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Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Soonil Kwon, Joonki Hong, Eue-Keun Choi, Byunghwan Lee, Changhyun Baik, Euijae Lee, Eui-Rim Jeong, Bon-Kwon Koo, Seil Oh, Yung Yi

J Med Internet Res 2020;22(5):e16443

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Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Soonil Kwon, Joonki Hong, Eue-Keun Choi, Euijae Lee, David Earl Hostallero, Wan Ju Kang, Byunghwan Lee, Eui-Rim Jeong, Bon-Kwon Koo, Seil Oh, Yung Yi

JMIR Mhealth Uhealth 2019;7(6):e12770

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The Effect of Mobile App Interventions on Influencing Healthy Maternal Behavior and Improving Perinatal Health Outcomes: Systematic Review

The primary outcome measure for the Choi et al study [23] of physical activity was weekly mean steps, and intervention participants had a greater increase in daily steps at 12 weeks with 1096 (SD 1898) steps, compared with 259 (SD 1604) steps among control

Lisa M Daly, Dell Horey, Philippa F Middleton, Frances M Boyle, Vicki Flenady

JMIR Mhealth Uhealth 2018;6(8):e10012

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Experiences of Patients With a Diabetes Self-Care App Developed Based on the Information-Motivation-Behavioral Skills Model: Before-and-After Study

The reliability of the DKT was measured by a Cronbach alpha of .67 in this study and of .71 in the study by Choi [20].Diabetes self-care personal motivation was measured using the third version of the Diabetes Attitude Scale (DAS-3).

Eunjoo Jeon, Hyeoun-Ae Park

JMIR Diabetes 2019;4(2):e11590

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Network Support Using Social Networking Services to Increase Exercise Adherence Among Korean-Chinese Middle-Aged Migrant Women: Mixed Methods Study

In the study, the Cronbach alpha was .73.Self-Efficacy for ExerciseSelf-efficacy for exercise was measured using the Korean version of the Barrier Self-Efficacy Scale developed by McAuley [33] and modified by Choi [34].

Hyeyeon Lee, Hyeonkyeong Lee, Youlim Kim, Sookyung Kim, Young-Me Lee

JMIR Mhealth Uhealth 2020;8(11):e19159

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Communication Technology Use by Caregivers of Adolescents With Mental Health Issues: Systematic Review

This critical appraisal of the full-text articles determined the selection and inclusion of articles in this systematic review.Table 3Summary of included articles’ study designs.Extracted dataReferenceDeitz et al [80]Cardamone-Breen et al [33]Choi et al [81

Ronelle Jansen, Marianne Reid

JMIR Mhealth Uhealth 2020;8(8):e13179

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Consensus on Use of the Term “App” Versus “Application” for Reporting of mHealth Research

LetterWe read with great interest a number of recent articles from the Journal of Medical Internet Research (JMIR) published by Bierbrier et al [1], Kim et al [2], and Choi et al [3], investigating different aspects of mHealth technology, specifically medical

Thomas Lorchan Lewis, Matthew Alexander Boissaud-Cooke, Timothy Dy Aungst, Gunther Eysenbach

J Med Internet Res 2014;16(7):e174

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Measurement of Physical Activity and Sedentary Behavior by Accelerometry Among a Nationwide Sample from the KiGGS and MoMo Study: Study Protocol

The algorithms invented by Choi [51] and Troiano [52] were considered for the determination of wear time in this study.

Alexander Burchartz, Kristin Manz, Bastian Anedda, Claudia Niessner, Doris Oriwol, Steffen CE Schmidt, Alexander Woll

JMIR Res Protoc 2020;9(7):e14370

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Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study

We can establish a chronology of studies that began to use internet search volumes following the study of Choi and Varian [9], who reviewed the pioneering studies that suggested that web search data are useful for forecasting in various fields.

Alberto Jimenez, Miguel-Angel Santed-Germán, Victoria Ramos

JMIR Public Health Surveill 2020;6(2):e10919

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COVID-19–Related Disruptions and Increased mHealth Emergency Use Intention: Experience Sampling Method Study

The results are shown in Table 2.Table 2Results of multilevel confirmatory factor analysis.ModelsChi-square (df)△ chi-squareP valueRMSEAaCFIbTLIcSRMRd (within)EUe, EDf, LSg, PFh151.22 (57)N/AiN/A0.060.960.950.03EU+ED, LS, PF451.15 (59)299.93<.0010.110.840.780.19EU

Zhenduo Zhang, Li Zhang, Junwei Zheng, Huan Xiao, Zhigang Li

JMIR Mhealth Uhealth 2020;8(12):e20642

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