What impacts the acceptability of wearable devices that detect opioid overdose in people who use opioids? A qualitative study

INTRODUCTION

Drug-related deaths involving an opioid are at all-time highs across the United Kingdom. Current overdose antidotes (naloxone) require events to be witnessed and recognised for reversal. Wearable technologies have potential for remote overdose detection or response but their acceptability among people who use opioids (PWUO) is not well understood. This study explored facilitators and barriers to wearable technology acceptability to PWUO.

METHODS

Twenty-four participants (79% male, average age 46 years) with current (n = 15) and past (n = 9) illicit heroin use and 54% (n = 13) who were engaged in opioid substitution therapy participated in semi-structured interviews (n = 7) and three focus groups (n = 17) in London and Nottingham from March to June 2022. Participants evaluated real devices, discussing characteristics, engagement factors, target populations, implementation strategies and preferences. Conversations were recorded, transcribed and thematically analysed.

RESULTS

Three themes emerged: device-, person- and environment-specific factors impacting acceptability. Facilitators included inconspicuousness under the device theme and targeting subpopulations of PWUO at the individual theme. Barriers included affordability of devices and limited technology access within the environment theme. Trust in device accuracy for high and overdose differentiation was a crucial facilitator, while trust between technology and PWUO was a significant environmental barrier.

DISCUSSION AND CONCLUSIONS

Determinants of acceptability can be categorised into device, person and environmental factors. PWUO, on the whole, require devices that are inconspicuous, comfortable, accessible, easy to use, controlled by trustworthy organisations and highly accurate. Device developers must consider how the type of end-user and their environment moderate acceptability of the device.

Soft Epidermal Paperfluidics for Sweat Analysis by Ratiometric Raman Spectroscopy

The expanding interest in digital biomarker analysis focused on non-invasive human bodily fluids, such as sweat, highlights the pressing need for easily manufactured and highly efficient soft lab-on-skin solutions. Here, we report, for the first time, the integration of microfluidic paper-based devices (μPAD) and non-enhanced Raman-scattering-enabled optical biochemical sensing (Raman biosensing). Their integration merges the enormous benefits of μPAD, with high potential for commercialization and use in resource-limited settings, with biorecognition-element-free (but highly selective) optical Raman biosensing. The introduced thin (0.36 mm), ultra-lightweight (0.19 g), and compact footprint (3 cm2) opto-paperfluidic sweat patch is flexible, stretchable, and conforms, irritation-free, to hairless or minimally haired body regions to enable swift sweat collection. As a great advantage, this new bio-chemical sensory system excels through its absence of onboard biorecognition elements (bioreceptor-free) and omission of plasmonic nanomaterials. The proposed easy fabrication process is adaptable to mass production by following a fully sustainable and cost-effective process utilizing only basic tools by avoiding typically employed printing or laser patterning. Furthermore, efficient collection and transportation of precise sweat volumes, driven exclusively by the wicking properties of porous materials, shows high efficiency in liquid transportation and reduces biosensing latency by a factor of 5 compared to state-of-the-art epidermal microfluidics. The proposed unit enables electronic chip-free and imaging-less visual sweat loss quantification as well as optical biochemical analysis when coupled with Raman spectroscopy. We investigated the multimodal quantification of sweat urea and lactate levels ex vivo (with syntactic sweat including +30 sweat analytes on porcine skin) and achieved a linear dynamic range from 0 to 100 mmol/L during fully dynamic continuous flow characterization.

mHealth Technology Experiences of Middle-Aged and Older Individuals With Visual Impairments: Cross-Sectional Interview Study

BACKGROUND

Current mobile health (mHealth) technology is predominantly designed with a visual orientation, often resulting in user interfaces that are inaccessible to visually impaired users. While mHealth technology offers potential for facilitating chronic illness management and enhancing health behaviors among visually impaired older populations, understanding its usage remains limited.

OBJECTIVE

This qualitative research aimed to explore the mHealth technology experiences of middle-aged and older individuals with visual impairments including the accessibility and usability issues they faced.

METHODS

The qualitative exploration was structured using the mHealth for Older Users framework. Cross-sectional interviews were conducted via Zoom between June 1 and July 31, 2023, using an interview protocol for data collection. A thematic analysis approach was employed to analyze the transcribed interview scripts.

