@Article{info:doi/10.2196/68757,
author="McCabe, Catherine
and Connolly, Leona
and Quintana, Yuri
and Weir, Arielle
and Moen, Anne
and Ingvar, Martin
and McCann, Margaret
and Doyle, Carmel
and Hughes, Mary
and Brenner, Maria",
title="How to Refine and Prioritize Key Performance Indicators for Digital Health Interventions: Tutorial on Using Consensus Methodology to Enable Meaningful Evaluation of Novel Digital Health Interventions",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="16",
volume="27",
pages="e68757",
keywords="digital health interventions",
keywords="key performance indicators",
keywords="Delphi technique",
keywords="consensus methodology",
keywords="drug-related side effects and adverse reactions",
keywords="referral",
keywords="consultation",
doi="10.2196/68757",
url="https://www.jmir.org/2025/1/e68757"
}


@Article{info:doi/10.2196/70481,
author="Nazar, Wojciech
and Nazar, Grzegorz
and Kami?ska, Aleksandra
and Danilowicz-Szymanowicz, Ludmila",
title="How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="18",
volume="27",
pages="e70481",
keywords="generative artificial intelligence",
keywords="large language models",
keywords="instruction-tuning datasets",
keywords="tutorials",
keywords="evaluation framework",
keywords="health care",
doi="10.2196/70481",
url="https://www.jmir.org/2025/1/e70481"
}


@Article{info:doi/10.2196/56790,
author="Vlake, H. Johan
and Drop, Q. Denzel L.
and Van Bommel, Jasper
and Riva, Giuseppe
and Wiederhold, K. Brenda
and Cipresso, Pietro
and Rizzo, S. Albert
and Rothbaum, O. Barbara
and Botella, Cristina
and Hooft, Lotty
and Bienvenu, J. Oscar
and Jung, Christian
and Geerts, Bart
and Wils, Evert-Jan
and Gommers, Diederik
and van Genderen, E. Michel
and ",
title="Reporting Guidelines for the Early-Phase Clinical Evaluation of Applications Using Extended Reality: RATE-XR Qualitative Study Guideline",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="29",
volume="26",
pages="e56790",
keywords="extended reality",
keywords="XR",
keywords="virtual reality",
keywords="augmented reality",
keywords="mixed reality",
keywords="reporting guideline",
keywords="Delphi process",
keywords="consensus",
keywords="computer-generated simulation",
keywords="simulation",
keywords="virtual world",
keywords="simulation experience",
keywords="clinical evaluation",
abstract="Background: Extended reality (XR), encompassing technologies such as virtual reality, augmented reality, and mixed reality, has rapidly gained prominence in health care. However, existing XR research often lacks rigor, proper controls, and standardization. Objective: To address this and to enhance the transparency and quality of reporting in early-phase clinical evaluations of XR applications, we present the ``Reporting for the early-phase clinical evaluation of applications using extended reality'' (RATE-XR) guideline. Methods: We conducted a 2-round modified Delphi process involving experts from diverse stakeholder categories, and the RATE-XR is therefore the result of a consensus-based, multistakeholder effort. Results: The guideline comprises 17 XR-specific (composed of 18 subitems) and 14 generic reporting items, each with a complementary Explanation \& Elaboration section. Conclusions: The items encompass critical aspects of XR research, from clinical utility and safety to human factors and ethics. By offering a comprehensive checklist for reporting, the RATE-XR guideline facilitates robust assessment and replication of early-stage clinical XR studies. It underscores the need for transparency, patient-centeredness, and balanced evaluation of the applications of XR in health care. By providing an actionable checklist of minimal reporting items, this guideline will facilitate the responsible development and integration of XR technologies into health care and related fields. ",
doi="10.2196/56790",
url="https://www.jmir.org/2024/1/e56790"
}


@Article{info:doi/10.2196/62761,
author="Harrison Ginsberg, Kristin
and Babbott, Katie
and Serlachius, Anna",
title="Exploring Participants' Experiences of Digital Health Interventions With Qualitative Methods: Guidance for Researchers",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="28",
volume="26",
pages="e62761",
keywords="qualitative methods",
keywords="content analysis",
keywords="thematic analysis",
keywords="digital health evaluation",
keywords="user engagement",
keywords="user experience",
keywords="digital health intervention",
keywords="innovation",
keywords="patient experience",
keywords="health care",
keywords="researcher",
keywords="technology",
keywords="mobile health",
keywords="mHealth",
keywords="telemedicine",
keywords="digital health",
keywords="behavior change",
keywords="usability",
keywords="tutorial",
keywords="research methods",
keywords="qualitative research",
keywords="study design",
doi="10.2196/62761",
url="https://www.jmir.org/2024/1/e62761"
}


@Article{info:doi/10.2196/58419,
author="Pogrebnoy, Dina
and Ashton, Lee
and Beh, A. Brian
and Burke, Meredith
and Cullen, Richard
and Czerenkowski, Jude
and Davey, Julie
and Dennett, M. Amy
and English, Kevin
and Godecke, Erin
and Harper, Nicole
and Lynch, Elizabeth
and MacDonald-Wicks, Lesley
and Patterson, Amanda
and Ramage, Emily
and Schelfhaut, Ben
and Simpson, B. Dawn
and Zacharia, Karly
and English, Coralie",
title="Adapting a Telehealth Physical Activity and Diet Intervention to a Co-Designed Website for Self-Management After Stroke: Tutorial",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="22",
volume="26",
pages="e58419",
keywords="stroke",
keywords="secondary prevention",
keywords="co-design",
keywords="how-to guide, website development",
keywords="accessibility",
keywords="navigation",
keywords="self-management",
doi="10.2196/58419",
url="https://www.jmir.org/2024/1/e58419"
}


@Article{info:doi/10.2196/58502,
author="Burns, James
and Chen, Kelly
and Flathers, Matthew
and Currey, Danielle
and Macrynikola, Natalia
and Vaidyam, Aditya
and Langholm, Carsten
and Barnett, Ian
and Byun, Soo) Andrew (Jin
and Lane, Erlend
and Torous, John",
title="Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial",
journal="J Med Internet Res",
year="2024",
month="Aug",
day="23",
volume="26",
pages="e58502",
keywords="digital phenotyping",
keywords="mental health",
keywords="data visualization",
keywords="data analysis",
keywords="smartphones",
keywords="smartphone",
keywords="Cortex",
keywords="open-source",
keywords="data processing",
keywords="mindLAMP",
keywords="app",
keywords="apps",
keywords="data set",
keywords="clinical",
keywords="real world",
keywords="methodology",
keywords="mobile phone",
doi="10.2196/58502",
url="https://www.jmir.org/2024/1/e58502",
url="http://www.ncbi.nlm.nih.gov/pubmed/39178032"
}


@Article{info:doi/10.2196/54407,
author="Pretorius, Kelly",
title="A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial",
journal="JMIR Form Res",
year="2024",
month="Jul",
day="9",
volume="8",
pages="e54407",
keywords="social media analysis",
keywords="data extraction",
keywords="health care research",
keywords="extraction tutorial",
keywords="Facebook extraction",
keywords="Facebook analysis",
keywords="safe sleep",
keywords="sudden unexpected infant death",
keywords="social media",
keywords="analysis",
keywords="systematic approach",
keywords="qualitative data",
keywords="Facebook",
keywords="health-related",
keywords="maternal perspective",
keywords="maternal perspectives",
keywords="sudden infant death syndrome",
keywords="mother",
keywords="mothers",
keywords="women",
keywords="United States",
keywords="SIDS",
keywords="SUID",
keywords="post",
keywords="posts",
doi="10.2196/54407",
url="https://formative.jmir.org/2024/1/e54407",
url="http://www.ncbi.nlm.nih.gov/pubmed/38980712"
}


@Article{info:doi/10.2196/50182,
author="Singla, Ashwani
and Khanna, Ritvik
and Kaur, Manpreet
and Kelm, Karen
and Zaiane, Osmar
and Rosenfelt, Scott Cory
and Bui, An Truong
and Rezaei, Navid
and Nicholas, David
and Reformat, Z. Marek
and Majnemer, Annette
and Ogourtsova, Tatiana
and Bolduc, Francois",
title="Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial",
journal="J Med Internet Res",
year="2024",
month="Jun",
day="18",
volume="26",
pages="e50182",
keywords="chatbot",
keywords="user interface",
keywords="knowledge graph",
keywords="neurodevelopmental disability",
keywords="autism",
keywords="intellectual disability",
keywords="attention-deficit/hyperactivity disorder",
doi="10.2196/50182",
url="https://www.jmir.org/2024/1/e50182",
url="http://www.ncbi.nlm.nih.gov/pubmed/38888947"
}


