Wearable Devices in Clinical Trials: Hype and Hypothesis

Methods for pragmatic randomized clinical trials of pain therapies: IMMPACT statement

ABSTRACT

Pragmatic, randomized, controlled trials hold the potential to directly inform clinical decision making and health policy regarding the treatment of people experiencing pain. Pragmatic trials are designed to replicate or are embedded within routine clinical care and are increasingly valued to bridge the gap between trial research and clinical practice, especially in multidimensional conditions, such as pain and in nonpharmacological intervention research. To maximize the potential of pragmatic trials in pain research, the careful consideration of each methodological decision is required. Trials aligned with routine practice pose several challenges, such as determining and enrolling appropriate study participants, deciding on the appropriate level of flexibility in treatment delivery, integrating information on concomitant treatments and adherence, and choosing comparator conditions and outcome measures. Ensuring data quality in real-world clinical settings is another challenging goal. Furthermore, current trials in the field would benefit from analysis methods that allow for a differentiated understanding of effects across patient subgroups and improved reporting of methods and context, which is required to assess the generalizability of findings. At the same time, a range of novel methodological approaches provide opportunities for enhanced efficiency and relevance of pragmatic trials to stakeholders and clinical decision making. In this study, best-practice considerations for these and other concerns in pragmatic trials of pain treatments are offered and a number of promising solutions discussed. The basis of these recommendations was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) meeting organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks.

Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention

The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. This article presents eight methodological questions, exploring issues such as the length of measurement period, choice of summary statistic and definition and handling of missing data as well as the potential for new estimands and new analyses to leverage the time series data from digital devices. The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials.

Environmental study and stress-related biomarkers modifications in a crew during analog astronaut mission EMMPOL 6

PURPOSE

Human presence in space is increasingly frequent, but we must not forget that it is a hostile environment. We aimed to study the characteristics of experimental scenarios, to obtain data on human response to isolation, disruption of circadian rhythm and high levels of psychophysical stress.

METHODS

In these experiments, we evaluated stress response in five young healthy subjects inside an earth-based moon-settlement-like habitat during a 1-week long analog astronaut mission. Wearable devices were used to monitor daily step count of the subjects, physical activity, heart rate during physical exercise and at rest, and sleep parameters. From saliva and urine samples collected every day at awakening, we studied oxy-inflammation biomarkers and hormones (stress and appetite) were studied too.

RESULTS

At the end of the week, all subjects revealed an increase in oxidative stress and cortisol levels but no inflammation biomarkers variations, in conjunction with increasing time/daily exercise. Furthermore, a significant decrease in hours of sleep/day, sleep quality, and REM phase of sleep was recorded and correlated with the increase of reactive oxygen species.

CONCLUSION

Oxidative stress increased in a short period of time and may be attributed to the influence of psychological stress during confinement, as well as increased exercise and decreased amount of sleep. On a long-term basis, this could impact performance.

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial

BACKGROUND

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design.

OBJECTIVE

The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems.

METHODS

The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership.

RESULTS

The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership.

CONCLUSIONS

Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines.

CLINICALTRIAL

ClinicalTrials.gov-Identifier: NCT04359238.

Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

The utility and impact of digital endpoints for improving breast cancer outcomes

INTRODUCTION

In breast cancer clinical trials utilizing digital endpoints, wearable sensors record participants' health information during activities of daily living.  These sensors are worn on the wrist or finger, placed as a skin patch or headband, or embedded on clothing. Data collected from wearable sensors form the basis of a digital endpoint, useful for determining effects of novel treatments on health outcomes, particularly quality-of-life outcomes.

AREAS COVERED

References for this article were selected from a PubMed search spanning from 1 January 2017,to 1 July 2024, using the terms 'wearable sensors,' 'digital endpoints,' 'virtualtrials,' 'breast cancer.'  Additional articles from the authors' personal collection of papers and reviewers suggestions were also used.

EXPERT OPINION

Digital endpoints must be validated as proper surrogate measures for healthcare outcomes, prior to their use in breast cancer trials.  Wearable sensors may introduce biases, such as 'missing not-at-random bias,' and perhaps even exacerbate disparities in healthcare outcomes if patients not comfortable with their use are excluded from clinical trials, or if the accuracy of sensors varies between racial and ethnic groups. Therefore, before embarking on trials with digital endpoints, validation studies are required, and limitations and risks of such trials need to be addressed.

