Comparing the Usability and Acceptability of Wearable Sensors Among Older Irish Adults in a Real-World Context: Observational Study

Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial

Introduction

Forensic psychiatric patients receive treatment to address their violent and aggressive behavior with the aim of facilitating their safe reintegration into society. On average, these treatments are effective, but the magnitude of effect sizes tends to be small, even when considering more recent advancements in digital mental health innovations. Recent research indicates that wearable technology has positive effects on the physical and mental health of the general population, and may thus also be of use in forensic psychiatry, both for patients and staff members. Several applications and use cases of wearable technology hold promise, particularly for patients with mild intellectual disability or borderline intellectual functioning, as these devices are thought to be user-friendly and provide continuous daily feedback.

Method

In the current randomized crossover trial, we addressed several limitations from previous research and compared the (continuous) usability and acceptance of four selected wearable devices. Each device was worn for one week by staff members and patients, amounting to a total of four weeks. Two of the devices were general purpose fitness trackers, while the other two devices used custom made applications designed for bio-cueing and for providing insights into physiological reactivity to daily stressors and events.

Results

Our findings indicated significant differences in usability, acceptance and continuous use between devices. The highest usability scores were obtained for the two fitness trackers (Fitbit and Garmin) compared to the two devices employing custom made applications (Sense-IT and E4 dashboard). The results showed similar outcomes for patients and staff members.

Discussion

None of the devices obtained usability scores that would justify recommendation for future use considering international standards; a finding that raises concerns about the adaptation and uptake of wearable technology in the context of forensic psychiatry. We suggest that improvements in gamification and motivational aspects of wearable technology might be helpful to tackle several challenges related to wearable technology.

Breaking down the Digital Fortress: The Unseen Challenges in Healthcare Technology-Lessons Learned from 10 Years of Research

Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring. However, despite its promise, the reality of implementing digital health tools in practice, and the scalability of innovations, remains stunted. Digital health is approaching a crossroads where we need to shift our focus away from simply looking at developing new innovations to seriously considering how we overcome the barriers that currently limit its impact. This paper summarises over 10 years of digital health experiences from a group of researchers with backgrounds in physical therapy-in order to highlight and discuss some of these key lessons-in the areas of validity, patient and public involvement, privacy, reimbursement, and interoperability. Practical learnings from this collective experience across patient cohorts are leveraged to propose a list of recommendations to enable researchers to bridge the gap between the development and implementation of digital health tools.

Advancing Wearable Biosensors for Congenital Heart Disease: Patient and Clinician Perspectives: A Science Advisory From the American Heart Association

Wearable biosensors (wearables) enable continual, noninvasive physiologic and behavioral monitoring at home for those with pediatric or congenital heart disease. Wearables allow patients to access their personal data and monitor their health. Despite substantial technologic advances in recent years, issues with hardware design, data analysis, and integration into the clinical workflow prevent wearables from reaching their potential in high-risk congenital heart disease populations. This science advisory reviews the use of wearables in patients with congenital heart disease, how to improve these technologies for clinicians and patients, and ethical and regulatory considerations. Challenges related to the use of wearables are common to every clinical setting, but specific topics for consideration in congenital heart disease are highlighted.

Utility and usability of a wearable system and progressive-challenge cued exercise program for encouraging use of the more involved arm at-home after stroke-a feasibility study with case reports

BACKGROUND

Understanding the role of adherence to home exercise programs for survivors of stroke is critical to ensure patients perform prescribed exercises and maximize effectiveness of recovery.

METHODS

Survivors of hemiparetic stroke with impaired motor function were recruited into a 7-day study designed to test the utility and usability of a low-cost wearable system and progressive-challenge cued exercise program for encouraging graded-challenge exercise at-home. The wearable system comprised two wrist-worn MetaMotionR+ activity monitors and a custom smartphone app. The progressive-challenge cued exercise program included high-intensity activities (one repetition every 30 s) dosed at 1.5 h per day, embedded within 8 h of passive activity monitoring per day. Utility was assessed using measures of system uptime and cue response rate. Usability and user experience were assessed using well-validated quantitative surveys of system usability and user experience. Self-efficacy was assessed at the end of each day on a visual analog scale that ranged from 0 to 100.

RESULTS

The system and exercise program had objective utility: system uptime was 92 ± 6.9% of intended hours and the rate of successful cue delivery was 99 ± 2.7%. The system and program also were effective in motivating cued exercise: activity was detected within 5-s of the cue 98 ± 3.1% of the time. As shown via two case studies, accelerometry data can accurately reflect graded-challenge exercise instructions and reveal differentiable activity levels across exercise stages. User experience surveys indicated positive overall usability in the home settings, strong levels of personal motivation to use the system, and high degrees of satisfaction with the devices and provided training. Self-efficacy assessments indicated a strong perception of proficiency across participants (95 ± 5.0).

CONCLUSIONS

This study demonstrates that a low-cost wearable system providing frequent haptic cues to encourage graded-challenge exercise after stroke can have utility and can provide an overall positive user experience in home settings. The study also demonstrates how combining a graded exercise program with all-day activity monitoring can provide insight into the potential for wearable systems to assess adherence to-and effectiveness of-home-based exercise programs on an individualized basis.

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.

Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges in monitoring and management due to the unpredictable nature of seizures. Wearable devices offer continuous monitoring and real-time data collection, providing insights into seizure patterns and trends. Wearable technology is important in epilepsy management because it enables early detection, prediction, and personalized intervention, empowering patients and healthcare providers. Key findings highlight the potential of wearable devices to improve seizure detection accuracy, enhance patient empowerment through real-time monitoring, and facilitate data-driven decision-making in clinical practice. However, further research is needed to validate the accuracy and reliability of these devices across diverse patient populations and clinical settings. Collaborative efforts between researchers, clinicians, technology developers, and patients are essential to drive innovation and advancements in wearable digital health technology for epilepsy management, ultimately improving outcomes and quality of life for individuals with this neurological condition.

Wearable technology use in long-term care facilities for older adults: a scoping review protocol

OBJECTIVE

The objective of this scoping review is to explore how wearable technology is being used to care for older adults in long-term care facilities.

