Continuous monitoring using thermography can capture the heat oscillations maintaining body temperature in neonates.

The body temperature of infants at equilibrium with their surroundings is balanced between heat production from metabolism and the transfer of heat to the environment. Total heat production is related to body size, which is closely related to metabolic rate and oxygen consumption. Body temperature control is a crucial aspect of neonatal medicine but we have often struggled with temperature measures. Contactless infrared thermography (IRT) is useful for vulnerable neonates and may be able to assess their spontaneous thermal metabolism. The present study focused on heat oscillations and their cause. IRT was used to measure the skin temperature every 15 s of neonates in an incubator. We analyzed the thermal data of 27 neonates (32 measurements), calculated the average temperature within specified regions, and extracted two frequency components-Components A and B-using the Savitzky-Golay method. Furthermore, we derived an equation describing the cycle-named cycle T-for maintaining body temperature according to body weight. A positive correlation was observed between cycle T and Component B (median [IQR]: 368 [300-506] s). This study sheds light on the physiological thermoregulatory function of newborns and will lead to improved temperature management methods for newborns, particularly premature, low-birth-weight infants.

The role of healthcare providers and caregivers in monitoring critically ill children: a qualitative study in a tertiary hospital, southern Malawi.

BACKGROUND: Critically ill children require close monitoring to facilitate timely interventions throughout their hospitalisation. In low- and middle-income countries with a high disease burden, scarce paediatric critical care resources complicates effective monitoring. This study describes the monitoring practices for critically ill children in a paediatric high-dependency unit (HDU) in Malawi and examines factors affecting this vital process. METHODS: A formative qualitative study based on 21 in-depth interviews of healthcare providers (n = 12) and caregivers of critically ill children (n = 9) in the HDU along with structured observations of the monitoring process. Interviews were transcribed and translated for thematic content analysis. RESULTS: The monitoring of critically ill children admitted to the HDU was intermittent, using devices and through clinical observations. Healthcare providers prioritised the most critically ill children for more frequent monitoring. The ward layout, power outages, lack of human resources and limited familiarity with available monitoring devices, affected monitoring. Caregivers, who were present throughout admission, were involved informally in monitoring and flagging possible deterioration of their child to the healthcare staff. CONCLUSION: Barriers to the monitoring of critically ill children in the HDU were related to ward layout and infrastructure, availability of accurate monitoring devices and limited human resources. Potential interventions include training healthcare providers to prioritise the most critically ill children, allocate and effectively employ available devices, and supporting caregivers to play a more formal role in escalation.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).

Background: Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. objectives: The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. Methods: This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. Results: A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

Contemporary Advances in Cardiac Remote Monitoring: A Comprehensive, Updated Mini-Review.

Over the past decade, remote monitoring (RM) has become an increasingly popular way to improve healthcare and health outcomes. Modern cardiac implantable electronic devices (CIEDs) are capable of recording an increasing amount of data related to CIED function, arrhythmias, physiological status and hemodynamic parameters, providing in-depth and updated information on patient cardiovascular function. The extensive use of RM for patients with CIED allows for early diagnosis and rapid assessment of relevant issues, both clinical and technical, as well as replacing outpatient follow-up improving overall management without compromise safety. This approach is recommended by current guidelines for all eligible patients affected by different chronic cardiac conditions including either brady- and tachy-arrhythmias and heart failure. Beyond to clinical advantages, RM has demonstrated cost-effectiveness and is associated with elevated levels of patient satisfaction. Future perspectives include improving security, interoperability and diagnostic power as well as to engage patients with digital health technology. This review aims to update existing data concerning clinical outcomes in patients managed with RM in the wide spectrum of cardiac arrhythmias and Hear Failure (HF), disclosing also about safety, effectiveness, patient satisfaction and cost-saving.

Remote Physiologic Monitoring Use Among Medicaid Population Increased, 2019-21.

