The Future of Sleep Measurements: A Review and Perspective

Symptom content analysis of OSA questionnaires: time to identify and improve relevance of diversity of OSA symptoms?

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a heterogeneous condition covering many clinical phenotypes in terms of the diversity of symptoms. Patient-based OSA screening questionnaires used in routine practice contain significantly varying contents that can impact the reliability and validity of the screening. We investigated to what extent common patient-based OSA screening questionnaires differ or overlap in their item content by conducting a rigorous, methodical, and quantified content overlap analysis.

METHODS

We conducted an item content analysis of 11 OSA screening questionnaires validated in adult populations and characterized their overlap using a 4-step approach: (1) selection of OSA screening questionnaires, (2) item extraction and selection, (3) extraction of symptoms from items, and (4) assessment of content overlap with the Jaccard index (from 0, no overlap, to 1, full overlap).

RESULTS

We extracted 72 items that provided 25 distinct symptoms from 11 selected OSA questionnaires. The overlap between them was weak (mean Jaccard index: 0.224; ranging from 0.138 to 0.329). All questionnaires contained symptoms of the "OSA symptom" dimension (eg, snoring or witnessed apneas). The STOP-BANG (0.329) and the Berlin (0.280) questionnaires exhibited the highest overlap content. Ten symptoms (40%) were investigated in only 1 questionnaire.

CONCLUSIONS

The heterogeneity of content and the low overlap across these questionnaires reflect the challenges of screening OSA. The different OSA questionnaires potentially capture varying aspects of the disorder, with the risk of biased results in studies. Suggestions are made for better OSA screening and refinement of clinical OSA phenotypes.

CITATION

Gauld C, Baillieul S, Martin VP, et al. Symptom content analysis of OSA questionnaires: time to identify and improve relevance of diversity of OSA symptoms? J Clin Sleep Med. 2024;20(7):1105-1117.

The training times of athletes could play a role in clinical sleep problems due to their associations with sleep difficulty scores

OBJECTIVES

Sleep is a key component of athletic recovery, yet training times could influence the sleep of athletes. The aim of the current study was to compare sleep difficulties in athletes across different training time groups (early morning, daytime, late evening, early morning plus late evening) and to investigate whether training time can predict sleep difficulties.

METHODS

Athletes from various sports who performed at a national-level (n = 273) answered the Athlete Sleep Screening Questionnaire (ASSQ) along with several other questionnaires related to demographics, exercise training, and mental health. From the ASSQ, a Sleep Difficulty Score (SDS) was calculated. Transformed SDS (tSDS) was compared across different training time categories using multiple one-way ANOVAs. A stepwise regression was then used to predict tSDS from various sleep-related factors.

RESULTS

SDSs ranged from none (31%), mild (38%), moderate (22%), and severe (9%). However, the one-way ANOVAs revealed training earlier or later vs. training daytime shifted the tSDS in a negative direction, a trend toward increased sleep difficulty. In particular, athletes training in the late evening (>20:00 or >21:00) had a significantly higher tSDS when compared to daytime training (p = .03 and p < .01, respectively). The regression model (p < .001) explained 27% of variance in the tSDS using depression score, age, training time, and chronotype score.

CONCLUSION

Among a heterogeneous sample of national-level athletes, 31% displayed moderate to severe SDSs regardless of their training time. However, when athletes trained outside daytime hours there was a tendency for the prevalence of sleep difficulties to increase.

Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight

Introduction

Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers.

Methods

A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow.

Results

We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ.

Discussion

We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.

Review and perspective on sleep-disordered breathing research and translation to clinics

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

The economic cost of obstructive sleep apnea: A systematic review

Obstructive sleep apnea (OSA) is a common disease associated with a high prevalence of costly comorbidities and accidents that add to the disease's economic impact. Although more attention has been focused on OSA in recent years, no previous systematic reviews have synthesized findings from existing studies that provide estimates of the economic cost of OSA. This study aims to summarize the findings of existing studies that provide estimates of the cost of OSA. Two bibliographic databases, PubMed and Scopus, were used to identify articles on the costs of OSA. The systematic literature review identified 5,938 publications, of which 31 met the inclusion criteria. According to the results, adjusted for inflation and converted to euros, the annual cost per patient ranged from €236 (the incremental cost of OSA) for New Zealand to €28,267 for the United States. The total annual cost per patient in Europe ranged from €1,669 to €5,186. OSA causes a significant burden on society, and OSA-related costs increase many years before the diagnosis and remain elevated for a long time after the diagnosis. Despite some well-conducted studies, the cost estimates for OSA are uncertain and specific to the context in which the study was conducted.

Online sleep diaries: considerations for system development and recommendations for data management

STUDY OBJECTIVES

To present development considerations for online sleep diary systems that result in robust, interpretable, and reliable data; furthermore, to describe data management procedures to address common data entry errors that occur despite those considerations.

METHODS

The online sleep diary capture component of the Sleep Healthy Using the Internet (SHUTi) intervention has been designed to promote data integrity. Features include diary entry restrictions to limit retrospective bias, reminder prompts and data visualizations to support user engagement, and data validation checks to reduce data entry errors. Despite these features, data entry errors still occur. Data management procedures relying largely on programming syntax to minimize researcher effort and maximize reliability and replicability. Presumed data entry errors are identified where users are believed to have incorrectly selected a date or AM versus PM on the 12-hour clock. Following these corrections, diaries are identified that have unresolvable errors, like negative total sleep time.

