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Interrater Reliability of mHealth App Rating Measures: Analysis of Top Depression and Smoking Cessation Apps

Interrater Reliability of mHealth App Rating Measures: Analysis of Top Depression and Smoking Cessation Apps

noImport/export capabilitiesKharrazi et al (2012)0-11=yes; 0=noUploaded by health care agencyPandey et al (2012)0-11=yes; 0=noEncryptionPowell et al (2014)0-11=yes; 0=noExplicit privacy policyPowell et al (2014)0-11=yes; 0=noEffectiveness tested (claimed by app)Powell

Adam C Powell, John Torous, Steven Chan, Geoffrey Stephen Raynor, Erik Shwarts, Meghan Shanahan, Adam B Landman

JMIR Mhealth Uhealth 2016;4(1):e15

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

the same parameter settings were used for the following 3 targets: relevance, commercial, and sentiment).ClassifiersScikit-learn functions (version)Parameter valuesLogistic regressionsklearn.linear_model.LogisticRegression (0.20.3)All default values except C=

Shyam Visweswaran, Jason B Colditz, Patrick O’Halloran, Na-Rae Han, Sanya B Taneja, Joel Welling, Kar-Hai Chu, Jaime E Sidani, Brian A Primack

J Med Internet Res 2020;22(8):e17478

The Development of an Automated Device for Asthma Monitoring for Adolescents: Methodologic Approach and User Acceptability

The Development of an Automated Device for Asthma Monitoring for Adolescents: Methodologic Approach and User Acceptability

Figure 6 shows the images of the prototype device with an external battery (a), and a teen wearing the device (b and c).Figure 5Schematized flow of the data processing used in the automated device for asthma monitoring (ADAM) application.

Hyekyun Rhee, Sarah Miner, Mark Sterling, Jill S. Halterman, Eileen Fairbanks

JMIR Mhealth Uhealth 2014;2(2):e27

Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design

Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design

These findings are useful for an initial understanding of the validity of ADAM and for providing direction for further studies.

Hyekyun Rhee, Michael J Belyea, Mark Sterling, Mark F Bocko

J Med Internet Res 2015;17(10):e234

Parent-Mediated Intervention Training Delivered Remotely for Children With Autism Spectrum Disorder Living Outside of Urban Areas: Systematic Review

Parent-Mediated Intervention Training Delivered Remotely for Children With Autism Spectrum Disorder Living Outside of Urban Areas: Systematic Review

The studies by Hamad et al [49], Heitzman-Powell et al [50], Vismara et al [12,48], and Wacker et al [47] demonstrated level IV evidence. The study by St.

Dave Parsons, Reinie Cordier, Sharmila Vaz, Hoe C Lee

J Med Internet Res 2017;19(8):e198

Using a Geolocation Social Networking Application to Calculate the Population Density of Sex-Seeking Gay Men for Research and Prevention Services

Using a Geolocation Social Networking Application to Calculate the Population Density of Sex-Seeking Gay Men for Research and Prevention Services

In Figure 8, we highlight the regions with density greater than the mean+2 standard deviations over the entire map, separately for all white (A), all black (B), and young black (<25 years old, C) users based on data in their observed profiles.

Kevin P Delaney, Michael R Kramer, Lance A Waller, W Dana Flanders, Patrick S Sullivan

J Med Internet Res 2014;16(11):e249