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The Internet-Based Cognitive Assessment Tool: System Design and Feasibility Study

The Internet-Based Cognitive Assessment Tool: System Design and Feasibility Study

user sorted a sequence incorrectly.Figure 5The user interface of the internet-based cognitive assessment tool visuomotor tracking task, where the user should enter the matching letter for each symbol as fast as possible.Visuomotor Tracking Task: Changing Morse

Pegah Hafiz, Kamilla Woznica Miskowiak, Lars Vedel Kessing, Andreas Elleby Jespersen, Kia Obenhausen, Lorant Gulyas, Katarzyna Żukowska, Jakob Eyvind Bardram

JMIR Form Res 2019;3(3):e13898


A Fuzzy-Match Search Engine for Physician Directories

A Fuzzy-Match Search Engine for Physician Directories

Beidar and Morse [5] developed the Beider-Morse Phonetic Matching system for decreasing the number of approximate matches by removing irrelevant ones.

Majid Rastegar-Mojarad, Christopher Kadolph, Zhan Ye, Daniel Wall, Narayana Murali, Simon Lin

JMIR Med Inform 2014;2(2):e30


Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data

Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data

, MAUnited States4Research and Development GroupHitachi, LtdTokyoJapan5Big Data LaboratoryHitachi America LtdSanta Clara, CAUnited StatesCorresponding Author: Neda Derakhshani snderakhshani@partners.orgJul-Dec20181709201842e118979820182982018©Sujay S

Sujay S Kakarmath, Neda Derakhshani, Sara B. Golas, Jennifer Felsted, Takuma Shibahara, Hideo Aoki, Mika Takata, Ken Naono, Joseph Kvedar, Kamal Jethwani, Stephen Agboola

iproc 2018;4(2):e11897


The Aachen Falls Prevention Scale: Multi-Study Evaluation and Comparison

The Aachen Falls Prevention Scale: Multi-Study Evaluation and Comparison

Scale—a77.086.0—Dynamic Gait Index13385.038.0—Elderly Fall Screening Test36193.078.0—Timed Up and Go3087.087.0—Performance-Oriented Mobility Assessment (Tinetti)7980.074.0—Oliver et al (2004) [26]Downton Fall Risk Index13590.626.8—Innes Score296889.373.5—Morse

Peter Rasche, Verena Nitsch, Lars Rentemeister, Mark Coburn, Benjamin Buecking, Christopher Bliemel, Leo Cornelius Bollheimer, Hans-Christoph Pape, Matthias Knobe

JMIR Aging 2019;2(1):e12114


Using a Medical Intranet of Things System to Prevent Bed Falls in an Acute Care Hospital: A Pilot Study

Using a Medical Intranet of Things System to Prevent Bed Falls in an Acute Care Hospital: A Pilot Study

Patients 18 years and older and deemed a high fall risk (Morse Fall Scale score ≥45) were eligible for inclusion in the study [21]. Vulnerable populations (eg, prisoners, patients undergoing stem cell transplant) were excluded.

Henri U Balaguera, Diana Wise, Chun Yin Ng, Han-Wen Tso, Wan-Lin Chiang, Aimee M Hutchinson, Tracy Galvin, Lee Hilborne, Cathy Hoffman, Chi-Cheng Huang, C Jason Wang

J Med Internet Res 2017;19(5):e150


Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study

Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study

The assessment of fall risk factors is the focus of a number of different screening methods, such as Morse Fall Scale [18], Berg Balance Scale [19], and Performance‐Oriented Assessment of Mobility Problems in Elderly Patients [20].Recently, in addition to traditional

Anabela Correia Martins, Juliana Moreira, Catarina Silva, Joana Silva, Cláudia Tonelo, Daniela Baltazar, Clara Rocha, Telmo Pereira, Inês Sousa

JMIR Res Protoc 2018;7(8):e10304


Authorship Correction: Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles

Authorship Correction: Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles

The authors of the paper entitled “Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles” [J Med Internet Res 2017;19(4):e118] inadvertently omitted Stephen M Schueller, PhD (Center for Behavioral Intervention Technologies, Department

Sohrab Saeb, Thaddeus R Cybulski, Stephen M Schueller, Konrad P Kording, David C Mohr

J Med Internet Res 2017;19(4):e143


Provider and Patient-Related Barriers to and Facilitators of Digital Health Adoption for Hypertension Management: Review

Provider and Patient-Related Barriers to and Facilitators of Digital Health Adoption for Hypertension Management: Review

States2Harvard Medical SchoolBoston, MAUnited States3American Medical AssociationChicago, MAUnited StatesCorresponding Author: Ramya Palacholla RPALACHOLLA@MGH.HARVARD.EDUJul-Dec20181709201842e119049820182982018©Ramya Palacholla, Nils Fischer, Amanda Coleman, Stephen

Ramya Palacholla, Nils Fischer, Amanda Coleman, Stephen Agboola, Jennifer Felsted, Kate Kirley, Chelsea Katz, Stacy Lloyd, Kamal Jethwani

iproc 2018;4(2):e11904


Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App

Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App

States2Harvard Medical SchoolBoston, MAUnited States3Massachusetts General HospitalBoston, MAUnited StatesCorresponding Author: Amanda Centi acenti@partners.orgJul-Dec20181709201842e118768820182982018©Amanda Centi, Ramya Palacholla, Sara Golas, Odeta Dyrmishi, Stephen

Amanda Centi, Ramya Palacholla, Sara Golas, Odeta Dyrmishi, Stephen Agboola, Kamal Jethwani, Joseph Kvedar

iproc 2018;4(2):e11876