Search Results (225)

This study explores the adoption of artificial intelligence (AI) in digital commerce platforms and whether such adoption is aligned with market positioning changes. Focusing on five of the largest e-commerce companies—Amazon, Apple, Shein, Temu, and IKEA—the study examines the application of AI in six key areas of operation: customer service, logistics, personalization, security, and supply chain management. A two-stage qualitative method was employed: a Scopus database-organized literature review, and a walkthrough examination of each company’s home page. There is extensive diversity in the deployment strategies of AI, which business models and digital maturity drive, the findings show. Amazon has end-to-end integration, but newer entrants such as Shein and Temu are concentrating on customer-facing AI tools. Apple, although it uses AI across its ecosystem, illustrates few examples in its online store. Notably, the rankings of firms under study align with their 2023 revenue rankings. Although no cause-and-effect relationship is assumed between the adoption of AI and revenue performance enhancement, the existence of a correlation suggests that AI could facilitate strategic differentiation. A comparative method for analyzing the adoption of AI is proposed in the study and highlights the importance of ethical, organizational, and regulatory concerns. Subsequent research should involve empirical measures of performance, longitudinal monitoring, and user-led assessments to enhance understanding of the impact of AI on digital trade. Full article

The Internet of Things (IoT) has emerged as a transformative technology in managing Alzheimer’s Disease (AD), offering novel solutions for early diagnosis, continuous patient monitoring, and assistive care. This review presents a comprehensive analysis of IoT-enabled systems tailored to AD care, focusing on wearable biosensors, cognitive monitoring tools, smart home automation, and Artificial Intelligence (AI)-driven analytics. A systematic literature survey was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify, screen, and synthesize 236 relevant studies primarily published between 2020 and 2025 across IEEE Xplore, PubMed, Scopus and Web of Science. The inclusion criteria targeted peer-reviewed articles that proposed or evaluated IoT-based solutions for AD detection, progression monitoring, or patient assistance. Key findings highlight the effectiveness of the IoT in detecting behavioral and cognitive changes, enhancing safety through real-time alerts, and improving patient autonomy. The review also explores integration challenges such as data privacy, system interoperability, and clinical adoption. The study reveals critical gaps in real-world deployment, clinical validation, and ethical integration of IoT-based systems for Alzheimer’s care. This study aims to serve as a definitive reference for researchers, clinicians, and developers working at the intersection of the IoT and neurodegenerative healthcare. Full article

Background: Maintaining long-term exercise adherence in occupational settings remains a challenge, particularly in remote or unsupervised environments. This study aimed to investigate the effect of individualized feedback on exercise adherence, body composition, and physical function during a remote home-based training intervention utilizing the video-based exercise system “SUKUBARA^®”. Methods: In total, 66 care facility workers were randomly categorized into either a feedback (FB) group or a non-feedback (NF) group. Both groups performed a combined exercise program comprising low-load resistance training (slow squats) and balance exercises (one-leg standing time of closed eye) for approximately 15 min, thrice weekly over 12 weeks. The FB group received individualized feedback sheets visualizing total video play time (TT), exercise frequency, and interruptions, alongside reminder emails. The primary outcome was TT. Secondary outcomes included body composition measures (body weight, fat-free mass, and body fat mass rate) and one-leg standing time of opened eye. Results: The FB group demonstrated significantly greater TT, approximately 1.5 times that of the NF group, indicating enhanced exercise adherence. Moreover, significant improvements in fat-free mass and body fat mass rate were observed in the FB group. A significant correlation was identified between changes in TT and body composition parameters, suggesting TT as a valid proxy for exercise engagement. Conclusions: Individualized feedback within a remote monitoring home exercise program effectively improved exercise adherence and body composition among care workers. The “SUKUBARA^®” system shows promise as a tool to support exercise continuity in occupational health and long-term care settings. Full article

Recent documents from leading international pediatric respiratory societies have strongly encouraged the use of lung function tests in clinical practice and research. These tests can explore ventilatory function across its volumetric and temporal domains, providing information on the intrapulmonary location and extent of damage caused by respiratory diseases. The choice of which test to use in each case to investigate presenting respiratory symptoms depends on the patient’s symptoms and the diagnostic–therapeutic phase being addresse d. In the most common and representative chronic pediatric condition—bronchial asthma—lung function tests play an especially important role due to the disease’s complexity and the fluctuating nature of airway obstruction. This review aims to examine the potential of various lung function tests in asthma, helping clinicians and researchers to optimize diagnosis and follow-up with the most appropriate methodology. While spirometry and flow resistance measurements using the interrupter technique have historically been the cornerstones of diagnosis and clinical monitoring in childhood asthma, the advent of new technologies—such as multiple breath nitrogen washout (MBNW) and the forced oscillation technique (FOT)—is opening up the door to a more nuanced view of the disease. These tools allow for an evaluation of asthma as a structurally complex and topographically and temporally disorganized condition. FOT, in particular, facilitates measurement acceptability in less cooperative subjects, both in respiratory physiology labs and even at the patient’s home. Full article

