Introduction: Digital technologies, such as telemedicine and wearable devices, are transforming health care by enhancing cross-sectoral care and targeted health responses. Despite these advancements, challenges such as data protection, lack of interoperability, reimbursement, and financial costs hinder telemedicine's broader implementation, especially within the German health care system. This study explores the use, acceptance, and barriers of telemedicine among cardiologists, internists, and general practitioners in Germany. Methods: A web-based survey was conducted from October 2023 to January 2024, targeting cardiologists, internists, and general practitioners. The survey assessed current telemedicine usage, acceptance, and barriers. Data analysis included descriptive statistics and exploratory cluster analysis. Results: Of the 172 physicians analyzed, 76.2% were cardiologists. Telemonitoring (45.9%) and wearable devices (26.2%) were the most used telemedicine applications, whereas video consultations (11.0%) and apps (19.2%) were less common. Despite high costs (57.7%), insufficient technical expertise (20.8%), and lack of system interoperability (45.8%), respondents rated telemedicine positively and saw several chances and potentials. Cluster analysis identified four user groups: The pioneers, the focused practitioners, the using skeptics, and the uninformed distanced, each with unique needs and challenges. Discussion: The acceptance of elemedicine among physicians indicates recognition of its benefits for patient care. Only half of the respondents felt reasonably well informed about telemedicine. Overall, our study shows the current use of telemedicine as well as the acceptance, barriers, and challenges perceived in the German ambulatory sector. It underlines the increasing importance of telemedicine for patient care and highlights existing barriers to enable wider implementation in the outpatient sector. The results show that telemedicine in Germany is on a promising path. The biggest obstacles still appear to be reimbursement and the technical infrastructure.

Postpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth that is often underscreened and underdetected. If not diagnosed and treated, PPD is associated with long-term developmental challenges in the child and maternal morbidity. Wearable technologies, such as smartwatches and fitness trackers (eg, Fitbit), offer continuous and longitudinal digital phenotyping for mood disorder diagnosis and monitoring, with device wear time being an important yet understudied aspect.

We aimed to suggest that wear time of a wearable device may provide additional information about perinatal mental health to facilitate screening and early detection of PPD. We proposed that wear time of a wearable device may also be valuable for managing other mental health disorders.

Using the All of Us Research Program dataset, we identified females who experienced childbirth with and without PPD using computational phenotyping. We compared the percentage of days and number of hours per day females with and without PPD wore Fitbit devices during prepregnancy, pregnancy, postpartum, and PPD periods, determined by electronic health records. Comparisons between females with and without PPD were conducted using linear regression models. We also assessed the correlation between Fitbit wear time consistency (measured as the maximum number of consecutive days the Fitbit was worn) during prepregnancy and PPD periods in females with and without PPD using the Pearson correlation. All analyses were run with Bonferroni correction.

Our findings showed a strong trend, although nonsignificant after multiple testing correction, that females in the PPD cohort wore their Fitbits more than those in non-PPD cohort during the postpartum (PPD cohort: mean 69.9%, 95% CI 42.7%-97%; non-PPD cohort: mean 50%, 95% CI 25.5%-74.4%; P=.02) and PPD periods (PPD cohort: mean 66.6%, 95% CI 37.9%-95.3%; non-PPD cohort: mean 46.4%, 95% CI 20.5%-72.2%; P=.02). We found no difference in the number of hours per day females in the PPD and non-PPD cohorts wore their Fitbit during any period of pregnancy. Finally, there was no relationship between the consistency of Fitbit wear time during prepregnancy and PPD periods (r=-0.05, 95% CI -0.46 to 0.38; P=.84); however, there was a trend, though nonsignificant, in Fitbit wear time consistency among females without PPD (r=0.25, 95% CI -0.02 to 0.49; P=.07).

We hypothesize that increased Fitbit wear time among females with PPD may be attributed to hypervigilance, given the common co-occurrence of anxiety symptoms. Future studies should assess the link between PPD, hypervigilance, and wear time patterns. We envision that wear time patterns of a wearable device combined with digital biomarkers such as sleep and physical activity could enhance early PPD detection using machine learning by alerting clinicians to potential concerns and facilitating timely screenings, which may have implications for other mental health disorders.

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Automated detection could improve survival by reducing delays in first responder activation. This study provides proof-of-principle for a device-independent technology that can (A) distinguish presence versus absence of spontaneous circulation, and (B) reliably alert emergency medical services (EMS).

Circulatory arrest data were collected from three groups: (1) volunteers undergoing temporarily restricted blood flow to the arm using a cuff, (2) patients undergoing cardioplegic cardiac arrest for heart surgery, and (3) domestic swine, slaughtered in food industry. Data were collected using Samsung Watch5 and Watch5 Pro. An algorithm was developed to analyze photoplethysmography signals and detect circulatory arrest. Emergency response was tested via the Dutch community first responder network HartslagNu, using their test environment to activate test responders and EMS.

