Push Notifications Research Articles

Time Efficiency and Cost-Benefit Analysis of New Student Admissions Mobile Application: A Case Study in A Vocational School Palembang

This study aims to analyze the effectiveness of using an Android mobile application in the new student admission system at a vocational school. The application is designed to replace the manual registration methods, which were previously conducted through Google Forms and WhatsApp communication. By integrating push notifications using Firebase Cloud Messaging (FCM) and a MySQL database, the system enables real-time communication between administrators and prospective students, as well as the automation of admission data management. The Time Efficiency method is used to measure the time saved in the registration and notification process compared to the manual method. Meanwhile, the Cost-Benefit Analysis method is applied to evaluate the ratio between the system implementation costs and the benefits produced, such as reduced operational costs, increased registration speed, and improved administrative staff efficiency.

The Medium is the Message: How Secure Messaging Apps Leak Sensitive Data to Push Notification Services

Like most modern software, secure messaging apps rely on thirdparty components to implement important app functionality. Although this practice reduces engineering costs, it also introduces the risk of inadvertent privacy breaches due to misconfiguration errors or incomplete documentation. Our research investigated secure messaging apps' usage of Google's Firebase Cloud Messaging (FCM) service to send push notifications to Android devices. We analyzed 21 popular secure messaging apps from the Google Play Store to determine what personal information these apps leak in the payload of push notifications sent via FCM. Of these apps, 11 leaked metadata, including user identifiers (10 apps), sender or recipient names (7 apps), and phone numbers (2 apps), while 4 apps leaked the actual message content. Furthermore, none of the data we observed being leaked to FCM was specifically disclosed in those apps' privacy disclosures. We also found several apps employing strategies to mitigate this privacy leakage to FCM, with varying levels of success. Of the strategies we identified, none appeared to be common, shared, or well-supported. We argue that this is fundamentally an economics problem: incentives need to be correctly aligned to motivate platforms and SDK providers to make their systems secure and private by default.

Willingness to Share Debunking Information on Social Media — An Exploration of Influencing Factors from the Perspective of Media Affordance Theory

With the rapid advancement of technology, social media platforms have become crucial channels for the dissemination of refutation information. This study investigates the underlying mechanisms through which media affordances influence users' willingness to share such information, adopting a technological perspective. The aim is to understand how the affordances provided by media platforms enhance the effectiveness of refutation efforts, enabling better utilization of these technological means. Building on the theoretical frameworks of media affordance and the Stimulus-Organism-Response (S-O-R) model, this research develops a model to examine social media users' willingness to share refutation information. Through a survey, 308 valid questionnaires were collected and analyzed using structural equation modeling. The results reveal that media flexibility, user agency, emotional expressiveness, social connectivity, and 24/7 push notifications—representing five key dimensions of social media platforms—significantly and positively impact users' perceptions of refutation information and their willingness to share it.

User Experience of a Large-Scale Smartphone-Based Observational Study in Multiple Sclerosis: Global, Open-Access, Digital-Only Study.

