Tag: nevin manimala

Wound Manag Prev. 2023 Mar;69(1):14-24.

ABSTRACT

BACKGROUND: Pressure injuries are associated with skin temperature changes, but little is known about skin temperature characteristics of the Kennedy Lesion (KL).

PURPOSE: The purpose of this study was to describe early skin temperature changes in KLs using long-wave infrared thermography.

METHODS: KLs were identified from chart review in 10 ICU patients. Skin assessments were performed within 24 hours of new skin discoloration. Temperature measurements were performed using a long-wave infrared thermography imaging system. Relative Temperature Differential (RTD) between the discolored area and a selected control point was calculated. RTDs of > +1.2 degrees C and < -1.2 degrees C were considered abnormal. Demographic data and observable characteristics of the KL were collected when available. Descriptive statistics (Mean plus/minus SD; % ) were used.

RESULTS: The major finding of this study was that there were no early skin temperature differences between the KLs and surrounding skin.

CONCLUSION: The early stage of the KL may be limited to microvascular injury which results in a normal skin temperature. More studies are needed to verify this finding and to ascertain whether KL skin temperature changes over time. The study also supports the bedside use of thermography in skin temperature assessment.

PMID:37014934

PLoS One. 2023 Apr 4;18(4):e0283811. doi: 10.1371/journal.pone.0283811. eCollection 2023.

ABSTRACT

While linking records across large administrative datasets [“big data”] has the potential to revolutionize empirical social science research, many administrative data files do not have common identifiers and are thus not designed to be linked to others. To address this problem, researchers have developed probabilistic record linkage algorithms which use statistical patterns in identifying characteristics to perform linking tasks. Naturally, the accuracy of a candidate linking algorithm can be substantially improved when an algorithm has access to “ground-truth” examples-matches which can be validated using institutional knowledge or auxiliary data. Unfortunately, the cost of obtaining these examples is typically high, often requiring a researcher to manually review pairs of records in order to make an informed judgement about whether they are a match. When a pool of ground-truth information is unavailable, researchers can use “active learning” algorithms for linking, which ask the user to provide ground-truth information for select candidate pairs. In this paper, we investigate the value of providing ground-truth examples via active learning for linking performance. We confirm popular intuition that data linking can be dramatically improved with the availability of ground truth examples. But critically, in many real-world applications, only a relatively small number of tactically-selected ground-truth examples are needed to obtain most of the achievable gains. With a modest investment in ground truth, researchers can approximate the performance of a supervised learning algorithm that has access to a large database of ground truth examples using a readily available off-the-shelf tool.

PMID:37014897 | DOI:10.1371/journal.pone.0283811

PLoS One. 2023 Apr 4;18(4):e0283875. doi: 10.1371/journal.pone.0283875. eCollection 2023.

ABSTRACT

INTRODUCTION: The prevalence of atrial fibrillation (AF) is increasing as the elderly population continues to increase. Chronic kidney disease, diabetes, and hypertension are known risk factors for AF. Since multimorbidity exists in chronic kidney disease, it is difficult to determine the impact of hypertension alone. Furthermore, little is known about the impact of hypertension on predicting AF in diabetic end-stage renal disease (ESRD). Here, we evaluated the effect of differential blood pressure control on AF prevalence among the diabetic ESRD population.

METHODS: From the Korean National Health Insurance Service database, 2 717 072 individuals with diabetes underwent health examinations during 2005-2019. Exactly 13 859 individuals with diabetic ESRD without a prior history of AF were selected and included in the analysis. Based on blood pressure level and previous hypertension medication history, we subdivided them into five groups: normal (normotensive), pre-hypertension, new onset hypertension, controlled hypertension, and uncontrolled hypertension. AF risk according to the blood pressure groups was estimated using Cox proportional-hazards models.

RESULTS: Among the five groups, the new onset hypertension, controlled hypertension, and uncontrolled hypertension groups showed a higher AF risk. In patients on antihypertensives, diastolic blood pressure ≥100 mmHg was significantly associated with AF risk. High pulse pressure showed a significant risk for AF in patients on antihypertensives.

CONCLUSION: In patients with diabetic ESRD, overt hypertension and a history of hypertension impacts AF. AF risk was higher in the ESRD population with diastolic blood pressure ≥100 mmHg and pulse pressure >60 mmHg.

PMID:37014888 | DOI:10.1371/journal.pone.0283875

Proc Natl Acad Sci U S A. 2023 Apr 11;120(15):e2211807120. doi: 10.1073/pnas.2211807120. Epub 2023 Apr 4.

