Actigraphy Data Research Articles

Association of the timing of evening eating with BMI Z-score and waist-to-height ratio among preschool-aged children in Finland.

Later timing of eating has been associated with higher adiposity among adults and children in several studies, but not all. Moreover, studies in younger children are scarce. Hence, this study investigated the associations of the timing of evening eating with BMI Z-score and waist-to-height ratio (WHtR), and whether these associations were moderated by chronotype among 627 preschoolers (3-6-year-olds) from the cross-sectional DAGIS survey in Finland. Food intake was measured with 3-d food records, and sleep was measured with hip-worn actigraphy. Three variables were formed to describe the timing of evening eating: (1) clock time of the last eating occasion (EO); (2) time between the last EO and sleep onset; and (3) percentage of total daily energy intake (%TDEI) consumed 2 h before sleep onset or later. Chronotype was assessed as a sleep debt-corrected midpoint of sleep on the weekend (actigraphy data). The data were analysed with adjusted linear mixed effects models. After adjusting for several confounders, the last EO occurring closer to sleep onset (estimate = -0·006, 95 % CI (-0·010, -0·001)) and higher %TDEI consumed before sleep onset (estimate = 0·0004, 95 % CI (0·00003, 0·0007)) were associated with higher WHtR. No associations with BMI Z-score were found after adjustments. Clock time of the last EO was not significantly associated with the outcomes, and no interactions with chronotype emerged. The results highlight the importance of studying the timing of eating relative to sleep timing instead of only as clock time.

Sleep and circadian rhythms during pregnancy, social disadvantage, and alterations in brain development in neonates.

Pregnant women in poverty may be especially likely to experience sleep and circadian rhythm disturbances, which may have downstream effects on fetal neurodevelopment. However, the associations between sleep and circadian rhythm disturbances, social disadvantage during pregnancy, and neonatal brain structure remains poorly understood. The current study explored the association between maternal sleep and circadian rhythm disturbances during pregnancy and neonatal brain outcomes, examining sleep and circadian rhythm disturbances as a mediator of the effect of social disadvantage during pregnancy on infant structural brain outcomes. The study included 148 mother-infant dyads, recruited during early pregnancy, who had both actigraphy and neuroimaging data. Mothers' sleep was assessed throughout their pregnancy using actigraphy, and neonates underwent brain magnetic resonance imaging in the first weeks of life. Neonatal structural brain outcomes included cortical gray matter, subcortical gray matter, and white matter volumes along with a measure of the total surface area of the cortex. Neonates of mothers who experienced greater inter-daily deviations in sleep duration had smaller total cortical gray and white matter volumes and reduced cortical surface areas. Neonates of mothers who had higher levels of circadian misalignment and later sleep timing during pregnancy showed smaller subcortical gray matter volumes. Inter-daily deviations in sleep duration during pregnancy mediated the association between maternal social disadvantage and neonatal structural brain outcomes. Findings highlight the importance of regularity and rhythmicity in sleep schedules during pregnancy and bring to light the role of chronodisruption as a potential mechanism underlying the deleterious neurodevelopmental effects of prenatal adversity. RESEARCH HIGHLIGHTS: Social disadvantage was associated with sleep and circadian rhythm disturbances during pregnancy, including later sleep schedules, increased variability in sleep duration, circadian misalignment, and a higher proportion of the sleep period spent awake. Maternal sleep and circadian rhythm disturbances during pregnancy were associated with decreased brain volume and reduced cortical surface area in neonates. Maternal inter-daily deviations in sleep duration during pregnancy mediated the association between social disadvantage and neonatal brain volume and cortical surface area.

