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The Oxygen Reserve Index (ORi™) is a dimensionless parameter with a value between 0 and 1. It is related to the real-time oxygenation status in the moderate hyperoxic range. The purpose of this study is to investigate the added warning time provided by different ORi alarm triggers and the continuous trends of ORi, SpO

Taiwan faces challenges in providing long-term family care as its population rapidly ages. This study aimed to construct a health application that could effectively meet family caregivers' needs in providing home care. Mixed methods were adopted to explore their acceptance of the application, as well as to gather their suggestions for improving the application. A nonrandomized controlled group pretest-posttest design was utilized. The participants were family caregivers who were recruited via purposive sampling at a regional hospital in Taipei, Taiwan. The caregivers used the application to monitor and manage the health of those they were caring for. Elderly family caregivers scored 1.57 and 1.16 points higher than their middle-aged counterparts in terms of performance expectancy (0.61-2.53, P = .01) and effort expectancy (0.25-2.06, P = .01), respectively. The caregivers opined that it was convenient to use the application for managing the health data of their patients, and resources and solutions for overcoming problems when using the application were easily within their reach. They also suggested that relevant information for promoting patient mental health could also be included in the application. Thus, the software is acceptable to all the family caregivers regardless of age, indicating that it can assist family caregivers in the future in performing home care management.

BACKGROUND Traditional Chinese medicine (TCM) theories assert that body constitution and meridian energy lay the foundation for disease prevention. TCM-based health concepts have not been incorporated into the mobile health application (mHealth app) for individuals with prediabetes. OBJECTIVE This study aimed to examine the effectiveness of a TCM mHealth app for individuals with prediabetes. METHODS This randomized controlled trial recruited 121 individuals with prediabetes at a teaching hospital in New Taipei City between February 2020 and May 2021. The participants were randomly assigned to the TCM mHealth app (TCMG; n=42), ordinary mHealth app (OMG; n=41), or control groups (CG; n=38). All participants received the usual care that included 15–20 minutes of health education about disease explanation, healthy diet, and exercise encouragement. The ordinary mHealth app included physical activity (PA), diet, disease education, and individual records. The TCM mHealth app additionally included qi and body constitution as well as constitution-based PA and diet advices. The CG received the usual care alone and did not use apps. Data were collected at baseline, the end of the 12-week intervention, and 1 month after the intervention. Body constitution, including yang-deficiency, yin-deficiency, and phlegm-stasis, was measured by the body constitution questionnaire, with higher scores indicating a greater deficiency. Body energy was examined by using the Meridian Energy Analysis Device. The Short-Form 36 was used to evaluate health-related quality of life (HRQOL), which yielded physical component scores (PCS) and mental component scores (MCS), with higher scores indicating better physical and mental aspects of HRQOL, respectively. RESULTS Compared to the CG, the TCMG improved more in hemoglobin A1c (HbA1c), yang-deficiency and phlegm-stasis body constitution, and body mass index (BMI); but no significant differences were found in those outcomes when the TCMG was compared to the OMG. The TCMG had better improvement in body energy and MCS than the OMG. There were no significant differences in fasting plasma glucose, yin-deficiency body constitution, Dietary Approaches to Stop Hypertension dietary behavior, and total PA among the three groups after the intervention. CONCLUSIONS Use of either the ordinary or TCM mHealth app improves HRQOL among individuals with prediabetes. Comparing to controls not using app, use of the TCM mHealth app is effective in improving HbA1c, BMI, yang-deficiency and phlegm-stasis body constitution, and HRQOL. Moreover, using the TCM mHealth app seems to improve the body energy and HRQOL more than using the ordinary mHealth app. Whether the differences favoring the TCM app were clinically meaningful may need further studies with a larger sample size and a longer follow-up period. CLINICALTRIAL ClinicalTrials.gov NCT04096989; https://clinicaltrials.gov/ct2/show/NCT04096989