Nineteen participants were analyzed. Across all three groups, 28 of 31 circulatory arrests were correctly identified, sensitivity 90.3% (95% CI: 74.2% - 98.0%), and hour-level specificity was 94.1% (95% CI: 71.3% - 99.9%). Triggering a circulatory arrest consistently resulted in an audiovisual smartwatch alarm and an instantaneous alert to the virtual EMS at the HartslagNu test server.

This study demonstrates the feasibility of detecting circulatory arrest using commercially available smartwatch sensors, achieving high sensitivity and specificity. Additionally, we integrated an automated alerting system with emergency networks to notify first responders. While this technology shows promise to improve survival, higher specificity is needed to prevent overburdening EMS. Future research should focus on real-world validation using actual cardiac arrest data.

Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

A holistic view on biological systems is today a reality with the application of multi-omic technologies. These technologies allow the profiling of genome, epigenome, transcriptome, proteome, metabolome as well as newly emerging 'omes.' While the multiple layers of data accumulate, their integration and reconciliation in a single system map is a cumbersome exercise that faces many challenges. Application to human health and disease requires large sample sizes, robust methodologies and high-quality standards.

We review the different methods used to integrate multi-omics, as recent ones including artificial intelligence. With proteomics as an anchor technology, we then present selected applications of its data combination with other omics layers in clinical research, mainly covering literature from the last five years in the Scopus and/or PubMed databases.

Multi-omics is powerful to comprehensively type molecular layers and link them to phenotype. Yet, technologies and data are very diverse and still strategies and methodologies to properly integrate these modalities are needed.

Despite advancements in wearable technologies1,2, barriers remain in achieving distributed computation located persistently on the human body. Here a textile fibre computer that monolithically combines analogue sensing, digital memory, processing and communication in a mass of less than 5 g is presented. Enabled by a foldable interposer, the two-dimensional pad architectures of microdevices were mapped to three-dimensional cylindrical layouts conforming to fibre geometry. Through connection with helical copper microwires, eight microdevices were thermally drawn into a machine-washable elastic fibre capable of more than 60% stretch. This programmable fibre, which incorporates a 32-bit floating-point microcontroller, independently performs edge computing tasks even when braided, woven, knitted or seam-sewn into garments. The universality of the assembly process allows for the integration of additional functions with simple modifications, including a rechargeable fibre power source that operates the computer for nearly 6 h. Finally, we surmount the perennial limitation of rigid interconnects by implementing two wireless communication schemes involving woven optical links and seam-inserted radio-frequency communications. To demonstrate its utility, we show that garments equipped with four fibre computers, one per limb, operating individually trained neural networks achieve, on average, 67% accuracy in classifying physical activity. However, when networked, inference accuracy increases to 95% using simple weighted voting.

Consumer-grade wearables are becoming increasingly popular in research and in clinical contexts. These technologies hold significant promise for advancing digital medicine, particularly in remote and rural areas in low-income settings like sub-Saharan Africa, where climate change is exacerbating health risks. This study evaluates the data agreement between consumer-grade and research-established devices under standardized conditions.

Twenty-two participants (11 women, 11 men) performed a structured protocol, consisting of six different activity phases (sitting, standing, and the first four stages of the classic Bruce treadmill test). We collected heart rate, (core) body temperature, step count, and energy expenditure. Each variable was simultaneously tracked by consumer-grade and established research-grade devices to evaluate the validity of the consumer-grade devices. We statistically compared the data agreement using Pearson's correlation r, Lin's concordance correlation coefficient (LCCC), Bland-Altman method, and mean absolute percentage error.

A good agreement was found between the wrist-worn Withings Pulse HR (consumer-grade) and the chest-worn Faros Bittium 180 in measuring heart rate while sitting, standing, and slow walking on a treadmill at a speed of 2.7 km/h (r ≥ 0.82, |bias| ≤ 3.1 bpm), but this decreased with increasing speed (r ≤ 0.33, |bias| ≤ 11.7 bpm). The agreement between the Withing device and the research-established device worn on the wrist (GENEActiv) for measuring the number of steps also decreased during the treadmill phases (first stage: r = 0.48, bias = 0.6 steps/min; fourth stage: r = 0.48, bias = 17.3 steps/min). Energy expenditure agreement between the Withings device and the indirect calorimetry method was poor during the treadmill test (|r| ≤ 0.29, |bias | ≥ 1.7 MET). The Tucky thermometer under the armpit (consumer-grade) and the Tcore sensor on the forehead were found to be in poor agreement in measuring (core) body temperature during resting phases (r ≤ 0.53, |bias| ≥ 0.8°C) and deteriorated during the treadmill test.