Remote physiologic monitoring use increased more than 1,300 percent from 2019 to 2021, and use varied by state. This increase was driven by a small number of (predominantly internal medicine) providers. Female beneficiaries, residents of metropolitan areas, and people diagnosed with diabetes or hypertension had the highest rates of use.

Daily weight monitoring for inpatients receiving chemotherapy.

ABSTRACT: Unlike intake and output documentation, which is often inaccurate and inconsistent, daily weight measurement is a reliable method to assess fluid volume status. Daily weight assessment and monitoring are crucial for preventing volume overload in patients receiving chemotherapy in the inpatient setting.

Value of integrated pulmonary index monitoring for the detection of adverse respiratory events in patients undergoing sedation for gastrointestinal system endoscopy.

Objectives: To determine if the integrated pulmonary index detects changes in ventilation status early in patients undergoing gastrointestinal endoscopy under sedation, and to determine the risk factors affecting hypoxia. METHODS: The retrospective study was conducted at the endoscopy unit of a tertiary university hospital in Turkey and comprised data between October 2018 and December 2019 related to patients of either gender aged >18 years who were assessed as American Society of Anaesthesiologists grade I-III and underwent elective lower and upper gastrointestinal endoscopy. Monitoring was done with capnography in addition to standard procedures. Data was analysed using SPSS 23. RESULTS: Of the 154 patients, 94(%) were females and 60(%) were males. The overall mean age was 50.88±11.8 years (range: 20-70 years). Mean time under anaesthesia was 23.58±4.91 minutes and mean endoscopy time was 21.73±5.06 minutes. During the procedure, hypoxia was observed in 42(27.3%) patients, severe hypoxia in 23(14.9%) and apnoea in 70(45.5%). Mean time between apnoea and hypoxia was 12.59±7.99 seconds, between apnoea and serious hypoxia 21.07±17.64 seconds, between integrated pulmonary index score 1 and hypoxia 12.91±8.17 sec, between integrated pulmonary index score 1 and serious hypoxia 21.59±14.13 seconds, between integrated pulmonary index score <7 and hypoxia 19.63±8.89 seconds, between integrated pulmonary index score <7 and serious hypoxia 28.39±12.66 seconds, between end-tidal carbon dioxide and hypoxia 12.95±8.33 seconds, and between end-tidal carbon dioxide and serious hypoxia 21.29±7.55 seconds. With integrated pulmonary index score 1, sensitivity value for predicting hypoxia and severe hypoxia was 88.1% and 95.7%, respectively, and specificity was 67% and 60.3%, respectively. With integrated pulmonary index score <7, the corresponding values were 100%, 100%, 42% and 64.1%, respectively. CONCLUSIONS: Capnographic monitoring, especially the follow-up integrated pulmonary index score, was found to be valuable and reliable in terms of finding both time and accuracy of the risk factor in the diagnosis of respiratory events.

Physiologic Determinants of Near-Infrared Spectroscopy-Derived Cerebral and Tissue Oxygen Saturation Measurements in Critically Ill Patients.

OBJECTIVES: Near-infrared spectroscopy (NIRS) is a potentially valuable modality to monitor the adequacy of oxygen delivery to the brain and other tissues in critically ill patients, but little is known about the physiologic determinants of NIRS-derived tissue oxygen saturations. The purpose of this study was to assess the contribution of routinely measured physiologic parameters to tissue oxygen saturation measured by NIRS. DESIGN: An observational sub-study of patients enrolled in the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) randomized feasibility trial. SETTING: Two ICUs in the United Kingdom. PATIENTS: Patients were recruited for the RADAR-2 study, which compared a conservative approach to fluid therapy and deresuscitation with usual care. Those included in this sub-study underwent continuous NIRS monitoring of cerebral oxygen saturations (SctO2) and quadriceps muscle tissue saturations (SmtO2). INTERVENTION: Synchronized and continuous mean arterial pressure (MAP), heart rate (HR), and pulse oximetry (oxygen saturation, Spo2) measurements were recorded alongside NIRS data. Arterial Paco2, Pao2, and hemoglobin concentration were recorded 12 hourly. Linear mixed effect models were used to investigate the association between these physiologic variables and cerebral and muscle tissue oxygen saturations. MEASUREMENTS AND MAIN RESULTS: Sixty-six patients were included in the analysis. Linear mixed models demonstrated that Paco2, Spo2, MAP, and HR were weakly associated with SctO2 but only explained 7.1% of the total variation. Spo2 and MAP were associated with SmtO2, but together only explained 0.8% of its total variation. The remaining variability was predominantly accounted for by between-subject differences. CONCLUSIONS: Our findings demonstrated that only a small proportion of variability in NIRS-derived cerebral and tissue oximetry measurements could be explained by routinely measured physiologic variables. We conclude that for NIRS to be a useful monitoring modality in critical care, considerable further research is required to understand physiologic determinants and prognostic significance.

