Literatura científica selecionada sobre o tema "Best Apple Watch for Seniors"

Abstract Introduction Consumer wearables use accelerometry and heart rate to predict sleep, the same data signals used by research-grade devices to classify sleep/wake patterns. However, metrics predicted by consumer wearables are based on black box algorithms limiting their use in research. The objective of this study was to develop algorithms for predicting sleep in children based on the raw accelerometry and heart rate data from a popular consumer wearable device, therefore bypassing the onboard black box algorithms. Methods 38 children (M=8.5 years, SD=2.4, 42% black, 61% male) underwent overnight laboratory-based polysomnography (PSG) while wearing an Apple Watch Series 7. Heart rate and accelerometry data were collected via the Apple Watch application program interface. Features extracted included (1) age, (2) heart rate, (3) y-axis offset angle, (4) y-axis angle relative to x-axis (y-angle), (5) x-axis fast Fourier transformation (FFT) 4Hz, (6) x-axis FFT 9Hz, (7) vector magnitude (VM) FFT 9Hz, (8) VM FFT 14Hz, (9) mean power dispersion (MPD), (10) bandpass filter followed by Euclidean norm/vector magnitude (BFEN), (11) dominant signal power at 0.6–2.5Hz (PMAXBAND), and (12) activity counts. Four machine learning models: logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and neural network (NN) predicted sleep or wake. Model performance was evaluated by F1 score, precision, sensitivity, and specificity. Feature importance was evaluated using the RF model. Results LR achieved the best performance according to F1 score (RF: 85.04, LR: 86.50, KNN: 82.52, NN: 85.13). LR also had the highest precision (RF:88.33, LR:88.94, KNN: 86.77, NN:87.95), and nearly the highest sensitivity (RF:84.52, LR:86.11, KNN:84.73, NN:86.55) and specificity (RF:75.72, LR:75.00, KNN:54.01, NN:60.57). BFEN was the most important feature, followed by MPD, age, PMAXBAND, VM FFT 14Hz, x-axis FFT 9Hz, heart rate, y-axis offset angle, y-angle, activity count, VM FFT 9Hz, x-axis FFT 4Hz. Conclusion Raw accelerometry data combined with heart rate data from the Apple Watch Series 7 was able to detect sleep and wake in children. Leveraging the raw accelerometry data is a viable alternative to relying on black-box algorithms for sleep classification in consumer wearable devices. Support (if any)

Background: Sickle cell disease (SCD) is an inherited blood disorder that impacts the structure of red blood cells. The most common complication of SCD are vaso-occlusive crises (VOCs), commonly called pain crisis. Frequency and intensity of VOCs can differ among individuals. The unpredictable nature of VOCs increases risk of further complications, limits daily activities, affects mental well-being, and decreases health related quality of life. The use of mHealth and wearables for those living with SCD allows the opportunity to better understand pain and general wellbeing through real time data and analysis. Objectives: We aimed to: 1) Establish a comprehensive baseline health profile for individuals with SCD; and 2) determine potential relationships between patient reported symptoms and biometric data. Methods: Following IRB approval, patients entering the SCD clinic or the SCD Day Hospital at Duke University were approached, consented, and provided with the Nanbar Health app. Participants were instructed to make entries at least once daily and wear an Apple Watch throughout the day for 6 months. If a participant did not have Apple devices, an iOS Apple smartphone and/or Apple Watch series SE were provided. Self-reported general symptoms were recorded in the Nanbar Health app on scales from 0 (none) to 10 (most severe). General wellbeing was rated on an emoji scale, converted to 1 (worst/'frowny face') to 5 (best/'smiley face'). The biometric data from wearable collected included heart rate, step count, heart rate variability (HRV), resting heart rate (RHR), respiratory rate, and O2 saturation. For participants that had their own Apple Watch, 3 months of retroactive data was acquired. Statistical and network analysis was performed to analyze the correlations between symptoms, biometric data, and the general wellbeing of the participant. Results: Over 3 months, 18 participants were enrolled in the study. Median age of participant was 25 (IQR 21-33), 10 females, all Black/African American, and most either HgbSS (72%) or HgbSC (16%). Participants logged symptoms in the app 597 times over 93 days with a median of 0.33 entries/day/patient (IQR 0.22-0.44). The average pain score participants reported was 5.9 (SD 2.4, n=220). The three most reported symptoms, besides pain, were tiredness (22.1%), headache (5.2%), and priapism (5%). The patient-reported general feeling average was 3.3 (n=597, SD 1.6). Heart rate (n=74646), HRV (n=1277), and step count (n=1566) were the most recorded biometrics from the wearables. The average heart rate, HRV, and step count were 105.1 bpm (SD 26.8), 31.9 ms (SD 13.3), and 4100 steps/day (SD 5960), respectively. Significant expected correlations include between pain and priapism (r=0.85, p<0.01), aching and pain (r=0.78, p<0.01), and feeling bad and aching (r= -0.74, p<0.01). We also found feeling well correlated positively to back pain and tired correlated negatively with aching (see Table 1). Using network analysis of biometrics, RHR and HRV had a higher expected influence on patient feeling compared to the other biometrics collected. Conclusion: Baseline biometric characteristics for patients were similar to other diseases with chronic pain and as expected, pain is the most common symptom reported. Our data reflected lower HRV compared to healthy individuals and consistent with other disorders characterized by chronic pain. However, compared to other chronic pain disorders, patients with SCD appeared to have lower HRV values. There was no strong correlation (>0.7) between biometrics obtained by the watch and SCD patient-reported pain scores. Our study found a strong significant correlation between priapism and feeling cold. The strongest correlation was between feeling tired and feeling cold, however, tired/fatigue was also significantly correlated with priapism and feeling cold, although negatively correlated with aching. Limitations to conducting comprehensive analyses and making direct comparisons with other conditions include discrepancies in biometrics due to high variability and participant adherence. These limitations underscore the need to enroll more patients and improve patient adherence to improve the reliability of biometric data in future studies. Future efforts will compare the use of other mHealth devices besides the Apple Watch and expand to patients outside our institution.