The Withings device showed adequate performance for heart rate at low activity levels and step count at higher activity levels, but had limited overall accuracy. The Tucky device showed poor agreement with the Tcore in all six different activity phases. The limited accuracy of consumer-grade devices suggests caution in their use for rigorous research, but points to their potential utility in capture general physiological trends in long-term field monitoring or population-health surveillance.

Digital therapy has gained popularity in the mental health field because of its convenience and accessibility. One major benefit of digital therapy is its ability to address therapist shortages. Posttraumatic stress disorder (PTSD) is a debilitating mental health condition that can develop after an individual experiences or witnesses a traumatic event. Digital therapy is an important resource for individuals with PTSD who may not have access to traditional in-person therapy. Cognitive behavioral therapy (CBT) and eye movement desensitization and reprocessing (EMDR) are two evidence-based psychotherapies that have shown efficacy in treating PTSD. This paper examines the mechanisms and clinical symptoms of PTSD as well as the principles and applications of CBT and EMDR. Additionally, the potential of digital therapy, including internet-based CBT, video conferencing-based therapy, and exposure therapy using augmented and virtual reality, is explored. This paper also discusses the engineering techniques employed in digital psychotherapy, such as emotion detection models and text analysis, for assessing patients' emotional states. Furthermore, it addresses the challenges faced in digital therapy, including regulatory issues, hardware limitations, privacy and security concerns, and effectiveness considerations. Overall, this paper provides a comprehensive overview of the current state of digital psychotherapy for PTSD treatment and highlights the opportunities and challenges in this rapidly evolving field.

To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine.

A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications. Included studies were categorized by application: athletic training, rehabilitation, and research.

In total, 43 studies met criteria for inclusion in this review. Forms of WSDs include pedometers, accelerometers, encoders (consisting of magnetometers and gyroscopes), force sensors, global positioning system trackers, and inertial measurement units. Outcome metrics include step counts; gait, limb motion, and angular positioning; foot and skin pressure; change of direction and inclination, including analysis of both body parts and athletes on a field; displacement and velocity of body segments and joints; heart rate; plethysmography; sport-specific kinematics; range of motion, symmetry, and alignment; head impact; sleep; throwing biomechanics; and kinetic and spatiotemporal running metrics. WSDs are used in athletic training to assess sport-specific biomechanics and workload with a goal of injury prevention and training optimization, as well as for rehabilitation monitoring and research such as for risk predicting and aiding diagnosis.

WSDs enable real-time monitoring of human performance across a variety of implementations and settings, allowing collection of metrics otherwise not achievable. WSDs are powerful tools with multiple applications within athletic training, patient rehabilitation, and orthopaedic and sports medicine research.

Wearable technology may represent the missing link to quantitatively addressing return to play and previous performance. WSDs are commercially available and portable adjuncts that allow clinicians, trainers, and individual athletes to monitor biomechanical parameters, workload, and recovery status to better contextualize personalized training, injury risk, and rehabilitation.

The increasing demand for personalized healthcare, particularly among individuals requiring continuous health monitoring, has driven significant advancements in sensor technology. Wearable, non-continuous monitoring, and non-contact sensors are leading this innovation, providing novel methods for monitoring vital signs and physiological data in both clinical and home settings. However, there is a lack of comprehensive comparative studies assessing the overall functionality of these technologies. This paper aims to address this gap by presenting a detailed comparative analysis of selected wearable, non-continuous monitoring, and non-contact sensors used for health monitoring. To achieve this, we conducted a comprehensive evaluation of various sensors available on the market, utilizing key indicators such as sensor performance, usability, associated platforms functionality, data management, battery efficiency, and cost-effectiveness. Our findings highlight the strengths and limitations of each sensor type, thus offering valuable insights for the selection of the most appropriate technology based on specific healthcare needs. This study has the potential to serve as a valuable resource for researchers, healthcare providers, and policymakers, contributing to a deeper understanding of existing user-centered health monitoring solutions.

Mental health is vital to human well-being, and prevention strategies to address mental illness have a significant impact on the burden of disease and quality of life. With the recent developments in body-worn sensors, it is now possible to continuously collect data that can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment, thereby improving patient experiences and quality of life. However, access to high-quality medical data for research purposes is limited, especially regarding diagnosed psychiatric patients. To this extent, we present the OBF-Psychiatric dataset which comprises motor activity recordings of patients with bipolar and unipolar major depression, schizophrenia, and ADHD (attention deficit hyperactivity disorder). The dataset also contains data from a clinical sample diagnosed with various mood and anxiety disorders, as well as a healthy control group, making it suitable for building machine learning models and other analytical tools. It contains recordings from 162 individuals totalling 1565 days worth of motor activity data with a mean of 9.6 days per individual.

