Journal of Medical Internet Research

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Differences in the Use and Opinions About New eHealth Technologies Among Patients With Psychosis: Structured Questionnaire

These technological health interventions are known as e Health [5]. The recent examples of these interventions that are being currently tested are Actissist [6], Prime [7], and Slow Mo [8]. Nevertheless, before proceeding further in developing these e Health interventions, it is important to better understand the relationship between patients with psychosis and technology resources.

Lucia Bonet, Blanca Llácer, Miguel Hernandez-Viadel, David Arce, Ignacio Blanquer, Carlos Cañete, Maria Escartí, Ana M González-Pinto, Julio Sanjuán

JMIR Ment Health 2018;5(3):e51

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Mobile Phone Cognitive Bias Modification Research Platform for Substance Use Disorders: Protocol for a Feasibility Study

In the past decade, there have been an increasing number of remote online therapies available and this has been attributed to the advances in e Health, or electronic health. e Health technologies facilitate the delivery of online psychotherapy at a low cost and allows therapy to be highly accessible and anonymous [9]. There have been an increasing number of studies examining the effectiveness of Web-based cognitive bias modification interventions.

Melvyn Wb Zhang, JiangBo Ying, Guo Song, Daniel SS Fung, Helen Smith

JMIR Res Protoc 2018;7(6):e153

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Developing a Mental Health eClinic to Improve Access to and Quality of Mental Health Care for Young People: Using Participatory Design as Research Methodologies

In the past decades, the development and use of e Health solutions in mental health care have expanded; however, these solutions have been developed to address specific problems or to replace different components of the traditional health care system. As an example, the majority of self-triage tools rely on people actively searching for these tools on the Internet; however, some health services provide self-triage tools on their websites, particularly when booking appointments online [26].

Laura Ospina-Pinillos, Tracey A Davenport, Cristina S Ricci, Alyssa C Milton, Elizabeth M Scott, Ian B Hickie

J Med Internet Res 2018;20(5):e188

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Internet-Based Cognitive Behavioral Therapy for Symptoms of Depression and Anxiety Among Patients With a Recent Myocardial Infarction: The U-CARE Heart Randomized Controlled Trial

To improve access to effective support, increased engagement in e Health solutions within the cardiac community has been called upon [10], with internet-based CBT (i CBT) representing an e Health solution that may improve access to acceptable, effective, and cost-effective psychological treatment [11]. i CBT has been found to reduce symptoms of depression and anxiety among adults with common mental health difficulties [12].

Fredrika Martin Gustaf Norlund, Emma Wallin, Erik Martin Gustaf Olsson, John Wallert, Gunilla Burell, Louise von Essen, Claes Held

J Med Internet Res 2018;20(3):e88

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The Development of Complex Digital Health Solutions: Formative Evaluation Combining Different Methodologies

The Horizon 2020 project e Health in Rheumatology (ELECTOR) was launched in 2014 with the aim of developing and implementing a digital health solution founded on the basis of an ICT platform for home-based monitoring of disease progression and treatment outcome for patients with RA. The goal of this project was to develop a digital health solution as an alternative to some of the standard visits to the hospital outpatient clinic.

Anne Lee, Marianne Sandvei, Hans Christian Asmussen, Marie Skougaard, Joanne Macdonald, Jakub Zavada, Henning Bliddal, Peter C Taylor, Henrik Gudbergsen

JMIR Res Protoc 2018;7(7):e165

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Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design

User attrition from or nonadherence to electronic health (e Health) technologies is well documented, both for patients and clinicians [17,18]. The few existing implementation studies of specific e Health decision support technologies have identified several barriers, including low user acceptance, poor face validity, and low user-friendliness [19,20].

Caroline Wachtler, Amy Coe, Sandra Davidson, Susan Fletcher, Antonette Mendoza, Leon Sterling, Jane Gunn

JMIR Mhealth Uhealth 2018;6(4):e95

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A Mobile App–Based Intervention for Depression: End-User and Expert Usability Testing Study

Unfortunately, many adolescents and young adults are reluctant to seek help for their depressive symptoms [7], citing barriers such as cost, access to help, lack of anonymity, and perceived stigma of mental illness [8,9]. e Health technology may help to overcome these barriers to treatment and ultimately assist sufferers in alleviating their depressive symptoms.

Matthew David Fuller-Tyszkiewicz, Ben Richardson, Britt Klein, Helen Skouteris, Helen Christensen, David Austin, David Castle, Cathrine Mihalopoulos, Renee O'Donnell, Lilani Arulkadacham, Adrian Shatte, Anna Ware

JMIR Ment Health 2018;5(3):e54

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Preferences for Health Information Technologies Among US Adults: Analysis of the Health Information National Trends Survey

Literature that does converge on specific diseases like diabetes and chronic kidney disease only concentrate on one type of electronic health (e Health) technology at a time [32,33]. The evidence that concentrates on the broad use of technology in addressing CVD specifically is limited [34]. There is also a lack of studies on differences in the preference for using information exchange technologies between patients with chronic and nonchronic diseases and factors affecting these differences.

Onur Asan, Farion Cooper II, Sneha Nagavally, Rebekah J Walker, Joni S Williams, Mukoso N Ozieh, Leonard E Egede

J Med Internet Res 2018;20(10):e277

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A Decision Support System to Enhance Self-Management of Low Back Pain: Protocol for the selfBACK Project

In addition, results for the trial will be reported according to the CONSORT statement [51,52] and the extended CONSORT-EHEALTH checklist [53]. Along with publications in peer-reviewed scientific journals, the results will be disseminated to a wider audience and key stakeholders, such as patient organizations, health care professionals, and relevant policy makers, through social media and other mechanisms.

Paul Jarle Mork, Kerstin Bach

JMIR Res Protoc 2018;7(7):e167

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Three-Factor Structure of the eHealth Literacy Scale Among Magnetic Resonance Imaging and Computed Tomography Outpatients: A Confirmatory Factor Analysis

Electronic health (e Health) refers to the organization and delivery of health services and information using the internet and related technologies [4]. e Health holds potential as a scalable form of service delivery that is accessible, low-cost, promotes patient empowerment, and enhances patient-provider information exchange [5].

Lisa L Lynne Hyde, Allison W Boyes, Tiffany-Jane Evans, Lisa J Mackenzie, Rob Sanson-Fisher

JMIR Hum Factors 2018;5(1):e6

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