BACKGROUND: Manual interpretation of single-lead ECGs (SL-ECGs) is often required to confirm a diagnosis of atrial fibrillation. However accuracy in detecting atrial fibrillation via SL-ECGs may vary according to clinical expertise and choice of smart device. AIMS: To compare the accuracy of cardiologists, internal medicine residents and medical students in detecting atrial fibrillation via SL-ECGs from five different smart devices (Apple Watch, Fitbit Sense, KardiaMobile, Samsung Galaxy Watch, Withings ScanWatch). Participants were also asked to assess the quality and readability of SL-ECGs. METHODS: In this prospective study (BaselWearableStudy, NCT04809922), electronic invitations to participate in an online survey were sent to physicians at major Swiss hospitals and to medical students at Swiss universities. Participants were asked to classify up to 50 SL-ECGs (from ten patients and five devices) into three categories: sinus rhythm, atrial fibrillation or inconclusive. This classification was compared to the diagnosis via a near-simultaneous 12-lead ECG recording interpreted by two independent cardiologists. In addition, participants were asked their preference of each manufacturer’s SL-ECG. RESULTS: Overall, 450 participants interpreted 10,865 SL-ECGs. Sensitivity and specificity for the detection of atrial fibrillation via SL-ECG were 72% and 92% for cardiologists, 68% and 86% for internal medicine residents, 54% and 65% for medical students in year 4–6 and 44% and 58% for medical students in year 1–3; p <0.001. Participants who stated prior experience in interpreting SL-ECGs demonstrated a sensitivity and specificity of 63% and 81% compared to a sensitivity and specificity of 54% and 67% for participants with no prior experience in interpreting SL-ECGs (p <0.001). Of all participants, 107 interpreted all 50 SL-ECGs. Diagnostic accuracy for the first five interpreted SL-ECGs was 60% (IQR 40–80%) and diagnostic accuracy for the last five interpreted SL-ECGs was 80% (IQR 60–90%); p <0.001. No significant difference in the accuracy of atrial fibrillation detection was seen between the five smart devices; p = 0.33. SL-ECGs from the Apple Watch were considered as having the best quality and readability by 203 (45%) and 226 (50%) participants, respectively. CONCLUSION: SL-ECGs can be challenging to interpret. Accuracy in correctly identifying atrial fibrillation depends on clinical expertise, while the choice of smart device seems to have no impact.

Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidences that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet.

