@Article{info:doi/10.2196/69757,
author="Chen, Ming-Che
and Chen, Yen-Chin
and Lin, Cheng-Yu",
title="Enhancing Adherence to Continuous Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea Using Augmented Reality: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2025",
month="May",
day="6",
volume="14",
pages="e69757",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="augmented reality",
keywords="adherence",
keywords="feasibility",
abstract="Background: Continuous positive airway pressure (CPAP) therapy is the gold standard treatment for treating obstructive sleep apnea (OSA). However, patient adherence to CPAP therapy remains a critical challenge, with many individuals finding it difficult to maintain consistent use due to discomfort, lack of understanding, or perceived inconvenience. Augmented reality (AR) offers a novel approach to overcoming these barriers by providing interactive real-time guidance to users, potentially enhancing adherence rates. Objective: This clinical trial aims to examine the feasibility of AR implementation and the effect of AR on improving CPAP adherence in patients with OSA. Methods: A randomized controlled trial with a parallel assignment will be conducted. Participants will be randomly assigned to either an experimental group receiving AR-guided CPAP therapy or a control group receiving standard care. The study will span 6 months, with assessments at baseline (T0), and with follow-ups at 1 month (T1), 3 months (T2), and 6 months (T3) post intervention. The primary outcome measure is CPAP adherence, defined as using the CPAP device for more than 70\% of sleep time, with a minimum of 4 hours per night. Secondary outcomes will evaluate the common adverse effects associated with CPAP therapy, device usability, and time required for CPAP machine use education. Results: This study is funded by the Ministry of Science and Technology, Taiwan (August 2023 to July 2026) and was registered in August 2024 (ClinicalTrials.gov NCT06520592). Participant recruitment is scheduled to begin in April 2025, and by September 2025, we expect to have enrolled 40 participants (50\% of the target sample of 80). Preliminary analyses of CPAP adherence at 1 month and usability data are currently underway. Final data collection is anticipated to be completed by December 2025, with results expected to be published by Fall 2026. Conclusions: Anticipated findings suggest that AR-guided CPAP therapy may significantly enhance patient adherence by improving mask fitting and providing effective, interactive education. If validated, this innovative approach could pave the way for more personalized technology-driven interventions in OSA management and other chronic conditions requiring long-term therapy adherence. Trial Registration: ClinicalTrials.gov NCT06520592; https://clinicaltrials.gov/study/NCT06520592 International Registered Report Identifier (IRRID): PRR1-10.2196/69757 ",
doi="10.2196/69757",
url="https://www.researchprotocols.org/2025/1/e69757",
url="http://www.ncbi.nlm.nih.gov/pubmed/40327384"
}


@Article{info:doi/10.2196/51434,
author="Clements, Frances
and Vedam, Hima
and Chung, Yewon
and Smoleniec, John
and Sullivan, Colin
and Shanmugalingam, Renuka
and Hennessy, Annemarie
and Makris, Angela",
title="Effect of Continuous Positive Airway Pressure or Positional Therapy Compared to Control for Treatment of  Obstructive Sleep Apnea on the Development of Gestational Diabetes Mellitus in Pregnancy: Protocol for Feasibility Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2025",
month="Apr",
day="11",
volume="14",
pages="e51434",
keywords="obstructive sleep apnoea",
keywords="OSA",
keywords="sleep disordered breathing",
keywords="pregnancy",
keywords="CPAP",
keywords="positional therapy",
keywords="gestational diabetes",
keywords="GDM",
keywords="preeclampsia",
keywords="fetomaternal",
keywords="maternal",
keywords="pregnant",
keywords="fetus",
keywords="fetal",
keywords="breathing",
keywords="apnoea",
keywords="sleep",
keywords="respiratory",
keywords="eclampsia",
keywords="pregnant women",
keywords="pregnancy complications",
keywords="hypertension",
abstract="Background: Obstructive sleep apnea (OSA) is a common sleep disorder, and in pregnancy, it is associated with an increased risk of complications, including gestational diabetes mellitus and preeclampsia. Supine sleep may worsen OSA, and in pregnancy, it is associated with an increased risk of stillbirth due to effects on fetomaternal blood flow. Continuous positive airway pressure (CPAP) therapy is considered the gold-standard treatment for moderate to severe OSA, although compliance is frequently poor; positional therapy (PT) is generally less effective than CPAP in nonpregnant patients but may be better tolerated and more accessible during pregnancy. There is limited data on whether widespread, early screening for sleep disorders in pregnant women with symptoms of sleep-disordered breathing or at high risk of metabolic complications and subsequent early intervention with CPAP or PT attenuates fetomaternal risks. Objective: This study aims to determine the feasibility of conducting a randomized controlled trial to assess improved fetomaternal outcomes in a high-risk pregnant population with OSA, using CPAP or PT, initiated by the 16th week of gestation. Methods: This study is a randomized, controlled, open-label feasibility study in which pregnant women with an apnea-hypopnea index (AHI) or respiratory disturbance index (RDI) ?5 are treated with CPAP (auto-titrating and fixed pressure) or positional therapy from early gestation (by 16 weeks) until delivery. The primary outcome is the feasibility of the study protocol and the development of gestational diabetes mellitus by the 28-week gestation period. Secondary outcomes include the development of hypertensive disorders of pregnancy (HDP), maternal weight gain, uterine artery blood flow, glycemic control during pregnancy (in participants who develop gestational diabetes), changes in maternal circulating biomarkers, and neonatal birthweight complications. Polysomnography at 28- to 32-week gestation period, postpartum polysomnography, therapy compliance, and patient acceptability are also assessed. Results: The trial commenced on September 30, 2019. The trial is ongoing as of August 6, 2024. Conclusions: The trial intends to contribute to the growing evidence base to support the need for the identification and treatment of OSA occurring during pregnancy and to assess the feasibility of the study protocol. This will be the first trial to compare the early initiation of CPAP (auto-titrating and fixed pressure) and positional therapy in pregnant women from early gestation, providing alternative therapies for the treatment of OSA in this important population. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619001530112; https://tinyurl.com/yctdzs4u International Registered Report Identifier (IRRID): DERR1-10.2196/51434 ",
doi="10.2196/51434",
url="https://www.researchprotocols.org/2025/1/e51434",
url="http://www.ncbi.nlm.nih.gov/pubmed/40215099"
}


@Article{info:doi/10.2196/64742,
author="Chen, Yuyin
and Zhang, Yuanyuan
and Long, Xiuhong
and Tu, Huiqiong
and Chen, Jibing",
title="Effectiveness of Virtual Reality--Complemented Pulmonary Rehabilitation on Lung Function, Exercise Capacity, Dyspnea, and Health Status in Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="7",
volume="27",
pages="e64742",
keywords="virtual reality",
keywords="video games",
keywords="exergaming",
keywords="pulmonary rehabilitation",
keywords="chronic obstructive pulmonary disease",
keywords="lung function",
keywords="exercise capacity",
keywords="dyspnea",
keywords="health status",
keywords="randomized controlled trial",
keywords="systematic review",
keywords="meta-analysis",
abstract="Background: Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition characterized by persistent airflow obstruction. Pulmonary rehabilitation (PR) is a cornerstone of COPD management but remains underutilized due to barriers such as low motivation and accessibility issues. Virtual reality (VR)--complemented PR offers a novel approach to overcoming these barriers by enhancing patient engagement and rehabilitation outcomes. Objective: This review aims to evaluate the effect of VR-complemented PR compared with comparators on lung function, exercise capacity, dyspnea, health status, and oxygenation in patients with COPD. Additionally, the study aimed to identify which comparator type (active exercise vs nonactive exercise control group) and intervention duration would result in the greatest improvements in rehabilitation outcomes. The study also assessed patient-reported experience measures, including acceptability and engagement. Methods: A comprehensive search of 11 international and Chinese databases identified randomized controlled trials (RCTs) published up to November 2024. Data were analyzed using RevMan 5.4, with pooled effect sizes reported as mean differences (MDs) and 95\% CIs. Results: A total of 16 RCTs involving 1052 participants were included. VR-complemented PR significantly improved lung function (forced expiratory volume in 1 second [FEV1] [L], MD 0.25, P<.001; FEV1/forced vital capacity [FVC], MD 6.12, P<.001; FVC, MD 0.28, P<.001) compared with comparators. Exercise capacity, assessed by the 6MWD, significantly improved (MD 23.49, P<.001) compared with comparators; however, it did not reach the minimally clinically important difference of 26 m, indicating limited clinical significance despite statistical significance. VR-complemented PR also significantly reduced dyspnea measured by the modified British Medical Research Council scale (MD --0.28, P<.001), improved health status measured by the COPD Assessment Test (MD --2.95, P<.001), and enhanced oxygenation status measured by SpO2 (MD 1.35, P=.04) compared with comparators. Subgroup analyses revealed that VR-complemented PR had a significantly greater effect on FEV1 (L) (MD 0.32, P=.005) and 6MWD (MD 40.93, P<.001) compared with the nonactive exercise control group. Additionally, VR-complemented PR showed a greater improvement in FEV1/FVC (MD 6.15, P<.001) compared with the active exercise control group. Intervention duration influenced outcomes, with 5-12-week programs showing the greatest improvement in 6MWD (MD 38.96, P<.001). VR-complemented PR was well-accepted, with higher adherence and engagement rates than comparators. Conclusions: VR-complemented PR significantly improves lung function, exercise capacity, dyspnea, health status, and oxygenation in patients with COPD compared with comparators, while enhancing adherence and engagement. Subgroup analyses showed greater effects on FEV1 (L) and 6MWD compared with the nonactive exercise control group, and a larger improvement in FEV1/FVC compared with the active exercise control group. Interventions (5-12 weeks) yielded the most significant benefits in exercise capacity. These findings highlight VR as a promising adjunct to traditional PR, with future research focusing on long-term outcomes and standardized protocols. ",
doi="10.2196/64742",
url="https://www.jmir.org/2025/1/e64742"
}


