@Article{info:doi/10.2196/70926,
author="Strickland, B. Isabella
and Ferketich, K. Amy
and Tackett, P. Alayna
and Patterson, G. Joanne
and Breitborde, K. Nicholas J.
and Davis, Jade
and Roberts, Megan",
title="Imposters, Bots, and Other Threats to Data Integrity in Online Research: Scoping Review of the Literature and Recommendations for Best Practices",
journal="Online J Public Health Inform",
year="2025",
month="Aug",
day="29",
volume="17",
pages="e70926",
keywords="review",
keywords="fraud",
keywords="data integrity",
keywords="bots",
keywords="online data collection",
keywords="PRISMA",
abstract="Background: Threats to data integrity have always existed in online human subjects research, but it appears these threats have become more common and more advanced in recent years. Researchers have proposed various techniques to address satisficers, repeat participants, bots, and fraudulent participants; yet, no synthesis of this literature has been conducted. Objective: This study undertakes a scoping review of recent methods and ethical considerations for addressing threats to data integrity in online research. Methods: A PubMed search was used to identify 90 articles published from 2020 to 2024 that were written in English, that discussed online human subjects research, and that had at least one paragraph dedicated to discussing threats to online data integrity. Results: We cataloged 16 types of techniques for addressing threats to online data integrity. Techniques to authenticate personal information (eg, videoconferencing and mailing incentives to a physical address) appear to be very effective at deterring or identifying fraudulent participants. Yet such techniques also come with ethical considerations, including participant burden and increased threats to privacy. Other techniques, such as Completely Automated Public Turing test to tell Computers and Humans Apart (reCAPTCHA; Google LLC), scores, and checking IP addresses, although very common, were also deemed by several researchers as no longer sufficient protections against advanced threats to data integrity. Conclusions: Overall, this review demonstrates the importance of shifting online research protocols as bots and fraudulent participants become more sophisticated. ",
doi="10.2196/70926",
url="https://ojphi.jmir.org/2025/1/e70926"
}


@Article{info:doi/10.2196/70537,
author="Tan, Ruimin
and Ge, Chen
and Li, Zhe
and Yan, Yating
and Guo, He
and Song, Wenjing
and Zhu, Qiong
and Du, Quansheng",
title="Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2025",
month="May",
day="20",
volume="27",
pages="e70537",
keywords="acute respiratory distress syndrome",
keywords="mortality",
keywords="machine learning",
keywords="systematic review",
keywords="meta-analysis",
abstract="Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high mortality rates. Despite advancements in critical care, reliable early prediction methods for ARDS-related mortality remain elusive. Accurate risk assessment is crucial for timely intervention and improved patient outcomes. Machine learning (ML) techniques have emerged as promising tools for mortality prediction in patients with ARDS, leveraging complex clinical datasets to identify key prognostic factors. However, the efficacy of ML-based models remains uncertain. This systematic review aims to assess the value of ML models in the early prediction of ARDS mortality risk and to provide evidence supporting the development of simplified, clinically applicable ML-based scoring tools for prognosis. Objective: This study systematically reviewed available literature on ML-based ARDS mortality prediction models, primarily aiming to evaluate the predictive performance of these models and compare their efficacy with conventional scoring systems. It also sought to identify limitations and provide insights for improving future ML-based prediction tools. Methods: A comprehensive literature search was conducted across PubMed, Web of Science, the Cochrane Library, and Embase, covering publications from inception to April 27, 2024. Studies developing or validating ML-based ARDS mortality predicting models were considered for inclusion. The methodological quality and risk of bias were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses were performed to explore heterogeneity in model performance based on dataset characteristics and validation approaches. Results: In total, 21 studies involving a total of 31,291 patients with ARDS were included. The meta-analysis demonstrated that ML models achieved relatively high predictive performance. In the training datasets, the pooled concordance index (C-index) was 0.84 (95\% CI 0.81-0.86), while for in-hospital mortality prediction, the pooled C-index was 0.83 (95\% CI 0.81-0.86). In the external validation datasets, the pooled C-index was 0.81 (95\% CI 0.78-0.84), and the corresponding value for in-hospital mortality prediction was 0.80 (95\% CI 0.77-0.84). ML models outperformed traditional scoring tools, which demonstrated lower predictive performance. The pooled area under the receiver operating characteristic curve (ROC-AUC) for standard scoring systems was 0.7 (95\% CI 0.67-0.72). Specifically, 2 widely used clinical scoring systems, the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score II (SAPS-II), demonstrated ROC-AUCs of 0.64 (95\% CI 0.62-0.67) and 0.70 (95\% CI 0.66-0.74), respectively. Conclusions: ML-based models exhibited superior predictive accuracy over conventional scoring tools, suggesting their potential use in early ARDS mortality risk assessment. However, further research is needed to refine these models, improve their interpretability, and enhance their clinical applicability. Future efforts should focus on developing simplified, efficient, and user-friendly ML-based prediction tools that integrate seamlessly into clinical workflows. Such advancements may facilitate the early identification of high-risk patients, enabling timely interventions and personalized, risk-based prevention strategies to improve ARDS outcomes. ",
doi="10.2196/70537",
url="https://www.jmir.org/2025/1/e70537"
}