P018 The Relationships Between Multidimensional Sleep Health and Work Productivity in Individuals with Neurological Conditions

Abstract Numerous studies have reported the negative impacts of poor sleep on work productivity in the general population. However, despite the known sleep issues individuals living with neurological conditions experience, no study has explored its impact on their work productivity. Sleep health is a concept that includes multiple domains of sleep, measured with a combination of objective and subjective measures. Therefore, this study aimed to ascertain the associations between sleep health and its domains and work productivity in individuals with neurological conditions. Sleep health domains were determined through actigraphy data collected over one week and sleep questionnaires. Work productivity was assessed via the Work Productivity and Activity Impairment Questionnaire. A comparison of sleep health scores between demographic variables was performed using Mann-Whitney U and Kruskal Wallis Tests. Associations between the sleep health domains and work productivity were performed using linear regression models. There were no significant differences in sleep health scores between sex, smoking status, education level, employment status or any work productivity domain. Individuals with extreme sleep timing had greater absenteeism (22.99%) than the non-extreme group. Individuals with extreme sleep quality had an increase in presenteeism (30.85%), work productivity loss (26.44%) and activity impairment (25.81%) compared to those in the non-extreme group. The findings from this study highlight that self-reported sleep quality has the largest impact on work productivity. Improving individuals' sleep quality through triage for potential sleep disorders or improving their sleep hygiene (sleep behaviour and environment) may positively impact work productivity.

Objective and subjective sleep in patients with non-seasonal major depressive disorder and eveningness – Results from a randomized controlled trial of bright light therapy

Study objectivesThis study examines the (dis)agreement between objective and subjective sleep and their prospective changes in a randomized controlled trial of bright light therapy (BLT) in patients with major depressive disorder (MDD) and eveningness. MethodsA total of 93 adults were randomized to receive either 30-min daily of 10,000 lux BLT or 50lux placebo dim red light therapy (DRL group) for a total of 5 weeks. Actigraphic data were collected at the baseline and during the last week of treatment. (Dis)Concordance of diary and actigraphic sleep parameters were assessed by partial correlations and Bland-Altman plots, and the associations between these discrepancies to depression severity was assessed by linear regression models. Changes of sleep parameters were assessed by linear mixed models. ResultsSignificant correlations were found between subjective sleep timings and chronotype to actigraphic parameters. Discrepancies between diary- and actigraphic-measures were observed, and patients with more severe depressive symptoms were associated with a greater under-estimation of total sleep time (TST). A greater advance in the diary-based time to fall sleep and rise time were achieved in the BLT group as compared to the DRL group, while diary-based wake after sleep onset (WASO), TST and sleep efficiency (SE) comparably improved with time in both groups. There was no significant difference between the two groups in the actigraphic parameters after treatment. ConclusionsIn this study, we found that depression severity influenced subjective report of sleep. BLT led to a greater advance in subjective sleep timings when compared to the placebo group.

Four State Sleep Staging From a Multilayered Algorithm Using Electrocardiographic and Actigraphic Data.

Sleep studies are important to evaluate sleep and sleep-related disorders. The standard test for evaluating sleep is polysomnography, during which several physiological signals are recorded separately and simultaneously with specialized equipment that requires a technologist. Simpler recordings that can model the results of a polysomnography would provide the benefit of expanding the possibilities of sleep recordings. Using the publicly available sleep data set from the multiethnic study of atherosclerosis and 1769 nights of sleep, we extracted a distinct data subset with engineered features of the biomarkers collected by actigraphic, oxygenation, and electrocardiographic sensors. We then applied scalable models with recurrent neural network and Extreme Gradient Boosting (XGBoost) with a layered approach to produce an algorithm that we then validated with a separate data set of 177 nights. The algorithm achieved an overall performance of 0.833 accuracy and 0.736 kappa in classifying into four states: wake, light sleep, deep sleep, and rapid eye movement (REM). Using feature analysis, we demonstrated that heart rate variability is the most salient feature, which is similar to prior reports. Our results demonstrate the potential benefit of a multilayered algorithm and achieved higher accuracy and kappa than previously described approaches for staging sleep. The results further the possibility of simple, wearable devices for sleep staging. Code is available at https://github.com/NovelaNeuro/nEureka-SleepStaging.

Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study.