RESULTS

Of the 7 participants who took part in the Zoom interviews, 3 were men and 4 were women, with ages ranging from 53 to 70 years. Most participants adopted mHealth apps and wearable devices for promoting health. They exhibited 3 distinct adoption patterns. Seven themes were emerged from the perceived challenges in using mHealth technologies: (1) a scarcity of accessible user manuals, (2) user interfaces that are not visually impaired-friendly, (3) health data visualizations that are not accessible, (4) unintuitive arrangement of app content, (5) health information that is challenging to comprehend, (6) cognitive overload caused by an excess of audible information, and (7) skepticism regarding the accuracy of health records. mHealth technologies seem to positively affect the health and health management of participants.

CONCLUSIONS

Design considerations for mHealth technologies should consider individuals' disabilities and chronic conditions and should emphasize the importance of providing accessible manuals and training opportunities when introducing new mHealth solutions.

Validation of Automatically Quantified Swim Stroke Mechanics Using an Inertial Measurement Unit in Paralympic Athletes

Biomechanics and training load monitoring are important for performance evaluation and injury prevention in elite swimming. Monitoring of performance and swim stroke parameters is possible with inertial measurement units (IMU) but has not been validated in para-swimmers. The purpose of this study was to validate a single IMU-based system to accurately estimate pool-swam lap time, stroke count (SC), stroke duration, instantaneous stroke rate (ISR), and distance per stroke (DPS). Eight Paralympic athletes completed 4 × 50 m swims with an IMU worn on the sacrum. Strokes cycles were identified using a zero-crossing algorithm on the medio-lateral (freestyle and backstroke) or forward-backward (butterfly and breaststroke) instantaneous velocity data. Video-derived metrics were estimated using Dartfish and Kinovea. Agreement analyses, including Bland-Altman and Intraclass Correlation Coefficient (ICC), were performed on all outcome variables. SC Bland-Altman bias was 0.13 strokes, and ICC was 0.97. ISR Bland-Altman biases were within 1.5 strokes/min, and ICCs ranged from 0.26 to 0.96. DPS Bland-Altman biases were within 0.20 m, and ICCs ranged from 0.39 to 0.93. A single-IMU system can provide highly valid performance and swim stroke monitoring data for elite para-swimmers for the majority of strokes, with the exception of backstroke. Future work should improve bilateral stroke detection algorithms in this population.

Sweat Sensor Based on Wearable Janus Textiles for Sweat Collection and Microstructured Optical Fiber for Surface-Enhanced Raman Scattering Analysis

Wearable sweat sensors provide real-time monitoring of biomarkers, enabling individuals to gain real-time insight into their health status. Current sensors primarily rely on electrochemical mechanisms, limiting their capacity for the concurrent detection of multiple analytes. Surface-enhanced Raman scattering spectroscopy offers an alternative approach by providing molecular fingerprint information to facilitate the identification of intricate analytes. In this study, we combine a wearable Janus fabric for efficient sweat collection and a grapefruit optical fiber embedded with Ag nanoparticles as a sensitive SERS probe. The Janus fabric features a superhydrophobic side in contact with the skin and patterned superhydrophilic regions on the opposite surface, facilitating the unidirectional flow of sweat toward these hydrophilic zones. Grapefruit optical fibers feature sharp tips with the ability to penetrate transparent dressings. Its microchannels extract sweat through capillary force, and nanoliter-scale volumes of sweat are sufficient to completely fill them. The Raman signal of sweat components is greatly enhanced by the plasmonic hot spots and accumulates along the fiber length. We demonstrate sensitive detection of sodium lactate and urea in sweat with a detection limit much lower than the physiological concentration levels. Moreover, the platform shows its capability for multicomponent detection and extends to the analysis of real human sweat.

The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions

The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.

Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study

Background

Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study.

Objective

The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy.

Methods

A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective.

Results

A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated "above average" in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes.

Conclusions

Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

BACKGROUND

Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive.

OBJECTIVE

This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders.

METHODS

We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs.

RESULTS

The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments.

CONCLUSIONS

Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Physical Noninvasive Attacks on Photoplethysmogram by Computer Controlled Blood Pressure Cuff

Sensor data has been used in social security and welfare infrastructures such as insurance and medical care to provide personalized products and services; there is a risk that attackers can alter sensor data to obtain unfair benefits. We consider that one of the attack methods to modify sensor data is to attack the wearer's body to modify biometric information. In this study, we propose a noninvasive attack method to modify the sensor value of a photoplethysmogram. The proposed method can disappear pulse wave peaks by pressurizing the upper arm with air pressure to control blood volume. Seven subjects experiencing a rest environment and five subjects experiencing an after-exercise environment wore five different models of smartwatches, and three pressure patterns were performed. It was confirmed in both situations that the displayed heart rate decreased from the true heart rate.

