Monitoring Apps Research Articles

An integrated smart water management system for efficient water conservation

Water is a fundamental resource that sustains life, supports ecosystems, and plays a crucial role in various natural processes on earth. Water wastage is a major problem in the world, with a variety of causes including leaks in infrastructure and inefficient usage methods. A typical cause of water wastage is overflow from reservoirs or tanks as a result of poor maintenance or monitoring. This paper proposes a novel water resource management using internet of things (WARM-IoT) system to monitor and regulate the water level remotely by integrating IoT technology with demand side management (DSM) strategies, real-time monitoring of water levels has been achieved. The approach utilizes an ultrasonic sensor and Raspberry Pi for data acquisition and processing, fuzzy logic for decision-making, and an Android app for remote monitoring and control. The WARM-IoT assesses the system's performance, showcasing its efficacy in managing water levels and lowering electricity expenses. By analyzing consumption costs under different activation timings, significant potential for cost savings is observed, with a notable reduction of up to 6% in electricity expenses. Overall, the proposed WARM-IoT offers a comprehensive solution to water wastage and inefficient electricity usage in water management systems.

P1192 Use of a mobile application for monitoring and communicating with Inflammatory Bowel Disease patients

Abstract Background Biological therapies and small molecules for patients with Inflammatory Bowel Diseases (IBD) have increased the effectiveness, but also adverse effects (AE) or problems in their administration. In addition, patients have greater formation and communication needs. In 2020, we developed a mobile application (app) to improve communication and monitoring of IBD patients. Aim and objectives To assess the implementation of an app for communication and remote monitoring with IBD patients. Methods A multidisciplinary group composed of pharmacists, dermatologists, rheumatologists, gastroenterologists, and nurses designed an app for immune-mediated inflammatory diseases (IMID) patients in a tertiary hospital. The app consists of the following modules: Medication, Questionnaires, Adverse Events, Useful Information, Messages, and Patient Profile. We offered the app to patients who initiated a new biological or small molecule therapy. We performed an observational, longitudinal study to assess the app's impact on medication adherence, safety, and communication. The inclusion period was from December 2020 to May 2024. The inclusion criteria were age ≥ 18 years, diagnosis of IBD, and ownership of a smartphone. Patients with language barriers were excluded. Results 183 patients were included (median age: 42 years [IQR: 29,5-52,1] and 47.5% were female). Concerning the type of IBD, 73.7% of patients had Crohn's Disease (CD) and 26.3% Ulcerative Colitis (UC). The median follow-up time for app use was 446 (243,5-626,5) days. In the Medication module, 100% of patients registered their biological therapy and 39 % also used this module to record each dose of medication administered. 95,1% adherent according 4-item Morisky Medication Adherence Scale (MMAS-4). A total of 98 AE were registered. 16,39% of patients registered at least 1 AE. The most frequent AEs were fatigue (28.6%), injection site reaction (16.3%), headache (13.3%), and diarrhea (8.2%). 27,3% of patients used the messages module to communicate with healthcare professionals. The most frequent messages concerned gratitude (34%), logistical and citation issues (20%), doubts about administration (14%), doubts about managing AEs (12%), and drug interactions (8%). Conclusion Our app reminds patients to take medication, enables them to record AEs, and helps them communicate with healthcare professionals. More than a third of the patients registered the administration of the biological therapies and 16,39% registered at least 1 AE. The most used functionality was communication with health professionals.

Consumer Protection of Girls from Cybercrime in a Gender Perspective

Cybercrime covers a wide range of illegal acts carried out through the Internet or other digital devices, including fraud, identity theft, hacking, the spread of malware, to sexual exploitation of children. Girls are often the primary targets of these various forms of cybercrime. This is due to several factors, such as higher emotional vulnerability, still strong gender stereotypes, as well as a lack of education and awareness about cybersecurity among girls. Therefore, special protection that takes into account a gender perspective is essential to reduce the risk and impact of cybercrime against girls. In addition to the above cases, there are many more cases of cybercrime against girls in Indonesia that have not been or received public attention. Cybercrime is a serious threat that needs to be addressed seriously and urgently. So it is necessary to study further the impact of cybercrime on girls and to analyze gender perspectives in protecting girls' consumers as vulnerable consumers. Education on cybersecurity should start early, with a special focus on girls. Strict regulations and policies are needed to protect girls from sexual exploitation and other cybercrimes. Using protective technologies such as antivirus software and parental monitoring apps can help reduce the risk of girls becoming victims of cybercrime. Psychological and social support is crucial in helping girls who are victims to cope with the negative impact of cybercrime. Collaboration between various institutions and communities is essential to creating a secure and supportive digital environment for girls.

