State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study

Abnormally short and long sleep are associated with premature mortality, and achieving optimal sleep duration has been the focus of sleep health guidelines. Emerging research demonstrates that sleep regularity, the day-to-day consistency of sleep-wake timing, can be a stronger predictor for some health outcomes than sleep duration. The role of sleep regularity in mortality, however, has not been investigated in a large cohort with objective data. We therefore aimed to compare how sleep regularity and duration predicted risk for all-cause and cause-specific mortality. We calculated Sleep Regularity Index (SRI) scores from > 10 million hours of accelerometer data in 60 977 UK Biobank participants (62.8 ± 7.8 years, 55.0% female, median[IQR] SRI: 81.0[73.8-86.3]). Mortality was reported up to 7.8 years after accelerometer recording in 1859 participants (4.84 deaths per 1000 person-years, mean (±SD) follow-up of 6.30 ± 0.83 years). Higher sleep regularity was associated with a 20%-48% lower risk of all-cause mortality (p < .001 to p = 0.004), a 16%-39% lower risk of cancer mortality (p < 0.001 to p = 0.017), and a 22%-57% lower risk of cardiometabolic mortality (p < 0.001 to p = 0.048), across the top four SRI quintiles compared to the least regular quintile. Results were adjusted for age, sex, ethnicity, and sociodemographic, lifestyle, and health factors. Sleep regularity was a stronger predictor of all-cause mortality than sleep duration, by comparing equivalent mortality models, and by comparing nested SRI-mortality models with and without sleep duration (p = 0.14-0.20). These findings indicate that sleep regularity is an important predictor of mortality risk and is a stronger predictor than sleep duration. Sleep regularity may be a simple, effective target for improving general health and survival.

Nightmare disorder and low back pain in veterans: cross-sectional association and effect over time

Low back pain (LBP) disproportionately impacts US military veterans compared with nonveterans. Although the effect of psychological conditions on LBP is regularly studied, there is little published to date investigating nightmare disorder (NMD) and LBP. The purpose of this study was to (1) investigate whether an association exists between NMD and LBP and (2) estimate the effect of NMD diagnosis on time to LBP. We used a retrospective cohort design with oversampling of those with NMD from the Veterans Health Administration (n = 15 983). We used logistic regression to assess for a cross-sectional association between NMD and LBP and survival analysis to estimate the effect of NMD on time to LBP, up to 60-month follow-up, conditioning on age, sex, race, index year, Charlson Comorbidity Index, depression, anxiety, insomnia, combat exposure, and prisoner of war history to address confounding. Odds ratios (with 95% confidence intervals [CIs]) indicated a cross-sectional association of 1.35 (1.13 to 1.60) and 1.21 (1.02 to 1.42) for NMD and LBP within 6 months and 12 months pre- or post-NMD diagnosis, respectively. Hazard ratios (HRs) indicated the effect of NMD on time to LBP that was time-dependent-HR (with 95% CIs) 1.27 (1.02 to 1.59), 1.23 (1.03 to 1.48), 1.19 (1.01 to 1.40), and 1.10 (0.94 to 1.29) in the first 3, 6, 9, and 12 months post-diagnosis, respectively-approximating the null (1.00) at >12 months. The estimated effect of NMD on LBP suggests that improved screening for NMD among veterans may help clinicians and researchers predict (or intervene to reduce) risk of future back pain.

Experimental methods to study sleep disruption and immune balance in urban children with asthma

Study Objectives

We describe research methods developed to examine effects of sleep disruption on changes in immune balance, lung function, and cognitive performance in a sample of urban, ethnically diverse children with persistent asthma. Two case examples (8- and 10-year-old males) are presented to highlight methods of the current study and illustrate effects of experimentally disrupted sleep on the immune balance profile (Th1/Th2 cytokines), key sleep variables from polysomnography data, and lung function in our sample.