Can Wrist-Worn Medical Devices Correctly Identify Ovulation?

(1) Background: Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary ovulation tests for predicting the fertile window. (2) Methods: We conducted a prospective observational study including women aged 18-45 years without current hormonal therapy who used a wrist-worn medical device and urinary ovulation tests for a minimum of three cycles. We analyzed the accuracy of both the retrospective and prospective algorithms using a generalized linear mixed-effects model. The findings were compared to real-world data from bracelet users who also reported urinary ovulation tests. (3) Results: A total of 61 study participants contributing 205 cycles and 6081 real-life cycles from 3268 bracelet users were included in the analysis. The mean error in identifying ovulation with the wrist-worn medical device retrospective algorithm in the clinical study was 0.31 days (95% CI -0.13 to 0.75). The retrospective algorithm identified 75.4% of fertile days, and the prospective algorithm identified 73.8% of fertile days correctly within the pre-specified equivalence limits (±2 days). The quality of the retrospective algorithm in the clinical study could be confirmed by real-world data. (4) Conclusion: Our data indicate that wearable sensors may be used to accurately detect the periovulatory period.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Using wearable activity trackers for research in the global south: Lessons learned from adolescent psychotherapy research in Kenya

Wearable activity trackers have emerged as valuable tools for health research, providing high-resolution data on measures such as physical activity. While most research on these devices has been conducted in high-income countries, there is growing interest in their use in the global south. This perspective discusses the challenges faced and strategies employed when using wearable activity trackers to test the effects of a school-based intervention for depression and anxiety among Kenyan youth. Lessons learned include the importance of validating data output, establishing an internal procedure for international procurement, providing on-site support for participants, designating a full-time team member for wearable activity tracker operation, and issuing a paper-based information sheet to participants. The insights shared in this perspective serve as guidance for researchers undertaking studies with wearables in similar settings, contributing to the evidence base for mental health interventions targeting youth in the global south. Despite the challenges to set up, deploy and extract data from wearable activity trackers, we believe that wearables are a relatively economical approach to provide insight into the daily lives of research participants, and recommend their use to other researchers.

Haptic Nudging Using a Wearable Device to Promote Upper Limb Activity during Stroke Rehabilitation: Exploring Diurnal Variation, Repetition, and Duration of Effect

Haptic nudging via wearable devices promotes physical activity and may increase upper limb movement in stroke rehabilitation. This study investigated the optimal approach to haptic nudging by examining diurnal variation, duration of effect, and repeated nudging. The study analysed data from a multiple-period randomised crossover study. A 12 h inpatient rehabilitation day was divided into 72 intervals in which participants with stroke (n = 20) randomly received either a 'nudge' or 'no nudge'. Upper limb movement was observed, classified, and analysed using longitudinal mixed models. The odds of affected upper limb movement following a nudge compared with no nudge were significantly higher during active periods such as breakfast, lunch, and morning and afternoon activities (odds ratios (ORs) 2.01-4.63, 95% CIs [1.27-2.67, 3.17-8.01]), but not dinner (OR 1.36, 95% CI [0.86, 2.16]). The effect of nudging was no longer statistically significant at 50-60 s post-nudge. Consecutive delays in nudging significantly decreased the odds of moving when a nudge was eventually delivered. Contrary to expectations, people with stroke appear more responsive to haptic nudging during active periods rather than periods of inactivity. By understanding the optimal timing and frequency of haptic nudging, the design of wearable devices can be optimised to maximise their therapeutic benefits.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Background

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices.

Objective

This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors.

Methods

People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Results

A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007).

Conclusions

This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Quantifying muscle contraction with a conductive electroactive polymer sensor: introduction to a novel surface mechanomyography device

Clinicians seek an accurate method to assess muscle contractility during activities to better guide treatment. We investigated application of a conductive electroactive polymer sensor as a novel wearable surface mechanomyography (sMMG) sensor for quantifying muscle contractility. The radial displacement of a muscle during a contraction is detected by the physically stretched dielectric elastomer component of the sMMG sensor which quantifies the changes in capacitance. The duration of muscle activation times for quadriceps, hamstrings, and gastrocnemius muscles demonstrated strong correlation between sMMG and EMG during a parallel squat activity and isometric contractions. A moderate to strong correlation was demonstrated between the sMMG isometric muscle activation times and force output times from a dynamometer. The potential wearable application of an electroactive polymer sensor to measure muscle contraction time is supported.