Obstructive sleep apnea (OSA) is a highly prevalent and underdiagnosed sleep disorder with significant cardiovascular implications, namely in atrial fibrillation (AF) patients. Despite its clinical relevance, OSA prevalence among AF patients and the diagnostic strategies used remain heterogeneous across studies, complicating screening, and treatment pathways. Our aim was to synthesize recent evidence on OSA prevalence in AF populations and to critically evaluate the diagnostic methods and screening strategies employed in clinical studies, by conducting a systematic review using PubMed and Google Scholar to identify original clinical studies published between January-2019 and December-2024. Inclusion criteria targeted adult AF populations assessed for OSA or sleep-disordered breathing. The results were analyzed by two independent reviewers. Non-concordances were resolved by consensus. Data extracted included study characteristics, population profiles, diagnostic approaches, prevalence rates, symptom profiles, and clinical correlates. Thirty-eight studies were included, comprising predominantly observational studies. Prevalence estimates of OSA in AF populations ranged from 5% to 90%, with most studies reporting rates > 60%. A consistent burden of moderate-to-severe OSA was observed. Diagnostic methods varied widely, from polysomnography (PSG) and home sleep apnea testing to pacemaker-derived monitoring and questionnaires such as STOP-Bang and Epworth Sleepiness Scale (ESS). Underdiagnosis was attributed to minimal symptomatology, lack of physician awareness, and reliance on subjective tools. Several studies highlighted the limited sensitivity of standard screening instruments in AF populations and advocated for objective testing even in asymptomatic patients. Marked heterogeneity in study designs, diagnostic methods, and populations precluded quantitative synthesis and limited direct comparisons. Objective diagnostic testing, particularly PSG, is essential to improve OSA detection rates and guide individualized management. Integration of structured screening protocols into AF care—especially for high-risk patients—and interdisciplinary collaboration are critical. Full article

Background: Wearable sensors are a promising tool for the remote, continuous monitoring of motor symptoms and physical activity, especially in individuals with neurological or chronic conditions. Despite many experimental trials, clinical adoption remains limited. A major barrier is the lack of awareness and confidence among healthcare professionals in these technologies. Methods: This systematic review analyzed the use of wearable sensors for continuous motor monitoring at home, focusing on their purpose, type, feasibility, and effectiveness in neurological, musculoskeletal, or rheumatologic conditions. This review followed PRISMA guidelines and included studies from PubMed, Scopus, and Web of Science. Results: Seventy-two studies with 7949 participants met inclusion criteria. Neurological disorders, particularly Parkinson’s disease, were the most frequently studied. Common sensors included inertial measurement units (IMUs), accelerometers, and gyroscopes, often integrated into medical devices, smartwatches, or smartphones. Monitoring periods ranged from 24 h to over two years. Feasibility studies showed high patient compliance (≥70%) and good acceptance, with strong agreement with clinical assessments. However, only half of the studies were controlled trials, and just 5.6% were randomized. Conclusions: Wearable sensors offer strong potential for real-world motor function monitoring. Yet, challenges persist, including ethical issues, data privacy, standardization, and healthcare access. Artificial intelligence integration may boost predictive accuracy and personalized care. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background: Nemaline myopathy is a rare congenital neuromuscular disease associated with progressive weakness and frequent respiratory complications. In emergency situations, families often serve as the first and only responders. The aim of this study is to explore how parents in Spain care for children with nemaline myopathy during emergency situations, focusing on the clinical responses performed at home and the organizational challenges encountered when interacting with healthcare systems. Methods: A qualitative phenomenological study was conducted with 17 parents from 10 families belonging to the Asociación Yo Nemalínica. Semi-structured interviews were performed via video calls, transcribed verbatim, and analyzed using Giorgi’s descriptive method and ATLAS.ti software (version 24). Methodological rigor was ensured through triangulation, reflexivity, and member validation. Results: Four themes were identified. First, families were described as acting under extreme pressure and in isolation during acute home emergencies, often providing cardiopulmonary resuscitation and respiratory support without professional backup. Second, families managed ambiguous signs of deterioration using clinical judgment and home monitoring tools, often preventing fatal outcomes. Third, parents frequently assumed guiding roles in emergency departments due to a lack of clinician familiarity with the disease, leading to delays or errors. Finally, the transition to the Pediatric Intensive Care Unit was marked by emotional distress and rapid decision-making, with families often participating in critical choices about invasive procedures. These findings underscore the complex, multidisciplinary nature of caregiving. Conclusions: Parents play an active clinical role during emergencies and episodes of deterioration. Their lived experience should be formally integrated into emergency protocols and the continuity of care strategies to improve safety and outcomes. Full article