Reduced sleep duration and work hour variability contribute to medical error and physician burnout. This study assesses the relationships between physician performance, burnout, and the dimensions of sleep beyond hours slept. This was an ancillary analysis of 3 years of data from an international prospective cohort study: the Intern Health Study. Actigraphy data from 3,654 intern physicians capturing sleep timing, regularity, efficiency, and duration were used individually and combined as a composite sleep health index to measure the association of multidimensional sleep patterns on self-reported medical errors and burnout. From 2017-2019, interns' work hours decreased by 4 hours per week and total sleep time also decreased (6.7 to 5.99 hours), and sleep efficiency, timing, and regularity all worsened (all P < .05). In the 21.2% of participants who committed an error, there was no difference in sleep duration, timing, or regularity. Lower sleep efficiency was associated with higher odds of committing an error (P = .003) and higher burnout scores (P < .001). Although overall sleep quality was poor in the entire cohort, interns in the lowest quintile of sleep duration, regularity, and efficiency had higher burnout scores than those in the best quintile. Sleep efficiency, not duration, was associated with increased self-reported medical errors and burnout in intern physicians. Overall sleep quality and duration worsened despite fewer hours worked. Future studies on physician burnout should measure all aspects of sleep health. Hassinger AB, Velez C, Wang J, Mador MJ, Wilding GE, Mishra A. Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study. J Clin Sleep Med. 2024;20(2):221-227.

Patterns of sedentary behaviour in adults with acute insomnia derived from actigraphy data.

Sleep disorders, such as insomnia, have been associated with extended periods of inactive, sedentary behaviour. Many factors contribute to insomnia, including stress, irregular sleep patterns, mental health issues, inadequate sleeping schedules, diseases, neurological disorders and prescription medications. Identification of the patterns of sedentary time and its duration in adults with acute insomnia and healthy controls to determine the statistically significant sedentary bouts; comparison of the sedentary behaviour patterns in acute insomnia adults with healthy controls. We investigate the daytime actigraphy data and identify temporal patterns of inactivity among adults with acute insomnia and healthy adults. Seven days of actigraphy data were utilised to calculate sedentary time and bouts of variable duration based on a threshold of activity counts (<100 counts per minute). Statistical analysis was applied to investigate sedentary bouts and total sedentary time during weekdays and weekend. A logistic regression model has been used to determine the significance of sedentary bouts. We found that individuals with acute insomnia accumulate a significant amount of their sedentary time in medium (6-30 minutes and 31-60 minutes) and longer (more than 60 minutes) duration bouts in comparison to healthy adults. Furthermore, a low p value for total sedentary time (2.54 ⋅ 10-4) association with acute insomnia supports the finding that acute insomnia individuals are significantly more engaged in sedentary activities compared to healthy controls. Also, as shown by the weekend vs weekday analysis, the physical and sedentary activity patterns of acute insomnia adults demonstrate higher variability during the weekdays in comparison to the weekend. The results of the present study demonstrate that adults with acute insomnia spend more time in low-intensity daily physical activities compared to healthy adults.

Near-Death Experiences and Sleep Disturbance: An Exploratory Study Using Wrist Actigraphy.

Near-death experiences (NDEs) are nonordinary states of consciousness typically occurring on the brink of death. Sleep changes after NDEs have been described, including shorter sleep duration, longer sleep latency, and more sleep disturbances; however, objective verification is lacking. In this exploratory research, 57 participants took part in a 14-day actigraphy study and were assigned to three groups: those who have had an NDE ( n = 26); those who experienced a near-death event but without NDE ( n = 12); and those who had never come close to death ( n = 19). No significant differences were found between groups for actigraphy data. Paired samples t tests indicated significant differences between subjective and objective measures of sleep onset latency, sleep duration, and sleep efficiency, notably among the NDE group. Findings are indicative of the phenomenon known as sleep state misperception (SSM), which may have clinical implications for the study of NDEs and SSM.