ABSTRACT

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible-a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.

PMID:37014867 | DOI:10.1073/pnas.2211807120

JMIR Pediatr Parent. 2023 Apr 4;6:e42272. doi: 10.2196/42272.

ABSTRACT

BACKGROUND: Parents play an influential role in the health behaviors of their children, such as physical activity, dietary intake, sleep, screen time, and substance use. However, further research is needed to inform the development of more effective and engaging parent-based interventions targeting adolescent risk behaviors.

OBJECTIVE: This study aimed to assess parents’ knowledge about adolescent risk behaviors, barriers and facilitators to engaging in healthy behaviors, and preferences for a parent-based prevention intervention.

METHODS: An anonymous web-based survey was conducted from June 2022 to August 2022. Eligible participants were parents of children aged 11 to 18 years and were residing in Australia at the time of this study. The survey assessed the parents’ perceived and actual knowledge about Australian health guidelines for youth, parent and adolescent engagement in health behaviors, parenting style and attitudes, barriers and facilitators to engaging in healthy behaviors, and delivery and component preferences for a parent-based preventive intervention. Descriptive statistics and logistic regressions were conducted to analyze the data.

RESULTS: A total of 179 eligible participants completed the survey. The mean age of the parents was 42.22 (SD 7.03) years, and 63.1% (101/160) were female. Parent-reported sleep duration was high for both parents (mean 8.31, SD 1.00 hours) and adolescents (mean 9.18, SD 0.94 hours). However, the proportion of parents who reported that their child met the national recommendations for physical activity (5/149, 3.4%), vegetable intake (7/126, 5.6%), and weekend recreational screen time (7/130, 5.4%) was very low. Overall, parents’ perceived knowledge of health guidelines was moderate, ranging from 50.6% (80/158) for screen time to 72.8% (115/158) for sleep guidelines (for children aged 5-13 years). Actual knowledge was lowest for vegetable intake and physical activity, with only 44.2% (46/104) and 42% (31/74) of parents reporting correct guidelines for these behaviors, respectively. The key issues of concern reported by parents were excessive use of technology, mental health, e-cigarette use, and negative peer relationships. The top-rated delivery method for a parent-based intervention was via a website (53/129, 41.1%). The highest rated intervention component was opportunities for goal-setting (89/126, 70.7% rated very or extremely important), and other important program features were ease of use (89/122, 72.9%), paced learning (79/126, 62.7%), and appropriate program length (74/126, 58.8%).

CONCLUSIONS: The findings suggest that such interventions should be brief and web based and should aim to increase parental knowledge of health guidelines; provide opportunities for skill-building, such as goal-setting; and include effective behavior change techniques, such as motivational interviewing and social support. This study will inform the development of future parent-based preventive interventions to prevent multiple lifestyle risk behaviors among adolescents.

PMID:37014696 | DOI:10.2196/42272

JMIR Form Res. 2023 Apr 4;7:e41222. doi: 10.2196/41222.

ABSTRACT

BACKGROUND: The prepandemic period saw a rise in web-based teaching. However, web-based tools for teaching the essential clinical skill of cognitive empathy (also known as perspective taking) remain limited. More of these tools are needed and require testing for ease of use and understanding by students.

OBJECTIVE: This study aimed to evaluate the usability of the In Your Shoes web-based empathy training portal application for students using quantitative and qualitative methods.

METHODS: This 3-phase formative usability study used a mixed methods design. In mid-2021, we conducted a remote observation of student participants interacting with our portal application. Their qualitative reflections were captured, followed by data analysis and iterative design refinements of the application. Overall, 8 third- and fourth-year nursing students from an undergraduate baccalaureate program at a Canadian university, in the western province of Manitoba, were included in this study. Participants in phases 1 and 2 were remotely observed by 3 research personnel while engaged in predefined tasks. In phase 3, two student participants were asked to use the application as they liked in their own environments, after which a video-recorded exit interview with a think-aloud process was conducted as participants responded to the System Usability Scale. We calculated descriptive statistics and performed content analysis to analyze the results.

RESULTS: This small study included 8 students with a range of technology skills. Usability themes were based on participants’ comments on the application’s appearance, content, navigation, and functionality. The biggest issues that participants experienced were with navigating the application’s “tagging” features during video analysis and the length of educational material. We also observed variations in 2 participants’ system usability scores in phase 3. This may be because of their different comfort levels with technology; however, additional research is required. We made iterative refinements to our prototype application (eg, added pop-up messages and provided a narrated video on the application’s “tagging” function) based on participant feedback.