@Article{info:doi/10.2196/67861,
author="Brown, Jeffrey
and Mitchell, Zachary
and Jiang, Albert Yu
and Archdeacon, Ryan",
title="Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="28",
volume="9",
pages="e67861",
keywords="snore detection",
keywords="snore tracking",
keywords="machine learning",
keywords="SleepWatch",
keywords="Bodymatter",
keywords="neural net",
keywords="mobile device",
keywords="smartphone",
keywords="smartphone application",
keywords="mobile health",
keywords="sleep monitoring",
keywords="sleep tracking",
keywords="sleep apnea",
abstract="Background: High-quality sleep is essential for both physical and mental well-being. Insufficient or poor-quality sleep is linked to numerous health issues, including cardiometabolic diseases, mental health disorders, and increased mortality. Snoring---a prevalent condition---can disrupt sleep and is associated with disease states, including coronary artery disease and obstructive sleep apnea. Objective: The SleepWatch smartphone app (Bodymatter, Inc) aims to monitor and improve sleep quality and has snore detection capabilities that were built through a machine-learning process trained on over 60,000 acoustic events. This study evaluated the accuracy of the SleepWatch snore detection algorithm in a simulated real-world setting. Methods: The snore detection algorithm was tested by using 36 simulated snoring audio files derived from 18 participants. Each file simulated a snoring index between 30 and 600 snores per hour. Additionally, 9 files with nonsnoring sounds were tested to evaluate the algorithm's capacity to avoid false positives. Sensitivity, specificity, and accuracy were calculated for each test, and results were compared by using Bland-Altman plots and Spearman correlation to assess the statistical association between detected and actual snores. Results: The SleepWatch algorithm showed an average sensitivity of 86.3\% (SD 16.6\%), an average specificity of 99.5\% (SD 10.8\%), and an average accuracy of 95.2\% (SD 5.6\%) across the snoring tests. The positive predictive value and negative predictive value were 98.9\% (SD 2.6\%) and 93.8\% (SD 14.4\%) respectively. The algorithm performed exceptionally well in avoiding false positives, with a specificity of 97.1\% (SD 3.5\%) for nonsnoring files. Inclusive of all snoring and nonsnore tests, the aggregated accuracy for all trials in this bench study was 95.6\% (SD 5.3\%). The Bland-Altman analysis indicated a mean bias of ?29.8 (SD 41.7) snores per hour, and the Spearman correlation analysis revealed a strong positive correlation (rs=0.974; P<.001) between detected and actual snore rates. Conclusions: The SleepWatch snore detection algorithm demonstrates high accuracy and compares favorably with other snore detection apps. Aside from its broader use in sleep monitoring, SleepWatch demonstrates potential as a tool for identifying individuals at risk for sleep-disordered breathing, including obstructive sleep apnea, on the basis of the snoring index. ",
doi="10.2196/67861",
url="https://formative.jmir.org/2025/1/e67861"
}


@Article{info:doi/10.2196/63230,
author="Egmose, Julie
and Kronborg, Thomas
and Hejlesen, Ole
and Hangaard, Stine",
title="Contactless Sleep Monitoring for the Detection of Exacerbations in People With Chronic Obstructive Pulmonary Disease: Protocol for a Longitudinal Observational Study",
journal="JMIR Res Protoc",
year="2025",
month="Mar",
day="14",
volume="14",
pages="e63230",
keywords="disease exacerbation",
keywords="chronic obstructive pulmonary disease",
keywords="contactless measurements",
keywords="sleep monitoring systems",
keywords="heart rate measurement",
keywords="respiration rate measurement",
keywords="radar technology",
keywords="health literacy",
keywords="patient remote monitoring",
abstract="Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are one of the main causes of mortality, and early detection of exacerbations is thus essential. Telemedicine solutions have shown promising results for the detection of exacerbations in COPD and have increasingly been used. However, the effect of telemedicine is divergent. According to several studies, respiration rate (RR) increases before, during, and after an exacerbation and the change is measurable with several contactless devices. Despite this, RR is rarely measured, and telemedicine solutions only use wearable devices for measuring RR, even though wearable respiratory monitoring devices have been associated with certain drawbacks. Contactless devices are often used during sleep, as measurements conducted during sleep minimize the risk of disturbance from physical activities. However, the potential of measuring RR and heart rate (HR) during sleep for the detection of exacerbations in COPD remains unclear. Objective: The aim of this observational study is to investigate whether contactless measurement of RR, HR, and sleep stages can be used to detect exacerbations in people with COPD. Methods: An observational study including 50 participants with COPD will be conducted. The participants reside in Aalborg municipality, located in the North Denmark Region. Participants will use a contactless monitor (Sleepiz One+) near their bed during sleep for a period of 4 months. After data collection, descriptive statistics will be used to identify any extremes or variations in RR, HR, or sleep stages in the nights preceding an exacerbation. Correlation analysis will be performed to evaluate the relationship between the number of exacerbations and extremes or variations in RR, HR, or sleep stages. Finally, qualitative interviews will be conducted with 12 participants to explore their experiences of sleeping with the monitor nearby. Results: Recruitment started at the end of April 2024. A total of 12 participants have been recruited, and the remaining participants are expected to be recruited during March and April 2025. Six out of 12 participants have completed the data collection and qualitative interview stages. Overall data collection is expected to be completed by September 2025. The results are expected to provide insight into the potential for identifying extremes or variations in RR, HR, or sleep stages in the days preceding an exacerbation. Additionally, the results are expected to assess the correlation between the number of exacerbations and extremes or variations in RR, HR, and sleep stages. Conclusions: The findings from this study may clarify the possibility of using a contactless monitor to detect exacerbations in COPD. Furthermore, the results may have the potential to improve the ability to predict exacerbations in the future. International Registered Report Identifier (IRRID): DERR1-10.2196/63230 ",
doi="10.2196/63230",
url="https://www.researchprotocols.org/2025/1/e63230"
}


@Article{info:doi/10.2196/65840,
author="Alami, Sarah
and Schaller, Manuella
and Blais, Sylvie
and Taupin, Henry
and Hern{\'a}ndez Gonz{\'a}lez, Marta
and Gagnadoux, Fr{\'e}d{\'e}ric
and Pinto, Paula
and Cano-Pumarega, Irene
and Bedert, Lieven
and Braithwaite, Ben
and Servy, Herv{\'e}
and Ouary, St{\'e}phane
and Fabre, C{\'e}line
and Bazin, Fabienne
and Texereau, Jo{\"e}lle",
title="Evaluating the Benefit of Home Support Provider Services for Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea: Protocol for an Ambispective International Real-World Study",
journal="JMIR Res Protoc",
year="2025",
month="Jan",
day="31",
volume="14",
pages="e65840",
keywords="obstructive sleep apnea",
keywords="positive airway pressure",
keywords="real-world evidence",
keywords="home support provider",
keywords="adherence",
keywords="electronic patient-reported outcome",
keywords="comparative real-world study",
abstract="Background: Adherence and persistence to positive airway pressure (PAP) therapy are key factors for positive health outcomes. Home support providers participate in the home implementation and follow-up of PAP therapy for patients with obstructive sleep apnea (OSA). In Europe, home support provider service levels are country (or area) specific, resulting in differences in content and frequency of patient interactions. However, no robust evaluation of the impact of these differences on clinical and patient outcomes has been performed. Objective: The AWAIR study aims to evaluate and compare the impact of different home support provider service levels on PAP adherence and persistence in 4 European countries. Methods: This real-world, ambispective, cohort study---conducted in France, Belgium, Spain, and Portugal---will recruit adults with OSA who started PAP therapy between 2019 and 2023 and were followed by an Air Liquide Healthcare home support provider. Given the large number of eligible participants (around 150,000), the study will use a decentralized and digital approach. A patient video will present the study objectives and the participation process. A secure electronic solution will be used to manage patient information and consent, as well as to administer a web-based questionnaire. Retrospective data, collected during routine patient follow-up by home support providers, include the level of service and device data, notably PAP use. Prospective data collected using an electronic patient-reported outcome tool include health status, OSA-related factors, patient-reported outcomes including quality of life and symptoms, OSA and PAP literacy, patient-reported experience, and satisfaction with PAP therapy and service. Hierarchical models, adjusted for preidentified confounding factors, will be used to assess the net effect of home support provider services on PAP adherence and persistence while minimizing real-world study biases and considering the influence of country-level contextual factors. We hypothesize that higher levels of home support provider services will be positively associated with adherence and persistence to PAP therapy. Results: As of December 2024, the study has received approval in France, Portugal, and 2 regions of Spain. The study began enrollment in France in October 2024. Results are expected in the second quarter of 2025. Conclusions: The AWAIR study has a unique design, leveraging an unprecedented number of eligible participants, decentralized technologies, and a real-world comparative methodology across multiple countries. This approach will highlight intercountry differences in terms of patient characteristics, PAP adherence, and persistence, as well as patient-reported outcomes, patient-reported experiences, and satisfaction with the home service provider. By assessing the added value of home support provider services, the results will support best practices for patient management and for decision-making by payers and authorities. International Registered Report Identifier (IRRID): PRR1-10.2196/65840 ",
doi="10.2196/65840",
url="https://www.researchprotocols.org/2025/1/e65840"
}


@Article{info:doi/10.2196/51615,
author="Kuo, Nai-Yu
and Tsai, Hsin-Jung
and Tsai, Shih-Jen
and Yang, C. Albert",
title="Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="19",
volume="26",
pages="e51615",
keywords="sleep apnea",
keywords="machine learning",
keywords="questionnaire",
keywords="oxygen saturation",
keywords="polysomnography",
keywords="screening",
keywords="sleep disorder",
keywords="insomnia",
keywords="utilization",
keywords="dataset",
keywords="training",
keywords="diagnostic",
abstract="Background: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time consuming and labor intensive, thus limiting diagnostic efficiency. Objective: This study aims to develop 2 sequential machine learning models to efficiently screen and differentiate OSA. Methods: We used 2 datasets comprising 8444 cases from the Sleep Heart Health Study (SHHS) and 1229 cases from Taipei Veterans General Hospital (TVGH). The Questionnaire Model (Model-Questionnaire) was designed to distinguish OSA from primary insomnia using demographic information and Pittsburgh Sleep Quality Index questionnaires, while the Saturation Model (Model-Saturation) categorized OSA severity based on multiple blood oxygen saturation parameters. The performance of the sequential machine learning models in screening and assessing the severity of OSA was evaluated using an independent test set derived from TVGH. Results: The Model-Questionnaire achieved an F1-score of 0.86, incorporating demographic data and the Pittsburgh Sleep Quality Index. Model-Saturation training by the SHHS dataset displayed an F1-score of 0.82 when using the power spectrum of blood oxygen saturation signals and reached the highest F1-score of 0.85 when considering all saturation-related parameters. Model-saturation training by the TVGH dataset displayed an F1-score of 0.82. The independent test set showed stable results for Model-Questionnaire and Model-Saturation training by the TVGH dataset, but with a slightly decreased F1-score (0.78) in Model-Saturation training by the SHHS dataset. Despite reduced model accuracy across different datasets, precision remained at 0.89 for screening moderate to severe OSA. Conclusions: Although a composite model using multiple saturation parameters exhibits higher accuracy, optimizing this model by identifying key factors is essential. Both models demonstrated adequate at-home screening capabilities for sleep disorders, particularly for patients unsuitable for in-laboratory sleep studies. ",
doi="10.2196/51615",
url="https://www.jmir.org/2024/1/e51615",
url="http://www.ncbi.nlm.nih.gov/pubmed/39699950"
}


