Health Monitoring Using Smart Home Technologies: A Scoping Review (Preprint)

Smartwatch-Detected Arrhythmias in Patients After Transcatheter Aortic Valve Replacement (TAVR): Analysis of the SMART TAVR Trial

BACKGROUND

There are limited data available on the development of arrhythmias in patients at risk of high-degree atrioventricular block (HAVB) or complete heart block (CHB) following transcatheter aortic valve replacement (TAVR).

OBJECTIVE

This study aimed to explore the incidence and evolution of arrhythmias by monitoring patients at risk of HAVB or CHB after TAVR using smartwatches.

METHODS

We analyzed 188 consecutive patients in the prospective SMART TAVR (smartwatch-facilitated early discharge in patients undergoing TAVR) trial. Patients were divided into 2 groups according to the risk of HAVB or CHB. Patients were required to trigger a single-lead electrocardiogram (ECG) recording and send it to the Heart Health App via their smartphone. Physicians in the central ECG core lab would then analyze the ECG. The incidence and timing of arrhythmias and pacemaker implantation within a 30-day follow-up were compared. All arrhythmic events were adjudicated in a central ECG core lab.

RESULTS

The mean age of the patients was 73.1 (SD 7.3) years, of whom 105 (55.9%) were men. The mean discharge day after TAVR was 2.0 (SD 1.8) days. There were no statistically significant changes in the evolution of atrial fibrillation or atrial flutter, Mobitz I, Mobitz II, and third-degree atrial ventricular block over time in the first month after TAVR. The incidence of the left bundle branch block (LBBB) increased in the first week and decreased in the subsequent 3 weeks significantly (P<.001). Patients at higher risk of HAVB or CHB received more pacemaker implantation after discharge (n=8, 9.6% vs n=2, 1.9%; P=.04). The incidence of LBBB was higher in the group with higher HAVB or CHB risk (n=47, 56.6% vs n=34, 32.4%; P=.001). The independent predictors for pacemaker implantation were age, baseline atrial fibrillation, baseline right bundle branch block, Mobitz II, and third-degree atrioventricular block detected by the smartwatch.

CONCLUSIONS

Except for LBBB, no change in arrhythmias was observed over time in the first month after TAVR. A higher incidence of pacemaker implantation after discharge was observed in patients at risk of HAVB or CHB. However, Mobitz II and third-degree atrioventricular block detected by the smartwatch during follow-ups were more valuable indicators to predict pacemaker implantation after discharge from the index TAVR.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04454177; https://clinicaltrials.gov/study/NCT04454177.

Closing the loop between environment, brain and mental health: how far we might go in real-life assessments?

PURPOSE OF REVIEW

Environmental factors such as climate, urbanicity, and exposure to nature are becoming increasingly important influencers of mental health. Incorporating data gathered from real-life contexts holds promise to substantially enhance laboratory experiments by providing a more comprehensive understanding of everyday behaviors in natural environments. We provide an up-to-date review of current technological and methodological developments in mental health assessments, neuroimaging and environmental sensing.

RECENT FINDINGS

Mental health research progressed in recent years towards integrating tools, such as smartphone based mental health assessments or mobile neuroimaging, allowing just-in-time daily assessments. Moreover, they are increasingly enriched by dynamic measurements of the environment, which are already being integrated with mental health assessments. To ensure ecological validity and accuracy it is crucial to capture environmental data with a high spatio-temporal granularity. Simultaneously, as a supplement to experimentally controlled conditions, there is a need for a better understanding of cognition in daily life, particularly regarding our brain's responses in natural settings.

SUMMARY

The presented overview on the developments and feasibility of "real-life" approaches for mental health and brain research and their potential to identify relationships along the mental health-environment-brain axis informs strategies for real-life individual and dynamic assessments.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review

BACKGROUND

The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.

OBJECTIVE

The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.

METHODS

A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.

RESULTS

Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.

CONCLUSION

Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

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.

The Impact of a Multicomponent Platform Intervention on the Daily Lives of Older Adults

Gerontechnology is an interdisciplinary field of research involving gerontology and technology in order to help older adults identify and slow down the effects of age-related physical and cognitive decline. It has enormous potential to allow individuals to remain in their own homes and improve their quality of life. This study aims to assess the impact of a multicomponent platform, consisting of an ambient sensor, wearable devices, and a cloud application, as an intervention in terms of usability and acceptance as primary outcomes and well-being, quality of life, and self-efficacy as secondary outcomes in a sample of 25 older adults aged over 65 after 21 days of non-supervised usage at home. This research involved the use of a mixed-methods approach, in which both qualitative and quantitative data were collected in three different measurements. Overall, the participants shared good engagement with the integrated platform. The system achieved positive results in terms of both usability and acceptance, especially the smartwatch. The state of complete well-being slightly improved over the period, whereas self-efficacy remained stable. This study demonstrates the ability of target users to use technology independently in their home environment: it strengthens the idea that this technology is ready for mainstream use and offers food for thought for developers who create products for the aging population.

The Lifespan of Human Activity Recognition Systems for Smart Homes

With the growing interest in smart home environments and in providing seamless interactions with various smart devices, robust and reliable human activity recognition (HAR) systems are becoming essential. Such systems provide automated assistance to residents or to longitudinally monitor their daily activities for health and well-being assessments, as well as for tracking (long-term) behavior changes. These systems thus contribute towards an understanding of the health and continued well-being of residents. Smart homes are personalized settings where residents engage in everyday activities in their very own idiosyncratic ways. In order to provide a fully functional HAR system that requires minimal supervision, we provide a systematic analysis and a technical definition of the lifespan of activity recognition systems for smart homes. Such a designed lifespan provides for the different phases of building the HAR system, where these different phases are motivated by an application scenario that is typically observed in the home setting. Through the aforementioned phases, we detail the technical solutions that are required to be developed for each phase such that it becomes possible to derive and continuously improve the HAR system through data-driven procedures. The detailed lifespan can be used as a framework for the design of state-of-the-art procedures corresponding to the different phases.