Online journal of public health informatics最新文献

{"title":"Reporting of Fairness Metrics in Clinical Risk Prediction Models: A Call for Change to Ensure Equitable Precision Health Benefits for All.","authors":"Lillian Rountree, Yi-Ting Lin, Chuyu Liu, Maxwell Salvatore, Andrew Admon, Brahmajee K Nallamothu, Karandeep Singh, Anirban Basu, Fan Bu, Bhramar Mukherjee","doi":"10.2196/66598","DOIUrl":"https://doi.org/10.2196/66598","url":null,"abstract":"<p><strong>Background: </strong>Clinical risk prediction models integrated in digitized healthcare 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/ethnicity-in the field of prediction modeling. However, fairness metric usage in clinical risk prediction models remain infrequent, sporadic and rarely empirically evaluated.</p><p><strong>Objective: </strong>We seek to assess the uptake of fairness metrics in clinical risk prediction modeling through an empirical evaluation of popular prediction models for two diseases, one chronic and one infectious disease.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>Our review resulted in a shortlist of 23 CVD-focused articles and 22 COVID-19 focused articles. No articles evaluated fairness metrics. Of the CVD articles, 26% used a sex-stratified model, and of those with race/ethnicity data, 92% had data from over 50% from one race/ethnicity. Of the COVID-19 models, 9% used a sex-stratified model, and of those that included race/ethnicity data, 50% had study populations that were more than 50% from one race/ethnicity. No articles for either disease stratified their models by race/ethnicity.</p><p><strong>Conclusions: </strong>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/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 adoption of fairness metrics for clinical risk prediction. We hypothesize that this integration will lead to a more equitable prediction world.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expansion and Assessment of a Web-Based 24-Hour Dietary Recall Tool, Foodbook24, for Use Among Diverse Populations Living in Ireland: Comparative Analysis.","authors":"Grace Bennett, Shuhua Yang, Laura A Bardon, Claire M Timon, Eileen R Gibney","doi":"10.2196/52380","DOIUrl":"10.2196/52380","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Currently, the methods used to collect dietary intake data in Ireland are inflexible to the needs of certain populations, who are poorly represented in nutrition and health data as a result. As the Irish population is becoming increasingly diverse, there is an urgent need to understand the habitual food intake and diet quality of multiple population subgroups, including different nationalities and ethnic minorities, in Ireland. Foodbook24 is an existing web-based 24-hour dietary recall tool, which has previously been validated for use within the general Irish adult population. Because of its design, Foodbook24 can facilitate the improved inclusion of dietary intake assessment in Ireland.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to examine the suitability of expanding the Foodbook24 tool, improving the reliability and accuracy of dietary intake data collected among prominent nationalities in Ireland.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study consisted of three distinct parts: (1) expansion of Foodbook24, (2) testing its usability (ie, acceptability study), and (3) examining the accuracy (ie, comparison study) of the updated Foodbook24 tool. To expand Foodbook24, national survey data from Brazil and Poland were reviewed and commonly consumed food items were added to the food list. All foods were translated into Polish and Portuguese. The acceptability study used a qualitative approach whereby participants provided a visual record of their habitual diet. The comparison study consisted of one 24-hour dietary recall using Foodbook24 and one interviewer-led recall completed on the same day, repeated again 2 weeks later. Comparison study data were analyzed using Spearman rank correlations, Mann-Whitney U tests, and κ coefficients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The expansion of the Foodbook24 food list resulted in 546 additional foods. The acceptability study reported that 86.5% (302/349) of foods listed by participants were available in the updated food list. From the comparison study, strong and positive correlations across 8 food groups (44% of a total of 18 food groups) and 15 nutrients (58% of a total of 26 nutrients) were identified (r=0.70-0.99). Only intakes of potatoes and potato dishes and nuts, herbs, and seeds significantly differed across methods of assessment, where correlations across these food groups were low (r=0.56 and r=0.47, respectively). The incidence of food omissions varied across samples, with Brazilian participants omitting a higher percentage of foods in self-administered recalls than other samples (6/25, 24% among the Brazilian vs 5/38, 13% among the Irish cohort).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The updated food list is representative of most foods consumed by Brazilian, Irish, and Polish adults in Ireland. Dietary intake data reported in Foodbook24 are not largely different from food groups and nutrient intakes reported via traditional methods. This study has demonstrated that Foodbo","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"17 ","pages":"e52380"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11845893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Patient Demand for Orthopedics Care by Region Through Google Trends Analysis: Descriptive Epidemiology Study.","authors":"Abram Qiu, Kristopher Meadows, Fei Ye, Osasu Iyawe, Kenneth Kenneth-Nwosa","doi":"10.2196/63560","DOIUrl":"10.2196/63560","url":null,"abstract":"<p><strong>Background: </strong>There is a growing gap between the supply of surgeons and the demand for orthopedic services in the United States.