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1.
Hum Factors ; : 187208231183874, 2023 Jun 30.
Artículo en Inglés | MEDLINE | ID: mdl-37387305

RESUMEN

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

2.
Sensors (Basel) ; 22(22)2022 Nov 09.
Artículo en Inglés | MEDLINE | ID: mdl-36433227

RESUMEN

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.


Asunto(s)
Apnea Obstructiva del Sueño , Humanos , Teorema de Bayes , Apnea Obstructiva del Sueño/diagnóstico , Polisomnografía , Antropometría , Aprendizaje Automático
3.
Sensors (Basel) ; 21(23)2021 Dec 03.
Artículo en Inglés | MEDLINE | ID: mdl-34884101

RESUMEN

Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over three subsequent nights. Scores on OSA indices-namely, the apnoea-hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into three groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons, and their correlations were examined by Pearson's correlation coefficient. The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All three groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment, compared with the hospital-based assessment. The percentage of supine sleep time (∆Supine%) exhibited a significant but weak to moderate positive correlation with each of the OSA indices. A significant but weak-to-moderate correlation between the ∆Supine% and ∆Rx index was still observed among the patients with high sleep efficiency (≥80%), who could reduce the effect of short sleep duration, leading to underestimation of the patients' OSA severity. The high supine percentage of sleep may cause OSA indices' overestimation in the hospital-based examination. Sleep recording at home with patch-type wearable devices may aid in accurate OSA diagnosis.


Asunto(s)
Apnea Obstructiva del Sueño , Electrocardiografía , Hospitales , Humanos , Polisomnografía , Sueño , Apnea Obstructiva del Sueño/diagnóstico
4.
Lasers Med Sci ; 30(7): 1959-65, 2015 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-26231231

RESUMEN

Intense pulsed light (IPL) technology has been popularly employed in clinical treatments for dermatological and cosmetic purposes in recent years; yet, the underlying mechanisms of its functions are not fully elucidated. On the other hand, aquaporin (AQP) 3, a member of a subgroup of the aquaporin family that transports both water and small solutes, such as glycerol, has been documented to play an important role in the skin homeostasis. We thus examined the possible involvement of AQP3 in the functional mechanisms of IPL irradiation. Rat dorsal skin areas were irradiated one to three times with IPL at doses of 15, 25, and 35 J/cm2. Skin specimens were collected 7 days after the final irradiation and analyzed for changes in histology, skin hydration, mRNA, and protein expressions of AQP3. IPL induced no significant variations in the mRNA expression levels. Twice or thrice irradiation at the dose of 25 or 35 J/cm2 significantly enhanced AQP3 protein expression. Immunofluorescence study revealed that AQP3 was mainly localized to keratinocyte membranes in the basal layer of epidermis, and the localization was unaltered by IPL. In addition, the pattern of IPL-induced changes in skin hydration was generally coincided with the expression profile of AQP3. These results suggest the possibility that one of the functional mechanisms of IPL might be related to the regulation of AQP3 protein expression.


Asunto(s)
Acuaporina 3/metabolismo , Expresión Génica/efectos de la radiación , Rayos Láser , Animales , Acuaporina 3/genética , Tratamiento de Luz Pulsada Intensa , Queratinocitos/metabolismo , Queratinocitos/efectos de la radiación , Masculino , ARN Mensajero/metabolismo , Ratas , Ratas Sprague-Dawley , Piel/citología , Piel/metabolismo , Piel/efectos de la radiación
5.
J Med Internet Res ; 16(3): e71, 2014 Mar 03.
Artículo en Inglés | MEDLINE | ID: mdl-24590187

