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A Mobile Gaming Intervention for Persons on Pre-Exposure Prophylaxis: Protocol for Intervention Development and Randomized Controlled Trial

A Mobile Gaming Intervention for Persons on Pre-Exposure Prophylaxis: Protocol for Intervention Development and Randomized Controlled Trial

The protocol was reviewed by the Institutional Review Boards of Brown University, Lifespan/ Miriam/Rhode Island Hospital (RIH), and the University of Mississippi Medical Center (UMMC).

Laura Whiteley, Elizabeth Olsen, Leandro Mena, Kayla Haubrick, Lacey Craker, Dylan Hershkowitz, Larry K Brown

JMIR Res Protoc 2020;9(9):e18640

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

In particular, we used the Brown clustering model and Word Vector Classes as word clustering features and applied raw word embedding as word vector features.We trained the Brown cluster model [56] on a large collection of biomedical text.

Tsendsuren Munkhdalai, Feifan Liu, Hong Yu

JMIR Public Health Surveill 2018;4(2):e29

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

One study investigated co-commenting behavior on K-pop videos by analyzing the weighted frequency and weighted sentiment scores of the co-comments.

Shasha Teng, Kok Wei Khong, Saeed Pahlevan Sharif, Amr Ahmed

JMIR Public Health Surveill 2020;6(4):e19618

A Mobile Gaming Intervention to Increase Adherence to Antiretroviral Treatment for Youth Living With HIV: Development Guided by the Information, Motivation, and Behavioral Skills Model

A Mobile Gaming Intervention to Increase Adherence to Antiretroviral Treatment for Youth Living With HIV: Development Guided by the Information, Motivation, and Behavioral Skills Model

Text message interventions among adults were associated with improved viral load and/or CD4+ count (k=3; OR  1.56, 95% CI 1.11-2.20) [38]. Less data are available on text messaging interventions for younger populations living with HIV.

Laura Whiteley, Larry Brown, Michelle Lally, Nicholas Heck, Jacob J van den Berg

JMIR Mhealth Uhealth 2018;6(4):e96

A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing

A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing

Spearman-Brown coefficient was used to test split-half reliability. For construct reliability only, items not on a 5-point scale were adjusted to a 5-point scale. These analyses were conducted using the first occasion apps were evaluated.

Kristen Nicole DiFilippo, Wenhao Huang, Karen M. Chapman-Novakofski

JMIR Mhealth Uhealth 2017;5(10):e163

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

There were more cross-sectional (k=15) [54,68,81,83,87,88,91,92,95] than longitudinal studies (k=11) [58,63,67,69,72,76,78,88]. Longitudinal studies ranged from 1 month to 6 months (see Multimedia Appendix 2).

Debaleena Chattopadhyay, Tengteng Ma, Hasti Sharifi, Pamela Martyn-Nemeth

J Med Internet Res 2020;22(7):e18839

Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment

Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment

evaluate MARS scores from the 4 raters, we calculated the interrater agreement based on Fleiss κ [46], the internal consistency was based on Cronbach α [47], and the interrater reliability was based on Guttman λ6 [48] as well as intraclass correlation—ICC(2,k)

Muntazir Mehdi, Michael Stach, Constanze Riha, Patrick Neff, Albi Dode, Rüdiger Pryss, Winfried Schlee, Manfred Reichert, Franz J Hauck

JMIR Mhealth Uhealth 2020;8(8):e21767

Development of the Gambling Disorder Identification Test (G-DIT): Protocol for a Delphi Method Study

Development of the Gambling Disorder Identification Test (G-DIT): Protocol for a Delphi Method Study

We conducted an extensive literature search of review articles on gambling measures [15-17] and a prior unpublished collection of gambling measures compiled by local colleagues (A Nilsson and K Magnusson, personal communication, February 2017), which resulted

Olof Molander, Rachel Volberg, Kristina Sundqvist, Peter Wennberg, Viktor Månsson, Anne H Berman

JMIR Res Protoc 2019;8(1):e12006