A practical guide for selecting continuous monitoring wearable devices for community-dwelling adults

Importance

The burgeoning landscape of wearable devices warrants a guide for the selection of devices. Existing guidelines and recommendations provide evaluation frameworks with theoretical principles but tend to lack a pragmatic application and systematic approach for device selection. While fitness trackers exemplify the convenience of wearable technologies, their selection for specific health monitoring purposes demands a nuanced understanding of varying functionalities and user compatibilities.

Objective

The objective is to develop and present a practical guide for researchers, healthcare professionals, and device users to systematically select wearable devices for continuous monitoring in community-dwelling adults.

Methods & results

Based on diverse sources, such as the United States Food and Drug Administration (FDA), the Clinical Trials Transformation Initiative (CTTI), the Electronic Patient-Reported Outcome (ePRO) Consortium, and comparative analyses of wearable technology performances from feasibility and usability studies, the guide incorporates five core criteria: continuous monitoring capability, device availability and suitability, technical performance (accuracy and precision), feasibility of use, and cost evaluation. The structured criteria can be applied in device selection as well as device evaluation.

Conclusions

This practical guide provides a step-by-step solution for researchers, healthcare professionals, and device users to choose suitable wearable devices for continuous monitoring. It provides a comprehensive starting point, outlining how to effectively navigate the selection process for wearable devices amidst a plethora of similar options.

Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial

Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .

Clinical trials in older patients with cancer - typical challenges, possible solutions, and a paradigm of study design in breast cancer

BACKGROUND AND PURPOSE

While the prevalence of older breast cancer patients is rapidly increasing, these patients are greatly underrepresented in clinical trials. We discuss barriers to recruitment of older patients to clinical trials and propose solutions on how to mitigate these challenges and design optimal clinical trials through the paradigm of IMPORTANT trial.

PATIENTS AND METHODS

This is a narrative review of the current literature evaluating barriers to including older breast cancer patients in clinical trials and how mitigating strategies can be implemented in a pragmatic clinical trial.

RESULTS

The recognized barriers can be roughly divided into trial design-related (e.g. the adoption of strict inclusion criteria, the lack of pre-specified age-specific analysis), patient-related (e.g. lack of knowledge, valuation of the quality-of-life instead of survival, transportation issues), or physician-related (e.g. concern for toxicity). Several strategies to mitigate barriers have been identified and should be considered when designing a clinical trial dedicated to older patients with cancer. The pragmatic, de-centralized IMPORTANT trial focusing on dose optimization of CDK4/6 -inhibitors in older breast cancer patients is a paradigm of a study design where different mitigating strategies have been adopted.

INTERPRETATION

Because of the existing barriers, older adults in clinical trials are considerably healthier than the average older patients treated in clinical practice. Thus, the study results cannot be generalized to the older population seen in daily clinical practice. Broader inclusion/exclusion criteria, offering telehealth visits, and inclusion of patient-reported, instead of physician-reported outcomes may increase older patient participation in clinical trials.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults

INTRODUCTION

Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices.

METHODS

The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults.

RESULTS

The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females).

CONCLUSIONS

The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data

Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset-a 700,000 person-days unlabelled dataset-in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5-130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study

BACKGROUND

Despite its importance to women's reproductive health and its impact on women's daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures.

OBJECTIVE

The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle.

METHODS

This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle.

RESULTS

The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458).

CONCLUSIONS

We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices.

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors' performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.

Circadian-like patterns in electrochemical skin conductance measured from home-based devices: a retrospective study

In this study, we investigated the potential of electrochemical skin conductance (ESC) measurements gathered from home-based devices to detect circadian-like patterns. We analyzed data from 43,284 individuals using the Withings Body Comp or Body Scan scales, which provide ESC measurements. Our results highlighted a circadian pattern of ESC values across different age groups and countries. Our findings suggest that home-based ESC measurements could be used to evaluate circadian rhythm disorders associated with neuropathies and contribute to a better understanding of their pathophysiology. However, further controlled studies are needed to confirm these results. This study highlights the potential of digital health devices to generate new scientific and medical knowledge.

