Physical therapy for sleep apnea: a smartphone application for home-based physical therapy for patients with obstructive sleep apnea.
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ABSTRACT: Purpose
In this study, we described "PT for Sleep Apnea", a smartphone application for home-based physical therapy of patients with Obstructive Sleep Apnea (OSA).Methods
The application was created in a joint program between the University of Medicine and Pharmacy at Ho Chi Minh City (UMP), Vietnam, and National Cheng Kung University (NCKU), Taiwan. Exercises maneuvers were derived from the exercise program previously published by the partner group at National Cheng Kung University. They included exercises for upper airway and respiratory muscle training and general endurance training.Results
The application provides video and in-text tutorials for users to follow at home and a schedule function to assist the user in organizing the training program, which may improve the efficacy of home-based physical therapy in patients with Obstructive Sleep Apnea.Conclusion
In the future, our group plans to conduct a user study and randomized-controlled trials to investigate whether our application can benefit patients with OSA.
SUBMITTER: Bui-Diem K
PROVIDER: S-EPMC10249728 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Publications
Physical therapy for sleep apnea: a smartphone application for home-based physical therapy for patients with obstructive sleep apnea.
Bui-Diem Khue K Hung Ching-Hsia CH Zhu Guan-Cheng GC Tho Nguyen Van NV Nguyen-Binh Thu T Vu-Tran-Thien Quan Q To-Truong Duy D Ngo-Thanh Hoan H Duong-Quy Sy S
Frontiers in neurology 20230525
<h4>Purpose</h4>In this study, we described "PT for Sleep Apnea", a smartphone application for home-based physical therapy of patients with Obstructive Sleep Apnea (OSA).<h4>Methods</h4>The application was created in a joint program between the University of Medicine and Pharmacy at Ho Chi Minh City (UMP), Vietnam, and National Cheng Kung University (NCKU), Taiwan. Exercises maneuvers were derived from the exercise program previously published by the partner group at National Cheng Kung Universi ...[more]
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Project description:ImportanceConsumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.ObjectiveTo validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.Design, setting, and participantsThis diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.Main outcomes and measuresSensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.ResultsOf the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.Conclusions and relevanceThis diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.
| S-EPMC10654929 | biostudies-literature
Project description:Study objectivesHome-based diagnosis of obstructive sleep apnea (OSA) with portable monitoring (PM) is increasingly utilized, but remains understudied in underserved and minority populations. We tested the feasibility of home PM in an urban population at risk for OSA compared to in-laboratory polysomnography (PSG) and examined patient preference with respect to home PM versus PSG.MethodsRandomized crossover study of home PM (WatchPAT200) and in-laboratory simultaneous PSG and PM in 75 urban African Americans with high pre-test probability of OSA, identified with the Berlin questionnaire.ResultsFifty-seven of 75 participants were women, average age 45 ± 11 years (mean ± SD), 35% with ≤ high school education, and 76% with annual household income < $50,000. Technical failure rates were 5.3% for home vs. 3.1% for in-laboratory PM. There was good agreement between apnea hypopnea index on PSG; AHIPSG and AHI on home PM (mean ± 2 SD of the differences = 0.64 ± 46.5 and intraclass correlation coefficient; ICC = 0.73). The areas under the curve for the receiver-operator characteristic curves for home PM were 0.90 for AHIPSG ≥ 5, 0.95 for AHIPSG ≥ 10, and 0.92 for AHIPSG ≥ 15. 62/75 (82%) participants preferred home over in-laboratory testing.ConclusionsHome PM for diagnosis of OSA in a high risk urban population is feasible, accurate, and preferred by patients. As home PM may improve access to care, the cost-effectiveness of this diagnostic strategy for OSA should be examined in underserved urban and rural populations.Clinical trials registrationClinicalTrials.gov, identifier: NCT01997723.
| S-EPMC4106942 | biostudies-literature
Project description:Rationale: Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses. Methods: Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for two nights prior to surgery. Visceral fat biopsies were obtained intra-operatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways. Results: 10 patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, body mass index between the two groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P-value 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P-value < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients including the pro-inflammatory NF-M-NM-:B pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs. Conclusions: OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder. Total RNA from visceral fat of 18 subjects (10 OSA, 8 Control) was hybridized to 18 Affymetrix Genechip Human Gene 1.0 ST microarrays.
