@Article{info:doi/10.2196/57718,
author="Jagomast, Tobias
and Finck, Jule
and Tangemann-M{\"u}nstedt, Imke
and Auth, Katharina
and Dr{\"o}mann, Daniel
and Franzen, F. Klaas",
title="Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis",
journal="Online J Public Health Inform",
year="2024",
month="Dec",
day="3",
volume="16",
pages="e57718",
keywords="internet",
keywords="coronavirus",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="pandemics",
keywords="public health",
keywords="smoking cessation",
keywords="tobacco products",
keywords="Google Trends",
keywords="relative search volume",
keywords="Europe",
keywords="online",
keywords="search",
keywords="smoking",
keywords="addiction",
keywords="quit",
keywords="cessation",
keywords="trend",
keywords="cluster",
keywords="public interest",
keywords="lockdown",
keywords="vaccination",
keywords="spread",
keywords="incidence",
abstract="Background: Smoking is a modifiable risk factor for SARS-CoV-2 infection. Evidence of smoking behavior during the pandemic is ambiguous. Most investigations report an increase in smoking. In this context, Google Trends data monitor real-time public information--seeking behavior and are therefore useful to characterize smoking-related interest over the trajectory of the pandemic. Objective: This study aimed to use Google Trends data to evaluate the effect of the pandemic on public interest in smoking-related topics with a focus on lockdowns, vaccination campaigns, and incidence. Methods: The weekly relative search volume was retrieved from Google Trends for England, Germany, Italy, and Spain from December 31, 2017, to April 18, 2021. Data were collected for keywords concerning consumption, cessation, and treatment. The relative search volume before and during the pandemic was compared, and general trends were evaluated using the Wilcoxon rank-sum test. Short-term changes and hereby temporal clusters linked to lockdowns or vaccination campaigns were addressed by the flexible spatial scan statistics proposed by Takahashi and colleagues. Subsequently, the numbers of clusters after the onset of the pandemic were compared by chi-square test. Results: Country-wise minor differences were observed while 3 overarching trends prevailed. First, regarding cessation, the statistical comparison revealed a significant decline in interest for 58\% (7/12) of related keywords, and fewer clusters were present during the pandemic. Second, concerning consumption, significantly reduced relative search volume was observed for 58\% (7/12) of keywords, while treatment-related keywords exhibited heterogeneous trends. Third, substantial clusters of increased interest were sparsely linked to lockdowns, vaccination campaigns, or incidence. Conclusions: This study reports a substantial decline in overall relative search volume and clusters for cessation interest. These results underline the importance of intensifying cessation aid during times of crisis. Lockdowns, vaccination, and incidence had less impact on information-seeking behavior. Other public measures that positively affect smoking behavior remain to be determined. ",
doi="10.2196/57718",
url="https://ojphi.jmir.org/2024/1/e57718",
url="http://www.ncbi.nlm.nih.gov/pubmed/39626237"
}