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents' overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in R2, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context.

Driving performance can be significantly impacted when a person experiences intense emotions behind the wheel. Research shows that emotions such as anger, sadness, agitation, and joy can increase the risk of traffic accidents. This study introduces a methodology to recognize four specific emotions using an intelligent model that processes and analyzes signals from motor activity and driver behavior, which are generated by interactions with basic driving elements, along with facial geometry images captured during emotion induction. The research applies machine learning to identify the most relevant motor activity signals for emotion recognition. Furthermore, a pre-trained Convolutional Neural Network (CNN) model is employed to extract probability vectors from images corresponding to the four emotions under investigation. These data sources are integrated through a unidimensional network for emotion classification. The main proposal of this research was to develop a multimodal intelligent model that combines motor activity signals and facial geometry images to accurately recognize four specific emotions (anger, sadness, agitation, and joy) in drivers, achieving a 96.0% accuracy in a simulated environment. The study confirmed a significant relationship between drivers' motor activity, behavior, facial geometry, and the induced emotions.

Randomized clinical trials are the gold standard for establishing the efficacy and safety of cardiovascular therapies. However, current pivotal trials are expensive, lengthy, and insufficiently diverse. Emerging artificial intelligence (AI) technologies can potentially automate and streamline clinical trial operations. This review describes opportunities to integrate AI throughout a trial's life cycle, including designing the trial, identifying eligible patients, obtaining informed consent, ascertaining physiological and clinical event outcomes, interpreting imaging, and analyzing or disseminating the results. Nevertheless, AI poses risks, including generating inaccurate results, amplifying biases against underrepresented groups, and violating patient privacy. Medical journals and regulators are developing new frameworks to evaluate AI research tools and the data they generate. Given the high-stakes role of randomized trials in medical decision making, AI must be integrated carefully and transparently to protect the validity of trial results.

Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear.

To conduct a 'living' (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes.

A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors' results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences.

Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of ± 3%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO2max by ± 15.24% during resting tests and ± 9.83% during exercise tests. Physical activity intensity measurements had a mean absolute error ranging from 29 to 80%, depending on the intensity of the activity being undertaken. Wearables mostly underestimated step counts (mean absolute percentage errors ranging from - 9 to  12%) and energy expenditure (mean bias =  - 3 kcal per minute, or - 3%, with error ranging from - 21.27 to 14.76%). For blood oxygen saturation, wearables showed a mean absolute difference of up to 2.0%. Sleep measurement showed a tendency to overestimate total sleep time (mean absolute percentage error typically > 10%).

While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments.

CRD42023402703.

The latest evolution of the healthcare industry from Industry 1.0 to 5.0, incorporating smart wearable devices and digital technologies, has revolutionized healthcare delivery and improved patient treatment. Integrating smart wearables such as fitness trackers, smartwatches, and biosensors has endowed healthcare Industry 5.0 with numerous advantages, including remote patient monitoring, personalized healthcare, patient empowerment and engagement, telemedicine, and virtual care. This digital healthcare paradigm embraces promising technologies like Machine Learning (ML) and the Internet of Medical Things (IoMT) to enhance patient care. The key contribution of digital healthcare Industry 5.0 lies in its ability to revolutionize patient care by leveraging smart wearables and digital technologies to provide personalized, proactive, and patient-centric healthcare solutions. Despite the remarkable growth of smart wearables, the exploration of ML-based applications still needs to be expanded. Motivated by this gap, our paper conducts a comprehensive examination and evaluation of advanced ML techniques pertinent to the digital healthcare Industry 5.0 and wearable technology. We propose a detailed taxonomy for digital healthcare Industry 5.0, transforming it into an innovative process model highlighting key research challenges such as wearable modes for data collection, health tracking, security, and privacy issues. The proposed ML-based process comprises data collection from wearables like smartwatches and performs data pre-processing. Several ML models are applied, such as Support Vector Machine (SVM), Decision Tree (DT), and Random Forest(RF), to predict and classify the activity of the person. ML algorithms are capable of analyzing extensive healthcare data encompassing electronic health records (EHR) from sensors to offer valuable insights to improve decision-making processes. A comparative study of the existing work is discussed in detail. Lastly, a case study is presented to render the process model, where the RF-based model shows its efficacy by obtaining the lowest RMSE of 0.94, MSE of 0.88, and MAE of 0.27 for the prediction of activity.

Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF).

Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF.

The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders.

In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0.

Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

We propose a categorization of smartwatch use in the health care sector into 3 key functional domains: monitoring, nudging, and predicting. Monitoring involves using smartwatches within medical treatments to track health data, nudging pertains to individual use for health purposes outside a particular medical setting, and predicting involves using aggregated user data to train machine learning algorithms to predict health outcomes. Each domain offers unique contributions to health care, yet there is a lack of nuanced discussion in existing research. This paper not only provides an overview of recent technological advancements in consumer smartwatches but also explores the 3 domains in detail, culminating in a comprehensive summary that anticipates the future value and impact of smartwatches in health care. By dissecting the interconnected challenges and potentials, this paper aims to enhance the understanding and effective deployment of smartwatches in value-based health care.

To enhance the digital competencies of pharmacy students, the Institute of Clinical Pharmacy and Pharmacotherapy at Heinrich-Heine University Duesseldorf developed and evaluated a theoretical module on digital health and data analysis. This innovative module integrated a continuous glucose-monitoring (CGM) wearable device into teaching, providing students with in-depth practical experience and a 2.5 h seminar on digital health and CGM systems. Students' knowledge of CGM and self-assessment of their CGM competencies were assessed in a pre-post manner. Additionally, students' satisfaction with the module and their perceptions of the future integration of digital health training and the role of wearables in pharmacy practice were also assessed after the module. A total of 39 final-year pharmacy students completed the module conducted in June 2024 as part of the clinical pharmacy seminar. In total, 32 students completed the pre- and post-knowledge tests and self-assessment questionnaires. Both the knowledge and the students' self-assessment of CGM-related skills after the module increased significantly (p < 0.05). Students expanded their knowledge regarding digital health solutions, in particular the CGM systems, and increased their self-reported competence in CGM-related skills. With this module, an important foundation was laid, as this is the first theoretical module including the essentials of CGM digital health tools for pharmacy students in Germany.

Postpartum depression (PPD) is a mood disorder affecting one in seven women after childbirth that is often under-screened and under-detected. If not diagnosed and treated, PPD is associated with long-term developmental challenges in the child and maternal morbidity. Wearable technologies, such as smartwatches and fitness trackers (e.g., Fitbit), offer continuous and longitudinal digital phenotyping for mood disorder diagnosis and monitoring, with device wear time being an important yet understudied aspect. Using the All of Us Research Program (AoURP) dataset, we assessed the percentage of days women with PPD wore Fitbit devices across pre-pregnancy, pregnancy, postpartum, and PPD periods, as determined by electronic health records. Wear time was compared in women with and without PPD using linear regression models. Results showed a strong trend that women in the PPD cohort wore their Fitbits more those without PPD during the postpartum (PPD: mean=72.9%, SE=13.8%; non-PPD: mean=58.9%, SE=12.2%, P-value=0.09) and PPD time periods (PPD: mean=70.7%, SE=14.5%; non-PPD: mean=55.6%, SE=12.9%, P-value=0.08). We hypothesize this may be attributed to hypervigilance, given the common co-occurrence of anxiety symptoms among women with PPD. Future studies should assess the link between PPD, hypervigilance, and wear time patterns. We envision that device wear patterns with digital biomarkers like sleep and physical activity could enhance early PPD detection using machine learning by alerting clinicians to potential concerns facilitating timely screenings, which may have implications for other mental health disorders.

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.

Telemonitoring technologies are rapidly evolving, offering a promising solution for remote monitoring and timely management of asthma acute episodes. We aimed to describe current pediatric asthma telemonitoring technologies. A systematic review was conducted until September 2023 on Medline, Scopus, and Web of Science. We included studies of children (0-18 years) with asthma or recurrent wheezing whose respiratory condition was telemonitored outside the healthcare setting. A narrative synthesis was performed. We identified 40 telemonitoring technologies described in 40 studies. The more frequently used technologies for telemonitoring were mobile applications (n = 21) and web-based systems (n = 14). Telemonitoring duration varied between 2 weeks and 32 months. Data collection included asthma symptoms (n = 30), patient-reported outcome measures (PROMs) (n = 11), spirometry/peak flow readings (n = 20), medication adherence (n = 17), inhaler technique (n = 3), air quality (n = 2), and respiratory sounds (n = 2). Both parents and children were the technology target users in most studies (n = 23). Technology training was reported in 23 studies of which 3 provided ongoing support. Automatic feedback was found in 30 studies, mostly related with asthma control. HCP were involved in data management in 27 studies. Technologies were tested in samples from 4 to 327 children, with most studies including school-aged children and/or adolescents (n = 38) and eight including preschool children. This review provides an overview of existing technologies for the outpatient telemonitoring of pediatric asthma. Specific technologies for preschool children represent a gap in the literature that needs to be specifically addressed in future research.

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized.

This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis.

We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences.

