Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

STUDY OBJECTIVES: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. METHODS: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. RESULTS: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. CONCLUSIONS: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

Using Wearable Skin Temperature Data to Advance Tracking and Characterization of the Menstrual Cycle in a Real-World Setting.

The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy.

Navigating menopause at work: a preliminary study about challenges and support systems.

OBJECTIVE: Women's increasing workforce participation necessitates understanding unique life phases like menopause for enhanced workplace inclusivity. This research investigates the challenges and needs of peri-menopausal women in work settings, using the Job Demands-Resources model as a foundation. METHODS: A cross-sectional survey was administered to 351 working women aged 40 to 65 years in the United States. Hierarchical multiple regression models were employed to assess the relationship between the severity of menopausal symptoms, emotional exhaustion, work engagement, and turnover intentions. RESULTS: Most of the respondents reported moderate (38.46%) to severe (35.9%) menopausal symptoms. Notably, 54% of the women were caregivers for children or adults. About 77.7% of participants reported work-related challenges due to menopause, with a perceived reduction in productivity (56.8%) being the most common issue. The severity of menopausal symptoms was found to significantly predict more emotional exhaustion ( P < 0.001), less work engagement ( P < 0.001), and greater turnover intentions ( P = 0.03). Concerns about being perceived as less capable in the workplace due to menopausal symptoms were reported by 51.2% of respondents. A striking gap exists between the workplace measures desired by women, such as formal menopause policies and managerial training (65.4%-68%), and their actual implementation (2%-6.3%). CONCLUSIONS: This study reveals an exigent need for increased awareness and structural changes to support working women going through menopause. The findings have far-reaching implications for not just promoting gender equity and well-being but are also pivotal for maintaining a diversified, engaged, and effective workforce.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table.

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

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.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study.

INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Brain structural covariance network features are robust markers of early heavy alcohol use.

BACKGROUND AND AIMS: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. DESIGN AND SETTING: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years). CASES: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. MEASUREMENTS: Graph theory metrics of segregation and integration were used to summarize SCN. FINDINGS: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. CONCLUSION: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

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.

Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence.

GOAL AND AIMS: To evaluate an automatic sleep scoring algorithm against manual polysomnography sleep scoring. FOCUS METHOD/TECHNOLOGY: Yet Another Spindle Algorithm automatic sleep staging algorithm. REFERENCE METHOD/TECHNOLOGY: Manual sleep scoring. SAMPLE: 327 nights (151 healthy adolescents), from the NCANDA study. DESIGN: Participants underwent one-to-three overnight polysomnography recordings, one consisting of an event-related-potential paradigm. CORE ANALYTICS: Epoch by Epoch and discrepancy analyses (Bland Altman plots) were conducted on the overall sample. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Epoch by Epoch and discrepancy analysis were repeated separately on standard polysomnography nights and event-related potential nights. Regression models were estimated on age, sex, scorer, and site of recording, separately on standard polysomnography nights and event-related potential nights. CORE OUTCOMES: The Yet Another Spindle Algorithm sleep scoring algorithm's average sensitivity of 93.04% for Wake, 87.67% for N2, 84.46% for N3, 86.02% for rapid-eye-movement, and 40.39% for N1. Specificity was 96.75% for Wake, 97.31% for N1, 88.87% for N2, 97.99% for N3, and 97.70% for rapid-eye-movement. The Matthews Correlation Coefficient was highest in rapid-eye-movement sleep (0.85) while lowest in N1 (0.39). Cohen's Kappa mirrored Matthews Correlation Coefficient results. In Bland-Altman plots, the bias between Yet Another Spindle Algorithm and human scoring showed proportionality to the manual scoring measurement size. IMPORTANT ADDITIONAL OUTCOMES: Yet Another Spindle Algorithm performance was reduced in event-related-potential/polysomnography nights for N3 and rapid-eye-movement. According to the Matthews Correlation Coefficient, the Yet Another Spindle Algorithm performance was affected by younger age, male sex, recording sites, and scorers. CORE CONCLUSION: Results support the use of Yet Another Spindle Algorithm to score adolescents' polysomnography sleep records, possibly with classification outcomes supervised by an expert scorer.