INTRODUCTION

The use of digital health technologies to support care delivery in long-term care facilities for older adults has grown significantly in recent years, especially since the COVID-19 pandemic. Wearable technology refers to devices worn or attached to the body that can track a variety of health-related data, such as vital signs, falls, and sleep patterns. Despite the evidence that wearable devices are playing an increasing role in older adults' care, no review has been conducted on how wearable technology is being used in long-term care facilities.

INCLUSION CRITERIA

This review will consider studies that include people aged over 65, with any health condition or level of disability, who live in long-term care facilities. Primary and secondary studies using quantitative, qualitative, and mixed methods study designs will be included. Dissertations and policy documents will also be considered.

METHODS

Data sources will include comprehensive searches of electronic databases (MEDLINE, Embase, CINAHL, and Scopus), gray literature, and reference scanning of relevant studies. Two independent reviewers will screen titles, abstracts, and full texts of the selected studies. Data extraction will be performed using a tool developed by the researchers. Data will be mapped and analyzed. Descriptive frequencies and content analysis will be included, along with the tabulated results, which will be used to present the findings with regard to the review objectives.

REVIEW REGISTRATION

Open Science Framework https://osf.io/r9qtd.

The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review

Background

As the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables.

Methods

A comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data.

Results

A total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%).

Conclusion

Commonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.

Electronic Wearable Device Use for Physical Activity in Older Adults: A Qualitative Study

Innovative solutions to help older adults increase physical activity are critically important. In this qualitative study, we explored older adults' acceptance, capability, and experiences of using three different types of electronic wearable devices over a period of 4-24 weeks for self-monitoring and promoting physical activity. We conducted 23 semistructured interviews with older adults who participated in three physical activity intervention studies. Two researchers analyzed the data using NVivo version 12, applying a directed content analysis that was partially guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Six themes emerged: (1) device learning, (2) hedonic motivation, (3) habit and adherence, (4) facilitating conditions, (5) effort expectancy, and (6) performance expectancy. Although most older adults (95.8%) from this study were first-time users, they reflected positive experiences and generally enjoyed using electronic wearable devices. Participants reported issues related to electronic wearable device functionalities that can be improved to better enhance user experience and motivate increased physical activity. Future research should explore the role of electronic wearable devices in older adults' physical activity with an emphasis on behavioral change over time.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high-resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

[Functionality and everyday suitability of commercially wristwear products for frail older people - a comparative product testing]

BACKGROUND AND AIM

There is a wide range of smartwatches and emergency watches on the market that are specifically designed for older people. The products are freely available, which is why there is an urgent need for information about the reliability and functionality of the products among potential users, but also health professionals and decision-makers. As part of a systematic product comparison test, the functionality and quality of seven smartwatches were investigated.

METHOD

Four watches for seniors, one watch for adults and two watches for children, but with comparable functionalities, were included in the test. For the test, real-life situations were simulated and, in addition to emergency calls, GPS tracking, fall detection and geofencing, the battery life, call quality, stability/robustness of the products and service/support were evaluated. From the total number of points, a grade was determined based on the German school grading system (1 = very good to 6 = insufficient).

RESULTS

All smartwatches evaluated were rated at least "3-satisfactory". The two best-rated watches received a score of 1.8. The differences were particularly evident in the emergency call functionality, battery life, precision of the tracking function, and service/support. The call quality, with one exception, and the stability/robustness were consistently rated as "1-very good". Three watches in the test were able to detect falls with variable results.

CONCLUSION

The functionality and usability of the tested products differed considerably. A focus on a few main functions can even provide added value for older, frail people. Continuous comparative testing of products for this target group with new and updated products is desirable.

Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Background

In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices.

Objective

Our study objective is twofold: (1) to propose a set of step-based fall risk parameters that can be obtained independently of the sensor placement by using a ubiquitous step detection method and (2) to evaluate their association with prospective falls.

Methods

A reanalysis was conducted on the 1-week ambulatory inertial data from the StandingTall study, which was originally described by Delbaere et al. The data were from 301 community-dwelling older people and contained fall occurrences over a 12-month follow-up period. Using the ubiquitous and robust step detection method Smartstep, which is agnostic to sensor placement, a range of step-based fall risk parameters can be calculated based on walking bouts of 200 steps. These parameters are known to describe different dimensions of gait (ie, variability, complexity, intensity, and quantity). First, the correlation between parameters was studied. Then, the number of parameters was reduced through stepwise backward elimination. Finally, the association of parameters with prospective falls was assessed through a negative binomial regression model using the area under the curve metric.

Results

The built model had an area under the curve of 0.69, which is comparable to models exclusively built on fixed sensor placement. A higher fall risk was noted with higher gait variability (coefficient of variance of stride time), intensity (cadence), and quantity (number of steps) and lower gait complexity (sample entropy and fractal exponent).

Conclusions

These findings highlight the potential of our method for comprehensive and accurate fall risk assessments, independent of sensor placement. This approach has promising implications for ambulatory gait monitoring and fall risk monitoring using consumer-grade devices.

Five-day evaluation of the acceptability and comfort of wearable technology at four anatomical locations during military training

INTRODUCTION

Wearable sensor technologies enable Defence to optimise human performance, remotely identify physiological abnormalities and enhance medical support. Maximising the acceptability of devices will ensure they are worn alongside other equipment. This study assessed the acceptability and comfort of four devices at different anatomical locations during military training.

METHOD

A cross-sectional pilot study during a live firing infantry exercise or adventurous training assessed four anatomical locations concurrently over 5 days: finger, wrist, upper arm and chest. Participants rated comfort, acceptability and preference using a standardised questionnaire after 12 hours and 5 days of wear.

