Digital health interventions like a sleep hygiene mobile application (app) designed specifically for night shift workers, can help improve health and on-the-job safety. Successful app development should consider user experience and economic demand in addition to sleep biology. This study reports the results of a market survey which aims to assess interest in a hypothetical sleep hygiene app designed for night shift workers. N = 97 night shift workers, predominantly from the healthcare industry (n = 52), completed an anonymous online survey about their sleep habits, fatigue, technology use, perceived importance of app features, preferred pricing models, and level of comfort sharing data with employers. Respondents reported sleeping less than 7 h on average with frequent sleep and fatigue issues in relation to their working schedules. Respondents ranked the ability of a sleep hygiene app to sync with their work schedule as the most important app feature. Slightly under half of respondents (n = 48) preferred a “free with ads” pricing model to one-time or recurring fees. n = 84 respondents were interested in using a fatigue management app; n = 93 would either be as interested or more interested if the app was paid for by their employer. The majority of respondents (n = 78) were either neutral or comfortable with sharing sleep data with their employers. Night shift workers experience sleep problems and fatigue at work. Night shift workers would prefer a sleep hygiene app that takes their schedules into account and would be more likely to use an app that is either free with ads or paid for by their employer.

Aviation is a global safety-sensitive industry that employs strict guidance about the monitoring and management of fatigue. Ecological sleep data is routinely collected to assess fatigue risk in flight crew during long-haul operations for safety and regulatory purposes. There is a growing body of scientific literature that supports the evaluation and use of consumer sleep technologies (CSTs) for ecological research. CSTs have the potential to facilitate longitudinal monitoring of sleep and fatigue in the aviation context and thus improve not only the health and well-being of flight crew but the safety of their passengers as well. However, CSTs have not been robustly studied for the measurement of in-flight sleep. Flight crew regularly take in-flight rest opportunities to mitigate fatigue when the opportunity arises and it is legally permitted. Technologies that cannot accurately capture in-flight sleep are not a reliable method to use for aviation research. The goal of this narrative review is to describe how CSTs could potentially be used to collect sleep data, specifically in the in-flight environment, based on existing guidance from the scientific and regulatory literature on fatigue risk management. Aviation stakeholders and the sleep science community should work together to develop criteria for the appropriate testing and use of CSTs as part of appropriate fatigue risk management systems (FRMS). This article is part of the Consumer Sleep Technology Special Collection.

We aimed to compare sleep problems in autistic and non-autistic adults with co-occurring depression and anxiety. The primary research question was whether autism status influences sleep quality, after accounting for the effects of depression and anxiety. We hypothesized that autistic adults would report higher levels of depression, anxiety, and sleep problems compared to non-autistic adults, after controlling for these covariates. We recruited 208 adults (109 non-autistic, 99 autistic) through a crowdsourcing platform, Prolific. Participants completed the Pittsburgh Sleep Quality Index, the Center for Epidemiologic Studies Depression Scale, and the Generalized Anxiety Disorder 7-item scale. Statistical analyses included Mann-Whitney U tests to compare group scores and a generalized linear model to assess the effect of autism status on sleep problems while controlling for depressive and anxiety symptoms. Autistic adults reported significantly higher levels of depressive and anxiety symptoms compared to non-autistic adults. However, after controlling for depression and anxiety, autism status alone did not have a statistically significant effect on overall sleep quality. The findings suggest that while autistic adults experience more severe sleep problems, these issues are closely related to higher levels of depression and anxiety rather than autism status itself. This study contributes to the understanding of sleep difficulties in autistic individuals, highlighting the importance of addressing co-occurring mental health conditions. Further research should explore the specific factors that exacerbate sleep problems in this population.

Biomathematical models of fatigue (BMMFs) are commonly used to predict cognitive alertness in commercial aviation. Accounting for workload in association with routine job tasks may help BMMFs to more accurately predict fatigue in real world operations. This study compared the accuracy of BMMF workload predictions (SF Workload) against pilot self-report of workload during normal flight operations. Ninety-nine (N=99) pilots from a major Asia-based airline completed the NASA Task Load Index (TLX) at top of descent (TOD) during a multiple-flight three-day roster that consisted of daytime flying. SF Workload predictions and TLX scores were normalized to a 100-point scale and compared using equivalence testing. SF Workload predictions were statistically non-different from pilot TLX scores at the same TOD (64 ± 7 vs. 65 ± 15; both t=1.56, p=0.06) using the two one-sided t-test (TOST) approach, indicating high workload and that BMMF predictions are non-inferior to pilot self-report as a means of estimating workload. Establishing the accuracy of workload predictions against real-world reports in a commercial pilot population is an important step towards risk management in situations where high workload may create a safety risk.

