Tracking sentiments toward fat acceptance over a decade on Twitter

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either “fat acceptance” or “#fatacceptance” from 2010–2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet’s stance toward the FA movement. Our model achieved nearly 80% average precision and recall in classifying “supporting” and “opposing” tweets. Applying this model to the complete dataset, we observed that the majority of tweets at the beginning of the last decade supported FA, but sentiment trended downward until 2016, when support was at its lowest. Overall, public sentiment is negative across Twitter. Users who tweet more about FA or use FA-related hashtags are more supportive than general users. Our findings reveal both challenges to and strengths of the modern FA movement, with implications for those who wish to reduce societal weight stigma.

Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution

A deep integration of routine care and research remains challenging in many respects. We aimed to show the feasibility of an automated transformation and transfer process feeding deeply structured data with a high level of granularity collected for a clinical prospective cohort study from our hospital information system to the study’s electronic data capture system, while accounting for study-specific data and visits. We developed a system integrating all necessary software and organizational processes then used in the study. The process and key system components are described together with descriptive statistics to show its feasibility in general and to identify individual challenges in particular. Data of 2051 patients enrolled between 2014 and 2020 was transferred. We were able to automate the transfer of approximately 11 million individual data values, representing 95% of all entered study data. These were recorded in n = 314 variables (28% of all variables), with some variables being used multiple times for follow-up visits. Our validation approach allowed for constant good data quality over the course of the study. In conclusion, the automated transfer of multi-dimensional routine medical data from HIS to study databases using specific study data and visit structures is complex, yet viable.

Development and validation of a progressive web application to educate partial denture wearers

Effective delivery of post-insertion instructions is essential for denture care and oral health. This study aimed to develop a progressive web application (PWA) to educate patients’ chairside and serve as a reference material. A need analysis was conducted before prototype development. Subsequently, the prototype was subjected to content verification, design appraisal and usability testing. The results of usability testing revealed a user task success rate of 94.4%, with an adjusted Wald 95% confidence interval of 83–100%. User satisfaction assessed using the Single Ease Questionnaire and System Usability Score reported a mean score of 6.13 (95% CI: 5.69–6.55) and 85.9 (95% CI: 82.2–89.6), respectively, indicating good usability. This study highlights the systematic approach of developing an evidence-based educational PWA to meet the usability standards for mobile applications. This PWA is useful in clinical studies to explore mobile technologies’ potential in educating denture wearers, especially in the older population.

Trend research of vitamin D receptor: Bibliometric analysis

Studies on vitamin D receptor (VDR) and its association with multiple disorders are expanding. This bibliometric study aims to find and summarize VDR-related publications, and compare them across various countries, organizations, and journals to demonstrate trends in VDR research. VOSviewer and Excel 2019 were used to classify and summarize Web of Science articles from 1900 to mid-2021. Total records of 8762 articles were analyzed, and maps of co-citations bibliometric keywords co-occurrence were designed. In conclusion, relative research interest and published papers related to VDR were growing in the past 30 years. The United States of America dominates the research regarding VDR. The highest quality of VDR research was achieved by the University of California System, University of Wisconsin System, and Harvard University. J Steroid Biochem Mol Biol, PLoS One, and J Biol Chem are the leading three productive journals on VDR. Various aspects of vitamin D deficiency associated disorders and genetic studies regarding VDR, including single nucleotide polymorphism, gene variants, epigenome, long non-coding ribonucleic acid (lncRNA), and small nucleolar RNA host gene 6 are potentially the recent research hotspot in this field. Moreover, coronavirus disease, polycystic ovary syndrome, non-alcoholic fatty liver disease, gut microbiota, gestational diabetes, systemic sclerosis, and chemoresistance are the trending medical conditions associated with VDR.

The smart pill sticker: Introducing a smart pill management system based on touch-point technology

Older adults tend to suffer from multi-morbidity, requiring complex treatment methodologies demanding poly-pharmacy. The increasing medication usage can tend towards the mismanagement of prescriptions and irregular or faulty administration. Thus, there arises an urgent need for a proper pill management system for these prescribed medicines. To tackle this grave concern, we propose a mobile, cost-effective, robust, and easy to use solution involving the extension to the human body-smartphones and conductive stickers. The technology utilizes a unique combination of touch-points on the smartphone screen to recognize the medication and give information regarding the proper usage and dosage and gives a reminder of the intake of the medicine. Our tool is comprised of two components—(1) the conductive ink stickers containing a unique combination of conductive inks to be applied to the pill container and (2) the mobile application utilizing touch-points generated by the conductive ink sticker to give information of the corresponding medicine. The following functionalities could be performed by the application-detection of pill container: providing essential information about pill container and dosage; keeping a count of pills already taken, to be taken and remaining pills; reordering the medication and reminding about the medicine intake at the correct designated time.

Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers

Wearable healthcare devices offer tremendous promise to effectively track and improve the well-being of older adults. Yet, little is known about the use of wearable devices by older adults. Drawing upon a national survey in US with 1481 older adults, we examine the use of wearable healthcare devices and the key predictors of use viz. sociodemographic factors, health conditions, and technology self-efficacy. We also examine if the predictors are associated with elders’ willingness to share health data from wearable devices with healthcare providers. We find low level of wearable use (17.49%) among US older adults. We find significant positive associations between technology self-efficacy, health conditions, and demographic factors (gender, race, education, and annual household income) and use of wearable devices. Men were less likely (OR = 0.62, 95% CI 0.36–1.04) and Asians were more likely (OR = 2.60, 95% CI 0.89–7.64) to use wearables, as did healthy adults (OR = 1.98, 95% CI 1.37–2.87). Those who electronically communicated with their doctors (OR = 1.86, 95% CI 1.16–2.97), and those who searched online for health information (OR = 1.79, 95% CI 1.03–3.10) were more likely to use wearables. Though 80.15% of wearable users are willing to share health data with providers, those with greater technology self-efficacy and favorable attitudes toward exercise are more willing.

Identifying data elements and key features of a mobile-based self-care application for patients with COVID-19 in Iran

Mobile Health applications have shown different usages in the COVID-19 pandemic, which consisted of empowering patient’s awareness, promoting patient’s self-care, and self-monitor behaviors. The purpose of this study is to identify key features and capabilities of a mobile-based application for self-care and self-management of people with COVID-19 disease. This study was a descriptive-analytical study that was conducted in two main phases in 2020. In the first phase, a literature review study was performed. In the second phase, using the information obtained from the review of similar articles, a questionnaire was designed to validate identified requirements. Based on the results of the first phase, 53 data elements and technical key features for mobile-based self-care application for people with COVID-19 were identified. According to the statistical population, 11 data elements for demographic requirements, 11 data elements for clinical requirements, 15 data elements for self-care specifications, and 16 features for the technical capability of this app were determined. Most of the items were selected by infectious and internal medicine specialists (94%). This study supports that the use of mobile-based applications can play an important role in the management of this disease. Software design and development could help manage and improve patients’ health status.

Teams managing civilian and military complex trauma: What are the competencies required in austere environments and the potential of simulation technology to address them?

Surgical training in civilian hospitals may not be sufficient for managing complex trauma in a setting where such care is not commonly practiced. Understanding the challenges that civilian teams face when moving to austere environments can inform the competencies that need to be trained. The aim of this study was to explore the competencies required in austere environments for teams managing complex trauma, and how they can be trained with simulation technologies. Ethnographic field observations were conducted, and field notes were synthesized. The field notes were structured with the elements of Activity Theory to generate the teams’ competencies that need to be trained. A literature review was conducted to verify the results and identify examples of relevant simulation modalities. The analysis resulted in a structured list of competencies for civilian teams to manage complex trauma in an austere environment and recommendations which simulation technologies could be used in training of those competencies based on published studies. Our study contributes to understanding the challenges that civilian teams face when operating in an austere environment. A systematized list of competencies with suggested simulation technologies directs future research to improve quality of complex trauma training in civilian and military collaboration.

A low-resource digital infrastructure to streamline viewing and interpretation of radiographic images: A case study of Uganda’s hospital-wide environment

Picture Archiving and Communication Systems (PACS) are said to improve patient quality of care through timely access to radiological images by clinicians. However, they are costly to be considered for hospital wide environment in low income countries. Ordinary core i3 computer systems (PCs) can provide an affordable and faster alternative solution for PACS workstations. This comparative study assessed the diagnostic accuracy, image quality of ordinary PC systems versus PACS workstations and patient turnaround time (PTAT). Forty images were randomly obtained and viewed by four raters from both PACS and PC. The findings showed modest agreement among raters (kappa 0.644 for PACS and 0.5164 PC) with acceptable diagnostic accuracy for PC (AUC = 0.7990), 97.5% reproduction of images on PC and significant reduction in PTAT after a switch to PC (4.8 min), p < 0.001, suggesting that PC display can improve quality of health care services through timely access to radiographic images.