@Article{info:doi/10.2196/54792,
author="Knowlden, P. Adam
and Winchester, J. Lee
and MacDonald, V. Hayley
and Geyer, D. James
and Higginbotham, C. John",
title="Associations Among Cardiometabolic Risk Factors, Sleep Duration, and Obstructive Sleep Apnea in a Southeastern US Rural Community: Cross-Sectional Analysis From the SLUMBRx-PONS Study",
journal="JMIR Form Res",
year="2024",
month="Nov",
day="8",
volume="8",
pages="e54792",
keywords="obstructive sleep apnea",
keywords="obesity",
keywords="adiposity",
keywords="cardiometabolic",
keywords="cardiometabolic disease",
keywords="risk factors",
keywords="sleep",
keywords="sleep duration",
keywords="sleep apnea",
keywords="Short Sleep Undermines Cardiometabolic Health-Public Health Observational study",
keywords="SLUMBRx study",
abstract="Background: Short sleep and obstructive sleep apnea are underrecognized strains on the public health infrastructure. In the United States, over 35\% of adults report short sleep and more than 80\% of individuals with obstructive sleep apnea remain undiagnosed. The associations between inadequate sleep and cardiometabolic disease risk factors have garnered increased attention. However, challenges persist in modeling sleep-associated cardiometabolic disease risk factors. Objective: This study aimed to report early findings from the Short Sleep Undermines Cardiometabolic Health-Public Health Observational study (SLUMBRx-PONS). Methods: Data for the SLUMBRx-PONS study were collected cross-sectionally and longitudinally from a nonclinical, rural community sample (n=47) in the southeast United States. Measures included 7 consecutive nights of wrist-based actigraphy (eg, mean of 7 consecutive nights of total sleep time [TST7N]), 1 night of sleep apnea home testing (eg, apnea-hypopnea index [AHI]), and a cross-sectional clinical sample of anthropometric (eg, BMI), cardiovascular (eg, blood pressure), and blood-based biomarkers (eg, triglycerides and glucose). Correlational analyses and regression models assessed the relationships between the cardiometabolic disease risk factors and the sleep indices (eg, TST7N and AHI). Linear regression models were constructed to examine associations between significant cardiometabolic indices of TST7N (model 1) and AHI (model 2). Results: Correlational assessment in model 1 identified significant associations between TST7N and AHI (r=--0.45, P=.004), BMI (r=--0.38, P=.02), systolic blood pressure (r=0.40, P=.01), and diastolic blood pressure (r=0.32, P=.049). Pertaining to model 1, composite measures of AHI, BMI, systolic blood pressure, and diastolic blood pressure accounted for 25.1\% of the variance in TST7N (R2adjusted=0.25; F2,38=7.37; P=.002). Correlational analyses in model 2 revealed significant relationships between AHI and TST7N (r=--0.45, P<.001), BMI (r=0.71, P<.001), triglycerides (r=0.36, P=.03), and glucose (r=0.34, P=.04). Results from model 2 found that TST7N, triglycerides, and glucose accounted for 37.6\% of the variance in the composite measure of AHI and BMI (R2adjusted=0.38; F3,38=8.63; P<.001). Conclusions: Results from the SLUMBRx-PONS study highlight the complex interplay between sleep-associated risk factors for cardiometabolic disease. Early findings underscore the need for further investigations incorporating the collection of clinical, epidemiological, and ambulatory measures to inform public health, health promotion, and health education interventions addressing the cardiometabolic consequences of inadequate sleep. International Registered Report Identifier (IRRID): RR2-10.2196/27139 ",
doi="10.2196/54792",
url="https://formative.jmir.org/2024/1/e54792"
}


@Article{info:doi/10.2196/60769,
author="Mak, Selene
and Ash, Garrett
and Liang, Li-Jung
and Der-McLeod, Erin
and Ghadimi, Sara
and Kewalramani, Anjali
and Naeem, Saadia
and Zeidler, Michelle
and Fung, Constance",
title="Testing a Consumer Wearables Program to Promote the Use of Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea: Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Sep",
day="19",
volume="13",
pages="e60769",
keywords="sleep apnea",
keywords="consumer wearables",
keywords="adherence",
keywords="self-management",
keywords="mobile phone",
abstract="Background: Although positive airway pressure (PAP) therapy is considered first-line treatment for obstructive sleep apnea (OSA), nonadherence is common. Numerous factors influence PAP use, including a belief that the therapy is important and effective. In theory, providing information to patients about their blood oxygen levels during sleep (which may be low when PAP is not used), juxtaposed to information about their PAP use, may influence a patient's beliefs about therapy and increase PAP use. With the advent of consumer wearable smartwatches' blood oxygen saturation monitoring capability (and the existing routine availability of PAP use data transmitted via modem to clinical dashboards), there is an opportunity to provide this combination of information to patients. Objective: This study aims to test the feasibility, acceptability, and preliminary efficacy of the Chronic Care Management With Wearable Devices in Patients Prescribed Positive Airway Pressure Therapy (mPAP), a program that augments current PAP therapy data with consumer-grade wearable device to promote self-management of PAP therapy for OSA in a pilot randomized waitlist-controlled clinical trial. Methods: This is a single-blinded randomized controlled trial. We will randomize 50 individuals with a history of OSA, who receive care from a Department of Veterans Affairs medical center in the Los Angeles area and are nonadherent to prescribed PAP therapy, into either an immediate intervention group or a waitlist control group. During a 28-day intervention, the participants will wear a study-provided consumer wearable device and complete a weekly survey about their OSA symptoms. A report that summarizes consumer wearable--provided oxygen saturation values, PAP use derived from modem data, and patient-reported OSA symptoms will be prepared weekly and shared with the patient. The immediate intervention group will begin intervention immediately after randomization (T1). Assessments will occur at week 5 (T3; 1 week after treatment for the immediate intervention group and repeat baseline for the waitlist control group) and week 11 (T5; follow-up for the immediate intervention group and 1 week after treatment for the waitlist control group). The primary outcome will be the change in 7-day PAP adherence (average minutes per night) from T1 to T3. The primary analysis will be a comparison of the primary outcome between the immediate intervention and the waitlist control groups (intention-to-treat design), using a 2-sample, 2-sided t test on change scores (unadjusted). Results: Recruitment began in October 2023. Data analysis is expected to begin in October 2024 when all follow-ups are complete, and a manuscript summarizing trial results will be submitted following completion of data analysis. Conclusions: Findings from the study may provide additional insights on how patients with OSA might use patient-generated health data collected by consumer wearables to inform self-management of OSA and possibly increase their use of PAP therapy. Trial Registration: ClinicalTrials.gov NCT06039865; https://clinicaltrials.gov/study/NCT06039865 International Registered Report Identifier (IRRID): DERR1-10.2196/60769 ",
doi="10.2196/60769",
url="https://www.researchprotocols.org/2024/1/e60769",
url="http://www.ncbi.nlm.nih.gov/pubmed/39207912"
}


@Article{info:doi/10.2196/58187,
author="Abd-alrazaq, Alaa
and Aslam, Hania
and AlSaad, Rawan
and Alsahli, Mohammed
and Ahmed, Arfan
and Damseh, Rafat
and Aziz, Sarah
and Sheikh, Javaid",
title="Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="10",
volume="26",
pages="e58187",
keywords="sleep apnea",
keywords="hypopnea",
keywords="artificial intelligence",
keywords="wearable devices",
keywords="machine learning",
keywords="systematic review",
keywords="mobile phone",
abstract="Background: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. Objective: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. Methods: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. Results: Among 615 studies, 38 (6.2\%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. Conclusions: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses. ",
doi="10.2196/58187",
url="https://www.jmir.org/2024/1/e58187",
url="http://www.ncbi.nlm.nih.gov/pubmed/39255014"
}


@Article{info:doi/10.2196/51901,
author="Roberge, Patrice
and Ruel, Jean
and B{\'e}gin-Drolet, Andr{\'e}
and Lemay, Jean
and Gakwaya, Simon
and Masse, Jean-Fran{\c{c}}ois
and S{\'e}ri{\`e}s, Fr{\'e}d{\'e}ric",
title="Preliminary Assessment of an Ambulatory Device Dedicated to Upper Airway Muscle Training in Patients With Sleep Apnea: Proof-of-Concept Study",
journal="JMIR Biomed Eng",
year="2024",
month="Apr",
day="15",
volume="9",
pages="e51901",
keywords="obstructive sleep apnea/hypopnea syndrome",
keywords="OSAHS",
keywords="myofunctional therapy",
keywords="myotherapy",
keywords="oral",
keywords="orofacial",
keywords="myology",
keywords="musculature",
keywords="labial",
keywords="buccal",
keywords="lingual",
keywords="speech therapy",
keywords="physiotherapy",
keywords="physical therapy",
keywords="oropharyngeal exercises",
keywords="oropharyngeal",
keywords="pharyngeal",
keywords="pharynx",
keywords="hypopnea",
keywords="lip",
keywords="home-based",
keywords="portable device",
keywords="devices",
keywords="ambulatory",
keywords="portable",
keywords="monitoring",
keywords="apnea",
keywords="mouth",
keywords="lips",
keywords="tongue",
keywords="facial",
keywords="exercise",
keywords="exercises",
keywords="myofunctional",
keywords="continuous monitoring",
keywords="sleep-disordered breathing",
keywords="sleep",
keywords="breathing",
keywords="tongue exercise",
keywords="lip exercise",
keywords="mHealth",
keywords="muscle",
keywords="muscles",
keywords="muscular",
keywords="airway",
keywords="sleep apnea",
abstract="Background: Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a prevalent condition affecting a substantial portion of the global population, with its prevalence increasing over the past 2 decades. OSAHS is characterized by recurrent upper airway (UA) closure during sleep, leading to significant impacts on quality of life and heightened cardiovascular and metabolic morbidity. Despite continuous positive airway pressure (CPAP) being the gold standard treatment, patient adherence remains suboptimal due to various factors, such as discomfort, side effects, and treatment unacceptability. Objective: Considering the challenges associated with CPAP adherence, an alternative approach targeting the UA muscles through myofunctional therapy was explored. This noninvasive intervention involves exercises of the lips, tongue, or both to improve oropharyngeal functions and mitigate the severity of OSAHS. With the goal of developing a portable device for home-based myofunctional therapy with continuous monitoring of exercise performance and adherence, the primary outcome of this study was the degree of completion and adherence to a 4-week training session. Methods: This proof-of-concept study focused on a portable device that was designed to facilitate tongue and lip myofunctional therapy and enable precise monitoring of exercise performance and adherence. A clinical study was conducted to assess the effectiveness of this program in improving sleep-disordered breathing. Participants were instructed to perform tongue protrusion, lip pressure, and controlled breathing as part of various tasks 6 times a week for 4 weeks, with each session lasting approximately 35 minutes. Results: Ten participants were enrolled in the study (n=8 male; mean age 48, SD 22 years; mean BMI 29.3, SD 3.5 kg/m2; mean apnea-hypopnea index [AHI] 20.7, SD 17.8/hour). Among the 8 participants who completed the 4-week program, the overall compliance rate was 91\% (175/192 sessions). For the tongue exercise, the success rate increased from 66\% (211/320 exercises; SD 18\%) on the first day to 85\% (272/320 exercises; SD 17\%) on the last day (P=.05). AHI did not change significantly after completion of training but a noteworthy correlation between successful lip exercise improvement and AHI reduction in the supine position was observed (Rs=--0.76; P=.03). These findings demonstrate the potential of the device for accurately monitoring participants' performance in lip and tongue pressure exercises during myofunctional therapy. The diversity of the training program (it mixed exercises mixed training games), its ability to provide direct feedback for each exercise to the participants, and the easy measurement of treatment adherence are major strengths of our training program. Conclusions: The study's portable device for home-based myofunctional therapy shows promise as a noninvasive alternative for reducing the severity of OSAHS, with a notable correlation between successful lip exercise improvement and AHI reduction, warranting further development and investigation. ",
doi="10.2196/51901",
url="https://biomedeng.jmir.org/2024/1/e51901",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875673"
}