Methods

Children follow an individualized structured sleep schedule consistent with their habitual sleep need (≥9.5 hours' time in bed) for six days before a laboratory-based experimental sleep protocol. Children then spend two successive nights in the sleep lab monitored by polysomnography: a baseline night consisting of uninterrupted sleep, and a disruption night, during which they are awoken for 2 minutes between 20-minute intervals of uninterrupted sleep. Evening and morning blood draws bracket baseline and disruption nights for immune biomarker assessment.

Results

A shift towards immune imbalance following the sleep disruption protocol was observed in these illustrative cases.

Conclusions

Data from these case examples provide evidence that the experimental protocol caused disruptions in sleep as observed on polysomnography and had the hypothesized downstream effects on immune balance associated with clinical asthma control. Documenting the effects of sleep disruption on immune function in children with persistent asthma is a crucial step towards understanding associations between sleep, immune balance, and asthma outcomes and provides important information for developing novel interventions for youth with asthma and suboptimal sleep.

Clinical Trials

Not applicable.

Who meets national early childhood sleep guidelines in Aotearoa New Zealand? A cross-sectional and longitudinal analysis

Study Objectives

To investigate the proportion of children in Aotearoa New Zealand (NZ) who do or do not meet sleep duration and sleep quality guidelines at 24 and 45 months of age and associated sociodemographic factors.

Methods

Participants were children (n = 6490) from the Growing Up in New Zealand longitudinal study of child development with sleep data available at 24 and/or 45 months of age (48.2% girls, 51.8% boys; 22.4% Māori [the Indigenous people of NZ], 12.9% Pacific, 13.4% Asian, 45.2% European/Other). Relationships between sociodemographic factors and maternally reported child sleep duration (across 24 hours) and night wakings were investigated cross-sectionally and longitudinally. Estimates of children in NZ meeting sleep guidelines were calculated using a range of analytical techniques including Bayesian linear regression, negative binomial multiple regression, and growth curve models.

Results

In NZ, 29.8% and 19.5% of children were estimated to have a high probability of not meeting sleep duration guidelines and 15.4% and 8.3% were estimated to have a high probability of not meeting night waking guidelines at 24 and 45 months respectively, after controlling for multiple sociodemographic variables. Factors associated cross-sectionally with children's sleep included ethnicity, socioeconomic deprivation, material standard of living, rurality, and heavy traffic, and longitudinal sleep trajectories differed by gender, ethnicity, and socioeconomic deprivation.

Conclusions

A considerable proportion of young children in NZ have a high probability of not meeting sleep guidelines but this declines across the ages of 24 and 45 months. Sleep health inequities exist as early as 24 months of age in NZ.

Sleep-wake parameters can be detected in patients with chronic stroke using a multisensor accelerometer: a validation study

STUDY OBJECTIVES

Sleep-wake dysfunction is bidirectionally associated with the pathogenesis and evolution of stroke. Longitudinal and prospective measurement of sleep after chronic stroke remains poorly characterized because of a lack of validated objective and ambulatory sleep measurement tools in neurological populations. This study aimed to validate a multisensor sleep monitor, the SenseWear Armband (SWA), in patients with ischemic stroke and control patients using at-home polysomnography.

METHODS

Twenty-eight radiologically confirmed patients with ischemic stroke (aged 69.61 ± 7.35 years; mean = 4.1 years poststroke) and 16 control patients (aged 73.75 ± 7.10 years) underwent overnight at-home polysomnography in tandem with the SWA. Lin's concordance correlation coefficient and reduced major axis regressions were employed to assess concordance of SWA vs polysomnography-measured total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Subsequently, data were converted to 30-second epochs to match at-home polysomnography. Epoch-by-epoch agreement between SWA and at-home polysomnography was estimated using crude agreement, Cohen's kappa, sensitivity, and specificity.