Personalized Smartphone-Enabled Assessment of Blood Pressure and Its Treatment During the SARS-CoV-2 COVID-19 Pandemic in Patients From the CURE-19 Study: Longitudinal Observational Study.

The use of digital interventions by patients for remote monitoring and management of health and disease is increasing. This observational study examined the feasibility, use, and safety of a digital smartphone app for routine monitoring of blood pressure (BP), medication, and symptoms of COVID-19 during the COVID-19 pandemic. The objective of this study was to deploy and test electronic data recording using a smartphone app developed for routine monitoring of BP in patients with primary hypertension. We tested the app for ease of data entry in BP management and tracking symptoms of new-onset COVID-19 to determine if participants found this app approach useful and sustainable. This remote, decentralized, 12-week, prospective, observational study was conducted in a community setting within the United States. Participants were approached and recruited from affiliated sites where they were enrolled in an ongoing remote decentralized study (CURE-19) of participants experiencing the COVID-19 pandemic. Potential participants were asked to complete a digital screener to determine eligibility and given informed consent forms to read and consent to using the Curebase digital platform. Following enrollment, participants downloaded the digital app to their smartphones for all data collection. Participants recorded daily BP, associated medication use, and emergent symptoms associated with SARS-CoV-2 infection. In addition, usability (adherence, acceptability, and user experience) was assessed using standard survey questions. Adverse events were collected based on participant self-report. Compliance and engagement were determined from user data entry rates. Feasibility and participant feedback were assessed upon study completion using the User Experience Questionnaire. Of the 389 participants who enrolled in and completed the study, 380 (98%) participants downloaded and entered BP routines in week 1. App engagement remained high; 239 (62.9%) of the 380 participants remained in the study for the full 12-week observation period, and 201 (84.1%) of the 239 participants entered full BP routines into the digital app 80% or more of the time. The smartphone app scored an overall positive evaluation as assessed by the User Experience Questionnaire and was benchmarked as "excellent" for domains of perspicuity, efficiency, and dependability and "above average" for domains of attractiveness and stimulation. Highly adherent participants with hypertension demonstrated well-controlled BP, with no significant changes in average systolic or diastolic BP between week 1 and week 12 (all P>.05). Participants were able to record BP medications and symptoms of SARS-CoV-2 infection. No adverse events attributable to the use of the smartphone app were reported during the observational period. The high retention, engagement and acceptability and positive feedback in this study demonstrates that routine monitoring of BP and medications using a smartphone app is feasible for patients with hypertension in a community setting. Remote monitoring of BP and data collection could be coupled with hypertensive medication in a combination product (drug+digital) for precision management of hypertension.

Symptom Monitoring App Use Associated With Medication Adherence Among Woman Survivors of Breast Cancer on Adjuvant Endocrine Therapy.