The Floodlight Open app is a digital health technology tool (DHTT) that comprises remote, smartphone sensor-based tests (daily activities) for assessing symptoms of multiple sclerosis (MS). User acquisition, engagement, and retention remain a barrier to successfully deploying such tools. This study aims to quantitatively and qualitatively investigate key user experience (UX) factors associated with the Floodlight Open app. Floodlight Open is a global, open-access, digital-only study designed to understand the drivers and barriers in deploying a DHTT in a naturalistic setting without supervision and onboarding by a clinician. Daily activities included tests assessing cognition (Information Processing Speed and Information Processing Speed Digit-Digit), hand-motor function (Pinching Test and Draw a Shape Test), and postural stability and gait (Static Balance Test, U-Turn Test, and Two-Minute Walk Test [2MWT]). All daily activities except the 2MWT were taken in a fixed sequence. Qualitative UX was studied through semistructured interviews in a substudy of US participants with MS. The quantitative UX analysis investigated the impact of new UX design features on user engagement and retention in US participants for 3 separate test series: all daily activities included in the fixed sequence (DA), all daily activities included in the fixed sequence except the Static Balance Test and U-Turn Test (DAx), and the 2MWT. The qualitative UX substudy (N=22) revealed the need for 2 new UX design features: a more seamless user journey during the activation process that eliminates the requirement of switching back and forth between the app and the email that the participants received upon registration, and configurable reminders and push notifications to help plan and remind the participants to complete their daily activities. Both UX design features were assessed in the quantitative UX analysis. Introducing the more seamless user journey (original user journey: n=608; more seamless user journey: n=481) improved the conversion rate of participants who enrolled in the study and proceeded to successfully activate the app from 53.9% (328/608) to 74.6% (359/481). Introducing reminders and push notifications (with reminders and notifications: n=350; without reminders and notifications: n=172) improved continuous usage time (proportion of participants with ≥3 consecutive days of usage: DA and DAx: ~30% vs ~12%; 2MWT: ~30% vs ~20%); test completion rates (maximum number of test series completed: DA: 279 vs 64; DAx: 283 vs 126; 2MWT: 302 vs 76); and user retention rates (at day 30: DA: 53/172, 30.8% vs 34/350, 9.7%; DAx: 53/172, 30.8% vs 60/350, 17.1%; 2MWT: 39/172, 22.6% vs 22/350, 6.2%). Inactivity times remained comparable. The remote assessment of MS with DHTTs is a relatively nascent but growing field of research. The continued assessment and improvement of UX design features can play a crucial role in the successful long-term adoption of new DHTTs.

AI detection of malicious push notifications in augmented reality in the workplace

Distraction caused by the visual processing of multiple objects during augmented reality (AR) immersion could make users more susceptible to malicious push notifications, thus potentially exposing organisations to unwitting insider threats. This case study consulted four experts in the field of AR application development to design a proposed artificial intelligence (AI) equipped feature that could detect possibly malicious artefacts entering the user’s line of sight during partial immersion in an augmented reality application at the workplace. Participants included a business partner at an AR company, a security engineering manager, an AI engineer focused on machine learning (ML) and a data analytics specialist. The case study determined that a security application natively implemented into the device could use heuristic analysis of user screen captured activity to assess potentially malicious push notifications in real time.

What Drives Deep Reader Engagement with Socially-Generated Content? The Critical Role of Dual-Layer Information

Social media platforms have rapidly gained popularity due to features such as personalized recommendations and real-time push notifications, attracting a large number of creators to produce content. However, with the explosive growth of content and information overload, readers’ expectations for content and interaction quality have increased, making it increasingly difficult to engage them deeply. This poses management challenges to the platform’s ecosystem, such as high visitor volume but low retention rates. This study, leveraging the latest GPT technology, provides a more scientific approach to content metrics and a detailed exploration of interaction categories to examine the impact of information, authors, and interactions on reader engagement behavior. It particularly considers the effects of internal and external layers of social-generated content and different levels of reader engagement. Combining real community data, it is found that the impact of content information is the most crucial, with this impact being distributed across content and interaction aspects. It is suggested that social media platforms should strive to balance vivacity and informativeness when producing content. Additionally, the role of content, authors, and interactions varies across different product categories, so differentiated strategies should be employed accordingly.

Mobile Application to Assist Traffic Officers in Managing Congestion

Peak-time traffic congestion on high-level roads has persisted for decades, significantly affecting the environment, drivers’ health, and the countries' economy. During peak hours, traffic officers can be seen managing traffic flow in congested areas, making significant contributions to alleviating congestion hotspots. Despite, numerous research findings on traffic congestion, few have addressed the specific role played by traffic officers. Therefore, the study aims to introduce an application to aid traffic officers in alleviating congestion on High–Level Road by proposing appropriate solutions. Initially, a structured questionnaire was distributed physically among 51 police officers stationed at 3 randomly selected locations along High-Level Road. Survey data analysis at a 95% confidence interval, revealed majority of officers identified the morning peak hour period as 6:00 am to 9:00 am, Furthermore, officers expressed a need for manual confirmation and selection of all duty points, push notifications, projected traffic for 15 min intervals, three modes of traffic data presentation (layer, chart, text) and sorting options for traffic details based on the nature of traffic. Following Agile methodologies, a prototype application was meticulously developed for this purpose utilizing daily backup traffic data from Google Maps, and APIs to predict traffic conditions. This study aims to enhance congestion management decision–making by furnishing real-time suggestions to officers.