Poor sleep is a risk factor for relapse in patients undergoing methadone maintenance therapy (MMT) for opioid use disorder (OUD). The aim of this pilot study was to examine the relationship between habitual sleep duration and opioid use over a 5-week period in MMT patients at the Institutes for Behavior Resources (IBR) Recovery Enhanced by Access to Comprehensive Healthcare (REACH) out-patient clinic in Baltimore, Maryland. N=19 opioid-dependent patients MMT wore MotionLogger actigraphs (AMI) continuously and provided a weekly urine sample for up to five weeks. Patients were split between those who tested positive for opioids at least once during the study period (Test+) and those who consistently tested negative (Test-). Changes in weekly average total sleep time (TST) and changes in average TST between the first and last two weeks of the study were compared between Test- and Test+ groups using paired samples t-tests and analysis of variance (ANOVA). Patients’ average TST over the course of the entire study period was less than 5 hours per 24-hour period (292±81 minutes). However, TST increased an average of 23±106 min per 24-hour period between the first 2 weeks of the study period (273±118 min) and the final 2 weeks of the study period (297±78 min) across all patients. Patients were unevenly split between Test+ (n=6) and Test- (n=13) groups. Test- patients had longer TST than Test+ patients during the first two weeks of the study (288±125 min vs. 243±104 min), the final two weeks of the study (299±71 min vs. 292±100 min), and across all study days (298±80 min vs. 279±89 min). Test+ patients had a greater increase in TST over time (49±25 min) than Test- patients (11±34 min). These data suggest that abstinence from opioid use may be related to habitual sleep duration at intake for MMT patients and that time in treatment may be related to increased sleep duration, particularly for individuals with worse sleep at intake. A larger study population and a longer observation period is needed in order to confirm the relationship between sleep duration and treatment outcomes. N/A

Background: Modeling tools should be tested against real-world outcomes to confirm their predictive ability compared to random chance. Insights is an analytical tool within the biomathematical modeling software SAFTE-FAST that identifies work patterns that consistently result in elevated fatigue risk. This study investigated the ability of Insights to correctly identify duties with an associated fatigue report using previously collected flight schedule and report data. Methods: Planned and completed flight roster schedules were analyzed using SAFTE-FAST Insights after the rosters had been flown. Fatigue reports were independently linked to planned and completed schedules at the duty level. Odds ratio (OR) analysis investigated the ability of Insights to predict which duties would be linked to a fatigue report. Differences in duties were compared using a one-way analysis of variance (ANOVA) and a two-sample t-test. Results: There were 157 fatigue reports out of 78,061 planned duties and 235 fatigue reports out of 82,612 completed duties. Insights had 3.04 odds of correctly identifying fatigue reports in planned duties but 0.41 odds for completed duties. Discussion: Insights showed good odds of correctly identifying a fatigue report duty using planned schedules but poor odds of identifying a fatigue report duty from completed schedules. Completed duties started later in the day and were shorter in duration than planned duties. Day-of-operations schedule changes may have reduced the fatigue risk in response to the fatigue reports.

INTRODUCTION:Fatigue from multiple sources (e.g., circadian, workload, stress, etc.) can create a compound safety risk. Pilots operating medium haul (M-H) routes may be susceptible to compound fatigue, but sources of fatigue in M-H operations have not been robustly quantified. METHODS:In an anonymous survey, airline pilots working M-H rosters were asked to rank on a scale of 0-10 the level of fatigue they experience from 40 separate factors across four domains: 1) circadian; 2) environmental; 3) operational; and 4) psychosocial, with higher scores indicating more fatigue. Pilots also reported habitual sleep duration. RESULTS:A total of 223 pilots (90 Captains; 133 First Officers) completed the survey. Pilots rated circadian factors as most fatiguing [mean (SD); 6 (1)], followed by factors in the psychosocial and environmental domains [both 5 (1)], and finally, the operational domain [4 (2)]. Pilots reported sleeping 7 h on average; sleep was not significantly related to fatigue ratings. DISCUSSION:Operational fatigue factors related to higher work volume (e.g., working longer hours, shorter breaks, etc.) were rated as more fatiguing. Schedule features that impinge on the window of circadian low (e.g., early starts, late ends) were fatiguing even in M-H pilots with daytime schedules that allow for sufficient sleep duration. Devine JK, Hursh SR, Behrend J. Compound fatigue risk in medium-haul pilots. Aerosp Med Hum Perform. 2025; 96(12):1063-1068.