The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies.

While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Regular physical activity is associated with improved quality of life in patients with inflammatory bowel diseases (IBDs), although much of the existing research is based on self-reported data. Wearable devices provide objective data on many rich physical activity dimensions including steps, duration, distance, and intensity. Little is known about how patients with IBDs engage in these varying dimensions of exercise and how it may influence their symptom and disease-specific patient-reported outcomes (PROs).

This study aims to (1) cluster physical activity patterns from consumer-grade wearable devices and (2) assess the relationship between the clusters and PROs in patients with IBDs.

We conducted a cross-sectional and longitudinal cohort study among adults with IBDs in the Crohn's and Colitis Foundation IBD Partners cohort. Participants contribute physical activity data through smartphone apps or wearable devices in a bring-your-own-device model. Participants also complete biannual PRO questionnaires from the Patient-Reported Outcomes Measurement Information System short forms and IBD-specific questionnaires. K-means cluster analysis was used to generate physical activity clusters based on 3 key features: number of steps, duration of moderate to vigorous activity (minutes), and distance of activity (miles). Based on the clusters, we conducted a cross-sectional analysis to examine differences in mean questionnaire scores and participant characteristics using one-way ANOVA and chi-square tests. We also conducted a longitudinal analysis to examine individual cluster transitions among participants who completed multiple questionnaires, and mean differences in questionnaire scores were compared using 2-tailed paired sample t tests across 6-month periods.

Among 430 participants comprising 1255 six-week physical activity periods, we identified clusters of low (33.7%, n=423), moderate (46%, n=577), and high (20.3%, n=255) physical activity. Scores varied across clusters for depression (P=.004), pain interference (P<.001), fatigue (P<.001), sleep disturbance (P<.001), social satisfaction (P<.001), and short Crohn Disease Activity Index (P<.001), with those in the low activity cluster having the worst scores. Sociodemographic characteristics also differed, and those with low physical activity were older (P=.002), had higher BMIs (P<.001), and had longer disease durations (P=.02) compared to other clusters. Among 246 participants who completed at least 2 consecutive questionnaires consisting of 726 questionnaire periods, 67.8% (n=492) remained in the same cluster, and only 1.2% (n=9) moved to or from the furthest clusters of low and high activity across 6-month periods.

For patients with IBDs, there were positive associations between physical activity and PROs related to disease activity and psychosocial domains. Physical activity patterns mostly did not fluctuate over time, suggesting little variation in exercise levels in the absence of an intervention. The use of real-world data to identify subgroups with similar lifestyle behaviors could be leveraged to develop targeted interventions that provide support for psychosocial symptoms and physical activity for personalized IBD care.

Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host's immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment.

The objective of this study is to explore the current state of research concerning the cost-effectiveness of wearable health technologies, excluding hearing aids, owing to extensive previous investigation. A systematic review was performed using PubMed, EMBASE/MEDLINE, Google Scholar, and Cumulated Index to Nursing and Allied Health Literature to search studies evaluating the cost-effectiveness of wearable health devices in terms of quality-adjusted life years and incremental cost-effectiveness ratio. The search was conducted on March 28, 2023, and the date of publication did not limit the search. The search yielded 10 studies eligible for inclusion. These studies, published between 2012 and 2023, spanned various locations globally. The studies used data from hypothetical cohorts, existing research, randomized controlled trials, and meta-analyses. They covered a diverse range of wearable technologies applied in different health care settings, including respiratory rate monitors, pedometers, fall-prediction devices, hospital-acquired pressure injury prevention monitors, seizure detection devices, heart rate monitors, insulin therapy sensors, and wearable cardioverter defibrillators. The time horizons in the cost-effectiveness analyses ranged from less than a year to a lifetime. The studies indicate that wearable technologies can increase quality-adjusted life years and be cost-effective and potentially cost-saving. However, the cost-effectiveness depends on various factors, such as the type of device, the health condition being addressed, the specific perspective of the health economic analysis, local cost and payment structure, and willingness-to-pay thresholds. The use of wearables in health care promises improving outcomes and resource allocation. However, more research is needed to fully understand the long-term benefits and to strengthen the evidence base for health care providers, policymakers, and patients.

The widespread adoption of wearable devices (wearables) for monitoring vital signs, including blood pressure and glucose levels, has experienced a considerable surge in recent times. This surge has led to the generation of a substantial amount of health data, accessible to pharmacists during patient consultations as the healthcare sector advances in digitalization. To enhance the digital competencies of future pharmacists required by the rapidly changing digital health landscape, the Institute of Clinical Pharmacy and Pharmacotherapy, Heinrich Heine University (HHU) Duesseldorf has developed an innovative elective practical course aimed to bolster pharmacy students' competencies in handling wearables and the health data generated. The three-week practical elective course employed wearables FreeStyle Libre® 3 (Continuous Glucose Monitoring, CGM) and Aktiia (Cuffless Blood Pressure Monitoring). The hands-on activities allowed participants to obtain and interpret wearable-generated health-related data and acquainted them with simulated patient cases. Final-year pharmacy students' subjective assessments before and after the course depicted the increased knowledge and competence regarding analysing wearables data.