RESUMEN

BACKGROUND: The traditional otorhinoendoscope is widely used in the diagnosis of a variety of ear and nose diseases, but only one doctor can use it at a time. It is also very difficult to share observations from one doctor with another doctor. With advances in electronic health technology, the extended potential application of smartphones to support medical practice or mobile health has grown steadily. OBJECTIVE: The first phase of the study discussed how smartphones may be used for otorhinoscopic imaging and image management via an innovative adaptor. The second phase of the study was to evaluate the diagnostic capability of the smartphone-based otorhinoendoscope, as compared to the traditional otorhinoendoscope, and its application in mobile health and teleotolaryngology. METHODS: We designed a unique adaptor to connect the otorhinoendoscope and smartphone in order to perform smartphone-based otorhinoendoscopy. The main aim was to transform the smartphone into an otorhinoendoscope. We devised a method that would allow us to use the smartphone's camera to capture otorhinoscopic images. Using a freely available Web-based real-time communication application platform and the 3G (or WIFI) network, the smartphone-based otorhinoendoscope could synchronize the smartphone-based otorhinoscopic image with smartphones, tablet PCs, computer notebooks, or personal computers. RESULTS: We investigated the feasibility of telemedicine using a smartphone, tablet PC, and computer notebook. Six types of clinical otorhinoscopic images were acquired via the smartphone-based otorhinoendoscope from six patients, which were examined in this study. Three teleconsultants (doctors A, B, and C) reviewed the six types of clinical otorhinoscopic images and made a telediagnosis. When compared to the face-to-face diagnosis, which was made in-person via a traditional otorhinoendoscope, the three teleconsultants obtained scores of a correct primary telediagnosis 83% (5/6), 100% (6/6), and 100% (6/6) of the time, respectively. When the clinical data were provided, the three teleconsultants obtained a correct secondary telediagnosis score of 100% (6/6), 100% (6/6), and 100% (6/6) of the time, respectively. CONCLUSIONS: The use of previously available technologies in the absence of any additional expensive devices could significantly increase the quality of diagnostics while lowering extraneous costs. Furthermore, this could also increase the connectivity between most isolated family doctors and remote referral centers.


Asunto(s)
Teléfono Celular , Endoscopios , Otolaringología/instrumentación , Telemedicina/instrumentación , Humanos , Enfermedades Otorrinolaringológicas/diagnóstico
6.
J Clin Sleep Med ; 20(8): 1267-1277, 2024 Aug 01.
Artículo en Inglés | MEDLINE | ID: mdl-38546033

RESUMEN

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. CITATION: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.


Asunto(s)
Aprendizaje Profundo , Polisomnografía , Radar , Índice de Severidad de la Enfermedad , Apnea Obstructiva del Sueño , Humanos , Apnea Obstructiva del Sueño/diagnóstico , Apnea Obstructiva del Sueño/fisiopatología , Masculino , Estudios Prospectivos , Polisomnografía/instrumentación , Polisomnografía/métodos , Femenino , Persona de Mediana Edad , Radar/instrumentación , Tecnología Inalámbrica/instrumentación , Taiwán , Adulto , Anciano
7.
Digit Health ; 9: 20552076231152751, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-36896329

RESUMEN

Objectives: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

8.
Digit Health ; 9: 20552076231205744, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37846406

RESUMEN

Objective: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

9.
BMJ Open Respir Res ; 10(1)2023 11.
Artículo en Inglés | MEDLINE | ID: mdl-37940353

RESUMEN

BACKGROUND: Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations. METHODS: In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices. RESULTS: First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea-Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI. CONCLUSION: Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.


Asunto(s)
Contaminantes Atmosféricos , Apnea Obstructiva del Sueño , Humanos , Contaminantes Atmosféricos/efectos adversos , Contaminantes Atmosféricos/análisis , Estudios Retrospectivos , Agua Corporal/química , Apnea Obstructiva del Sueño/epidemiología , Material Particulado/efectos adversos , Material Particulado/análisis , Oxígeno , Nivel de Alerta , Agua
10.
Int J Occup Saf Ergon ; 29(4): 1429-1439, 2023 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-36281493

RESUMEN

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.