@Article{info:doi/10.2196/66598,
author="Rountree, Lillian
and Lin, Yi-Ting
and Liu, Chuyu
and Salvatore, Maxwell
and Admon, Andrew
and Nallamothu, Brahmajee
and Singh, Karandeep
and Basu, Anirban
and Bu, Fan
and Mukherjee, Bhramar",
title="Reporting of Fairness Metrics in Clinical Risk Prediction Models Used for Precision Health: Scoping Review",
journal="Online J Public Health Inform",
year="2025",
month="Mar",
day="19",
volume="17",
pages="e66598",
keywords="bias",
keywords="cardiovascular disease",
keywords="COVID-19",
keywords="risk stratification",
keywords="sensitive features",
keywords="clinical risk prediction",
keywords="equity",
abstract="Background: Clinical risk prediction models integrated into digitized health care informatics systems hold promise for personalized primary prevention and care, a core goal of precision health. Fairness metrics are important tools for evaluating potential disparities across sensitive features, such as sex and race or ethnicity, in the field of prediction modeling. However, fairness metric usage in clinical risk prediction models remains infrequent, sporadic, and rarely empirically evaluated. Objective: We seek to assess the uptake of fairness metrics in clinical risk prediction modeling through an empirical evaluation of popular prediction models for 2 diseases, 1 chronic and 1 infectious disease. Methods: We conducted a scoping literature review in November 2023 of recent high-impact publications on clinical risk prediction models for cardiovascular disease (CVD) and COVID-19 using Google Scholar. Results: Our review resulted in a shortlist of 23 CVD-focused articles and 22 COVID-19 pandemic--focused articles. No articles evaluated fairness metrics. Of the CVD-focused articles, 26\% used a sex-stratified model, and of those with race or ethnicity data, 92\% had study populations that were more than 50\% from 1 race or ethnicity. Of the COVID-19 models, 9\% used a sex-stratified model, and of those that included race or ethnicity data, 50\% had study populations that were more than 50\% from 1 race or ethnicity. No articles for either disease stratified their models by race or ethnicity. Conclusions: Our review shows that the use of fairness metrics for evaluating differences across sensitive features is rare, despite their ability to identify inequality and flag potential gaps in prevention and care. We also find that training data remain largely racially and ethnically homogeneous, demonstrating an urgent need for diversifying study cohorts and data collection. We propose an implementation framework to initiate change, calling for better connections between theory and practice when it comes to the adoption of fairness metrics for clinical risk prediction. We hypothesize that this integration will lead to a more equitable prediction world. ",
doi="10.2196/66598",
url="https://ojphi.jmir.org/2025/1/e66598",
url="http://www.ncbi.nlm.nih.gov/pubmed/39962044"
}


@Article{info:doi/10.2196/59906,
author="Dritsakis, Giorgos
and Gallos, Ioannis
and Psomiadi, Maria-Elisavet
and Amditis, Angelos
and Dionysiou, Dimitra",
title="Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review",
journal="Online J Public Health Inform",
year="2024",
month="Oct",
day="25",
volume="16",
pages="e59906",
keywords="policy making",
keywords="public health",
keywords="noncommunicable diseases",
keywords="data analytics",
keywords="digital tools",
keywords="descriptive",
keywords="predictive",
keywords="decision support",
keywords="implementation",
abstract="Background: There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making. Objective: The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation. Methods: A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years. Results: Nine articles that presented 7 data analytics tools designed to inform policy making for NCDs were reviewed. The tools incorporated descriptive and predictive analytics. Some tools were designed to include recommendations for decision support, but no pilot studies applying prescriptive analytics have been published. The tools were piloted with various conditions, with cancer being the least studied condition. Implementation of the tools included use cases, pilots, or evaluation workshops that involved policy makers. However, our findings demonstrate very limited real-world use of analytics by policy makers, which is in line with previous studies. Conclusions: Despite the availability of tools designed for different purposes and conditions, data analytics is not widely used to support policy making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy making. Based on the findings, we make suggestions for researchers developing digital tools to support public health policy making. The findings will also serve as input for the European Union--funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform. ",
doi="10.2196/59906",
url="https://ojphi.jmir.org/2024/1/e59906",
url="http://www.ncbi.nlm.nih.gov/pubmed/39454197"
}