RESULTS

Twenty-one regular British Army personnel soldiers participated, aged 24.4 (4.3) years. The upper arm location received the highest rating by participants for comfort, followed in order by wrist, finger and chest (p=0.002, Χ2=40.0). The finger was most commonly identified as uncomfortable during specific activities (76%), followed by chest (48%), wrist (23%) and upper arm devices (14%). There was no significant difference in participant confidence in the devices to collect data or allow movement, but there was a trend towards greater confidence in upper arm and wrist locations to stay in position than the others (p=0.059, Χ2=28.0). After 5 days of wear, 43% of participants said they preferred the upper arm for comfort, followed by wrist (36%), finger (24%) and chest (10%). 73% and 71% would wear the wrist and upper arm devices on deployed operations, compared with 29% and 24% for chest and finger devices, respectively.

CONCLUSION

The upper arm location offered greater acceptability and comfort than finger, wrist or chest locations. It is essential to consider such findings from occupationally relevant settings when selecting wearable technology. A larger service evaluation in diverse settings is recommended to guide the choice of the most acceptable wearable devices across different equipment, roles and environments.

Determinants of physical activity in community-dwelling older adults: an umbrella review

INTRODUCTION

Physical activity (PA) is critical for disease prevention and maintaining functional ability with aging. Despite this, as many as 50% of older adults in populations worldwide are considered insufficiently active. There is a recognized need to mobilize policies targeted toward modifiable determinants of healthy aging like PA. This umbrella review aimed to summarize the evidence for determinants of PA in community-dwelling older adults.

METHODS

A research librarian searched six databases. Systematic and scoping reviews were included if they investigated community-dwelling people with a mean age of 60 + years and examined a relationship between a determinant and any type of PA. Two independent reviewers screened and extracted data from all reviews. JBI methodology and Critical Appraisal Checklist for Systematic Reviews and Research Syntheses were followed and information on the quality of the evidence was extracted.

RESULTS

From 17,277 records screened,11 reviews representing > 300 unique primary papers were ultimately included. Only 6% of studies included in all reviews had longitudinal designs. Included studies used a large variety of PA measures, with 76% using only self-report, 15% using only direct measures (e.g., accelerometry), 3% using both types, and 6% with no outcome measure reported. Only four reviews provided a definition of PA and there was substantial inconsistency in the way PA was categorised. Community level influences, which only included the physical environment, were the most commonly assessed (6/11) with more than 70% of the summarized relationships demonstrating null associations. Three out of four reviews reported a positive relationship between walkability and PA in general community-dwelling older adults. There was also evidence supporting relationships between presence of social support for PA, younger age, and men having higher PA from a single systematic review. None of the included reviews assessed the quality of evidence but over 60% performed a risk of bias assessment.

CONCLUSIONS

Walkability, age, gender, and social support for PA were the most supported PA determinants identified. Further research should focus on interpersonal and intrapersonal influences and incorporate direct measures of PA with clear operational definitions. There is a need for longitudinal study designs to further understand determinants of PA behaviour trajectories.

Data Mining and Fusion Framework for In-Home Monitoring Applications

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.

A Deep Regression Approach for Human Activity Recognition Under Partial Occlusion

In real-life scenarios, Human Activity Recognition (HAR) from video data is prone to occlusion of one or more body parts of the human subjects involved. Although it is common sense that the recognition of the majority of activities strongly depends on the motion of some body parts, which when occluded compromise the performance of recognition approaches, this problem is often underestimated in contemporary research works. Currently, training and evaluation is based on datasets that have been shot under laboratory (ideal) conditions, i.e. without any kind of occlusion. In this work, we propose an approach for HAR in the presence of partial occlusion, in cases wherein up to two body parts are involved. We assume that human motion is modeled using a set of 3D skeletal joints and also that occluded body parts remain occluded during the whole duration of the activity. We solve this problem using regression, performed by a novel deep Convolutional Recurrent Neural Network (CRNN). Specifically, given a partially occluded skeleton, we attempt to reconstruct the missing information regarding the motion of its occluded part(s). We evaluate our approach using four publicly available human motion datasets. Our experimental results indicate a significant increase of performance, when compared to baseline approaches, wherein networks that have been trained using only nonoccluded or both occluded and nonoccluded samples are evaluated using occluded samples. To the best of our knowledge, this is the first research work that formulates and copes with the problem of HAR under occlusion as a regression task.

Acceptance and Usability of an Innovative mDentistry eHygiene Model Amid the COVID-19 Pandemic Within the US National Dental Practice-Based Research Network: Mixed Methods Study

BACKGROUND

Amid the COVID-19 pandemic and other possible future infectious disease pandemics, dentistry needs to consider modified dental examination regimens that render quality care and ensure the safety of patients and dental health care personnel (DHCP).

OBJECTIVE

This study aims to assess the acceptance and usability of an innovative mDentistry eHygiene model amid the COVID-19 pandemic.

METHODS

This pilot study used a 2-stage implementation design to assess 2 critical components of an innovative mDentistry eHygiene model: virtual hygiene examination (eHygiene) and patient self-taken intraoral images (SELFIE), within the National Dental Practice-Based Research Network. Mixed methods (quantitative and qualitative) were used to assess the acceptance and usability of the eHygiene model.

RESULTS

A total of 85 patients and 18 DHCP participated in the study. Overall, the eHygiene model was well accepted by patients (System Usability Scale [SUS] score: mean 70.0, SD 23.7) and moderately accepted by dentists (SUS score: mean 51.3, SD 15.9) and hygienists (SUS score: mean 57.1, SD 23.8). Dentists and patients had good communication during the eHygiene examination, as assessed using the Dentist-Patient Communication scale. In the SELFIE session, patients completed tasks with minimum challenges and obtained diagnostic intraoral photos. Patients and DHCP suggested that although eHygiene has the potential to improve oral health care services, it should be used selectively depending on patients' conditions.

CONCLUSIONS

The study results showed promise for the 2 components of the eHygiene model. eHygiene offers a complementary modality for oral health data collection and examination in dental offices, which would be particularly useful during an infectious disease outbreak. In addition, patients being able to capture critical oral health data in their home could facilitate dental treatment triage and oral health self-monitoring and potentially trigger oral health-promoting behaviors.

Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is highly prevalent and is associated with a heavy burden on patients and health systems alike. Exacerbations of COPD (ECOPDs) are a leading cause of acute hospitalization among all adult chronic diseases. There is currently a paradigm shift in the way that ECOPDs are conceptualized. For the first time, objective physiological parameters are being used to define/classify what an ECOPD is (including heart rate, respiratory rate, and oxygen saturation criteria) and therefore a mechanism to monitor and measure their changes, particularly in an outpatient ambulatory setting, are now of great value. In addition to pre-existing challenges on traditional 'in-person' health models such as geography and seasonal (ex. winter) impacts on the ability to deliver in-person visit-based care, the COVID-19 pandemic imposed additional stressors including lockdowns, social distancing, and the closure of pulmonary function labs. These health system stressors, combined with the new conceptualization of ECOPDs, rapid advances in sophistication of hardware and software, and a general openness by stakeholders to embrace this technology, have all influenced the propulsion of remote patient monitoring (RPM) and wearable technology in the modern care of COPD. The present article reviews the use of RPM and wearable technology in COPD. Context on the influences, factors and forces which have helped shape this health system innovation is provided. A focused summary of the literature of RPM in COPD is presented. Finally, the practical and ethical principles which must guide the transition of RPM in COPD into real-world clinical use are reviewed.

Human Activity Recognition in the Presence of Occlusion

The presence of occlusion in human activity recognition (HAR) tasks hinders the performance of recognition algorithms, as it is responsible for the loss of crucial motion data. Although it is intuitive that it may occur in almost any real-life environment, it is often underestimated in most research works, which tend to rely on datasets that have been collected under ideal conditions, i.e., without any occlusion. In this work, we present an approach that aimed to deal with occlusion in an HAR task. We relied on previous work on HAR and artificially created occluded data samples, assuming that occlusion may prevent the recognition of one or two body parts. The HAR approach we used is based on a Convolutional Neural Network (CNN) that has been trained using 2D representations of 3D skeletal motion. We considered cases in which the network was trained with and without occluded samples and evaluated our approach in single-view, cross-view, and cross-subject cases and using two large scale human motion datasets. Our experimental results indicate that the proposed training strategy is able to provide a significant boost of performance in the presence of occlusion.

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP).

Methods

Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback.

Results

Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS.

Conclusion

This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP.

An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

BACKGROUND

Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status.

OBJECTIVE

This study's aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.

METHODS

Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.

RESULTS

The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.

CONCLUSIONS

Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users' walking activity and help researchers gain insights on users' health status.

Usability of a wearable device for home-based upper limb telerehabilitation in persons with stroke: A mixed-methods study

Background

The use of wearable technology offers a promising home-based self-directed option for upper limb training. Although product usability is a crucial aspect of users' acceptance of a wearable device, usability studies in wearable devices are rare, with most studies focusing primarily on clinical validity.

Objective

This study aimed to explore the usability of a wristwatch device called "Smart reminder" for home-based upper limb telerehabilitation for persons with stroke.

Methods

Eleven stroke participants used the proposed wristwatch for at least two weeks and underwent a home-based telerehabilitation program. A mixed-methods design was used to explore the usability of the wristwatch. Quantitative data were collected through the System Usability Scale (SUS) questionnaire, and the participants' rate of therapy compliance (gathered from the device) was reported descriptively. In addition, qualitative data were collected through semi-structured interviews with the participants and were analyzed using thematic analysis.

Results

The results demonstrated that the usability of the proposed wristwatch and telerehabilitation system was rated highly by the participants, with a high SUS mean score of 84.3 (12.3) and high therapy compliance rate (mean  =  91%). Qualitatively, all participants reported positive experiences with the wristwatch and indicated keenness to use it again. Participants reported physical improvements and felt motivated to exercise after using the wristwatch. They found the device easy and convenient and appreciated the remote monitoring function. Meanwhile, they highlighted critical considerations for the design of the device and program, including technical support, a wearable design of the device, graded exercise content according to ability, and flexibility in exercise schedules. Finally, they suggested that an interim review with the therapist on their progress might help them continue using the wristwatch.

Conclusions

This study's results supported the proposed wearable device's usability and showed strong acceptance by the participants for using it as a home-based upper limb telerehabilitation intervention.

ChroniSense National Early Warning Score Study: Comparison Study of a Wearable Wrist Device to Measure Vital Signs in Patients Who Are Hospitalized

BACKGROUND

Wearable devices could be used to continuously monitor vital signs in patients who are hospitalized, but they require validation.

OBJECTIVE

This study aimed to evaluate the clinical validity of the prototype of a semiautomated wearable wrist device (ChroniSense Polso) to measure vital signs and provide National Early Warning Scores (NEWSs).

METHODS

Vital signs and NEWSs measured using the wearable device were compared with standard, nurse-lead manual measurements. We enrolled adult patients (aged ≥18 years) who required vital sign measurements at least every 6 hours in a UK teaching district general hospital. Wearable device measurements were not used for clinical decision-making. The primary outcome was the agreement on the individual National Early Warning parameter scores and vital sign measurements: respiratory rate, oxygen saturation, body temperature, systolic blood pressure, and heart rate. Secondary outcomes were the agreement on the total NEWS, incidence of adverse events, and user acceptance. To compare the wearable device measurements with the standard measurements, we analyzed vital sign measurements by limits of agreement (Bland-Altman analysis) and conducted κ agreement analyses for NEWSs. A user experience survey was conducted with questions about comfort of the wrist device, safety, preference, and use.

RESULTS

We included 132 participants in the study, with a mean age of 62 (SD 15.81) years; most of them were men (102/132, 77.3%). The highest weighted κ values were found for heart rate (0.69, 95% CI 0.57-0.81 for all 385 measurements) and systolic blood pressure (0.39, 95% CI 0.30-0.47 for all 339 measurements). Weighted κ values were low for respiration rate (0.03, 95% CI -0.001 to 0.05 for all 445 measurements), temperature (0, 95% CI 0-0 for all 231 measurements), and oxygen saturation (-0.11, 95% CI -0.20 to -0.02 for all 187 measurements). Weighted κ using Cicchetti-Allison weights showed κ of 0.20 (95% CI 0.03-0.38) when using all 56 total NEWSs. The user acceptance survey found that approximately half (45/91, 49%) of the participants found it comfortable to wear the device and liked its appearance. Most (85/92, 92%) of them said that they would wear the device during their next hospital visit, and many (74/92, 80%) said that they would recommend it to others.

