The use of wearable sleep tracking devices is rapidly expanding and provides an opportunity to engage individuals in monitoring of their sleep patterns. However, there are a growing number of patients who are seeking treatment for self-diagnosed sleep disturbances such as insufficient sleep duration and insomnia due to periods of light or restless sleep observed on their sleep tracker data. The patients' inferred correlation between sleep tracker data and daytime fatigue may become a perfectionistic quest for the ideal sleep in order to optimize daytime function. To the patients, sleep tracker data often feels more consistent with their experience of sleep than validated techniques, such as polysomnography or actigraphy. The challenge for clinicians is balancing educating patients on the validity of these devices with patients' enthusiasm for objective data. Incorporating the use of sleep trackers into cognitive behavioral therapy for insomnia will be important as use of these devices is rapidly expanding among our patient population.

Introduction: Disruption of circadian rhythms is one of the proposed mechanisms linking late sleep timing to obesity risk but few studies have evaluated biological markers outside of the laboratory. The goal of this study was to determine the relationship between the timing and alignment of melatonin and sleep onset (phase angle) with body mass index (BMI), body fat and obesity-related behaviors. We hypothesized that circadian alignment (relationship of melatonin to sleep timing) rather than circadian (melatonin) timing would be associated with higher BMI, body fat, dietary intake and lower physical activity. Subjects/Methods: Adults with sleep duration ⩾6.5 h completed 7 days of wrist actigraphy, food diaries and SenseWear arm band monitoring. Circadian timing, measured by dim light melatonin onset was measured in the clinical research unit. Circadian alignment was calculated as the duration between dim light melatonin onset and average sleep onset time in the prior week (phase angle). Body fat was evaluated using dual-energy X-ray absorptiometry. Data were analyzed using bivariate correlations and multivariable regression analyses controlling for age, sex, sleep duration and evening light exposure. Results: Participants included 97 adults (61 F, age 26.8±7.3 years) with average sleep duration 443.7 (s.d.=50.4) minutes. Average phase angle was 2.2 h (s.d.=1.5). Circadian alignment was associated with circadian timing (P<0.001) and sleep duration (P=0.005). In multivariable analyses, later circadian timing was associated with lower BMI (P=0.04). Among males only, circadian alignment was associated with percent body fat (P=0.02) and higher android/gynoid fat ratio (P=0.04). Circadian alignment was associated with caloric intake (P=0.049) carbohydrate intake (P=0.04) and meal frequency (P=0.03) among both males and females. Conclusion: Circadian timing and alignment were not associated with increased BMI or body fat, among healthy adults with ⩾6.5 h of sleep, but circadian alignment was associated with dietary intake. There may be sex differences in the relationship between circadian alignment and body fat.

Background: The rapid growth of consumer sleep technology demonstrates the population’s interest in measuring sleep. However, the extent to which these devices can be used in the delivery of behavioral sleep interventions is currently unknown. The objectives of this systematic review were to evaluate the use of consumer sleep technology (wearable and mobile) in behavioral sleep medicine interventions, identify gaps in the literature and potential future directions. Methods: We completed a scoping review of studies conducted in adult populations that used consumer sleep tracking technology to deliver sleep-related interventions. Results: Our initial search returned 4,538 articles and 14 articles met our inclusion/exclusion criteria. Results demonstrated that wearable devices are being used for two main purposes: 1. To deliver treatment for insomnia and 2. Sleep monitoring as part of overall wellness programs. Half of the articles reviewed (n = 7) used consumer sleep technology in a cognitive behavioral therapy for insomnia. The majority of the studies reviewed (n = 10) were fully digital, without human intervention, and only two small studies evaluated interventions delivered with and without a sleep tracking device. Conclusions: These studies demonstrate opportunities to utilize consumer sleep trackers in insomnia treatment and wellness programs, but most new and innovative interventions are in the early, feasibility stages. Future research is needed to determine how to leverage wearables to improve existing behavioral sleep treatments and determine how this technology can engage patients and reduce barriers to behavioral sleep medicine interventions.