RESULTS

Total sleep time was the most robustly quantified sleep-wake variable (concordance correlation coefficient = 0.49). The SWA performed poorest for sleep measures requiring discrimination of wakefulness (sleep onset latency; concordance correlation coefficient = 0.16). The sensitivity of the SWA was high (95.90%) for patients with stroke and for control patients (95.70%). The specificity of the SWA was fair-moderate for patients with stroke (40.45%) and moderate for control patients (45.60%). Epoch-by-epoch agreement rate was fair (78%) in patients with stroke and fair (74%) in controls.

CONCLUSIONS

The SWA shows promise as an ambulatory tool to estimate macro parameters of sleep-wake; however, agreement at an epoch level is only moderate-fair. Use of the SWA warrants caution when it is used as a diagnostic tool or in populations with significant sleep-wake fragmentation.

Motivational enhancement to improve adherence to positive airway pressure in patients with obstructive sleep apnea: a randomized controlled trial

BACKGROUND

Obstructive sleep apnea (OSA) is associated with a variety of medical conditions. Positive airway pressure (PAP) is an effective treatment for improving sleep, yet adherence rates are low. The aim of the current study is to test two treatments versus standard care in improving adherence to PAP.

METHOD

Two hundred twenty-seven patients with OSA were randomized to standard care (SC), education (ED) and motivational enhancement therapy (MET). Adherence was measured objectively and the first week of adherence (prior to the intervention) was used as an a priori moderator of the effect of the various interventions. Mediators of treatment response were also examined using theory-based measures of decisional balance and self-efficacy.

RESULTS

Adherence declined over time for all three groups. There was a significant interaction between level of adherence during the first week of treatment and treatment group. Those who had moderate levels of adherence during their first week of PAP were more likely to adhere to treatment at follow-up if they had MET; those who had high levels of adherence during their first week of PAP were more likely to adhere to treatment at follow-up if they had ED. MET treatment increased the perception of the positive aspects of PAP, but ED did not.

CONCLUSIONS

Initial adherence to positive airway pressure could help guide subsequent treatment plans. The results also support social cognitive theory in that educational approaches might be best suited for those who are ready for change whereas more motivational approaches might be best for those who are ambivalent about change.

The more the merrier? Working towards multidisciplinary management of obstructive sleep apnea and comorbid insomnia

OBJECTIVES

The goal of this article was to provide an overview of the diagnostic considerations, clinical features, pathophysiology, and treatment approaches for patients with obstructive sleep apnea (OSA) and comorbid insomnia.

METHOD

We begin with a review of the literature on OSA and comorbid insomnia. We then present a multidisciplinary approach using pulmonary and behavioral sleep medicine treatments.

RESULTS

OSA and insomnia co-occur at a high rate and such patients have distinct clinical features. Empirically supported treatments are available for OSA and insomnia independently but there are no standards or guidelines for how to implement these treatments for patients who suffer from both disorders.

CONCLUSIONS

Multidisciplinary treatment holds promise for patients with comorbid sleep disorders. Further research should be aimed at optimizing treatments and developing standards of practice for this population.

Parent-based sleep education workshops in autism

To determine if parents can successfully teach their children with autism spectrum disorders to become better sleepers, we piloted small group parent education workshops focused on behavioral sleep strategies. Workshops consisted of three 2-hour sessions conducted over consecutive weeks by 2 physicians. Curricula included establishing effective daytime and nighttime habits, initiating a bedtime routine, and optimizing parental interactions at bedtime and during night wakings. Baseline and treatment questionnaires and actigraphy were analyzed in 20 children, ages 3 to 10 years. Improvements after treatment were seen in the total scale and several insomnia-related subscales of the Children's Sleep Habits Questionnaire. Actigraphy documented reduced sleep latency in children presenting with sleep onset delay. Improvements were also noted in measures of sleep habits and daytime behavior. Brief parent-based behavioral sleep workshops in children with autism spectrum disorders appear effective in improving subjective and objective measures of sleep, sleep habits, and daytime behavior.