Exploring the feasibility of pupillometry training and perceptions of potential use for intracranial pressure monitoring in Uganda: A mixed methods study.

INTRODUCTION: Traumatic brain injury (TBI) accounts for the majority of Uganda's neurosurgical disease burden; however, invasive intracranial pressure (ICP) monitoring is infrequently used. Noninvasive monitoring could change the care of patients in such a setting through quick detection of elevated ICP. PURPOSE: Given the novelty of pupillometry in Uganda, this mixed methods study assessed the feasibility of pupillometry for noninvasive ICP monitoring for patients with TBI. METHODS: Twenty-two healthcare workers in Kampala, Uganda received education on pupillometry, practiced using the device on healthy volunteers, and completed interviews discussing pupillometry and its implementation. Interviews were assessed with qualitative analysis, while quantitative analysis evaluated learning time, measurement time, and accuracy of measurements by participants compared to a trainer's measurements. RESULTS: Most participants (79%) reported a positive perception of pupillometry. Participants described the value of pupillometry in the care of patients during examination, monitoring, and intervention delivery. Commonly discussed concerns included pupillometry's cost, understanding, and maintenance needs. Perceived implementation challenges included device availability and contraindications for use. Participants suggested offering continued education and engaging hospital leadership as implementation strategies. During training, the average learning time was 13.5 minutes (IQR 3.5), and the measurement time was 50.6 seconds (IQR 11.8). Paired t-tests to evaluate accuracy showed no statistically significant difference in comparison measurements. CONCLUSION: Pupillometry was considered acceptable for noninvasive ICP monitoring of patients with TBI, and pupillometer use was shown to be feasible during training. However, key concerns would need to be addressed during implementation to aid device utilization.

Bioreactance assessment of cardiac output lacks reliability for the follow-up of patients with pulmonary hypertension.

Cardiac output (CO) is one of the primary prognostic factors evaluated during the follow-up of patients treated for pulmonary hypertension (PH). It is recommended that it be measured using the thermodilution technique during right heart catheterization. The difficulty to perform iterative invasive measurements on the same individual led us to consider a non-invasive option. The aims of the present study were to assess the agreement between CO values obtained using bioreactance (Starling™ SV) and thermodilution, and to evaluate the ability of the bioreactance monitor to detect patients whose CO decreased by more than 15% during follow-up and, accordingly, its usefulness for patient monitoring. A prospective cohort study evaluating the performance of the Starling™ SV monitor was conducted in patients with clinically stable PH. Sixty patients referred for hemodynamic assessment were included. CO was measured using both the thermodilution technique and bioreactance during two follow-up visits. A total of 60 PH patients were included. All datasets were available at the baseline visit (V0) and 50 of them were usable during the follow-up visit (V1). Median [IQR] CO was 4.20 l/min [3.60-4.70] when assessed by bioreactance, and 5.30 l/min [4.57-6.20] by thermodilution (p<0.001). The Spearman correlation coefficient was 0.51 [0.36-0.64], and the average deviation on Bland-Altman plot was -1.25 l/min (95% CI [-1.48-1.01], p<0.001). The ability of the monitor to detect a variation in CO of more than 15% between two follow-up measurements, when such variation existed using thermodilution, was insufficient for clinical practice (AUC = 0.54, 95% CI [0.33-0.75]).