@Article{info:doi/10.2196/47809,
author="Haverinen, Jari
and Harju, Terttu
and Mikkonen, Hanna
and Liljamo, Pia
and Turpeinen, Miia
and Reponen, Jarmo",
title="Digital Care Pathway for Patients With Sleep Apnea in Specialized Care: Mixed Methods Study",
journal="JMIR Hum Factors",
year="2024",
month="Feb",
day="22",
volume="11",
pages="e47809",
keywords="health services",
keywords="telehealth",
keywords="telemedicine",
keywords="health personnel",
keywords="sleep apnea syndromes",
keywords="mobile phone",
abstract="Background: Sleep apnea is a significant public health disorder in Finland, with a prevalence of 3.7\%. Continuous positive airway pressure (CPAP) therapy is the first-line treatment for moderate or severe sleep apnea. From November 18, 2019, all patients who started their CPAP therapy at Oulu University Hospital were attached to a sleep apnea digital care pathway (SA-DCP) and were instructed on its use. Some patients still did not use the SA-DCP although they had started their CPAP therapy. Objective: We aimed to study health care professionals' (HCPs') perspectives on the SA-DCP and its usefulness for their work; whether the main targets of SA-DCP can be reached: shortening the initial guiding sessions of CPAP therapy, reducing patient calls and contact with HCPs, and improving patients' adherence to CPAP therapy; and patients' perspectives on the SA-DCP and its usefulness to them. Methods: Overall, 6 HCPs were interviewed in May and June 2021. The survey for SA-DCP users (58/91, 64\%) and SA-DCP nonusers (33/91, 36\%) was conducted in 2 phases: from May to August 2021 and January to June 2022. CPAP device remote monitoring data were collected from SA-DCP users (80/170, 47.1\%) and SA-DCP nonusers (90/170, 52.9\%) in May 2021. The registered phone call data were collected during 2019, 2020, and 2021. Feedback on the SA-DCP was collected from 446 patients between February and March 2022. Results: According to HCPs, introducing the SA-DCP had not yet significantly improved their workload and work practices, but it had brought more flexibility in some communication situations. A larger proportion of SA-DCP users familiarized themselves with prior information about CPAP therapy before the initial guiding session than nonusers (43/58, 74\% vs 16/33, 49\%; P=.02). Some patients still had not received prior information about CPAP therapy; therefore, most of the sessions were carried out according to their needs. According to the patient survey and remote monitoring data of CPAP devices, adherence to CPAP therapy was high for both SA-DCP users and nonusers. The number of patients' phone calls to HCPs did not decrease during the study. SA-DCP users perceived their abilities to use information and communications technology to be better than nonusers (mean 4.2, SD 0.8 vs mean 3.2, SD 1.2; P<.001). Conclusions: According to this study, not all the goals set for the introduction of the SA-DCP have been achieved. Despite using the SA-DCP, some patients still wanted to communicate with HCPs by phone. The most significant factors explaining the nonuse of the SA-DCP were lower digital literacy and older age of the patients. In the future, more attention should be paid to these user groups when designing and introducing upcoming digital care pathways. ",
doi="10.2196/47809",
url="https://humanfactors.jmir.org/2024/1/e47809",
url="http://www.ncbi.nlm.nih.gov/pubmed/38386368"
}


@Article{info:doi/10.2196/47146,
author="Vaussenat, Fabrice
and Bhattacharya, Abhiroop
and Payette, Julie
and Benavides-Guerrero, A. Jaime
and Perrotton, Alexandre
and Gerlein, Felipe Luis
and Cloutier, G. Sylvain",
title="Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study",
journal="JMIR Biomed Eng",
year="2023",
month="Oct",
day="25",
volume="8",
pages="e47146",
keywords="relative humidity sensor",
keywords="design",
keywords="develop",
keywords="development",
keywords="tidal volume",
keywords="pulmonary volume",
keywords="COPD",
keywords="pulmonary",
keywords="respiratory",
keywords="sensor",
keywords="sensors",
keywords="wearables",
keywords="humidity",
keywords="medical device",
keywords="breathing",
keywords="wearable",
keywords="ventilation",
keywords="air",
abstract="Background: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea. Objective: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard. Methods: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods. Results: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of --0.25 and 0.33. The RR bias was 0.018, and the LoAs were --1.89 and 1.89. Conclusions: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals. ",
doi="10.2196/47146",
url="https://biomedeng.jmir.org/2023/1/e47146",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875670"
}


@Article{info:doi/10.2196/47460,
author="Hnatiak, Jakub
and Zikmund Galkova, Lujza
and Winnige, Petr
and Batalik, Ladislav
and Dosbaba, Filip
and Ludka, Ondrej
and Krejci, Jan",
title="Obstructive Sleep Apnea and a Comprehensive Remotely Supervised Rehabilitation Program: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Sep",
day="18",
volume="12",
pages="e47460",
keywords="obstructive sleep apnea",
keywords="telerehabilitation",
keywords="telemonitoring",
keywords="CPAP",
keywords="apnea-hypopnea index",
keywords="telehealth",
keywords="telemedicine",
keywords="sleep",
keywords="respiratory",
keywords="home based",
keywords="rehabilitation",
keywords="RCT",
keywords="randomized controlled trial",
abstract="Background: Obstructive sleep apnea (OSA) is characterized by recurrent, intermittent partial or complete obstruction of the upper respiratory tract during sleep, which negatively affects the patient's daily quality of life (QoL). Middle-aged and older men who smoke and have obesity are most at risk. Even though the use of continuous positive airway pressure (CPAP) during sleep remains the gold standard treatment, various rehabilitation methods, such as exercise, respiratory therapy, myofunctional therapy, and nutritional lifestyle interventions, also appear to be effective. Moreover, it is increasingly recommended to use alternative or additional therapy options in combination with CPAP therapy. Objective: This study aims to evaluate if a comprehensive home-based, remotely supervised rehabilitation program (tele-RHB), in combination with standard therapy, can improve OSA severity by decreasing the apnea-hypopnea index (AHI); improve objective parameters of polysomnographic, spirometric, anthropometric, and body composition examinations; improve lipid profile, maximal mouth pressure, and functional capacity tests; and enhance the subjective perception of QoL, as well as daytime sleepiness in male participants with moderate to severe OSA. Our hypothesis is that a combination of the tele-RHB program and CPAP therapy will be more effective by improving OSA severity and the abovementioned parameters. Methods: This randomized controlled trial aims to recruit 50 male participants between the ages of 30 and 60 years with newly diagnosed moderate to severe OSA. Participants will be randomized 1:1, either to a 12-week tele-RHB program along with CPAP therapy or to CPAP therapy alone. After the completion of the intervention, the participants will be invited to complete a 1-year follow-up. The primary outcomes will be the polysomnographic value of AHI, Epworth Sleepiness Scale score, 36-Item Short Form Health Survey (SF-36) score, percentage of body fat, 6-minute walk test distance covered, as well as maximal inspiratory and expiratory mouth pressure values. Secondary outcomes will include polysomnographic values of oxygen desaturation index, supine AHI, total sleep time, average heart rate, mean oxygen saturation, and the percentage of time with oxygen saturation below 90\%; anthropometric measurements of neck, waist, and hip circumference; BMI values; forced vital capacity; forced expiratory volume in 1 second; World Health Organization's tool to measure QoL (WHOQOL-BREF) score; and lipid profile values. Results: Study recruitment began on October 25, 2021, and the estimated study completion date is December 2024. Analyses will be performed to examine whether the combination of the tele-RHB program and CPAP therapy will be more effective in the reduction of OSA severity and improvement of QoL, body composition and circumferences, exercise tolerance, lipid profile, as well as respiratory muscle and lung function, compared to CPAP therapy alone. Conclusions: The study will evaluate the effect of a comprehensive tele-RHB program on selected parameters mentioned above in male participants. The results of this intervention could help the further development of novel additional therapeutic home-based options for OSA. Trial Registration: ClinicalTrials.gov NCT04759456; https://clinicaltrials.gov/ct2/show/NCT04759456 International Registered Report Identifier (IRRID): DERR1-10.2196/47460 ",
doi="10.2196/47460",
url="https://www.researchprotocols.org/2023/1/e47460",
url="http://www.ncbi.nlm.nih.gov/pubmed/37721786"
}


