Background No treatment for COVID-19 is yet available; therefore, providing access to information about SARS-CoV-2, the transmission route of the virus, and ways to prevent the spread of infection is a critical sanitary measure worldwide. Serious games have advantages in the dissemination of reliable information during the pandemic; they can provide qualified content while offering interactivity to the user, and they have great reach over the internet. Objective This study aimed to develop a serious game with the purpose of providing science-based information on the prevention of COVID-19 and personal care during the pandemic while assessing players’ knowledge about COVID-19–related topics. Methods The study was conducted with the interdisciplinary collaboration of specialists in health sciences, computing, and design at the Federal University of Minas Gerais, Brazil. The health recommendations were grouped into six thematic blocks, presented in a quiz format. The software languages were based on the progressive web app development methodology with the Ionic framework, JavaScript, HTML5, cascading style sheets, and TypeScript (Angular). Open data reports of how users interact with the serious game were obtained using the Google Analytics application programming interface. The visual identity, logo, infographics, and icons were carefully developed by considering a selection of colors, typography, sounds, and images that are suitable for young audiences. Cards with cartoon characters were introduced at the end of each thematic topic to interact with the player, reinforcing their correct answers or alerting them to the need to learn more about the disease. The players’ performance was assessed by the rate of incorrect and correct answers and analyzed by linear correlation coefficient over 7 weeks. The agile SCRUM development methodology enabled quick and daily interactions of developers through a webchat and sequential team meetings. Results The game “COVID-19–Did You Know?” was made available for free on a public university website on April 1, 2020. The game had been accessed 17,571 times as of September 2020. Dissemination actions such as reports on social media and television showed a temporal correspondence with the access number. The players’ error rate in the topic “Mask” showed a negative trend (r=–.83; P=.01) over the weeks of follow-up. The other topics showed no significant trend over the weeks. Conclusions The gamification strategy for health education content on the theme of COVID-19 reached a young audience, which is considered to be a priority in the strategy of orientation toward social distancing. Specific educational reinforcement measures were proposed and implemented based on the players’ performance. The improvement in the users’ performance on the topic about the use of masks may reflect an increase in information about or adherence to mask use over time.

Background A low birth weight is an independent risk factor for adverse infant outcomes and a predictor of chronic disease in adulthood. In these situations, differentiating between prematurity and small for gestational age (SGA) or simultaneous conditions is essential to ensuring adequate care. Such diagnoses, however, depend on reliable pregnancy dating, which can be challenging in developing countries. A new medical optoelectronic device was developed to estimate gestational age (GA) at birth based on newborn skin reflection. Objective This study will aim to evaluate the device’s ability to detect prematurity or SGA, or both conditions simultaneously as well as predict short-term pulmonary complications in a cohort of low-birth-weight newborns. Methods This study protocol was designed for a multicenter cohort including referral hospitals in Brazil and Mozambique. Newborns weighing 500-2500 g will be eligible for inclusion with the best GA available, considering the limited resources of low-income countries. Comparator-GA is based on reliable last menstrual period dating or ultrasound assessment before 24 weeks’ gestation. Estimated GA at birth (Test-GA) will be calculated by applying a novel optoelectronic device to the newborn’s skin over the sole. The average difference between Test-GA and Comparator-GA will be analyzed, as will the percentage of newborns who are correctly diagnosed as preterm or SGA. In addition, in a nested case–control study, the accuracy of skin reflection in the prediction of prematurity-related respiratory problems will be evaluated. The estimated required sample size is 298 newborns. Results Teams of health professionals were trained, and standard operating procedures were developed following the good practice guidelines for the clinical investigation of medical devices for human participants. The first recruitment started in March 2019 in Brazil. Data collection is planned to end in December 2020, and the results should be available in March 2021. Conclusions The results of this clinical study have the potential to validate a new device to easily assess postnatal GA, supporting SGA identification when pregnancy dating is unreliable or unknown. Trial Registration ReBec: RBR-33rnjf; http://www.ensaiosclinicos.gov.br/rg/RBR-33rnjf/ International Registered Report Identifier (IRRID) DERR1-10.2196/16477