Background Individuals of all ages are becoming more health conscious, and wearable technology devices (eg, Fitbit and Apple Watch) are becoming increasingly popular in encouraging healthy lifestyles. Objective The aim of this paper was to explore how consumers use wearable devices. Methods A retrospective review was done on the top-rated verified purchase reviews of the Fitbit One posted on Amazon.com between January 2014 and August 2018. Relevant themes were identified by qualitatively analyzing open-ended reviews. Results On retrieval, there were 9369 reviews with 7706 positive reviews and 1663 critical reviews. The top 100 positive and top 100 critical comments were subsequently analyzed. Four major themes were identified: sleep hygiene (“charts when you actually fall asleep, when you wake up during the night, when you're restless--and gives you a cumulative time of “actual sleep” as well as weekly averages.”), motivation (“25 lbs lost after 8 months – best motivator ever!”), accountability (“platform to connect with people you know and set little competitions or group…fun accountability if you set a goal with a friend/family.”), and discretion (“able to be clipped to my bra without being seen.”). Alternatively, negative reviewers felt that the wearable device’s various tracking functions, specifically steps and sleep, were inaccurate. Conclusions Wearable technology devices are an affordable, user-friendly application that can support all individuals throughout their everyday lives and potentially be implemented into medical surveillance, noninvasive medical care, and mobile health and wellness monitoring. This study is the first to explore wearable technology device use among consumers, and further studies are needed to examine the limitless possibilities of wearable devices in health care.

AIMS: Validate the application for iPad (Apple, California, USA) “Teste seu Cérebro” as well as establish cutoff point between normal seniors and Mild Neurocognitive Impairment.METHODS: In a prospective cross-sectional study, elderly subjects who attended the neuropsychology clinic of a tertiary health hospital in the southern region of Brazil and the community in general underwent cognitive assessment using two instruments: the Montreal Cognitive Assessment (Gold Standard Test) and the “Teste seu Cérebro”. These results served as a parameter to validate the said application from a diagnostic test and to establish the cutoff point between normal elderly and mild cognitive impairment; the following statistical measures were determined: sensitivity and specificity, internal consistency and reliability reached by the McDonald’s Omega coefficient and Pearson’s correlation coefficient, respectively. The average “Teste seu Cérebro” cutoff point to detect cases classified as mild neurocognitive impairment by the Montreal Cognitive Assessment was obtained through the ROC curve. Evaluations include functions such as memory, attention / orientation, fluency, language, and visuospatial skills. RESULTS: The sample consisted of 104 participants with mean age of 70.3 (standard deviation = 6.6), with a minimum age of 60 and a maximum of 87 years. An acceptable reliability was achieved for the “Teste seu Cérebro” application by analyzing the internal consistency. In the comparison between the general scores of the two instruments (Teste seu Cérebro and Montreal Cognitive Assessment), where the result showed a statistically significant correlation, positive and classified as moderate. The cutoff point of the “Teste seu Cérebro” scores that best discriminated patients with mild neurocognitive impairment diagnosed by the Montreal Cognitive Assessment was 89.5%, that is, scores below or equal to that percentage reached higher sensitivity and specificity for the instrument. No influence of sociodemographic variables such as sex, age and schooling were identified on the linearity relationship between the “Teste seu Cérebro” and Montreal Cognitive Assessment instruments. CONCLUSIONS: The results suggest that the “Teste seu Cérebro” instrument can be safely used to identify early and accurately the presence of Mild Neurocognitive Impairment in the elderly population. New studies will be directed to the validation of the instrument “Teste seu Cérebro” in the identification of other types of cognitive disorders, in addition to Mild Neurocognitive Impairment.

Background Sickle cell disease (SCD) is an inherited disorder of red blood cells affecting millions of people worldwide. SCD is characterized by recurrent episodes of severe pain attacks, also called vaso-occlusive crisis (VOC), and are the most common reason for hospitalization. Approximately 90% of the hospital admissions are for pain treatment and the hospital readmission rate is alarmingly high, as 30-50% are re-admitted within 30 days. We previously were successful in developing machine learning models to predict pain using inpatient vital signs data as well as Apple Watch data in patients with SCD hospitalized for VOC. We now aimed to predict reutilization of care within 30 days in patients with SCD treated for a VOC. Methods Patients with SCD aged 18 years and above, who were admitted for a VOC to the day hospital or Duke University Hospital between April and June 2022, were eligible for this study. Following informed consent, demographics, SCD genotype, details from the hospitalization such as length of stay, pain scores, as well as vital signs measured per standard of care were collected from the electronic medical records including blood pressure, pulse rate, respiratory rate, oxygen saturation and temperature. Baseline vital signs during regular visits were collected 6 months before and after the hospitalization. We used the values of nearest neighbors to fill in the empty entries. The vital signs over the whole hospital stay were averaged and those numbers were used as the predictor values for the model. The primary outcome was reutilization of care defined as readmission within 30 days to the day hospital and/or hospital. The predictors were used to fit 3 different machine learning classification models for the prediction of reutilization of care: random forest, logistic regression, gradient boosting. By fitting random forest model on the whole dataset, we were able to rank all the features using mean decrease in impurities. To avoid overfitting, we only used the best four predictors which were diastolic blood pressure, pulse rate, respiratory rate and pain score.The performance of the machine learning models was evaluated using the following metrics: accuracy, precision, recall, F1 score and area under the receiver-operating-curve (AUC). Results Eighteen participants with SCD were included in this study. The median age at inclusion was 30 years (IQR 22-34). The majority of the participants had SCD genotype HbSS (68%), and all were Black of African-American. There were 10 participants treated at day hospital (56%), while the other 8 participants were admitted to the hospital with a median length of stay of 7.5 days (IQR 2.5-10). After discharge, 15 participants sought medical care at least once within 30 days (83%); 8 were hospitalized (44%), and 13 were readmitted to the day hospital (72%). This pilot study consisted of 88 vital sign data points across the 18 patients. The metrics of our best-performing machine learning model, the random forest model, were: accuracy 70%, precision 0.94, recall 0.71, F1 score 0.81 and AUC 0.61 (Figure 1 and 2). The difference in precisions and recalls for all the models reflects the class imbalance in the dataset. To test how the model will perform for independent data sets, we used 5-fold cross-validation, and the cross-validation accuracy was 66% with a standard deviation of 7.7%. Conclusion In this pilot study, our machine learning model was able to accurately predict health care reutilization within 30 days following discharge, with real-time vital signs data collected during clinic visits and hospital admissions in participants with SCD. Prediction of reutilization may help healthcare providers identify those at high risk and allow considerations for inpatient and outpatient strategies for patient management. Our next efforts include prediction of re-utilization from data collected from consumer wearables and in a larger number of participants.

