Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study.

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72-94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (ß = 0.30, 95% CI: 0.16-0.44) and executive function scores (ß = 0.38, 95% CI: 0.22-0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22-0.67) or dementia (OR = 0.25, 95% CI: 0.08-0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia.

Impact of Remote Blood Pressure Monitoring Device Connectivity on Engagement Among Pregnant Individuals Enrolled in the Delfina Care Platform: Observational Study.

Unlabelled: User engagement with remote blood pressure monitoring during pregnancy is critical to optimize the associated benefits of blood pressure control and early detection of hypertensive disorders of pregnancy. In our study population of pregnant individuals, we found that connected blood pressure cuffs, which automatically sync measures to a monitoring platform or health record, increase engagement (2.13 [95% CI 1.36-3.35] times more measures per day) with remote blood pressure monitoring compared to unconnected cuffs that require manual entry of measures.

Implementation of an interprofessional model for the management of postpartum hypertension.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: Postpartum hypertension (PPHTN) poses increased risks, including of stroke. Timely assessment and management by clinicians is imperative but challenging. Team-based care involving pharmacists has shown promise in improving blood pressure control, yet its application in PPHTN management remains unexplored. The objective of this study was to determine the impact and feasibility of an interprofessional model for PPHTN management. SUMMARY: This initiative implemented a novel interprofessional model at a safety-net hospital to address previous workflow limitations. Ambulatory care pharmacists collaborated with an obstetric nurse (OBRN) and a maternal fetal medicine specialist to manage high-risk patients with PPHTN utilizing electronic consults (e-consults). Data collection and symptom assessment were completed by an OBRN via telemedicine appointments. Pharmacists employed a collaborative practice agreement based on a preestablished algorithm to initiate medications. Data on patient demographics, consult volume, prescriptions, and pharmacist comfort were collected during the first quarter of full integration. Pharmacists completed 55 e-consults and generated 54 prescriptions. The average time spent per chart review was 12.5 minutes, and the average time to completion of e-consults was 54 minutes. Forty-five unique patients received care, who were primarily non-English-speaking and non-Hispanic Black patients. Pharmacists reported moderate to high comfort levels in managing PPHTN based on the algorithm and provided feedback leading to workflow adjustments. CONCLUSION: Integration of pharmacists into PPHTN care enables prompt medication initiation and titration. This innovative model, involving remote blood pressure monitoring, telemedicine visits with an OBRN, and e-consults completed by pharmacists, ensures delivery of timely and equitable care and improved access across a diverse population.

Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review.

BACKGROUND: The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions. OBJECTIVE: This scoping review aims to investigate the impact of artificial intelligence (AI)-based conversational agents (CAs)-including chatbots, voicebots, and anthropomorphic digital avatars-as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies. METHODS: A broad literature search was conducted in July 2023 in 6 electronic databases-Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science-using the search terms "conversational agents," "artificial intelligence," and "noncommunicable diseases," including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care-oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies. RESULTS: The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs' potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs. CONCLUSIONS: This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management. TRIAL REGISTRATION: Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX.

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management.

The aim of this study was to describe the implementation of a novel 50-bed continuous remote monitoring service for high-risk acute inpatients treated in non-critical wards, known as Health in a Virtual Environment (HIVE). We report the initial results, presenting the number and type of patients connected to the service, and assess key outcomes from this cohort. This was a prospective, observational study of characteristics and outcomes of patients connected to the HIVE continuous monitoring service at a major tertiary hospital and a smaller public hospital in Western Australia between January 2021 and June 2023. In the first two and a half years following implementation, 7541 patients were connected to HIVE for a total of 331,118 h. Overall, these patients had a median length of stay of 5 days (IQR 2, 10), 11.0% (n = 833) had an intensive care unit admission, 22.4% (n = 1691) had an all-cause emergency readmission within 28 days from hospital discharge, and 2.2% (n = 167) died in hospital. Conclusions: Our initial results show promise, demonstrating that this innovative approach to inpatient care can be successfully implemented to monitor high-risk patients in medical and surgical wards. Future studies will investigate the effectiveness of the program by comparing patients receiving HIVE supported care to comparable patients receiving routine care.

Optimisation of Remote Monitoring Programmes in Heart Failure: Evaluation of Patient Drop-Out Behaviour and Healthcare Professionals' Perspectives.

