Fatigue Risk Management Preferences for Consumer Sleep Technologies and Data Sharing in Aviation.

INTRODUCTION: Employees from any type of aviation services industry were asked to give their opinions about the usefulness of consumer sleep technologies (CSTs) during operations and their willingness to share data from CSTs with their organizations for fatigue risk management purposes under a variety of circumstances.METHODS: Respondents provided information about position in aviation and use of CST devices. Respondents ranked sleep issues and feedback metrics by perceived level of importance to operational performance. Respondents rated their likelihood to share data with their organization under a series of hypothetical situations.RESULTS: Between January-July 2023, 149 (N = 149) aviation professionals responded. Pilots comprised 72% (N = 108) of respondents; 84% (N = 125) of all respondents worked short- or medium-haul operations. "Nighttime operations" and "inconsistent sleep routines" ranked as the most important issues affecting sleep. "Sleep quality history" and "projected alertness levels" ranked as most important feedback metrics for personal management of fatigue. Respondents were split between CST users (N = 64) and nonusers (N = 68). CST users did not indicate a strong preference for a specific device brand. The most-reported reason for not using a CST was due to not owning one or no perceived need. Respondents indicated greater likelihood of data sharing under conditions where the device was provided to them by their organization.DISCUSSION: These results suggest that aviation professionals are more concerned about schedule-related disturbances to sleep than they are about endogenous sleep problems. Organizations may be able to increase compliance to data collection for fatigue risk management by providing employees with company-owned CSTs of any brand.Devine JK, Choynowski J, Hursh SR. Fatigue risk management preferences for consumer sleep technologies and data sharing in aviation. Aerosp Med Hum Perform. 2024; 95(5):265-272.

Consumer preferences for sleep-tracking wearables: The role of scientific evaluation and endorsement.

OBJECTIVES: Accuracy and relevance to health outcomes are important to researchers and clinicians who use consumer sleep technologies, but economic demand motivates consumer sleep technology design. This report quantifies the value of scientific relevance to the general consumer in a dollar amount to convey the importance of device accuracy in terms that consumer sleep technology manufacturers can appreciate. METHODS: Survey data were collected from 368 participants on Amazon mTurk. Participants ranked sleep metrics, evaluation methods, and scientific endorsement by perceived level of importance. Participants indicated their likelihood of purchasing a hypothetical consumer sleep technology that had either (1) not been evaluated or endorsed; (2) had been evaluated but not endorsed, and; (3) had been evaluated and endorsed by a sleep science authority. Demand curves determined the relative value of each consumer sleep technology. RESULTS: Devices that were evaluated and endorsed had the most value, followed by those only evaluated, and then those with no evaluation. The unit price at which there was 50% probability of purchase increased by $30 or $48 for evaluation or endorsement, respectively, relative to a nonvalidated device. Respondents indicated the most valuable sleep metric was sleep duration, the most important evaluation method was against laboratory/hospital standards for sleep, and that the highest value of endorsement came from a medical institution. CONCLUSIONS: Consumer demand is greatest for a device that has been evaluated by an independent laboratory and is endorsed by a medical institution. Consumer sleep technology manufacturers may be able to increase sales by partnering with sleep science authorities to produce a scientifically superior device.

Alterations of pain pathways by experimental sleep disturbances in humans: central pain-inhibitory, cyclooxygenase, and endocannabinoid pathways.

