Evaluation of Nasal Conditions on Sleep: Integrating Wearable Tech in Surgical Outcomes.

Objective: The primary objective of this study was to explore and identify the impacts of nasal septum deviation and turbinate hypertrophy on respiratory function, sleep quality, and overall well-being. Additionally, the study aimed to establish the therapeutic efficacy of surgical intervention and comprehensively analyse the additional advantages of wearable sleep trackers when combined with established diagnostic techniques. Methods: A prospective cohort of 150 participants (75 with nasal septum deviation and 75 with turbinate hypertrophy) underwent surgical intervention. The NOSE scale, PSQI, SF-36, and wearable sleep tracker data were employed for pre- and post-surgical evaluations. Objective measurements, such as nasal airflow and acoustic rhinometry, were also used. Multivariate regression was utilised to identify potential predictors of post-surgical outcomes. Results: The cohort had a mean age of 41 years with evenly balanced gender distribution. Both conditions showed post-surgical improvements in respiratory function, sleep quality, and quality-of-life. Wearable sleep tracker data provided insights into REM sleep duration and interruptions during sleep. The results indicated significant disturbances in sleep patterns in individuals with nasal septum deviation before undergoing surgery. Duration of the nasal condition was found to be a significant factor in predicting outcomes. Conclusion: Nasal septum deviation and turbinate hypertrophy had a significant impact on sleep patterns, overall well-being, and respiratory function. Surgical interventions provided significant relief, and wearable sleep tracker integration provides deeper insights into sleep disorders. The study highlights the importance of early intervention and the benefit of modern technologies in clinical evaluations. Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-024-04524-y.

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. (AU)

Assessing the immediate impact of Twin-block appliance insertion on adolescents' sleep using a wearable device.

OBJECTIVE: This prospective clinical study aimed to evaluate the immediate impact of Twin-block appliance insertion on the sleep of adolescents using a wearable device. MATERIALS AND METHODS: A total of 24 girls, aged 11-13 years, with Class II division 1 molar relationship, skeletal class 2 malocclusion (ANB ≥5) and overjet measuring ≥5 mm were selected. Exclusion criteria included a history of previous orthodontic treatment, systemic disease, irregular sleep pattern, obstructive sleep apnea, medical history of breathing disorders, or concurrent use of medications. Participants wore a wearable device to measure sleep parameters, including deep sleep, light sleep, minutes awake during sleep, wake-up times, bedtimes and total sleep times. The participants wore the device for 10 days prior to Twin-block insertion and sleep data were collected for another 10 days after insertion. RESULTS: Following the insertion of the Twin-block appliance, there was a highly statistically significant shift in bedtime and wake-up time to later hours (P < .001). All participants experienced a highly significant delay in bedtime compared to the recommended 10 pm time (P < .001). Additionally, there was a significant increase in the duration of light sleep (P < .05). However, the effect on deep sleep, minutes awake during sleep and sleep duration was not statistically significant. None of the sleep parameters tested showed statistically significant changes between the first 5 days after Twin-block insertion with the subsequent 5 days. CONCLUSION: The immediate insertion of the Twin-block appliance disrupts sleep onset, wake-up time and light sleep during the specified period of 10 days.

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.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights.

GOAL AND AIMS: Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography. SAMPLE: Twenty-one university students (10 females). DESIGN: Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes. CORE ANALYTICS: Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Variability of specificity and negative predictive value across subjects and across nights. CORE OUTCOMES: Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively). IMPORTANT ADDITIONAL OUTCOMES: Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights. CORE CONCLUSION: This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers.

Consumer wearable activity trackers, such as Fitbit are widely used in ubiquitous and longitudinal sleep monitoring in free-living environments. However, these devices are known to be inaccurate for measuring sleep stages. In this study, we develop and validate a novel approach that leverages the processed data readily available from consumer activity trackers (i.e., steps, heart rate, and sleep metrics) to predict sleep stages. The proposed approach adopts a selective correction strategy and consists of two levels of classifiers. The level-I classifier judges whether a Fitbit labeled sleep epoch is misclassified, and the level-II classifier re-classifies misclassified epochs into one of the four sleep stages (i.e., light sleep, deep sleep, REM sleep, and wakefulness). Best epoch-wise performance was achieved when support vector machine and gradient boosting decision tree (XGBoost) with up sampling were used, respectively at the level-I and level-II classification. The model achieved an overall per-epoch accuracy of 0.731 ± 0.119, Cohen's Kappa of 0.433 ± 0.212, and multi-class Matthew's correlation coefficient (MMCC) of 0.451 ± 0.214. Regarding the total duration of individual sleep stage, the mean normalized absolute bias (MAB) of this model was 0.469, which is a 23.9% reduction against the proprietary Fitbit algorithm. The model that combines support vector machine and XGBoost with down sampling achieved sub-optimal per-epoch accuracy of 0.704 ± 0.097, Cohen's Kappa of 0.427 ± 0.178, and MMCC of 0.439 ± 0.180. The sub-optimal model obtained a MAB of 0.179, a significantly reduction of 71.0% compared to the proprietary Fitbit algorithm. We highlight the challenges in machine learning based sleep stage prediction with consumer wearables, and suggest directions for future research.

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls.

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16-19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for "light" and "deep" sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for "light" sleep, 59% for "deep" sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2 = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code.

Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.