Oral adjuvant endocrine therapy (AET) reduces the risk of cancer recurrence and death for women with hormone receptor-positive (HR+) breast cancer. Because of adverse symptoms and socioecologic barriers, AET adherence rates are low. We conducted post hoc analyses of a randomized trial of a remote symptom and adherence monitoring app to evaluate characteristics associated with higher app use, satisfaction, and how app use was associated with AET adherence. Patients prescribed AET were randomly assigned to receive one of three intervention conditions: app, app + feedback, or enhanced usual care. Baseline and 6-month follow-up surveys, app use, and pillbox-monitored AET adherence data for app and app + feedback participants were used. Logistic regression evaluated the association between sociodemographic/clinical characteristics and app utilization and satisfaction, and how app use was associated with AET adherence (>80%). Overall, 163 women with early-stage HR+ breast cancer were included; 35.0% had high app use (≥75% of weeks enrolled). No sociodemographic characteristics were associated with app use. Satisfaction with the app was higher among those who were younger (88.9% for age 31-49 years v 54.9% for age 65+ years, P < .001), identified as White (76.8% v 60.1% for Black, P = .045), had lower health literacy (85.4% v 68.2% with higher health literacy, P = .017), or were nonurban residents (85.7% v 68.6% for urban, P = .021). Most participants (90.3%) with high app use were AET-adherent compared with 66.8% for those with lower app use (P < .001). Use of a remote monitoring app was similar across sociodemographic characteristics, and more frequent app use was associated with a higher likelihood of 6-month AET adherence. Encouraging women to monitor medication adherence and communicate adverse symptoms could improve AET adherence.

A Hybrid Care Model to Manage Diabetes Mellitus in the United Arab Emirates

The GluCare.Health hybrid care model represents a potentially groundbreaking approach to diabetes management that integrates on-site and remote components. With the global burden of diabetes projected to affect 783 million people by 2045, this model addresses the shortcomings of both traditional care and virtual care as stand-alone modalities, emphasizing patient engagement. Hybrid care combines the strengths of both in-person visits and digital health technologies to provide a continuous-engagement care model. Under the GluCare.Health hybrid model, the physical clinic, aligned with American Diabetes Association standards, offers personalized consultations and an intuitive layout. The virtual component employs remote continuous data monitoring and utilizes wearables such as the Oura ring for real-time tracking and mobile apps for remote monitoring and engagement, primarily around habits and behaviors across specific parameters that affect metabolic health. Implementation was effective in December 2022 when the model went live. Within just 3 months, significant improvements in certain health parameters were observed, and data at 12 months show similar results. Challenges for this model include scalability, talent recruitment, the physician adoption of continuous care models, reimbursement issues, resistance from older patients, and ethical considerations. The model’s success hinges on proactive policy advocacy, community engagement, and iterative refinement to navigate evolving health care landscapes. In essence, GluCare.Health’s hybrid model has the potential to become a transformative, patient-centric solution to the escalating global diabetes burden, driving innovation in chronic disease management.

Symptom experience of patients undergoing treatment for multiple myeloma: a longitudinal real-world electronic patient-reported outcomes study.

Patients with multiple myeloma (MM) experience significant symptom burden. We used a symptom monitoring app to longitudinally characterize the MM treatment experience in detail based on line of therapy (LOT). Adults with MM on active treatment completed weekly symptom monitoring surveys. Patients on their 4th LOT or greater were considered heavily pretreated. We characterized moderate to very severe (MOD-VS) symptom prevalence, weekly symptom burden, symptom bother (FACT-GP5), and health-related quality of life (HR-QoL) (EORTC QLQ-C30 Item 30) per LOT. We considered 109 patients on LOT < 4 and 47 on LOT ≥ 4. The top MOD-VS symptoms were fatigue (71.6% of patients), muscle pain (59.8%), general pain (51.6%), numbness/tingling (48.4%), and insomnia (47.6%). More patients on LOT ≥ 4 experienced numbness/tingling (66.7% vs. 41.3%; OR 2.84, 95% CI 1.27-6.37; p = 0.0098) and fatigue (83.3% vs. 65.6%; OR 2.60, 95% CI 0.96-7.09; p = 0.0557). Some symptoms (fatigue, muscle pain, anxiety) persisted for months among patients on LOT ≥ 4, but patients on LOT < 4 also had unmet longitudinal needs (numbness/tingling, dyspnea). Patients on LOT ≥ 4 had more weeks with ≥ 3 MOD-VS symptoms (27.1% of weeks vs. 15.7%; OR 2.56, 95% CI 1.07-6.08; p = 0.0337) and experienced high symptom bother more often (39.1% of surveys vs. 30.0%; OR 4.23, 95% CI 1.37-13.10; p = 0.0123). HR-QoL was similar between groups. Heavily pretreated patients experienced greater symptom burden and bother, but patients at earlier LOTs also had unmet needs. Interventions are needed to improve symptom management in MM regardless of LOT, but most pressingly for heavily pretreated patients.