STUDY OBJECTIVES: There is strong evidence that sleep disturbances are an independent risk factor for the development of chronic pain conditions. The mechanisms underlying this association, however, are still not well understood. We examined the effect of experimental sleep disturbances (ESDs) on three pathways involved in pain initiation/resolution: (1) the central pain-inhibitory pathway, (2) the cyclooxygenase (COX) pathway, and (3) the endocannabinoid (eCB) pathway. METHODS: Twenty-four healthy participants (50% females) underwent two 19-day long in-laboratory protocols in randomized order: (1) an ESD protocol consisting of repeated nights of short and disrupted sleep with intermittent recovery sleep; and (2) a sleep control protocol consisting of nights with an 8-hour sleep opportunity. Pain inhibition (conditioned pain modulation, habituation to repeated pain), COX-2 expression at monocyte level (lipopolysaccharide [LPS]-stimulated and spontaneous), and eCBs (arachidonoylethanolamine, 2-arachidonoylglycerol, docosahexaenoylethanolamide [DHEA], eicosapentaenoylethanolamide, docosatetraenoylethanolamide) were measured every other day throughout the protocol. RESULTS: The central pain-inhibitory pathway was compromised by sleep disturbances in females, but not in males (p < 0.05 condition × sex effect). The COX-2 pathway (LPS-stimulated) was activated by sleep disturbances (p < 0.05 condition effect), and this effect was exclusively driven by males (p < 0.05 condition × sex effect). With respect to the eCB pathway, DHEA was higher (p < 0.05 condition effect) in the sleep disturbance compared to the control condition, without sex-differential effects on any eCBs. CONCLUSIONS: These findings suggest that central pain-inhibitory and COX mechanisms through which sleep disturbances may contribute to chronic pain risk are sex specific, implicating the need for sex-differential therapeutic targets to effectively reduce chronic pain associated with sleep disturbances in both sexes. CLINICAL TRIALS REGISTRATION: NCT02484742: Pain Sensitization and Habituation in a Model of Experimentally-induced Insomnia Symptoms. https://clinicaltrials.gov/ct2/show/NCT02484742.

Psychomotor Vigilance Performance in Participants with Excessive Daytime Sleepiness in Obstructive Sleep Apnea or Narcolepsy Compared with SAFTE-FAST Model Predictions.

INTRODUCTION: Excessive daytime sleepiness (EDS) associated with narcolepsy or obstructive sleep apnea (OSA) can impair vigilance/attention. Solriamfetol, a dopamine/norepinephrine reuptake inhibitor, is approved to treat EDS associated with narcolepsy (75-150 mg/day) or OSA (37.5-150 mg/day). The analysis reported here explored the use of the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model (used in transport industries to model performance based on accumulated sleep and circadian variability) as a substitute for healthy controls using psychomotor vigilance task (PVT) data collected during clinical studies. METHODS: Data were analyzed from two phase 2 studies of solriamfetol in adults with OSA (NCT02806895, EudraCT 2015-003930-28) or narcolepsy (NCT02806908, EudraCT 2015-003931-36). Participants were randomly assigned 1:1 to solriamfetol 150 mg/day (3 days) followed by 300 mg/day (4 days), or placebo (7 days), then crossed over to the other treatment. Actual task effectiveness scores were calculated from average PVT inverse reaction time (pre-dose; 2 h post-dose; 6 h post-dose). Actigraphy-derived sleep intervals were used in SAFTE to determine modeled healthy control task effectiveness scores. RESULTS: In participants with OSA (N = 31) on placebo or solriamfetol, actual and modeled healthy control task effectiveness did not differ at any time point. In participants with narcolepsy (N = 20) on placebo, actual task effectiveness at 2 h post-dose was lower than modeled healthy control task effectiveness (nominal P = 0.03), a difference not present with solriamfetol. There was no main effect of solriamfetol on actual or modeled healthy control task effectiveness across time points. CONCLUSION: This study represents a novel application of the SAFTE biomathematical model to approximate healthy controls in sleep disorder research and provides valuable lessons that may optimize future research. Future studies should perform a priori power analyses for model-tested outcomes and use sleep measures that capture sleep fragmentation characteristic of sleep disorders for sleep input (e.g., total sleep time rather than time in bed). TRIAL REGISTRATION: NCT02806895, EudraCT 2015-003930-28: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Obstructive Sleep Apnea. NCT02806908, EudraCT 2015-003931-36: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Narcolepsy.