@Article{info:doi/10.2196/58088,
author="Patel, Mohammed Ahmed
and Baxter, Weston
and Porat, Talya",
title="Toward Guidelines for Designing Holistic Integrated Information Visualizations for Time-Critical Contexts: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="20",
volume="26",
pages="e58088",
keywords="visualization",
keywords="design",
keywords="holistic",
keywords="integrated",
keywords="time-critical",
keywords="guidelines",
keywords="pre-attentive processing",
keywords="gestalt theory",
keywords="situation awareness",
keywords="decision-making",
keywords="mobile phone",
abstract="Background: With the extensive volume of information from various and diverse data sources, it is essential to present information in a way that allows for quick understanding and interpretation. This is particularly crucial in health care, where timely insights into a patient's condition can be lifesaving. Holistic visualizations that integrate multiple data variables into a single visual representation can enhance rapid situational awareness and support informed decision-making. However, despite the existence of numerous guidelines for different types of visualizations, this study reveals that there are currently no specific guidelines or principles for designing holistic integrated information visualizations that enable quick processing and comprehensive understanding of multidimensional data in time-critical contexts. Addressing this gap is essential for enhancing decision-making in time-critical scenarios across various domains, particularly in health care. Objective: This study aims to establish a theoretical foundation supporting the argument that holistic integrated visualizations are a distinct type of visualization for time-critical contexts and identify applicable design principles and guidelines that can be used to design for such cases. Methods: We systematically searched the literature for peer-reviewed research on visualization strategies, guidelines, and taxonomies. The literature selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted across 6 databases: ACM Digital Library, Google Scholar, IEEE Xplore, PubMed, Scopus, and Web of Science. The search was conducted up to August 2024 using the terms (``visualisations'' OR ``visualizations'') AND (``guidelines'' OR ``taxonomy'' OR ``taxonomies''), with studies restricted to the English language. Results: Of 936 papers, 46 (4.9\%) were included in the final review. In total, 48\% (22/46) related to providing a holistic understanding and overview of multidimensional data; 28\% (13/46) focused on integrated presentation, that is, integrating or combining multidimensional data into a single visual representation; and 35\% (16/46) pertained to time and designing for rapid information processing. In total, 65\% (30/46) of the papers presented general information visualization or visual communication guidelines and principles. No specific guidelines or principles were found that addressed all the characteristics of holistic, integrated visualizations in time-critical contexts. A summary of the key guidelines and principles from the 46 papers was extracted, collated, and categorized into 60 guidelines that could aid in designing holistic integrated visualizations. These were grouped according to different characteristics identified in the systematic review (eg, gestalt principles, reduction, organization, abstraction, and task complexity) and further condensed into 5 main proposed guidelines. Conclusions: Holistic integrated information visualizations in time-critical domains are a unique use case requiring a unique set of design guidelines. Our proposed 5 main guidelines, derived from existing design theories and guidelines, can serve as a starting point to enable both holistic and rapid processing of information, facilitating better-informed decisions in time-critical contexts. ",
doi="10.2196/58088",
url="https://www.jmir.org/2024/1/e58088"
}


@Article{info:doi/10.2196/60128,
author="Sven{\vs}ek, Adrijana
and Lorber, Mateja
and Gosak, Lucija
and Verbert, Katrien
and Klemenc-Ketis, Zalika
and Stiglic, Gregor",
title="The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review",
journal="JMIR Public Health Surveill",
year="2024",
month="Oct",
day="14",
volume="10",
pages="e60128",
keywords="cardiovascular disease prevention",
keywords="risk factors",
keywords="visual analytics",
keywords="visualization",
keywords="mobile phone",
keywords="PRISMA",
abstract="Background: Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. Objective: This review aims to present the most-used visualization techniques to estimate CVD risk. Methods: In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. Results: We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Giron{\'i} del COR, were more complex and required laboratory testing--related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94\% of the studies); smoking status (14/17, 82\%); diabetes status (11/17, 65\%); family history (10/17, 59\%); high-density lipoprotein and total cholesterol (9/17, 53\%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35\%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59\%), followed by bar charts (5/17, 29\%) and graphs (4/17, 24\%). Conclusions: On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited. ",
doi="10.2196/60128",
url="https://publichealth.jmir.org/2024/1/e60128",
url="http://www.ncbi.nlm.nih.gov/pubmed/39401079"
}


@Article{info:doi/10.2196/54861,
author="Lin, Ting-Yu
and Yen, Ming-Fang Amy
and Chen, Li-Sheng Sam
and Hsu, Chen-Yang
and Lai, Chao-Chih
and Luh, Dih-Ling
and Yeh, Yen-Po
and Chen, Hsiu-Hsi Tony",
title="Kinetics of Viral Shedding for Outbreak Surveillance of Emerging Infectious Diseases: Modeling Approach to SARS-CoV-2 Alpha and Omicron Infection",
journal="JMIR Public Health Surveill",
year="2024",
month="Sep",
day="19",
volume="10",
pages="e54861",
keywords="COVID-19",
keywords="PCR testing",
keywords="Ct values",
keywords="viral load",
keywords="kinetics of viral shedding",
keywords="emerging infectious disease",
keywords="SARS-CoV-2 variants",
keywords="infection surveillance",
abstract="Background: Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values. Objective: This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery. Methods: This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the ``zero-COVID-19'' policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the ``zero-COVID-19'' policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model. Results: The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19\% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25. Conclusions: The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks. ",
doi="10.2196/54861",
url="https://publichealth.jmir.org/2024/1/e54861"
}