@Article{info:doi/10.2196/40193,
author="Lacroix, Joyca
and Tatousek, Jan
and Den Teuling, Niek
and Visser, Thomas
and Wells, Charles
and Wylie, Paul
and Rosenberg, Russell
and Bogan, Richard",
title="Effectiveness of an Intervention Providing Digitally Generated Personalized Feedback and Education on Adherence to Continuous Positive Airway Pressure: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2023",
month="May",
day="22",
volume="25",
pages="e40193",
keywords="therapy adherence",
keywords="personalized feedback",
keywords="personalized education",
keywords="tailored communication",
keywords="psychological profile",
keywords="continuous positive airway pressure therapy",
keywords="CPAP therapy",
keywords="obstructive sleep apnea",
abstract="Background: Many people worldwide experience obstructive sleep apnea, which is associated with medical and psychological problems. Continuous positive airway pressure (CPAP) is an efficacious therapy for obstructive sleep apnea, but its effect is limited by nonadherence. Studies show that personalized education and feedback can increase CPAP adherence. Moreover, tailoring the style of information to the psychological profile of a patient has been shown to enhance the impact of interventions. Objective: This study aimed to assess the effect of an intervention providing digitally generated personalized education and feedback on CPAP adherence and the additional effect of tailoring the style of the education and feedback to an individual's psychological profile. Methods: This study was a 90-day, multicenter, parallel, single-blinded, and randomized controlled trial with 3 conditions: personalized content in a tailored style (PT) in addition to usual care (UC), personalized content in a nontailored style (PN) in addition to UC, and UC. To test the effect of personalized education and feedback, the PN + PT group was compared with the UC group. To test the additional effect of tailoring the style to psychological profiles, the PN and PT groups were compared. Overall, 169 participants were recruited from 6 US sleep clinics. The primary outcome measures were adherence based on minutes of use per night and on nights of use per week. Results: We found a significant positive effect of personalized education and feedback on both primary adherence outcome measures. The difference in the estimated average adherence based on minutes of use per night between the PT + PN and UC groups on day 90 was 81.3 minutes in favor of the PT + PN group (95\% CI ?134.00 to ?29.10; P=.002). The difference in the average adherence based on nights of use per week between the PT + PN and UC groups at week 12 was 0.9 nights per week in favor of the PT + PN group (difference in odds ratio 0.39, 95\% CI 0.21-0.72; P=.003). We did not find an additional effect of tailoring the style of the intervention to psychological profiles on the primary outcomes. The difference in nightly use between the PT and PN groups on day 90 (95\% CI ?28.20 to 96.50; P=.28) and the difference in nights of use per week between the PT and PN groups at week 12 (difference in odds ratio 0.85, 95\% CI 0.51-1.43; P=.054) were both nonsignificant. Conclusions: The results show that personalized education and feedback can increase CPAP adherence substantially. Tailoring the style of the intervention to the psychological profiles of patients did not further increase adherence. Future research should investigate how the impact of interventions can be enhanced by catering to differences in psychological profiles. Trial Registration: ClinicalTrials.gov NCT02195531; https://clinicaltrials.gov/ct2/show/NCT02195531 ",
doi="10.2196/40193",
url="https://www.jmir.org/2023/1/e40193",
url="http://www.ncbi.nlm.nih.gov/pubmed/37213195"
}


@Article{info:doi/10.2196/41049,
author="Fonseca, Catarina
and Cavadas, Francisca
and Fonseca, Patr{\'i}cia",
title="Upper Airway Assessment in Cone-Beam Computed Tomography for Screening of Obstructive Sleep Apnea Syndrome: Development of an Evaluation Protocol in Dentistry",
journal="JMIR Res Protoc",
year="2023",
month="May",
day="5",
volume="12",
pages="e41049",
keywords="cone-beam computed tomography",
keywords="three-dimensional image",
keywords="3D image",
keywords="airway obstructions",
keywords="sleep medicine specialty",
keywords="dentistry",
keywords="obstructive sleep apnea",
keywords="protocol",
abstract="Background: The upper airways are formed by the nasal cavities, pharynx, and larynx. There are several radiographic methods that allow evaluation of the craniofacial structure. Upper airway analysis in cone-beam computed tomography (CBCT) may be useful in diagnosing some pathologies such as obstructive sleep apnea syndrome (OSAS). OSAS prevalence has increased significantly in recent decades, justified by increased obesity and average life expectancy. It can be associated with cardiovascular, respiratory, and neurovascular diseases, diabetes, and hypertension. In some individuals with OSAS, the upper airway is compromised and narrowed. Nowadays, CBCT is widely used in dentistry by clinicians. Its use for upper airway assessment would be an advantage for screening some abnormalities related to an increased risk of pathologies such as OSAS. CBCT helps to calculate the total volume of the airways and their area in different anatomical planes (sagittal, coronal, and transverse). It also helps identify regions with the highest anteroposterior and laterolateral constriction of the airways. Despite its undoubted advantages, airway assessment is not routinely performed in dentistry. There is no protocol that allows comparisons between studies, which makes it difficult to obtain scientific evidence in this area. Hence, there is an urgent need to standardize the protocol for upper airway measurement to help clinicians identify at-risk patients. Objective: Our main aim is to develop a standard protocol for upper airway evaluation in CBCT for OSAS screening in dentistry. Methods: To measure and evaluate the upper airways, data are obtained using Planmeca ProMax 3D (Planmeca). Patient orientation is performed in accordance with the manufacturer's indications at the time of image acquisition. The exposure corresponds to 90 kV, 8 mA, and 13,713 seconds. The software used for upper airway analysis is Romexis (version 5.1.O.R; Planmeca). The images are exhibited in accordance with the field of view of 20.1{\texttimes}17.4 cm, size of 502{\texttimes}502{\texttimes}436 mm, and voxel size of 400 $\mu$m. Results: The protocol described and illustrated here allows for automatic calculation of the total volume of the pharyngeal airspace, its area of greatest narrowing, its location, and the smallest anteroposterior and laterolateral dimensions of the pharynx. These measurements are carried out automatically by the imaging software whose reliability is proven by the existing literature. Thus, we could reduce the possible bias of manual measurement, aiming at data collection. Conclusions: The use of this protocol by dentists will allow for standardization of the measurements and constitutes a valuable screening tool for OSAS. This protocol may also be suitable for other imaging software. The anatomical points used as reference are most relevant for standardizing studies in this field. International Registered Report Identifier (IRRID): RR1-10.2196/41049 ",
doi="10.2196/41049",
url="https://www.researchprotocols.org/2023/1/e41049",
url="http://www.ncbi.nlm.nih.gov/pubmed/37145857"
}


@Article{info:doi/10.2196/39489,
author="Pfammatter, Fidler Angela
and Hughes, Olivia Bonnie
and Tucker, Becky
and Whitmore, Harry
and Spring, Bonnie
and Tasali, Esra",
title="The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="5",
volume="24",
number="12",
pages="e39489",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="CPAP adherence",
keywords="weight loss",
keywords="lifestyle",
abstract="Background: Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA. Objective: This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA. Methods: We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback. Results: During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal (``Goal: 100\%''). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning. Conclusions: We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted. ",
doi="10.2196/39489",
url="https://www.jmir.org/2022/12/e39489",
url="http://www.ncbi.nlm.nih.gov/pubmed/36469406"
}


@Article{info:doi/10.2196/31698,
author="Kumar, Shefali
and Rudie, Emma
and Dorsey, Cynthia
and Blase, Amy
and Benjafield, V. Adam
and Sullivan, S. Shannon",
title="Assessment of Patient Journey Metrics for Users of a Digital Obstructive Sleep Apnea Program: Single-Arm Feasibility Pilot Study",
journal="JMIR Form Res",
year="2022",
month="Jan",
day="12",
volume="6",
number="1",
pages="e31698",
keywords="obstructive sleep apnea",
keywords="virtual care",
keywords="remote care",
keywords="OSA diagnosis",
keywords="sleep apnea",
keywords="OSA",
keywords="underdiagnosed",
keywords="feasibility",
keywords="patient-centered",
keywords="treatment pathway",
keywords="diagnostic",
keywords="eHealth",
abstract="Background: Despite the importance of diagnosis and treatment, obstructive sleep apnea (OSA) remains a vastly underdiagnosed condition; this is partially due to current OSA identification methods and a complex and fragmented diagnostic pathway. Objective: This prospective, single-arm, multistate feasibility pilot study aimed to understand the journey in a nonreferred sample of participants through the fully remote OSA screening and diagnostic and treatment pathway, using the Primasun Sleep Apnea Program (formally, Verily Sleep Apnea Program). Methods: Participants were recruited online from North Carolina and Texas to participate in the study entirely virtually. Eligible participants were invited to schedule a video telemedicine appointment with a board-certified sleep physician who could order a home sleep apnea test (HSAT) to be delivered to the participant's home. The results were interpreted by the sleep physician and communicated to the participant during a second video telemedicine appointment. The participants who were diagnosed with OSA during the study and prescribed a positive airway pressure (PAP) device were instructed to download an app that provides educational and support-related content and access to personalized coaching support during the study's 90-day PAP usage period. Surveys were deployed throughout the study to assess baseline characteristics, prior knowledge of sleep apnea, and satisfaction with the program. Results: For the 157 individuals who were ordered an HSAT, it took a mean of 7.4 (SD 2.6) days and median 7.1 days (IQR 2.0) to receive their HSAT after they completed their first televisit appointment. For the 114 individuals who were diagnosed with OSA, it took a mean of 13.9 (SD 9.6) days and median 11.7 days (IQR 10.1) from receiving their HSAT to being diagnosed with OSA during their follow-up televisit appointment. Overall, the mean and median time from the first televisit appointment to receiving an OSA diagnosis was 21.4 (SD 9.6) days and 18.9 days (IQR 9.2), respectively. For those who were prescribed PAP therapy, it took a mean of 8.1 (SD 9.3) days and median 6.0 days (IQR 4.0) from OSA diagnosis to PAP therapy initiation. Conclusions: These results demonstrate the possibility of a highly efficient, patient-centered pathway for OSA workup and treatment. Such findings support pathways that could increase access to care, reduce loss to follow-up, and reduce health burden and overall cost. The program's ability to efficiently diagnose patients who otherwise may have not been diagnosed with OSA is important, especially during a pandemic, as the United States shifted to remote care models and may sustain this direction. The potential economic and clinical impact of the program's short and efficient journey time and low attrition rate should be further examined in future analyses. Future research also should examine how a fast and positive diagnosis experience impacts success rates for PAP therapy initiation and adherence. Trial Registration: ClinicalTrials.gov NCT04599803; https://clinicaltrials.gov/ct2/show/NCT04599803 ",
doi="10.2196/31698",
url="https://formative.jmir.org/2022/1/e31698",
url="http://www.ncbi.nlm.nih.gov/pubmed/34792470"
}