Digital Tools-Regulatory Considerations for Application in Clinical Trials

During the last few years, the pharmaceutical industry has adopted digital technologies/digital health technology (DHT) to improve the drug development process and the commercialization of new products. Technological advances are strongly supported by both the US-FDA and the EMA, but the regulatory landscape in the US seems to be more suitable to promote innovation in the digital health sector (e.g. Cures Act). In contrast, the new Medical Device Regulation sets high hurdles for Medical Device software to pass regulatory scrutiny.On both sides of the Atlantic, a digital tool must be fit-for-purpose for the intended use in the clinical drug trial. Irrespective of its status as a Medical Device, at least the basic safety and performance requirements according to local regulations should be met, quality system and surveillance requirements should be fulfilled, and the sponsor must ensure conformity with GxP and the local data privacy and cybersecurity legislations.There is an overlap in technical and clinical validation for drug development tool qualification in both regions to ensure that the digital tools deliver reliable data with tangible clinical benefits. Based on an analysis of the regulatory framework of the FDA and the EMA, this study proposes regulatory strategies for a global pharma company: It would be prudent for drug development companies to a) use approved solutions or b) consider qualification of drug development tools early and in parallel to clinical development. Early engagement with the FDA and the EMA/CA is recommended to define evidentiary standards and corresponding regulatory pathways for different contexts-of-use and to clarify regulator's expectations as to what extent data collected by digital tools are acceptable to support marketing authorization applications (MAA).Hence a harmonization of the partly disparate regulatory requirements in the US and the EU accompanied by further development of the regulatory landscape in the EU, could further foster the use of digital tools in drug clinical development. The outlook for the use of digital tools in clinical trials is hopeful.

Digital outcome measures in pulmonary clinical trials

PURPOSE OF REVIEW

We highlight recent advances in the development and use of digital outcome measures in clinical trials, focusing on how to select the appropriate technology, use digital data to define trial endpoints, and glean important lessons from current experiences with digital outcome measures in pulmonary medicine.

RECENT FINDINGS

A review of emerging literature demonstrates that the use of digital health technologies, particularly pulse oximeters, remote spirometers, accelerometers, and Electronic Patient-Reported Outcomes, has surged in both pulmonary practice and clinical trials. Lessons learned from their use can help researchers to design the next generation of clinical trials leveraging digital outcomes to improve health.

SUMMARY

In pulmonary diseases, digital health technologies provide validated, reliable, and usable data on patients in real-world environments. More broadly, digital endpoints have accelerated innovation in clinical trial design, improved clinical trial efficiency, and centered patients. As investigators adopt digital health technologies, it is important to follow a framework informed by both the opportunities and challenges of digitization. Successful use of digital health technologies will transform clinical trials by improving accessibility, efficiency, patient-centricity, and expanding opportunities for personalized medicine.

Digital health in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

Involvement of Human Volunteers in the Development and Evaluation of Wearable Devices Designed to Improve Medication Adherence: A Scoping Review

Wearable devices designed to improve medication adherence can emit audible and vibrating alerts or send text messages to users. However, there is little information on the validation of these technologies. The aim of this scoping review was to investigate the involvement of human volunteers in the development and evaluation of wearable devices. A literature search was conducted using six databases (MEDLINE, Embase, Scopus, CINAHL, PsycInfo, and Web of Science) up to March 2020. A total of 7087 records were identified, and nine studies were included. The wearable technologies most investigated were smartwatches (n = 3), patches (n = 3), wristbands (n = 2), and neckwear (n = 1). The studies involving human volunteers were categorized into idea validation (n = 4); prototype validation (n = 5); and product validation (n = 1). One of them involved human volunteers in idea and prototype validation. A total of 782 participants, ranging from 6 to 252, were included. Only five articles reported prior approval by a research ethics committee. Most studies revealed fragile methodological designs, a lack of a control group, a small number of volunteers, and a short follow-up time. Product validation is essential for regulatory approval and encompasses the assessment of the effectiveness, safety, and performance of a wearable device. Studies with greater methodological rigor and the involvement of human volunteers can contribute to the improvement of the process before making them available on the market.

A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics

In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user's perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as "soft", "robot", "wearable", and "exoskeleton" was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

Exploring human biology with N-of-1 clinical trials

Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.