@Article{info:doi/10.2196/44443,
author="Weng, Huiqin Janice
and Hu, Yanyan
and Heaukulani, Creighton
and Tan, Clarence
and Chang, Kuiyu Julian
and Phang, Sheng Ye
and Rajendram, Priyanka
and Tan, Mooi Weng
and Loke, Chiong Wai
and Morris, T. Robert J.",
title="Mental Wellness Self-Care in Singapore With mindline.sg: A Tutorial on the Development of a Digital Mental Health Platform for Behavior Change",
journal="J Med Internet Res",
year="2024",
month="Jun",
day="4",
volume="26",
pages="e44443",
keywords="digital mental health",
keywords="artificial intelligence",
keywords="AI",
keywords="AI chatbot",
keywords="digital therapeutics",
keywords="mental health",
keywords="mental wellness",
keywords="mobile phone",
abstract="Background: Singapore, like the rest of Asia, faces persistent challenges to mental health promotion, including stigma around unwellness and seeking treatment and a lack of trained mental health personnel. The COVID-19 pandemic, which created a surge in mental health care needs and simultaneously accelerated the adoption of digital health solutions, revealed a new opportunity to quickly scale innovative solutions in the region. Objective: In June 2020, the Singaporean government launched mindline.sg, an anonymous digital mental health resource website that has grown to include >500 curated local mental health resources, a clinically validated self-assessment tool for depression and anxiety, an artificial intelligence (AI) chatbot from Wysa designed to deliver digital therapeutic exercises, and a tailored version of the website for working adults called mindline at work. The goal of the platform is to empower Singapore residents to take charge of their own mental health and to be able to offer basic support to those around them through the ease and convenience of a barrier-free digital solution. Methods: Website use is measured through click-level data analytics captured via Google Analytics and custom application programming interfaces, which in turn drive a customized analytics infrastructure based on the open-source platforms Titanium Database and Metabase. Unique, nonbounced (users that do not immediately navigate away from the site), engaged, and return users are reported. Results: In the 2 years following launch (July 1, 2020, through June 30, 2022), the website received >447,000 visitors (approximately 15\% of the target population of 3 million), 62.02\% (277,727/447,783) of whom explored the site or engaged with resources (referred to as nonbounced visitors); 10.54\% (29,271/277,727) of those nonbounced visitors returned. The most popular features on the platform were the dialogue-based therapeutic exercises delivered by the chatbot and the self-assessment tool, which were used by 25.54\% (67,626/264,758) and 11.69\% (32,469/277,727) of nonbounced visitors. On mindline at work, the rates of nonbounced visitors who engaged extensively (ie, spent ?40 seconds exploring resources) and who returned were 51.56\% (22,474/43,588) and 13.43\% (5,853/43,588) over a year, respectively, compared to 30.9\% (42,829/138,626) and 9.97\% (13,822/138,626), respectively, on the generic mindline.sg site in the same year. Conclusions: The site has achieved desired reach and has seen a strong growth rate in the number of visitors, which required substantial and sustained digital marketing campaigns and strategic outreach partnerships. The site was careful to preserve anonymity, limiting the detail of analytics. The good levels of overall adoption encourage us to believe that mild to moderate mental health conditions and the social factors that underly them are amenable to digital interventions. While mindline.sg was primarily used in Singapore, we believe that similar solutions with local customization are widely and globally applicable. ",
doi="10.2196/44443",
url="https://www.jmir.org/2024/1/e44443",
url="http://www.ncbi.nlm.nih.gov/pubmed/38833294"
}


@Article{info:doi/10.2196/50890,
author="Cho, Hunyong
and She, Jane
and De Marchi, Daniel
and El-Zaatari, Helal
and Barnes, L. Edward
and Kahkoska, R. Anna
and Kosorok, R. Michael
and Virkud, V. Arti",
title="Machine Learning and Health Science Research: Tutorial",
journal="J Med Internet Res",
year="2024",
month="Jan",
day="30",
volume="26",
pages="e50890",
keywords="health science researcher",
keywords="machine learning pipeline",
keywords="machine learning",
keywords="medical machine learning",
keywords="precision medicine",
keywords="reproducibility",
keywords="unsupervised learning",
doi="10.2196/50890",
url="https://www.jmir.org/2024/1/e50890",
url="http://www.ncbi.nlm.nih.gov/pubmed/38289657"
}


@Article{info:doi/10.2196/51125,
author="Henry, M. Lauren
and Hansen, Eleanor
and Chimoff, Justin
and Pokstis, Kimberly
and Kiderman, Miryam
and Naim, Reut
and Kossowsky, Joe
and Byrne, E. Meghan
and Lopez-Guzman, Silvia
and Kircanski, Katharina
and Pine, S. Daniel
and Brotman, A. Melissa",
title="Selecting an Ecological Momentary Assessment Platform: Tutorial for Researchers",
journal="J Med Internet Res",
year="2024",
month="Jan",
day="4",
volume="26",
pages="e51125",
keywords="ecological momentary assessment",
keywords="methodology",
keywords="psychology and psychiatry",
keywords="child and adolescent",
keywords="in vivo and real time",
abstract="Background: Although ecological momentary assessment (EMA) has been applied in psychological research for decades, delivery methods have evolved with the proliferation of digital technology. Technological advances have engendered opportunities for enhanced accessibility, convenience, measurement precision, and integration with wearable sensors. Notwithstanding, researchers must navigate novel complexities in EMA research design and implementation. Objective: In this paper, we aimed to provide guidance on platform selection for clinical scientists launching EMA studies. Methods: Our team includes diverse specialties in child and adolescent behavioral and mental health with varying expertise on EMA platforms (eg, users and developers). We (2 research sites) evaluated EMA platforms with the goal of identifying the platform or platforms with the best fit for our research. We created a list of extant EMA platforms; conducted a web-based review; considered institutional security, privacy, and data management requirements; met with developers; and evaluated each of the candidate EMA platforms for 1 week. Results: We selected 2 different EMA platforms, rather than a single platform, for use at our 2 research sites. Our results underscore the importance of platform selection driven by individualized and prioritized laboratory needs; there is no single, ideal platform for EMA researchers. In addition, our project generated 11 considerations for researchers in selecting an EMA platform: (1) location; (2) developer involvement; (3) sample characteristics; (4) onboarding; (5) survey design features; (6) sampling scheme and scheduling; (7) viewing results; (8) dashboards; (9) security, privacy, and data management; (10) pricing and cost structure; and (11) future directions. Furthermore, our project yielded a suggested timeline for the EMA platform selection process. Conclusions: This study will guide scientists initiating studies using EMA, an in vivo, real-time research tool with tremendous promise for facilitating advances in psychological assessment and intervention. ",
doi="10.2196/51125",
url="https://www.jmir.org/2024/1/e51125",
url="http://www.ncbi.nlm.nih.gov/pubmed/38175682"
}


@Article{info:doi/10.2196/44206,
author="Keogh, Alison
and Mc Ardle, R{\'i}ona
and Diaconu, Gabriela Mara
and Ammour, Nadir
and Arnera, Valdo
and Balzani, Federica
and Brittain, Gavin
and Buckley, Ellen
and Buttery, Sara
and Cantu, Alma
and Corriol-Rohou, Solange
and Delgado-Ortiz, Laura
and Duysens, Jacques
and Forman-Hardy, Tom
and Gur-Arieh, Tova
and Hamerlijnck, Dominique
and Linnell, John
and Leocani, Letizia
and McQuillan, Tom
and Neatrour, Isabel
and Polhemus, Ashley
and Remmele, Werner
and Saraiva, Isabel
and Scott, Kirsty
and Sutton, Norman
and van den Brande, Koen
and Vereijken, Beatrix
and Wohlrab, Martin
and Rochester, Lynn
and ",
title="Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial",
journal="J Med Internet Res",
year="2023",
month="Oct",
day="27",
volume="25",
pages="e44206",
keywords="patient involvement",
keywords="patient engagement",
keywords="public-private partnership",
keywords="research consortium",
keywords="digital mobility outcomes",
keywords="real-world mobility",
keywords="digital mobility measures",
doi="10.2196/44206",
url="https://www.jmir.org/2023/1/e44206",
url="http://www.ncbi.nlm.nih.gov/pubmed/37889531"
}