The double-edged sword of self-tracking: investigating factors of technostress in performance-oriented cycling and triathlon.

This study is dedicated to the investigation of technostress caused by self-tracking in performance-oriented amateur sports and thus addresses a significant research gap in the understanding of stress factors operating in this specific context. Although technostress in occupational and private settings has been extensively researched, there is a lack of knowledge about the effects and specifics of technostress caused by the use of self-tracking technologies such as wearables and performance monitoring apps in sports. A total of 16 stress factors were identified, eight of which - information overload, distraction, unavailability, loss of control, lack of sense of achievement, unreliability, complexity and self-monitoring - are already known from the professional context and were transferred to the sports context. In addition, eight new stress factors specific to performance-oriented amateur sport were identified: Performance enhancement imperative, lack of context, digital visibility, feedback incorporation, measurement data fixation, comparison pressure, permanent monitoring and perception discrepancy. The study is based on a qualitative research approach with guided interviews conducted with performance-oriented amateur triathletes. The findings of this study contribute to a deeper understanding of the dynamic and contextual nature of technostress in sport and provide a basis for the development of targeted intervention strategies aimed to reducing technostress, such as adaptive training programs or personalized feedback systems. The results thus provide a valuable starting point for future research, particularly for the investigation of coping strategies in relation to the identified stress factors. In addition to identifying eight new sport-specific technostress factors, this study clearly delineates how traditional work-related technostress factors are applicable to amateur sports. This contextual adaptation helps in understanding the unique pressures faced by amateur athletes and distinguishes this study within the field.

Smart Sensors and Intelligent Analysis: A Literature Review on More Effective Early Warning Systems with IoT and Machine Learning

The IoT system described in the article "LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring" monitors air quality in real-time and transmits data through a LoRaWAN network to a public IoT platform. It measures seven key air quality parameters: nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon dioxide (CO₂), carbon monoxide (CO), PM2.5, temperature, and humidity. These parameters were chosen for their significant effects on air quality and human health. NO₂ and SO₂ come from fossil fuel combustion and can cause respiratory issues and acid rain. CO₂ contributes to climate change, while CO is toxic and harmful to health. PM2.5 particles can lead to respiratory and cardiovascular problems. The system uses sensors connected to an Arduino microcontroller to collect data, which is transmitted through a LoRa Shield to a LoRaWAN gateway. Data is then sent to The Things Network (TTN), integrated with ThingSpeak, and displayed on a web dashboard. Additionally, it is synchronized with the Virtuino smartphone app for mobile monitoring. The system has been validated by comparing its data to Aeroqual air quality monitors, demonstrating reliable real-time monitoring and transmission of air quality information over the internet.

ENERGY EFFICIENT ROUTING PROTOCOL FOR SECURITY ANALYSIS SCHEME USING HOMOMORPHIC ENCRYPTION

Wireless Sensor Networks (WSNs) have gained widespread interest as a result of developments in IT technology and the electronics industry. This ground-breaking sensing technology consists of multiple sensor nodes or motes that are placed in an atmosphere to detect constantly changing physical phenomena. These compact sensor nodes gather and interpret data via radio waves. The tiny size of these sensors is advantageous since they may be readily incorporated into any system or system. This capability has prompted the adoption of WSNs, particularly for form monitoring and tracking; most notably, monitoring apps. However, this small scale of sensor nodes limits the capacity of resources. Usually, the WSNs are installed in environments of unsafe or difficult human interference. Critical decisions in emergency applications can require sensible knowledge. It is necessary to check the network security. To extend the network security using Homomorphic Encryption effectively, the available resources must be expanded to a full view. This refers to the development of energy-efficient routing protocol strategies to ensure low energy consumption of common routing protocols and improve the availability and security of WSNs. Homomorphic encryption is effective in improving the security of wireless sensor networks.

