Obstructive sleep apnea and obesity: A review of epidemiology, pathophysiology and the effect of weight-loss treatments.

Despite the commonly-accepted paradigm that patients with obstructive sleep apnea (OSA) also invariably have obesity, OSA prevalence extends beyond obesity. This necessitates a reevaluation of screening strategies, biomarkers of increased OSA risk, and heightened awareness among healthcare providers about the array of OSA treatments for diverse adult populations. While obesity contributes importantly to OSA pathogenesis, there is substantial evidence that non-anatomical factors also play a crucial role, especially in patients who do not have obesity. In recent years, notwithstanding the recognition of diverse contributors to OSA pathogenesis, research has frequently focused on weight reduction to address OSA. Insights from past experiences with bariatric surgery in OSA serve as a lens to anticipate potential outcomes of emerging anti-obesity pharmacotherapies. Pharmacological alternatives, particularly incretin agonists, exhibit promise in weight reduction and OSA improvement, but encounter obstacles such as potential side effects and high costs. With this comprehensive narrative review, we delve into the complex epidemiological and pathophysiological connections between OSA and obesity. Additionally, we emphasize the importance of a multifaceted approach to OSA treatment, recognizing that while weight management is crucial, there is a need for comprehensive strategies that go beyond traditional weight-centric perspectives.

Regulatory Pathways for Qualification and Acceptance of Digital Health Technology-Derived Clinical Trial Endpoints: Considerations for Sponsors.

Despite widespread interest and substantial investment in the adoption of sensor-based digital health technologies (sDHTs) for remote data capture in drug development trials, no drug has been approved based on an sDHT-derived primary endpoint in the United States (US). One reason for this lack of advancement is the complexity of obtaining regulatory endorsement for those endpoints within current US regulatory pathways. The goal of our review is to describe the two choices currently available to pharmaceutical study Sponsors: (i) they may navigate the traditional route of compiling the evidence to support the sDHT-derived endpoint in their investigational new drug (IND) application, requiring specific expertise and substantial resources; or (ii) they may navigate the drug development tool (DDT) pathway with the goal of qualifying their sDHT-derived endpoint as a biomarker or clinical outcome assessment applicable to a broader context of use (COU), either alone or as part of a partnership or consortium. We describe the nuances of each pathway; the evidentiary requirements for supporting an sDHT-derived endpoint and the technology used to capture it; and the impact that an sDHT's regulatory status may have on a Sponsor's decision to use it for data capture. By systematically comparing the IND and DDT pathways, our over-arching goals are to support the increasing deployment of sDHTs within the clinical research setting and help advance regulatory science in the field of digital medicine.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology.

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high-resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

Adenotonsillectomy for Snoring and Mild Sleep Apnea in Children: A Randomized Clinical Trial.