IntroductionOpioid use disorder (OUD) is a serious and persistent problem in the United States with limited non-pharmacological treatment options, especially for the concomitant sleep disorders experienced by most individuals with addiction. While new, non-invasive interventions such as low-intensity focused ultrasound (LIFU) have shown promise in targeting the brain regions impacted throughout addiction and recovery, the devices used are not amenable to outpatient treatment in their current form factor and cannot be used at night during sleep. To bridge this gap and provide a much-needed treatment option for repeated, at-home use, we developed a wearable LIFU device out-of-clinic use.MethodsThis study evaluated the feasibility and acceptability of the portable treatment device among individuals recovering from OUD in an unsupervised, at-home setting. 31 subjects were recruited from a Baltimore, Maryland (USA) outpatient treatment facility and, along with a separate group of 14 healthy controls (HC), were asked to wear a prototype EEG-only (non-LIFU) device for 7 consecutive nights to assess their willingness and adherence to nightly use. Participants used a smartphone application, TrialKit (ePRO), to self-report nightly sleep data (e.g. duration, quality, possible disturbances, and device comfort).ResultsOf the 31 OUD participants recruited, 30 (97%) successfully completed the at-home study, and the majority responded that they would participate in future studies using the head wearable device (OUD, 87%; HC, 71%). OUD participants were statistically more likely than HCs to respond that they would consider using the device in the future to help them sleep (OUD, 70%; HC, 29%). Despite some participants facing technological issues (e.g. lack of reliable phone access or cellular data plans), the OUD group demonstrated high study compliance on par with the healthy control group.DiscussionParticipant’s daily ePRO and exit interview results established that at-home use of advanced treatment technology is feasible in a population group challenged with recovering from OUD. Even more so, numerous participants noted strong willingness to participate in future LIFU-enabled intervention studies to address their persistent sleep issues during recovery.

Abstract Introduction Sleep difficulties are common in Autism Spectrum Disorder (ASD) populations. While much literature exists on sleep difficulties in ASD children, there is relatively scant research on adults. ASD is frequently co-morbid with symptoms of anxiety or depression, which are independently related to sleep problems. Personalized feedback from wearables may appeal to ASD adults looking to improve their sleep. This study examined sleep quality and severity of anxiety/depression in ASD adults with comorbid anxiety/depression and matched controls and establish demand for information about sleep from wearables in each group. Methods Participants were recruited through Prolific; 100 ASD adults with anxiety/depression and 100 matched controls with anxiety/depression completed the survey. Participants completed the Pittsburgh Sleep Quality Index (PSQI), General Anxiety Scale (GAD-7), Center for Epidemiologic Studies Depression Scale (CESD-10), and a task to establish demand for wearables that provide sleep or blood oxygenation information. Student’s t-tests and Cohen’s d examined between-group differences. Pearson's chi-squared test (χ²) examined differences between expected and observed frequencies of GAD-7 categories and poor sleepers (PSQI ≥ 5) between groups. Results Participants in the ASD group had higher anxiety (t=3.42, d=0.47, p=0.001) and depression (t=3.23, d=.45, p=0.001) than controls. Bad sleepers had higher anxiety (t=5.20, d=0.99, p< 0.001) and depression (t=5.17, d=0.98, p< 0.001) than good sleepers. Individuals in the autistic group had higher frequencies of moderate or severe anxiety than expected (χ²=12.90, p=0.05); control participants had lower frequencies of poor sleepers than expected (χ²=3.19, p=0.07). Both groups demonstrated more robust demand for sleep data than blood oxygenation data. ASD participants had more robust demand than matched controls for any wearable data. Conclusion ASD participants may have a higher burden of mental health and sleep issues relative to controls. The effect sizes suggest that self-report sleep quality was more strongly related to self-report depression and anxiety than autism diagnosis status. The demand task offers a novel framework to examine interest in potential sleep solutions in adult populations with co-morbid mental health diagnoses. ASD participants may have a greater interest in data from wearables; next steps will explore whether feedback from wearables can improve sleep in an adult ASD population. Support (if any) NA