CONCLUSIONS: With increasing engagement in web-based teaching, technology has become an essential medium for receiving health care education. We developed a novel prototype application as a supplemental classroom tool to foster students’ self-directed learning of empathy. This study provided direction for refinements to optimize the usability of and satisfaction with this innovative application. Qualitative feedback revealed favorable input toward learning perspective taking place on the web and helpful recommendations for improving user experiences with the application. We could not fully assess the application’s key functions owing to the COVID-19 protocols. Thus, our next step is to obtain feedback from a larger sample of student users, whose experiences performing “live” video capture, annotation, and analysis will be more authentic and wholesome with the refined application. We discuss our findings in relation to research on nursing education, perspective taking, and adaptive e-learning.

PMID:37014693 | DOI:10.2196/41222

J Med Internet Res. 2023 Apr 4;25:e39259. doi: 10.2196/39259.

ABSTRACT

BACKGROUND: The majority of Germans see a deficit in information availability for choosing a physician. An increasing number of people use physician rating websites and decide upon the information provided. In Germany, the most popular physician rating website is Jameda.de, which offers monthly paid membership plans. The platform operator states that paid memberships have no influence on the rating indicators or list placement.

OBJECTIVE: The goal of this study was to investigate whether a physician’s membership status might be related to his or her quantitative evaluation factors and to possibly quantify these effects.

METHODS: Physician profiles were retrieved through the search mask on Jameda.de website. Physicians from 8 disciplines in Germany’s 12 most populous cities were specified as search criteria. Data Analysis and visualization were done with Matlab. Significance testing was conducted using a single factor ANOVA test followed by a multiple comparison test (Tukey Test). For analysis, the profiles were grouped according to member status (nonpaying, Gold, and Platinum) and analyzed according to the target variables-physician rating score, individual patient’s ratings, number of evaluations, recommendation quota, number of colleague recommendations, and profile views.

RESULTS: A total of 21,837 nonpaying profiles, 2904 Gold, and 808 Platinum member profiles were acquired. Statistically significant differences were found between paying (Gold and Platinum) and nonpaying profiles in all parameters we examined. The distribution of patient reviews differed also by membership status. Paying profiles had more ratings, a better overall physician rating, a higher recommendation quota, and more colleague recommendations, and they were visited more frequently than nonpaying physicians’ profiles. Statistically significant differences were found in most evaluation parameters within the paid membership packages in the sample analyzed.

CONCLUSIONS: Paid physician profiles could be interpreted to be optimized for decision-making criteria of potential patients. With our data, it is not possible to draw any conclusions of mechanisms that alter physicians’ ratings. Further research is needed to investigate the causes for the observed effects.

PMID:37014690 | DOI:10.2196/39259

J Med Internet Res. 2023 Apr 4;25:e40337. doi: 10.2196/40337.

ABSTRACT

BACKGROUND: This paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owing to their promise as well as challenges. For AI to fulfill its potential, it must not only be adopted by physicians and health providers but also by patients and other members of the public.

OBJECTIVE: This study reviews the existing survey research on the United States’ public attitudes toward AI in health care and reveals the challenges and opportunities for more effective and inclusive engagement on the use of AI in health settings.

METHODS: We conducted a systematic review of public opinion surveys, reports, and peer-reviewed journal articles published on Web of Science, PubMed, and Roper iPoll between January 2010 and January 2022. We include studies that are nationally representative US public opinion surveys and include at least one or more questions about attitudes toward AI in health care contexts. Two members of the research team independently screened the included studies. The reviewers screened study titles, abstracts, and methods for Web of Science and PubMed search results. For the Roper iPoll search results, individual survey items were assessed for relevance to the AI health focus, and survey details were screened to determine a nationally representative US sample. We reported the descriptive statistics available for the relevant survey questions. In addition, we performed secondary analyses on 4 data sets to further explore the findings on attitudes across different demographic groups.

RESULTS: This review includes 11 nationally representative surveys. The search identified 175 records, 39 of which were assessed for inclusion. Surveys include questions related to familiarity and experience with AI; applications, benefits, and risks of AI in health care settings; the use of AI in disease diagnosis, treatment, and robotic caregiving; and related issues of data privacy and surveillance. Although most Americans have heard of AI, they are less aware of its specific health applications. Americans anticipate that medicine is likely to benefit from advances in AI; however, the anticipated benefits vary depending on the type of application. Specific application goals, such as disease prediction, diagnosis, and treatment, matter for the attitudes toward AI in health care among Americans. Most Americans reported wanting control over their personal health data. The willingness to share personal health information largely depends on the institutional actor collecting the data and the intended use.