Importance: The utility of adenotonsillectomy in children who have habitual snoring without frequent obstructive breathing events (mild sleep-disordered breathing [SDB]) is unknown. Objectives: To evaluate early adenotonsillectomy compared with watchful waiting and supportive care (watchful waiting) on neurodevelopmental, behavioral, health, and polysomnographic outcomes in children with mild SDB. Design, Setting, and Participants: Randomized clinical trial enrolling 459 children aged 3 to 12.9 years with snoring and an obstructive apnea-hypopnea index (AHI) less than 3 enrolled at 7 US academic sleep centers from June 29, 2016, to February 1, 2021, and followed up for 12 months. Intervention: Participants were randomized 1:1 to either early adenotonsillectomy (n = 231) or watchful waiting (n = 228). Main Outcomes and Measures: The 2 primary outcomes were changes from baseline to 12 months for caregiver-reported Behavior Rating Inventory of Executive Function (BRIEF) Global Executive Composite (GEC) T score, a measure of executive function; and a computerized test of attention, the Go/No-go (GNG) test d-prime signal detection score, reflecting the probability of response to target vs nontarget stimuli. Twenty-two secondary outcomes included 12-month changes in neurodevelopmental, behavioral, quality of life, sleep, and health outcomes. Results: Of the 458 participants in the analyzed sample (231 adenotonsillectomy and 237 watchful waiting; mean age, 6.1 years; 230 female [50%]; 123 Black/African American [26.9%]; 75 Hispanic [16.3%]; median AHI, 0.5 [IQR, 0.2-1.1]), 394 children (86%) completed 12-month follow-up visits. There were no statistically significant differences in change from baseline between the 2 groups in executive function (BRIEF GEC T-scores: -3.1 for adenotonsillectomy vs -1.9 for watchful waiting; difference, -0.96 [95% CI, -2.66 to 0.74]) or attention (GNG d-prime scores: 0.2 for adenotonsillectomy vs 0.1 for watchful waiting; difference, 0.05 [95% CI, -0.18 to 0.27]) at 12 months. Behavioral problems, sleepiness, symptoms, and quality of life each improved more with adenotonsillectomy than with watchful waiting. Adenotonsillectomy was associated with a greater 12-month decline in systolic and diastolic blood pressure percentile levels (difference in changes, -9.02 [97% CI, -15.49 to -2.54] and -6.52 [97% CI, -11.59 to -1.45], respectively) and less progression of the AHI to greater than 3 events/h (1.3% of children in the adenotonsillectomy group compared with 13.2% in the watchful waiting group; difference, -11.2% [97% CI, -17.5% to -4.9%]). Six children (2.7%) experienced a serious adverse event associated with adenotonsillectomy. Conclusions: In children with mild SDB, adenotonsillectomy, compared with watchful waiting, did not significantly improve executive function or attention at 12 months. However, children with adenotonsillectomy had improved secondary outcomes, including behavior, symptoms, and quality of life and decreased blood pressure, at 12-month follow-up. Trial Registration: ClinicalTrials.gov Identifier: NCT02562040.

Study protocol: A comparison of mobile and clinic-based spirometry for capturing the treatment effect in moderate asthma.

Several inefficiencies in drug development trial implementation may be improved by moving data collection from the clinic to mobile, allowing for more frequent measurements and therefore increased statistical power while aligning to a patient-centric approach to trial design. Sensor-based digital health technologies such as mobile spirometry (mSpirometry) are comparable to clinic spirometry for capturing outcomes, such as forced expiratory volume in 1 s (FEV1); however, the impact of remote spirometry measurements on the detection of treatment effect has not been investigated. A protocol for a multicenter, single-arm, open-label interventional trial of long-acting beta agonist (LABA) therapy among 60 participants with uncontrolled moderate asthma is described. Participants will complete twice-daily mSpirometry at home and clinic spirometry during weekly visits, alongside continuous use of a wrist-worn wearable and regular completion of several diaries capturing asthma symptoms as well as participant- and site-reported satisfaction and ease of use of mSpirometry. The co-primary objectives of this study are (A) to quantify the treatment effect of LABA therapy among participants with moderate asthma, using both clinical spirometry (FEV1c ) and mSpirometry (FEV1m ); and (B) to investigate whether FEV1m is as accurate as FEV1c in detecting the treatment effect using a mixed-effect model for repeated measures. Study results will help inform whether the deployment of mSpirometry and a wrist-worn wearable for remote data collection are feasible in a multicenter setting among participants with moderate asthma, which may then be generalizable to other populations with respiratory disease.

Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring.

STUDY OBJECTIVES: To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. METHODS: We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. RESULTS: The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). CONCLUSIONS: Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

Impact of an Extended Telemonitoring and Coaching Program on Continuous Positive Airway Pressure Adherence.