Abstract Introduction The Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model is a framework that predicts individual performance changes based on variations in circadian phase and sleep/wake schedules. The SAFTE model is used by industries in which fatigue risk is relevant to safety through licensed software like SAFTE-FAST. The SAFTE model may provide helpful context during the analysis of objective and performance sleep data but a full software license may be cost-prohibitive. SAFTEr was created as an open-source R package to allow the use of the original patented SAFTE model as a freely available research tool. Methods The SAFTE algorithm was rebuilt from patent equations in collaboration with SAFTE inventor Steven Hursh using the R-coding language. The R package was broken down into three primary steps: 1) formatting data into 1-minute epochs; 2) fitting SAFTE model predictions to the data; and 3) graphing the dataset. SAFTEr includes 17 constants identified in the model and 20 output variables that can be computed on an epoch-by-epoch basis. SAFTEr is best suited for schedules that do not induce a phase change, because the simplified model does not detect and automatically shift circadian phase. Results The current version of the SAFTEr package (0.1.2) consists of six functions that model and graph time-based datasets of sleep/wake data and include the ability to customize bedtimes, study start and end times, and events markers. The six functions include: 1) formatting data; 2) identification of possible missing data; 3) modeling; 4) generation of individual graphs with graphic overlay of event markers against the modeled data; 5) graph overlay to compare variables of interest against the SAFTE model as a line chart or; 6) as a scatter plot. Conclusion The SAFTEr package is freely available for use by researchers who wish to predict cognitive performance as a function of objective sleep data. The SAFTEr package is an open-source recreation of the SAFTE model that gives anyone with a basic knowledge of R the ability to apply the model to their own data as well as graph it against other measurable tests. Support (if any) N/A

INTRODUCTION:Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule. MATERIALS AND METHODS:Royal Australian Navy volunteers agreed to wear a Fitbit Versa 2 with the SleepTank app (SleepTank condition), or without the SleepTank app (Controls), for up to 10 weeks from May to July 2023 during the trial of a novel shift rotation schedule. Participants from across 6 units worked a combination of early (6:00 AM to 2:00 PM), day (7:30 AM to 4:30 PM), late (4:00 PM to 12:00 AM), and night shifts (12:00 AM to 6:00 AM) or stable day shifts (6:00 AM to 4:00 PM). Differences in sleep behavior (time in bed, total sleep time, bedtime, wake time, sleep efficiency [SE]) between conditions and shift types were tested using Analysis of Variance. This study was approved by the Australian Departments of Defence and Veterans' Affairs Human Research Ethics Committee. RESULTS:Thirty-four participants completed the full study (n = 17 Controls; n = 17 SleepTank). There was a significant effect of shift type on 24-hour time in bed (TIB24; F(4,9) = 8.15, P < .001, η2 = 0.15) and total sleep time (TST24; F(4,9) = 8.54, P < .001, η2 = 0.18); both were shorter in early shifts and night shifts compared to other shift types. TIB24 and TST24 were not significantly different between conditions, but there was a trend for greater SE in the SleepTank condition relative to Controls (F(1,9) = 2.99, P = .08, η2 = 0.11). CONCLUSIONS:Sleep data collected by Fitbit Versa 2s indicated shorter sleep duration (TIB24, TST24) for Royal Australian Navy workers during early and late shifts relative to stable day shifts. Access to the SleepTank app did not greatly influence measures of sleep duration but may be protective against fatigue by affecting SE. Further research is needed to evaluate the utility of the SleepTank app as a means of improving sleep hygiene in real-world, shift-working environments.

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.

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.

The goal of this report was to examine the behavioral economic demand for consumer sleep technologies with different levels of validation and endorsement. The value or importance consumers place in different validation methods and the organizations conducting the evaluations was also assessed. Survey data were collected from 113 participants on Amazon mTurk. Participants indicated their likelihood of purchasing devices that varied in level of validation across a series of increasing prices. Demand curves were analyzed to determine the relative value of each watch type. Participants also reported how valuable or important different aspects of device validation were to them. Devices that were both evaluated against laboratory measures and endorsed by sleep researchers had the most value, followed by those only evaluated against laboratory measures, and then those not evaluated against any laboratory measures. The unit price at which there was 50% probability of purchase was increased by $25 or $44 for evaluation or endorsement, respectively. Respondents indicated the most valuable features were a measure of sleep duration, that it was most important that devices were validated against measures of sleep from a laboratory or hospital, and that they would put a high value on sleep tracker endorsements from a university or academic institution. Consumer demand is greatest for a device that has been evaluated by an independent laboratory for accuracy in measuring sleep and is endorsed by an academic, medical, or government institution. These results indicate a role for scientific evaluation and endorsement in consumer preference for sleep trackers.