Background Early access to antenatal care and high-cost technologies for pregnancy dating challenge early neonatal risk assessment at birth in resource-constrained settings. To overcome the absence or inaccuracy of postnatal gestational age (GA), we developed a new medical device to assess GA based on the photobiological properties of newborns’ skin and predictive models. Objective This study aims to validate a device that uses the photobiological model of skin maturity adjusted to the clinical data to detect GA and establish its accuracy in discriminating preterm newborns. Methods A multicenter, single-blinded, and single-arm intention-to-diagnosis clinical trial evaluated the accuracy of a novel device for the detection of GA and preterm newborns. The first-trimester ultrasound, a second comparator ultrasound, and data regarding the last menstrual period (LMP) from antenatal reports were used as references for GA at birth. The new test for validation was performed using a portable multiband reflectance photometer device that assessed the skin maturity of newborns and used machine learning models to predict GA, adjusted for birth weight and antenatal corticosteroid therapy exposure. Results The study group comprised 702 pregnant women who gave birth to 781 newborns, of which 366 (46.9%) were preterm newborns. As the primary outcome, the GA as predicted by the new test was in line with the reference GA that was calculated by using the intraclass correlation coefficient (0.969, 95% CI 0.964-0.973). The paired difference between predicted and reference GAs was −1.34 days, with Bland-Altman limits of −21.2 to 18.4 days. As a secondary outcome, the new test achieved 66.6% (95% CI 62.9%-70.1%) agreement with the reference GA within an error of 1 week. This agreement was similar to that of comparator-LMP-GAs (64.1%, 95% CI 60.7%-67.5%). The discrimination between preterm and term newborns via the device had a similar area under the receiver operating characteristic curve (0.970, 95% CI 0.959-0.981) compared with that for comparator-LMP-GAs (0.957, 95% CI 0.941-0.974). In newborns with absent or unreliable LMPs (n=451), the intent-to-discriminate analysis showed correct preterm versus term classifications with the new test, which achieved an accuracy of 89.6% (95% CI 86.4%-92.2%), while the accuracy for comparator-LMP-GA was 69.6% (95% CI 65.3%-73.7%). Conclusions The assessment of newborn’s skin maturity (adjusted by learning models) promises accurate pregnancy dating at birth, even without the antenatal ultrasound reference. Thus, the novel device could add value to the set of clinical parameters that direct the delivery of neonatal care in birth scenarios where GA is unknown or unreliable. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2018-027442

IntroductionRecognising prematurity is critical in order to attend to immediate needs in childbirth settings, guiding the extent of medical care provided for newborns. A new medical device has been developed to carry out the preemie-test, an innovative approach to estimate gestational age (GA), based on the photobiological properties of the newborn’s skin. First, this study will validate the preemie-test for GA estimation at birth and its accuracy to detect prematurity. Second, the study intends to associate the infant’s skin reflectance with lung maturity, as well as evaluate safety, precision and usability of a new medical device to offer a suitable product for health professionals during childbirth and in neonatal care settings.Methods and analysisResearch protocol for diagnosis, single­group, single­blinding and single­arm multicenter clinical trial with a reference standard. Alive newborns, with 24 weeks or more of pregnancy age, will be enrolled during the first 24 hours of life. Sample size is 787 subjects. The primary outcome is the difference between the GA calculated by the photobiological neonatal skin assessment methodology and the GA calculated by the comparator antenatal ultrasound or reliable last menstrual period (LMP). Immediate complications caused by pulmonary immaturity during the first 72 hours of life will be associated with skin reflectance in a nested case–control study.Ethics and disseminationEach local independent ethics review board approved the trial protocol. The authors intend to share the minimal anonymised dataset necessary to replicate study findings.Trial registration numberRBR-3f5bm5.

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