Social-Ecological Measurement of Daily Life: How Relationally Focused Ambulatory Assessment can Advance Clinical Intervention Science

Individuals’ daily behaviors and social interactions play a central role in the diagnosis and treatment of psychological disorders. Despite this, observational ambulatory assessment methods—research methods that allow for direct and passive assessment of individuals’ momentary activities and interactions—have a remarkably scant history in the clinical science field. Prior discussions of ambulatory assessment methods in clinical science have focused on subjective methods (e.g., ecological momentary assessment) and physiological methods (e.g., wearable heart rate monitoring). Comparatively less attention has been dedicated to ambulatory assessment methods that collect objective, relational data about individuals’ social behaviors and their interactions with their momentary environmental contexts. Drawing on extant social-ecological measurement frameworks, this article first provides a conceptual and psychometric rationale for the integration of daily relational data into clinical science research. Next, the nascent research applying such methods to clinical science is reviewed, and priorities for further research organized by the NIH Stage Model for Clinical Science Research are recommended. These data can provide unique information about the social contexts of diverse patient populations; identify social-ecological targets for transdiagnostic, precision, and culturally responsive interventions; and contribute novel data about the effectiveness of established interventions at creating behavioral and relational change.

LifeSnaps, a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral and psychological patterns due to challenges in collecting and releasing such datasets, including waning user engagement or privacy considerations. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n = 71 participants. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71 M rows of data. The participants contributed their data through validated surveys, ecological momentary assessments, and a Fitbit Sense smartwatch and consented to make these data available to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data will open novel research opportunities and potential applications in multiple disciplines.

[Evidence-based health care with pharmaceuticals for rare diseases: the role of digitalisation]

Knowledge generation in the field of drug development for people with rare diseases (RDs) faces particular difficulties. This paper will show what improvements are expected from increasing digitalisation from the perspective of three healthcare institutions: the Federal Institute for Drugs and Medical Devices, the Institute for Quality and Efficiency in Health Care and the Federal Joint Committee.First, the potential of digitalisation to increase the efficiency of clinical development and regulatory decision-making through earlier collaboration of all stakeholders is proposed. Subsequently, it is argued that digitalisation should be used to reduce barriers to the implementation of care-associated randomised controlled trials, including those based on registries. High-quality registry studies should not only be started after approval but during the approval process, so that the evidence necessary for therapy decisions is available promptly after approval. Finally, it is stated that improving the evidence base through qualitative improvement of the data sources and their linkages directly benefits patients. Usable evidence that can be generated over a longer period of time - also beyond approval - and contribute to decisions within healthcare system ensures effective drug provision.The institutions agree that high-quality indication registries should be developed as product-independent, standing infrastructures so that high-quality data can be accessed early in the development of medicines for RD.

Functionality of Flexible Pressure Sensors in Cardiovascular Health Monitoring: A Review

As the highest percentage of global mortality is caused by several cardiovascular diseases (CVD), maintenance and monitoring of a healthy cardiovascular condition have become the primary concern of each and every individual. Simultaneously, recent progress and advances in wearable pressure sensor technology have provided many pathways to monitor and detect underlying cardiovascular illness in terms of irregularities in heart rate, blood pressure, and blood oxygen saturation. These pressure sensors can be comfortably attached onto human skin or can be implanted on the surface of vascular grafts for uninterrupted monitoring of arterial blood pressure. While the traditional monitoring systems are time-consuming, expensive, and not user-friendly, flexible sensor technology has emerged as a promising and dynamic practice to collect important health information at a comparatively low cost in a reliable and user-friendly way. This Review explores the importance and necessity of cardiovascular health monitoring while emphasizing the role of flexible pressure sensors in monitoring patients' health conditions to avoid adverse effects. A comprehensive discussion on the current research progress along with the real-time impact and accessibility of pressure sensors developed for cardiovascular health monitoring applications has been provided.

Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.