Background Stress urinary incontinence (SUI) is a major health problem affecting approximately 50% of the female population over 45 years of age. We evaluated the therapeutic effects of a home-based non-invasive wireless sensor pelvic floor muscle training (PFMT) device with assisted Kegel exercise for SUI. Methods We included sixty women aged 40 to 60 years who were diagnosed with urodynamic SUI (mean pad test, 10.52 g). The PFMT device applicator was clamped on the upper inner thigh, and the patients could self-train at home. The signal was recorded and delivered to a 3G/4G smartphone via Bluetooth, which also allows guided feedback via the smartphone's voice. To evaluate the therapeutic effect, all patients completed the following questionnaires: a 3-day bladder diary, the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), the Urogenital Distress Inventory-Short Form (UDI-6-SF), and the Incontinence Impact Questionnaire-7 (IIQ-7). One-hour pad test measurements were performed before the test (M0) and at 1 (M1), 2 months (M2), and 3 months (M3) after the PFMT device-assisted Kegel exercise. Results The one-hour pad test and the scores of the ICIQ-SF, UDI-6, and IIQ-7 questionnaires were improved at M1, M2, and M3, compared with the M0 values. The mean value of the post-voiding residual urine (PVR) significantly decreased at M2 and M3. The subjective and objective improvement rates at M3 were 80% and 72%, respectively. Conclusion The data demonstrated that 3 months of Kegel exercise assisted with a home-based PFMT device improved the number and severity of episodes, post-voiding residual urine, and quality of life (QoL) in patients with SUI, suggesting that this device might serve as an alternative non-invasive therapy for mild and moderate SUI.

Due to the global spreading of the COVID-19 virus, countries all over the world are faced with the need to conduct centralized quarantine or home quarantine for "persons who have been in contact with individuals diagnosed with the COVID-19 virus" and "visitors who have travel histories via COVID-19 hot zones". We have presented the community home quarantine service platform design that was utilized in Nanjing, China when the first wave of citizens returns to work after the Chinese New Year holidays on 10th Feb 2020. The main functions of the home quarantine monitoring system include (1) community grid management,(2) GPS positioning application in home isolation movement management,(3) Bluetooth body temperature patch data transmission integration, (4) health assessment scale (physical and mental health state) and (5) multilingual language options.

BACKGROUND As the population of Taiwan continues to age rapidly, middle-aged and elderly people will increasingly begin to care for their older relatives. Therefore, the responsibility and complexity of care required from family caregivers will increase. The mobile health technology (mHealth) model has been successfully applied to numerous care services. OBJECTIVE To develop a mobile health app that supports family caregivers of various ages by meeting their home care needs, thus improving their quality of life, decreasing their severity of depression, and making them more empowered. METHODS This study applied a nonrandomized control-group pretest–posttest design. Purposive sampling was employed to recruit 30 family caregivers—16 middle-aged and 14 old family caregivers—from the home care cases of a regional hospital in Taipei. The developed home care mHealth app was used for management and monitoring of patient health. The outcomes measured were quality of life, level of depression, and sense of empowerment. RESULTS The results indicated that use of the mHealth app improved the technology acceptance, quality of life, depression level, and empowerment of the family caregivers. The old caregivers obtained quality of life scores that were 2.23 points higher, on average, than those obtained by the middle-aged caregivers P = .01, a significant difference. On the quality of life questionnaire, the old caregivers’ obtained scores for the health and family factors that were 2.75 P = .01 and 2.49 P = .01 points higher, respectively, than those obtained by the middle-aged caregivers, indicating significant differences. Regarding empowerment and depression, the scores obtained at different time points indicated that both groups became more empowered and less depressed over time, but no significant differences were discovered between the two groups. CONCLUSIONS According to our results, using the home care mHealth app had a positive influence the app is concluded to be a favorable tool for care education and management. Old caregivers found the software useful for managing patients’ physiological data, and they wished that other assistive methods were available to help them understand the internal functions. The software contributes to quality of life improvement, a decrease in depression severity, and empowerment enhancement and can be used by family caregivers as a reference for home care management in the future.

This research provided a postoperative system for osteosarcoma which is seperated into two parts, Bone Patient and Bone Care. We focus on using smartphone features and proper interface design to facilitate self-monitoring, health analysis, and to make appointments. It achieves the effects of shared decision making (SDM). During the two weeks of research, we achieved satisfied with 4.03 ± 0.67 TAM score.

Pulmonary embolism is a difficult disease to diagnose in the emergency department. It may be fatal if the diagnosis is missed. Clinical practice guidelines and textbooks publish pretest diagnostic tools and algorithms that facilitate the diagnosis of pulmonary embolism. We developed and administered a questionnaire that determined that such tools can be difficult to remember to use. We also designed an Android application to facilitate diagnosis of pulmonary embolism and used a questionnaire to evaluate the application.