@Article{info:doi/10.2196/56804,
author="Callaghan-Koru, A. Jennifer
and Newman Chargois, Paige
and Tiwari, Tanvangi
and Brown, C. Clare
and Greenfield, William
and Koru, G{\"u}ne?",
title="Public Maternal Health Dashboards in the United States: Descriptive Assessment",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="17",
volume="26",
pages="e56804",
keywords="dashboard",
keywords="maternal health",
keywords="data visualization",
keywords="data communication",
keywords="perinatal health",
abstract="Background: Data dashboards have become more widely used for the public communication of health-related data, including in maternal health. Objective: We aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States. Methods: Through systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience. Results: Most reviewed dashboards reported state-level data (58/80, 72\%) and were hosted on a state health department website (48/80, 60\%). Most dashboards reported data from only 1 (33/80, 41\%) or 2 (23/80, 29\%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12\%) and severe maternal morbidity rate (12/80, 15\%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81\%), geography (65/80, 81\%), and race or ethnicity (55/80, 69\%). Among dashboards that identified their audience (30/80, 38\%), legislators or policy makers and public health agencies or organizations were the most common audiences. Conclusions: While maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design. ",
doi="10.2196/56804",
url="https://www.jmir.org/2024/1/e56804"
}


@Article{info:doi/10.2196/51525,
author="Fareed, Naleef
and Olvera, G. Ramona
and Wang, Yiting
and Hayes, Michael
and Larimore, Liz Elizabeth
and Balvanz, Peter
and Langley, Ronald
and Noel, A. Corinna
and Rock, Peter
and Redmond, Daniel
and Neufeld, Jessica
and Kosakowski, Sarah
and Harris, Daniel
and LaRochelle, Marc
and Huerta, R. Timothy
and Glasgow, LaShawn
and Oga, Emmanuel
and Villani, Jennifer
and Wu, Elwin",
title="Lessons Learned From Developing Dashboards to Support Decision-Making for Community Opioid Response by Community Stakeholders: Mixed Methods and Multisite Study",
journal="JMIR Hum Factors",
year="2024",
month="Sep",
day="9",
volume="11",
pages="e51525",
keywords="data visualizations",
keywords="dashboards",
keywords="public health",
keywords="overdose epidemic",
keywords="human-centered design",
abstract="Background: Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings. Objective: Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders' perceptions of the usability and use of the CTH dashboards for decision-making. Methods: We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors. Results: Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders' perceptions of the dashboard's usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders. Conclusions: We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders. Trial Registration: ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939 ",
doi="10.2196/51525",
url="https://humanfactors.jmir.org/2024/1/e51525",
url="http://www.ncbi.nlm.nih.gov/pubmed/39250216"
}


@Article{info:doi/10.2196/56406,
author="Chang, Annie
and O'Hagan, Ross
and Young, N. Jade
and Wei, Nancy
and Gulati, Nicholas",
title="Geographic Disparities in Online Searches for Psoriasis Biologics in the United States: Google Trends Analysis",
journal="JMIR Dermatol",
year="2024",
month="Jul",
day="31",
volume="7",
pages="e56406",
keywords="psoriasis",
keywords="biologics",
keywords="health disparities",
keywords="Google Trends",
keywords="online search",
keywords="web-based search",
keywords="USA",
keywords="United States",
keywords="Google",
keywords="awareness",
keywords="skin",
keywords="patient awareness",
keywords="psoriasis treatment",
keywords="US",
keywords="psoriasis medication",
keywords="patient education",
doi="10.2196/56406",
url="https://derma.jmir.org/2024/1/e56406"
}


@Article{info:doi/10.5210/ojphi.v14i1.11651,
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="e11651",
doi="10.5210/ojphi.v14i1.11651",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/36120164"
}


@Article{info:doi/10.5210/ojphi.v13i2.11465,
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="2",
pages="e11465",
doi="10.5210/ojphi.v13i2.11465",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/34659646"
}