Heart failure (HF) is a growing epidemic, affecting millions of people worldwide, and is a major cause of mortality, morbidity, and impaired quality of life. Traditional cardiac rehabilitation is a valuable approach to the physical and quality-of-life recovery of patients with cardiovascular disease. The innovative approach of remote monitoring through telemedicine offers a solution based on modern technologies, enabling continuous collection of health data outside the hospital environment. Remote monitoring devices present challenges that could adversely affect patient adherence, resulting in the risk of dropout. By applying a cognitive-behavioral model, we aim to identify the antecedents of dropout behavior among patients adhering to traditional cardiac rehabilitation programs and remote monitoring in order to improve the latter. Our study was conducted from October 2023 to January 2024. In the first stage, we used data from literature consultation. Subsequently, data were collected from the direct experience of 49 health workers related to both remote monitoring and traditional treatment, recruited from the authors' workplace. Results indicate that patients with cardiovascular disease tend to abandon remote monitoring programs more frequently than traditional cardiac rehabilitation therapies. It is critical to design approaches that take these barriers into account to improve adherence and patient satisfaction. This analysis identified specific antecedents to address, helping to improve current monitoring models. This is crucial to promote care continuity and to achieve self-management by patients in the future.

Silk fibroin wetting stability film induced by polyamide-amine-epichlorohydrin (PAE) for intelligent sensing system.

Protein materials gain new functions and applicability through redesigns in protein structure and engineering confer. However, the application and development of proteins for use in flexible devices that fit in flexible devices that fit the surface of human skin is hindered by their poor wet stability. Here, we described the design of wet-stable materials based on the reconstruction of silk fibroin (SF). The combination of polyamide-amine-epichlorohydrin (PAE) was used as a traction rope to bring SF molecular chains closer to each other, to facilitate the self-assembly of SF through branching and lengthening of molecular chains, and change its crystalline structure. SF/PAE composite films that exhibited huge improvement in ductility and wet stability were combined with flexible SF substrates via patterning and ion sputtering to prepare flexible sensors. In addition, the SF/PAE sensing system equipped with a microprocessor and Bluetooth module enabled the real-time remote acquisition of human health signals such as vocal cords, joints, pulse and meridians. This reconfiguration of the SF structure will advance the systematic exploration of protein structures and the development of protein materials for intelligent device applications.

Quantifying lumbar mobility using a single tri-axial accelerometer.

Background: Lumbar mobility is regarded as important for assessing and managing low back pain (LBP). Inertial Measurement Units (IMUs) are currently the most feasible technology for quantifying lumbar mobility in clinical and research settings. However, their gyroscopes are susceptible to drift errors, limiting their use for long-term remote monitoring. Research question: Can a single tri-axial accelerometer provide an accurate and feasible alternative to a multi-sensor IMU for quantifying lumbar flexion mobility and velocity? Methods: In this cross-sectional study, 18 healthy adults performed nine repetitions of full spinal flexion movements. Lumbar flexion mobility and velocity were quantified using a multi-sensor IMU and just the tri-axial accelerometer within the IMU. Correlations between the two methods were assessed for each percentile of the lumbar flexion movement cycle, and differences in measurements were modelled using a Generalised Additive Model (GAM). Results: Very high correlations (r > 0.90) in flexion angles and velocities were found between the two methods for most of the movement cycle. However, the accelerometer overestimated lumbar flexion angle at the start (-4.7° [95 % CI -7.6° to -1.8°]) and end (-4.8° [95 % CI -7.7° to -1.9°]) of movement cycles, but underestimated angles (maximal difference of 4.3° [95 % CI 1.4° to 7.2°]) between 7 % and 92 % of the movement cycle. For flexion velocity, the accelerometer underestimated at the start (16.6°/s [95%CI 16.0 to 17.2°/s]) and overestimated (-12.3°/s [95%CI -12.9 to -11.7°/s]) at the end of the movement, compared to the IMU. Significance: Despite the observed differences, the study suggests that a single tri-axial accelerometer could be a feasible tool for continuous remote monitoring of lumbar mobility and velocity. This finding has potential implications for the management of LBP, enabling more accessible and cost-effective monitoring of lumbar mobility in both clinical and research settings.

Monitoring landfill volatile organic compounds emissions by an uncrewed aerial vehicle platform with infrared and visible-light cameras, remote monitoring, and sampling systems.