@Article{info:doi/10.2196/49949,
author="Thirunavukarasu, James Arun
and Elangovan, Kabilan
and Gutierrez, Laura
and Li, Yong
and Tan, Iris
and Keane, A. Pearse
and Korot, Edward
and Ting, Wei Daniel Shu",
title="Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial",
journal="J Med Internet Res",
year="2023",
month="Oct",
day="12",
volume="25",
pages="e49949",
keywords="machine learning",
keywords="automated machine learning",
keywords="autoML",
keywords="artificial intelligence",
keywords="democratization",
keywords="autonomous AI",
keywords="imaging",
keywords="image analysis",
keywords="automation",
keywords="AI engineering",
doi="10.2196/49949",
url="https://www.jmir.org/2023/1/e49949",
url="http://www.ncbi.nlm.nih.gov/pubmed/37824185"
}


@Article{info:doi/10.2196/50638,
author="Mesk{\'o}, Bertalan",
title="Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial",
journal="J Med Internet Res",
year="2023",
month="Oct",
day="4",
volume="25",
pages="e50638",
keywords="artificial intelligence",
keywords="AI",
keywords="digital health",
keywords="future",
keywords="technology",
keywords="ChatGPT",
keywords="GPT-4",
keywords="large language models",
keywords="language model",
keywords="LLM",
keywords="prompt",
keywords="prompts",
keywords="prompt engineering",
keywords="AI tool",
keywords="engineering",
keywords="healthcare professional",
keywords="decision-making",
keywords="LLMs",
keywords="chatbot",
keywords="chatbots",
keywords="conversational agent",
keywords="conversational agents",
keywords="NLP",
keywords="natural language processing",
doi="10.2196/50638",
url="https://www.jmir.org/2023/1/e50638",
url="http://www.ncbi.nlm.nih.gov/pubmed/37792434"
}


@Article{info:doi/10.2196/42154,
author="Naef, C. Aileen
and Jeitziner, Marie-Madlen
and Jakob, M. Stephan
and M{\"u}ri, M. Ren{\'e}
and Nef, Tobias",
title="Creating Custom Immersive 360-Degree Videos for Use in Clinical and Nonclinical Settings: Tutorial",
journal="JMIR Med Educ",
year="2023",
month="Sep",
day="14",
volume="9",
pages="e42154",
keywords="360-degree video",
keywords="head-mounted display",
keywords="healthcare",
keywords="relaxing content",
keywords="technology",
keywords="video content",
keywords="video production",
keywords="virtual reality",
keywords="VR",
doi="10.2196/42154",
url="https://mededu.jmir.org/2023/1/e42154",
url="http://www.ncbi.nlm.nih.gov/pubmed/37707883"
}


@Article{info:doi/10.2196/42187,
author="de Batlle, Jordi
and Ben{\'i}tez, D. Ivan
and Moncus{\'i}-Moix, Anna
and Androutsos, Odysseas
and Angles Barbastro, Rosana
and Antonini, Alessio
and Arana, Eunate
and Cabrera-Umpierrez, Fernanda Maria
and Cea, Gloria
and Dafoulas, $\Epsilon$ George
and Folkvord, Frans
and Fullaondo, Ane
and Giuliani, Francesco
and Huang, Hsiao-Ling
and Innominato, F. Pasquale
and Kardas, Przemyslaw
and Lou, Q. Vivian W.
and Manios, Yannis
and Matsangidou, Maria
and Mercalli, Franco
and Mokhtari, Mounir
and Pagliara, Silvio
and Schellong, Julia
and Stieler, Lisa
and Votis, Konstantinos
and Curr{\'a}s, Paula
and Arredondo, Teresa Maria
and Posada, Jorge
and Guill{\'e}n, Sergio
and Pecchia, Leandro
and Barb{\'e}, Ferran
and Torres, Gerard
and Fico, Giuseppe
and ",
title="GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="28",
volume="25",
pages="e42187",
keywords="big data",
keywords="chronic diseases",
keywords="eHealth",
keywords="healthy aging",
keywords="integrated care",
keywords="large-scale pilots",
abstract="Background: The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. Methods: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. Results: Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence--based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. Conclusions: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space. ",
doi="10.2196/42187",
url="https://www.jmir.org/2023/1/e42187",
url="http://www.ncbi.nlm.nih.gov/pubmed/37379060"
}


@Article{info:doi/10.2196/45662,
author="Hou, Jue
and Zhao, Rachel
and Gronsbell, Jessica
and Lin, Yucong
and Bonzel, Clara-Lea
and Zeng, Qingyi
and Zhang, Sinian
and Beaulieu-Jones, K. Brett
and Weber, M. Griffin
and Jemielita, Thomas
and Wan, Sabrina Shuyan
and Hong, Chuan
and Cai, Tianrun
and Wen, Jun
and Ayakulangara Panickan, Vidul
and Liaw, Kai-Li
and Liao, Katherine
and Cai, Tianxi",
title="Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies",
journal="J Med Internet Res",
year="2023",
month="May",
day="25",
volume="25",
pages="e45662",
keywords="electronic health records",
keywords="real-world evidence",
keywords="data curation",
keywords="medical informatics",
keywords="randomized controlled trials",
keywords="reproducibility",
doi="10.2196/45662",
url="https://www.jmir.org/2023/1/e45662",
url="http://www.ncbi.nlm.nih.gov/pubmed/37227772"
}


@Article{info:doi/10.2196/37269,
author="Sudre, Gustavo
and Bagi{\'c}, I. Anto
and Becker, T. James
and Ford, P. John",
title="An Emerging Screening Method for Interrogating Human Brain Function: Tutorial",
journal="JMIR Form Res",
year="2023",
month="Apr",
day="27",
volume="7",
pages="e37269",
keywords="screening",
keywords="brain function",
keywords="cognition",
keywords="magnetoencephalography",
keywords="MEG",
keywords="neuroimaging",
keywords="tutorial",
keywords="tool",
keywords="cognitive test",
keywords="signal",
keywords="cognitive function",
doi="10.2196/37269",
url="https://formative.jmir.org/2023/1/e37269",
url="http://www.ncbi.nlm.nih.gov/pubmed/37103988"
}


@Article{info:doi/10.2196/40730,
author="Bendtsen, Marcus",
title="Avoiding Under- and Overrecruitment in Behavioral Intervention Trials Using Bayesian Sequential Designs: Tutorial",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="16",
volume="24",
number="12",
pages="e40730",
keywords="digital alcohol intervention",
keywords="Bayesian sequential design",
keywords="sample size",
keywords="randomized controlled trial",
keywords="trial recruitment",
keywords="behavioural intervention",
keywords="participant recruitment",
keywords="research participants",
keywords="research methods",
keywords="effect size",
keywords="trial procedure",
doi="10.2196/40730",
url="https://www.jmir.org/2022/12/e40730",
url="http://www.ncbi.nlm.nih.gov/pubmed/36525297"
}