@Article{info:doi/10.5210/ojphi.v12i1.10588,
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="2020",
volume="12",
number="1",
pages="e10588",
doi="10.5210/ojphi.v12i1.10588",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/32908643"
}


@Article{info:doi/10.5210/ojphi.v12i1.10321,
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="2020",
volume="12",
number="1",
pages="e10321",
doi="10.5210/ojphi.v12i1.10321",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/32577151"
}


@Article{info:doi/10.5210/ojphi.v11i2.10114,
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="2019",
volume="11",
number="2",
pages="e10114",
doi="10.5210/ojphi.v11i2.10114",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/31632599"
}


@Article{info:doi/10.5210/ojphi.v11i2.9483,
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="2019",
volume="11",
number="2",
pages="e9483",
doi="10.5210/ojphi.v11i2.9483",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/31632597"
}


@Article{info:doi/10.5210/ojphi.v11i1.9675,
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="2019",
volume="11",
number="1",
pages="e9675",
doi="10.5210/ojphi.v11i1.9675",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9690,
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="2019",
volume="11",
number="1",
pages="e9690",
doi="10.5210/ojphi.v11i1.9690",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9665,
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="2019",
volume="11",
number="1",
pages="e9665",
doi="10.5210/ojphi.v11i1.9665",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9676,
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="2019",
volume="11",
number="1",
pages="e9676",
doi="10.5210/ojphi.v11i1.9676",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9691,
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="2019",
volume="11",
number="1",
pages="e9691",
doi="10.5210/ojphi.v11i1.9691",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9666,
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="2019",
volume="11",
number="1",
pages="e9666",
doi="10.5210/ojphi.v11i1.9666",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9677,
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="2019",
volume="11",
number="1",
pages="e9677",
doi="10.5210/ojphi.v11i1.9677",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9692,
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="2019",
volume="11",
number="1",
pages="e9692",
doi="10.5210/ojphi.v11i1.9692",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9670,
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="2019",
volume="11",
number="1",
pages="e9670",
doi="10.5210/ojphi.v11i1.9670",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9678,
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="2019",
volume="11",
number="1",
pages="e9678",
doi="10.5210/ojphi.v11i1.9678",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9693,
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="2019",
volume="11",
number="1",
pages="e9693",
doi="10.5210/ojphi.v11i1.9693",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9671,
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="2019",
volume="11",
number="1",
pages="e9671",
doi="10.5210/ojphi.v11i1.9671",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9686,
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="2019",
volume="11",
number="1",
pages="e9686",
doi="10.5210/ojphi.v11i1.9686",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9672,
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="2019",
volume="11",
number="1",
pages="e9672",
doi="10.5210/ojphi.v11i1.9672",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9687,
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="2019",
volume="11",
number="1",
pages="e9687",
doi="10.5210/ojphi.v11i1.9687",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9673,
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="2019",
volume="11",
number="1",
pages="e9673",
doi="10.5210/ojphi.v11i1.9673",
url=""
}


@Article{info:doi/10.5210/ojphi.v11i1.9688,
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="2019",
volume="11",
number="1",
pages="e9688",
doi="10.5210/ojphi.v11i1.9688",
url=""
}


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@Article{info:doi/10.5210/ojphi.v11i1.9689,
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@Article{info:doi/10.5210/ojphi.v9i1.7628,
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@Article{info:doi/10.5210/ojphi.v9i1.7613,
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@Article{info:doi/10.5210/ojphi.v9i1.7621,
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@Article{info:doi/10.5210/ojphi.v9i1.7629,
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@Article{info:doi/10.5210/ojphi.v9i1.7614,
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@Article{info:doi/10.5210/ojphi.v9i1.7622,
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@Article{info:doi/10.5210/ojphi.v9i1.7630,
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@Article{info:doi/10.5210/ojphi.v9i1.7607,
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@Article{info:doi/10.5210/ojphi.v9i1.7608,
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@Article{info:doi/10.5210/ojphi.v9i1.7609,
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@Article{info:doi/10.5210/ojphi.v8i3.7052,
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