An uncrewed aerial vehicle (UAV) platform equipped with dual imaging cameras, a gas sampling system, and a remote synchronous monitoring system was developed to sample and analyze volatile organic compounds (VOCs) emitted from landfills. The remote synchronous monitoring system provided real-time video to administrators with specific permissions to assist in identifying sampling sites within extensive landfill areas. The sampling system included four kits capable of collecting samples from different locations during a single flight mission. Each kit comprised a 1 L Tedlar bag for measuring landfill VOC concentrations according to the TO-15 method prescribed by the US Environmental Protection Agency. The air sample was introduced into a Tedlar bag via pumping. A known volume of the sample was subsequently concentrated using a solid multisorbent concentrator. Following this, the sample underwent cold trap concentration and thermal desorption. The concentrated sample was then transferred to a chromatography-mass spectrometry system for separation and analysis. Since the anaerobic catabolism of organic waste is exothermic and emits VOCs, this study employed UAV thermal imaging to locate principal emission sources for sampling. Visible-light imaging helped identify newer or older landfill sections, aiding in the selection of appropriate sampling sites, particularly when surfaces were thermally disturbed by solar radiation. Field measurements were conducted under three meteorological conditions: sunny morning, cirrus morning, and thin cloud evening (2 h after sunset), identifying 119, 122, and 111 chemical species respectively. The sequence of total VOC concentrations measured correlated with the meteorological conditions as follows: cirrus morning > thin cloud evening > sunny morning. The results indicated that ambient temperature and global solar radiation significantly influenced daytime gas emissions from landfills. Evening thermal images, unaffected by solar heating, facilitated more accurate identification of major VOC emission points, resulting in higher VOC concentrations compared to those recorded in the sunny morning. VOCs from the landfill were categorized into nine organic groups: alkanes, alkenes, carbonyls, aromatics, alcohols, esters, ethers, organic oxides, and others. The classification was based on carbon-containing compounds (Cn, where the compound contains n carbon atoms). Alkanes were predominant in terms of Cn presence, followed by alcohols and carbonyls. Among the organic groups, organic oxides, particularly 2-heptyl-1,3-dioxolane, exhibited the highest concentrations, succeeded by alkenes. Sampling under cloudy conditions or in the evening is recommended to minimize the effects of surface temperature anomalies caused by solar radiation, which vary due to differences in land composition.

Too much ado about data: Continuous remote monitoring of water temperatures, circulation and throughput can assist in the reduction of hospital-associated waterborne infections.

BACKGROUND: National and international guidance provides advice on maintenance and management of water systems in healthcare buildings, however, healthcare-associated waterborne infections (HAWI) are increasing. This narrative review identifies parameters critical to water quality in healthcare buildings and assesses if remote sensor monitoring can deliver safe water systems thus reducing HAWI. METHOD: A narrative review was performed using the following search terms 1) consistent water temperature AND waterborne pathogen control OR nosocomial infection 2) water throughput AND waterborne pathogen control OR nosocomial infection 3) remote monitoring of in-premise water systems AND continuous surveillance for temperature OR throughput OR flow OR use. Databases employed were PubMed, CDSR (Clinical Study Data Request) and DARE (Database of Abstracts of Reviews of Effects) from Jan 2013 - Mar 2024. FINDINGS: Single ensuite-patient rooms, expansion of wash-hand basins, widespread glove use, alcohol gel and wipes have increased water system stagnancy resulting in amplification of waterborne pathogens and transmission risk of Legionella, Pseudomonas and Non-Tuberculous Mycobacteria. Manual monitoring does not represent temperatures across large complex water systems. This review deems that multiple point continuous remote sensor monitoring is effective at identifying redundant and low use outlets, hydraulic imbalance and inconsistent temperature delivery across in-premise water systems. CONCLUSION: As remote monitoring becomes more common there will be greater recognition of failures in temperature control, hydraulics and balancing in water systems and there remains much to learn as we adopt this developing technology within our hospitals.

Prediction of heart failure events based on physiologic sensor data in HINODE defibrillator patients.