Asunto(s)
Conducción de Automóvil , Humanos , Accidentes de Tránsito , Frecuencia Cardíaca/fisiología , Proyectos Piloto , Teorema de Bayes , Aprendizaje Automático
11.
Life (Basel) ; 13(5)2023 May 19.
Artículo en Inglés | MEDLINE | ID: mdl-37240863

RESUMEN

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

12.
Front Public Health ; 11: 1175203, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37397706

RESUMEN

Background: Exposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations. Objectives: This study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution. Methods: This retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined. Results: Significant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA. Conclusion: Because exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Ambientales , Apnea Obstructiva del Sueño , Humanos , Contaminantes Atmosféricos/efectos adversos , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Exposición a Riesgos Ambientales/efectos adversos , Material Particulado/efectos adversos , Material Particulado/análisis , Estudios Retrospectivos , Apnea Obstructiva del Sueño/epidemiología , Agua Corporal
13.
Ear Nose Throat J ; : 1455613221123361, 2022 Aug 22.
Artículo en Inglés | MEDLINE | ID: mdl-35993670

RESUMEN

OBJECTIVES: Chronic otitis media is a long-term infection of the middle ear. It is characterized by persistent discharge from the middle ear through a perforated tympanic membrane. It is one of the most common causes of preventable hearing loss, especially in developing countries. Precise estimation of the size of tympanic membrane perforation is essential for successful clinical management. In this study, we developed a smartphone-based application to calculate the ratio of the area of tympanic membrane perforation to the area of the tympanic membrane. Twelve standardized patients and 60 medical students were involved to assess the area of tympanic membrane perforation, in particular, the percentage of perforation size. METHODS: In total, 60 student doctors (including year 5 and year 6 medical students, intern and post-graduate year training of doctors) were recruited during their rotation at the Otolaryngology department of Taipei Medical University Shuang-Ho Hospital. Twelve standardized patients with chronic otitis media were recruited through a single otology practice. Oto-endoscopic examination was performed for all patients by using a commercially-available digital oto-endoscope, and clinical images of the tympanic membrane perforation were obtained. To demonstrate the variability of perforation size estimation by different student doctors, we calculated the percentage of perforation using the smartphone-based application for 12 tympanic membranes objectively and compared the results with those visually estimated by the 60 student doctors subjectively. RESULTS: The variance in the visual estimation by the 60 student doctors was large. By contrast, variances in smartphone-based application calculations were smaller, indicating consistency in the results obtained from different users. The smartphone-based application accurately estimated the presence of perforation for tympanic membranes with high consistency. The differences in visual estimations can be considerably great and the variances can be large among different individuals. CONCLUSIONS: The smartphone-based application is a dependable tool for precisely evaluating the size of tympanic membrane perforation.

14.
Front Neurol ; 13: 1038735, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-36530623

RESUMEN

Objectives: Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD. Methods: Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aß42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aß42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared. Results: We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product. Conclusions: Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.

15.
Inform Health Soc Care ; 47(4): 373-388, 2022 Oct 02.
Artículo en Inglés | MEDLINE | ID: mdl-34886766

RESUMEN

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.


Asunto(s)
Apnea Obstructiva del Sueño , Masculino , Humanos , Femenino , Taiwán/epidemiología , Teorema de Bayes , Polisomnografía , Apnea Obstructiva del Sueño/diagnóstico , Apnea Obstructiva del Sueño/epidemiología , Aprendizaje Automático
16.
J Pers Med ; 11(10)2021 Oct 16.
Artículo en Inglés | MEDLINE | ID: mdl-34683176