@Article{info:doi/10.2196/37036,
author="Etling, Ann Mary
and Musili, Michael
and Eastes, Kaytlin
and Oyungu, Eren
and McHenry, S. Megan",
title="Creating the Map of Interactive Services Aiding and Assisting Persons With Disabilities (MSAADA) Project: Tutorial for the Novel Use of a Store Locator App",
journal="Interact J Med Res",
year="2022",
month="Dec",
day="8",
volume="11",
number="2",
pages="e37036",
keywords="map",
keywords="virtual",
keywords="interactive",
keywords="disability",
keywords="resources",
keywords="inclusion",
keywords="mHealth",
keywords="Kenya",
keywords="global health",
keywords="public health",
abstract="Background: An estimated 15\% of the global population is living with a disability. In Kenya, children with disabilities remain among the most vulnerable populations, experiencing substantial barriers to wellness and inclusion. Smartphone ownership and internet access have been increasing across sub-Saharan Africa, including in Kenya. Despite these advances, online or mobile resources remain limited and difficult to find and navigate. Objective: This paper aims to describe the novel use of a store locator app to develop an interactive map of organizations that provide medical, educational, and socioeconomic resources to individuals with disabilities in Kenya. The target audience is individuals with disabilities, medical professionals, and organization leaders. Methods: A comprehensive list of organizations, government county offices, educational assessment and resource centers, and institutions was compiled. Organizations were contacted via email, WhatsApp, or in person for semistructured interviews. Based on the services offered, each organization was assigned categorical search tags. The data were entered into a third-party store locator app. The resulting map was inserted into a page on the Academic Model Providing Access to Healthcare (AMPATH) website. Results: The Map of Interactive Services Aiding and Assisting Persons With Disabilities (MSAADA; this abbreviation is also Swahili for ``help'') was launched in July 2020 in both English and Swahili. The map included 89 organizations across Kenya. Of these, 51 were reached for an interview (for a 57\% response rate). Interviewees cited limited paid staff and dependence on grant-based funding as primary challenges to growth and sustainability. Conclusions: MSAADA is an interactive, virtual map that aims to connect individuals with disabilities, medical professionals, and organization leaders to resources in Kenya. The novel use of a store locator app to compile resources in remote settings has the potential to improve access to health care for a wide variety of specialties and patient populations. Innovators in global health should consider the use of store locator apps to connect individuals to resources in regions with limited mapping. ",
doi="10.2196/37036",
url="https://www.i-jmr.org/2022/2/e37036",
url="http://www.ncbi.nlm.nih.gov/pubmed/36480245"
}


@Article{info:doi/10.2196/26339,
author="Olson, Jenny
and Hadjiconstantinou, Michelle
and Luff, Carly
and Watts, Karen
and Watson, Natasha
and Miller, Venus
and Schofield, Deborah
and Khunti, Kamlesh
and Davies, J. Melanie
and Calginari, Sara",
title="From the United Kingdom to Australia---Adapting a Web-Based Self-management Education Program to Support the Management of Type 2 Diabetes: Tutorial",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="20",
volume="24",
number="4",
pages="e26339",
keywords="diabetes mellitus",
keywords="type 2",
keywords="technology",
keywords="self-management",
doi="10.2196/26339",
url="https://www.jmir.org/2022/4/e26339",
url="http://www.ncbi.nlm.nih.gov/pubmed/35442198"
}


@Article{info:doi/10.2196/28291,
author="Fundingsland Jr, Lauritz Edwin
and Fike, Joseph
and Calvano, Joshua
and Beach, Jeffrey
and Lai, Deborah
and He, Shuhan",
title="Methodological Guidelines for Systematic Assessments of Health Care Websites Using Web Analytics: Tutorial",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="15",
volume="24",
number="4",
pages="e28291",
keywords="Google Analytics",
keywords="website usability",
keywords="conversion rate",
keywords="website engagement",
keywords="user demographics",
keywords="website traffic",
keywords="website content",
keywords="internet browsers",
keywords="healthcare websites",
keywords="web analytics",
keywords="healthcare industry",
keywords="usability",
doi="10.2196/28291",
url="https://www.jmir.org/2022/4/e28291",
url="http://www.ncbi.nlm.nih.gov/pubmed/35436216"
}


@Article{info:doi/10.2196/32365,
author="Szinay, Dorothy
and Cameron, Rory
and Naughton, Felix
and Whitty, A. Jennifer
and Brown, Jamie
and Jones, Andy",
title="Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="11",
volume="23",
number="10",
pages="e32365",
keywords="discrete choice experiment",
keywords="stated preference methods",
keywords="mHealth",
keywords="digital health",
keywords="quantitative methodology",
keywords="uptake",
keywords="engagement",
keywords="methodology",
keywords="preference",
keywords="Bayesian",
keywords="design",
keywords="tutorial",
keywords="qualitative",
keywords="user preference",
doi="10.2196/32365",
url="https://www.jmir.org/2021/10/e32365",
url="http://www.ncbi.nlm.nih.gov/pubmed/34633290"
}


@Article{info:doi/10.2196/29157,
author="Milligan, John Kevin
and Daulton, Scott Robert
and St Clair, Taylor Zachary
and Epperson, Veronica Madison
and Holloway, Mackenzie Rachel
and Schlaudecker, David Jeffrey",
title="Creation of a Student-Run Medical Education Podcast: Tutorial",
journal="JMIR Med Educ",
year="2021",
month="Jul",
day="8",
volume="7",
number="3",
pages="e29157",
keywords="podcast",
keywords="medical student",
keywords="near-peer",
keywords="medical education",
abstract="Background: Podcasting has become a popular medium for medical education content. Educators and trainees of all levels are turning to podcasts for high-quality, asynchronous content. Although numerous medical education podcasts have emerged in recent years, few student-run podcasts exist. Student-run podcasts are a novel approach to supporting medical students. Near-peer mentoring has been shown to promote medical students' personal and professional identity formation. Student-run podcasts offer a new medium for delivering near-peer advice to medical students in an enduring and accessible manner. Objective: This paper describes the creation of the UnsCripted Medicine Podcast---a student-run medical education podcast produced at the University of Cincinnati College of Medicine. Methods: The planning and preparatory phases spanned 6 months. Defining a target audience and establishing a podcast mission were key first steps. Efforts were directed toward securing funding; obtaining necessary equipment; and navigating the technical considerations of recording, editing, and publishing a podcast. In order to ensure that high professionalism standards were met, key partnerships were created with faculty from the College of Medicine. Results: The UnsCripted Medicine Podcast published 53 episodes in its first 2 years. The number of episodes released per month ranges from 0 to 5, with a mean of 2.0 episodes. The podcast has a Twitter account with 217 followers. The number of listeners who subscribed to the podcast via Apple Podcasts grew to 86 in the first year and then to 218 in the second year. The show has an average rating of 4.8 (out of 5) on Apple Podcasts, which is based on 24 ratings. The podcast has hosted 70 unique guests, including medical students, resident physicians, attending physicians, nurses, physicians' family members, graduate medical education leadership, and educators. Conclusions: Medical student--run podcasts are a novel approach to supporting medical students and fostering professional identity formation. Podcasts are widely available and convenient for listeners. Additionally, podcast creators can publish content with lower barriers of entry compared to those of other forms of published content. Medical schools should consider supporting student podcast initiatives to allow for near-peer mentoring, augment the community, facilitate professional identity formation, and prepare the rising physician workforce for the technological frontier of medical education and practice. ",
doi="10.2196/29157",
url="https://mededu.jmir.org/2021/3/e29157",
url="http://www.ncbi.nlm.nih.gov/pubmed/34255694"
}


@Article{info:doi/10.2196/25077,
author="Acquaviva, D. Kimberly",
title="Establishing and Facilitating Large-Scale Manuscript Collaborations via Social Media: Novel Method and Tools for Replication",
journal="J Med Internet Res",
year="2021",
month="May",
day="17",
volume="23",
number="5",
pages="e25077",
keywords="social media",
keywords="crowdsourcing",
keywords="collaboration",
keywords="health professions",
keywords="medicine",
keywords="scholarship",
keywords="literature",
keywords="research",
abstract="Background: Authorship teams in the health professions are typically composed of scholars who are acquainted with one another before a manuscript is written. Even if a scholar has identified a diverse group of collaborators outside their usual network, writing an article with a large number of co-authors poses significant logistical challenges. Objective: This paper describes a novel method for establishing and facilitating large-scale manuscript collaborations via social media. Methods: On September 11, 2020, I used the social media platform Twitter to invite people to collaborate on an article I had drafted. Anyone who wanted to collaborate was welcome, regardless of discipline, specialty, title, country of residence, or degree completion. During the 25 days that followed, I used Google Docs, Google Sheets, and Google Forms to manage all aspects of the collaboration. Results: The collaboration resulted in the completion of 2 manuscripts in a 25-day period. The International Council of Medical Journal Editors authorship criteria were met by 40 collaborators for the first article (``Documenting Social Media Engagement as Scholarship: A New Model for Assessing Academic Accomplishment for the Health Professions'') and 35 collaborators for the second article (``The Benefits of Using Social Media as a Health Professional in Academia''). The authorship teams for both articles were notably diverse, with 17\%-18\% (7/40 and 6/35, respectively) of authors identifying as a person of color and/or underrepresented minority, 37\%-38\% (15/40 and 13/35, respectively) identifying as LGBTQ+ (lesbian, gay, bisexual, transgender, gender non-conforming, queer and/or questioning), 73\%-74\% (29/40 and 26/35, respectively) using she/her pronouns, and 20\%-23\% (9/40 and 7/35, respectively) identifying as a person with a disability. Conclusions: Scholars in the health professions can use this paper in conjunction with the tools provided to replicate this process in carrying out their own large-scale manuscript collaborations. ",
doi="10.2196/25077",
url="https://www.jmir.org/2021/5/e25077",
url="http://www.ncbi.nlm.nih.gov/pubmed/33999002"
}