Delivering remote monitoring, through primary care, to patients with chronic heart failure

Abstract It is estimated that over 90,000 patients in Ireland have a diagnosis of Heart Failure. The HeartCare at Home study was commenced in 2021 as a collaboration between Centric Health and Roche Diagnostic. The study aimed to examine the feasibility of implementing a remote monitoring programme for patients with heart failure, through primary care, and the impact on hospitalisations, A&amp;E attendances, and GP visits. Patients were referred to the study by their GP. Patients were provided with a blood pressure monitor, weighing scales and smart device in line with the study protocol. The remote monitoring app, a CE-certified Medical Device Class IIa, was provided by Luscii Healthtech. Patients were requested to input their weight, blood pressure, heart rate and symptoms for two weeks after a undergoing a clinical onboard to establish a baseline. Thereafter, readings were entered by patients twice weekly. Patients participated for a minimum of 12 months. If measurements fell outside pre-determined parameters or a deterioration was noted, the team was alerted through the Luscii app dashboard. Where clinically appropriate and in line with cardiologist approved protocols, the team supported the patient to titrate their diuretic medication at home for a maximum of 3 days, with continuous daily monitoring. The patient’s GP was informed of all deteriorations and changes in the patient’s condition and of all recommendations made by the HeartCare at Home team. One hundred and sixty-two patients were onboarded to the programme while 120 patients completed one year follow up (42% female; median age 76 years, range 31-100 years). Thirty five percent of patients had a diagnosis of HFrEF, 13% HFpEF, and 6% HFmrEF while 43% were undifferentiated. Seventy five percent of patients had a NYHA I or II classification. Over 95,000 clinical measurements were recorded through the Luscii app over the course of the study. Of the decompensations managed directly by the HeartCare at Home team with medication titration and daily monitoring, 70% were clinically resolved following the HeartCare at Home protocol. Adherence to inputting monitoring data remained strong throughout the intervention period with over 89% of participating patients continuing to upload readings as scheduled at 12 months. Using established and secure communication pathways, such as HealthLink and Healthmail, the clinical team were able to interact with each patients GP to ensure continuity of care and optimised patient treatment. The HeartCare at Home study demonstrates the feasibility of implementing a remote monitoring programme through primary care. Remote monitoring with direct access to clinical support has potential benefits for patients, clinicians, and the health service at large. Further research, in a larger cohort, and examination of the cost of implementation of remote monitoring in primary care, is warranted.

Improving Exposure Therapy Through Projection-Based Augmented Reality for the Treatment of Cockroach Phobia: A Feasibility, Multiple-Baseline, Single-Case Study

Augmented Reality (AR) is helpful for overcoming the challenges of in vivo exposure therapy for Specific Phobia (SP). Specifically, Projection-based AR exposure therapy (P-ARET) allows the individual to face the feared animal without intrusive hardware, the phobic stimulus can be controlled, and it can maximize “variability”, producing a positive effect in the generalization of the results. The goal of this work is to assess the feasibility of P-ARET for SP, comparing multiple stimuli (MS) versus single stimulus (SS) conditions and evaluating the participants’ user experience. Adherence to a daily monitoring app (Emotional Monitor) and preliminary efficacy of the P-ARET treatment were assessed. Four participants diagnosed with SP of cockroaches (DSM-5) were randomly assigned to different baselines. Episodic and daily evaluations were performed. Participants considered the MS condition more aversive but more effective than the SS condition. Adherence to the mobile app was 83% for three participants and 55% for the remaining person. Analyses of non-overlap of all pairs and changes in the functionality levels showed a decrease in the SP symptoms at post-treatment and follow-ups. This study offers preliminary feasibility results for a novel form of P-ARET to treat participants with cockroach phobia, which may also apply to other phobias.

Integrating Real-Time Air Quality Monitoring, Ecological Momentary Assessment, and Spirometry to Evaluate Asthma Symptoms: Usability Study.