RESUMEN

Hearing impairment is a frequent human sensory impairment. It was estimated that over 50% of those aged >75 years experience hearing impairment in the United States. Several hearing impairment-related factors are detectable through screening; thus, further deterioration can be avoided. Early identification of hearing impairment is the key to effective management. However, hearing screening resources are scarce or inaccessible, underlining the importance of developing user-friendly mobile health care systems for universal hearing screening. Mobile health (mHealth) applications (apps) act as platforms for personalized hearing screening to evaluate an individual's risk of developing hearing impairment. We aimed to evaluate and compare the accuracy of smartphone-based air conduction and bone conduction audiometry self-tests with that of standard air conduction and bone conduction pure-tone audiometry tests. Moreover, we evaluated the use of smartphone-based air conduction and bone conduction audiometry self-tests in conductive hearing loss diagnosis. We recruited 103 patients (206 ears) from an otology clinic. All patients were aged ≥20 years. Patients who were diagnosed with active otorrhea was excluded. Moderate hearing impairment was defined as hearing loss with mean hearing thresholds >40 dB. All patients underwent four hearing tests performed by a board-certified audiologist: a smartphone-based air conduction audiometry self-test, smartphone-based bone conduction audiometry self-test, standard air-conduction pure-tone audiometry, and standard bone conduction pure-tone audiometry. We compared and analyzed the results of the smartphone-based air conduction and bone conduction audiometry self-tests with those of the standard air conduction and bone conduction pure-tone audiometry tests. The sensitivity of the smartphone-based air conduction audiometry self-test was 0.80 (95% confidence interval CI = 0.71-0.88) and its specificity was 0.84 (95% CI = 0.76-0.90), respectively. The sensitivity of the smartphone-based bone conduction audiometry self-test was 0.64 (95% CI = 0.53-0.75) and its specificity was 0.71 (95% CI = 0.62-0.78). Among all the ears, 24 were diagnosed with conductive hearing loss. The smartphone-based audiometry self-tests correctly diagnosed conductive hearing loss in 17 of those ears. The personalized smartphone-based audiometry self-tests correctly diagnosed hearing loss with high sensitivity and high specificity, and they can be a reliable screening test to rule out moderate hearing impairment among the population. It provided patients with moderate hearing impairment with personalized strategies for symptomatic control and facilitated individual case management for medical practitioners.

17.
Artículo en Inglés | MEDLINE | ID: mdl-34639340

RESUMEN

As part of the new measures to prevent the spread of the 2019 coronavirus disease (COVID-19), medical students were advised to wear a mask in class and avoid touching their faces. Few studies have analyzed the influence of health education on the frequency of face- and smartphone-touching behaviors during the COVID-19 pandemic. This research compared the frequency of in-class face- and smartphone-touching behaviors of medical students before and after the delivery of personal hygiene education during the COVID-19 pandemic. A behavioral observational study was conducted involving medical students at Taipei Medical University. Eighty medical students were recruited during a lecture on otorhinolaryngology. All medical students were required to wear a mask. Their face- and smartphone-touching behavior was observed by viewing the 4 k resolution video tape recorded in class. The recording lasted for 2 h, comprising 1 h prior to the health educational reminder and 1 h afterwards. The frequencies of hand-to-face contact and hand-to-smartphone contact were analyzed before and after the delivery of health education emphasizing personal hygiene. Comprehensive health education and reminders effectively reduce the rate of face- and smartphone-touching behaviors.


Asunto(s)
COVID-19 , Pandemias , Humanos , Higiene , Pandemias/prevención & control , SARS-CoV-2 , Teléfono Inteligente
18.
Diagnostics (Basel) ; 12(1)2021 Dec 27.
Artículo en Inglés | MEDLINE | ID: mdl-35054218

RESUMEN

Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.

19.
JMIR Mhealth Uhealth ; 8(10): e17213, 2020 10 27.
Artículo en Inglés | MEDLINE | ID: mdl-33107828

RESUMEN

BACKGROUND: Hearing impairment is the most frequent sensory deficit in humans, affecting more than 360 million people worldwide. In fact, hearing impairment is not merely a health problem, but it also has a great impact on the educational performance, economic income, and quality of life. Hearing impairment is therefore an important social concern. OBJECTIVE: We aimed to evaluate and compare the accuracy of self-perception, Hearing Handicap Inventory for the Elderly-Screening (HHIE-S) questionnaire, free-field voice test, and smartphone-based audiometry as tests for screening moderate hearing impairment in older adults in China. METHODS: In this study, 41 patients were recruited through a single otology practice. All patients were older than 65 years. Patients with otorrhea and cognitive impairment were excluded. Moderate hearing impairment was defined as mean hearing thresholds at 500, 1000, 2000, and 4000 Hz >40 dB hearing loss (pure-tone average > 40 dB hearing loss). All patients completed 5 hearing tests, namely, the self-perception test, HHIE-S questionnaire test, free-field voice test, smartphone-based audiometry test, and standard pure-tone audiometry by the same audiologist. We compared the results of these tests to the standard audiogram in the better-hearing ear. RESULTS: The sensitivity and the specificity of the self-perception test were 0.58 (95% CI 0.29-0.84) and 0.34 (95% CI 0.19-0.54), respectively. The sensitivity and the specificity of the HHIE-S questionnaire test were 0.67 (95% CI 0.35-0.89) and 0.31 (95% CI 0.316-0.51), respectively. The sensitivity and the specificity of the free-field voice test were 0.83 (95% CI 0.51-0.97) and 0.41 (95% CI 0.24-0.61), respectively. The sensitivity and the specificity of the smartphone-based audiometry test were 0.92 (95% CI 0.60-0.99) and 0.76 (95% CI 0.56-0.89), respectively. Smartphone-based audiometry correctly diagnosed the presence of hearing loss with high sensitivity and high specificity. CONCLUSIONS: Smartphone-based audiometry may be a dependable screening test to rule out moderate hearing impairment in the older population.