@Article{info:doi/10.2196/20803,
author="Nguyen, Xuan-Lan Anne
and Trinh, Xuan-Vi
and Wang, Y. Sophia
and Wu, Y. Albert",
title="Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions",
journal="J Med Internet Res",
year="2021",
month="May",
day="17",
volume="23",
number="5",
pages="e20803",
keywords="sentiment analysis",
keywords="emotions analysis",
keywords="natural language processing",
keywords="online forums",
keywords="social media",
keywords="patient attitudes",
keywords="medicine",
keywords="infodemiology",
keywords="infoveillance",
keywords="digital health",
abstract="Background: Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences. Objective: This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application. Methods: We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools. Results: Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ?500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients' attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (?0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of ?0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied. Conclusions: This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients' perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results. ",
doi="10.2196/20803",
url="https://www.jmir.org/2021/5/e20803",
url="http://www.ncbi.nlm.nih.gov/pubmed/33999001"
}


@Article{info:doi/10.2196/25502,
author="Lalande, Kathleen
and Greenman, S. Paul
and Bouchard, Karen
and Johnson, M. Susan
and Tulloch, Heather",
title="The Healing Hearts Together Randomized Controlled Trial and the COVID-19 Pandemic: A Tutorial for Transitioning From an In-Person to a Web-Based Intervention",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="6",
volume="23",
number="4",
pages="e25502",
keywords="web-based intervention",
keywords="internet-based intervention",
keywords="randomized controlled trial",
keywords="COVID-19",
keywords="research",
keywords="tutorial",
keywords="digital medicine",
keywords="behavioral medicine",
keywords="telehealth",
keywords="telemedicine",
keywords="cardiovascular rehabilitation",
doi="10.2196/25502",
url="https://www.jmir.org/2021/4/e25502",
url="http://www.ncbi.nlm.nih.gov/pubmed/33729984"
}


@Article{info:doi/10.2196/25173,
author="Shepperd, A. James
and Pogge, Gabrielle
and Hunleth, M. Jean
and Ruiz, Sienna
and Waters, A. Erika",
title="Guidelines for Conducting Virtual Cognitive Interviews During a Pandemic",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="11",
volume="23",
number="3",
pages="e25173",
keywords="cognitive interview",
keywords="COVID-19",
keywords="guidelines",
keywords="teleresearch",
keywords="pandemic",
keywords="tablet computer",
keywords="telehealth",
keywords="training",
doi="10.2196/25173",
url="https://www.jmir.org/2021/3/e25173",
url="http://www.ncbi.nlm.nih.gov/pubmed/33577464"
}


@Article{info:doi/10.2196/23379,
author="Do, Quan
and Marc, David
and Plotkin, Marat
and Pickering, Brian
and Herasevich, Vitaly",
title="Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper",
journal="JMIR Form Res",
year="2020",
month="Dec",
day="24",
volume="4",
number="12",
pages="e23379",
keywords="geographic mapping",
keywords="medicalGIS guidelines",
keywords="information storage and retrieval",
keywords="mapping",
keywords="geotagging",
keywords="data visualization",
keywords="population",
keywords="public health",
abstract="Background: Geotagging is the process of attaching geospatial tags to various media data types. In health care, the goal of geotagging is to gain a better understanding of health-related questions applied to populations. Although there has been a prevalence of geographic information in public health, in order to effectively use and expand geotagging across health care there is a requirement to understand other factors such as the disposition, standardization, data sources, technologies, and limitations. Objective: The objective of this document is to serve as a resource for new researchers in the field. This report aims to be comprehensive but easy for beginners to understand and adopt in practice. The optimal geocodes, their sources, and a rationale for use are suggested. Geotagging's issues and limitations are also discussed. Methods: A comprehensive review of technical instructions and articles was conducted to evaluate guidelines for geotagging, and online resources were curated to support the implementation of geotagging practices. Summary tables were developed to describe the available geotagging resources (free and for fee) that can be leveraged by researchers and quality improvement personnel to effectively perform geospatial analyses primarily targeting US health care. Results: This paper demonstrated steps to develop an initial geotagging and geovisualization project with clear structure and instructions. The geotagging resources were summarized. These resources are essential for geotagging health care projects. The discussion section provides better understanding of geotagging's limitations and suggests suitable way to approach it. Conclusions: We explain how geotagging can be leveraged in health care and offer the necessary initial resources to obtain geocodes, adjustment data, and health-related measures. The resources outlined in this paper can support an individual and/or organization in initiating a geotagging health care project. ",
doi="10.2196/23379",
url="http://formative.jmir.org/2020/12/e23379/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33361054"
}


@Article{info:doi/10.2196/22420,
author="Patel, Devika
and Hawkins, Jessica
and Chehab, Zena Lara
and Martin-Tuite, Patrick
and Feler, Joshua
and Tan, Amy
and Alpers, S. Benjamin
and Pink, Sophia
and Wang, Jerome
and Freise, Jonathan
and Kim, Phillip
and Peabody, Christopher
and Bowditch, John
and Williams, R. Eric
and Sammann, Amanda",
title="Developing Virtual Reality Trauma Training Experiences Using 360-Degree Video: Tutorial",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="16",
volume="22",
number="12",
pages="e22420",
keywords="virtual reality",
keywords="cineVR",
keywords="360-degree video",
keywords="trauma training",
keywords="medical education",
doi="10.2196/22420",
url="http://www.jmir.org/2020/12/e22420/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33325836"
}


@Article{info:doi/10.2196/19217,
author="Martin, L. Christie
and Kramer-Kostecka, N. Eydie
and Linde, A. Jennifer
and Friend, Sarah
and Zuroski, R. Vanessa
and Fulkerson, A. Jayne",
title="Leveraging Interdisciplinary Teams to Develop and Implement Secure Websites for Behavioral Research: Applied Tutorial",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="23",
volume="22",
number="9",
pages="e19217",
keywords="research ethics and compliance",
keywords="website development",
keywords="behavioral research",
keywords="digital interventions",
keywords="website authentication",
keywords="website security",
doi="10.2196/19217",
url="http://www.jmir.org/2020/9/e19217/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32965234"
}


@Article{info:doi/10.2196/15878,
author="Robinson, Heather
and Appelbe, Duncan
and Dodd, Susanna
and Flowers, Susan
and Johnson, Sonia
and Jones, H. Steven
and Mateus, C{\'e}u
and Mezes, Barbara
and Murray, Elizabeth
and Rainford, Naomi
and Rosala-Hallas, Anna
and Walker, Andrew
and Williamson, Paula
and Lobban, Fiona",
title="Methodological Challenges in Web-Based Trials: Update and Insights From the Relatives Education and Coping Toolkit Trial",
journal="JMIR Ment Health",
year="2020",
month="Jul",
day="17",
volume="7",
number="7",
pages="e15878",
keywords="randomized controlled trial",
keywords="research design",
keywords="methods",
keywords="internet",
keywords="web",
keywords="mental health",
keywords="relatives",
keywords="carers",
abstract="International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-016965 ",
doi="10.2196/15878",
url="https://mental.jmir.org/2020/7/e15878",
url="http://www.ncbi.nlm.nih.gov/pubmed/32497018"
}


@Article{info:doi/10.2196/17316,
author="Hadjiconstantinou, Michelle
and Schreder, Sally
and Brough, Christopher
and Northern, Alison
and Stribling, Bernie
and Khunti, Kamlesh
and Davies, J. Melanie",
title="Using Intervention Mapping to Develop a Digital Self-Management Program for People With Type 2 Diabetes: Tutorial on MyDESMOND",
journal="J Med Internet Res",
year="2020",
month="May",
day="11",
volume="22",
number="5",
pages="e17316",
keywords="diabetes mellitus, type 2",
keywords="technology",
keywords="self-management",
doi="10.2196/17316",
url="https://www.jmir.org/2020/5/e17316",
url="http://www.ncbi.nlm.nih.gov/pubmed/32391797"
}