@Article{info:doi/10.2196/26524,
author="Akbarian, Sina
and Ghahjaverestan, Montazeri Nasim
and Yadollahi, Azadeh
and Taati, Babak",
title="Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation",
journal="J Med Internet Res",
year="2021",
month="Nov",
day="1",
volume="23",
number="11",
pages="e26524",
keywords="sleep apnea",
keywords="deep learning",
keywords="noncontact monitoring",
keywords="computer vision",
keywords="positional sleep apnea",
keywords="3D convolutional neural network",
keywords="3D-CNN",
abstract="Background: Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective: The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods: A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results: The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83\% accuracy and an F1-score of 86\%. Conclusions: This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app. ",
doi="10.2196/26524",
url="https://www.jmir.org/2021/11/e26524",
url="http://www.ncbi.nlm.nih.gov/pubmed/34723817"
}


@Article{info:doi/10.2196/24072,
author="Turino, Cecilia
and Ben{\'i}tez, D. Ivan
and Rafael-Palou, Xavier
and Mayoral, Ana
and Lopera, Alejandro
and Pascual, Lydia
and Vaca, Rafaela
and Cortijo, Anunciaci{\'o}n
and Moncus{\'i}-Moix, Anna
and Dalmases, Mireia
and Vargiu, Eloisa
and Blanco, Jordi
and Barb{\'e}, Ferran
and de Batlle, Jordi",
title="Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="18",
volume="23",
number="10",
pages="e24072",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="patient compliance",
keywords="remote monitoring",
keywords="machine learning",
abstract="Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. Objective: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. Methods: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea--Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient's CPAP compliance from the very first days of usage; (2) machine learning--based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient's compliance, satisfaction, and health care costs. Results: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13\% (8/60) were women. Patients in the intervention arm had a mean (95\% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients' satisfaction was excellent in both arms, and up to 88\% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean {\texteuro}90.2 (SD 53.14) (US \$105.76 [SD 62.31]); intervention: mean {\texteuro}96.2 (SD 62.13) (US \$112.70 [SD 72.85]); P=.70; {\texteuro}1=US \$1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. Conclusions: A machine learning--based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients' empowerment in the management of chronic diseases. Trial Registration: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958 ",
doi="10.2196/24072",
url="https://www.jmir.org/2021/10/e24072",
url="http://www.ncbi.nlm.nih.gov/pubmed/34661550"
}


@Article{info:doi/10.2196/29200,
author="Conway, Aaron
and Jungquist, R. Carla
and Chang, Kristina
and Kamboj, Navpreet
and Sutherland, Joanna
and Mafeld, Sebastian
and Parotto, Matteo",
title="Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study",
journal="JMIR Perioper Med",
year="2021",
month="Oct",
day="5",
volume="4",
number="2",
pages="e29200",
keywords="procedural sedation and analgesia",
keywords="conscious sedation",
keywords="nursing",
keywords="informatics",
keywords="patient safety",
keywords="machine learning",
keywords="capnography",
keywords="anesthesia",
keywords="anaesthesia",
keywords="medical informatics",
keywords="sleep apnea",
keywords="apnea",
keywords="apnoea",
keywords="sedation",
abstract="Background: Capnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a ``smart alarm'' that can alert clinicians to apneic events that are predicted to be prolonged. Objective: To determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds). Methods: A secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds). Results: A total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8\%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9\%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy. Conclusions: Decision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds. ",
doi="10.2196/29200",
url="https://periop.jmir.org/2021/2/e29200",
url="http://www.ncbi.nlm.nih.gov/pubmed/34609322"
}


@Article{info:doi/10.2196/30500,
author="Borrmann, Francisca Paz
and O'Connor-Reina, Carlos
and Ignacio, M. Jose
and Rodriguez Ruiz, Elisa
and Rodriguez Alcala, Laura
and Dzembrovsky, Florencia
and Baptista, Peter
and Garcia Iriarte, T. Maria
and Casado Alba, Carlos
and Plaza, Guillermo",
title="Muscular Assessment in Patients With Severe Obstructive Sleep Apnea Syndrome: Protocol for a Case-Control Study",
journal="JMIR Res Protoc",
year="2021",
month="Aug",
day="6",
volume="10",
number="8",
pages="e30500",
keywords="myofunctional therapy",
keywords="sleep apnea",
keywords="sleep disordered breathing",
keywords="speech therapy",
keywords="phenotype",
keywords="sleep",
keywords="therapy",
keywords="protocol",
keywords="muscle",
keywords="assessment",
keywords="case study",
keywords="exercise",
keywords="airway",
keywords="respiratory",
abstract="Background: Myofunctional therapy is currently a reasonable therapeutic option to treat obstructive sleep apnea-hypopnea syndrome (OSAHS). This therapy is based on performing regular exercises of the upper airway muscles to increase their tone and prevent their collapse. Over the past decade, there has been an increasing number of publications in this area; however, to our knowledge, there are no studies focused on patients who can most benefit from this therapy. Objective: This protocol describes a case-control clinical trial aimed at determining the muscular features of patients recently diagnosed with severe OSAHS compared with those of healthy controls. Methods: Patients meeting set criteria will be sequentially enrolled up to a sample size of 40. Twenty patients who meet the inclusion criteria for controls will also be evaluated. Patients will be examined by a qualified phonoaudiologist who will take biometric measurements and administer the Expanded Protocol of Orofacial Myofunctional Evaluation with Scores (OMES), Friedman Staging System, Epworth Sleepiness Scale, and Pittsburgh Sleep Quality Index questionnaires. Measures of upper airway muscle tone will also be performed using the Iowa Oral Performance Instrument and tongue digital spoon devices. Evaluation will be recorded and reevaluated by a second specialist to determine concordance between observers. Results: A total of 60 patients will be enrolled. Both the group with severe OSAHS (40 patients) and the control group (20 subjects) will be assessed for differences between upper airway muscle tone and OMES questionnaire responses. Conclusions: This study will help to determine muscle patterns in patients with severe OSAHS and can be used to fill the gap currently present in the assessment of patients suitable to be treated with myofunctional therapy. Trial Registration: ISRCTN Registry ISRCTN12596010; https://www.isrctn.com/ISRCTN12596010 International Registered Report Identifier (IRRID): PRR1-10.2196/30500 ",
doi="10.2196/30500",
url="https://www.researchprotocols.org/2021/8/e30500",
url="http://www.ncbi.nlm.nih.gov/pubmed/34115605"
}


@Article{info:doi/10.2196/24171,
author="Zhang, Zhongxing
and Qi, Ming
and H{\"u}gli, Gordana
and Khatami, Ramin",
title="The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="29",
volume="23",
number="7",
pages="e24171",
keywords="obstructive sleep apnea",
keywords="wearable devices",
keywords="smartwatch",
keywords="oxygen saturation",
keywords="near-infrared spectroscopy",
keywords="continuous positive airway pressure therapy",
keywords="photoplethysmography",
abstract="Background: Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9\% to 38\% of the general population. About 90\% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ?30\% drop in breathing and an at least 3\% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. Objective: We aimed to compare event-by-event arm OD (arm\_OD) with fingertip OD (finger\_OD) in sleep hypopneas during both na{\"i}ve sleep and CPAP therapy. Methods: Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm\_OD of the left biceps muscle and finger\_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm\_OD and finger\_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. Results: In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57\%) were collected during baseline sleep and 1517 (69.43\%), during CPAP sleep. The mean difference between finger\_OD and arm\_OD was 2.86\% (95\% CI 2.67\%-3.06\%, t667=28.28; P<.001; 95\% limits of agreement [LoA] --2.27\%, 8.00\%) during baseline sleep and 1.83\% (95\% CI 1.72\%-1.94\%, t1516=31.99; P<.001; 95\% LoA --2.54\%, 6.19\%) during CPAP. Using the standard criterion of 3\% saturation drop, arm\_OD only recognized 16.32\% (109/668) and 14.90\% (226/1517) of hypopneas at baseline and during CPAP, respectively. Conclusions: arm\_OD is 2\% to 3\% lower than standard finger\_OD in sleep hypopnea, probably because the measured arm\_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ?3\% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness. ",
doi="10.2196/24171",
url="https://www.jmir.org/2021/7/e24171",
url="http://www.ncbi.nlm.nih.gov/pubmed/34326039"
}


@Article{info:doi/10.2196/27062,
author="Laursen, Hjorth Ditte
and Rom, Gitte
and Bangh{\o}j, Margareta Anne
and Tarnow, Lise
and Schou, Lone",
title="Improving Diabetes Self-management by Providing Continuous Positive Airway Pressure Treatment to Patients With Obstructive Sleep Apnea and Type 2 Diabetes: Qualitative Exploratory Interview Study",
journal="JMIR Form Res",
year="2021",
month="Jul",
day="20",
volume="5",
number="7",
pages="e27062",
keywords="diabetes",
keywords="diabetes self-management",
keywords="obstructive sleep apnea",
keywords="continued positive airway pressure",
keywords="sleep patterns",
keywords="sleepiness in daily life",
keywords="sleep apnea",
keywords="elderly",
keywords="sleep",
abstract="Background: There is a high prevalence of unexplained and unexplored obstructive sleep apnea (OSA) among patients with type 2 diabetes. The daytime symptoms of OSA include severe fatigue, cognitive problems, a decreased quality of life, and the reduced motivation to perform self-care. These symptoms impair the management of both diabetes and daily life. OSA may therefore have negative implications for diabetes self-management. Continuous positive airway pressure (CPAP) therapy is used to treat OSA. This treatment improves sleep quality, insulin resistance, and glycemic control. Although the benefits of using CPAP as a treatment for OSA are clear, the noncompliance rate is high, and the evidence for the perceived effect that CPAP treatment has on patients with type 2 diabetes and OSA is poor. Objective: The purpose of this study was to analyze the impacts that comorbid diabetes and OSA have on the daily lives of older adults and to investigate the perceived effect that CPAP treatment for OSA has on patients' diabetes self-management. Methods: A qualitative follow-up study that involved in-depth, semistructured dyad interviews with couples before and after CPAP treatment (N=22) was conducted. Patients were recruited from the Hilleroed Hospital in Denmark and were all diagnosed with type 2 diabetes, aged >18 years, and had an apnea-hypopnea index of ?15. All interviews were coded and analyzed via thematic analysis. Results: The results showed that patients and their partners did not consider OSA to be a serious disorder, as they believed that OSA symptoms were similar to those of the process of aging. Patients experienced poor nocturnal sleep, took frequent daytime naps, exhibited reduced cognitive function, and had low levels of physical activity and a high-calorie diet. These factors negatively influenced their diabetes self-management. Despite the immediate benefit of CPAP treatment, most patients (11/12, 92\%) faced technical challenges when using the CPAP device. Only the patients with severe OSA symptoms that affected their daily lives overcame the challenges of using the CPAP device and thereby improved their diabetes self-management. Patients with less severe symptoms rated CPAP-related challenges as more burdensome than their symptoms. Conclusions: If used correctly, CPAP has the potential to significantly improve OSA, resulting in better sleep quality; improved physical activity; improved diet; and, in the end, better diabetes self-management. However, there are many barriers to undergoing CPAP treatment, and only few patients manage to overcome these barriers and comply with correct treatment. ",
doi="10.2196/27062",
url="https://formative.jmir.org/2021/7/e27062",
url="http://www.ncbi.nlm.nih.gov/pubmed/34283032"
}