Latent factor structure of behavioral economic heroin and cocaine demand curves.

Research has shown that behavioral economic demand curve indices can be characterized by a two-factor latent structure and that these factors can predict dimensions of substance use. No study to date has examined the latent factor structure of heroin and cocaine demand curves. The objective of this study was to use exploratory factor analysis to examine the underlying factor structure of the facets of heroin and cocaine reinforcement derived from heroin and cocaine demand curves. Participants were 143 patients from two samples that met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association [APA], 2013) criteria for opioid dependance and were undergoing medication-assisted treatment (methadone or buprenorphine). Heroin and cocaine demand curves were generated via hypothetical purchase tasks (HPT) that assessed consumption at 9 or 17 levels of prices from $0 to $500. Five facets of demand were generated from the tasks (Q0, 1/α, Pmax, Omax, and break point). Principal components analysis was used to examine the latent structure among the variables. The results revealed a two-factor solution for both heroin and cocaine demand. These factors were interpreted as persistence, consisting of 1/α, Pmax, Omax, and break point, and amplitude, consisting of Q0 and Omax, and in one case, 1/α. Heroin factors had some predictive power for future substance use, but cocaine factors did not. These findings suggest that heroin and cocaine demand indices can be reduced to two factors indicating sensitivity and volume of consumption, and that these factors may be able to predict substance use for heroin. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Differential effects of an experimental model of prolonged sleep disturbance on inflammation in healthy females and males.

Sleep disturbances, including disrupted sleep and short sleep duration, are highly prevalent and are prospectively associated with an increased risk for various widespread diseases, including cardiometabolic, neurodegenerative, chronic pain, and autoimmune diseases. Systemic inflammation, which has been observed in populations experiencing sleep disturbances, may mechanistically link disturbed sleep with increased disease risks. To determine whether sleep disturbances are causally responsible for the inflammatory changes reported in population-based studies, we developed a 19-day in-hospital experimental model of prolonged sleep disturbance inducing disrupted and shortened sleep. The model included delayed sleep onset, frequent nighttime awakenings, and advanced sleep offset, interspersed with intermittent nights of undisturbed sleep. This pattern aimed at providing an ecologically highly valid experimental model of the typical sleep disturbances often reported in the general and patient populations. Unexpectedly, the experimental sleep disturbance model reduced several of the assessed proinflammatory markers, namely interleukin(IL)-6 production by monocytes and plasma levels of IL-6 and C-reactive protein (CRP), presumably due to intermittent increases in the counterinflammatory hormone cortisol. Striking sex differences were observed with females presenting a reduction in proinflammatory markers and males showing a predominantly proinflammatory response and reductions of cortisol levels. Our findings indicate that sleep disturbances causally dysregulate inflammatory pathways, with opposing effects in females and males. These results have the potential to advance our mechanistic understanding of the pronounced sexual dimorphism in the many diseases for which sleep disturbances are a risk factor.

Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market.

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.

Predictive Biomathematical Modeling Compared to Objective Sleep During COVID-19 Humanitarian Flights.

BACKGROUND: Biomathematical modeling software like the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model and Fatigue Avoidance Scheduling Tool (FAST) help carriers predict fatigue risk for planned rosters. The ability of a biomathematical model to accurately estimate fatigue risk during unprecedented operations, such as COVID-19 humanitarian ultra-long-range flights, is unknown. Azul Cargo Express organized and conducted five separate humanitarian missions to China between May and July 2020. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within SAFTE-FAST was used to predict in-flight sleep duration and pilot effectiveness. Here we compare AutoSleep predictions against pilots' sleep diary and a sleep-tracking actigraphy device (Zulu watch, Institutes for Behavior Resources) from Azul's COVID-19 humanitarian missions.METHODS: Pilots wore Zulu watches throughout the mission period and reported sleep duration for their in-flight rest periods using a sleep diary. Agreement between AutoSleep, diary, and Zulu watch measures was compared using intraclass correlation coefficients (ICC). Goodness-of-fit between predicted effectiveness distribution between scenarios was evaluated using the R² statistic.RESULTS: A total of 20 (N = 20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. ICC and R² values were >0.90, indicating excellent agreement between sleep measures and predicted effectiveness distribution, respectively.DISCUSSION: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances. Pilots may overestimate the amount of sleep that they receive during extreme flight-duty periods, which could constitute a fatigue risk.Devine JK, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco PC, Choynowski J, Hursh SR. Predictive biomathematical modeling compared to objective sleep during COVID-19 humanitarian flights. Aerosp Med Hum Perform. 2022; 93(1):4-12.