Visual field (VF) testing remains a cornerstone in assessing retinal function by measuring how well different parts of the retina detect light. It is essential for early detection, monitoring, and management of many retinal diseases. By mapping retinal sensitivity, VF exams can reveal functional loss before structural changes become visible. This review summarizes how VF testing is applied across key conditions: hydroxychloroquine (HCQ) retinopathy, age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular edema (DME), and inherited disorders including inherited dystrophies such as retinitis pigmentosa (RP). Traditional methods like the Goldmann kinetic perimetry and simple tools such as the Amsler grid help identify large or central VF defects. Automated perimetry (e.g., Humphrey Field Analyzer) provides detailed, quantitative data critical for detecting subtle paracentral scotomas in HCQ retinopathy and central vision loss in AMD. Frequency-doubling technology (FDT) reveals early neural deficits in DR before blood vessel changes appear. Microperimetry offers precise, localized sensitivity maps for macular diseases. Despite its value, VF testing faces challenges including patient fatigue, variability in responses, and interpretation of unreliable results. Recent advances in artificial intelligence, virtual reality perimetry, and home-based perimetry systems are improving test accuracy, accessibility, and patient engagement. Integrating VF exams with these emerging technologies promises more personalized care, earlier intervention, and better long-term outcomes for patients with retinal disease. Full article

Background/Objectives: For aging men experiencing lower urinary tract symptoms (LUTS), bladder diaries (BD) and uroflowmetry (UFM) are commonly used non-invasive diagnostic tools. However, bladder diaries often suffer from subjectivity and incomplete data, while traditional hospital-based uroflowmetry lacks convenience and repeatability. Therefore, there is a growing need for a user-friendly, artificial intelligence (AI)-powered at-home uroflow monitoring solution. This study aims to develop a novel, vibration-based home uroflowmetry system capable of recognizing uroflow curve patterns and measuring voiding parameters, and to compare its performance with other existing home-based uroflowmetry methods. Methods: Seventy-six male participants, all of whom provided informed consent, underwent uroflowmetry to assess voiding symptoms. An accelerometer affixed to the uroflowmeter’s urine container captured vibration signals, which were used to calculate the root mean square (RMS) values and maximum amplitude (Mmax). Simultaneously, the uroflowmeter recorded standard voiding parameters and generated uroflow curves. These vibration signals were then analyzed using a convolutional neural network (CNN) to classify six distinct uroflow curve patterns, aiding in diagnostic evaluation. Results: Seventy-six participants’ voiding volume ranged from 50 mL to 690 mL (median [Q1, Q3]: 160 [70.00, 212.50] mL). The correlation analysis revealed positive correlations between the vibration signals and voiding parameters, including the voided volume and RMS (R = 0.768, p < 0.001), Qmax and Mmax (R = 0.684, p < 0.001), voiding time and signal time (R = 0.838, p < 0.001), time to Qmax and time to Mmax (R = 0.477, p < 0.001). AI pattern recognition demonstrated high accuracy with all three indicators (precision, recall, and F1 score) surpassing 0.97. Conclusions: This AI-assisted vibration-based home uroflowmetry enables accurate voiding parameter measurement and uroflow pattern recognition, showing high precision, recall, and F1-score. It might offer a convenient solution for continuous and subjective bladder monitoring outside clinical settings. Full article

Introduction: Home-based cardiac telerehabilitation (HBCTR) is a multidisciplinary intervention aimed at optimizing functional, psychological, and social recovery in patients undergoing percutaneous coronary intervention (PCI). This rehabilitation model serves as an effective alternative to traditional center-based rehabilitation, providing a cost-effective and clinically advantageous approach. Methods: Following PRISMA guidelines, we conducted a systematic literature search across multiple databases (PubMed, CINAHL, Cochrane, Scopus, Web of Science). We included randomized controlled trials (RCTs), cohort, and observational studies assessing telerehabilitation in post-PCI patients. Primary outcomes focused on health-related quality of life (HRQoL) and adherence, while secondary outcomes included functional capacity (6 min walk test, VO₂max), cardiovascular risk factor control, and psychological well-being. Risk of bias was assessed using the Cochrane RoB 2.0 and ROBINS-I tools. Results: A total of 3575 articles were identified after removing duplicates, of which 877 were selected based on title and abstract, and 17 met the inclusion criteria, with strong RCT representation ensuring robust evidence synthesis. HBCTR was associated with significant improvements in exercise capacity, with increases in VO₂max ranging from +1.6 to +3.5 mL/kg/min and in 6 min walk distance from +34.7 to +116.6 m. HRQoL scores improved significantly, with physical and mental component scores increasing by +6.75 to +14.18 and +4.27 to +11.39 points, respectively. Adherence to telerehabilitation programs was consistently high, often exceeding 80%, and some studies reported reductions in hospital readmissions of up to 40%. Wearable devices and smartphone applications facilitated self-monitoring, enhancing adherence and reducing readmissions. Several studies also highlighted improvements in anxiety and depression scores ranging from 10% to 35%. Conclusions: HBCTR is a promising strategy for rehabilitation and quality-of-life improvement after PCI. It offers a patient-centered solution that leverages technology to enhance long-term outcomes. By integrating structured telerehabilitation programs, healthcare systems can expand accessibility, promote adherence, and improve equity in cardiovascular care. Full article