Associations between alcohol use and sex-specific maturation of subcortical gray matter morphometry from adolescence to adulthood: Replication across two longitudinal samples.

Subcortical brain morphometry matures across adolescence and young adulthood, a time when many youth engage in escalating levels of alcohol use. Initial cross-sectional studies have shown alcohol use is associated with altered subcortical morphometry. However, longitudinal evidence of sex-specific neuromaturation and associations with alcohol use remains limited. This project used generalized additive mixed models to examine sex-specific development of subcortical volumes and associations with recent alcohol use, using 7 longitudinal waves (n = 804, 51% female, ages 12-21 at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). A second, independent, longitudinal dataset, with up to four waves of data (n = 467, 43% female, ages 10-18 at baseline), was used to assess replicability. Significant, replicable non-linear normative volumetric changes with age were evident in the caudate, putamen, thalamus, pallidum, amygdala and hippocampus. Significant, replicable negative associations between subcortical volume and alcohol use were found in the hippocampus in all youth, and the caudate and thalamus in female but not male youth, with significant interactions present in the caudate, thalamus and putamen. Findings suggest a structural vulnerability to alcohol use, or a predisposition to drink alcohol based on brain structure, with female youth potentially showing heightened risk, compared to male youth.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions.

GOAL AND AIMS: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY: Standard manual PSG sleep scoring. SAMPLE: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

Effects of emerging alcohol use on developmental trajectories of functional sleep measures in adolescents.

STUDY OBJECTIVES: Adolescence is characterized by significant brain development, accompanied by changes in sleep timing and architecture. It also is a period of profound psychosocial changes, including the initiation of alcohol use; however, it is unknown how alcohol use affects sleep architecture in the context of adolescent development. We tracked developmental changes in polysomnographic (PSG) and electroencephalographic (EEG) sleep measures and their relationship with emergent alcohol use in adolescents considering confounding effects (e.g. cannabis use). METHODS: Adolescents (n = 94, 43% female, age: 12-21 years) in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study had annual laboratory PSG recordings across 4-years. Participants were no/low drinkers at baseline. RESULTS: Linear mixed effect models showed developmental changes in sleep macrostructure and EEG, including a decrease in slow wave sleep and slow wave (delta) EEG activity with advancing age. Emergent moderate/heavy alcohol use across three follow-up years was associated with a decline in percentage rapid eye movement (REM) sleep over time, a longer sleep onset latency (SOL) and shorter total sleep time (TST) in older adolescents, and lower non-REM delta and theta power in males. CONCLUSIONS: These longitudinal data show substantial developmental changes in sleep architecture. Emergent alcohol use during this period was associated with altered sleep continuity, architecture, and EEG measures, with some effects dependent on age and sex. These effects, in part, could be attributed to the effects of alcohol on underlying brain maturation processes involved in sleep-wake regulation.

Morning perception of sleep, stress, and mood, and its relationship with overnight physiological sleep: findings from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study.

This cross-sectional study investigated objective-subjective sleep discrepancies and the physiological basis for morning perceptions of sleep, mood, and readiness, in adolescents. Data collected during a single in-laboratory polysomnographic assessment from 137 healthy adolescents (61 girls; age range: 12-21 years) in the United States National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study were analysed. Upon awakening, participants completed questionnaires assessing sleep quality, mood, and readiness. We evaluated the relationship between overnight polysomnographic, electroencephalographic, sleep autonomic nervous system functioning measures, and next morning self-reported indices. Results showed that older adolescents reported more awakenings, yet they perceived their sleep to be deeper and less restless than younger adolescents. Prediction models including sleep physiology measures (polysomnographic, electroencephalographic, and sleep autonomic nervous system) explained between 3% and 29% of morning sleep perception, mood, and readiness indices. The subjective experience of sleep is a complex phenomenon with multiple components. Distinct physiological sleep processes contribute to the morning perception of sleep and related measures of mood and readiness. More than 70% of the variance (based on a single observation per person) in the perception of sleep, mood, and morning readiness is not explained by overnight sleep-related physiological measures, suggesting that other factors are important for the subjective sleep experience.

Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children.

Importance: The use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people's understanding of and willingness to join and remain engaged in remote health studies. Objective: To examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children. Design, Setting, and Participants: This cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022. Main Outcomes and Measures: The 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined. Results: The mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (-59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P < .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P < .001). Children from low-income households (<$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P < .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P < .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (ß = -43.00 hours; 95% CI, -55.11 to -30.88 hours; P < .001). Conclusions and Relevance: In this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals' health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.

Effects of the COVID-19 pandemic on screen time and sleep in early adolescents.

OBJECTIVE: During the COVID-19 pandemic, adolescents and families have turned to online activities and social platforms more than ever to maintain well-being, connect remotely with friends and family, and online schooling. However, excessive screen use can have negative effects on health (e.g., sleep). This study examined changes in sleep habits and recreational screen time (social media, video gaming), and their relationship, before and across the first year of the pandemic in adolescents in the Adolescent Brain Cognitive Development (ABCD) Study. METHOD: Mixed-effect models were used to examine associations between self-reported sleep and screen time using longitudinal data of 5,027 adolescents in the ABCD Study, assessed before the pandemic (10-13 years) and across six time points between May 2020 and March 2021 (pandemic). RESULTS: Time in bed varied, being higher during May-August 2020 relative to pre-pandemic, partially related to the school summer break, before declining in October 2020 to levels lower than pre-pandemic. Screen time steeply increased and remained high across all pandemic time points relative to pre-pandemic. Higher social media use and video gaming were associated with shorter time in bed, later bedtimes, and longer sleep onset latency. CONCLUSIONS: Sleep behavior and screen time changed during the pandemic in early adolescents. More screen time was associated with poorer sleep behavior, before and during the pandemic. While recreational screen usage is an integral component of adolescent's activities, especially during the pandemic, excessive use can have negative effects on essential health behaviors, highlighting the need to promote balanced screen usage. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Good sleep is a mood buffer for young women during menses.

STUDY OBJECTIVES: We sought to elucidate the interaction between sleep and mood considering menstrual cycle phase (menses and non-menses portions of the cycle) in 72 healthy young women (18-33 years) with natural, regular menstrual cycles and without menstrual-associated disorders. This work fills a gap in literature of examining mood in context of sleep and menstrual cycle jointly, rather than individually. METHODS: Daily subjective measures of sleep and mood, and date of menses were remotely, digitally collected over a 2-month period. Each morning, participants rated their sleep on the previous night, and each evening participants rated the extent of positive and negative mood for that day. Objective sleep was tracked with a wearable (OURA ring) during month 2 of the study. Time-lag cross-correlation and mixed linear models were used to analyze the significance and directionality of the sleep-mood relationship, and how the interaction between menstrual cycle status and sleep impacted mood levels. RESULTS: We found that menstrual status alone did not impact mood. However, subjective sleep quality and menstrual status interacted to impact positive mood (p < .05). After a night of perceived poor sleep quality, participants reported lower positive mood during menses compared to non-menses portions of the cycle, while after a night of perceived good sleep quality participants reported equivalent levels of positive mood across the cycle. CONCLUSIONS: We suggest that the perception of good sleep quality acts as a mood equalizer, with good sleep providing a protective buffer to positive mood across the menstrual cycle.

Stress, hypothalamic pituitary adrenal axis activity and autonomic nervous system function in adolescents with insomnia.

