Gotowa bibliografia na temat „Apple watch for under 200”

The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children’s free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.

Abstract Introduction Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM). Methods Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase. Results Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements. Conclusion This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables. Support (if any) This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

Background: We investigate the feasibility of using an Apple Watch for sleep monitoring by comparing its performance to the clinically validated Philips Actiwatch Spectrum Pro (the gold standard in this study), under free-living conditions. Methods: We recorded 27 nights of sleep from 14 healthy adults (9 male, 5 female). We extracted activity counts from the Actiwatch and classified 15-second epochs into sleep/wake using the Actiware Software. We extracted triaxial acceleration data (at 50 Hz) from the Apple Watch, calculated Euclidean norm minus one (ENMO) for the same epochs, and classified them using a similar algorithm. We used a range of analyses, including Bland-Altman plots and linear correlation, to visualize and assess the agreement between Actiwatch and Apple Watch. Results: The Apple Watch had high overall accuracy (97%) and sensitivity (99%) in detecting actigraphy-defined sleep, and adequate specificity (79%) in detecting actigraphy defined wakefulness. Over the 27 nights, total sleep time was strongly linearly correlated between the two devices (r=0.85). On average, the Apple Watch over-estimated total sleep time by 6.31 minutes and under-estimated Wake After Sleep Onset by 5.74 minutes. The performance of the Apple Watch compares favorably to the clinically validated Actiwatch in a normal environment. Conclusions: This study suggests that the Apple Watch could be an acceptable alternative to the Philips Actiwatch for sleep monitoring, paving the way for larger-scale sleep studies using Apple’s consumer-grade mobile device and publicly available sleep classification algorithms. Further study is needed to assess longer-term performance in natural conditions, and against polysomnography in clinical settings.

Abstract Background The level of physical activity (PA) of people with spinal cord injury (SCI) has an impact on long-term complications. Currently, PA is mostly assessed by interviews. Wearable activity trackers are promising tools to objectively measure PA under everyday conditions. The only off-the-shelf, wearable activity tracker with specific measures for wheelchair users is the Apple Watch. Objectives This study analyzes the measurement performance of Apple Watch Series 4 for wheelchair users and compares it with an earlier generation of the device. Methods Fifteen participants with subacute SCI during their first in-patient phase followed a test course using their wheelchair. The number of wheelchair pushes was counted manually by visual inspection and with the Apple Watch. Difference between the Apple Watch and the rater was analyzed with mean absolute percent error (MAPE) and a Bland–Altman plot. To compare the measurement error of Series 4 and an older generation of the device a t-test was calculated using data for Series 1 from a former study. Results The average of differences was 12.33 pushes (n = 15), whereas participants pushed the wheelchair 138.4 times on average (range 86–271 pushes). The range of difference and the Bland–Altman plot indicate an overestimation by Apple Watch. MAPE is 9.20% and the t-test, testing for an effect of Series 4 on the percentage of error compared with Series 1, was significant with p < 0.05. Conclusion Series 4 shows a significant improvement in measurement performance compared with Series 1. Series 4 can be considered as a promising data source to capture the number of wheelchair pushes on even grounds. Future research should analyze the long-term measurement performance during everyday conditions of Series 4.

“When Giants Pass” Frederick O. Leh, M.D. When giants pass, they leave giant footprints and giant shoes to fill. Dr. Leh Siu Chuan passed away last August 2013, after suffering multi-organ failure following a stroke secondary to sick sinus syndrome. As in life, he was a fighter, refusing to give up the ghost for 3 years and 3 months, living in an intensive care unit at the hospital he spent his life serving and loving. Siu Chuan Y. Leh was born in Manila August 22, 1935, the third generation of Chinese immigrants from the Fukien Province in China. He was the second child in a brood of twelve, easily the brightest child and the apple of his father’s eye. He completed his medical studies at the Pontifical University of Santo Tomas. During the ignominous Vietnam War of the 60’s, he was able to get a position for a residency position in Otolaryngology at the University of Pennsylvania, and trained under the venerable Dr. Atkins, a protégé of both Dr. Jackson Sr. and Dr. Tucker of endoscopic fame. He had to leave his family behind – his wife Benita Leh, and three children – Shirley, Frederick and Sandra. On his second year of training, he sent for his wife and son, Frederick who would later follow in his footsteps as an otolaryngologist. Life was difficult during that time for a married resident. He received a stipend of only $200 a month, and had to moonlight in emergency rooms on weekends to make ends meet. When he finally completed his residency and passed the American Board of Otolaryngology exams, he gave up a possible lucrative partnership with his mentor to go back to the Philippines to serve his countrymen. Dr. Leh was invited to the Chinese General Hospital and Medical Center, and he served there prominently as its brightest Ear Nose and Throat practitioner. He became well-known in the Chinese community, taking time to hold clinic in the Ong’s Association Building along Benavidez in Chinatown. He later served as Chinese General Hospital’s Executive Assistant Medical Director until his health started to fail. He was also very active in the Philippine Otolaryngology scene, serving continuously as a Board Examiner, much feared by examinees for his strict and no-nonsense grilling of would-be diplomats. Dr. Leh rose rapidly through the ranks to become President of the Philippine Society of Otolaryngology Head and Neck Surgery. Under his watch, the PSOHNS expanded exponentially, gaining many new member hospitals and programs. He organized and professionalized the criteria for the accreditation program, ensuring high quality from all applicant programs. With all the kudos, fame and fortune, Dr. Leh was still not done. He was asked to take over a fledgling Tzu Chi Philippine Chapter, part of a Taiwanese Buddhist Foundation seeking to bring relief to the poor of the world. Dr. Leh organized and founded TIMA (the Tzu Chi International Medical Missions and Assistance), which later became the model for other medical missions in the world. For this Dr. Leh was awarded many times by Tzu Chi Foundation. His dream continues as the TIMA continues to treat thousands of people daily, and will soon open a clinic and perhaps a hospital to serve the less fortunate. Dr. Leh Siu Chuan is survived by his wife of 54 years, Benita Leh, and two doctor sons – Patrick, an orthopaedic surgeon, and Frederick, an otolaryngologist, and two daughters – Shirley, an auditor in New York, and Sandra, district manager for E. Excel Pharmaceuticals of Taiwan. He will live on in the memory of his colleagues and loved ones, and all who had the good fortune of knowing him.