Instant messaging (IM) is one kind of online chat that provides real-time text transmission over the Internet. It becomes one of the popular communication tools. Even it is currnetly an era of smartphones, it still a great challenge to teach and promote the elderly to use smart phone. Besides, the acceptance of the elderly to use IM remains unknown. This study describes the usability and evaluates the acceptance of the IM in the elderly, who use the smartphone for the first time. This study is a quasi-experimental design study. The study period started from October, 2012 to December, 2013. There were totally 41 elderly recruited in the study. All of them were the first time to use LINE app on the smartphones. The usability was evaluated by using the Technology Acceptance Model which consisted of four constructs: cognitive usability, cognitive ease of use, attitude and willingness to use. Overall, the elderly had the best "attitude" for LINE APP communication software, with the highest rating averaging 4.07 points on four constructs, followed by an average of 4 points on "cognitive usefulness". The socres of "cognitive ease of use" and "willingness to use" scores were equal which are an average score of 3.86. It can be interpreted that (1) the elders thought that the LINE APP as an excellent communication tool for them; (2) they found the software is useful (3) it was convenient for them to communicate. However, it was necessary to additionally assist and explain the certain functions such as the options. It would play a great role in the "willingness to use". The positive acceptance of LINE APP in elderly refer to the probable similar acceptance for them to use other communication software. Encouraging the willingness the elderly to explore more technology products and understanding their behavior will be the basic knowledge to develop further software.

High fidelity simulation-based teaching has played an important role in medical education, especially in anesthesiology and emergency. But there is not any currently validated scoring system or prediction model for high fidelity simulation. We will develop a validated prediction model to enhance the efficiency and validation of clinical training with high fidelity simulation.

This study aimed at developing an assessment of smartphone usage competence and constructing a training program for the elderly. A list of smartphone usage competencies with 34 items was defined through expert survey and panel. Based on the competence and previous literature, a training program and learning aids were designed in this study. There were 41 participants in our program. The results of self-administrated smartphone usage ability questionnaire indicated that all competencies were significantly improved after training. However, the results also demonstrated that some items were still difficult for the elderly to comprehend. Overall, this study provided a first exploration of defining smartphone usage competency and built a training program for the elderly. With strong suggestion, future mobile health (mHealth) services can follow this study to insure the smartphone usage ability of the elderly.

Near field communications (NFC) is an emerging technology that may potentialy assist with disaster management. A smartphone-based app was designed to help track patient flow in real time. A table-drill was held as a brief evaluation and it showed significant imporvement in both efficacy and accuracy of patient management. It is feasible to use NFC-embedded smartphones to clarify the ambiguous and chaotic patient flow in a mass casualty incident.

Pregnancy weight gain may be associated with adverse pregnancy outcomes. The article aims to explore the relationship between weight change and pregnancy outcome in the Taiwanese pregnant women. The retrospective cohort study enrolled women with vertex singleton pregnancy at University-associated Hospital between 2011 and 2014. Pregnancy weight change was separated into 3 groups, based on the Institute of Medicine (IOM) guidelines: below (n = 221); within (n = 544); and above (n = 382). Analysis of variance, χ2 tests, generalized linear models, and logistic regression models were used for statistical comparisons. Pregnant women with weight change above IOM guidelines had a significant increase in both maternal and perinatal complications compared with normal controls (odds ratio [OR] 1.65, 95% confidence interval [CI] 1.03–1.98; P = .043; OR 1.45, 95% CI 1.01–1.87; P = .049, respectively). This finding was not found in pregnant women with weight gain below IOM guidelines. Moreover, age (OR 1.08, 95% CI 1.02–1.15; P = .0011), pre-pregnancy weight (OR 1.04, 95% CI 1.01–1.09; P = .0008), pre-pregnancy body mass index (BMI; OR 1.15, 95% CI 1.06–1.30; P

Highway accidents significantly impact normal traffic flow. Consequently, automatic detection of abnormal traffic events has gradually attracted the attention of researchers interested in intelligent transportation system. This work presents a vision-based approach for automatic traffic congestion and incident detection. The proposed approach involves extracting entropy-based features to create a grid model that simulates dynamic traffic flow behavior. When an unusual event occurs in the lane of the vehicle employing the system, the system can immediately detect it and issue signals to approaching vehicles to prevent accidents. Experiments conducted using various simulation results clearly demonstrate the validity and effectiveness of the proposed approach for managing traffic congestion and detecting incidents.

We propose a novel adaptive fast learning (AFL) algorithm for two-dimensional principal component analysis (2DPCA) in this paper. As opposite to conventional PCA which is based on 1D data vectors, 2DPCA is based on 2D image matrices and thus has higher accuracy than conventional PCA when applied to applications such as face recognition, facial expression recognition, palmprint recognition, etc. Our proposed AFL algorithm simultaneously estimates both eigenvectors and corresponding eigenvalues, and then adaptively sets the learning rate parameters of neurons to ensure all neurons learning with almost the same fast speed. Requiring no image covariance matrix evaluation, the desired multiple eigenvectors of 2DPCA can thus be learned effectively in the form of weight vectors of neurons. The proposed AFL algorithm can also be applied to learning for T-2DPCA. Simulation experiments performed on face database such as the YaleB database clearly demonstrate that the proposed AFL algorithm performs very well and thus is a very effective computational tool for both 2DPCA and T-2DPCA.