Estimating causal effects for binary outcomes using per-decision inverse probability weighting.

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW." The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.

Feasibility and Acceptability of a Prospective Syphilis Sexual Network Study for Sexual Minority Men.

In preparation for a prospective syphilis network study of sexual minority men, we conducted a mixed-methods formative study with the following objectives: a) assess acceptability of respondent driven sampling (RDS), b) assess acceptability of study procedures, c) social network seed selection, and d) pilot an ecological momentary assessment (EMA) study to assess social networking, sexual and substance use behavior. We conducted in-depth interviews with eight providers serving sexual minority men and five focus group discussions with 34 sexual minority men, prioritizing four target populations: young Black sexual minority men, 2) on PrEP, 3) living with HIV, 4) not engaged in care. The 4-week EMA pilot was conducted with 40 sexual minority men. EMA survey responses were analyzed to evaluate how different compensation levels influenced response rates. Brief exit surveys were used to assess EMA app acceptability. Primary themes identified through qualitative data collection: 1) importance of developing trust and maintaining confidentiality during proposed recruitment activities, 2) importance of compensating participants appropriately for study activities, and 3) cultural considerations for increasing visibility and participation of young Black sexual minority men. All EMA participants reported being "completely comfortable" reporting sexual behavior through the app. Most (78%) preferred the app to in-person interviews. Several participants identified technical issues with the app, including not receiving push notifications and spontaneous closure. This mixed-methods formative study allowed for adjustments to and tailoring of the planned network study, including recruitment protocols, compensation type and amount, and EMA survey wording and response items.

Relating caregiver experiences to personalized "push" content in mobile applications among caregivers of pediatric patients with oncology conditions.

With the evolution of data algorithms and personalized push systems in mobile applications, patients who have searched for disease-related information may repeatedly receive similar content on app homepages or through notifications. This study aims to assess the influence of health-related content delivered through mobile applications on the anxiety and depression levels of caregivers of pediatric oncology patients. A survey consisting of 16 questions was conducted among 91 caregivers of pediatric oncology patients at the Children's Hospital affiliated with Chongqing Medical University. The questionnaire was designed by oncologists and the Hospital Anxiety and Depression Scale was used to assess the caregivers' psychological states. The study found that 31.5% of caregivers exhibited borderline anxiety symptoms, while 20.2% displayed borderline depression symptoms. Caregivers who noticed changes in homepage recommendations reported higher levels of anxiety (p=.004) and depression (p=.034). Additionally, 50.6% occasionally felt anxious or uneasy due to personalized notifications and 19.1% frequently felt this way. Moreover, 53.9% of the caregivers reported a negative impact on their emotions or daily life. Personalized push notifications related to disease information in mobile applications can impose a significant psychological burden on patients and their caregivers. Mobile application developers and healthcare providers must strengthen their support in the digital health domain to enhance the emotional well-being of cancer patients and their caregivers.