Assessing the potential of wearable health monitors for health system strengthening in low- and middle-income countries: a prospective study of technology adoption in Cambodia

Wearable health monitors are a rapidly evolving technology that may offer new opportunities for strengthening health system responses to cardiovascular and other non-communicable diseases (NCDs) in low- and middle-income countries (LMICs). In light of this, we explored opportunities for, and potential challenges to, technology adoption in Cambodia, considering the complexity of contextual factors that may influence product uptake and sustainable health system integration. Data collection for this study involved in-depth interviews with national and international stakeholders and a literature review. The analytical approach was guided by concepts and categories derived from the non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework-an evidence-based framework that was developed for studying health technology adoption and the challenges to scale-up, spread and sustainability of such technologies in health service organizations. Three potential applications of health wearables for the prevention and control of NCDs in Cambodia were identified: health promotion, follow-up and monitoring of patients and surveys of NCD risk factors. However, several challenges to technology adoption emerged across the research domains, associated with the intended adopters, the organization of the national health system, the wider infrastructure, the regulatory environment and the technology itself. Our findings indicate that, currently, wearables could be best used to conduct surveys of NCD risk factors in Cambodia and in other LMICs with similar health system profiles. In the future, a more integrated use of wearables to strengthen monitoring and management of patients could be envisaged, although this would require careful consideration of feasibility and organizational issues.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

Ethics review of decentralized clinical trials (DCTs): Results of a mock ethics review

Decentralized clinical trials (DCTs) can be a valuable addition to the clinical trial landscape. However, the practice of DCTs is dependent on a regulatory system designed for conventional (site-based) trials. This study provides insight into the ethics review of DCTs. A 'mock ethics review' was performed in which members of European ethics committees (ECs) and national competent authorities (NCAs) discussed and reviewed a DCT protocol. Respondents expressed hesitancy toward DCTs and focused on potential risks and burdens. We advise to address these aspects explicitly when submitting a DCT protocol. We propose that both the benefits and risks of DCTs should be carefully monitored to advance the review and practice of this innovative approach to ethically optimize drug development.

Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies

Background

Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own “device” (BYOD) manner where participants use their technologies to generate study data.

Summary

The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving.

Key Messages

We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.

Remote and wearable ECG devices with diagnostic abilities in adults: A state-of-the-science scoping review

The electrocardiogram (ECG) records the electrical activity in the heart in real time, providing an important opportunity to detecting various cardiac pathologies. The 12-lead ECG currently serves as the "standard" ECG acquisition technique for diagnostic purposes for many cardiac pathologies other than arrhythmias. However, the technical aspects of acquiring a 12-lead ECG are not easy. and its usage currently is restricted to trained medical personnel, which limits the scope of its usefulness. Remote and wearable ECG devices have attempted to bridge this gap by enabling patients to take their own ECG using a simplified method at the expense of a reduced number of leads, usually a single-lead ECG. In this review, we summarize the studies that investigated the use of remote ECG devices and their clinical utility in diagnosing cardiac pathologies. Eligible studies discussed Food and Drug Administration-cleared, commercially available devices that were validated in an adult population. We summarize technical logistics of signal quality and device reliability, dimensional and functional features, and diagnostic value. Our synthesis shows that reduced-set ECG wearables have huge potential for long-term monitoring, particularly if paired with real-time notification techniques. Such capabilities make them primarily useful for abnormal rhythm detection, and there is sufficient evidence that a remote ECG device can be superior to the traditional 12-lead ECG in diagnosing specific arrhythmias such as atrial fibrillation. However, this review identifies important challenges faced by this technology and highlights the limited availability of clinical research examining their usefulness.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model's prediction accuracy on the Earable device's classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment

Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Targeting Anxiety to Improve Sleep Disturbance: A Randomized Clinical Trial of App-Based Mindfulness Training

OBJECTIVE

Sleep disturbance is experienced by nearly 20% of Americans and is highly comorbid with anxiety. Sleep disturbances may predict the development of anxiety disorders. Mindfulness training (MT) has shown efficacy for anxiety yet remains limited by in-person-based delivery. Digitally delivered MT may target habitual worry processes, yet its effects on sleep have not been studied. This study tested if app-based MT for anxiety could reduce worry and improve sleep and examined the underlying mechanisms.

METHODS

Individuals reporting worry interfering with sleep were randomized to treatment as usual (TAU; n = 40) or TAU + app-based MT (n = 40). Treatment-related changes in worry-related sleep disturbances (WRSDs), worry, nonreactivity, and anxiety were evaluated via self-report questionnaires at 1 and 2 months after treatment initiation. Fitbit devices were used to record total sleep time and estimate sleep efficiency. At 2 months, TAU received access to app-based MT, and both groups were reassessed at 4 months.