@Article{info:doi/10.2196/26059,
author="Mulgund, Pavankumar
and Sharman, Raj
and Rifkin, Daniel
and Marrazzo, Sam",
title="Design, Development, and Evaluation of a Telemedicine Platform for Patients With Sleep Apnea (Ognomy): Design Science Research Approach",
journal="JMIR Form Res",
year="2021",
month="Jul",
day="19",
volume="5",
number="7",
pages="e26059",
keywords="design science research",
keywords="telemedicine platform",
keywords="sleep apnea care",
keywords="mHealth",
keywords="telemedicine",
keywords="sleep apnea",
keywords="mobile health",
keywords="web application",
keywords="mobile phone",
abstract="Background: With an aging population and the escalating cost of care, telemedicine has become a societal imperative. Telemedicine alternatives are especially relevant to patients seeking care for sleep apnea, with its prevalence approaching one billion cases worldwide. Increasing awareness has led to a surge in demand for sleep apnea care; however, there is a shortage of the resources and expertise necessary to cater to the rising demand. Objective: The aim of this study is to design, develop, and evaluate a telemedicine platform, called Ognomy, for the consultation, diagnosis, and treatment of patients with sleep apnea. Methods: Using the design science research methodology, we developed a telemedicine platform for patients with sleep apnea. To explore the problem, in the analysis phase, we conducted two brainstorming workshops and structured interviews with 6 subject matter experts to gather requirements. Following that, we conducted three design and architectural review sessions to define and evaluate the system architecture. Subsequently, we conducted 14 formative usability assessments to improve the user interface of the system. In addition, 3 trained test engineers performed end-to-end system testing to comprehensively evaluate the platform. Results: Patient registration and data collection, physician appointments, video consultation, and patient progress tracking have emerged as critical functional requirements. A telemedicine platform comprising four artifacts---a mobile app for patients, a web app for providers, a dashboard for reporting, and an artificial intelligence--based chatbot for customer onboarding and support---was developed to meet these requirements. Design reviews emphasized the need for a highly cohesive but loosely coupled interaction among the platform's components, which was achieved through a layered modular architecture using third-party application programming interfaces. In contrast, critical findings from formative usability assessments focused on the need for a more straightforward onboarding process for patients, better status indicators during patient registration, and reorganization of the appointment calendar. Feedback from the design reviews and usability assessments was translated into technical improvements and design enhancements that were implemented in subsequent iterations. Conclusions: Sleep apnea is an underdiagnosed and undertreated condition. However, with increasing awareness, the demand for quality sleep apnea care is likely to surge, and creative alternatives are needed. The results of this study demonstrate the successful application of a framework using a design science research paradigm to design, develop, and evaluate a telemedicine platform for patients with sleep apnea and their providers. ",
doi="10.2196/26059",
url="https://formative.jmir.org/2021/7/e26059",
url="http://www.ncbi.nlm.nih.gov/pubmed/34279237"
}


@Article{info:doi/10.2196/25124,
author="Ferreira-Santos, Daniela
and Rodrigues, Pereira Pedro",
title="Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation",
journal="JMIR Med Inform",
year="2021",
month="Jun",
day="22",
volume="9",
number="6",
pages="e25124",
keywords="obstructive sleep apnea",
keywords="screening",
keywords="risk factors",
keywords="phenotypes",
keywords="Bayesian network classifiers",
abstract="Background: The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard. Objective: This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors. Methods: A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B). Results: A total of 318 patients at risk were included, of whom 207 (65.1\%) individuals were diagnosed with OSA (111, 53.6\% with mild; 50, 24.2\% with moderate; and 46, 22.2\% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7\% low; 104/207, 50.2\% medium; and 29/207, 14.1\% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26\%, 95\% CI 24-29, to 38\%, 95\% CI 35-40) while maintaining a high sensitivity (93\%, 95\% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening. ",
doi="10.2196/25124",
url="https://medinform.jmir.org/2021/6/e25124",
url="http://www.ncbi.nlm.nih.gov/pubmed/34156340"
}


@Article{info:doi/10.2196/20779,
author="Kooij, Laura
and Vos, JE Petra
and Dijkstra, Antoon
and Roovers, A. Elisabeth
and van Harten, H. Wim",
title="Video Consultation as an Adequate Alternative to Face-to-Face Consultation in Continuous Positive Airway Pressure Use for Newly Diagnosed Patients With Obstructive Sleep Apnea: Randomized Controlled Trial",
journal="JMIR Form Res",
year="2021",
month="May",
day="11",
volume="5",
number="5",
pages="e20779",
keywords="video consultation",
keywords="eHealth",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="randomized controlled trial",
abstract="Background: The effectiveness of continuous positive airway pressure (CPAP) is dependent on the degree of use, so adherence is essential. Cognitive components (eg, self-efficacy) and support during treatment have been found to be important in CPAP use. Video consultation may be useful to support patients during treatment. So far, video consultation has rarely been evaluated in thorough controlled research, with only a limited number of outcomes assessed. Objective: The aim of the study was to evaluate the superiority of video consultation over face-to-face consultation for patients with obstructive sleep apnea (OSA) on CPAP use (minutes per night), adherence, self-efficacy, risk outcomes, outcome expectancies, expectations and experiences with video consultation, and satisfaction of patients and nurses. Methods: A randomized controlled trial was conducted with an intervention (video consultation) and a usual care group (face-to-face consultation). Patients with confirmed OSA (apnea-hypopnea index >15), requiring CPAP treatment, no history of CPAP treatment, having access to a tablet or smartphone, and proficient in the Dutch language were recruited from a large teaching hospital. CPAP use was monitored remotely, with short-term (weeks 1 to 4) and long-term (week 4, week 12, and week 24) assessments. Questionnaires were completed at baseline and after 4 weeks on self-efficacy, risk perception, outcome expectancies (Self-Efficacy Measure for Sleep Apnea), expectations and experiences with video consultation (covering constructs of the unified theory of acceptance and use of technology), and satisfaction. Nurse satisfaction was evaluated using questionnaires. Results: A total of 140 patients were randomized (1:1 allocation). The use of video consultation for OSA patients does not lead to superior results on CPAP use and adherence compared with face-to-face consultation. A significant difference in change over time was found between groups for short-term (P-interaction=.008) but not long-term (P-interaction=.68) CPAP use. CPAP use decreased in the long term (P=.008), but no significant difference was found between groups (P=.09). Change over time for adherence was not significantly different in the short term (P-interaction=.17) or long term (P-interaction=.51). A relation was found between CPAP use and self-efficacy (P=.001), regardless of the intervention arm (P=.25). No significant difference between groups was found for outcome expectancies (P=.64), self-efficacy (P=.41), and risk perception (P=.30). The experiences were positive, and 95\% (60/63) intended to keep using video consultation. Patients in both groups rated the consultations on average with an 8.4. Overall, nurses (n=3) were satisfied with the video consultation system. Conclusions: Support of OSA patients with video consultation does not lead to superior results on CPAP use and adherence compared with face-to-face consultation. The findings of this research suggest that self-efficacy is an important factor in improving CPAP use and that video consultation may be a feasible way to support patients starting CPAP. Future research should focus on blended care approaches in which self-efficacy receives greater emphasis. Trial Registration: Clinicaltrials.gov NCT04563169; https://clinicaltrials.gov/show/NCT04563169 ",
doi="10.2196/20779",
url="https://formative.jmir.org/2021/5/e20779",
url="http://www.ncbi.nlm.nih.gov/pubmed/33973866"
}


@Article{info:doi/10.2196/18050,
author="Huynh, Pauline
and Shan, Rongzi
and Osuji, Ngozi
and Ding, Jie
and Isakadze, Nino
and Marvel, A. Francoise
and Sharma, Garima
and Martin, S. Seth",
title="Heart Rate Measurements in Patients with Obstructive Sleep Apnea and Atrial Fibrillation: Prospective Pilot Study Assessing Apple Watch's Agreement With Telemetry Data",
journal="JMIR Cardio",
year="2021",
month="Feb",
day="8",
volume="5",
number="1",
pages="e18050",
keywords="mHealth",
keywords="wearables",
keywords="atrial fibrillation",
keywords="obstructive sleep apnea",
keywords="digital health",
abstract="Background: Patients with obstructive sleep apnea (OSA) are at a higher risk for atrial fibrillation (AF). Consumer wearable heart rate (HR) sensors may be a means for passive HR monitoring in patients with AF. Objective: The aim of this study was to assess the Apple Watch's agreement with telemetry in measuring HR in patients with OSA in AF. Methods: Patients with OSA in AF were prospectively recruited prior to cardioversion/ablation procedures. HR was sampled every 10 seconds for 60 seconds using telemetry and an Apple Watch concomitantly. Agreement of Apple Watch with telemetry, which is the current gold-standard device for measuring HR, was assessed using mixed effects limits agreement and Lin's concordance correlation coefficient. Results: A total of 20 patients (mean 66 [SD 6.5] years, 85\% [n=17] male) participated in this study, yielding 134 HR observations per device. Modified Bland--Altman plot revealed that the variability of the paired difference of the Apple Watch compared with telemetry increased as the magnitude of HR measurements increased. The Apple Watch produced regression-based 95\% limits of agreement of 27.8 -- 0.3 {\texttimes} average HR -- 15.0 to 27.8 -- 0.3 {\texttimes} average HR + 15.0 beats per minute (bpm) with a mean bias of 27.8 -- 0.33 {\texttimes} average HR bpm. Lin's concordance correlation coefficient was 0.88 (95\% CI 0.85-0.91), suggesting acceptable agreement between the Apple Watch and telemetry. Conclusions: In patients with OSA in AF, the Apple Watch provided acceptable agreement with HR measurements by telemetry. Further studies with larger sample populations and wider range of HR are needed to confirm these findings. ",
doi="10.2196/18050",
url="http://cardio.jmir.org/2021/1/e18050/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33555260"
}