Rationale: The benefits of continuous positive airway pressure (CPAP) therapy in obstructive sleep apnea are limited by adherence. Telemonitoring and coaching have been demonstrated to increase adherence, but the ideal duration of such support is unclear. Objectives: To compare the impact of a 12-month versus a 3-month behavioral support program on CPAP adherence. Methods: We evaluated real-world CPAP adherence data from all patients initiating CPAP between July 1, 2018, and April 1, 2020, by any durable medical equipment (DME) providers who had used a 12-month commercially available telemonitoring/coaching program (Long Term Adherence Management, Philips Respironics) in this timeframe. Patients receiving either 12 months or 3 months of support (Patient Adherence Management Service) were compared with those initiated on CPAP without support. Mean CPAP adherence was computed monthly over the initial 18 months. Missing usage was imputed as zero use. All analyses were adjusted for age, sex, and DME provider. Results: The nine DME providers using the 12-month telemonitoring/coaching service cared for a total of 26,489 patients (3,264 receiving 12-month support, 15,424 receiving 3-month support, and 7,801 receiving no support) in the timeframe under study. In adjusted analyses, mean CPAP use in the 3-month support group was greater than the no support group in Month 3 (4.6 h vs. 4.3 h; P < 0.001) but subsequently, usage declined to match the no support group at both Month 12 and Month 18. In contrast, mean CPAP use was greater in the 12-month support group than in the no support group at Month 3 (4.6 h vs. 4.3 h; P < 0.001), Month 12 (4.0 h vs. 3.6 h; P < 0.001), and Month 18 (3.3 h vs. 3.2 h; P = 0.02). Conclusions: A 3-month telemonitoring/coaching program increases CPAP use in the short term but does not lead to sustained improvements. In contrast, a 12-month program leads to sustained improvements but results still diminish once coaching ceases. Implementation of longer-term telemonitoring and coaching programs may be vital to obtaining long-term benefits from CPAP therapy.

Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review.

BACKGROUND: Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. OBJECTIVE: We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. METHODS: We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). RESULTS: Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. CONCLUSIONS: We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers.

Background: Light exposure is an important driver and modulator of human physiology, behavior and overall health, including the biological clock, sleep-wake cycles, mood and alertness. Light can also be used as a directed intervention, e.g., in the form of light therapy in seasonal affective disorder (SAD), jetlag prevention and treatment, or to treat circadian disorders. Recently, a system of quantities and units related to the physiological effects of light was standardized by the International Commission on Illumination (CIE S 026/E:2018). At the same time, biometric monitoring technologies (BioMeTs) to capture personalized light exposure were developed. However, because there are currently no standard approaches to evaluate the digital dosimeters, the need to provide a firm framework for the characterization, calibration, and reporting for these digital sensors is urgent. Objective: This article provides such a framework by applying the principles of verification, analytic validation and clinical validation (V3) as a state-of-the-art approach for tools and standards in digital medicine to light dosimetry. Results: This article describes opportunities for the use of digital dosimeters for basic research, for monitoring light exposure, and for measuring adherence in both clinical and non-clinical populations to light-based interventions in clinical trials.

Prevalence of sleepiness and associations with quality of life in patients with sleep apnea in an online cohort.