Individuals are exposed to a variety of indoor residential toxins including volatile organic compounds and particulates. In adults with asthma, such exposures are associated with asthma symptoms, asthma exacerbations, and decreased lung function. However, data on these exposures and asthma-related outcomes are generally collected at different times and not in real time. The integration of multiple platforms to collect real-time data on environmental exposure, asthma symptoms, and lung function has rarely been explored. This paper describes how adults with asthma perceive the acceptability and usability of three integrated devices: (1) residential indoor air quality monitor, (2) ecological momentary assessment (EMA) surveys delivered via a smartphone app, and (3) home spirometry, over 14 days. Participants (N=40) with uncontrolled asthma were mailed the Awair Omni indoor air quality monitor, ZEPHYRx home spirometer, and detailed instructions required for the in-home monitoring. The air quality monitor, spirometer, and EMA app were set up and tested during a videoconference or phone orientation with a research team member. Midway through the 14-day data collection period, participants completed an interview about the acceptability of the study devices or apps, instructional materials provided, and the setup process. At the end of the 14-day data collection period, participants completed a modified System Usability Scale. A random sample of 20 participants also completed a phone interview regarding the acceptability of the study and the impact of the study on their asthma. Participants ranged in age from 26 to 77 (mean 45, SD 13.5) years and were primarily female (n=36, 90%), White (n=26, 67%), college graduates (n=25, 66%), and residing in a single-family home (n=30, 75%). Most indicated that the air quality monitor (n=23, 58%), the EMA (n=20, 50%), and the spirometer (n=17, 43%) were easy to set up and use. Challenges with the EMA included repetitive surveys, surveys arriving during the night, and technical issues. While the home spirometer was identified as a plausible means to evaluate lung function in real time, the interpretation of the readings was unclear, and several participants reported side effects from home spirometer use. Overall, the acceptability of the study and the System Usability Scale scores were high. The study devices were highly acceptable and usable. Participant feedback was instrumental in identifying technical challenges that should be addressed in future studies.

Understanding health literacy's moderating role in remote monitoring for adjuvant endocrine therapy: Findings from a randomized clinical trial.

45 Background: Despite the efficacy of oral adjuvant endocrine therapy (AET) for improving survival among women with hormone receptor-positive early-stage breast cancer, adherence rates remain low. Our THRIVE study (NCT03592771) investigated the effectiveness of a mobile health remote monitoring app with and without tailored educational messages on AET adherence among women with breast cancer and found no statistically significant treatment effects on 1-year AET adherence overall. Health literacy contributes to patients’ understanding of their treatment plans. We conducted a post hoc analysis of the THRIVE study to examine if health literacy moderates the effectiveness of this intervention. Methods: This non-blinded randomized controlled trial included women with early-stage breast cancer prescribed AET at a large cancer center with 14 clinics across three states. Participants used a pillbox to electronically monitor AET adherence for 1 year and completed surveys at enrollment. Consented participants were randomized into (1) “App”, receiving access to the study adherence and symptom monitoring app for 6 months, with increasing/severe symptoms and missed doses reported in the app triggering follow-ups from the oncology team; (2) “App+Feedback”, receiving additional weekly text messages about managing symptoms, adherence, and communication for 6 months; or (3) “Enhanced Usual Care (EUC).” The primary outcome was 1-year AET adherence captured with the pillbox (≥80% Proportion of Days Covered [PDC] vs. &lt; 80%). The enrollment survey captured participant’s sociodemographic characteristics, including race and ethnicity, education, household income, and health literacy. We used a linear probability model to measure the interaction between the study arm and health literacy on AET adherence. Results: Among 304 women randomized (104 EUC, 98 App, and 102 App+Feedback), the 12-month follow-up retention rate was 88% (n = 266) and 19.4% reported low health literacy at enrollment. Low health literacy was more prevalent among Black (29.4%) vs. White (13.4%) participants (p &lt; 0.001), those with incomes below the federal poverty level (34.4% vs. 17.2% of those with higher incomes, p = 0.02), and those with only a high school degree or lower education (31.7%) vs. with those with some college or higher education (16.4%, p &lt; 0.01). In the low health literacy group, 80.0% of App+Feedback were AET adherent vs. 42.1% of EUC, a 37.9 percentage point (ppt) difference (95% CI: 4.1 to 71.7, p = 0.03); in higher literacy group adherence, 47.1% of App+Feedback vs. 59.0% of EUC were adherent, a -11.8 ppt difference (95% CI: -27.9 to 4.3, p = 0.15). Conclusions: A remote monitoring app with tailored educational text messages led to higher 1-year AET adherence among participants with low health literacy, but not for those with high health literacy. Clinical trial information: NCT03592771 .