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials.

This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV.

This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase 1, a total of 15 participants completed interviews exploring HIV-related illnesses of concern that would be used to tailor the virtual environment. In phase 2, usability testing and beta testing were conducted with 10 participants to assess interest, features, and content.

In phase 1, we found that CVD risk factors included high blood pressure, myocardial infarction, stroke, and diabetes. Cancer (prostate, colon, and others) was a common concern, as were mental health conditions. In phase 2, all participants completed the 12-item usability checklist with favorable feedback within 30 to 60 minutes. Beta-testing interviews suggested (1) mixed perceptions of health and HIV, (2) high risk for comorbid conditions, (3) virtual environment features were promising, and (4) the need for diverse avatar representations.

We identified several comorbid conditions of concern, and findings carry significant implications for mitigating barriers to preventive health screenings, given the shared risk factors between HIV and related comorbidities. Highly rated aspects of the virtual environment were anonymity; meeting others with HIV who identify as gay or bisexual; validating lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) images and content; and accessibility to CVD prevention education. Critical end-user feedback from beta testing suggested more options for avatar customization in skin, hair, and body representation. Our next phase will test the virtual environment as a new approach to advancing cardiovascular health equity in ethnic and racial sexual minority men with HIV.

ClinicalTrials.gov NCT04061915; https://clinicaltrials.gov/study/NCT05242952.

RR2-10.2196/38348.

Bioelectronic Medicine (BEM), which uses implantable electronic medical devices to interface with electrically active tissues, aspires to revolutionize the way we understand and fight disease. By leveraging knowledge from microelectronics, materials science, information technology, neuroscience and medicine, BEM promises to offer novel solutions that address unmet clinical needs and change the concept of therapeutics. This perspective communicates our vision for the future of BEM and presents the necessary steps that need to be taken and the challenges that need to be faced before this new technology can flourish.

Although wearable technology has become increasingly common, comprehensive studies examining its ownership across different sociodemographic groups are limited.

The aims of this study were to (1) measure wearable device ownership by sociodemographic characteristics in a cohort of US consumers and (2) investigate how these devices are acquired and used for health-related purposes.

Data from the Rock Health Digital Health Consumer Adoption Survey collected from 2020 to 2022 with 23,974 US participants were analyzed. The sample was US Census-matched for demographics, including age, race/ethnicity, gender, and income. The relationship between sociodemographic factors and wearable ownership was explored using descriptive analysis and multivariate logistic regression.

Of the 23,974 respondents, 10,679 (44.5%) owned wearables. Ownership was higher among younger individuals, those with higher incomes and education levels, and respondents living in urban areas. Compared to those aged 18-24 years, respondents 65 years and older had significantly lower odds of wearable ownership (odds ratio [OR] 0.18, 95% CI 0.16-0.21). Higher annual income (≥US $200,000; OR 2.27, 95% CI 2.01-2.57) and advanced degrees (OR 2.23, 95% CI 2.01-2.48) were strong predictors of ownership. Living in rural areas reduced ownership odds (OR 0.65, 95% CI 0.60-0.72). There was a notable difference in ownership based on gender and health insurance status. Women had slightly higher ownership odds than men (OR 1.10, 95% CI 1.04-1.17). Private insurance increased ownership odds (OR 1.28, 95% CI 1.17-1.40), whereas being uninsured (OR 0.41, 95% CI 0.36-0.47) or on Medicaid (OR 0.75, 95% CI 0.68-0.82) decreased the odds of ownership. Interestingly, minority groups such as non-Hispanic Black (OR 1.14, 95% CI 1.03-1.25) and Hispanic/Latine (OR 1.20, 95% CI 1.10-1.31) respondents showed slightly higher ownership odds than other racial/ethnic groups.

Our findings suggest that despite overall growth in wearable ownership, sociodemographic divides persist. The data indicate a need for equitable access strategies as wearables become integral to clinical and public health domains.

Wearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals' physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a three-dimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.

Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.

Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

RR2-10.2196/39389.

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

There is increasing interest in using patient-generated health data (PGHD) to improve patient-centered care during pregnancy. However, little research has examined the perspectives of patients and providers as they report, collect, and use PGHD to inform obstetric care.