STUDY OBJECTIVES: Excessive daytime sleepiness (EDS) is a treatment target for many patients with obstructive sleep apnea (OSA). We aimed to understand the prevalence, risk factors, and quality of life associated with EDS in a nonclinical, "real world" sample of patients with OSA. METHODS: Cross-sectional survey of patients with OSA participating in an online peer support community, assessing demographics, comorbidities, treatment, and quality of life. Differences in those with and without EDS (Epworth Sleepiness Scale > and ≤ 10) were assessed. RESULTS: The sample (n = 422) was 54.2% male, 65.9% were ≥ 55 years, and 43.3% reported sleeping ≤ 6 hours/night. EDS was identified among 31.0% of respondents and 51.7% reported sleepiness as a precipitating factor for seeking initial OSA treatment. EDS was more prevalent in individuals reporting asthma, insomnia symptoms, positive airway pressure (PAP) use less than 6 hours/night on ≥ 5 nights/week, or sleep duration < 6 hours/night. After adjusting for demographics and comorbidities, patients with EDS reported poorer mental and physical health and well-being, lower disease-specific functional status, more activity and work impairment, and more driving impairment (P values < .05). In the subsample (n = 265) with high PAP adherence, 26.0% reported EDS, and similar associations between EDS and outcomes were observed. CONCLUSIONS: These "real world" data suggest that patients seeking online OSA support experience a high prevalence of EDS, which was associated with poorer quality of life and worse functional status. Associations persisted among respondents with high self-reported PAP-therapy adherence, potentially driving these individuals to seek online support for sleepiness-related symptoms. CITATION: Wanberg LJ, Rottapel RE, Reid ML, et al. Prevalence of sleepiness and associations with quality of life in patients with sleep apnea in an online cohort. J Clin Sleep Med. 2021;17(12):2363-2372.

Neighborhoods with Greater Prevalence of Minority Residents Have Lower Continuous Positive Airway Pressure Adherence.

Rationale: Limited data suggest racial disparities in continuous positive airway pressure (CPAP) adherence exist.Objectives: To assess whether CPAP adherence varies by neighborhood racial composition at a national scale.Methods: Telemonitoring data from a CPAP manufacturer database were used to assess adherence in adult patients initiating CPAP therapy between November 2015 and October 2018. Mapping ZIP code to ZIP code tabulation areas, age- and sex-adjusted CPAP adherence data at a neighborhood level was computed as a function of neighborhood racial composition. Secondary analyses adjusted for neighborhood education and poverty.Measurements and Main Results: Among 787,236 patients living in 26,180 ZIP code tabulation areas, the prevalence of CPAP adherence was 1.3% (95% confidence interval [CI], 1.0-1.6%) lower in neighborhoods with high (⩾25%) versus low (<1%) percentages of Black residents and 1.2% (95% CI, 0.9-1.5%) lower in neighborhoods with high versus low percentages of Hispanic residents (P < 0.001 for both), even after adjusting for neighborhood differences in poverty and education. Mean CPAP usage was similar across neighborhoods for the first 2 days, but by 90 days, differences in CPAP usage increased to 22 minutes (95% CI, 18-27 min) between neighborhoods with high versus low percentages of Black residents and 22 minutes (95% CI 17-27 min) between neighborhoods with high versus low percentages of Hispanic residents (P < 0.001 for both).Conclusions: CPAP adherence is lower in neighborhoods with greater proportions of Black and Hispanic residents, independent of education or poverty. These differences lead to a lower likelihood of meeting insurance coverage requirements for CPAP therapy, potentially exacerbating sleep health disparities.

Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity.

STUDY OBJECTIVES: We have developed the CardioRespiratory Sleep Staging (CReSS) algorithm for estimating sleep stages using heart rate variability and respiration, allowing for estimation of sleep staging during home sleep apnea tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold standard of manual polysomnography sleep staging. METHODS: Using 296 polysomnographs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (N1 + N2), deep sleep (N3), or rapid eye movement (REM) sleep. We calculated Cohen's kappa and the percentage of accurately identified epochs. We repeated our analyses after stratification by sleep-disordered breathing (SDB) severity, and after adding thoracic respiratory effort as a backup signal for periods of invalid airflow. RESULTS: CReSS discriminated wake/light sleep/deep sleep/REM sleep with 78% accuracy; the kappa value was 0.643 (95% confidence interval, 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%, non-REM sleep/REM sleep demonstrated a kappa of 0.790 and accuracy of 94%, and light sleep/deep sleep demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across subgroups of no SDB, mild SDB, moderate SDB, and severe SDB. Accuracy increased to 80%, with a kappa value of 0.680 (95% confidence interval, 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal. CONCLUSIONS: We observed substantial agreement between CReSS and the gold-standard comparator of manual sleep staging of polysomnographic signals, which was consistent across the full range of SDB severity. Future research should focus on the extent to which CReSS reduces the discrepancy between the apnea-hypopnea index and the respiratory event index, and the ability of CReSS to identify REM sleep-related obstructive sleep apnea.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience.