CONCLUSIONS

This study shows the promising use of a prototype wearable device to measure vital signs in a hospital setting. Agreement between the standard measurements and wearable device measurements was acceptable for systolic blood pressure and heart rate, but needed to be improved for respiration rate, temperature, and oxygen saturation. Future studies need to improve the clinical validity of this wearable device. Large studies are required to assess clinical outcomes and cost-effectiveness of wearable devices for vital sign measurement.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2018-028219.

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study

Background

This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health.

Methods

Participants (N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants (n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis.

Results

The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0-20) on a scale from 0-20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0-5.0) on a scale from 1-5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants' as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals.

Conclusions

Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.

A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review

Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote rehabilitation architecture and enabling technology mandates the elaboration of sensing mechanisms tailored to individual clinical needs. This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensing mechanism included a multimodal sensor, the application of multiple sensors, sensor fusion, and machine learning. Most of the stroke studies utilized biofeedback control systems (78%) while the majority of studies for neurodegenerative disorders used sensors for remote monitoring (57%). Functional assessment tools that the sensing mechanism may emulate to produce clinically valid information were proposed and factors affecting user adoption were described. Lastly, the limitations and directions for further development were discussed.

A Multimodal Fusion Approach for Human Activity Recognition

The problem of human activity recognition (HAR) has been increasingly attracting the efforts of the research community, having several applications. It consists of recognizing human motion and/or behavior within a given image or a video sequence, using as input raw sensor measurements. In this paper, a multimodal approach addressing the task of video-based HAR is proposed. It is based on 3D visual data that are collected using an RGB + depth camera, resulting to both raw video and 3D skeletal sequences. These data are transformed into six different 2D image representations; four of them are in the spectral domain, another is a pseudo-colored image. The aforementioned representations are based on skeletal data. The last representation is a "dynamic" image which is actually an artificially created image that summarizes RGB data of the whole video sequence, in a visually comprehensible way. In order to classify a given activity video, first, all the aforementioned 2D images are extracted and then six trained convolutional neural networks are used so as to extract visual features. The latter are fused so as to form a single feature vector and are fed into a support vector machine for classification into human activities. For evaluation purposes, a challenging motion activity recognition dataset is used, while single-view, cross-view and cross-subject experiments are performed. Moreover, the proposed approach is compared to three other state-of-the-art methods, demonstrating superior performance in most experiments.

Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers

Digital gait biomarkers (including walking speed) indicate functional decline and predict hospitalization and mortality. However, waist or lower-limb devices often used are not designed for continuous life-long use. While wrist devices are ubiquitous and many large research repositories include wrist-sensor data, widely accepted and validated digital gait biomarkers derived from wrist-worn accelerometers are not available yet. Here we describe the development of advanced signal processing algorithms that extract digital gait biomarkers from wrist-worn devices and validation using 1-week data from 78,822 UK Biobank participants. Our gait biomarkers demonstrate good test-retest-reliability, strong agreement with electronic walkway measurements of gait speed and self-reported pace and significantly discriminate individuals with poor self-reported health. With the almost universal uptake of smart-watches, our algorithms offer a new approach to remotely monitor life-long population level walking speed, quality, quantity and distribution, evaluate disease progression, predict risk of adverse events and provide digital gait endpoints for clinical trials.

Cardiovascular disease risk and all-cause mortality associated with accelerometer-measured physical activity and sedentary time ‒ a prospective population-based study in older adults

Background

Low levels of physical activity (PA) and high sedentary time (ST) are common in older adults and lack of PA is a risk factor for cardiovascular disease (CVD). Knowledge about associations with accelerometer-measured PA, ST and CVD risk in older adults is insufficient. This study examines the associations of accelerometer-measured PA and ST with cardiovascular risk measured using the Framingham risk score (FRS) and all-cause mortality in older adults.

Methods

A population-based sample of 660 (277 men, 383 women) older people (mean age 68.9) participated in the Oulu45 cohort study from 2013‒2015. PA and ST were measured with wrist-worn accelerometers at baseline for two weeks. Ten-year CVD risk (%) was estimated with FRS. The data for all-cause mortality were identified from the Digital and Population Data Services Agency, Finland after an average of 6.2 years follow-up. The associations between moderate to vigorous physical activity (MVPA), light physical activity (LPA), ST and FRS were analyzed using the multivariable linear regression analysis. Associations between LPA, ST and mortality were analyzed using the Cox proportional-hazard regression models.

Results

Each 10 min increase in MVPA (β = -0.779, 95% CI -1.186 to -0.371, p < 0.001) and LPA (β = -0.293, 95% CI -0.448 to -0.138, p < 0.001) was negatively associated with FRS while a 10 min increase in ST (β = 0.290, 95% CI 0.158 to 0.421, p < 0.001) was positively associated with FRS. After adjustment for waist circumference, only ST was significantly associated with FRS. Each 10 min increase in LPA was associated with 6.5% lower all-cause mortality risk (HR = 0.935, 95% CI 0.884 to 0.990, p = 0.020) and each 10 min increase in ST with 5.6% increased mortality risk (HR = 1.056, 95% CI 1.007 to 1.108, p = 0.025).

Conclusion

A higher amount of daily physical activity, at any intensity level, and avoidance of sedentary time are associated with reduced cardiovascular disease risk in older people. Higher time spent in light physical activity and lower sedentary time are associated with lower all-cause mortality.

Usability of a smartwatch for atrial fibrillation detection in older adults after stroke

Background

Smartwatches can be used for atrial fibrillation (AF) detection, but little is known about how older adults at risk for AF perceive their usability.