Pilot Sleep Behavior across Time during Ultra-Long-Range Flights.

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.

Addressing fatigue in medical residents with biomathematical fatigue modeling.

Fatigue in resident physicians has been identified as a factor that contributes to burnout and a decline in overall wellbeing. Fatigue risk exists because of poor sleep habits and demanding work schedules that have only increased due to the COVID-19 pandemic. At this time, it is important not to lose sight of how fatigue can impact residents and how fatigue risk can be mitigated. While fatigue mitigation is currently addressed by duty hour restrictions and education about fatigue, Fatigue Risk Management Systems (FRMSs) offer a more comprehensive strategy for addressing these issues. An important component of FRMS in other shiftwork industries, such as aviation and trucking, is the use of biomathematical models to prospectively identify fatigue risk in work schedules. Such an approach incorporates decades of knowledge of sleep and circadian rhythm research into shift schedules, taking into account not just duty hour restrictions but the temporal placement of work schedules. Recent research has shown that biomathematical models of fatigue can be adapted to a resident physician population and can help address fatigue risk. Such models do not require subject matter experts and can be applied in graduate medical education program shift scheduling. It is important for graduate medical education program providers to consider these alternative methods of fatigue mitigation. These tools can help reduce fatigue risk and may improve wellness as they allow for a more precise fatigue management strategy without reducing overall work hours.

Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents.

OBJECTIVE: To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue. DESIGN: 8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness"). SETTING: Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001). CONCLUSIONS: Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.

Trends in Strategic Napping in Surgical Residents by Gender, Postgraduate Year, Work Schedule, and Clinical Rotation.

OBJECTIVE: To identify surgical resident and clinical rotation attributes which predict on-shift napping through objectively measured sleep patterns and work schedules over a 2-month period. DESIGN: In a cross-sectional study, participants provided schedules, completed the Epworth Sleepiness Scale (ESS), and wore sleep-tracking devices (Zulu watch) continuously for 8 weeks. Multiple linear regression predicted percent days with on-shift napping from resident and rotation characteristics. SETTING: Greater Washington, DC area hospitals. PARTICIPANTS: Twenty-two (n = 22) surgical residents rotating in at least 1 of 5 different clinical rotation categories. RESULTS: Residents slept 6 hours within a 24-hour period (370 ± 129 minutes) with normal sleep efficiency (sleep efficiency (SE): 87.13% ± 7.55%). Resident ESS scores indicated excessive daytime sleepiness (11.64 ± 4.03). Ninety-five percent (n = 21) of residents napped on-shift. Residents napped on-shift approximately 32% of their working days and were most likely to nap when working between 23:00 and 05:00 hours. Earlier shift start times predicted less on-shift napping (B = -0.08, SE = 0.04, ß = -2.40, t = -2.09, p = 0.05) while working more night shifts (B = 1.55, SE = 0.44, ß = 4.12, t = 3.52, p = 0.003) and shifts over 24 hours (B = 1.45, SE = 0.55, ß = 1.96, t = 2.63, p = 0.01) predicted more frequent on-shift napping. CONCLUSIONS: Residents are taking advantage of opportunities to nap on-shift. Working at night seems to drive on-shift napping. However, residents still exhibit insufficient sleep and daytime sleepiness which could reduce competency and represent a safety risk to themselves and/or patients. These findings will help inform intervention strategies which are tailored to surgical residents using a biomathematical model of fatigue.

Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation.

Traditional measures of sleep or commercial wearables may not be ideal for use in operational environments. The Zulu watch is a commercial sleep-tracking device designed to collect longitudinal sleep data in real-world environments. Laboratory testing is the initial step towards validating a device for real-world sleep evaluation; therefore, the Zulu watch was tested against the gold-standard polysomnography (PSG) and actigraphy. Eight healthy, young adult participants wore a Zulu watch and Actiwatch simultaneously over a 3-day laboratory PSG sleep study. The accuracy, sensitivity, and specificity of epoch-by-epoch data were tested against PSG and actigraphy. Sleep summary statistics were compared using paired samples t-tests, intraclass correlation coefficients, and Bland-Altman plots. Compared with either PSG or actigraphy, both the accuracy and sensitivity for Zulu watch sleep-wake determination were >90%, while the specificity was low (~26% vs. PSG, ~33% vs. actigraphy). The accuracy for sleep scoring vs. PSG was ~87% for interrupted sleep, ~52% for light sleep, and ~49% for deep sleep. The Zulu watch showed mixed results but performed well in determining total sleep time, sleep efficiency, sleep onset, and final awakening in healthy adults compared with PSG or actigraphy. The next step will be to test the Zulu watch's ability to evaluate sleep in industrial operations.

Practice parameters for the use of actigraphy in the military operational context: the Walter Reed Army Institute of Research Operational Research Kit-Actigraphy (WORK-A).

BACKGROUND: The Walter Reed Army Institute of Research (WRAIR) Operational Research Kit-Actigraphy (WORK-A) is a set of unique practice parameters and actigraphy-derived measures for the analysis of operational military sleep patterns. The WORK-A draws on best practices from the literature and comprises 15 additional descriptive variables. Here, we demonstrate the WORK-A with a sample of United States Army Reserve Officers' Training Corps (ROTC) cadets (n = 286) during a month-long capstone pre-commissioning training exercise. METHODS: The sleep of ROTC cadets (n = 286) was measured by Philips Actiwatch devices during the 31-day training exercise. The preliminary effectiveness of the WORK-A was tested by comparing differences in sleep measures collected by Actiwatches as calculated by Philips Actiware software against WORK-A-determined sleep measures and self-report sleep collected from a subset of ROTC cadets (n = 140). RESULTS: Actiware sleep summary statistics were significantly different from WORK-A measures and self-report sleep (all P ≤ 0.001). Bedtimes and waketimes as determined by WORK-A major sleep intervals showed the best agreement with self-report bedtime (22:21 ± 1:30 vs. 22:13 ± 0:40, P = 0.21) and waketime (04:30 ± 2:17 vs. 04:31 ± 0:47, P = 0.68). Though still significantly different, the discrepancy was smaller between the WORK-A measure of time in bed (TIB) for major sleep intervals (352 ± 29 min) and self-report nightly sleep duration (337 ± 57 min, P = 0.006) than that between the WORK-A major TIB and Actiware TIB (177 ± 42, P ≤ 0.001). CONCLUSIONS: Default actigraphy methods are not the most accurate methods for characterizing soldier sleep, but reliable methods for characterizing operational sleep patterns is a necessary first step in developing strategies to improve soldier readiness. The WORK-A addresses this knowledge gap by providing practice parameters and a robust variety of measures with which to profile sleep behavior in service members.

Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

BACKGROUND: The impact of sleep disorders on active-duty soldiers' medical readiness is not currently quantified. Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members. The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S. military healthcare records from fiscal year 2018 (FY2018). METHODS: Medical diagnosis information and deployability profiles (e-Profiles) were queried for all active-duty U.S. Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018. Nondeployability was predicted from medical reasons for having an e-Profile (categorized as sleep, behavioral health, musculoskeletal, cardiometabolic, injury, or accident) using binomial logistic regression. Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability. RESULTS: Out of 582,031 soldiers, 48.4% (n = 281,738) had a sleep-related diagnosis in their healthcare records, 9.7% (n = 56,247) of soldiers had e-Profiles, and 1.9% (n = 10,885) had a sleep e-Profile. Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident (pOR (prevalence odds ratio) =4.7, 95% CI 2.63-8.39, P ≤ 0.001) or work/duty-related injury (pOR = 1.6, 95% CI 1.32-1.94, P ≤ 0.001). The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile (pOR = 4.25, 95% CI 3.75-4.81, P ≤ 0.001) or work/duty-related injury (pOR = 2.62, 95% CI 1.63-4.21, P ≤ 0.001). CONCLUSION: Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018, but their sleep problems are largely not profiled as limitations to medical readiness. Musculoskeletal issues and physical injury predict nondeployability, and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues. Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.

Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions.

The "International Biomarkers Workshop on Wearables in Sleep and Circadian Science" was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies. The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice. The goals of the workshop were: (1) characterize the term "wearable" for use in sleep and circadian science and identify relevant sleep and circadian metrics for wearables to measure; (2) assess the current use of wearables in sleep and circadian science; (3) identify current barriers for applying wearables to sleep and circadian science; and (4) identify goals and opportunities for wearables to advance sleep and circadian science. For the purposes of biomarker development in the sleep and circadian fields, the workshop included the terms "wearables," "nearables," and "ingestibles." Given the state of the current science and technology, the limited validation of wearable devices against gold standard measurements is the primary factor limiting large-scale use of wearable technologies for sleep and circadian research. As such, the workshop committee proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use. To complement validation studies, the workshop committee recommends the development of a public data repository for wearable data. Finally, sleep and circadian scientists must actively engage in the development and use of wearable devices to maintain the rigor of scientific findings and public health messages based on wearable technology.

Impact of daily yoga-based exercise on pain, catastrophizing, and sleep amongst individuals with fibromyalgia.

BACKGROUND: Fibromyalgia (FM) is a chronic widespread pain disorder characterized by negative affect, sleep disturbance, and fatigue. This uncontrolled pilot study investigated the efficacy of daily yoga-based exercise to improve FM symptoms and explored baseline phenotypic characteristics associated with the greatest benefit. METHODS: FM patients (n=46, with 36 completers) reported psychosocial functioning and a range of FM symptoms using validated instruments before and after participation in Satyananda yoga, which included weekly in-person pain-tailored group classes for 6 weeks and daily home yoga video practice. RESULTS: Changes in FM symptoms from pre- to post-yoga were variable amongst participants. Group means for pain decreased, as reported by average daily diary and Brief Pain Inventory, with greater home practice minutes associated with a greater decrease in pain. Average daily ratings of sleep and fatigue improved. Pain catastrophizing was decreased overall, with greater change correlated to a decrease in FM symptoms. We did not observe any group mean changes in actigraphy sleep efficiency, Patient-Reported Outcomes Measurement Information System-anxiety and the Revised Fibromyalgia Impact Questionnaire. Multilevel Modeling analysis revealed a significant interaction between anxiety and catastrophizing for end-study sleep efficiency, fatigue, and pain, such that patients with higher baseline catastrophizing and lower baseline anxiety reported less pain and fatigue, and higher sleep efficiency after the sixth week of yoga practice. CONCLUSION: This pilot study suggests that yoga may reduce pain and catastrophizing, as well as improve sleep, but these changes were modest across study participants. Greater uptake of home yoga practice as well as a phenotype of higher baseline catastrophizing combined with lower baseline anxiety were associated with greater impact. Future randomized, controlled trials comparing different types of yoga or exercise will allow determination of the most effective treatments for FM and allow closer targeting to the patients who will benefit most from them.

Objective changes in activity levels following sleep extension as measured by wrist actigraphy.