2012-09-01 | E-GEOD-38792 | biostudies-arrayexpress
Project description:Rationale: Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses. Methods: Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for two nights prior to surgery. Visceral fat biopsies were obtained intra-operatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways. Results: 10 patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, body mass index between the two groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P-value 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P-value < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients including the pro-inflammatory NF-κB pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs. Conclusions: OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder.
2012-09-01 | GSE38792 | GEO
Project description:Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.
| S-EPMC10708697 | biostudies-literature
Project description:Study objectivesWe conducted an economic analysis of the HomePAP study, a multicenter randomized clinical trial that compared home-based versus laboratory-based testing for the diagnosis and management of obstructive sleep apnea (OSA).DesignA cost-minimization analysis from the payer and provider perspectives was performed, given that 3-mo clinical outcomes were equivalent.SettingSeven academic sleep centers.ParticipantsThere were 373 subjects at high risk for moderate to severe OSA.InterventionsSubjects were randomized to either home-based limited channel portable monitoring followed by unattended autotitration with continuous positive airway pressure (CPAP), versus a traditional pathway of in-laboratory sleep study and CPAP titration.Measurements and resultsFrom the payer perspective, per subject costs for the laboratory-based pathway were $1,840 (95% confidence interval [CI] $1,660, $2,015) compared to $1,575 (95% CI $1,439, $1,716) for the home-based pathway under the base case. Costs were $264 (95% CI $39, $496, P = 0.02) in favor of the home arm. From the provider perspective, per subject costs for the laboratory arm were $1,697 (95% CI $1,566, $1,826) compared to $1,736 (95% CI $1,621, $1,857) in the home arm, for a difference of $40 (95% CI -$213, $142, P = 0.66) in favor of the laboratory arm under the base case. The provider operating margin was $142 (95% CI $85, $202,P < 0.01) in the laboratory arm, compared to a loss of -$161 (95% CI -$202, -$120, P < 0.01) in the home arm.ConclusionsFor payers, a home-based diagnostic pathway for obstructive sleep apnea with robust patient support incurs fewer costs than a laboratory-based pathway. For providers, costs are comparable if not higher, resulting in a negative operating margin.Clinicaltrialsgov identifierNCT00642486.
| S-EPMC4481018 | biostudies-literature
Project description:Study objectivesThe objective of the study is to validate the performance of Belun Ring Platform, a novel home sleep apnea testing system using a patented pulse oximeter sensor and a proprietary cloud-based neural networks algorithm.MethodsThe Belun Ring captures oxygen saturation, photoplethysmography, and accelerometer signals. The Belun Ring total sleep time is derived from features extracted from accelerometer, oxygen saturation, and photoplethysmography signals. The Belun Ring respiratory event index is derived from Belun Ring total sleep time and features extracted from heart rate variability and oxygen saturation changes. A total of 50 adults without significant cardiopulmonary or neuromuscular comorbidities and heart rate affecting medications were evaluated. In-lab sleep studies were performed simultaneously with the Ring and the studies were manually scored using the American Academy of Sleep Medicine Scoring Manual 4% desaturation criteria.ResultsThe Belun Ring respiratory event index correlated well with the polysomnography-apnea-hypopnea index (AHI; r = .894, P < .001). The sensitivity and specificity in categorizing AHI ≥ 15 events/h were 0.85 and 0.87, respectively, and the positive predictive value and negative predictive value were 0.88 and 0.83, respectively. The Belun Ring total sleep time also correlated well with the polysomnography-total sleep time (r = .945, P < .001). Although the Belun Ring Platform has a good overall performance, it tends to overestimate AHI in individuals with AHI under 15 events/h and underestimate AHI in those with AHI over 15 events/h. Conclusions: In this proof-of-concept study, the Belun Ring Platform demonstrated a reasonable accuracy in predicting AHI and total sleep time in patients without significant comorbidities and heart rate-affecting medications.Clinical trial registrationRegistry: ClinicalTrials.gov; Name: Validation of a Novel Device for Screening Patients With Symptoms of Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/NCT04121923; Identifier: NCT04121923.