RESULTS

In a modified intent-to-treat analysis, WRSD scores decreased by 27% in TAU + MT (n = 36) and 6% in TAU (n = 35) at 2 months (median [IQR] change = 11 [4.3] versus 15 [5.0], p = .001). These WRSD reductions were mediated by decreased worry, particularly improved nonreactivity (p values < .001). At 4 months, TAU reported a significant 29% reduction after beginning app-based MT at 2 months and TAU + MT maintained its gains. No significant between-group differences in average estimated total sleep time or sleep efficiency were found after 2 months of using the app.

CONCLUSIONS

Few mindfulness-related apps have been evaluated for clinical efficacy and/or mechanism. Results from this study demonstrate a mechanistic link between MT and increased emotional nonreactivity, decreased worry, and reduction in reported sleep disturbances, suggesting that app-based MT may be a viable option to help individuals who report that worry interferes with their sleep.Trial Registration: ClinicalTrials.gov identifier: NCT03684057.

IoT-Based Wearable Devices for Patients Suffering from Alzheimer Disease

The disorder of Alzheimer's (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life's daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices.

Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures

Low-dimensional nanostructures have many advantages when used in sensors compared to the traditional bulk materials, in particular in their sensitivity and specificity. In such nanostructures, the motion of carriers can be confined from one, two, or all three spatial dimensions, leading to their unique properties. New advancements in nanosensors, based on low-dimensional nanostructures, permit their functioning at scales comparable with biological processes and natural systems, allowing their efficient functionalization with chemical and biological molecules. In this article, we provide details of such sensors, focusing on their several important classes, as well as the issues of their designs based on mathematical and computational models covering a range of scales. Such multiscale models require state-of-the-art techniques for their solutions, and we provide an overview of the associated numerical methodologies and approaches in this context. We emphasize the importance of accounting for coupling between different physical fields such as thermal, electromechanical, and magnetic, as well as of additional nonlinear and nonlocal effects which can be salient features of new applications and sensor designs. Our special attention is given to nanowires and nanotubes which are well suited for nanosensor designs and applications, being able to carry a double functionality, as transducers and the media to transmit the signal. One of the key properties of these nanostructures is an enhancement in sensitivity resulting from their high surface-to-volume ratio, which leads to their geometry-dependant properties. This dependency requires careful consideration at the modelling stage, and we provide further details on this issue. Another important class of sensors analyzed here is pertinent to sensor and actuator technologies based on smart materials. The modelling of such materials in their dynamics-enabled applications represents a significant challenge as we have to deal with strongly nonlinear coupled problems, accounting for dynamic interactions between different physical fields and microstructure evolution. Among other classes, important in novel sensor applications, we have given our special attention to heterostructures and nucleic acid based nanostructures. In terms of the application areas, we have focused on chemical and biomedical fields, as well as on green energy and environmentally-friendly technologies where the efficient designs and opportune deployments of sensors are both urgent and compelling.

Opportunities and Challenges for Decentralized Clinical Trials: European Regulators' Perspective

Decentralized clinical trials (DCTs) have the potential to improve accessibility, diversity, and retention in clinical trials by moving trial activities to participants’ homes and local surroundings. In this study, we conducted semi‐structured interviews with 20 European regulators to identify regulatory challenges and opportunities for the implementation of DCTs in the European Union. The key opportunities for DCTs that were recognized by regulators include a reduced participation burden, which could facilitate the participation of underserved patients. In addition, regulators indicated that data collected in DCTs are expected to be more representative of the real world. Key challenges recognized by regulators for DCTs include concerns regarding investigator oversight and participants’ safety when physical examinations and face‐to‐face contact are limited. To facilitate future learning, hybrid clinical trials with both on‐site and decentralized elements are proposed by the respondents.