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

In addition to axial motor complications such as abnormal posture, instability, falls, and gait variability, neurodegenerative diseases like Parkinsonian syndromes include executive dysfunction, Parkinson’s disease dementia, and neuropsychiatric symptoms. These motor disorders significantly affect mobility, quality of life, and well-being. Recently, physical activity of various intensities monitored both remotely and face-to-face via digital health technologies, mobile platforms, or sensory cues has gained relevance in managing idiopathic and atypical Parkinson’s disease (PD and APD). Remote monitoring solutions, including home-based digital health assessments using semi-structured activities, offer unique advantages. Real-world gait parameters like walking speed can now be continuously assessed with body-worn sensors. Developing effective strategies to slow pathological aging and mitigate neurodegenerative progression is essential. This study presents outcomes of using digital health technologies (DHTs) for remote assessment of motor function, physical activity, and daily living tasks, aiming to reduce disease progression in PD and APD. In addition to wearable inertial sensors, clinical rating scales and digital biomarkers enhance the ability to characterize and monitor motor symptoms. By reviewing recent literature, we identified emerging trends in quantifying and intervening in neurodegeneration using tools that evaluate both remote and face-to-face physical activity. Our findings confirm that DHTs offer accurate detection of motor fluctuations and support clinical evaluations. In conclusion, DHTs represent a scalable, effective strategy for improving the clinical management of PD and APD. Their integration into healthcare systems may enhance patient outcomes, support early intervention, and help delay the progression of both motor and cognitive symptoms in aging individuals. Full article

Postoperative pain management after hospital discharge remains one of the main clinical challenges. The use of digital and telemedicine tools offers new opportunities for the continuous monitoring of, and timely intervention in, patients discharged and followed at home. This scoping review, conducted according to the PRISMA-ScR checklist and the Joanna Briggs Institute methodology, analyzed 26 studies selected through a search of PubMed, Scopus, and Web of Science databases. Inclusion criteria comprised studies published between 2015 and 2025 that involved patients discharged home after surgery, that used digital or telemedicine tools for pain management, and that included active involvement of healthcare professionals and reported clinical outcomes. Studies show the use of a variety of digital tools, including mobile applications, web platforms, wearable sensors, automated messaging systems, and virtual reality technologies, alternating across settings for the assessment and management of pain at home, educational and therapeutic support, and to enhance communication between healthcare professionals and patients. Most reported outcomes focus on improved home-based pain control, a reduction in opioid consumption, and a high level of patient satisfaction. However, some challenges remain, particularly the low level of digital literacy among certain segments of the population. In conclusion, the implementation of telemedicine and digital technologies for managing postoperative pain at home proves to be a promising strategy. Nonetheless, it requires further scientific investigation and, from policymakers, significant investments in professional training and technological infrastructure to ensure an increasingly equitable and sustainable distribution of home healthcare services. Full article

The development of automatic methods for early cognitive impairment (CI) detection has a crucial role to play in helping people obtain suitable treatment and care. Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). Following prior work, we improve the multiple thresholds (MTs) approach specifically for EBR calculation to enhance performance and robustness, while the HTR and HMSFs are extracted using methods from previous work. The EBR, HTR, and HMSFs are evaluated using an in-the-wild video dataset captured in challenging environments. This method leverages clinically validated cues and automatically extracts features to enable classification. Experiments show that the proposed approach achieves competitive performance in distinguishing between ND, MCI, FMD, and HCs on in-the-wild datasets, with results comparable to audiovisual-based methods conducted in a lab-controlled environment. The findings highlight the potential of visual-based approaches to complement existing diagnostic tools and provide an efficient home-based monitoring system. This work advances the field by addressing traditional limitations and offering a scalable, cost-effective solution for early detection. Full article