| S-EPMC7970584 | biostudies-literature
Project description:BackgroundObstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ≥15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a non-contact OSA screener App that can run on both Apple and Android smartphones is presented.MethodsThe subtle breathing patterns of a person in bed can be measured via a smartphone using the "Firefly" app technology platform [and underpinning software development kit (SDK)], which utilizes advanced digital signal processing (DSP) technology and artificial intelligence (AI) algorithms to identify detailed sleep stages, respiration rate, snoring, and OSA patterns. The smartphone is simply placed adjacent to the subject, such as on a bedside table, night stand or shelf, during the sleep session. The system was trained on a set of 128 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous full polysomnography (PSG), and "Firefly" smartphone app analysis. A separate independent test set of 120 recordings was collected across a range of Apple iOS and Android smartphones, and withheld for performance evaluation by a different team. An operating point tuned for mid-sensitivity (i.e., balancing sensitivity and specificity) was chosen for the screener.ResultsThe performance on the test set is comparable to ambulatory OSA screeners, and other smartphone screening apps, with a sensitivity of 88.3% and specificity of 80.0% [with receiver operating characteristic (ROC) area under the curve (AUC) of 0.92], for a clinical threshold for the AHI of ≥15 events/hour of detected sleep time.ConclusionsThe "Firefly" app based sensing technology offers the potential to significantly lower the barrier of entry to OSA screening, as no hardware (other than the user's personal smartphone) is required. Additionally, multi-night analysis is possible in the home environment, without requiring the wearing of a portable PSG or other home sleep test (HST).
| S-EPMC7475565 | biostudies-literature
Project description:Obstructive sleep apnea (OSA) is a disease characterized by intermittent and repetitive narrowing of the airway during sleep. Surgical therapies for the treatment of OSA aim to improve airway patency by addressing selected site(s) of obstruction. Because several areas may each be responsible for the narrowing, different surgical modalities have also been developed. In this review, we give an overview of surgery for each of potential obstruction site(s). As a consequence of the multi-factorial and heterogeneous etiology of OSA, surgical therapies need to be selected and performed specifically for each patient, as there is no perfect surgery that will fit all patients. As with any other treatment modalities for OSA, surgical therapies have variable efficacy, but are a very important tool on OSA management in selected patients and have been shown effectiveness in decreasing the morbidity and mortality associated with the disease.
| S-EPMC3480570 | biostudies-literature
Project description:STUDY OBJECTIVES:To study the feasibility and accuracy of home sleep apnea testing (HSAT) in the diagnosis of obstructive sleep apnea (OSA) in a stroke rehabilitation unit. METHODS:Stroke patients referred to a neurorehabilitation center underwent OSA screening by means of HSAT within the Home Polygraphic Recording with Telemedicine Monitoring for Diagnosis and Treatment of Sleep Apnea in Stroke, or HOPES study (ClinicalTrials.gov identifier: NCT02748681). Feasibility was determined by evaluating the acceptability of recording quality. Patients in whom moderate OSA was diagnosed subsequently underwent unattended polysomnography (PSG) confirmation. Accuracy was studied by comparing the respiratory event index (REI)/monitoring time (MT) of screening HSAT with the apnea-hypopnea index (AHI)/total sleep time (TST) obtained during subsequent PSG with Bland-Altman plots. The influence of PSG-evaluated wake time and arousals on OSA classification was studied by comparing the AHI and REI of the same night. RESULTS:A total of 265 patients (58 ± 9 years, 70% male) were screened. A total of 92% of HSAT studies were performed with acceptable recording quality. In total, 33 patients (63 ± 5 years, 58% male) with moderate OSA (REI ? 15 to < 30 events/h) were included in the HSAT/PSG comparison. The Bland-Altman plot shows acceptable limits of agreement from -19.5 to +16.4, with a mean difference of -1.33. The REI detected in the PSG night demonstrated no significant differences to the AHI and a high correlation (r = .97; P < .001). The 95% confidence interval of the Bland-Altman plots varied from -7.61 to +4.80. CONCLUSIONS:These findings confirm a good feasibility and sufficient accuracy of HSAT attached in a stroke rehabilitation unit. Therefore, the authors suggest that American Academy of Sleep Medicine recommendations for HSAT should include stroke patients.
| S-EPMC6134231 | biostudies-literature