Abstract Introduction With the recent developments in wearable technology, detecting arrhythmias has become much easier. Although smartwatches are commonly used to detect patients with atrial fibrillation, there is no consensus on their efficiency in detecting supraventricular tachycardias (SVT). Methods & Results Electrophysiological procedures were performed on 47 patients that have been previously documented to have SVT by a 12-lead ECG. All patients had sinus rhythm before the procedure. A narrow complex SVT which patients had at least once was induced by electrophysiological methods. Induced SVTs were recorded with a 6th-generation Apple Watch as one rhythm strip. Among the induced tachycardias, 27 typical AVNRT, 11 AVRT and 9 AT/AFLwere diagnosed. These records obtained from the smartwatch were evaluated by 3 cardiology residents and 3 attending cardiologists. Evaluated records were predicted among attending physicians with a minimum sensitivity of 66.0% and maximum sensitivity of 76.6%. Among residents, minimum and maximum sensitivity rates were 68.1% and 74.5%, respectively. The reliability between residents and attendings was assessed separately using Fleiss’s kappa method. The interrater reliability was found to be Kappa=0.465 (p<0.001), 95% CI (0.30-0.63) within the group of residents and Kappa=0.519, (p<0.001), 95% CI (0.35-0.68) within the group of attendings. Overall Kappa value was 0.417 (p<0.001), 95% CI (0.34-0.49), indicating a significantly important moderate level of agreement (p<0.001). Conclusion To the best of our knowledge, this is the first study showing how accurately clinicians can interpret the recording of SVTs with wearable devices such as smart watches, whose definitive diagnosis was made invasively by electrophysiological study. As presented in our study, induced, and subsequently recorded SVTs were predicted with high sensitivity and a moderate reliability rate. Adoption of smartwatches could significantly limit the delays in diagnosis and treatment of patients admitted to the hospitals with complaints of palpitations that cannot be documented.Statistics

This case study analyses the dilemma and strategic decision choices faced by Titan Watches (TW) regarding its growth aspirations and competitive challenges in the Indian context. The case reflects in detail the global watch industry and the competitive landscape from the company’s standpoint and highlights the journey covering more than three decades of its history. The case analysis is conducted based on secondary data gathered from various sources. The period considered to study the company and its competitive landscape is over three decades, that is, from its inception in 1984 to 2020. The financial data and other related data used in this case study were collected from different databases, that is, Euromonitor passport, Ace Knowledge Portal, CRISIL Research and ProQuest databases. It is found that the company is planning to achieve profitable growth in its revenue but faces tough competition from both analogue watchmakers (e.g. Seiko Holdings Corporation, Citizen Watch Co. Ltd and Swatch Group) and digital watch makers (e.g. Apple Inc., Fossil Group Inc., Samsung Electronics, Garmin Ltd. and Fitbit Inc.). To the best of our knowledge, there is no case study on TW highlighting the dilemma and strategic challenges the chief managing director faced in 2019. These include the challenges of capturing market share, achieving profitable growth and balancing the image of TW between a mass-market brand and a premium brand, etc. Furthermore, it discusses how TW needs to compete with global smartwatch companies like Apple and Samsung.