Background: Permanent Daylight Savings Time (DST) may improve road safety by providing more daylight in the evening but could merely shift risk to morning commutes or increase risk due to fatigue and circadian misalignment. Methods: To identify how potential daylight exposure and fatigue risk could differ between permanent DST versus permanent Standard Time (ST) or current time arrangements (CTA), generic work and school schedules in five United States cities were modeled in SAFTE-FAST biomathematical modeling software. Commute data were categorized by morning (0700-0900) and evening (1600-1800) rush hours. Results: Percent darkness was greater under DST compared to ST for the total waking day (t=2.59, p=0.03) and sleep periods (t=2.46, p=0.045). Waketimes occurred before sunrise 63%±41% percent of the time under DST compared to CTA (42%±37%) or ST (33%±38%; F(2, 74)=76.37; p<0.001). Percent darkness was greater during morning (16%±31%) and lower during evening rush hour (0%±0%) in DST compare to either CTA (morning:7%±23%; evening:7%±14%) or ST (morning:7%±23%; evening:7%±15%). Discussion: Morning rush hour overlapped with students’ commutes and shift worker reverse commutes, which may increase traffic congestion and risk compared to evening rush hour. Switching to permanent DST may be more disruptive than either switching to ST or keeping CTA without noticeable benefit to fatigue or potential daylight exposure.

This chapter discuss the history, implementation, limitations, and proper use of biomathematical models of fatigue (BMMFs) for fatigue risk management in transportation. BMMFs estimate fatigue and alertness based on sleep need, time of day, sleep inertia, work history, and sleep history. BMMFs can be used prospectively to evaluate upcoming schedules or retrospectively to evaluate fatigue risk in previously collected data. BMMFs predict performance or alertness outcomes based on population averages, but cannot be used to assess the performance or accident risk for a specific individual. BMMFs are a technological tool to be used as part of a successful fatigue risk management system (FRMS).

Abstract Introduction Nurses are prone to fatigue and sleep disturbances due to extended hour or shiftwork schedules. Fatigue in nurses not only compromises their own health and safety, but jeopardizes patient care as well. Nurses' work hours and overtime are currently not federally regulated. Fatigue risk mitigation strategies for nurses would benefit from understanding the relationship between sleep duration and work patterns in order to develop tools and regulations to protect against fatigue in the nursing profession. Methods Thirteen (N=13) nurses wore a Fitbit Versa 2 and used the SleepTank™ mobile app during a 6-week work rotation. Nurses in this study selected their own work shifts but did have duty-hour limitations. Sleep data was extracted via the mobile app; work schedules were self-reported. Sleep duration data was summed by date to provide a 24-hour estimate of time in bed (TIB24). Repeated measures analysis of variance (rmANOVA) explored differences in TIB24 by number of consecutive days worked and time. Pearson’s correlations evaluated the relationship between work and sleep. Results Nurses (N=13) wore the device with the app for an average of 54±15 days. Forty percent (40%) of the study period were work days (22±10). Nurses slept longer on non-work days (525±153 min) compared to work days (481±143; t=3.21, p=0.001). Nurses worked 2 days in a row on average (Range: 1-5). TIB24 was ~30 minutes shorter with each consecutive day worked (F5, 81=8.11, η2=0.08, p=< 0.001). Routinely working a greater number of consecutive days was negatively correlated to TIB24 (r=0.71, p=0.009). Later average work start time was positively correlated with later bedtime (r=0.83, p< 0.001) and shorter TIB24 (r=0.65, p=0.01). Conclusion Working more days consecutively and working later shifts was related to shorter sleep duration and later bedtime in nurses. However, given that nurses in this study routinely slept longer than 7 hours per night, poor sleep hygiene does not appear to be an issue in this population. The ability to select their own work shifts may allow nurses to select a schedule that permits healthy sleep behavior. Support (if any) N/A

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.