in the laboratory, where actigraphy was recorded alongside PSG to check whether sleep, in principle, is well estimated. Despite moderate to strong agreement between actigraphy and PSG sleep scoring in the laboratory, ambulatory actigraphic estimates of average sleep and circadian rhythm variables failed to successfully differentiate patients with insomnia from controls in the home environment. Only total sleep time differed between the groups. Additionally, within-subject variability of sleep efficiency and wake after sleep onset was higher in patients. Insomnia research may therefore benefit from shifting attention from average sleep variables to day-to-day variability or from the development of non-motor home-assessed indicators of sleep quality.

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

Wearable Light-and-Motion Dataloggers for Sleep/Wake Research: A Review

Long-term recording of a person’s activity (actimetry or actigraphy) using devices typically worn on the wrist is increasingly applied in sleep/wake, chronobiological, and clinical research to estimate parameters of sleep and sleep-wake cycles. With the recognition of the importance of light in influencing these parameters and with the development of technological capabilities, light sensors have been introduced into devices to correlate physiological and environmental changes. Over the past two decades, many such new devices have appeared from different manufacturers. One of the aims of this review is to help researchers and clinicians choose the data logger that best fits their research goals. Seventeen currently available light-and-motion recorders entered the analysis. They were reviewed for appearance, dimensions, weight, mounting, battery, sensors, features, communication interface, and software. We found that all devices differed from each other in several features. In particular, six devices are equipped with a light sensor that can measure blue light. It is noteworthy that blue light most profoundly influences the physiology and behavior of mammals. As the wearables market is growing rapidly, this review helps guide future developments and needs to be updated every few years.

Daytime rest: Association with 24-h rest-activity cycles, circadian timing and cognition in older adults

Growing epidemiological evidence points toward an association between fragmented 24-h rest-activity cycles and cognition in the aged. Alterations in the circadian timing system might at least partially account for these observations. Here, we tested whether daytime rest (DTR) is associated with changes in concomitant 24-h rest probability profiles, circadian timing and neurobehavioural outcomes in healthy older adults. Sixty-three individuals (59-82 years) underwent field actigraphy monitoring, in-lab dim light melatonin onset assessment and an extensive cognitive test battery. Actimetry recordings were used to measure DTR frequency, duration and timing and to extract 24-h rest probability profiles. As expected, increasing DTR frequency was associated not only with higher rest probabilities during the day, but also with lower rest probabilities during the night, suggesting more fragmented night-time rest. Higher DTR frequency was also associated with lower episodic memory performance. Moreover, later DTR timing went along with an advanced circadian phase as well as with an altered phase angle of entrainment between the rest-activity cycle and circadian phase. Our results suggest that different DTR characteristics, as reflective indices of wake fragmentation, are not only underlined by functional consequences on cognition, but also by circadian alteration in the aged.

Development and testing of methods for detecting off-wrist in actimetry recordings

Study Objectives

In field studies using wrist-actimetry, not identifying/handling off-wrist intervals may result in their misclassification as immobility/sleep and biased estimations of rhythmic patterns. By comparing different solutions for detecting off-wrist, our goal was to ascertain how accurately they detect nonwear in different contexts and identify variables that are useful in the process.

Methods

We developed algorithms using heuristic (HA) and machine learning (ML) approaches. Both were tested using data from a protocol followed by 10 subjects, which was devised to mimic contexts of actimeter wear/nonwear in real-life. Self-reported data on usage according to the protocol were considered the gold standard. Additionally, the performance of our algorithms was compared to that of visual inspection (by 2 experienced investigators) and Choi algorithm. Data previously collected in field studies were used for proof-of-concept analyses.

Results

All methods showed similarly good performances. Accuracy was marginally higher for one of the raters (visual inspection) than for heuristically developed algorithms (HA, Choi). Short intervals (especially &lt; 2 h) were either not or only poorly identified. Consecutive stretches of zeros in activity were considered important indicators of off-wrist (for both HA and ML). It took hours for raters to complete the task as opposed to the seconds or few minutes taken by the automated methods.