A preliminary study on the performance of the Nanit auto-videosomnography scoring system against observed video scoring and actigraphy to estimate sleep-wake states in infants

Goal and aimsTo examine the performance of the Nanit auto-videosomnography scoring system as a measure of sleep-wake states in infants compared to observed video scoring and actigraphy. Focus technologyNanit’s auto-videosomnography scoring system. ReferenceObserved video scoring and actigraphy. SampleSixteen U.S. infants (age: 4-8 months). DesignInfants’ sleep was assessed with the Nanit camera and actigraphy (micromotion logger sleep watch). Fifty nights were included in the analyses. Nanit’s videos were processed via a computer vision algorithm and were scored by trained observers. Actigraphic data were scored with the validated Sadeh algorithm. Core analyticsBland-Altman plots and epoch-by-epoch analyses (sensitivity, specificity, and total accuracy). Additional analyticsSpecificity values for each night separately. Core outcomesNanit estimates of sleep minute were not significantly different from observed sleep minute, but Nanit overestimated sleep minute relative to actigraphy by 17 minutes. Nanit overestimated wake minutes (wake after sleep onset by 5.3 minutes relative to observed scoring and underestimated wake after sleep onset by 19.1 minutes relative to actigraphy. The epoch-by-epoch analyses revealed that Nanit reached 97.8% sensitivity in classifying sleep and 60.4% specificity in classifying wake compared to observed scoring. The rates compared to actigraphy were 99.3% for sensitivity and 51.7% for specificity. Important additional outcomesSpecificity values were lower for infants with lower wake after sleep onset. Core conclusionThis preliminary study suggests that Nanit is highly accurate in detecting infant sleep compared to observed scoring and actigraphy. Specificity values are within the range of those reported previously for actigraphy devices that are commonly used in pediatric sleep research.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual’s own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

Measurement of Sleep in Chronic Traumatic Brain Injury: Relationship Between Self-report and Actigraphy.

To examine the relationship between self-report and actigraphy measurement of sleep in people with and without traumatic brain injury (TBI) by addressing 2 aims: (1) to assess the relationship between self-report and actigraphy for sleep quantity in people with and without TBI; and (2) to explore how self-report and actigraphy capture sleep quality in TBI. Participants completed the study over 2 weeks in their own homes. They wore activity monitors, day and night, throughout the experiment and completed morning sleep diaries while interacting with an experimenter on videoconference. This project was embedded in a larger study on sleep and word learning in 100 adults: 50 with chronic, moderate-severe TBI and 50 demographically matched noninjured peers. Of the 100 participants who completed the larger study, 92 participants (45 with TBI and 47 noninjured peers) had sufficient actigraphy data for inclusion in the current study. We used multilevel linear regression models and correlation analyses to assess how well participants' self-report corresponded to actigraphy measurement of sleep. Actigraphy measures included nightly sleep duration and nighttime wakeups. Sleep diary measures included self-reported nightly sleep duration, nighttime wakeups, sleep quality, and morning fatigue. People with and without TBI did not differ in the relationship between self-reported and actigraphy measurement of sleep quantity. Performance on a neuropsychological memory assessment did not correlate with the difference in self-reported and actigraphy-measured sleep in the TBI group. Sleep characteristics that were measured by actigraphy did not predict subjective experiences of sleep quality or fatigue. Short-term self-report diaries capture accurate information about sleep quantity in individuals with TBI and may support self-report of other daily habits. Future research is needed to identify reliable metrics of sleep quality, and how they relate to other domains such as memory and mood, in the chronic phase of TBI.

Actigraphic characterization of sleep and circadian phenotypes of PER3 gene VNTR genotypes