INTRODUCTION: Abnormal stress responses have been linked to the etiology of insomnia. We investigated the relationship between insomnia, stress, hypothalamic-pituitary-adrenal (HPA) axis activity, and autonomic nervous system (ANS) function in adolescence. METHODS: Forty-seven post-pubertal adolescents (16-20 years old, 28 female) with (N = 16; insomnia group) and without (N = 31; control group) DSM-5 insomnia symptoms were assessed for stress levels and stress reactivity and underwent a standardized stress protocol (Trier Social Stress Test (TSST)), after an overnight laboratory stay. Cortisol was measured upon awakening and 30-minutes later to calculate the cortisol awakening response (CAR). During the TSST, perceived stress, salivary cortisol (HPA activity), heart rate (HR) and blood pressure (BP) measures were collected. RESULTS: Participants in the insomnia group reported more stress from school performance and work overload, with insomnia girls experiencing more stress from peer pressure and future uncertainty than control girls (p < 0.05). No group differences were detected in the CAR and pre-TSST stress levels. All participants showed significant increases in perceived stress (~19 %), HR (~33 %), systolic (~13 %), and diastolic (~15 %) BP in response to the TSST (p < 0.05). Overall HR stress response did not differ between groups, but was lower in boys with insomnia than in girls with insomnia (p < 0.05). Cortisol stress responses were inconclusive, possibly due to a masking effect of CAR, as the task was performed shortly after awakening and larger CARs were associated with blunted cortisol stress responses. DISCUSSION: Results mostly show no group difference in physiological stress responses, although some interaction effects suggest a potential sex by insomnia interaction. Larger samples are needed to understand the physiological disturbances of insomnia in adolescence.

Wearable and mobile technology to characterize daily patterns of sleep, stress, presleep worry, and mood in adolescent insomnia.

OBJECTIVES: Characterizing daily patterns of sleep, stress, presleep worry, and mood in adolescents with and without insomnia symptomatology. DESIGN: Two months of continuous wearable tracking and daily diary ratings. SETTING: Free-living conditions. PARTICIPANTS: Ninety-three adolescents (59 girls; 16-19 years old) with (N = 47; 26 with clinical and 21 with sub-clinical) and without (N = 46; control) DSM-5 insomnia symptomatology. MEASUREMENTS: Fitbit Charge 3 tracked sleep, heart rate, and steps. Evening electronic diaries collected ratings of daily stress, presleep worry, and mood. RESULTS: While sleep duration (control: 6.88 ± 1.41 hours; insomnia: 6.92 ± 1.28 hours), architecture, timing, and night-to-night variability were similar between groups, the insomnia group reported higher levels of stress and worry, being mainly related to "school". At the intraindividual level, stress and worry predicted shorter sleep duration and earlier wake up times, which, in turn, predicted higher stress the following day. Moreover, higher-than-usual stress predicted higher sleep-time heart rate, with a more consistent effect in adolescents with insomnia. Results were overall consistent after controlling for covariates and several robustness checks. CONCLUSIONS: There is a bidirectional relationship between daily stress and sleep, with daily stress negatively impacting sleep, which in turn leads to more stress in adolescents with and without insomnia symptoms. Findings also highlight the complexity of insomnia in adolescence, in which the core clinical features (perceived sleep difficulties) and the critical factors (stress/worry) implicated in the pathophysiology of the disorder are not necessarily reflected in objective sleep indicators.

The complexities of the sleep-pain relationship in adolescents: A critical review.

Chronic pain is a common and disabling condition in adolescents. Disturbed sleep is associated with many detrimental effects in adolescents with acute and chronic pain. While sleep and pain are known to share a reciprocal relationship, the sleep-pain relationship in adolescence warrants further contextualization within normally occurring maturation of several biopsychological processes. Since sleep and pain disorders begin to emerge in early adolescence and are often comorbid, there is a need for a comprehensive picture of their interrelation especially related to temporal relationships and mechanistic drivers. While existing reviews provide a solid foundation for the interaction between disturbed sleep and pain in youth, we will extend this review by highlighting current methodological challenges for both sleep and pain assessments, exploring the recent evidence for directionality in the sleep-pain relationship, reviewing potential mechanisms and factors underlying the relationship, and providing direction for future investigations. We will also highlight the potential role of digital technologies in advancing the understanding of the sleep and pain relationship. Ultimately, we anticipate this information will facilitate further research and inform the management of pain and poor sleep, which will ultimately improve the quality of life in adolescents and reduce the risk of pain persisting into adulthood.