</p><p><strong>Objective: </strong>We analyzed publicly available online data to assess the correlation between the supply of orthopedic surgeons and patient demand across the United States. The geographic trends of this gap were assessed by using the relative demand index (RDI) to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions.</p><p><strong>Methods: </strong>The data used were from the US Census Bureau, Association of American Medical Colleges (AAMC) through their 2024 Electronic Residency Application Service (ERAS) directory, AAMC State Physician Workforce Data Report, and Google Trends. We calculated the normalized relative search volume (RSV) and the RDI and compared them to the densities of orthopedic surgeons across the United States. We examined the disparities with the Spearman rank correlation coefficient.</p><p><strong>Results: </strong>The supply of orthopedic surgeons varied greatly across the United States, with a significantly higher demand for them in southern states (P=.02). The orthopedic surgeon concentration, normalized to the highest density, was the highest in Alaska (n=100), the District of Columbia (n=96), and Wyoming (n=72); and the lowest in Texas (n=0), Arkansas (n=6), and Oklahoma (n=64). The highest RDI values were observed in Utah (n=97), Florida (n=88), and Texas (n=83), while the lowest were observed in Alaska (n=0), the District of Columbia (n=5), and New Hampshire (n=7). The 7 states of Alaska, Maine, South Dakota, Wyoming, Montana, Delaware, and Idaho lacked orthopedic surgery residencies. In 2023, New York (n=19), Michigan (n=17), Ohio (n=17), Pennsylvania (n=16), and California (n=16) had the most residency programs. Demand and supply, represented by the RDI and orthopedic surgeon concentration, respectively, were strongly correlated negatively (ρ=-0.791, P<.001). States that were in the top quartile of residency programs (≥4 residency programs) exhibited a high demand for orthopedic surgeons (ρ=.6035, P=.02).</p><p><strong>Conclusions: </strong>This study showed that regional disparities in access to orthopedic care can be addressed by increasing orthopedic residencies. The study highlights the novel application of the RDI to mapping the regional need for orthopedics, and this map allows for better targeted resource allocation to expand orthopedic surgery training.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"17 ","pages":"e63560"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11804898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nowcasting to Monitor Real-Time Mpox Trends During the 2022 Outbreak in New York City: Evaluation Using Reportable Disease Data Stratified by Race or Ethnicity.","authors":"Rebecca Rohrer, Allegra Wilson, Jennifer Baumgartner, Nicole Burton, Ray R Ortiz, Alan Dorsinville, Lucretia E Jones, Sharon K Greene","doi":"10.2196/56495","DOIUrl":"10.2196/56495","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends despite data lags and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City, we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity (Black or African American, Hispanic or Latino, and White). However, in real time, it was unclear if the estimates were accurate.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We evaluated the accuracy of estimated mpox case counts across a range of NobBS implementation options.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We evaluated NobBS performance for New York City residents with a confirmed or probable mpox diagnosis or illness onset from July 8 through September 30, 2022, as compared with fully accrued cases. We used the exponentiated average log score (average score) to compare moving window lengths, stratifying or not by race or ethnicity, diagnosis and onset dates, and daily and weekly aggregation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;During the study period, 3305 New York City residents were diagnosed with mpox (median 4, IQR 3-5 days from diagnosis to diagnosis report). Of these, 812 (25%) had missing onset dates, and of these, 230 (28%) had unknown race or ethnicity. The median lag in days from onset to onset report was 10 (IQR 7-14). For daily hindcasts by diagnosis date, the average score was 0.27 for the 14-day moving window used in real time. Average scores improved (increased) with longer moving windows (maximum: 0.47 for 49-day window). Stratifying by race or ethnicity improved performance, with an overall average score of 0.38 for the 14-day moving window (maximum: 0.57 for 49 day-window). Hindcasts for White patients performed best, with average scores of 0.45 for the 14-day window and 0.75 for the 49-day window. For unstratified, daily hindcasts by onset date, the average score ranged from 0.16 for the 42-day window to 0.30 for the 14-day window. Performance was not improved by weekly aggregation. Hindcasts underestimated diagnoses in early August after the epidemic peaked, then overestimated diagnoses in late August as the epidemic waned. Estimates were most accurate during September when cases were low and stable.