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

Age and Sex Disparities in Adherence to CPAP.

BACKGROUND: CPAP effectiveness is limited by suboptimal adherence. Prior studies of adherence have focused on middle-aged men. RESEARCH QUESTION: Does CPAP adherence vary by age and sex? STUDY DESIGN AND METHODS: Telemonitoring data from a CPAP manufacturer database were used to assess adherence in patients initiating CPAP therapy between November 2015 and October 2018. Analyses were restricted to patients in the United States aged 18 to 90 years. RESULTS: Across 789,260 patients initiated on CPAP (mean age, 55 ± 14 years; 58.2% male), overall adherence by US Centers of Medicare & Medicaid Services criteria was 72.6%, but it varied dramatically by age and sex, ranging from 51.3% in 18- to 30-year-old women to 80.6% in 71- to 80-year-old men. Patterns of use over the first 90 days demonstrated that younger age groups had peak CPAP use by the 2nd night, with a subsequent decay in use, including abandonment of CPAP, which was greatest among 18- to 30-year-old women. In contrast, older patients steadily increase use, taking more than a week to maximize usage, and then they have much slower decays in use over time. Younger, but not older, patients have lower use of CPAP on weekends compared with weekday nights. INTERPRETATION: CPAP adherence rates vary substantially by demographics, with 18- to 30-year-old women having the lowest adherence. The pattern of use over the first 90 days also varies substantially by age and sex. Further research to understand and address the causes of disparities will be crucial to maximizing the benefits of CPAP therapy.

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

Pediatric Adenotonsillectomy Trial for Snoring (PATS): protocol for a randomised controlled trial to evaluate the effect of adenotonsillectomy in treating mild obstructive sleep-disordered breathing.

INTRODUCTION: Mild obstructive sleep-disordered breathing (oSDB), characterised by habitual snoring without frequent apnoeas and hypopnoeas on polysomnography, is prevalent in children and commonly treated with adenotonsillectomy (AT). However, the absence of high-level evidence addressing the role of AT in improving health and behavioural outcomes has contributed to significant geographical variations in care and potential for surgery to be both overused and underused. METHODS AND ANALYSIS: The Pediatric Adenotonsillectomy Trial for Snoring (PATS) is a single-blinded, multicentre randomised controlled trial designed to evaluate the effect of AT in treating mild oSDB. Four hundred sixty eligible children, aged 3.0-12.9 years old, will be randomised to either early adenotonsillectomy or to watchful waiting with supportive care (WWSC) with a 1:1 ratio. The study's coprimary endpoints are (1) change from baseline in executive behaviour relating to self-regulation and organisation skills as measured by the Behavioural Rating Inventory of Executive Function (BRIEF) Global Composite Score (GEC); and (2) change from baseline in vigilance as measured on the Go-No-Go (GNG) signal detection parameter (d-prime). A mixed effects model will be used to compare changes in the BRIEF GEC score and GNG score at 6 and 12 months from baseline between the AT arm and the WWSC arm. ETHICS AND DISSEMINATION: The study protocol was approved by the institutional review board (IRB) at Children's Hospital of Philadelphia (CHOP) on 3 October 2014 (14-0 11 214). The approval of CHOP as the central IRB of record was granted on 29 February 2016. The results will be published in peer-reviewed journals and presented at academic conferences. The data collected from the PATS study will be deposited in a repository (National Sleep Research Resource, sleepdata.org) after completion of the study to maximise use by the scientific community. TRIAL REGISTRATION NUMBER: NCT02562040; Pre-results.

