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Developing an Adaptive Mobile Intervention to Address Risky Substance Use Among Adolescents and Emerging Adults: Usability Study

Developing an Adaptive Mobile Intervention to Address Risky Substance Use Among Adolescents and Emerging Adults: Usability Study

, as described in a study by Nahum-Shani et al (unpublished data, 2021), we conducted a larger MRT (N=68) of SARA with youth reporting past-month binge drinking or cannabis use to further refine the SARA app.

Lara N Coughlin, Inbal Nahum-Shani, Meredith L Philyaw-Kotov, Erin E Bonar, Mashfiqui Rabbi, Predrag Klasnja, Susan Murphy, Maureen A Walton

JMIR Mhealth Uhealth 2021;9(1):e24424


Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults

Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults

SARA consists of 2 modules: the data collection module and the engagement module (Figure 1).Data Collection ModuleSARA’s data collection module deals with procuring self-reported data. SARA currently supports 2 types of self-reported data collection.

Mashfiqui Rabbi, Meredith Philyaw Kotov, Rebecca Cunningham, Erin E. Bonar, Inbal Nahum-Shani, Predrag Klasnja, Maureen Walton, Susan Murphy

JMIR Res Protoc 2018;7(7):e166


Correction: Digital Health Coaching Programs Among Older Employees in Transition to Retirement: Systematic Literature Review

Correction: Digital Health Coaching Programs Among Older Employees in Transition to Retirement: Systematic Literature Review

ItalyThis has been corrected to:Model of Care and New Technologies, IRCCS INRCA-National Institute of Health and Science on Aging, Istituto di Ricovero e Cura a Carattere Scientifico Istituto Nazionale Ricovero e Cura per Anziani, Ancona, ItalyThe affiliation of Sara

Vera Stara, Sara Santini, Johannes Kropf, Barbara D'Amen

J Med Internet Res 2020;22(12):e25065


Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App

Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App

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

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

iproc 2018;4(2):e11876


Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

HRR is an indicator of vagal reactivation and SNS deactivation [63].Bartels-Ferreira et al [63] used the first-order exponential method to measure postexercise time-independent HRR based on HR decay curves.

Mahnoosh Sadeghi, Farzan Sasangohar, Anthony D McDonald

JMIR Ment Health 2020;7(7):e16654


Use of Featforward Mobile Phone App Associated with Decreased Cardiometabolic Risk Factors in Patients with Chronic Conditions

Use of Featforward Mobile Phone App Associated with Decreased Cardiometabolic Risk Factors in Patients with Chronic Conditions

/0000-0002-7517-2291JethwaniKamalMD123http://orcid.org/0000-0002-0122-80021Partners Connected Health InnovationBoston, MAUnited States2Massachusetts General HospitalBoston, MAUnited States3Harvard Medical SchoolBoston, MAUnited StatesCorresponding Author: Sara

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

iproc 2018;4(2):e11882


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

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

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


Exploring How Evidence is Used in Care Through an Organizational Ethnography of Two Teaching Hospitals

Exploring How Evidence is Used in Care Through an Organizational Ethnography of Two Teaching Hospitals

Sara tells the team that “mine is DIMS UFO where U is urine, F is fecal, and O is more involved.

Bryn Lander, Ellen Balka

J Med Internet Res 2019;21(3):e10769