AIMS: Hospitalizations are common in patients with heart failure and are associated with high mortality, readmission and economic burden. Detecting early signs of worsening heart failure may enable earlier intervention and reduce hospitalizations. The HeartLogic algorithm is designed to predict worsening heart failure using diagnostic data from multiple device sensors. The main objective of this analysis was to evaluate the sensitivity of the HeartLogic alert calculation in predicting worsening heart failure events (HFEs). We also evaluated the false positive alert rate (FPR) and compared the incidence of HFEs occurring in a HeartLogic alert state to those occurring out of an alert state. METHODS: The HINODE study enrolled 144 patients (81 ICD and 63 CRT-D) with device sensor data transmitted via a remote monitoring system. HeartLogic alerts were then retrospectively simulated using relevant sensor data. Clinicians and patients were blinded to calculated alerts. Reported adverse events with HF symptoms were adjudicated and classified by an independent HFE committee. Sensitivity was defined as the ratio of the number of detected usable HFEs (true positives) to the total number of usable HFEs. A false positive alert was defined as an alert with no usable HFE between the alert onset date and the alert recovery date plus 30 days. The patient follow-up period was categorized as in alert state or out of alert state. The event rate ratio was the HFE rate calculated in alert to out of alert. RESULTS: The patient cohort was 79% male and had an average age of 68 ± 12 years. This analysis yielded 244 years of follow-up data with 73 HFEs from 37 patients. A total of 311 HeartLogic alerts at the nominal threshold (16) occurred across 106 patients providing an alert rate of 1.27 alerts per patient-year. The HFE rate was 8.4 times greater while in alert compared with out of alert (1.09 vs. 0.13 events per patient-year; P < 0.001). At the nominal alert threshold, 80.8% of HFEs were detected by a HeartLogic alert [95% confidence interval (CI): 69.9%-89.1%]. The median time from first true positive alert to an adjudicated clinical HFE was 53 days. The FPR was 1.16 (95% CI: 0.98-1.38) alerts per patient-year. CONCLUSIONS: Results suggest that signs of worsening HF can be detected successfully with remote patient follow-up. The use of HeartLogic may predict periods of increased risk for HF or clinically significant events, allowing for early intervention and reduction of hospitalization in a vulnerable patient population.

Determinants of global cardiac implantable electrical device remote monitoring utilization - Results from an international survey.

Background: Despite near-global availability of remote monitoring (RM) in patients with cardiac implantable electronic devices (CIED), there is a high geographical variability in the uptake and use of RM. The underlying reasons for this geographic disparity remain largely unknown. Objectives: To study the determinants of worldwide RM utilization and identify locoregional barriers of RM uptake. Methods: An international survey was administered to all CIED clinic personnel using the Heart Rhythm Society global network collecting demographic information, as well as information on the use of RM, the organization of the CIED clinic, and details on local reimbursement and clinic funding. The most complete response from each center was included in the current analysis. Stepwise forward multivariate linear regression was performed to identify determinants of the percentage of patients with a CIED on RM. Results: A total of 302 responses from 47 different countries were included, 61.3% by physicians and 62.3% from hospital-based CIED clinics. The median percentage of CIED patients on RM was 80% (interquartile range, 40-90). Predictors of RM use were gross national income per capita (0.76% per US$1000, 95% CI 0.72-1.00, P < .001), office-based clinics (7.48%, 95% CI 1.53-13.44, P = .014), and presence of clinic funding (per-patient payment model 7.90% [95% CI 0.63-15.17, P = .033); global budget 3.56% (95% CI -6.14 to 13.25, P = .471]). Conclusion: The high variability in RM utilization can partly be explained by economic and structural barriers that may warrant specific efforts by all stakeholders to increase RM utilization.

Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm.

Background: Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation. Objective: This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm. Methods: The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th-75th percentile: 15-37). Results: During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02-9.72; P = .045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74-9.06; P=.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%-98%]) and 55% specificity (95% CI [51%-60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%-88%]) and 81% specificity (95% CI [77%-85%]). Conclusion: A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.

A biobehavioral observational study to understand the multilevel determinants of cardiovascular health in Black women: the BLOOM Study protocol.