@Article{info:doi/10.2196/23123,
author="O'Connor-Reina, Carlos
and Ignacio Garcia, Maria Jose
and Rodriguez Ruiz, Elisa
and Morillo Dominguez, Carmen Maria Del
and Ignacio Barrios, Victoria
and Baptista Jardin, Peter
and Casado Morente, Carlos Juan
and Garcia Iriarte, Teresa Maria
and Plaza, Guillermo",
title="Myofunctional Therapy App for Severe Apnea--Hypopnea Sleep Obstructive Syndrome: Pilot Randomized Controlled Trial",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Nov",
day="9",
volume="8",
number="11",
pages="e23123",
keywords="myofunctional therapy",
keywords="oropharyngeal exercises",
keywords="mHealth",
keywords="sleep apnea",
keywords="smartphone app",
keywords="app",
keywords="sleep",
keywords="therapy",
keywords="apnea",
keywords="randomized trial",
keywords="efficacy",
abstract="Background: Myofunctional therapy has demonstrated efficacy in treating sleep-disordered breathing. We assessed the clinical use of a new mobile health (mHealth) app that uses a smartphone to teach patients with severe obstructive sleep apnea--hypopnea syndrome (OSAHS) to perform oropharyngeal exercises. Objective: We conducted a pilot randomized trial to evaluate the effects of the app in patients with severe OSAHS. Methods: Forty patients with severe OSAHS (apnea--hypoxia index [AHI]>30) were enrolled prospectively and randomized into an intervention group that used the app for 90 sessions or a control group. Anthropometric measures, Epworth Sleepiness Scale (0-24), Pittsburgh Sleep Quality Index (0-21), Iowa Oral Performance Instrument (IOPI) scores, and oxygen desaturation index were measured before and after the intervention. Results: After the intervention, 28 patients remained. No significant changes were observed in the control group; however, the intervention group showed significant improvements in most metrics. AHI decreased by 53.4\% from 44.7 (range 33.8-55.6) to 20.88 (14.02-27.7) events/hour (P<.001). The oxygen desaturation index decreased by 46.5\% from 36.31 (27.19-43.43) to 19.4 (12.9-25.98) events/hour (P=.003). The IOPI maximum tongue score increased from 39.83 (35.32-45.2) to 59.06 (54.74-64.00) kPa (P<.001), and the IOPI maximum lip score increased from 27.89 (24.16-32.47) to 44.11 (39.5-48.8) kPa (P<.001). The AHI correlated significantly with IOPI tongue and lip improvements (Pearson correlation coefficient ?0.56 and ?0.46, respectively; both P<.001). The Epworth Sleepiness Scale score decreased from 10.33 (8.71-12.24) to 5.37 (3.45-7.28) in the app group (P<.001), but the Pittsburgh Sleep Quality Index did not change significantly. Conclusions: Orofacial exercises performed using an mHealth app reduced OSAHS severity and symptoms, and represent a promising treatment for OSAHS. Trial Registration: Spanish Registry of Clinical Studies AWGAPN-2019-01, ClinicalTrials.gov NCT04438785; https://clinicaltrials.gov/ct2/show/NCT04438785 ",
doi="10.2196/23123",
url="http://mhealth.jmir.org/2020/11/e23123/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33093013"
}


@Article{info:doi/10.2196/18297,
author="Sadek, Ibrahim
and Heng, Soon Terry Tan
and Seet, Edwin
and Abdulrazak, Bessam",
title="A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="18",
volume="22",
number="9",
pages="e18297",
keywords="ballistocardiography",
keywords="sleep apnea",
keywords="vital signs",
keywords="eHealth",
keywords="mobile health",
keywords="home care",
abstract="Background: At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient's care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient's everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective: This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods: In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient's mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results: For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96\% (SD 6.39), a sensitivity of 57.07\% (SD 12.63), and a specificity of 45.26\% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42\% (SD 0.57), an NRMSE of 6.54\% (SD 0.56), and an MAPE of 5.41\% (SD 0.58) for HR, whereas an NMAE of 11.42\% (SD 2.62), an NRMSE of 13.85\% (SD 2.78), and an MAPE of 11.60\% (SD 2.84) for RR. Conclusions: Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection. ",
doi="10.2196/18297",
url="http://www.jmir.org/2020/9/e18297/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32945773"
}


@Article{info:doi/10.2196/17252,
author="Akbarian, Sina
and Montazeri Ghahjaverestan, Nasim
and Yadollahi, Azadeh
and Taati, Babak",
title="Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study",
journal="J Med Internet Res",
year="2020",
month="May",
day="22",
volume="22",
number="5",
pages="e17252",
keywords="noncontact monitoring",
keywords="sleep apnea",
keywords="motion analysis",
keywords="computer vision",
keywords="obstructive apnea",
keywords="central apnea",
keywords="machine learning",
keywords="deep learning",
abstract="Background: Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). Objective: The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. Methods: Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. Results: Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95\% accuracy and an F1 score of 89\% in differentiating OA vs CA. Conclusions: In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition. ",
doi="10.2196/17252",
url="http://www.jmir.org/2020/5/e17252/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32441656"
}


@Article{info:doi/10.2196/16972,
author="Aardoom, Jo{\"e}lle Jiska
and Loheide-Niesmann, Lisa
and Ossebaard, C. Hans
and Riper, Heleen",
title="Effectiveness of eHealth Interventions in Improving Treatment Adherence for Adults With Obstructive Sleep Apnea: Meta-Analytic Review",
journal="J Med Internet Res",
year="2020",
month="Feb",
day="18",
volume="22",
number="2",
pages="e16972",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="treatment adherence",
keywords="patient adherence",
keywords="telemedicine",
keywords="eHealth",
keywords="meta-analysis",
keywords="systematic review",
abstract="Background: Poor adherence to continuous positive airway pressure (CPAP) treatment by adults with obstructive sleep apnea (OSA) is a common issue. Strategies delivered by means of information and communication technologies (ie, eHealth) can address treatment adherence through patient education, real-time monitoring of apnea symptoms and CPAP adherence in daily life, self-management, and early identification and subsequent intervention when device or treatment problems arise. However, the effectiveness of available eHealth technologies in improving CPAP adherence has not yet been systematically studied. Objective: This meta-analytic review was designed to investigate the effectiveness of a broad range of eHealth interventions in improving CPAP treatment adherence. Methods: We conducted a systematic literature search of the databases of Cochrane Library, PsycINFO, PubMed, and Embase to identify relevant randomized controlled trials in adult OSA populations. The risk of bias in included studies was examined using seven items of the Cochrane Collaboration risk-of-bias tool. The meta-analysis was conducted with comprehensive meta-analysis software that computed differences in mean postintervention adherence (MD), which was defined as the average number of nightly hours of CPAP use. Results: The meta-analysis ultimately included 18 studies (N=5429 adults with OSA) comprising 22 comparisons between experimental and control conditions. Postintervention data were assessed at 1 to 6 months after baseline, depending on the length of the experimental intervention. eHealth interventions increased the average nightly use of CPAP in hours as compared with care as usual (MD=0.54, 95\% CI 0.29-0.79). Subgroup analyses did not reveal significant differences in effects between studies that used eHealth as an add-on or as a replacement to care as usual (P=.95), between studies that assessed stand-alone eHealth and blended strategies combining eHealth with face-to-face care (P=.23), or between studies of fully automated interventions and guided eHealth interventions (P=.83). Evidence for the long-term follow-up effectiveness of eHealth adherence interventions remains undecided owing to a scarcity of available studies and their mixed results. Conclusions: eHealth interventions for adults with OSA can improve adherence to CPAP in the initial months after the start of treatment, increasing the mean nightly duration of use by about half an hour. Uncertainty still exists regarding the timing, duration, intensity, and specific types of eHealth interventions that could be most effectively implemented by health care providers. ",
doi="10.2196/16972",
url="https://www.jmir.org/2020/2/e16972",
url="http://www.ncbi.nlm.nih.gov/pubmed/32130137"
}


@Article{info:doi/10.2196/cardio.9894,
author="Treskes, Willem Roderick
and Maan, C. Arie
and Verwey, Florence Harriette
and Schot, Robert
and Beeres, Anna Saskia Lambertha Maria
and Tops, F. Laurens
and Van Der Velde, Tjeerd Enno
and Schalij, Jan Martin
and Slats, Margaretha Annelies",
title="Mobile Health for Central Sleep Apnea Screening Among Patients With Stable Heart Failure: Single-Cohort, Open, Prospective Trial",
journal="JMIR Cardio",
year="2019",
month="Mar",
day="19",
volume="3",
number="1",
pages="e9894",
keywords="mobile health",
keywords="central sleep apnea",
keywords="heart failure",
keywords="prevention",
keywords="screening",
keywords="mobile phone",
abstract="Background: Polysomnography is the gold standard for detection of central sleep apnea in patients with stable heart failure. However, this procedure is costly, time consuming, and a burden to the patient and therefore unsuitable as a screening method. An electronic health (eHealth) app to measure overnight oximetry may be an acceptable screening alternative, as it can be automatically analyzed and is less burdensome to patients. Objective: This study aimed to assess whether overnight pulse oximetry using a smartphone-compatible oximeter can be used to detect central sleep apnea in a population with stable heart failure. Methods: A total of 26 patients with stable heart failure underwent one night of both a polygraph examination and overnight saturation using a smartphone-compatible oximeter. The primary endpoint was agreement between the oxygen desaturation index (ODI) above or below 15 on the smartphone-compatible oximeter and the diagnosis of the polygraph. Results: The median age of patients was 66.4 (interquartile range, 62-71) years and 92\% were men. The median body mass index was 27.1 (interquartile range, 24.4-30.8) kg/m2. Two patients were excluded due to incomplete data, and two other patients were excluded because they could not use a smartphone. Seven patients had central sleep apnea, and 6 patients had obstructive sleep apnea. Of the 7 (of 22, 32\%) patients with central sleep apnea that were included in the analysis, 3 (13\%) had an ODI?15. Of all patients without central sleep apnea, 8 (36\%) had an ODI<15. The McNemar test yielded a P value of .55. Conclusions: Oxygen desaturation measured by this smartphone-compatible oximeter is a weak predictor of central sleep apnea in patients with stable heart failure. ",
doi="10.2196/cardio.9894",
url="http://cardio.jmir.org/2019/1/e9894/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758786"
}