Access and Management of Sweat for Non-Invasive Biomarker Monitoring: A Comprehensive Review

Sweat is an important biofluid presents in the body since it regulates the internal body temperature, and it is relatively easy to access on the skin unlike other biofluids and contains several biomarkers that are also present in the blood. Although sweat sensing devices have recently displayed tremendous progress, most of the emerging devices primarily focus on the sensor development, integration with electronics, wearability, and data from in vitro studies and short-term on-body trials during exercise. To further the advances in sweat sensing technology, this review aims to present a comprehensive report on the approaches to access and manage sweat from the skin toward improved sweat collection and sensing. It is begun by delineating the sweat secretion mechanism through the skin, and the historical perspective of sweat, followed by a detailed discussion on the mechanisms governing sweat generation and management on the skin. It is concluded by presenting the advanced applications of sweat sensing, supported by a discussion of robust, extended-operation epidermal wearable devices aiming to strengthen personalized healthcare monitoring systems.

Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey

Background

Physical inactivity is a global health issue, and mobile health (mHealth) apps are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity.

Objective

This study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample.

Methods

We recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used.

Results

Approximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders.

Conclusions

Regardless of the current trend in app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Use of Consumer Wearables in Health Research: Issues and Considerations

As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.

Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study

BACKGROUND

Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.

OBJECTIVE

Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("cross-body" validation), manually ascertained ground truth ("visually assessed" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("commercial wearable" validation).

METHODS

We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.

RESULTS

In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%.

CONCLUSIONS

This study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis

BACKGROUND

Recent studies have linked low heart rate variability (HRV) with COVID-19, indicating that this parameter can be a marker of the onset of the disease and its severity and a predictor of mortality in infected people. Given the large number of wearable devices that capture physiological signals of the human body easily and noninvasively, several studies have used this equipment to measure the HRV of individuals and related these measures to COVID-19.

OBJECTIVE

The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators of COVID-19, as well as the onset and worsening of symptoms in affected individuals.

METHODS

A systematic review was conducted searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE Xplore. Studies had to include (1) measures of HRV in patients with COVID-19 and (2) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce possible biases and increase the statistical power of the primary research.

RESULTS

The main finding was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported that a reduction in HRV parameters is associated with COVID-19. Individuals with COVID-19 presented a reduction in the SD of the normal-to-normal interbeat intervals and root mean square of the successive differences compared with healthy individuals. The decrease in the SD of the normal-to-normal interbeat intervals was 3.25 ms (95% CI -5.34 to -1.16 ms), and the decrease in the root mean square of the successive differences was 1.24 ms (95% CI -3.71 to 1.23 ms).

CONCLUSIONS

Wearable devices that measure changes in HRV, such as smartwatches, rings, and bracelets, provide information that allows for the identification of COVID-19 during the presymptomatic period as well as its worsening through an indirect and noninvasive self-diagnosis.

Predicting Obsessive-Compulsive Disorder Events in Children and Adolescents in the Wild using a Wearable Biosensor (Wrist Angel): Protocol for the Analysis Plan of a Nonrandomized Pilot Study

BACKGROUND

Physiological signals such as heart rate and electrodermal activity can provide insight into an individual's mental state, which are invaluable information for mental health care. Using recordings of physiological signals from wearable devices in the wild can facilitate objective monitoring of symptom severity and evaluation of treatment progress.

OBJECTIVE

We designed a study to evaluate the feasibility of predicting obsessive-compulsive disorder (OCD) events from physiological signals recorded using wrist-worn devices in the wild. Here, we present an analysis plan for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study.

METHODS

In total, 18 children and adolescents aged between 8 and 16 years were included in this study. Nine outpatients with an OCD diagnosis were recruited from a child and adolescent mental health center. Nine youths without a psychiatric diagnosis were recruited from the catchment area. Patients completed a clinical interview to assess OCD severity, types of OCD, and number of OCD symptoms in the clinic. Participants wore a biosensor on their wrist for up to 8 weeks in their everyday lives. Patients were asked to press an event tag button on the biosensor when they were stressed by OCD symptoms. Participants without a psychiatric diagnosis were asked to press this button whenever they felt really scared. Before and after the 8-week observation period, participants wore the biosensor under controlled conditions of rest and stress in the clinic. Features are extracted from 4 different physiological signals within sliding windows to predict the distress event logged by participants during data collection. We will test the prediction models within participants across time and multiple participants. Model selection and estimation using 2-layer cross-validation are outlined for both scenarios.