Conclusions

Automated strategies of off-wrist detection are similarly effective to visual inspection, but have the important advantage of being faster, less costly, and independent of raters’ attention/experience. In our study, detecting short intervals was a limitation across methods.

PHASE: An Open-Source Program for the Analysis of Drosophila Phase, Activity, and Sleep Under Entrainment

The problem of entrainment is central to circadian biology. In this regard, Drosophila has been an important model system. Owing to the simplicity of its nervous system and the availability of powerful genetic tools, the system has shed significant light on the molecular and neural underpinnings of entrainment. However, much remains to be learned regarding the molecular and physiological mechanisms underlying this important phenomenon. Under cyclic light/dark conditions, Drosophila melanogaster displays crepuscular patterns of locomotor activity with one peak anticipating dawn and the other anticipating dusk. These peaks are characterized through an estimation of their phase relative to the environmental light cycle and the extent of their anticipation of light transitions. In Drosophila chronobiology, estimations of phases are often subjective, and anticipation indices vary significantly between studies. Though there is increasing interest in building flexible analysis tools in the field, none incorporates objective measures of Drosophila activity peaks in combination with the analysis of fly activity/sleep in the same program. To this end, we have developed PHASE, a MATLAB-based program that is simple and easy to use and (i) supports the visualization and analysis of activity and sleep under entrainment, (ii) allows analysis of both activity and sleep parameters within user-defined windows within a diurnal cycle, (iii) uses a smoothing filter for the objective identification of peaks of activity (and therefore can be used to quantitatively characterize them), and (iv) offers a series of analyses for the assessment of behavioral anticipation of environmental transitions.

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

STUDY OBJECTIVES

The objective assessment of insomnia has remained difficult. Multisensory devices collecting heart rate (HR) and motion are regarded as the future of ambulatory sleep monitoring. Unfortunately, reports on altered average HR or heart rate variability (HRV) during sleep in insomnia are equivocal. Here, we evaluated whether the objective quantification of insomnia improves by assessing state-related changes in cardiac measures.

METHODS

We recorded electrocardiography, posture, and actigraphy in 33 people without sleep complaints and 158 patients with mild to severe insomnia over 4 d in their home environment. At the microscale, we investigated whether HR changed with proximity to gross (body) and small (wrist) movements at nighttime. At the macroscale, we calculated day-night differences in HR and HRV measures. For both timescales, we tested whether outcome measures were related to insomnia diagnosis and severity.

RESULTS

At the microscale, an increase in HR was often detectable already 60 s prior to as well as following a nocturnal chest, but not wrist, movement. This increase was slightly steeper in insomnia and was associated with insomnia severity, but future EEG recordings are necessary to elucidate whether these changes occur prior to or simultaneously with PSG-indicators of wakefulness. At the macroscale, we found an attenuated cardiac response to sleep in insomnia: patients consistently showed smaller day-night differences in HR and HRV.

CONCLUSIONS

Incorporating state-related changes in cardiac features in the ambulatory monitoring of sleep might provide a more sensitive biomarker of insomnia than the use of cardiac activity averages or actigraphy alone.

pyActigraphy: Open-source python package for actigraphy data visualization and analysis

Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.

The possibility to continuously record locomotor movements using accelerometers (actigraphy) has allowed field studies of sleep and rest-activity patterns. It has also enabled large-scale data collections, opening new avenues for research. However, each brand of actigraph devices encodes recordings in its own format and closed-source proprietary softwares are typically used to read and analyse actigraphy data. In order to provide an alternative to these softwares, we developed a comprehensive open-source toolbox for actigraphy data analysis, pyActigraphy. It allows researchers to read actigraphy data from 7 different file formats and gives access to a variety of rest-activity rhythm variables, automatic sleep detection algorithms and more advanced signal processing techniques. Besides, in order to empower researchers and clinicians with respect to their analyses, we created a series of interactive tutorials that illustrate how to implement the key steps of typical actigraphy data analyses. As an open-source project, all kind of user’s contributions to our toolbox are welcome. As increasing evidence points to the predicting value of rest-activity patterns derived from actigraphy for brain integrity, we believe that the development of the pyActigraphy package will not only benefit the sleep and chronobiology research, but also the neuroscientific community at large.