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

Actigraphic sleep dimensions and associations with academic functioning among adolescents

STUDY OBJECTIVES

There is mixed evidence regarding associations of sleep duration with academic functioning in adolescents and a lack of research on other sleep dimensions, particularly using objective sleep measures. We examined associations of multiple actigraphic sleep dimensions with academic functioning among adolescents.

METHODS

Data were from the sleep sub-study of the age 15 wave of the Future of Families and Child Wellbeing Study (n = 774-782; 52% female), a national, diverse sample of teens. Adolescents wore wrist-actigraphs for ~1 week and completed a survey reporting academic performance and school-related behavioral problems. Regression models assessed whether average sleep duration, timing, maintenance efficiency, and SD-variability were associated with self-reported academic functioning in cross-sectional analyses adjusted for demographic characteristics, depressive symptoms, and anxious symptoms.

RESULTS

Later sleep timing (hours) and greater sleep variability (SD-hours) were associated with poorer academic outcomes, including sleep onset variability with higher odds of receiving a D or lower (OR = 1.29), sleep onset (β = -.07), sleep offset (β = -.08), and sleep duration variability (β = -.08) with fewer A grades, sleep offset with lower GPA (β = -.07), sleep offset (OR = 1.11), sleep duration variability (OR = 1.31), and sleep onset variability (OR = 1.42) with higher odds of being suspended or expelled in the past 2 years, and sleep duration variability with greater trouble at school (β = .13). Sleep duration, sleep maintenance efficiency, and sleep regularity index were not associated with academic functioning.

CONCLUSIONS

Later sleep timing and greater sleep variability are risk factors for certain academic problems among adolescents. Promoting sufficient, regular sleep timing across the week may improve adolescent academic functioning.

REM sleep breathing: Insights beyond conventional respiratory metrics

Breathing and sleep state are tightly linked. The traditional approach to evaluation of breathing in rapid eye movement sleep has been to focus on apneas and hypopneas, and associated hypoxia or hypercapnia. However, rapid eye movement sleep breathing offers novel insights into sleep physiology and pathology, secondary to complex interactions of rapid eye movement state and cardiorespiratory biology. In this review, morphological analysis of clinical polysomnogram data to assess respiratory patterns and associations across a range of health and disease is presented. There are several relatively unique insights that may be evident by assessment of breathing during rapid eye movement sleep. These include the original discovery of rapid eye movement sleep and scoring of neonatal sleep, control of breathing in rapid eye movement sleep, rapid eye movement sleep homeostasis, sleep apnea endotyping and pharmacotherapy, rapid eye movement sleep stability, non-electroencephalogram sleep staging, influences on cataplexy, mimics of rapid eye movement behaviour disorder, a reflection of autonomic health, and insights into cardiac arrhythmogenesis. In summary, there is rich clinically actionable information beyond sleep apnea encoded in the respiratory patterns of rapid eye movement sleep.

Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use

GOAL AND AIMS

To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement.

FOCUS TECHNOLOGY

Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X.

REFERENCE TECHNOLOGY

Polysomnography.

SAMPLE

Sixty-three participants (36 female) aged 20-68.

DESIGN

Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message.

CORE ANALYTICS

Detection and timing accuracy for the sleep onset times and wake times.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods.

CORE OUTCOMES

Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency.

IMPORTANT ADDITIONAL OUTCOMES

With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit.

CORE CONCLUSIONS

Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

Is it Time to Include Wearable Sleep Trackers in the Applied Psychologists' Toolbox?

Wearable sleep trackers are increasingly used in applied psychology. Particularly, the recent boom in the fitness tracking industry has resulted in a number of relatively inexpensive consumer-oriented devices that further enlarge the potential applications of ambulatory sleep monitoring. While being largely positioned as wellness tools, wearable sleep trackers could be considered useful health devices supported by a growing number of independent peer-reviewed studies evaluating their accuracy. The inclusion of sensors that monitor cardiorespiratory physiology, diurnal activity data, and other environmental signals allows for a comprehensive and multidimensional approach to sleep health and its impact on psychological well-being. Moreover, the increasingly common combination of wearable trackers and experience sampling methods has the potential to uncover within-individual processes linking sleep to daily experiences, behaviors, and other psychosocial factors. Here, we provide a concise overview of the state-of-the-art, challenges, and opportunities of using wearable sleep-tracking technology in applied psychology. Specifically, we review key device profiles, capabilities, and limitations. By providing representative examples, we highlight how scholars and practitioners can fully exploit the potential of wearable sleep trackers while being aware of the most critical pitfalls characterizing these devices. Overall, consumer wearable sleep trackers are increasingly recognized as a valuable method to investigate, assess, and improve sleep health. Incorporating such devices in research and professional practice might significantly improve the quantity and quality of the collected information while opening the possibility of involving large samples over representative time periods. However, a rigorous and informed approach to their use is necessary.