Real world data and data science in medical research: present and future

Real world data (RWD) are generating greater interest in recent times despite being not new. There are various purposes of the RWD analytics in medical research as follows: effectiveness and safety of medical treatment, epidemiology such as incidence and prevalence of disease, burden of disease, quality of life and activity of daily living, medical costs, etc. The RWD research in medicine is a mixture of digital transformation, statistics or data science, public health, and regulatory science. Most of the articles describing the RWD or real-world evidence (RWE) in medical research cover only a portion of these specializations, which might lead to an incomplete understanding of the RWD. This article summarizes the overview and challenges of the RWD analysis in medical fields from methodological perspectives. As the first step for the RWD analysis, data source of the RWD should be comprehended. The progress of the RWD is closely related to the digitization, especially of medical administrative data and medical records. Second, the selection of appropriate statistical and epidemiological methods is highly critical for an RWD analysis than those for randomized clinical trials. This is because it contains greater varieties of bias, which should be controlled by balancing the underlying risk between treatment groups. Last, the future of the RWD is discussed in terms of overcoming limited data by proxy confounders, using unstructured text data, linking of multiple databases, using the RWD or RWE for a regulatory purpose, and evaluating values and new aspects in medical research brought by the RWD.

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Background

Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters.

Objective

The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management.

Methods

A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts.

Results

On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices.

Conclusions

Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

Implementing Digital Technologies in Clinical Trials: Lessons Learned

Multiple digital health technologies have been evaluated across clinical development programs, including external, wearable, implantable, and ingestible devices and sensors, along with digital mobile health applications (apps) that are accessible via users' personal electronic devices (e.g., smartphones, tablets, and computers). Several of these technologies have been incorporated into our ongoing neurology and respiratory clinical development programs. Based on our experience, one of the greatest potential benefits of digital health technologies is the ability to collect objective and/or biological data continuously or at regular intervals outside of office visits during a patient's normal daily activities to provide additional efficacy and safety information, versus data capture from traditional episodic, time point-based office visits. Many challenges encountered with digital health technologies can be successfully addressed by providing the appropriate training to staff and patients, ensuring availability of appropriate infrastructure support, and conducting pilot studies before scaling up to larger trials. Overall, our experience with digital health technologies demonstrated their potential to increase the amount of objective data collected in clinical trials, expand patient access to trials, and facilitate further improvement of clinical outcomes.

Use of a Wearable Biosensor to Study Heart Rate Variability in Chronic Obstructive Pulmonary Disease and Its Relationship to Disease Severity

The purpose of this study was to explore the relationships between heart rate variability (HRV) and various phenotypic measures that relate to health and functional status in chronic obstructive pulmonary disease (COPD), and secondly, to demonstrate the feasibility of ascertaining HRV via a chest-worn wearable biosensor in COPD patients. HRV analysis was performed using SDNN (standard deviation of the mean of all normal R-R intervals), low frequency (LF), high frequency (HF), and LF/HF ratio. We evaluated the associations between HRV and COPD severity, class of bronchodilator therapy prescribed, and patient reported outcomes. Seventy-nine participants with COPD were enrolled. There were no differences in SDNN, HF, and LF/HF ratio according to COPD severity. The SDNN in participants treated with concurrent beta-agonists and muscarinic antagonists was lower than that in other participants after adjusting heart rate (beta coefficient −3.980, p = 0.019). The SDNN was positively correlated with Veterans Specific Activity Questionnaire (VSAQ) score (r = 0.308, p = 0.006) and handgrip strength (r = 0.285, p = 0.011), and negatively correlated with dyspnea by modified Medical Research Council (mMRC) questionnaire (r = −0.234, p = 0.039), health status by Saint George’s Respiratory Questionnaire (SGRQ) (r = −0.298, p = 0.008), symptoms by COPD Assessment Test (CAT) (r = −0.280, p = 0.012), and BODE index (r = −0.269, p = 0.020). When measured by a chest-worn wearable device, reduced HRV was observed in COPD participants receiving inhaled beta-sympathomimetic agonist and muscarinic antagonists. HRV was also correlated with various health status and performance measures.

Recent Progress in Materials Chemistry to Advance Flexible Bioelectronics in Medicine

Designing bioelectronic devices that seamlessly integrate with the human body is a technological pursuit of great importance. Bioelectronic medical devices that reliably and chronically interface with the body can advance neuroscience, health monitoring, diagnostics, and therapeutics. Recent major efforts focus on investigating strategies to fabricate flexible, stretchable, and soft electronic devices, and advances in materials chemistry have emerged as fundamental to the creation of the next generation of bioelectronics. This review summarizes contemporary advances and forthcoming technical challenges related to three principal components of bioelectronic devices: i) substrates and structural materials, ii) barrier and encapsulation materials, and iii) conductive materials. Through notable illustrations from the literature, integration and device fabrication strategies and associated challenges for each material class are highlighted.