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

Personalized Detection of Motion Artifacts for Telemonitoring Applications.

Among its main benefits, telemonitoring enables personalized management of chronic diseases by means of biomarkers extracted from signals. In these applications, a thorough quality assessment is required to ensure the reliability of the monitored parameters. Motion artifacts are a common problem in recordings with wearable devices. In this work, we propose a fully automated and personalized method to detect motion artifacts in multimodal recordings devoted to the monitoring of the Cardiac Time Intervals (CTIs). The detection of motion artifacts was carried out by using template matching with a personalized template. The method yielded a balanced accuracy of 86%. Moreover, it proved effective to decrease the variability of the estimated CTIs by at least 17%. Our preliminary results show that personalized detection of motion artifacts improves the robustness of the assessment CTIs and opens to the use in wearable systems.

Privacy Barriers in Health Monitoring: Scoping Review.

BACKGROUND: Health monitoring technologies help patients and older adults live better and stay longer in their own homes. However, there are many factors influencing their adoption of these technologies. Privacy is one of them. OBJECTIVE: The aim of this study was to provide an overview of the privacy barriers in health monitoring from current research, analyze the factors that influence patients to adopt assisted living technologies, provide a social psychological explanation, and propose suggestions for mitigating these barriers in future research. METHODS: A scoping review was conducted, and web-based literature databases were searched for published studies to explore the available research on privacy barriers in a health monitoring environment. RESULTS: In total, 65 articles met the inclusion criteria and were selected and analyzed. Contradictory findings and results were found in some of the included articles. We analyzed the contradictory findings and provided possible explanations for current barriers, such as demographic differences, information asymmetry, researchers' conceptual confusion, inducible experiment design and its psychological impacts on participants, researchers' confirmation bias, and a lack of distinction among different user roles. We found that few exploratory studies have been conducted so far to collect privacy-related legal norms in a health monitoring environment. Four research questions related to privacy barriers were raised, and an attempt was made to provide answers. CONCLUSIONS: This review highlights the problems of some research, summarizes patients' privacy concerns and legal concerns from the studies conducted, and lists the factors that should be considered when gathering and analyzing people's privacy attitudes.

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series.

INTRODUCTION: Diabetic foot ulcers (DFU) are a devastating complication of diabetes. There are numerous challenges with preventing diabetic foot complications and barriers to achieving the care processes suggested in established foot care guidelines. Multi-faceted digital health solutions, which combine multimodal sensing, patient-facing biofeedback, and remote patient monitoring (RPM), show promise in improving our ability to understand, prevent, and manage DFUs. METHODS: Patients with a history of diabetic plantar foot ulcers were enrolled in a prospective cohort study and equipped with custom sensory insoles to track plantar pressure, plantar temperature, step count, and adherence data. Sensory insole data enabled patient-facing biofeedback to cue active plantar offloading in response to sustained high plantar pressures, and RPM assessments in response to data trends of concern in plantar pressure, plantar temperature, or sensory insole adherence. Three non-consecutive case participants that ultimately presented with pre-ulcerative lesions (a callus and/or erythematous area on the plantar surface of the foot) during the study were selected for this case series. RESULTS: Across three illustrative patients, continuous plantar pressure monitoring demonstrated promise for empowering both the patient and provider with information for data-driven management of pressure offloading treatments. CONCLUSION: Multi-faceted digital health solutions can naturally enable and reinforce the integrative foot care guidelines. Multi-modal sensing across multiple physiologic domains supports the monitoring of foot health at various stages along the DFU pathogenesis pathway. Furthermore, digital health solutions equipped with remote patient monitoring unlock new opportunities for personalizing treatments, providing periodic self-care reinforcement, and encouraging patient engagement-key tools for improving patient adherence to their diabetic foot care plan.

Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform.

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors.

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.