Feedback and Financial Incentives for Reducing Cell Phone Use While Driving

Handheld phone use while driving is a major factor in vehicle crashes. Scalable interventions are needed to encourage drivers not to use their phones. To test whether interventions involving social comparison feedback and/or financial incentives can reduce drivers' handheld phone use. In a randomized clinical trial, interventions were administered nationwide in the US via a mobile application in the context of a usage-based insurance program (Snapshot Mobile application). Customers were eligible to be invited to participate in the study if enrolled in the usage-based insurance program for 30 to 70 days. The study was conducted from May 13 to June 30, 2019. Analysis was completed December 22, 2023. Participants were randomly assigned to 1 of 6 trial arms for a 7-week intervention period: (1) control; (2) feedback, with weekly push notification about their handheld phone use compared with that of similar others; (3) standard incentive, with a maximum $50 award at the end of the intervention based on how their handheld phone use compared with similar others; (4) standard incentive plus feedback, combining interventions of arms 2 and 3; (5) reframed incentive plus feedback, with a maximum $7.15 award each week, framed as participant's to lose; and (6) doubled reframed incentive plus feedback, a maximum $14.29 weekly loss-framed award. Proportion of drive time engaged in handheld phone use in seconds per hour (s/h) of driving. Analyses were conducted with the intention-to-treat approach. Of 17 663 customers invited by email to participate, 2109 opted in and were randomized. A total of 2020 drivers finished the intervention period (68.0% female; median age, 30 [IQR, 25-39] years). Median baseline handheld phone use was 216 (IQR, 72-480) s/h. Relative to control, feedback and standard incentive participants did not reduce their handheld phone use. Standard incentive plus feedback participants reduced their use by -38 (95% CI, -69 to -8) s/h (P = .045); reframed incentive plus feedback participants reduced their use by -56 (95% CI, -87 to -26) s/h (P < .001); and doubled reframed incentive plus feedback participants reduced their use by -42 s/h (95% CI, -72 to -13 s/h; P = .007). The 5 active treatment arms did not differ significantly from each other. In this randomized clinical trial, providing social comparison feedback plus incentives reduced handheld phone use while individuals were driving. ClinicalTrials.gov Identifier: NCT03833219.

Enhancing Secure Messaging in Electronic Health Records: Evaluating the Impact of Emoji Chat Reactions on the Volume of Interruptive Notifications

Abstract Background Electronic health record secure messaging (EHRSM) is an increasingly utilized tool for communication among clinicians. However, there is concern about the growing quantity of disruptions it presents via interruptive notification. Objectives The primary aim of this study is to assess whether introducing emoji reactions, which do not trigger push notifications in EHRSM, can alleviate the burden of interruptive notifications. The second aim is to use messaging notification metadata to identify subgroups that might benefit from targeted interventions to aid the adoption of this innovation. Methods We implemented the emoji reaction feature into EHRSM across a large academic health system. We evaluated the volume of push notifications 11 weeks before (pre-emoji period) and after (post-emoji period) introducing emoji reactions in EHRSM. Notification metadata was categorized by user type, and users were stratified based on notification volume. Results There were 1,387,506 fewer push notifications in the post-emoji period (a decrease of 4.7%). Subgroups of users with increasing mean daily push notifications in the pre-emoji period were associated with decreasing mean daily push notifications in the post-emoji period. Among the eight user subgroups, six experienced a significant reduction in interruptive notifications, with the pharmacy and “other” subgroups not observing a reduction. Users in the top quartile of notification volume saw the greatest reduction in burden across each user subgroup. Conclusion Integrating emoji reactions into EHRSM across a large academic health system significantly reduced the burden of push notifications among EHRSM users. Utilizing messaging notification metadata allowed us to identify subgroups that require additional intervention.

IoT-based Environmental Monitoring with Data Analysis of Temperature, Humidity, and Air Quality

Environment monitoring has been linked to the use of the IoT. To raise awareness for the environment, the IoT system is built as an instrument tool based on a Prototyping model with an experimental approach. The hardware consists of sensors, microcontrollers, a Wi-Fi modem, powered with solar cells, and electricity integrated with IoT platforms Blynk and ThingSpeak. The prototype detectors were installed in two different locations at the Universitas Klabat. The IoT systems can store data, display information, and send push notifications as alerts to the user’s smartphone when critical conditions emerge. In the two locations for a specified time of May 2023, the data analysis shows average temperatures are 28,39˚C and 28,44˚C, where 28˚C is the optimal value. The average humidity shows 90,18%RH and 85,28%RH. These humidity values are critical because the humidity outside 40-60%RH can significantly impact health. The average air quality shows 59,62 AQI as “moderate” and 3.7 AQI as “good”. While “good” air quality is the best, “moderate” is safe because only when a value higher than 100 is unhealthy. The IoT system can help to monitor and provide real-time information about the environmental parameters.