OBJECTIVE/BACKGROUND: It is widely established that insufficient sleep can lead to adverse health outcomes. Paradoxically, epidemiologic research suggests that individuals who report habitual nightly sleep greater than 9 h also are at risk for adverse health outcomes. Further, studies have shown that long sleepers have decreased activity levels, which may partially explain the relationship between long sleep duration and mortality. The influence of sleep extension (longer time in bed) on levels of daily activity has not yet been established. The current study examined whether a week of sleep extension altered activity levels within the subsequent daily waking active and sleep period in order to determine whether increased time in bed indeed is related to decreased activity levels. METHODS: A total of 26 healthy volunteers wore wrist accelerometer devices (Actiwatch 2.0, Philips) in order to objectively measure sleep and activity for six days during their normal schedules and for six days during a sleep extension (10 h time in bed) intervention. RESULTS: There were no significant or clinically-relevant differences in 24-h activity or activity during the active or sleep period between baseline and sleep extension conditions. There were no main or interaction effects of day and condition when daily activity counts were compared between baseline and sleep extension conditions for the 24 h period (Day: F(5, 21) = 1.92, p = 0.12; Condition: F(1,25) = 2.93, p = 0.09; Day by Condition: F(5,21) = 0.32, p = 0.83), Active Waking Period (Day: F(5,25) = 1.53, p = 0.18; Condition: F(1,25) = 0.26, p = 0.61; Day by Condition: F(5,21) = 0.55, p = 0.74) or Nightly Sleep (Day: F(5,21) = 0.86, p = 0.51; Condition: F(1,25) = 1.78, p = 0.19; Day by Condition: F(5,21) = 0.79, p = 0.56) periods. In contrast, there was a main effect of condition when examining sleep duration by day between conditions (Day: F(5,21) = 1.60, p = 0.16; Condition: F(1,25) = 167.31, p < 0.001; Day by Condition: F(5,21) = 2.31, p = 0.07), such that sleep duration was longer during the sleep extension condition. DISCUSSION: Sleep duration increased during six days of a sleep extension protocol but activity levels remained similar to their baseline (normal) sleep schedule. The current findings suggest that extending time in bed alone does not alter waking activity counts in young healthy adults. The link between extended sleep and adverse health outcomes may be attributable to other phenotypic factors, or other biological correlates of extended sleep and poor health.

Integrating nap and night-time sleep into sleep patterns reveals differential links to health-relevant outcomes.

Both night-time sleep and nap behaviour have been linked consistently to health outcomes. Although reasons for napping are usually tied to night-time sleep, the majority of studies assess their effects independently. The current study thus aimed to examine the health relevance of patterns of sleep behaviour that take into account both night-time and daytime sleep habits. Night-time sleep, recorded during 7 days via actigraphy from 313 participants (aged 34-82 years) of the Midlife in the United States II Biomarker study, was assessed. Blood and urine specimens were assayed for noradrenaline, interleukin-6 and C-reactive protein. Participants self-reported nap behaviour, depressive symptoms, perceived chronic stress and the presence of medical symptoms and conditions. Overall, nappers (n = 208) showed elevated waist-hip ratios, C-reactive protein and interleukin-6 levels compared to non-nappers and reported more physiological symptoms and conditions (all P ≤ 0.019). Within nappers, cluster analysis revealed three patterns of sleep behaviour-infrequent nappers with good night-time sleep, frequent nappers with good night-time sleep and nappers with poor night-time sleep. Nappers with poor night-time sleep thereby exhibited elevated noradrenaline levels, depressive symptoms and perceived stress scores compared to other groups (all P ≤ 0.041). These findings support the idea that nap-health relationships are complex, in that frequency of napping and accumulation of nap sleep is not related linearly to health consequences. Assessing nap behaviour in conjunction with night-time sleep behaviour appeared crucial to elucidate further the health relevance of napping, particularly in terms of psychological health outcomes, including chronic stress and depressive symptoms.