Methods

We employed a mixed-methods study design using data from the ongoing Pulsewatch study, a randomized clinical trial (NCT03761394) examining the accuracy of a smartwatch-smartphone app dyad (Samsung/Android) compared to usual care with a patch monitor (Cardea SOLO™ ECG System) for detection of AF among older stroke survivors. To be eligible to participate in Pulsewatch, participants needed to be at least 50 years of age, have had an ischemic stroke, and have no major contraindications to anticoagulation therapy should AF be detected. After 14 days of use, usability was measured by the System Usability Scale (SUS) and investigator-generated questions. Qualitative interviews were conducted, transcribed, and coded via thematic analysis.

Results

Ninety participants in the Pulsewatch trial were randomized to use a smartwatch-smartphone app dyad for 14 days (average age: 65 years, 41% female, 87% White), and 46% found it to be highly usable (SUS ≥68). In quantitative surveys, participants who used an assistive device (eg, wheelchair) and those with history of anxiety or depression were more likely to report anxiety associated with watch use. In qualitative interviews, study participants reported wanting a streamlined system that was more focused on rhythm monitoring and a smartwatch with a longer battery life. In-person training and support greatly improved their experience, and participants overwhelmingly preferred use of a smartwatch over traditional cardiac monitoring owing to its comfort, appearance, and convenience.

Conclusion

Older adults at high risk for AF who were randomized to use a smartwatch-app dyad for AF monitoring over 14 days found it to be usable for AF detection and preferred their use to the use of a patch monitor. However, participants reported that a simpler device interface and longer smartwatch battery life would increase the system's usability.

The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment

Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland−Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings.

Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards?

Aim

To investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.

Findings

Wearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system.

Message

Despite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts.

Purpose

We assess feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.

Methods

Gait parameters of 83 patients (83.34 ± 5.88 years, 58/25 female/male) were recorded at admission and/or discharge to/from two geriatric inpatient wards. Gait parameters were tested for statistically significant differences between admission and discharge. Walking distance measured by a wearable gait analysis system was correlated with distance assessed by physiotherapists. Examiners rated usability using the system usability scale. Patients reported acceptability on a five-point Likert-scale.

Results

The total distance measures highly correlate (r = 0.89). System Usability Scale is above the median threshold of 68, indicating good usability. Majority of patients does not have objections regarding the use of the system. Among other gait parameters, mean heel strike angle changes significantly between admission and discharge.

Conclusion

Wearable gait analysis system is objectively and subjectively usable in a clinical setting and accepted by patients. It offers a reasonably valid assessment of gait parameters and is a feasible way for instrumented gait analysis.

Usability, Acceptability, and Satisfaction of a Wearable Activity Tracker in Older Adults: Observational Study in a Real-Life Context in Northern Portugal

BACKGROUND

The use of activity trackers has significantly increased over the last few years. This technology has the potential to improve the levels of physical activity and health-related behaviors in older adults. However, despite the potential benefits, the rate of adoption remains low among older adults. Therefore, understanding how technology is perceived may potentially offer insight to promote its use.

OBJECTIVE

This study aimed to (1) assess acceptability, usability, and user satisfaction with the Xiaomi Mi Band 2 in Portuguese community-dwelling older adults in a real-world context; (2) explore the mediating effect of the usability on the relationship between user characteristics and satisfaction; and (3) examine the moderating effect of user characteristics on the relationship between usability and user satisfaction.

METHODS

Older adults used the Xiaomi Mi Band 2 over 15 days. The user experience was evaluated through the Technology Acceptance Model 3, System Usability Scale, and User Satisfaction Evaluation Questionnaire. An integrated framework for usability and user satisfaction was used to explore user experience. Statistical data analysis included descriptive data analysis, reliability analysis, confirmatory factor analysis, and mediation and moderation analyses.

RESULTS

A sample of 110 older adults with an average age of 68.41 years (SD 3.11) completed the user experience questionnaires. Mean user acceptance was very high-perceived ease of use: 6.45 (SD 0.78); perceptions of external control: 6.74 (SD 0.55); computer anxiety: 6.85 (SD 0.47); and behavioral intention: 6.60 (SD 0.97). The usability was excellent with an average score of 92.70 (SD 10.73), and user satisfaction was classified as a good experience 23.30 (SD 2.40). The mediation analysis confirmed the direct positive effect of usability on satisfaction (β=.530; P<.01) and the direct negative effect of depression on usability (β=-.369; P<.01). Lastly, the indirect effect of usability on user satisfaction was higher in individuals with lower Geriatric Depression Scale levels.

CONCLUSIONS

Findings demonstrate that the Xiaomi Mi Band 2 is suitable for older adults. Furthermore, the results confirmed usability as a determinant of satisfaction with the technology and extended the existing knowledge about wearable activity trackers in older adults.

Accuracy of Heart Rate and Respiratory Rate Measurements Using Two Types of Wearable Devices

Objectives:

Wearable devices such as fitness trackers have become popular in the healthcare field. Tracking heart rate and respiratory rate, in addition to physical activity, may provide an accurate picture of daily health. We believe that a combination of two types of devices can simultaneously measure and record physical activity, heart rate, and respiratory rate. However, the measurement accuracies of these two types of devices are not clear. This study aimed to determine the measurement accuracies of two wearable devices for heart and respiratory rate measurements.

Methods:

Ten healthy men performed incremental load tests (ILTs) and constant load tests (CLTs) on a cycle ergometer. The heart and respiratory rates were measured using wrist-worn (Silmee W22, TDK, Japan, Tokyo) and respiratory tracking devices (Spire Stone, Spire Health, San Francisco, CA, USA), respectively. A 12-lead electrocardiograph and the breath-by-breath method were used as external standards for heart and respiratory rates, respectively.

Results:

Bland–Altman analysis showed that heart rate had a fixed bias at rest and during ILT and CLT and had a proportional bias during CLT. The standard error values of the regression at rest and during CLT were less than 10 bpm for heart rate and less than 5.0 /min for respiratory rate. During ILT, the standard error was greater than 10 bpm for heart rate and approximately 5.0 /min for respiratory rate.

Conclusions:

The heart and respiratory rate measurements obtained using wearable devices were accurate within the practical margin of error.

