Associations Between Sleep Quality, Sleep Architecture and Sleep Disordered Breathing and Memory After Continuous Positive Airway Pressure in Patients with Obstructive Sleep Apnea in the Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Quan, Stuart F.; Budhiraja, Rohit; Kushida, Clete A.

doi:10.5935/1984-0063.20180037

Cited by 21 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Before collecting data, we review the factors relating to OSA. According to the recently published literature [13][14][15][16][17], OSA relates closely with the following aspects: age, gender, body mass index (BMI), sleep quality including daytime tiredness, snore, health status, and underlying diseases. Thus, we collected questionnaire data considering these factors -the data used for analysis comes from the Sleep Heart Health Study (SHHS) database [18,19].…”

Section: Data Preparationmentioning

confidence: 99%

Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model

Sawaragi

Horiguchi

2021

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

With the development of artificial intelligence technologies, the high accuracy of machine learning methods has become a non-unique standard. People are beginning to be more concerned about the understandability between humans and machines. The interference procedure of the machines is hoped to accord with human thinking as much as possible, which has spawned the recent and ongoing demands for developing explainable models. The present study proposes a new explainable and persuasive model for machine learning problems by introducing Structural Equation Modelling into the picture. Six parts make up the model, from data collection to model evaluation. The model can be used for data analysis, machine learning, and causal analysis. The proposed model is also transparent and can be interpreted from design to application. A practical experiment shows its effectiveness in a healthcare problem.

show abstract

Section: Data Preparationmentioning

confidence: 99%

Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model

Sawaragi

Horiguchi

2021

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

show abstract

“…время сна, приводит к нормализации структуры сна [18]. Также среди пациентов, находящихся на длительной ПАП-терапии, нами была выявлена сильная корреляционная связь между средним временем использования ПАП-аппарата и качеством сна (PSQI): r=-0,438, p=0,000.…”

Section: Discussionunclassified

The effect of adherence with long-term PAP therapy on the psycho-emotional state of patients with obstructive sleep apnea syndrome

et al. 2020

View full text Add to dashboard Cite

Aim. To assess the impact of adherence and effectiveness of long-term positive airway pressure therapy (PAP-therapy) on the clinical and psychological characteristics of patients with obstructive sleep apnea syndrome. Materials and methods. The study included 80 patients who were examined in the sleep apnea laboratory of the Myasnikov Institute of Clinical Cardiology of the National Medical Research Center for Cardiology and who have been on PAP-therapy for more than 12 months. The average age of the patients was 65.0 years [59.0; 71.0], body mass index 35.0 kg/m2 [31.0; 38.0], sleep apnea-hypopnea index (AHI) 39.5/h [31.0; 62.6]. The average duration of PAP therapy was 3.5 years [2.0; 6.0], while the minimum use was 1 year, the maximum 15 years. In the sample of patients who came in person, the percentage of days using PAP-therapy was 87.5% [62.0; 98.0] and the average usage time 6.3 hours [5.2; 7.3]. Results. On long-term PAP-therapy a persistent decrease in AHI was seen on average from 39.5/h [31.0; 62.6] to 2.7/h [1.2; 6.2], p=0,000. The criteria for good adherence to PAP-therapy (use 4 hours/night, more than 70% of nights) were met by the percentage of days of use by 67.5% of patients, the average time of use by 87.5% of patients. Both criteria for good adherence to PAP-therapy was met by 64.8% of patients. With long-term PAP-therapy, 71.7% of patients met the criteria of effectiveness (AHI5/h), AHI remained 5/h in 22.9% of patients and the average residual AHI was 10/h in 5.4% of patients. Correlation analysis showed associations between the PAP-therapy usage parameters and the severity of obstructive sleep apnea syndrome, and sleep quality: percentage of days of use and AHI (r=0.374, p=0.001), average time of use and PSQI (r=-0.438, p=0.000). Patients with a lower adherence to PAP-therapy (63.0% of days [22.0; 96.0] and 3.6 hours [2.4; 4.5] of use) did not differ in daytime sleepiness (ESS 5.0 points [2.0; 9.0] and 5.0 points [3.0; 8.0 ], p=0.891), but had a significantly lower quality of sleep (PSQI): 18.0 points [14.0; 20.0] versus 10.0 points [7.0; 18.0], p=0.004 compared with patients with high adherence to PAP-therapy (98.0% of the days [92.0; 99.0] and 7.9 hours [7.5; 8.2] of use). Conclusions. Patients with the higher adherence to PAP-therapy had significantly better sleep quality. But even the use of PAP-therapy for less than 4 hours is associated with a decrease in daytime sleepiness severity.

show abstract

“…Multidimensionality opens the possibility of 'indirect' or 'root-cause' targeting, such as targeting OSA to improve multiple sleep health metrics such as alertness, quality, continuity, and % NR on a population level. Indeed, the high prevalence of OSA, its underdiagnosis, and causal effects on other sleep dimensions suggest OSA as a driver of poor sleep health (19,(33)(34)(35), and thus an attractive intervention target to improve multiple sleep health dimensions (36)(37)(38).…”

Section: Statistical Correlation Among Sleep Variables: Patterns Of Association and Implications For Intervention Targeting And Evaluatiomentioning

confidence: 99%

Multidimensional Sleep Health: Concepts, Advances, and Implications for Research and Intervention

Chung

Goodman

Huang

et al. 2021

Preprint

View full text Add to dashboard Cite

The new paradigm of multidimensional sleep health (‘sleep health’) offers both challenges and opportunities for sleep science. Buysse (2014) has described sleep health to be multidimensional, framed as positive attributes, operationalizable into composite measures of global sleep health, sensitive to upstream exposures, and consequential for downstream health. We highlight two paradigm-shifting effects of a multidimensional sleep health perspective. The first is the use of composite sleep metrics which i) enable quantification of population shifts in sleep health, ii) with possibly reduced measurement error, iii) greater statistical stability, and iv) reduced multiple-testing burdens. The second is that sleep dimensions do not occur in isolation, that is, they are commonly biologically or statistically dependent. These dependencies complicate hypothesis tests yet can be leveraged to inform scale construction, model interpretation, and inform targeted interventions. To illustrate these points, we i) extended Buysse’s Ru SATED model; ii) constructed a conceptual model of sleep health; and iii) showed exemplar analyses from the Multi-Ethnic Study of Atherosclerosis (n=735). Our findings support that sleep health is a distinctively useful paradigm to facilitate interpretation of a multitude of sleep dimensions. Nonetheless, the field of sleep health is still undergoing rapid development and is currently limited by: i) a lack of evidence-based cut-offs for defining optimal sleep health; ii) longitudinal data to define utility for predicting health outcomes; and iii) methodological research to inform how to best combine multiple dimensions for robust and reproducible composites.

show abstract

Associations Between Sleep Quality, Sleep Architecture and Sleep Disordered Breathing and Memory After Continuous Positive Airway Pressure in Patients with Obstructive Sleep Apnea in the Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Cited by 21 publications

References 24 publications

Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model

Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model

The effect of adherence with long-term PAP therapy on the psycho-emotional state of patients with obstructive sleep apnea syndrome

Multidimensional Sleep Health: Concepts, Advances, and Implications for Research and Intervention

Contact Info

Product

Resources

About