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Performance was better when hindcasting by diagnosis date than by onset date, consistent with shorter lags and higher completeness for diagnoses. For daily hindcasts by diagnosis date, longer moving windows performed better, but direct comparisons are limited because longer windows could only be assessed after case counts in this outbreak had stabilized. Stratification by race or ethnicity improved performance and identified differences in epidemic trends across patient groups. Contributors to differences in performance across strata might include differences in case volume, epidemic trends, delay distributions","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"17 ","pages":"e56495"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-Screening for Prenatal Depression in Kampala, Uganda Using the Edinburgh Postnatal Depression Scale: Survey Results.","authors":"Hasifah Kasujja Namatovu, Mark Abraham Magumba, Dickens Akena","doi":"10.2196/51602","DOIUrl":"10.2196/51602","url":null,"abstract":"<p><strong>Background: </strong>Perinatal depression remains a substantial public health challenge, often overlooked or incorrectly diagnosed in numerous low-income nations.</p><p><strong>Objective: </strong>The goal of this study was to establish statistical baselines for the prevalence of perinatal depression in Kampala and understand its relationship with key demographic variables.</p><p><strong>Methods: </strong>We employed an Android-based implementation of the Edinburgh Postnatal Depression Scale (EPDS) to survey 12,913 women recruited from 7 government health facilities located in Kampala, Uganda. We used the standard EPDS cutoff, which classifies women with total scores above 13 as possibly depressed and those below 13 as not depressed. The χ2 test of independence was used to determine the most influential categorical variables. We further analyzed the most influential categorical variable using odds ratios. For continuous variables such as age and the weeks of gestation, we performed a simple correlation analysis.</p><p><strong>Results: </strong>We found that 21.5% (2783/12,913, 95% CI 20.8%-22.3%) were possibly depressed. Respondents' relationship category was found to be the most influential variable (χ21=806.9, P<.001; Cramer's V=0.25), indicating a small effect size. Among quantitative variables, we found a weak negative correlation between respondents' age and the total EPDS score (r=-0.11, P<.001). Similarly, a weak negative correlation was also observed between the total EPDS score and the number of previous children of the respondent (r=-0.07, P<.001). Moreover, a weak positive correlation was noted between weeks of gestation and the total EPDS score (r=0.02, P=.05).</p><p><strong>Conclusions: </strong>This study shows that demographic factors such as spousal employment category, age, and relationship status have an influence on the respondents' EPDS scores. These variables may serve as proxies for latent factors such as financial stability and emotional support.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"17 ","pages":"e51602"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In the Shadow of Medicine: The Glaring Absence of Occurrence Records of Human-Hosted Biodiversity.","authors":"Rémy Poncet, Olivier Gargominy","doi":"10.2196/60140","DOIUrl":"10.2196/60140","url":null,"abstract":"<p><strong>Unlabelled: </strong>Microbial diversity is vast, with bacteria playing a crucial role in human health. However, occurrence records (location, date, observer, and host interaction of human-associated bacteria) remain scarce. This lack of information hinders our understanding of human-microbe relationships and disease prevention. In this study, we show that existing solutions such as France's Système d'Information sur le Patrimoine Naturel framework, can be used to efficiently collect and manage occurrence data on human-associated bacteria. This user-friendly system allows medical personnel to easily share and access data on bacterial pathogens. By adopting similar national infrastructures and treating human-associated bacteria as biodiversity data, we can significantly improve public health management and research, and our understanding of the One Health concept, which emphasizes the interconnectedness of human, animal, and environmental health.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e60140"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis.","authors":"Tobias Jagomast, Jule Finck, Imke Tangemann-Münstedt, Katharina Auth, Daniel Drömann, Klaas F Franzen","doi":"10.2196/57718","DOIUrl":"10.2196/57718","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e57718"},"PeriodicalIF":0.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychometric Properties of Measuring Antiretroviral Therapy Adherence Among Young Latino Sexual Minority Men With HIV: Ecological Momentary Assessment and Electronic Pill Dispenser Study.","authors":"Diana M Sheehan, Tendai Gwanzura, Cynthia Ibarra, Daisy Ramirez-Ortiz, Dallas Swendeman, Dustin T Duncan, Miguel Muñoz-Laboy, Jessy G Devieux, Mary Jo Trepka","doi":"10.2196/51424","DOIUrl":"10.2196/51424","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Increasing HIV rates among young Latino sexual minority men (YLSMM) warrant innovative and rigorous studies to assess prevention and treatment strategies. Ecological momentary assessments (EMAs) and electronic pill dispensers (EPDs) have been used to measure antiretroviral therapy (ART) adherence repeatedly in real time and in participants' natural environments, but their psychometric properties among YLSMM are unknown.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study's objective was to assess the concurrent validity, acceptability, compliance, and behavioral reactivity of EMAs and EPDs among YLSMM with HIV.