@Article{info:doi/10.2196/57618,
author="Anderson, Euan
and Lennon, Marilyn
and Kavanagh, Kimberley
and Weir, Natalie
and Kernaghan, David
and Roper, Marc
and Dunlop, Emma
and Lapp, Linda",
title="Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review",
journal="Online J Public Health Inform",
year="2024",
month="Aug",
day="7",
volume="16",
pages="e57618",
keywords="telecare",
keywords="telehealth",
keywords="telemedicine",
keywords="data analytics",
keywords="predictive models",
keywords="scoping review",
keywords="predictive",
keywords="predict",
keywords="prediction",
keywords="predictions",
keywords="synthesis",
keywords="review methods",
keywords="review methodology",
keywords="search",
keywords="searches",
keywords="searching",
keywords="scoping",
keywords="home",
abstract="Background: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. Objective: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. Methods: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. Results: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. Conclusions: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested. ",
doi="10.2196/57618",
url="https://ojphi.jmir.org/2024/1/e57618",
url="http://www.ncbi.nlm.nih.gov/pubmed/39110501"
}


@Article{info:doi/10.2196/51991,
author="Sun, Yehao
and Prabhu, Prital
and Rahman, Ryan
and Li, Dongmei
and McIntosh, Scott
and Rahman, Irfan",
title="e-Cigarette Tobacco Flavors, Public Health, and Toxicity: Narrative Review",
journal="Online J Public Health Inform",
year="2024",
month="May",
day="27",
volume="16",
pages="e51991",
keywords="vaping",
keywords="e-cigarettes",
keywords="tobacco flavors",
keywords="toxicity",
keywords="regulation",
keywords="tobacco",
keywords="public health",
keywords="smoking",
keywords="menthol",
keywords="social media",
keywords="nicotine",
keywords="symptoms",
keywords="symptom",
keywords="risk",
keywords="risks",
keywords="toxicology",
keywords="health risk",
abstract="Background: Recently, the US Food and Drug Administration implemented enforcement priorities against all flavored, cartridge-based e-cigarettes other than menthol and tobacco flavors. This ban undermined the products' appeal to vapers, so e-cigarette manufacturers added flavorants of other attractive flavors into tobacco-flavored e-cigarettes and reestablished appeal. Objective: This review aims to analyze the impact of the addition of other flavorants in tobacco-flavored e-cigarettes on both human and public health issues and to propose further research as well as potential interventions. Methods: Searches for relevant literature published between 2018 and 2023 were performed. Cited articles about the toxicity of e-cigarette chemicals included those published before 2018, and governmental websites and documents were also included for crucial information. Results: Both the sales of e-cigarettes and posts on social media suggested that the manufacturers' strategy was successful. The reestablished appeal causes not only a public health issue but also threats to the health of individual vapers. Research has shown an increase in toxicity associated with the flavorants commonly used in flavored e-cigarettes, which are likely added to tobacco-flavored e-cigarettes based on tobacco-derived and synthetic tobacco-free nicotine, and these other flavors are associated with higher clinical symptoms not often induced solely by natural, traditional tobacco flavors. Conclusions: The additional health risks posed by the flavorants are pronounced even without considering the toxicological interactions of the different tobacco flavorants, and more research should be done to understand the health risks thoroughly and to take proper actions accordingly for the regulation of these emerging products. ",
doi="10.2196/51991",
url="https://ojphi.jmir.org/2024/1/e51991",
url="http://www.ncbi.nlm.nih.gov/pubmed/38801769"
}