Patients’ experience using an app for home remote monitoring of heart failure for a university hospital in Quebec, Canada

BackgroundThe use of mobile health apps for remote monitoring has considerable potential for patient care. However, more research is needed to gather patients’ experience with their use. This study evaluates heart failure patients’ experience of the effects of remote monitoring on the management of heart failure (HF) using a smartphone app, the challenges faced in such use, and patients’ suggestions for improving the efficacy of this device.MethodA qualitative study was conducted with all patients who had used the TakeCare™ app for at least three months during the exploratory phase of the Continuum project. Patients were recruited before COVID-19 began, and they used the app during this period. The app allows patients to submit their daily vital signs and answer questions about their symptoms to help the clinical professionals adjust their treatment plan as needed. Individual interviews were conducted by telephone or videoconference using a semi-structured individual interview guide. All interviews were recorded, transcribed and analyzed using the thematic analysis method.ResultsOf the 29 patients invited, 5 had since died, 10 did not respond, two gave positive feedback on the app but declined the interview, and 12 patients agreed to participate in an individual interview. The participants all liked the app’s ease of use. They particularly praised the contribution it made to monitoring their disease, both by themselves and by clinicians. The app also allowed them to stay connected to their clinical team and have better access to healthcare professionals. Patients suggested, as an improvement, that the feedback and communication features should be personalized to make them more individualized and interactive.ConclusionThe TakeCare™ smartphone app was well received by patients with heart failure. Its use could facilitate better remote monitoring of heart conditions in the home as well as improve access to clinical teams.

The effect of brief in-survey product experience on preferences for smart energy technologies

Survey research on the adoption of smart energy technologies is growing rapidly, generating important knowledge about factors on the consumer side that may help facilitate transition towards sustainable energy systems. However, much of this research uses survey measures to elicit consumer preferences without explicit consideration of whether and how in-survey experience with the technologies affects preference estimates. For this reason, we experimentally test (for the first time) whether brief in-survey product experience, mainly in the form of additional time spent deliberating about relevant products, influences stated consumer preferences for smart energy monitoring and management apps. Findings obtained in our first experiment conducted in the United Kingdom suggest modest effects of in-survey product experience on consumer preferences, where consumer preferences can be both strengthened or weakened depending on the type of in-survey product experience. These findings are, however, not replicated in our second experiment conducted in Spain. The Spanish experiment, nonetheless, suggests that brief in-survey product experience can help participants make more reasoned choices better reflecting their environmental concern and income constraints. Our results point to possible ways how to improve the reliability of stated preference surveys by providing respondents with adequate in-survey experience with unfamiliar products.

Development and Evaluation of a New Algorithm for Real-Time Image Processing

When working with real-time images, creating and testing new methods is very important for making computers work faster and more accurately. This study describes a new method that is meant to solve problems in real-time picture processing applications by making them faster and better at what they do. It was created because of the need to deal with picture data that was getting more complicated in many areas, including autonomous systems, medical imaging, monitoring, and multimedia apps. Several new methods are built into the suggested program to help it reach its goals. To begin, it uses advanced parallel processing designs to speed up calculations without lowering their quality. The method cuts down on processing delays by making the best use of resources and job ordering. This makes it ideal for time-sensitive apps that need to make quick decisions based on real-time picture data. The algorithm also uses strong feature extraction techniques that are designed to work well with a wide range of picture traits and noise levels. This improves the algorithm's ability to work in a variety of settings, making sure it always works well in different working situations. Adaptive filtering mechanisms and advanced edge recognition algorithms are two important parts of the algorithm that work together to make it quick and easy to get useful information. A lot of testing was done to see how well the algorithm worked against the best available methods, using normal image processing datasets and real-life situations. A lot of attention was paid to performance measures like processing speed, accuracy in feature recognition, and computer economy. When compared to traditional methods, comparative research showed big gains in processing speed without sacrificing accuracy.