Beyond wellness monitoring: Continuous multiparameter remote automated monitoring of patients

The pursuit of more efficient patient-friendly health systems and reductions in tertiary health services use has seen enormous growth in the application and study of remote patient monitoring systems for cardiovascular patient care. While there are many consumer-grade products available to monitor patient wellness, the regulation of these technologies varies considerably, with most products having little to no evaluation data. As the science and practice of virtual care continues to evolve, clinicians and researchers can benefit from an understanding of more comprehensive solutions, capable of monitoring three or more biophysical parameters (e.g., oxygen saturation, heart rate) continuously and simultaneously. These devices, herein referred to as continuous multiparameter remote automated monitoring (CM-RAM) devices, have the potential to revolutionize virtual patient care. Through seamless integration of multiple biophysical signals, CM-RAM technologies can allow for the acquisition of high-volume big data for the development of algorithms to facilitate early detection of negative changes in patient health status and timely clinician response. In this article, we review key principles, architecture, and components of CM-RAM technologies. Work to date in this field and related implications are also presented, including strategic priorities for advancing the science and practice of CM-RAM.

Assessment of an Innovative Mobile Dentistry eHygiene Model Amid the COVID-19 Pandemic in the National Dental Practice-Based Research Network: Protocol for Design, Implementation, and Usability Testing

BACKGROUND

Amid COVID-19, and other possible future infectious disease pandemics, dentistry needs to consider modified dental examination regimens that render quality care, are cost effective, and ensure the safety of patients and dental health care personnel (DHCP). Traditional dental examinations, which number more than 300 million per year in the United States, rely on person-to-person tactile examinations, pose challenges to infection control, and consume large quantities of advanced-level personal protective equipment (PPE). Therefore, our long-term goal is to develop an innovative mobile dentistry (mDent) model that takes these issues into account. This model supplements the traditional dental practice with virtual visits, supported by mobile devices such as mobile telephones, tablets, and wireless infrastructure. The mDent model leverages the advantages of digital mobile health (mHealth) tools such as intraoral cameras to deliver virtual oral examinations, treatment planning, and interactive oral health management, on a broad population basis. Conversion of the traditional dental examinations to mDent virtual examinations builds upon (1) the reliability of teledentistry, which uses intraoral photos and live videos to make diagnostic decisions, and (2) rapid advancement in mHealth tool utilization.

OBJECTIVE

In this pilot project, we designed a 2-stage implementation study to assess 2 critical components of the mDent model: virtual hygiene examination (eHygiene) and patient self-taken intraoral photos (SELFIE). Our specific aims are to (1) assess the acceptance and barriers of mDent eHygiene among patients and DHCP, (2) assess the economic impact of mDent eHygiene, and (3) assess the patient's capability to generate intraoral photos using mHealth tools (exploratory aim, SELFIE).

METHODS

This study will access the rich resources of the National Dental Practice-Based Research Network to recruit 12 dentists, 12 hygienists, and 144 patients from 12 practices. For aims 1 and 2, we will use role-specific questionnaires to collect quantitative data on eHygiene acceptance and economic impact. The questionnaire components include participant characteristics, the System Usability Scale, a dentist-patient communication scale, practice operation cost, and patient opportunity cost. We will further conduct a series of iterative qualitative research activities using individual interviews to further elicit feedback and suggestion for changes to the mDent eHygiene model. For aim 3, we will use mixed methods (quantitative and qualitative) to assess the patient's capability of taking intraoral photos, by analyzing obtained photos and recorded videos.

RESULTS

The study is supported by the US National Institute of Dental and Craniofacial Research. This study received "single" institutional review board approval in August 2021. Data collection and analysis are expected to conclude by December 2021 and March 2022, respectively.

CONCLUSIONS

The study results will inform the logistics of conducting virtual dental examinations and empowering patients with mHealth tools, providing better safety and preserving PPE amid the COVID-19 and possible future pandemics.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/32345.

Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review

BACKGROUND

The World Health Organisation's global strategy for digital health emphasises the importance of patient involvement. Understanding the usability and acceptability of wearable devices is a core component of this. However, usability assessments to date have focused predominantly on healthy adults. There is a need to understand the patient perspective of wearable devices in participants with chronic health conditions.

METHODS

A systematic review was conducted to identify any study design that included a usability assessment of wearable devices to measure mobility, through gait and physical activity, within five cohorts with chronic conditions (Parkinson's disease [PD], multiple sclerosis [MS], congestive heart failure, [CHF], chronic obstructive pulmonary disorder [COPD], and proximal femoral fracture [PFF]).

RESULTS

Thirty-seven studies were identified. Substantial heterogeneity in the quality of reporting, the methods used to assess usability, the devices used, and the aims of the studies precluded any meaningful comparisons. Questionnaires were used in the majority of studies (70.3%; n = 26) with a reliance on intervention specific measures (n = 16; 61.5%). For those who used interviews (n = 17; 45.9%), no topic guides were provided, while methods of analysis were not reported in over a third of studies (n = 6; 35.3%).

CONCLUSION

Usability of wearable devices is a poorly measured and reported variable in chronic health conditions. Although the heterogeneity in how these devices are implemented implies acceptance, the patient voice should not be assumed. In the absence of being able to make specific usability conclusions, the results of this review instead recommends that future research needs to: (1) Conduct usability assessments as standard, irrespective of the cohort under investigation or the type of study undertaken. (2) Adhere to basic reporting standards (e.g. COREQ) including the basic details of the study. Full copies of any questionnaires and interview guides should be supplied through supplemental files. (3) Utilise mixed methods research to gather a more comprehensive understanding of usability than either qualitative or quantitative research alone will provide. (4) Use previously validated questionnaires alongside any intervention specific measures.

Association between accelerometer-measured physical activity, glucose metabolism, and waist circumference in older adults

AIMS

To examine the association of physical activity (PA) and sedentary time (ST) with glucose metabolism according to waist circumference (WC) in older people.

METHODS

A population-based sample of 702 individuals (aged 67-70 years) wore wrist-worn accelerometers for two weeks and underwent an oral glucose tolerance test. The associations between moderate-to-vigorous (MVPA) and light (LPA) PA, ST, and glucose metabolism across the tertiles of WC were analysed using general linear regression.