ABSTRACT The molecular circadian timing system involves genes known as “clock genes,” such as the PER3 gene. Studies have demonstrated associations among a repeat polymorphism (VNTR) of the PER3 gene with chronotypes, with the occurrence of circadian rhythm disorders and with sleep homeostasis phenotypes. The aim of this study was to investigate, by actigraphy, sleep and circadian rhythm profiles of people with different genotypes for the VNTR polymorphism of the PER3 gene. We genotyped 467 individuals (46,39% male) for the PER3 VNTR polymorphism. The mean age of the participants was 21.84 2.64, ranging from 18 to 30 y old. Actigraphy data were collected from a subsample of 81 subjects with PER3 4-repeats homozygous (PER3 4/4 ) or 5-repeats homozygous (PER3 5/5 ) genotypes from April to June of 2021. From this sample, 48 PER3 4/4 and 33 PER3 5/5 subjects wore a wrist actigraph between 12 and 19 d. The sleep onset (weekdays, p = 0.015; weekend, p = 0.022) and offset (weekdays, p = 0.004; weekend, p = 0.041) of the PER3 5/5 group occurred later than the PER3 4/4 group. Similar results were observed for the mid-sleep phase of weekdays (MSW) (p = 0.008) and free days (MSF) (p = 0.019), and for the mid-sleep phase corrected for sleep debt accumulated over the week (MSFsc) (p = 0.024). Despite the phase differences found between the PER3 4/4 and PER3 5/5 groups, no differences were found in sleep duration and social jet lag. However, the PER3 4/4 group presented, on average, a longer sleep rebound on the days off when compared to the PER3 5/5 (p = 0.002). The PER3 5/5 group showed lower interdaily stability (IS) (p = 0.032) and higher daily activity rhythm variability (IV) (p = 0.035). The findings of the present study revealed associations between the PER3 gene, sleep, and circadian rhythms. In general, we found that the gene is associated with the expression of sleep timing and duration and to the phase of the activity rhythm. The experiments carried out here occurred in the COVID-19 pandemic scenario, which should be considered as an environmental element with potential effects on the results obtained.

Rest-activity rhythms across the lifespan: cross-sectional findings from the US representative National Health and Nutrition Examination Survey.

Rest-activity rhythms (RAR) may mark development, aging, and physical and mental health. Understanding how they differ between people may inform intervention and health promotion efforts. However, RAR characteristics across the lifespan have not been well-studied. Therefore, we investigated the association between RAR measures with demographic and lifestyle factors in a US nationally representative study. RAR metrics of interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and mean amplitude and timing of high (M10) and low (L5) activity were derived from 2011 to 2012 and 2013 to 2014 National Health and Nutrition Examination Survey (NHANES) actigraphy data. Population-weighted linear and logistic regression models were fit to examine the associations of age, gender, smoking, alcohol, season, body mass index (BMI), income-to-poverty ratio, and race/ethnicity with RAR. Significance was based on a false-discovery rate-corrected P-value of <0.05. Among n = 12 526 NHANES participants (3-≥80 years), IS (higher = greater day-to-day regularity) and RA (higher = greater rhythm strength) generally decreased with age and were lower among males, whereas IV (higher = greater rhythm fragmentation) increased with age (p < 0.05). Dynamic changes in RAR trajectories were observed during childhood and adolescence. Income, BMI, smoking, and alcohol use were associated with RAR metrics, as well as season among children and teenagers (p < 0.05). RAR also differed by race/ethnicity (p < 0.05), with trajectories initially diverging in childhood and continuing into adulthood. RAR differed by demographic and health-related factors, representing possible windows for public health intervention and sleep health promotion. RAR differences by race/ethnicity begin in childhood, are evident in early adolescence, and persist throughout adulthood.

Describing Physical Activity Patterns of Truck Drivers Using Actigraphy

BackgroundTruck driving is a highly sedentary occupation that places workers at risk for chronic health conditions, such as obesity and high blood pressure. The primary purpose of this study was to objectively describe truck drivers' typical physical activity (PA) patterns. MethodsWe used ∼7–10-day baseline PA actigraphy data samples from drivers in the Safety & Health Involvement For Truckers (SHIFT) study (n = 394). Driver PA patterns (e.g., average number of ≥10 minute Freedson bouts per week, time in bouts, and common days/times for PA) were summarized with descriptive analyses. We also compared objective accelerometer data to self-reports. ResultsDrivers' weekly PA averaged 14.4 minutes (SD = 37.0), and most PA occurred between 5–6 pm on Tuesdays and Wednesdays. Drivers overestimated self-reported weekly exercise by over 60 min/week compared to accelerometer data. ConclusionOur results suggest that objective PA assessment may be warranted over self-report when possible, and timing may be key in future PA intervention work with truck drivers.

Effect of Data Reduction Techniques on Daily Moderate to Vigorous Physical Activity Collected with ActiGraph® in People with COPD.