Inexhaustible solar power is booming for carbon reduction in recent years. The current efficiency of solar power generation is still low. Therefore, improvement of the power generation efficiency will be the research objective to scientists all over the world. The maximum power point tracking (MPPT) is an effective way to improve efficiency. Existing maximum power point tracking methods are incremental conductance method, perturbation and observation method, voltage feedback method and so on. Perturbation and observation method has the simple, easy to understand and the most common structure. In this paper, the nonlinear phenomena of buck-boost DC-DC converting solar cell system with perturbation and observation method for MPPT, considering converter operation in continuous conduction mode (CCM) with inductor current by varying load resistance is studied. The system is from periodic steady-state into period-doubling bifurcation to the chaos of nonlinear phenomena. Firstly the solar cell maximum power tracking system and converter working principle are explained. Secondly, the construction of solar maximum power tracking with perturbation and observation method and buck-boost DC - DC converter equivalent circuit model, then mathematical model is derived, and simulated by MATLAB / SIMULINK software package. Through the time-domain waveforms, phase plane and bifurcation diagrams, to explore the changing of load resistance system from a periodic steady-state into the chaos of nonlinear phenomena, chaos evolution mechanism of the system is analyzed.

Human eye state identification can be applied not only to monitoring of the drowsiness of a human car driver but also to medical treatment facilitating system for monitoring neonate or stuporous patient. Once the patient awake and open his eyes, human eye state identification system can notify nurses to take care of the patient. In this work, we propose an intelligent human eye state identification algorithm based on 2DPCA and skin color. Adaboost face detection function of OpenCV is first adopted to detect the human faces in color images acquired from camera. Then, we develop a more precise HSV skin color model and use it to eliminate the false alarms in the previous stage. Next, a heuristic segmentation method based on skin color and face geometry is proposed to segment the region of eyes, from which 2DPCA is then adopted to extract the features and identify the opening or closing state of eyes. We study three kinds of 2DPCA, i.e. 2DPCA, T-2DPCA and (2D)2PCA, and compare their performance. Experimental results reveal that our algorithm can achieve over 90% accuracy rate.

The back propagation (BP) algorithm for function approximation is multilayer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter is used to partition the nonlinear system's domain into several piecewise linear subspaces to be represented by neural networks. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm is proposed to greatly mitigate the influence of data noise and outliers. (2) A fuzzy-based data sifter is proposed to locate good turning-points to partition a given nonlinear data domain into piecewise clusters so that a neural network can be constructed with fewer rules. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers.

This paper studies the nonlinear behaviors of the half-bridge buck converter when the load resistance changes. The characteristics of the study are considered as the inductor current in the continuous conduction mode. Firstly, the converter is equivalent to two switching states and is derived into the equivalent mathematical model. According to this model, using MATLAB/ SIMULINK tool to build a half-bridge buck converter block diagram for simulation. When the load resistance changes as the varying parameter in the converter, this paper observes the time-domain waveforms and phase portraits of steady-state to period-doubling bifurcation into chaos. The nonlinear phenomena of converter are verified through simulation results.

This paper investigates the nonlinear phenomena in a current-mode controlled buck-boost DC-DC converter with solar cells. The converter is operated in continuous conduction mode (CCM) with inductor current. It is also controlled by varying reference current to study the occurrence of nonlinear phenomena from the initial periodic steady-state to period-doubling bifurcation into the last chaos condition. Firstly, this paper introduces the basic principles of solar cells and current-mode controlled buck-boost converter. Secondly, the equivalent circuit model of solar cells and converter is constructed and the mathematical model is derived. Then, this study adopts MATLAB / SIMULINK for modeling and simulation. The converter nonlinear phenomena are verified through time-domain waveforms, phase portraits and bifurcation diagrams.

This paper investigates the nonlinear behaviors when the switching frequency changed in the UC3842 current-mode controlled flyback converter. First, State equations of the flyback converter are derived. The converter in continuous conduction mode (CCM) is then considered. Second, MATLAB/SIMULINK is used to construct simulation of the converter whose operation under varying switching frequency parameters. The output voltage, inductor current waveforms and phase-plane portraits are observed. It can be seen that the system exhibits nonlinear dynamics from period-one operation through period-doubling to chaos phenomena as the switching frequency are changed. From the simulation results, it is clearly evident that SIMULINK provide consistent results and verify the mathematical models and able to really understand the nonlinear behavior of the flyback converter.