BACKGROUND: The racial/ethnic and gender disparities in cardiovascular disease (CVD) morbidity and mortality in the United States are evident. Across nearly every metric, non-Hispanic Black women have poorer overall cardiovascular health. Emerging evidence shows a disproportionately high burden of increased CVD risk factors in Black women of childbearing age, which has a far-reaching impact on both maternal and child outcomes, resulting in premature onset of CVD and further widens the racial disparities in CVD. There is growing recognition that the fundamental driver of persistent racial/ethnic disparities in CVD, as well as disparities in behavioral risk factors such as physical activity and sleep, is structural racism. Further, the lived personal experience of racial discrimination not only has a negative impact on health behaviors, but also links to various physiological pathways to CVD risks, such as internalized stress resulting in a pro-inflammatory state. Limited research, however, has examined the interaction between daily experience and health behaviors, which are influenced by upstream social determinants of health, and the downstream effect on biological/physiological indicators of cardiovascular health in non-pregnant Black women of childbearing age. METHODS/DESIGN: The BLOOM Study is an observational study that combines real-time ambulatory assessments over a 10-day monitoring period with in-depth cross-sectional lab-based physiological and biological assessments. We will use a wrist-worn actigraphy device to capture 24-h movement behaviors and electronic ecological momentary assessment to capture perceived discrimination, microaggression, and stress. Blood pressure will be captured continuously through a wristband. Saliva samples will be self-collected to assess cortisol level as a biomarker of psychological stress. Lab assessments include a fasting venous blood sample, and assessment of various indices of peripheral and cerebral vascular function/health. Participants' address or primary residence will be used to obtain neighborhood-level built environmental and social environmental characteristics. We plan to enroll 80 healthy Black women who are between 18 and 49 years old for this study. DISCUSSION: Results from this study will inform the development of multilevel (i.e., individual, interpersonal, and social-environmental levels) lifestyle interventions tailored to Black women based on their lived experiences with the goal of reducing CVD risk. GOV IDENTIFIER: NCT06150989.

Temperature and ST-segment morphology remote monitoring: new perspectives for implantable cardiac monitors in Brugada syndrome.

INTRODUCTION: Patients with Brugada syndrome (BrS) face an increased risk of ventricular arrhythmias and sudden cardiac death. Implantable cardiac monitors (ICMs) have emerged as effective tools for detecting arrhythmias in BrS. Technological advancements, including temperature sensors and improved subcutaneous electrocardiogram (subECG) signal quality, hold promise for further enhancing their utility in this population. METHODS AND RESULTS: We present a case of a 40-year-old man exhibiting a BrS type 2 pattern on 12-lead ECG, who underwent ICM insertion (BIOMONITOR IIIm, BIOTRONIK) due to drug-induced BrS type 1 pattern and a history of syncope, with a negative response to programmed ventricular stimulation. The device contains an integrated temperature sensor and can transmit daily vital data, such as mean heart rate and physical activity. Several months later, remote alerts indicated a temperature increase, along with transmitted subECGs suggesting a fever-induced BrS type 1 pattern. The patient was promptly advised to commence antipyretic therapy. Over the following days, remotely monitored parameters showed decreases in mean temperature, physical activity, and mean heart rate, without further recurrence of abnormal subECGs. CONCLUSION: ICMs offer valuable insights beyond arrhythmia detection in BrS. Early detection of fever using embedded temperature sensors may improve patient management, while continuous subECG morphological analysis has the potential to enhance risk stratification in BrS patients.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Mechanical Method for Rapid Determination of Step Count Sensor Settings.

With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30-60, 20-90, and 30-110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials.

Technological advances in lower-limb tele-rehabilitation: A review of literature.

Tele-rehabilitation is a healthcare practice that leverages technology to provide rehabilitation services remotely to individuals in their own homes or other locations. With advancements in remote monitoring and Artificial Intelligence, automatic tele-rehabilitation systems that can measure joint angles, recognize exercises, and provide feedback based on movement analysis are being developed. Such platforms can offer valuable information to clinicians for improved care planning. However, with various methods and sensors being used, understanding their pros, cons, and performance is important. This paper reviews and compares the performance of recent vision-based, wearable, and pressure-sensing technologies used in lower limb tele-rehabilitation systems over the past 10 years (from 2014 to 2023). We selected studies that were published in English and focused on joint angle estimation, activity recognition, and exercise assessment. Vision-based approaches were the most common, accounting for 42% of studies. Wearable technology followed at approximately 37%, and pressure-sensing technology appeared in 21% of studies. Identified gaps include a lack of uniformity in reported performance metrics and evaluation methods, a need for cross-subject validation, inadequate testing with patients and older adults, restricted sets of exercises evaluated, and a scarcity of comprehensive datasets on lower limb exercises, especially those involving movements while lying down.