Overcoming Communication Barriers in Clinical Care: A Digital Translation Platform.

Language barriers in healthcare can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients' relatives for interpretation, compromising the quality of care. This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating: 1. how healthcare professionals overcome language barriers and their acceptance of an on-demand video telephony platform, 2. the feasibility of the platform during medical consultations, and 3. identifying potential challenges for future development. The study included ethnographic interviews with healthcare professionals and an observational pilot to assess the use of the Tranlatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system's backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that healthcare professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer non-professional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari and Arabic. Ethnographic research conducted by interviewing healthcare professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools like Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5, 31.25%) and emergency (4, 25%). Challenges were identified around translator availability, with 23 (59%) of requests going unanswered, which was further evidenced by user feedback. The pilot study demonstrates the feasibility of the Translatly platform in real-world healthcare settings. It shows potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development.

A novel smartphone application to prevent postpartum depression adapted from an evidence-based cognitive behavioral therapy program: Individual end-user interviews to optimize the intervention.

Postpartum depression (PPD) is more common among pregnant patients who have unmet social needs, such as financial stress or food insecurity, compared to those who do not. Mothers and Babies (MB) is a cognitive behavioral therapy (CBT)-based program that prevents up to 50% of de novo PPD when provided in-person to low-income Spanish- and English-speaking pregnant people without depression. MB's reach has been limited by the need for trained personnel to support the program. Transforming MB into a smartphone application (app) may mitigate this key barrier to scaling MB. To utilize qualitative data from target end-users to create and optimize MBapp, a novel app centered on the MB program. Draft wireframes of MBapp were created in English and Spanish with CBT-based modules adapted from MB. These wireframes included several features shown previously to sustain app engagement: 1) push notifications delivered at participant-preferred times; 2) text-, graphic-, and video-based content; and 3) gamification with digital rewards for app engagement. English- or Spanish-speaking individuals with public health insurance who were between 32 weeks' gestation and six months postpartum and owned smartphones were eligible to consent for individual in-depth interviews. Individuals with prior or current depression were excluded. Interviews were recorded, transcribed, and analyzed using deductive and inductive codes to characterize opinions about MBapp and perceptions of challenges and facilitators of use of MBapp or other perinatal or mental health apps. End-user feedback led to major modifications to the wireframes. Each of these changes was categorized according to the Framework for Modification and Adaptation (FRAME), an established method of systematically reporting adaptations and modifications to evidence-based interventions via end-user feedback. Recruitment ceased with content saturation, defined as three successive participants providing only positive feedback on MBapp's wireframe, without further suggestions for improvement. 25 interviews were completed. Participants were racially and ethnically diverse, generally representing our target end-user population, and 48% of interviews were conducted in Spanish. Participants' suggestions to improve MBapp were categorized within the FRAME as adaptations that improved either content or context to optimize reach, retention, engagement, and fit for end users. Specifically, the following features were added to MBapp secondary to end-user feedback: 1) audio narration; 2) "ask a clinician" non-urgent questions; 3) on-demand module summaries accessible upon module completion; and 4) choice to defer assessments and start the next module. Participants also provided insights into features of perinatal or mental health apps they found appealing or unappealing to understand preferences, challenges, and (non)negotiables for MBapp. Adapting MBapp to incorporate end users' perspectives optimized our digital PPD prevention intervention, ideally increasing its appeal to future users. Our team's next steps will confirm that MBapp is a feasible, acceptable intervention among English- and Spanish-speaking perinatal people at risk of PPD.