@Article{info:doi/10.2196/50898,
author="Bindhu, Shwetha
and Nattam, Anunita
and Xu, Catherine
and Vithala, Tripura
and Grant, Tiffany
and Dariotis, K. Jacinda
and Liu, Hexuan
and Wu, Y. Danny T.",
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2024",
month="Mar",
day="20",
volume="16",
pages="e50898",
keywords="health literacy",
keywords="social determinants of health",
keywords="SDoH",
keywords="social determinants",
keywords="systematic review",
keywords="patient education",
keywords="health education",
keywords="health information",
keywords="information needs",
keywords="information comprehension",
keywords="patient counseling",
keywords="barriers to care",
keywords="language proficiency",
abstract="Background: Health literacy (HL) is the ability to make informed decisions using health information. As health data and information availability increase due to online clinic notes and patient portals, it is important to understand how HL relates to social determinants of health (SDoH) and the place of informatics in mitigating disparities. Objective: This systematic literature review aims to examine the role of HL in interactions with SDoH and to identify feasible HL-based interventions that address low patient understanding of health information to improve clinic note-sharing efficacy. Methods: The review examined 2 databases, Scopus and PubMed, for English-language articles relating to HL and SDoH. We conducted a quantitative analysis of study characteristics and qualitative synthesis to determine the roles of HL and interventions. Results: The results (n=43) were analyzed quantitatively and qualitatively for study characteristics, the role of HL, and interventions. Most articles (n=23) noted that HL was a result of SDoH, but other articles noted that it could also be a mediator for SdoH (n=6) or a modifiable SdoH (n=14) itself. Conclusions: The multivariable nature of HL indicates that it could form the basis for many interventions to combat low patient understandability, including 4 interventions using informatics-based solutions. HL is a crucial, multidimensional skill in supporting patient understanding of health materials. Designing interventions aimed at improving HL or addressing poor HL in patients can help increase comprehension of health information, including the information contained in clinic notes shared with patients. ",
doi="10.2196/50898",
url="https://ojphi.jmir.org/2024/1/e50898",
url="http://www.ncbi.nlm.nih.gov/pubmed/38506914"
}


@Article{info:doi/10.2196/50927,
author="Joshi, Vibha
and Joshi, Kumar Nitin
and Bhardwaj, Pankaj
and Singh, Kuldeep
and Ojha, Deepika
and Jain, Kumar Yogesh",
title="The Health Impact of mHealth Interventions in India: Systematic Review and Meta-Analysis",
journal="Online J Public Health Inform",
year="2023",
month="Sep",
day="4",
volume="15",
pages="e50927",
keywords="mobile applications",
keywords="mobile apps",
keywords="cost-benefit analysis",
keywords="telemedicine",
keywords="technology",
keywords="India",
keywords="patient satisfaction",
keywords="pregnancy",
abstract="Background: Considerable use of mobile health (mHealth) interventions has been seen, and these interventions have beneficial effects on health and health service delivery processes, especially in resource-limited settings. Various functionalities of mobile phones offer a range of opportunities for mHealth interventions. Objective: This review aims to assess the health impact of mHealth interventions in India. Methods: This systematic review and meta-analysis was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies conducted in India, and published between April 1, 2011, and March 31, 2021, were considered. A literature search was conducted using a combination of MeSH (Medical Subject Headings) terms in different databases to identify peer-reviewed publications. Thirteen out of 1350 articles were included for the final review. Risk of bias was assessed using the Risk of Bias 2 tool for RCTs and Risk Of Bias In Non-randomised Studies - of Interventions tool (for nonrandomized trials), and a meta-analysis was performed using RevMan for 3 comparable studies on maternal, neonatal, and child health. Results: The meta-analysis showed improved usage of maternal and child health services including iron--folic acid supplementation (odds ratio [OR] 14.30, 95\% CI 6.65-30.75), administration of both doses of the tetanus toxoid (OR 2.47, 95\% CI 0.22-27.37), and attending 4 or more antenatal check-ups (OR 1.82, 95\% CI 0.65-5.09). Meta-analysis for studies concerning economic evaluation and chronic diseases could not be performed due to heterogeneity. However, a positive economic impact was observed from a societal perspective (ReMiND [reducing maternal and newborn deaths] and ImTeCHO [Innovative Mobile Technology for Community Health Operation] interventions), and chronic disease interventions showed a positive impact on clinical outcomes, patient and provider satisfaction, app usage, and improvement in health behaviors. Conclusions: This review provides a comprehensive overview of mHealth technology in all health sectors in India, analyzing both health and health care usage indicators for interventions focused on maternal and child health and chronic diseases. Trial Registration: PROSPERO 2021 CRD42021235315; https://tinyurl.com/yh4tp2j7 ",
doi="10.2196/50927",
url="https://ojphi.jmir.org/2023/1/e50927",
url="http://www.ncbi.nlm.nih.gov/pubmed/38046564"
}


@Article{info:doi/10.5210/ojphi.v14i1.12731,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2022",
volume="14",
number="1",
pages="e12731",
doi="10.5210/ojphi.v14i1.12731",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/36457350"
}


@Article{info:doi/10.5210/ojphi.v14i1.12577,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2022",
volume="14",
number="1",
pages="e12577",
doi="10.5210/ojphi.v14i1.12577",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/36120162"
}