@Article{info:doi/10.2196/15561,
author="Chow, I. Philip",
title="Developing Mental or Behavioral Health Mobile Apps for Pilot Studies by Leveraging Survey Platforms: A Do-it-Yourself Process",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Apr",
day="20",
volume="8",
number="4",
pages="e15561",
keywords="app",
keywords="mental health",
keywords="mHealth",
abstract="Background: Behavioral health researchers are increasingly recognizing the potential of mobile phone apps to deliver empirically supported treatments. However, current options for developing apps typically require large amounts of expertise or money. Objective: This paper aims to describe a pragmatic do-it-yourself approach for researchers to create and pilot an Android mobile phone app using existing survey software (eg, Qualtrics survey platform). Methods: This study was conducted at an academic research center in the United States focused on developing and evaluating behavioral health technologies. The process outlined in this paper was derived and condensed from the steps to building an existing app intervention, iCanThrive, which was developed to enhance mental well-being in women cancer survivors. Results: This paper describes an inexpensive, practical process that uses a widely available survey software, such as Qualtrics, to create and pilot a mobile phone intervention that is presented to participants as a Web viewer app that is downloaded from the Google Play store. Health researchers who are interested in using this process to pilot apps are encouraged to inquire about the survey platforms available to them, the level of security those survey platforms provide, and the regulatory guidelines set forth by their institution. Conclusions: As app interventions continue to gain interest among researchers and consumers alike, it is important to find new ways to efficiently develop and pilot app interventions before committing a large amount of resources. Mobile phone app interventions are an important component to discovering new ways to reach and support individuals with behavioral or mental health disorders. ",
doi="10.2196/15561",
url="https://mhealth.jmir.org/2020/4/e15561",
url="http://www.ncbi.nlm.nih.gov/pubmed/32310143"
}


@Article{info:doi/10.2196/16335,
author="Parga-Belinkie, Joanna
and Merchant, M. Raina",
title="Voices in Evidence-Based Newborn Care: A How-to-Guide on Developing a Parent-Facing Podcast",
journal="JMIR Pediatr Parent",
year="2019",
month="Dec",
day="20",
volume="2",
number="2",
pages="e16335",
keywords="neonatology",
keywords="social media",
keywords="medical education",
keywords="patient education",
doi="10.2196/16335",
url="http://pediatrics.jmir.org/2019/2/e16335/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31859674"
}


@Article{info:doi/10.2196/11966,
author="Tobore, Igbe
and Li, Jingzhen
and Yuhang, Liu
and Al-Handarish, Yousef
and Kandwal, Abhishek
and Nie, Zedong
and Wang, Lei",
title="Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Aug",
day="02",
volume="7",
number="8",
pages="e11966",
keywords="machine learning",
keywords="deep learning",
keywords="big data",
keywords="mHealth",
keywords="medical imaging",
keywords="electronic health record",
keywords="biologicals",
keywords="biomedical",
keywords="ECG",
keywords="EEG",
keywords="artificial intelligence",
doi="10.2196/11966",
url="https://mhealth.jmir.org/2019/8/e11966/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31376272"
}


@Article{info:doi/10.2196/13439,
author="Mavragani, Amaryllis
and Ochoa, Gabriela",
title="Google Trends in Infodemiology and Infoveillance: Methodology Framework",
journal="JMIR Public Health Surveill",
year="2019",
month="May",
day="29",
volume="5",
number="2",
pages="e13439",
keywords="big data",
keywords="health",
keywords="infodemiology",
keywords="infoveillance",
keywords="internet behavior",
keywords="Google Trends",
doi="10.2196/13439",
url="http://publichealth.jmir.org/2019/2/e13439/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31144671"
}


@Article{info:doi/10.2196/12128,
author="Adam, Maya
and McMahon, A. Shannon
and Prober, Charles
and B{\"a}rnighausen, Till",
title="Human-Centered Design of Video-Based Health Education: An Iterative, Collaborative, Community-Based Approach",
journal="J Med Internet Res",
year="2019",
month="Jan",
day="30",
volume="21",
number="1",
pages="e12128",
keywords="human-centered design",
keywords="health promotion",
keywords="health behavior",
keywords="health knowledge, attitudes, practice",
keywords="community health workers",
keywords="telemedicine",
keywords="eHealth",
keywords="mHealth",
doi="10.2196/12128",
url="http://www.jmir.org/2019/1/e12128/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30698531"
}


@Article{info:doi/10.2196/jmir.9372,
author="Akers, Laura
and Gordon, S. Judith",
title="Using Facebook for Large-Scale Online Randomized Clinical Trial Recruitment: Effective Advertising Strategies",
journal="J Med Internet Res",
year="2018",
month="Nov",
day="08",
volume="20",
number="11",
pages="e290",
keywords="research subject recruitment",
keywords="advertisements",
keywords="social media",
doi="10.2196/jmir.9372",
url="http://www.jmir.org/2018/11/e290/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30409765"
}


@Article{info:doi/10.2196/10347,
author="Zafar, Sidra
and Habboush, Yacob
and Beidas, Sary",
title="Use of Grading of Recommendations, Assessment, Development, and Evaluation to Combat Fake News: A Case Study of Influenza Vaccination in Pregnancy",
journal="JMIR Med Educ",
year="2018",
month="Nov",
day="07",
volume="4",
number="2",
pages="e10347",
keywords="GRADE",
keywords="influenza",
keywords="vaccination",
keywords="spontaneous abortion",
keywords="miscarriage",
abstract="Background: The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework is a validated evaluation tool used to assess the quality of scientific publications. It helps in enhancing clinicians' decision-making process and supports production of informed healthy policy. Objective: The purpose of this report was two-fold. First, we reviewed the interpretation of observational studies. The second purpose was to share or provide an example using the GRADE criteria. Methods: To illustrate the use of the GRADE framework to assess publications, we selected a study evaluating the risk of spontaneous abortion (SAB) after influenza vaccine administration. Results: Since 2004, the Centers for Disease Control and Prevention and the Advisory Committee on Immunization Practice have recommended influenza vaccination of pregnant women. Previous studies have not found an association between influenza vaccination and SAB. However, in a recent case-control study by Donahue et al, a correlation with SAB in women who received the H1N1 influenza vaccine was identified. For women who received H1N1--containing vaccine in the previous and current influenza season, the adjusted odds ratio (aOR) for SAB was 7.7 (95\% CI, 2.2-27.3), while the aOR for women not vaccinated in the previous season but vaccinated in the current season was 1.3 (95\% CI, 0.7-2.7). Conclusions: Our goal is to enable the readers to critique published literature using appropriate evaluation tools such as GRADE. ",
doi="10.2196/10347",
url="http://mededu.jmir.org/2018/2/e10347/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30404772"
}


@Article{info:doi/10.2196/10873,
author="Bendtsen, Marcus",
title="A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials",
journal="J Med Internet Res",
year="2018",
month="Oct",
day="24",
volume="20",
number="10",
pages="e10873",
keywords="null hypothesis testing",
keywords="Bayesian analysis",
keywords="randomized controlled trials",
keywords="Bayes theorem",
keywords="randomized controlled trials as topic",
doi="10.2196/10873",
url="http://www.jmir.org/2018/10/e10873/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30148453"
}


@Article{info:doi/10.2196/jmir.9596,
author="O'Sullivan, Benjamin
and Alam, Fahad
and Matava, Clyde",
title="Creating Low-Cost 360-Degree Virtual Reality Videos for Hospitals: A Technical Paper on the Dos and Don'ts",
journal="J Med Internet Res",
year="2018",
month="Jul",
day="16",
volume="20",
number="7",
pages="e239",
keywords="360-degree video",
keywords="VR",
keywords="virtual reality",
keywords="video production",
keywords="anesthetic preparation",
keywords="preoperative anxiety",
keywords="preoperative preparation",
doi="10.2196/jmir.9596",
url="http://www.jmir.org/2018/7/e239/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30012545"
}