The individual alternative minimum tax (AMT) was designed in 1970 to apply reduce aggressive tax sheltering, but under current law will grow to cover tens of millions of households in the next decade. The growth occurs because the AMT is not indexed for inflation and the 2001 tax cut reduced regular income taxes but not the AMT. AMT growth is troubling because the tax has questionable effects on equity and efficiency and is inordinately complex. This paper describes the AMT, discusses economic issues related to the alternative minimum tax, and examines options for reform.

Background Smartphone and wearable-based activity data provide an opportunity to remotely monitor functional capacity in patients. In this study, we assessed the ability of a home-based 6-minute walk test (6MWT) as well as passively collected activity data to supplement or even replace the in-clinic 6MWTs in patients with cardiovascular disease. Methods We enrolled 110 participants who were scheduled for vascular or cardiac procedures. Each participant was supplied with an iPhone and an Apple Watch running the VascTrac research app and was followed for 6 months. Supervised 6MWTs were performed during clinic visits at scheduled intervals. Weekly at-home 6MWTs were performed via the VascTrac app. The app passively collected activity data such as daily step counts. Logistic regression with forward feature selection was used to assess at-home 6MWT and passive data as predictors for “frailty” as measured by the gold-standard supervised 6MWT. Frailty was defined as walking <300m on an in-clinic 6MWT. Results Under a supervised in-clinic setting, the smartphone and Apple Watch with the VascTrac app were able to accurately assess ‘frailty’ with sensitivity of 90% and specificity of 85%. Outside the clinic in an unsupervised setting, the home-based 6MWT is 83% sensitive and 60% specific in assessing “frailty.” Passive data collected at home were nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT, with area under curve (AUC) of 0.643 and 0.704, respectively. Conclusions In this longitudinal observational study, passive activity data acquired by an iPhone and Apple Watch were an accurate predictor of in-clinic 6MWT performance. This finding suggests that frailty and functional capacity could be monitored and evaluated remotely in patients with cardiovascular disease, enabling safer and higher resolution monitoring of patients.

The number of mixed cropland—apple orchard system has gradually increased in the Changwu Tableland region of the Loess Plateau, China. However, the soil water content (SWC) is not sufficient to maintain the sustainable development of apple trees in this agroforestry system. It is unclear whether the growing fruit trees would compete with crops for soil water. To systematically analyze the temporal and spatial distribution of soil moisture and to understand the effect of orchard hydrology in that cropland, the SWC was measured at different depths at different locations on cropland and in an apple orchard. The results show that: (1) The SWC of each soil layer in the cropland (0–20, 20–60, 60–100, 100–200, 200–300 cm) is higher than that of the orchard. The soil moisture changes dramatically in the 0–200 cm soil layer. (2) As the soil moisture monitoring distance from the apple orchard increases, the SWC gradually increases, the loss of soil water storage gradually decreases, and the drying effect gradually disappears. This is related to the different distribution ranges of the roots of apple trees and crops. Therefore, the government should control the proportion of the orchard and cropland, and then adjust the planting period of the orchard in the appropriate range to keep the green use of water in the region.