@Article{info:doi/10.5210/ojphi.v14i1.11090,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2022",
volume="14",
number="1",
pages="e11090",
doi="10.5210/ojphi.v14i1.11090",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/36120163"
}


@Article{info:doi/10.5210/ojphi.v13i3.11081,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2021",
volume="13",
number="3",
pages="e11081",
doi="10.5210/ojphi.v13i3.11081",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/35082976"
}


@Article{info:doi/10.5210/ojphi.v10i2.8270,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2018",
volume="10",
number="2",
pages="e8270",
doi="10.5210/ojphi.v10i2.8270",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/30349633"
}


@Article{info:doi/10.5210/ojphi.v9i2.7985,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2017",
volume="9",
number="2",
pages="e7985",
doi="10.5210/ojphi.v9i2.7985",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/29026457"
}


@Article{info:doi/10.5210/ojphi.v9i2.7437,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2017",
volume="9",
number="2",
pages="e7437",
doi="10.5210/ojphi.v9i2.7437",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/29026456"
}


@Article{info:doi/10.5210/ojphi.v7i2.5853,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2015",
volume="7",
number="2",
pages="e5853",
doi="10.5210/ojphi.v7i2.5853",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/26392847"
}


@Article{info:doi/10.5210/ojphi.v7i2.5595,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2015",
volume="7",
number="2",
pages="e5595",
doi="10.5210/ojphi.v7i2.5595",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/26392851"
}


@Article{info:doi/10.5210/ojphi.v7i2.6031,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2015",
volume="7",
number="2",
pages="e6031",
doi="10.5210/ojphi.v7i2.6031",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/26284150"
}


@Article{info:doi/10.5210/ojphi.v7i2.5931,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2015",
volume="7",
number="2",
pages="e5931",
doi="10.5210/ojphi.v7i2.5931",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/26392846"
}


@Article{info:doi/10.5210/ojphi.v6i3.5571,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2014",
volume="6",
number="3",
pages="e5571",
doi="10.5210/ojphi.v6i3.5571",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/25598870"
}


@Article{info:doi/10.5210/ojphi.v6i2.5484,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2014",
volume="6",
number="2",
pages="e5484",
doi="10.5210/ojphi.v6i2.5484",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/25422725"
}


@Article{info:doi/10.5210/ojphi.v6i2.4903,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2014",
volume="6",
number="2",
pages="e4903",
doi="10.5210/ojphi.v6i2.4903",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/25422724"
}


@Article{info:doi/10.5210/ojphi.v5i3.4943,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2014",
volume="5",
number="3",
pages="e4943",
doi="10.5210/ojphi.v5i3.4943",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/24678382"
}


@Article{info:doi/10.5210/ojphi.v5i3.4814,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2014",
volume="5",
number="3",
pages="e4814",
doi="10.5210/ojphi.v5i3.4814",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/24683442"
}


@Article{info:doi/10.5210/ojphi.v5i2.4623,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="2",
pages="e4623",
doi="10.5210/ojphi.v5i2.4623",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23923099"
}


@Article{info:doi/10.5210/ojphi.v5i1.4376,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4376",
doi="10.5210/ojphi.v5i1.4376",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i1.4375,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4375",
doi="10.5210/ojphi.v5i1.4375",
url=""
}


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title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2012",
volume="4",
number="3",
pages="e4267",
doi="10.5210/ojphi.v4i3.4267",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569643"
}


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title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2012",
volume="4",
number="2",
pages="e4198",
doi="10.5210/ojphi.v4i2.4198",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569634"
}


@Article{info:doi/10.5210/ojphi.v4i2.4191,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2012",
volume="4",
number="2",
pages="e4191",
doi="10.5210/ojphi.v4i2.4191",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569633"
}


@Article{info:doi/10.5210/ojphi.v4i1.3684,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2012",
volume="4",
number="1",
pages="e3684",
doi="10.5210/ojphi.v4i1.3684",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569625"
}


@Article{info:doi/10.5210/ojphi.v4i1.4011,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2012",
volume="4",
number="1",
pages="e4011",
doi="10.5210/ojphi.v4i1.4011",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569629"
}


@Article{info:doi/10.5210/ojphi.v3i1.3385,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2011",
volume="3",
number="1",
pages="e3385",
doi="10.5210/ojphi.v3i1.3385",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569601"
}


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title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2010",
volume="2",
number="1",
pages="e2855",
doi="10.5210/ojphi.v2i1.2855",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569577"
}