@Article{info:doi/10.2196/jmir.8622,
author="Hekler, B. Eric
and Rivera, E. Daniel
and Martin, A. Cesar
and Phatak, S. Sayali
and Freigoun, T. Mohammad
and Korinek, Elizabeth
and Klasnja, Predrag
and Adams, A. Marc
and Buman, P. Matthew",
title="Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions",
journal="J Med Internet Res",
year="2018",
month="Jun",
day="28",
volume="20",
number="6",
pages="e214",
keywords="adaptive interventions",
keywords="mHealth",
keywords="eHealth",
keywords="digital health",
keywords="control systems engineering",
keywords="behavior change",
keywords="optimization",
keywords="multiphase optimization strategy",
keywords="physical activity",
keywords="behavioral maintenance",
abstract="Background: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions. Objective: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions. Overview: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step. Implications: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities. ",
doi="10.2196/jmir.8622",
url="http://www.jmir.org/2018/6/e214/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29954725"
}


@Article{info:doi/10.2196/resprot.6452,
author="Sieverink, Floor
and Kelders, Saskia
and Poel, Mannes
and van Gemert-Pijnen, Lisette",
title="Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data",
journal="JMIR Res Protoc",
year="2017",
month="Aug",
day="07",
volume="6",
number="8",
pages="e156",
keywords="eHealth",
keywords="black box",
keywords="evaluation",
keywords="log data analysis",
doi="10.2196/resprot.6452",
url="http://www.researchprotocols.org/2017/8/e156/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28784592"
}


@Article{info:doi/10.2196/mhealth.4423,
author="Heffernan, Joanne Kayla
and Chang, Shanton
and Maclean, Tamara Skye
and Callegari, Teresa Emma
and Garland, Marie Suzanne
and Reavley, Jane Nicola
and Varigos, Andrew George
and Wark, Dennis John",
title="Guidelines and Recommendations for Developing Interactive eHealth Apps for Complex Messaging in Health Promotion",
journal="JMIR mHealth uHealth",
year="2016",
month="Feb",
day="09",
volume="4",
number="1",
pages="e14",
keywords="mhealth",
keywords="complex messaging",
keywords="vitamin D",
keywords="eHealth smartphone apps",
keywords="interactive",
abstract="Background: The now ubiquitous catchphrase, ``There's an app for that,'' rings true owing to the growing number of mobile phone apps. In excess of 97,000 eHealth apps are available in major app stores. Yet the effectiveness of these apps varies greatly. While a minority of apps are developed grounded in theory and in conjunction with health care experts, the vast majority are not. This is concerning given the Hippocratic notion of ``do no harm.'' There is currently no unified formal theory for developing interactive eHealth apps, and development is especially difficult when complex messaging is required, such as in health promotion and prevention. Objective: This paper aims to provide insight into the creation of interactive eHealth apps for complex messaging, by leveraging the Safe-D case study, which involved complex messaging required to guide safe but sufficient UV exposure for vitamin D synthesis in users. We aim to create recommendations for developing interactive eHealth apps for complex messages based on the lessons learned during Safe-D app development. Methods: For this case study we developed an Apple and Android app, both named Safe-D, to safely improve vitamin D status in young women through encouraging safe ultraviolet radiation exposure. The app was developed through participatory action research involving medical and human computer interaction researchers, subject matter expert clinicians, external developers, and target users. The recommendations for development were created from analysis of the development process. Results: By working with clinicians and implementing disparate design examples from the literature, we developed the Safe-D app. From this development process, recommendations for developing interactive eHealth apps for complex messaging were created: (1) involve a multidisciplinary team in the development process, (2) manage complex messages to engage users, and (3) design for interactivity (tailor recommendations, remove barriers to use, design for simplicity). Conclusions: This research has provided principles for developing interactive eHealth apps for complex messaging as guidelines by aggregating existing design concepts and expanding these concepts and new learnings from our development process. A set of guidelines to develop interactive eHealth apps generally, and specifically those for complex messaging, was previously missing from the literature; this research has contributed these principles. Safe-D delivers complex messaging simply, to aid education, and explicitly, considering user safety. ",
doi="10.2196/mhealth.4423",
url="http://mhealth.jmir.org/2016/1/e14/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26860623"
}


@Article{info:doi/10.2196/jmir.5086,
author="Pagoto, Sherry
and Waring, E. Molly
and May, N. Christine
and Ding, Y. Eric
and Kunz, H. Werner
and Hayes, Rashelle
and Oleski, L. Jessica",
title="Adapting Behavioral Interventions for Social Media Delivery",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="29",
volume="18",
number="1",
pages="e24",
keywords="social media",
keywords="behavioral interventions",
keywords="health behavior",
keywords="online social networks",
doi="10.2196/jmir.5086",
url="http://www.jmir.org/2016/1/e24/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26825969"
}


@Article{info:doi/10.2196/mhealth.4917,
author="Abroms, C. Lorien
and Whittaker, Robyn
and Free, Caroline
and Mendel Van Alstyne, Judith
and Schindler-Ruwisch, M. Jennifer",
title="Developing and Pretesting a Text Messaging Program for Health Behavior Change: Recommended Steps",
journal="JMIR mHealth uHealth",
year="2015",
month="Dec",
day="21",
volume="3",
number="4",
pages="e107",
keywords="mHealth",
keywords="telemedicine",
keywords="SMS",
keywords="text messaging",
keywords="behavior change",
keywords="behavior modification",
abstract="Background: A growing body of evidence demonstrates that text messaging-based programs (short message service [SMS]) on mobile phones can help people modify health behaviors. Most of these programs have consisted of automated and sometimes interactive text messages that guide a person through the process of behavior change. Objective: This paper provides guidance on how to develop text messaging programs aimed at changing health behaviors. Methods: Based on their collective experience in designing, developing, and evaluating text messaging programs and a review of the literature, the authors drafted the guide. One author initially drafted the guide and the others provided input and review. Results: Steps for developing a text messaging program include conducting formative research for insights into the target audience and health behavior, designing the text messaging program, pretesting the text messaging program concept and messages, and revising the text messaging program. Conclusions: The steps outlined in this guide may help in the development of SMS-based behavior change programs. ",
doi="10.2196/mhealth.4917",
url="http://mhealth.jmir.org/2015/4/e107/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26690917"
}


@Article{info:doi/10.2196/jmir.4521,
author="Bergmo, Strand Trine",
title="How to Measure Costs and Benefits of eHealth Interventions: An Overview of Methods and Frameworks",
journal="J Med Internet Res",
year="2015",
month="Nov",
day="09",
volume="17",
number="11",
pages="e254",
keywords="eHealth",
keywords="telemedicine",
keywords="telehealth",
keywords="telemonitoring",
keywords="health economics",
keywords="economic evaluation",
keywords="cost-benefit analysis",
keywords="cost-effectiveness analysis",
keywords="cost-utility analysis",
keywords="quality-adjusted life years (QALYs)",
doi="10.2196/jmir.4521",
url="http://www.jmir.org/2015/11/e254/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26552360"
}


@Article{info:doi/10.2196/mhealth.3779,
author="Bock, C. Beth
and Rosen, K. Rochelle
and Barnett, P. Nancy
and Thind, Herpreet
and Walaska, Kristen
and Foster, Robert
and Deutsch, Christopher
and Traficante, Regina",
title="Translating Behavioral Interventions Onto mHealth Platforms: Developing Text Message Interventions for Smoking and Alcohol",
journal="JMIR mHealth uHealth",
year="2015",
month="Feb",
day="24",
volume="3",
number="1",
pages="e22",
keywords="mHealth",
keywords="text message",
keywords="smoking cessation",
keywords="alcohol",
keywords="qualitative methods",
doi="10.2196/mhealth.3779",
url="http://mhealth.jmir.org/2015/1/e22/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25714907"
}


@Article{info:doi/10.2196/jmir.4055,
author="Yardley, Lucy
and Morrison, Leanne
and Bradbury, Katherine
and Muller, Ingrid",
title="The Person-Based Approach to Intervention Development: Application to Digital Health-Related Behavior Change Interventions",
journal="J Med Internet Res",
year="2015",
month="Jan",
day="30",
volume="17",
number="1",
pages="e30",
keywords="person-based approach",
keywords="Internet",
keywords="qualitative research",
keywords="evaluation studies",
keywords="feasibility studies",
keywords="health promotion",
keywords="patient education",
keywords="professional education",
keywords="behavior change.",
doi="10.2196/jmir.4055",
url="http://www.jmir.org/2015/1/e30/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25639757"
}