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A convenience sample of 56 YLSMM with HIV with suboptimal ART adherence, aged 18-34 years, was recruited into a 28-consecutive-day EMA study. Concurrent validity was analyzed by comparing median ART adherence rates and calculating Spearman correlations between ART adherence measured by EMA, EPD, and baseline retrospective validated 3-item and single-item measures. Acceptability was assessed in exit interviews asking participants to rate EMA and EPD burden. Compliance was assessed by computing the percent lost to follow-up, the percent of EMAs missed, and the percentage of days the EPD was not opened that had corresponding EMA data self-reporting adherence to ARTs. Behavioral reactivity was assessed by computing the median change in ART adherence during the study period, using generalized mixed models to assess whether the cumulative number of EMAs completed and days of EPD use predicted ART adherence over time, and by asking participants to rate perceived reactivity using a Likert scale.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;EMA ART adherence was significantly correlated with baseline validated 3-item (r=0.41, P=.003) and single-item (r=0.52, P&lt;.001) measures, but correlations were only significant for participants that reported EMA was not burdensome. Correlations for EPD ART adherence were weaker but significant (r=0.36, P=.009; r=0.34, P=.01, respectively). Acceptability was high for EMAs (48/54, 89%) and EPDs (52/54, 96%) per self-report. Loss to follow-up was 4% (2/56), with the remaining participants completing 88.6% (1339/1512) of study-prompted EMAs. The percentage of missed EMA surveys increased from 5.8% (22/378) in week 1 of the study to 16.7% (63/378) in week 4. Of 260 days when EPDs were not opened, 68.8% (179) had a corresponding EMA survey self-reporting ART adherence. Reactivity inferred from the median change in ART adherence over time was 8.8% for EMAs and -0.8% for EPDs. Each completed EMA was associated with 1.03 odds (95% CI 1-1.07) of EMA ART adherence over time, and each day of EPD use with 0.97 odds (95% CI 0.96-0.99) of EPD ART adherence over time. Self-reported perceived behavioral reactivity was 39% for EMAs and 35% for EPDs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study provides evidence of concurrent validity with retrospective validated measures for EMA- and E","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e51424"},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rank Ordered Design Attributes for Health Care Dashboards Including Artificial Intelligence: Usability Study.","authors":"Melina Malkani, Eesha Madan, Dillon Malkani, Arav Madan, Neel Singh, Tara Bamji, Harman Sabharwal","doi":"10.2196/58277","DOIUrl":"10.2196/58277","url":null,"abstract":"<p><strong>Background: </strong>On average, people in the United States visit a doctor 4 times a year, and many of them have chronic illnesses. Because of the increased use of technology, people frequently rely on the internet to access health information and statistics. People use health care information to make better-educated decisions for themselves and others. Health care dashboards should provide pertinent and easily understood data, such as information on timely cancer screenings, so the public can make better-informed decisions. In order to enhance health outcomes, effective dashboards should provide precise data in an accessible and easily digestible manner.</p><p><strong>Objective: </strong>This study identifies the top 15 attributes of a health care dashboard. The objective of this research is to enhance health care dashboards to benefit the public by making better health care information available for more informed decisions by the public and to improve population-level health care outcomes.</p><p><strong>Methods: </strong>The authors conducted a survey of health care dashboards with 218 individuals identifying the best practices to consider when creating a public health care dashboard. The data collection was conducted from June 2023 to August 2023. The analyses performed were descriptive statistics, frequencies, and a comparison to a prior study.</p><p><strong>Results: </strong>From May 2023 to June 2023, we collected 3259 responses in multiple different states around the United States from 218 people aged 18 years or older. The features ranking in descending order of importance are as follows: (1) easy navigation, (2) historical data, (3) simplicity of design, (4) high usability, (5) use of clear descriptions, (6) consistency of data, (7) use of diverse chart types, (8) compliance with the Americans with Disabilities Act, (9) incorporated user feedback, (10) mobile compatibility, (11) comparison data with other entities, (12) storytelling, (13) predictive analytics with artificial intelligence, (14) adjustable thresholds, and (15) charts with tabulated data.</p><p><strong>Conclusions: </strong>Future studies can extend the research to other types of dashboards such as bioinformatics, financial, and managerial dashboards as well as confirm these top 15 best practices for medical dashboards with further evidentiary support. The medical informatics community may benefit from standardization to improve efficiency and effectiveness as dashboards can communicate vital information to patients worldwide on critically prominent issues. Furthermore, health care professionals should use these best practices to help increase population health care outcomes by informing health care consumers to make better decisions with better data.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e58277"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11618005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population Digital Health: Continuous Health Monitoring and Profiling at Scale.","authors":"Naser Hossein Motlagh, Agustin Zuniga, Ngoc Thi Nguyen, Huber Flores, Jiangtao Wang, Sasu Tarkoma, Mattia Prosperi, Sumi Helal, Petteri Nurmi","doi":"10.2196/60261","DOIUrl":"10.2196/60261","url":null,"abstract":"<p><strong>Unlabelled: </strong>This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices' sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"16 ","pages":"e60261"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}