A Mobile Post Anesthesia Care Unit Order Reminder System Improves Timely Order Entry

Transition to the postanesthesia care unit (PACU) requires timely order placement by anesthesia providers. Computerized ordering enables automated order reminder systems, but their value is not fully understood. We performed a single-center, retrospective cohort study to estimate the association between automated PACU order reminders and primary outcomes (1) on-time order placement and (2) the degree of delay in placement. As a secondary post-hoc analysis, we studied the association between late order placement and PACU outcomes. We included patients with a qualifying postprocedure order from January 1, 2019, to May 31, 2023. We excluded cases transferred directly to the ICU, whose anesthesia provider was involved in the pilot testing of the reminder system, or those with missing covariate data. Order reminder system usage was defined by the primary attending anesthesiologist’s receipt of a push notification reminder on the day of surgery. We estimated the association between reminder system usage and timely order placement using a logistic regression. For patients with late orders, we performed a survival analysis of order placement. The significance level was 0.05. Patient (e.g., age, race), procedural (e.g., anesthesia duration), and provider-based (e.g., ordering privileges) variables were used as covariates within the analyses. Reminders were associated with 51% increased odds of order placement prior to PACU admission (Odds Ratio: 1.51; 95% Confidence Interval: 1.43, 1.58; p ≤ 0.001), reducing the incidence of late PACU orders from 17.5% to 12.6% (p ≤ 0.001). In patients with late orders, the reminders were associated with 10% quicker placement (Hazard Ratio: 1.10; 95% CI 1.05, 1.15; p < 0.001). On-time order placement was associated with decreased PACU duration (p < 0.001), decreased odds of peak PACU pain score (p < 0.001), and decreased odds of multiple administration of antiemetics (p = 0.02). An order reminder system was associated with an increase in order placement prior to PACU arrival and a reduction in delay in order placement after arrival.

Mobile-based Electronic Health Card Application using Realtime Push Notification

Mobile-based Electronic Health Card Application using Realtime Push Notification

How Marketing Gain Benifites in the Digital Age

With the advent of the digital age, the ecological environment of marketing has undergone profound changes. Traditional marketing methods are no longer able to meet the diverse and personalized needs of modern consumers, and digital marketing has emerged as a new way for enterprises to gain competitive advantages and benefits. Digital marketing not only improves the accuracy and efficiency of marketing activities, but also establishes a closer connection between enterprises and consumers. Through technologies such as big data analysis and artificial intelligence, enterprises can gain a deeper understanding of consumer needs, develop personalized marketing strategies, and achieve precise push notifications and efficient conversions.

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation

PurposeDiabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area. DesignRetrospective cohort study Subjects1,115 individuals with diabetes. MethodsA retrospective analysis of a screening initiative conducted in four municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment. Main outcome measuresProof-of-concept implementation success. ResultsOut of 2,052 invited individuals, 1,115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years, of whom 66.03% were women. The screening covered 2,222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). AI performance for overall detection of referable cases (both DR and other conditions) was: sensitivity- 84.23% (95% CI- 82.63-85.84), specificity- 80.79% (95% CI- 79.05- 82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI- 84.12-87.22), specificity was 98.86 (95% CI- 98.39-99.33), and AUC was 0.92 (95% CI- 0.91-0.94). ConclusionsThe integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.

Pollution Based Ventilation Control in Automobiles

Abstract: Growing worries about air pollution have driven new approaches to confined spaces, such autos. The proposed system includes air quality sensors, ventilation control mechanisms, and data processing units inside the car. It continuously monitors the amount of CO2 and other air pollutants. It emits warning sounds and turns on automated window ventilation when thresholds are passed. Furthermore, the system proposes closing windows in high-pollution areas and detects foul odors to put on air fresheners accordingly. Alerts are sent by push notifications, sirens that sound, or dashboard lights. This innovation combines sophisticated ventilation control, real-time monitoring, and alerts to make driving safer while prioritizing both environmental sustainability and passenger safety