The Effect of Continuous Positive Airway Pressure on Vascular Function and Cardiac Structure in Diabetes and Sleep Apnea. A Randomized Controlled Trial.

Rationale: Although both type 2 diabetes mellitus (T2DM) and obstructive sleep apnea (OSA) are independently recognized as risk factors for cardiovascular disease, little is known about their interaction.Objectives: We hypothesized that T2DM and OSA act synergistically to increase vascular risk, and that treatment of OSA would improve vascular reactivity in patients with T2DM plus OSA.Methods: Cross-sectional study of 141 adults with T2DM, OSA, T2DM plus OSA, and control subjects, followed by a 3-month, parallel-arm, randomized, placebo-controlled trial comparing active and sham continuous positive airway pressure (CPAP) in 53 adults with T2DM plus OSA. Endothelium-dependent macro- and microvascular reactivity (flow-mediated dilation [FMD] of the brachial artery and acetylcholine-induced dilation of forearm microvasculature, respectively) and cardiovascular magnetic resonance to assess left- and right-ventricular mass/volume.Results: Mean (±SD) FMD was 6.1 (±4.0)%, 7.3 (±3.6)%, 6.8 (±4.5)%, and 4.8 (±2.9)% in control subjects, T2DM only, OSA only, and T2DM plus OSA, respectively. We observed a significant T2DM × OSA interaction on FMD, such that the mean effect of OSA in those with T2DM was 3.1% (95% confidence interval [CI], 0.6 to 5.6) greater than the effect of OSA in those without T2DM. A total of 3 months of CPAP resulted in a mean absolute increase in FMD of 0.3% (95% CI, -1.9 to 2.5; primary endpoint), with a net improvement of 1.1% (95% CI, -1.4 to 3.6) among those with adherence of 4 h/night or greater. A significant T2DM × OSA interaction was found for both left ventricular (LV) and right ventricular end-diastolic volume, such that OSA was associated with a 22.4 ml (95% CI, 3.2 to 41.6) greater LV end-diastolic volume and 23.2 ml (95% CI, 2.6 to 43.8) greater right ventricular end-diastolic volume in those with T2DM compared with the impact of OSA in those without T2DM. We observed a net improvement in LV end-diastolic volume of 8.7 ml (95% CI, -7.0 to 24.4).Conclusions: The combination of T2DM plus OSA is associated with macrovascular endothelial dysfunction beyond that observed with either disease alone. CPAP for 3 months did not significantly improve macrovascular endothelial function in the intent-to-treat analysis; however, cardiovascular magnetic resonance results suggest that there may be a beneficial effect of CPAP on LV diastolic volume.Clinical trial registered with www.clinicaltrials.gov (NCT01629862).

A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.

Mobile technologies, such as smart phone applications, wearables, ingestibles, and implantables, are increasingly used in clinical research to capture study endpoints. On behalf of the Clinical Trials Transformation Initiative, we aimed to conduct a systematic scoping review and compile a database summarizing pilot studies addressing mobile technology sensor performance, algorithm development, software performance, and/or operational feasibility, in order to provide a resource for guiding decisions about which technology is most suitable for a particular trial. Our systematic search identified 275 publications meeting inclusion criteria. From these papers, we extracted data including the medical condition, concept of interest captured by the mobile technology, outcomes captured by the digital measurement, and details regarding the sensors, algorithms, and study sample. Sixty-seven percent of the technologies identified were wearable sensors, with the remainder including tablets, smartphones, implanted sensors, and cameras. We noted substantial variability in terms of reporting completeness and terminology used. The data have been compiled into an online database maintained by the Clinical Trials Transformation Initiative that can be filtered and searched electronically, enabling a user to find information most relevant to their work. Our long-term goal is to maintain and update the online database, in order to promote standardization of methods and reporting, encourage collaboration, and avoid redundant studies, thereby contributing to the design and implementation of efficient, high-quality trials.