This study aims to explore the perspectives of patients and providers about the use of PGHD during pregnancy, including the benefits and challenges of reporting, collecting, and using these data, as well as considerations for expanding the use of PGHD to improve obstetric care.

We conducted one-on-one interviews with 30 pregnant or postpartum patients and 14 health care providers from 2 obstetrics clinics associated with an academic medical center. Semistructured interview guides included questions for patients about their experience and preferences for sharing PGHD and questions for providers about current processes for collecting PGHD, opportunities to improve or expand the collection of PGHD, and challenges faced when collecting and using this information. Interviews were conducted by phone or videoconference and were audio recorded, transcribed verbatim, and deidentified. Interview transcripts were analyzed deductively and inductively to characterize and explore themes in the data.

Patients and providers described how PGHD, including physiologic measurements and experience of symptoms, were currently collected during and between in-person clinic visits for obstetric care. Both patients and providers reported positive perceptions about the collection and use of PGHD during pregnancy. Reported benefits of collecting PGHD included the potential to use data to directly inform patient care (eg, identify issues and adjust medication) and to encourage ongoing patient involvement in their care (eg, increase patient attention to their health). Patients and providers had suggestions for expanding the collection and use of PGHD during pregnancy, and providers also shared considerations about strategies that could be used to expand PGHD collection and use. These strategies included considering the roles of both patients and providers in reporting and interpreting PGHD. Providers also noted the need to consider the unintended consequences of using PGHD that should be anticipated and addressed.

Acknowledging the challenges, suggestions, and considerations voiced by patients and providers can inform the development and implementation of strategies to effectively collect and use PGHD to support patient-centered care during pregnancy.

E-textiles have been the focus of interest in health technology, but little research has been done so far on how they could support persons with complex communication needs. A global estimate is that 97 million people may benefit from Augmentative and Alternative Communication (AAC). Unfortunately, despite the growing body of research, many persons with complex communication needs are left without functional means to communicate. This study aimed to address the lack of research in textile-based AAC and to build a picture of the issues that affect novel textile-based technology development.

We arranged a focus group study for altogether 12 speech and language therapists to elicit user scenarios to understand needs, activities, and contexts when implementing a novel, textile-based technology in a user-centred approach.

As a result, we present six user scenarios that were created for children to enhance their social interaction in everyday life when using textile-based technology that recognizes touch or detects motion. The persistent availability and the individual design to meet a person's capability along with ease of use and personalization were perceived important requirements. Through these scenarios, we identified technological constraints regarding the development of e-textile technology and its use in the AAC field, such as issues regarding sensors and providing power supply. Resolving the design constraints will lead to a feasible and portable e-textile AAC system.

Heightened stress and insufficient sleep are common in the transition to college, often co-occur, and have both been linked to negative health outcomes. A challenge concerns disentangling whether perceived stress precedes or succeeds changes in sleep. These day-to-day associations may vary across individuals, but short study periods and group-level analyses in prior research may have obscured person-specific phenotypes.

This study aims to obtain stable estimates of lead-lag associations between perceived stress and objective sleep duration in the individual, unbiased by the group, by developing an individual-level linear model that can leverage intensive longitudinal data while remaining parsimonious.

In total, 55 college students (n=6, 11% second-year students and n=49, 89% first-year students) volunteered to provide daily self-reports of perceived stress via a smartphone app and wore an actigraphy wristband for the estimation of daily sleep duration continuously throughout the academic year (median usable daily observations per participant: 178, IQR 65.5). The individual-level linear model, developed in a Bayesian framework, included the predictor and outcome of interest and a covariate for the day of the week to account for weekly patterns. We validated the model on the cohort of second-year students (n=6, used as a pilot sample) by applying it to variables expected to correlate positively within individuals: objective sleep duration and self-reported sleep quality. The model was then applied to the fully independent target sample of first-year students (n=49) for the examination of bidirectional associations between daily stress levels and sleep duration.

Proof-of-concept analyses captured expected associations between objective sleep duration and subjective sleep quality in every pilot participant. Target analyses revealed negative associations between sleep duration and perceived stress in most of the participants (45/49, 92%), but their temporal association varied. Of the 49 participants, 19 (39%) showed a significant association (probability of direction>0.975): 8 (16%) showed elevated stress in the day associated with shorter sleep later that night, 5 (10%) showed shorter sleep associated with elevated stress the next day, and 6 (12%) showed both directions of association. Of note, when analyzed using a group-based multilevel model, individual estimates were systematically attenuated, and some even reversed sign.

The dynamic interplay of stress and sleep in daily life is likely person specific. Paired with intensive longitudinal data, our individual-level linear model provides a precision framework for the estimation of stable real-world behavioral and psychological dynamics and may support the personalized prioritization of intervention targets for health and well-being.

Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.