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Academic literature on the topic 'Best apple watch for teenager'

Author: Grafiati

Published: 28 July 2024

Last updated: 31 July 2025

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Journal articles on the topic "Best apple watch for teenager"

1

Hu, Xiangyu, Hongpeng Yang, James White, et al. "0280 Unlocking the Potential of Consumer Wearables for Predicting Sleep in Children: A Device-Agnostic Machine Learning Approach." SLEEP 46, Supplement_1 (2023): A124—A125. http://dx.doi.org/10.1093/sleep/zsad077.0280.

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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 o

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2

Fernandez, Olivia, Logan Long, Arvind Mallikarjunan, Abhinav Gundala, and Nirmish Shah. "Use of Apple Watch to Acquire Baseline Wearable Biometric Data for Patients with Sickle Cell Disease and Correlations to Symptoms Such As Pain and Fatigue." Blood 142, Supplement 1 (2023): 5290. http://dx.doi.org/10.1182/blood-2023-188096.

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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 dat

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3

Weidlich, Simon, Diego Mannhart, Teodor Serban, et al. "Accuracy in detecting atrial fibrillation in single-lead ECGs: an online survey comparing the influence of clinical expertise and smart devices." Swiss Medical Weekly 153, no. 9 (2023): 40096. http://dx.doi.org/10.57187/smw.2023.40096.

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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.

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4

Cajal, Diego, David Hernando, Jesús Lázaro, Pablo Laguna, Eduardo Gil, and Raquel Bailón. "Effects of Missing Data on Heart Rate Variability Metrics." Sensors 22, no. 15 (2022): 5774. http://dx.doi.org/10.3390/s22155774.

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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 alleviat

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5

Zhai, Bing, Yu Guan, Michael Catt, and Thomas Plötz. "Ubi-SleepNet." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (2021): 1–33. http://dx.doi.org/10.1145/3494961.

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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 mo

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6

Chong, Kimberly P. L., Julia Z. Guo, Xiaomeng Deng, and Benjamin K. P. Woo. "Consumer Perceptions of Wearable Technology Devices: Retrospective Review and Analysis." JMIR mHealth and uHealth 8, no. 4 (2020): e17544. http://dx.doi.org/10.2196/17544.

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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

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7

Vuong, Caroline, Kumar Utkarsh, Rebecca Stojancic, et al. "Use of Machine Learning to Predict 30-Day Reutilization of Care for Patients with Sickle Cell Disease Treated for Vaso-Occlusive Crisis." Blood 142, Supplement 1 (2023): 3896. http://dx.doi.org/10.1182/blood-2023-186272.

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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 patient

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8

Soysal, A., E. Golcuk, A. Atici, et al. "Detection of supraventricular arrhythmias with apple watch." European Heart Journal 44, Supplement_2 (2023). http://dx.doi.org/10.1093/eurheartj/ehad655.2949.

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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.

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9

Kumar, Rohit, and Deeksha Gupta. "Strategic Shift at Titan Watches: Will it work?" Vision: The Journal of Business Perspective, November 26, 2022, 097226292211308. http://dx.doi.org/10.1177/09722629221130866.

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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

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10

Walch, Olivia, Yitong Huang, Daniel Forger, and Cathy Goldstein. "Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device." Sleep 42, no. 12 (2019). http://dx.doi.org/10.1093/sleep/zsz180.

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Abstract Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using th

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Books on the topic "Best apple watch for teenager"

1

Best Apple Watch for Epic Adventures. Barcodeliveorg, 2023.

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Best apple watch for swimmers. Barcodeliveorg, 2023.

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Best Apple Watch for Stylish Seniors. Barcodeliveorg, 2023.

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Best Affordable Apple Watch for Nurses on a Budget. Barcodeliveorg, 2023.

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Productivity at Your Fingertips with the Best Apple Watch for Professionals. Barcodeliveorg, 2023.

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6

Jordan, Alex. Apple Watch Series 4 User Manual: Best Tips / Tricks, Siri Commands and Troubleshooting Guide to Help You Take Full Advantage of the New Apple Watch Series 4. Independently Published, 2019.

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Rodriguez, Alexis. Apple Watch Series 5 for Elderly: The Ultimate Tips and Tricks on How to Master Your Apple Watch Series 5 and WatchOS 6 in the Best Optimal Way. Independently Published, 2019.

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Rodriguez, Alexis. Apple Watch Series 5 User Guide: The Ultimate Tips and Tricks on How to Master Your Apple Watch Series 5 and WatchOS 6 in the Best Optimal Way. Independently Published, 2019.

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Apple Watch Series 7 User Guide: The Complete Beginners and Pro User Manual for the New Apple Watch Series 7 Best Hidden Features Including Tips and Tricks for WatchOS 8. Independently Published, 2021.

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Paul, Steve Andrew. How to Watch and Stream on Apple TV for Free: The Latest and Best Method to Watch and Stream on Apple TV 4th Gen and Other Versions in Less Than 15 Minutes. Independently Published, 2017.

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