Predicting Internet addiction in college students using a 1D-CNN model: analysis of influencing factors

This study constructs a deep learning-based model to predict internet addiction among college students and analyzes significant influencing factors. A random survey of 4,895 students from a university in Shandong Province was conducted using questionnaires on general information, internet addiction (CIAS-R), personality (CBF-PI-B), psychological traits (SDS, SAS), parenting styles (EMBU), behavioral issues (SAS-C), and social support (ASSRS) to establish a database. A predictive model was developed using a 1D Convolutional Neural Network (1D-CNN), extracting key influencing factors of internet addiction. The model showed 92.77% accuracy, with high precision and recall rates for predicting normal users and addicts. The gradient calculation indicates that in second-year students, negative and withdrawal behaviors, depression, over-interfering families, and anxiety significantly contribute to Internet addiction, with factors exceeding 0.5. The 1D-CNN model offers robust performance and accuracy in predicting internet addiction, identifying significant factors for early prevention and potential integration with apps for real-time monitoring.

Improvements in Digestive Symptoms After Participation in an App-Based Chronic Digestive Disease Management Program: A Prospective Cohort Evaluation.

Fewer than 20% of adults with chronic gastrointestinal (GI) symptoms have accessed care to evaluate or manage their symptoms. We sought to characterize whether adults with chronic GI symptoms would use an app for symptom monitoring and the effects of participation in a digitally delivered GI chronic care program. We provided a digital digestive care management app to adults via their employer-sponsored benefits. We evaluated participants' self-reported GI symptoms at baseline and between 30 and 90 days post-registration. GI symptoms (e.g., abdominal pain and constipation) were rated on a scale of 0 (no symptoms) to 4 (very severe symptoms). A total of 1936 participants were enrolled (75% female; 67% White, 11% Asian/Pacific Islander, 6% Hispanic, 7% Black; mean age: 43 years). Their most common GI conditions were irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), and acid reflux. Participants of all genders and races reported statistically significant improvements in all symptoms between baseline and the end of the intervention (P< 0.05). At baseline, 79.5% of participants reported at least moderate GI symptom severity for at least one symptom. In contrast, at the end of the intervention, only 47.8% of participants reported moderate or severe symptoms, and 310 (16.0%) participants reported no symptoms. Participants who were scheduled with their care team reported greater symptom improvement than those who were not scheduled (P = 0.004). Participants reported feeling greater control of their health (83%), better management of their digestive symptoms (83%), increased happiness (76%), and greater productivity at work (54%). Demographically diverse participants engaged with a digital digestive chronic care program and reported significant improvements in digestive symptom severity.

Integrating vision-based AI and large language models for real-time water pollution surveillance.

Water pollution has become a major concern in recent years, affecting over 2 billion people worldwide, according to UNESCO. This pollution can occur by either naturally, such as algal blooms, or man-made when toxic substances are released into water bodies like lakes, rivers, springs, and oceans. To address this issue and monitor surface-level water pollution in local water bodies, an informative real-time vision-based surveillance system has been developed in conjunction with large language models (LLMs). This system has an integrated camera connected to a Raspberry Pi for processing input frames and is further linked to LLMs for generating contextual information regarding the type, causes, and impact of pollutants on both human health and the environment. This multi-model setup enables local authorities to monitor water pollution and take necessary steps to mitigate it. To train the vision model, seven major types of pollutants found in water bodies like algal bloom, synthetic foams, dead fishes, oil spills, wooden logs, industrial waste run-offs, and trashes were used for achieving accurate detection. ChatGPT API has been integrated with the model to generate contextual information about pollution detected. Thus, the multi-model system can conduct surveillance over water bodies and autonomously alert local authorities to take immediate action, eliminating the need for human intervention. PRACTITIONER POINTS: Combines cameras and LLMs with Raspberry Pi for processing and generating pollutant information. Uses YOLOv5 to detect algal blooms, synthetic foams, dead fish, oil spills, and industrial waste. Supports various modules and environments, including drones and mobile apps for broad monitoring. Educates on environmental healthand alerts authorities about waterpollution.