ActiGraph® is a valid, frequently used, accelerometer to quantify moderate to vigorous physical activities (MVPA) in people with COPD. The impact of ActiGraph processing techniques on this population is unknown. This study aimed to explore the effect of data reduction techniques on MVPA in people with COPD. MVPA/day, through ActiGraph GT3X+, was estimated using: Troiano, Freedson 98 and FreedsonVM3 cutoffs, 15-s and 60-s epochs, and normal and low-frequency extension (LFE) filters. Cutoff, epoch, and filter effects were explored with Aligned Rank Transform-ANOVA. Lin's concordance correlation coefficients and Bland-Altman plots were used to evaluate agreement and bias between different techniques. The analysis included 136 people with COPD (79% male; 68 ± 8 years; FEV1 51 ± 17% predicted). MVPA/day differed according to cutoff, filter, and epoch selection (p-value < 0.001). FreedsonVM3 cutoff, 15-s epochs, and LFE yielded the highest MVPA (45 min/day, 68% of physically active participants). Troiano cutoff, 60-s epochs, and normal filter yielded the lowest MVPA (8 min/day, 20% of physically active participants). Only comparisons between Troiano and Freedson98 cutoffs presented an almost perfect agreement. ActiGraph data reduction techniques affected MVPA/day estimates and their interpretation at the individual and group level. Studies using different processing criteria should not be compared in people with COPD. Future studies with a gold standard are required to ascertain which processing technique produces the most accurate MVPA estimates in COPD. Meanwhile, future trials employing the ActiGraph GT3X+ may consider estimating MVPA based on Freedson VM3 cutofffs, 60-s epochs, and normal filter.

Rest-activity rhythm associated with depressive symptom severity and attention among patients with major depressive disorder: a 12-month follow-up study.

Patients with depressive disorder demonstrate rest-activity rhythm disturbances and cognitive function impairment. This study examined the association of individual rest-activity rhythm changes over time with mood symptoms and attention. We recruited 15 adult outpatients with a diagnosis of major depressive disorder from a single medical center and observed them for 12 months. Weekly rest-activity parameters, including rhythm characteristics generated from nonparametric circadian rhythm analysis, were retrieved from actigraphy data. Attention was evaluated weekly with a smartphone-based psychomotor vigilance test upon awakening. Depressive symptom severity was evaluated using the Beck Depression Inventory (BDI) fortnightly. The association of rest-activity parameters with BDI score and attention was examined using generalized linear mixed regression. A fixed-effects analysis was used to examine the association between rest-activity parameters and depressive episodes. An advanced bedtime and most active continuous 10 h starting time were associated with depressive symptom severity but also associated with higher vigilance test performance. A longer sleep duration, mainly due to an earlier bedtime, was associated with depressive symptom severity. Compared to remission, sleep duration was 27.8 min longer during depressive episodes, and bed time was 24 min earlier. A shorter sleep duration and increased activity during sleep were associated with poorer attention. Rest-activity rhythms change with mood symptoms among patients with depressive disorder. The circadian rhythms of rest-activity among patients with depressive disorder should be distinguished during various mood states in future studies.

Circadian Activity Rhythms and Psychopathology in Major Depressive Episodes

Background: Identifying suicidal risk based on clinical assessment is challenging. Suicidal ideation fluctuates, can be downplayed or denied, and seems stigmatizing if divulged. In contrast, vitality is foundational to subjectivity in being immediately conscious before reflection. Including its assessment may improve detection of suicidal risk compared to relying on suicidal ideation alone. We hypothesized that objective motility measures would be associated with vitality and enhance assessment of suicidal risk. Methods: We evaluated 83 adult-psychiatric outpatients with a DSM-5 bipolar (BD) or major depressive disorder (MDD): BD-I (n = 48), BD-II (20), and MDD (15) during a major depressive episode. They were actigraphically monitored continuously over 3 weekdays and self-rated their subjective states at regular intervals. We applied cosinor analysis to actigraphic data and analyzed associations of subjective psychopathology measures with circadian activity parameters. Results: Actigraphic circadian mesor, amplitude, day- and nighttime activity were lower with BD versus MDD. Self-rated vitality (wish-to-live) was significantly lower, self-rated suicidality (wish-to-die) was higher, and their difference was lower, with BD versus MDD. There were no other significant diagnostic differences in actigraphic sleep parameters or in self-rated depression, dysphoria, or anxiety. By linear regression, the difference between vitality and passive suicidal ideation was strongly positively correlated with mesor (p < 0.0001), daytime activity (p < 0.0001), and amplitude (p = 0.001). Conclusions: Higher circadian activity measures reflected enhanced levels of subjective vitality and were associated with lesser suicidal ideation. Current suicidal-risk assessment might usefully include monitoring of motility and vitality in addition to examining negative affects and suicidal thinking.

Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia.

This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID). Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen's d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months. Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62). AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

Phase I pilot safety and feasibility of a novel restraint device for critically ill patients requiring mechanical ventilation

Background: Mechanically ventilated Intensive Care Unit (ICU) patients often require wrist restraints, contributing to immobility and agitation, over-sedation, and delirium. The Exersides® Refraint® (Healthy Design, LLC), a novel restraint alternative, may be safe and facilitate greater mobility than traditional restraints. Objective: This National Institutes of Health Small Business Technology Transfer (STTR) Program Grant-funded single-site Phase I feasibility study evaluated Exersides® safety and feasibility in anticipation of a multi-site Phase II randomized controlled trial (RCT). Methods: In two academic ICUs, mechanically ventilated adults ⩾25 years old who were non-comatose, required restraints and had an expected stay of ⩾2 days were enrolled to wear Exersides® and traditional wrist restraints for 4 h on day 1, in a randomized order, and in the reverse order on day 2. Main outcomes were Exersides® safety (i.e., patient/clinician lacerations/injuries), feasibility (i.e., ⩾90% of required data collected), and patient/family/clinician feedback. Results: Eight patients were enrolled; one no longer required restraints at initiation, yielding seven subjects (median [interquartile range (IQR)] age 65 [55, 70] years, 86% men). All seven wore Exersides®, averaging (SD) 2.5 (1.0) hours per session, with no safety events reported. Across restraint time periods, 92% and 100% of Richmond Agitation-Sedation Scale (RASS) and wrist actigraphy data, respectively, were collected. Feedback was positive (more movement and comfortable than traditional restraints) and constructive (bulky, intimidating to apply). Conclusions: This pilot study provided key safety and feasibility data for a Phase II RCT evaluating Exersides® versus traditional wrist restraints. Feedback motivated minor device modifications before RCT initiation.

Automatic sleep-stage classification of heart rate and actigraphy data using deep and transfer learning approaches

Manual sleep-stage scoring based on full-night polysomnography data recorded in a sleep lab has been the gold standard of clinical sleep medicine. This costly and time-consuming approach is unfit for long-term studies as well as assessment of sleep on a population level. With the vast amount of physiological data becoming available from wrist-worn devices, deep learning techniques provide an opportunity for fast and reliable automatic sleep-stage classification tasks. However, training a deep neural network requires large annotated sleep databases, which are not available for long-term epidemiological studies. In this paper, we introduce an end-to-end temporal convolutional neural network able to automatically score sleep stages from raw heartbeat RR interval (RRI) and wrist actigraphy data. Moreover, a transfer learning approach enables the training of the network on a large public database (Sleep Heart Health Study, SHHS) and its subsequent application to a much smaller database recorded by a wristband device. The transfer learning significantly shortens training time and improves sleep-scoring accuracy from 68.9% to 73.8% and inter-rater reliability (Cohen’s kappa) from 0.51 to 0.59. We also found that for the SHHS database, automatic sleep-scoring accuracy using deep learning shows a logarithmic relationship with the training size. Although deep learning approaches for automatic sleep scoring are not yet comparable to the inter-rater reliability among sleep technicians, performance is expected to significantly improve in the near future when more large public databases become available. We anticipate those deep learning techniques, when combined with our transfer learning approach, will leverage automatic sleep scoring of physiological data from wearable devices and enable the investigation of sleep in large cohort studies.