Globally, noise exposure from occupational and nonoccupational sources is common, and, as a result, noise-induced hearing loss affects tens of millions of people. Occupational noise exposures have been studied and regulated for decades, but nonoccupational sound exposures are not well understood. The nationwide Apple Hearing Study, launched using the Apple research app in November 2019 (Apple Inc., Cupertino, CA), is characterizing the levels at which participants listen to headphone audio content, as well as their listening habits. This paper describes the methods of the study, which collects data from several types of hearing tests and uses the Apple Watch noise app to measure environmental sound levels and cardiovascular metrics. Participants, all of whom have consented to participate and share their data, have already contributed nearly 300 × 106 h of sound measurements and 200 000 hearing assessments. The preliminary results indicate that environmental sound levels have been higher, on average, than headphone audio, about 10% of the participants have a diagnosed hearing loss, and nearly 20% of the participants have hearing difficulty. The study’s analyses will promote understanding of the overall exposures to sound and associated impacts on hearing and cardiovascular health. This study also demonstrates the feasibility of collecting clinically relevant exposure and health data outside of traditional research settings.

Abstract Several acaricides were compared with an untreated control in a 'Delicious' apple block at Steele's Tavern, Va. Four single-tree replicates were used in a completely randomized design. Applications were made using a truck-mounted Swanson sprayer with a handgun attachment. Treatment applications were applied when the ERM population exceeded an average of 7 mites/leaf, which occurred in the last week of Jul. Trees were sprayed on 30 Jul to the drip point (200 gal/acre). Control trees were sprayed with water. Mites were evaluated using a mite brushing machine and a single 20-leaf sample/tree. Mites were counted under a binocular microscope. The data on 30 Jul are pretreatment counts.

This chapter considers the application of alkaline (NaOH) based catalyzed methanolysis of seed oil from Chrysophyllum albidum (African star apple) as a viable route for synthesis of methyl esters (biodiesel). Specific consideration was given to the chemical kinetics and thermodynamics of the irreversible consecutive mechanism of the process on the basis of higher application of methanol/molar ratio (>3:1) as a feasible approach for generating required data for commercial scale-up of the process. The application of power rate law revealed that second order model was the best fitted model on the 328 K, 333 K and 338 K temperature and 0–100 min ranges studied. Rate constants of the glyceride hydrolysis were 0.00710, 0.00870 and 0.00910 wt% min−1 for the triglyceride (TG), 0.02390, 0.03040 and 0.03210 wt% min−1 for the diglycerides (DG) and 0.01600, 0.03710 and 0.04090 wt% min−1 for the monoglycerides (MG) at the above respective temperatures. The activation energies were 2.707, 7.30 and 23.33 kcal/mol respectively. TG hydrolysis to DG was the rate determining step. Rates of reactions were found to increase with increase temperature and mixing rate (200, 400 and 800 rpm). No optimal mixing rate was detected and the highest mixing rate of 800 rpm was the most favorable in the mixing range under investigation. The possible reason for the absence of lag period is formation of methyl esters, which acted as a solvent for the reactants, and consequently, made the reaction mixture a homogeneous single phase. The quality of the produced methyl esters were found to compare with international standards. All the results lead to more diverse and novel applications of the seed oil in biodiesel productions.

In this paper, we compare the prediction accuracy of a deep learning model and three classification algorithms on very similar motions in the field of dementia diagnostics.The basic aim is to gain insights into the retrieval, provision and classification of interaction and health data in the course of the disease of dementia patients. This work shows how the smartwatch "Apple Watch Series 7" can be used to record interaction data from dementia patients and recognise corresponding movement sequences.A data transfer platform was developed that enables communication with a watchOS application on the smartwatch via a Node.js WebSocket. This data transfer platform can be used to control smartwatches and retrieve data from sensors in different frequencies (1 to 100 Hz) in real time. The sensor technology used includes accelerometers, position sensors, gyroscopes, magnetometers and heart rate monitors.In this work, the main focus is on the recognition of motion sequences. For this purpose, two different approaches of supervised learning are compared: recurrent neural network versus classification algorithms. The recurrent neural network is a special form of neural network in which neurons of the same layer or different layers are fed back. Through these feedbacks, temporally coded information can be extracted from data. Typical areas of application are handwriting recognition, translations or speech recognition. A recurrent neural network processes data with a memory called Long Short-Term Memory (LSTM). LSTMs represent the state of the art in human activity recognition and are ideal for analysing sequential streams of sensor data. An LSTM is a memory-based, powerful model that can dynamically capture and analyse contextual information whose timing is relevant.This approach of recognising motion sequences is contrasted with the classification algorithms Logistic Regression, Support Vector Machine and Decision Tree. The classification takes place under consideration of features of the respective class. An algorithm tries to work out a dividing line between combinations of features of data and to group them.Records of the activities of dementia patients by the nursing staff from two dementia care communities are available. Consultation with various care teams who work with dementia patients on a daily basis revealed that many patients wear smartwatches. Such watches keep the adjustment effort for sensor positioning low.The records of the activities of dementia patients serve as a template in this work; the movement patterns of the activities eating, drinking and writing are classified. These activities are very similar in their movement patterns, which makes classification challenging.The contribution of this work is the comparison of two possibilities for the recognition of similar movements of patients by means of smartwatches with regard to their correctness. We evaluate our prototype in a test series with five test subjects. In doing so, we demonstrate the accuracy of the memory-based classification network and classification in interaction with the latest wearable sensor technologies and discuss future directions and possibilities in the wearable IoT field of dementia