CONCLUSIONS: Americans in general report seeing health care as an area in which AI applications could be particularly beneficial. However, they have substantial levels of concern regarding specific applications, especially those in which AI is involved in decision-making and regarding the privacy of health information.

PMID:37014676 | DOI:10.2196/40337

JMIR Res Protoc. 2023 Apr 4;12:e45178. doi: 10.2196/45178.

ABSTRACT

BACKGROUND: Women often experience many unique health issues and conditions throughout their working lives. The Internet of Things (IoT) is a system of interrelated digital devices that can enable data exchanges over a network without human-to-human or human-to-computer interaction. The usage of applications and IoT in improving women’s health has recently increased worldwide. However, there has been no consensus on the effectiveness of IoT in improving women’s health outcomes.

OBJECTIVE: This systematic review and network meta-analysis (NMA) aims to assess and synthesize the role of apps and the IoT in improving women’s health and to identify the ranking of interventions for ensuring better results for each stated outcome.

METHODS: Our systematic review and NMA will be conducted in accordance with the guidelines of the Cochrane Handbook. We will comprehensively search the following electronic databases: PubMed (including MEDLINE), Cochrane Central Register of Controlled Trials, Embase, Cumulative Index to Nursing and Allied Health Literature (ie, CINAHL), PsycINFO, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry, along with other resources to identify relevant randomized controlled trials that have assessed the effects of various apps and the IoT with regard to improving working-aged women’s health in high-income countries. We will segment and analyze the results of the included studies based on age categories (women undergoing a preconception period, those undergoing gestational and postpartum periods, and menopausal and pre- and postmenopausal women) and the medical history (women who have a specific medical condition-eg, cancer or diabetes-and women who do not have them) separately. Two independent reviewers will perform the study selection, data extraction, and quality assessment. Our primary outcomes include health status, well-being, and quality of life. We will perform pairwise meta-analysis and NMA to estimate the direct, indirect, and relative effects of apps and the IoT on women’s health outcomes. We will also assess the hierarchy of interventions, statistical inconsistencies, and certainties of evidence for each outcome.

RESULTS: We plan to conduct the search in January 2023 and are currently discussing search strategies with the literature search specialists. The final report is planned for submission to a peer-reviewed journal in September 2023.

CONCLUSIONS: To the best of our knowledge, this review will be the first to identify the ranking of IoT intervention for ensuring working-aged women’s health outcomes. These findings may be of great use to researchers, policy makers, and others with an interest in the field.

TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42022384620; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=384620.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45178.

PMID:37014674 | DOI:10.2196/45178

Transl Vis Sci Technol. 2023 Apr 3;12(4):2. doi: 10.1167/tvst.12.4.2.

ABSTRACT

PURPOSE: To design and validate a high-sensitivity semiautomated algorithm, based on adaptive contrast image, able to identify and quantify tear meniscus height (TMH) from optical coherence tomography (OCT) images by using digital image processing (DIP) techniques.

METHODS: OCT images of the lacrimal meniscus of healthy patients and with dry eye are analyzed by our algorithm, which is composed of two stages: (1) the region of interest and (2) TMH detection and measurement. The algorithm performs an adaptive contrast sequence based on morphologic operations and derivative image intensities. Trueness, repeatability, and reproducibility for TMH measurements are computed and the algorithm performance is statistically compared against the corresponding negative obtained manually by using a commercial software.

RESULTS: The algorithm showed excellent repeatability supported by an intraclass correlation coefficient equal to 0.993, a within-subject standard deviation equal to 9.88, and a coefficient of variation equal to 2.96%, and for the reproducibility test, the results did not show a significant difference as the mean value was 244.4 ± 114.9 µm for an expert observer versus 242.4 ± 111.2 µm for the inexperienced observer (P = 0.999). The method strongly suggests the algorithm can predict measurements that are manually performed with commercial software.

CONCLUSIONS: The presented algorithm possess high potential to identify and measure TMH from OCT images in a reproducible and repeatable way with minimal dependency on user.

TRANSLATIONAL RELEVANCE: The presented work shows a methodology on how, by using DIP, it is possible to process OCT images to calculate TMH and aid ophthalmologists in the diagnosis of dry eye disease.

PMID:37014649 | DOI:10.1167/tvst.12.4.2