@Article{info:doi/10.2196/jmir.3770,
author="Horvath, J. Keith
and Ecklund, M. Alexandra
and Hunt, L. Shanda
and Nelson, F. Toben
and Toomey, L. Traci",
title="Developing Internet-Based Health Interventions: A Guide for Public Health Researchers and Practitioners",
journal="J Med Internet Res",
year="2015",
month="Jan",
day="23",
volume="17",
number="1",
pages="e28",
keywords="Internet",
keywords="public health",
keywords="intervention",
keywords="development",
abstract="Background: Researchers and practitioners interested in developing online health interventions most often rely on Web-based and print resources to guide them through the process of online intervention development. Although useful for understanding many aspects of best practices for website development, missing from these resources are concrete examples of experiences in online intervention development for health apps from the perspective of those conducting online health interventions. Objective: This study aims to serve as a series of case studies in the development of online health interventions to provide insights for researchers and practitioners who are considering technology-based interventional or programmatic approaches. Methods: A convenience sample of six study coordinators and five principal investigators at a large, US-based land grant university were interviewed about the process of developing online interventions in the areas of alcohol policy, adolescent health, medication adherence, and human immunodeficiency virus prevention in transgender persons and in men who have sex with men. Participants were asked questions that broadly addressed each of the four phases of the User-Centered Design Process Map from the US Department of Health and Human Services' Research-Based Web Design \& Usability Guidelines. Interviews were audio recorded and transcribed. Qualitative codes were developed using line-by-line open coding for all transcripts, and all transcripts were coded independently by at least 2 authors. Differences among coders were resolved with discussion. Results: We identified the following seven themes: (1) hire a strong (or at least the right) research team, (2) take time to plan before beginning the design process, (3) recognize that vendors and researchers have differing values, objectives, and language, (4) develop a detailed contract, (5) document all decisions and development activities, (6) use a content management system, and (7) allow extra time for testing and debugging your intervention. Each of these areas is discussed in detail, with supporting quotations from principal investigators and study coordinators. Conclusions: The values held by members of each participating organization involved in the development of the online intervention or program, as well as the objectives that are trying to be met with the website, must be considered. These defined values and objectives should prompt an open and explicit discussion about the scope of work, budget, and other needs from the perspectives of each organization. Because of the complexity of developing online interventions, researchers and practitioners should become familiar with the process and how it may differ from the development and implementation of in-person interventions or programs. To assist with this, the intervention team should consider expanding the team to include experts in computer science or learning technologies, as well as taking advantage of institutional resources that will be needed for successful completion of the project. Finally, we describe the tradeoff between funds available for online intervention or program development and the complexity of the project. ",
doi="10.2196/jmir.3770",
url="http://www.jmir.org/2015/1/e28/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25650702"
}


@Article{info:doi/10.2196/resprot.3459,
author="Agboola, Stephen
and Hale, M. Timothy
and Masters, Caitlin
and Kvedar, Joseph
and Jethwani, Kamal",
title="``Real-World'' Practical Evaluation Strategies: A Review of Telehealth Evaluation",
journal="JMIR Res Protoc",
year="2014",
month="Dec",
day="17",
volume="3",
number="4",
pages="e75",
keywords="telehealth",
keywords="eHealth",
keywords="evaluation",
keywords="evaluation framework",
keywords="diabetes mellitus",
keywords="technology",
abstract="Background: Currently, the increasing interest in telehealth and significant technological breakthroughs of the past decade create favorable conditions for the widespread adoption of telehealth services. Therefore, expectations are high that telehealth can help alleviate prevailing challenges in health care delivery. However, in order to translate current research to policy and facilitate adoption by patients and health care providers, there is need for compelling evidence of the effectiveness of telehealth interventions. Such evidence is gathered from rigorously designed research studies, which may not always be practical in many real-world settings. Objective: Our aim was to summarize current telehealth evaluation strategies and challenges and to outline practical approaches to conduct evaluation in real-world settings using one of our previously reported telehealth initiatives, the Diabetes Connect program, as a case study. Methods: We reviewed commonly used current evaluation frameworks and strategies, as well as best practices based on successful evaluative efforts to date to address commonly encountered challenges in telehealth evaluation. These challenges in telehealth evaluation and commonly used frameworks are described relevant to the evaluation of Diabetes Connect, a 12-month Web-based blood glucose monitoring program. Results: Designers of telehealth evaluation frameworks must give careful consideration to the elements of planning, implementation, and impact assessment of interventions. Evaluating performance at each of these phases is critical to the overall success of an intervention. Although impact assessment occurs at the end of a program, our review shows that it should begin at the point of problem definition. Critical to the success of an evaluative strategy is early planning that involves all stakeholders to identify the overall goals of the program and key measures of success at each phase of the program life cycle. This strategy should enable selection of an appropriate evaluation strategy and measures to aid in the ongoing development and implementation of telehealth and provide better evidence of program impact. Conclusions: We recommend a pragmatic, multi-method, multi-phase approach to telehealth evaluation that is flexible and can be adapted to the characteristics and challenges unique to each telehealth program. ",
doi="10.2196/resprot.3459",
url="http://www.researchprotocols.org/2014/4/e75/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25524892"
}


@Article{info:doi/10.2196/mhealth.3425,
author="Zhang, WB Melvyn
and Tsang, Tammy
and Cheow, Enquan
and Ho, SH Cyrus
and Yeong, Beng Ng
and Ho, CM Roger",
title="Enabling Psychiatrists to be Mobile Phone App Developers: Insights Into App Development Methodologies",
journal="JMIR mHealth uHealth",
year="2014",
month="Nov",
day="11",
volume="2",
number="4",
pages="e53",
keywords="smartphone application",
keywords="mobile application",
keywords="creation",
abstract="Background: The use of mobile phones, and specifically smartphones, in the last decade has become more and more prevalent. The latest mobile phones are equipped with comprehensive features that can be used in health care, such as providing rapid access to up-to-date evidence-based information, provision of instant communications, and improvements in organization. The estimated number of health care apps for mobile phones is increasing tremendously, but previous research has highlighted the lack of critical appraisal of new apps. This lack of appraisal of apps has largely been due to the lack of clinicians with technical knowledge of how to create an evidence-based app. Objective: We discuss two freely available methodologies for developing Web-based mobile phone apps: a website builder and an app builder. With these, users can program not just a Web-based app, but also integrate multimedia features within their app, without needing to know any programming language. Methods: We present techniques for creating a mobile Web-based app using two well-established online mobile app websites. We illustrate how to integrate text-based content within the app, as well as integration of interactive videos and rich site summary (RSS) feed information. We will also briefly discuss how to integrate a simple questionnaire survey into the mobile-based app. A questionnaire survey was administered to students to collate their perceptions towards the app. Results: These two methodologies for developing apps have been used to convert an online electronic psychiatry textbook into two Web-based mobile phone apps for medical students rotating through psychiatry in Singapore. Since the inception of our mobile Web-based app, a total of 21,991 unique users have used the mobile app and online portal provided by WordPress, and another 717 users have accessed the app via a Web-based link. The user perspective survey results (n=185) showed that a high proportion of students valued the textbook and objective structured clinical examination videos featured in the app. A high proportion of students concurred that a self-designed mobile phone app would be helpful for psychiatry education. Conclusions: These methodologies can enable busy clinicians to develop simple mobile Web-based apps for academic, educational, and research purposes, without any prior knowledge of programming. This will be beneficial for both clinicians and users at large, as there will then be more evidence-based mobile phone apps, or at least apps that have been appraised by a clinician. ",
doi="10.2196/mhealth.3425",
url="http://mhealth.jmir.org/2014/4/e53/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25486985"
}


@Article{info:doi/10.2196/jmir.2076,
author="Regola, Nathan
and Chawla, V. Nitesh",
title="Storing and Using Health Data in a Virtual Private Cloud",
journal="J Med Internet Res",
year="2013",
month="Mar",
day="13",
volume="15",
number="3",
pages="e63",
keywords="medical informatics",
keywords="HIPAA",
doi="10.2196/jmir.2076",
url="http://www.jmir.org/2013/3/e63/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23485880"
}