RESULTS

Among highest WC tertile, LPA negatively associated with fasting insulin (β =  - 0.047, 95% CI - 0.082 to - 0.012), HOMA-IR (β =  - 0.098, 95% CI - 0.184 to - 0.012), and HOMA-β (β =  - 3.367, CI - 6.570 to - 0.783). ST associated with 120 min glucose (β = 0.140, CI 0.021 to 0.260). Among lowest WC tertile, MVPA negatively associated with 30 min insulin (β =  - 0.086, 95% CI - 0.168 to - 0.004) and 120 min insulin (β =  - 0.160, 95% CI - 0.257 to - 0.063) and positively associated with Matsuda index (β = 0.076, 95% CI 0.014 to 0.139). Light PA negatively associated with 120 min insulin (β =  - 0.054, 95% CI - 0.104 to - 0.005).

CONCLUSION

With the limitation of the cross-sectional study, reducing ST and increasing LPA may be beneficial for glucose metabolism among abdominally obese older adults. Lean older adults could benefit more from increasing MVPA.

It's not about the capture, it's about what we can learn": a qualitative study of experts' opinions and experiences regarding the use of wearable sensors to measure gait and physical activity

BACKGROUND

The use of wearable sensor technology to collect patient health data, such as gait and physical activity, offers the potential to transform healthcare research. To maximise the use of wearable devices in practice, it is important that they are usable by, and offer value to, all stakeholders. Although previous research has explored participants' opinions of devices, to date, limited studies have explored the experiences and opinions of the researchers who use and implement them. Researchers offer a unique insight into wearable devices as they may have access to multiple devices and cohorts, and thus gain a thorough understanding as to how and where this area needs to progress. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices to measure gait and physical activity.

METHODS

Twenty professionals with experience using wearable devices in research were recruited from academic, industry and clinical backgrounds. Independent, semi-structured interviews were conducted, audio-recorded and transcribed. Transcribed texts were analysed using inductive thematic analysis.

RESULTS

Five themes were identified: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Researchers overwhelmingly supported the use of wearable sensor technology due to the insights that they may provide. Though barriers remain, researchers were pragmatic towards these, believing that there is a paradigm shift happening in this area of research that ultimately requires mistakes and significant volumes of further research to allow it to progress.

CONCLUSIONS

Multiple barriers to the use of wearable devices in research and clinical practice remain, including data management and clear clinical utility. However, researchers strongly believe that the potential benefit of these devices to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary research integrating the expertise of both academia, industry and clinicians is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders.

The Characterization of the Toll of Caring for Coronavirus Disease 2019 on ICU Nursing Staff

Objectives

Coronavirus disease 2019 pandemic exercised a significant demand on healthcare workers. We aimed to characterize the toll of caring for coronavirus disease 2019 patients by registered nurses.

Design

An observational study of two registered nurses cohorts.

Setting

ICUs in a large academic center.

Subjects

Thirty-nine ICU registered nurses assigned to coronavirus disease 2019 versus noncoronavirus disease 2019 patients.

Interventions

None.

Measurements and Main Results

Skin temperature (t [°C]), galvanic skin stress response (GalvStress), blood pulse wave, energy expenditure (Energy [cal]), number of steps (hr^-1), heart rate (min^-1), and respiratory rate (min^-1) were collected using biosensors during the shift. National Aeronautics and Space Administration Task Loading Index measured the subjective perception of an assignment load. Elevated skin temperatures during coronavirus disease 2019 shifts were recorded (Δt_COVID vs t_non-COVID = +1.3 [°C]; 95% CI, 0.1-2.5). Registered nurses staffing coronavirus disease patients self-reported elevated effort (ΔEffort_COVID vs Effort_non-COVID = +28.6; 95% CI, 13.3-43.9) concomitant with higher energy expenditure (ΔEnergy_COVID vs Energy_non-COVID = +21.5 [cal/s]; 95% CI, 4.2-38.7). Galvanic skin stress responses were more frequent among coronavirus disease registered nurse (ΔGalStress_COVID vs GalvStress_non-COVID = +10.7 [burst/hr]; 95% CI, 2.6-18.7) and correlated with self-reported increased mental burden (ΔTLXMental_COVID vs ΔTLXMental_non-COVID = +15.3; 95% CI, 1.0-29.6).

Conclusions

There are indications that registered nurses providing care for coronavirus disease 2019 in the ICU reported increased thermal discomfort coinciding with elevated energy expenditure and a more pronounced self-perception of effort, stress, and mental demand.

Time-frequency time-space LSTM for robust classification of physiological signals

Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time-frequency and time-space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial

Biosensors represent one of the numerous promising technologies envisioned to extend healthcare delivery. In perioperative care, the healthcare delivery system can use biosensors to remotely supervise patients who would otherwise be admitted to a hospital. This novel technology has gained a foothold in healthcare with significant acceleration due to the COVID-19 pandemic. However, few studies have attempted to narrate, or systematically analyze, the process of their implementation. We performed an observational study of biosensor implementation. The data accuracy provided by the commercially available biosensors was compared to those offered by standard clinical monitoring on patients admitted to the intensive care unit/perioperative unit. Surveys were also conducted to examine the acceptance of technology by patients and medical staff. We demonstrated a significant difference in vital signs between sensors and standard monitoring which was very dependent on the measured variables. Sensors seemed to integrate into the workflow relatively quickly, with almost no reported problems. The acceptance of the biosensors was high by patients and slightly less by nurses directly involved in the patients’ care. The staff forecast a broad implementation of biosensors in approximately three to five years, yet are eager to learn more about them. Reliability considerations proved particularly troublesome in our implementation trial. Careful evaluation of sensor readiness is most likely necessary prior to system-wide implementation by each hospital to assess for data accuracy and acceptance by the staff.

"It's Not as Simple as Just Looking at One Chart": A Qualitative Study Exploring Clinician's Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data

Background

Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient's physical activity patterns.

Objective

To explore clinicians' opinions on different visualisations of actigraphy data.

Methods

Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis.

Results

Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret.

Conclusion

The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.

An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment

Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.