RESULTS

Using the example of one of our fully-powered, U.S. national SHUTi randomized controlled trials, we demonstrate the application of these procedures: of 45,598 total submitted diaries, 487 diaries (0.01%) required modification due to date and/or AM/PM errors and 27 diaries (<0.001%) were eliminated due to unresolvable errors.

CONCLUSION

To secure the most complete and valid data from online sleep diary systems, it is critical to consider the design of the data collection system and to develop replicable processes to manage data.

CLINICAL TRIAL REGISTRATION

Sleep Healthy Using The Internet for Older Adult Sufferers of Insomnia and Sleeplessness (SHUTiOASIS); https://clinicaltrials.gov/ct2/show/NCT03213132; ClinicalTrials.gov ID: NCT03213132.

Evaluating User Compliance in Mobile Health Apps: Insights from a 90-Day Study Using a Digital Sleep Diary

Sleep diaries are the gold standard for subjective assessment of sleep variables in clinical practice. Digitization of sleep diaries is needed, as paper versions are prone to human error, memory bias, and difficulties monitoring compliance.

METHODS

45 healthy eligible participants (Mage = 50.3 years, range 23-74, 56% female) were asked to use a sleep diary mobile app for 90 consecutive days. Univariate and bivariate analysis was used for group comparison and linear regression for analyzing reporting trends and compliance over time.

RESULTS

Overall compliance was high in the first two study months but tended to decrease over time (p < 0.001). Morning and evening diary entries were highly correlated (r = 0.932, p < 0.001) and participants significantly answered on average 4.1 days (95% CI [1.7, 6.6]) more often in the morning (M = 60.2, sd = 22.1) than evening ((M = 56.1, sd = 22.2), p < 0.001).

CONCLUSION

Using a daily diary assessment in a longitudinal sleep study with a sleep diary delivered through a mobile application was feasible, and compliance in this study was satisfactory.

Sleep from Infancy Through Adolescence

This article describes the changes in normal sleep regulation, structure, and organization and sleep-related changes in respiration from infancy to adolescence. The first 2 years of age are striking, with more time asleep than awake. With development, the electroencephalogram architecture has a marked reduction in rapid eye movement sleep and the acquisition of K-complexes, sleep spindles, and slow-wave sleep. During adolescence there is a reduction in slow-wave sleep and a delay in the circadian phase. Infants have a more collapsible upper airway and lower lung volumes than older children, which predisposes them to obstructive sleep apnea and sleep-related hypoxemia.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

The Sleep Revolution project: the concept and objectives

Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea-hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.

In-person versus video hookup instructions: a comparison of home sleep apnea testing quality

STUDY OBJECTIVES

The high prevalence of obstructive sleep apnea (OSA) in the general population makes diagnosing OSA a high priority. Typically, patients receive in-person instructions to hook up the home sleep apnea test (HSAT) devices. Using recorded video instructions would save healthcare personnel time and improve access to OSA diagnostics for patients in remote areas. The aim of this study was to compare the quality of HSAT recordings when using in-person and video hookup instructions in a randomized study.

METHODS

A total of 100 patients aged 18 to 70 years with suspected OSA were randomized to receive either in-person or video hookup instructions for the Nox T3 device (Nox Medical, Reykjavik, Iceland). The overall quality of the resulting sleep studies was analyzed by determining the number of technically invalid studies. The recording quality of four sensors (pulse oximeter, nasal cannula, thorax and abdominal respiratory inductance plethysmography belts) was assessed by checking for signal artifacts.

RESULTS

No significant difference was found between the two groups in any quality index. Only 1 (2%) and 2 (3.9%) sleep studies were technically invalid in the in-person and video instructions group, respectively. The average ± standard deviation recording quality of the four sensors combined was 94.8% ± 13.6% for the in-person and 96.0% ± 11.0% for the video instructions group.

CONCLUSIONS

This study found no difference in HSAT recording quality between the two groups. Video hookup instructions are therefore viable, and an important step towards a telemedicine-based way of diagnosing OSA.

Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Background

The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used.

Objective

The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary.

Methods

Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels.

Results

Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time.

Conclusions

We demonstrate that >2 months’ worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

The Role of Patient-Reported Outcomes in Sleep Measurements

Several questionnaires aka patient-reported outcome measures (PROMs) have been developed for specific use in sleep medicine. Some PROMS are "disease-specific," that is, related to a specific sleep disorder, whereas others are generic. These PROMS constitute a valuable add-on to the conventional history taking. They can be used in the areas of research, clinical practice, and quality of health care appraisal. Still, these instruments have inherent limitations, requiring proficient application in the various areas of interest. Disease-specificity includes a risk for nosologic bias that may confound diagnostic and therapeutic results. Future research should provide solutions for shortcomings of presently available questionnaires.

Sleep Measurement in Children-Are We on the Right Track?

Sleep plays a critical role in the development of healthy children. Detecting sleep and sleep disorders and the effectiveness of interventions for improving sleep in children require valid sleep measures. Assessment of sleep in children, in particular infants and young children, can be a quite challenging task. Many subjective and objective methods are available to evaluate various aspects of sleep in childhood, each with their strengths and limitations. None can, however, replace the importance of thorough clinical interview with detailed history and clinical examination by a sleep specialist.