Asunto(s)
Pérdida Auditiva , Teléfono Inteligente , Anciano , Audiometría de Tonos Puros , China , Pérdida Auditiva/diagnóstico , Pérdida Auditiva/epidemiología , Humanos , Calidad de Vida , Estándares de Referencia , Autoimagen , Encuestas y Cuestionarios
20.
Ear Hear ; 30(1): 90-9, 2009 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-19125031

RESUMEN

OBJECTIVE: This study aimed to develop a set of 50- and 25-item word lists that exhibited familiarity, homogeneity, and phonemic balance for a Mandarin monosyllable recognition test (MMRT). DESIGN: To achieve the design goal of high subject familiarity with the test material, we selected the 700 most frequently occurring monosyllables to be the test material. The homogeneity of the test material was achieved by evaluating five psychometric characteristics of these 700 monosyllables to obtain 348 homogeneous monosyllables with similar psychometric functions for constructing the word lists. The phonemic balance of the 50-item word lists was achieved by deriving the desired numbers of initials, finals, and tones in these lists according to their occurrence frequencies in 4733 monosyllabic words. The phonemic balance of the 25-item word lists was achieved by equally dividing the desired numbers of initials, finals, and tones in the 50-item word lists into two groups, called half-A and half-B lists. Three half-A lists and three half-B lists were constructed from the 348 homogeneous monosyllables, and they could be paired to form nine 50-item word lists. Accordingly, all of the MMRT word lists are familiar, homogeneous, and phonemically balanced. RESULTS: The homogeneity of the MMRT word lists was examined by evaluating the interlist equivalence, interitem variability, and intersubject variability. A chi test was used to evaluate the interlist equivalence among the MMRT word lists; that is, to determine whether the values of percent correct recognition were distributed identically at 12 different presentation levels among the six 25-item word lists and nine 50-item word lists. No statistically significant differences were found among the word lists: chi(55) = 12.004 and p = 1.000 for the six 25-item word lists, chi(88) = 7.695 and p = 1.000 for the nine 50-item word lists, and chi(154) = 19.699 and p = 1.000 for the six 25-item word lists plus nine 50-item word lists. The chi tests indicated that the six 25-item word lists and nine 50-item word lists exhibited interlist equivalence. The mean interitem variability (2.0 dB) and mean intersubject variability (2.3 dB) are lower for the MMRT word lists than for previously reported word recognition tests. CONCLUSION: This study applied an innovative approach to design MMRT word lists from familiar and homogeneous monosyllables, in which the familiarity, homogeneity, and phonemic balance of the six 25-item and nine 50-item word lists were strictly controlled. These word lists exhibit interlist equivalence with respect to their psychometric functions and five psychometric characteristics; moreover, their interitem and intersubject variability are lower than those of previously reported lists. Future clinical experiments should examine whether such a design approach can improve the reliability and diagnostic sensitivity of word recognition tests for hearing-impaired listeners.


Asunto(s)
Pueblo Asiatico , Lenguaje , Reconocimiento en Psicología , Pruebas de Discriminación del Habla , Adulto , Femenino , Humanos , Masculino , Psicometría , Retención en Psicología , Adulto Joven
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