RESULTS

Participants were included between December 2021 and December 2022. Participants included 10 female and 8 male participants with an even sex distribution between groups. Patients were aged between 10 and 16 years, and adolescents without a psychiatric diagnosis were between the ages of 8 and 16 years. Most patients had moderate to moderate to severe OCD, except for 1 patient with mild OCD.

CONCLUSIONS

The strength of the planned study is the investigation of predictions of OCD events in the wild. Major challenges of the study are the inherent noise of in-the-wild data and the lack of contextual knowledge associated with the recorded signals. This preregistered analysis plan discusses in detail how we plan to address these challenges and may help reduce interpretation bias of the upcoming results. If the obtained results from this study are promising, we will be closer to automated detection of OCD events outside of clinical experiments. This is an important tool for the assessment and treatment of OCD in youth.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/study/NCT05064527.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48571.

Monitoring Changes in Oxygen Muscle during Exercise with High-Flow Nasal Cannula Using Wearable NIRS Biosensors

Exercise increases the cost of breathing (COB) due to increased lung ventilation (V˙E), inducing respiratory muscles deoxygenation (∇SmO2), while the increase in workload implies ∇SmO2 in locomotor muscles. This phenomenon has been proposed as a leading cause of exercise intolerance, especially in clinical contexts. The use of high-flow nasal cannula (HFNC) during exercise routines in rehabilitation programs has gained significant interest because it is proposed as a therapeutic intervention for reducing symptoms associated with exercise intolerance, such as fatigue and dyspnea, assuming that HFNC could reduce exercise-induced ∇SmO2. SmO2 can be detected using optical wearable devices provided by near-infrared spectroscopy (NIRS) technology, which measures the changes in the amount of oxygen bound to chromophores (e.g., hemoglobin, myoglobin, cytochrome oxidase) at the target tissue level. We tested in a study with a cross-over design whether the muscular desaturation of m.vastus lateralis and m.intercostales during a high-intensity constant-load exercise can be reduced when it was supported with HFNC in non-physically active adults. Eighteen participants (nine women; age: 22 ± 2 years, weight: 65.1 ± 11.2 kg, height: 173.0 ± 5.8 cm, BMI: 21.6 ± 2.8 kg·m-2) were evaluated in a cycle ergometer (15 min, 70% maximum watts achieved in ergospirometry (V˙O2-peak)) breathing spontaneously (control, CTRL) or with HFNC support (HFNC; 50 L·min-1, fiO2: 21%, 30 °C), separated by seven days in randomized order. Two-way ANOVA tests analyzed the ∇SmO2 (m.intercostales and m.vastus lateralis), and changes in V˙E and ∇SmO2·V˙E-1. Dyspnea, leg fatigue, and effort level (RPE) were compared between trials by the Wilcoxon matched-paired signed rank test. We found that the interaction of factors (trial × exercise-time) was significant in ∇SmO2-m.intercostales, V˙E, and (∇SmO2-m.intercostales)/V˙E (p < 0.05, all) but not in ∇SmO2-m.vastus lateralis. ∇SmO2-m.intercostales was more pronounced in CTRL during exercise since 5' (p < 0.05). Hyperventilation was higher in CTRL since 10' (p < 0.05). The ∇SmO2·V˙E-1 decreased during exercise, being lowest in CTRL since 5'. Lower dyspnea was reported in HFNC, with no differences in leg fatigue and RPE. We concluded that wearable optical biosensors documented the beneficial effect of HFNC in COB due to lower respiratory ∇SmO2 induced by exercise. We suggest incorporating NIRS devices in rehabilitation programs to monitor physiological changes that can support the clinical impact of the therapeutic intervention implemented.

Exploring Microphone Technologies for Digital Auscultation Devices

The aim of this work is to present a preliminary study for the design of a digital auscultation system, i.e., a novel wearable device for patient chest auscultation and a digital stethoscope. The development and testing of the electronic stethoscope prototype is reported with an emphasis on the description and selection of sound transduction systems and analog electronic processing. The focus on various microphone technologies, such as micro-electro-mechanical systems (MEMSs), electret condensers, and piezoelectronic diaphragms, intends to emphasize the most suitable transducer for auscultation. In addition, we report on the design and development of a digital acquisition system for the human body for sound recording by using a modular device approach in order to fit the chosen analog and digital mics. Tests were performed on a designed phantom setup, and a qualitative comparison between the sounds recorded with the newly developed acquisition device and those recorded with two commercial digital stethoscopes is reported.

Assessing the Effect of Extreme Weather on Population Health Using Consumer-Grade Wearables in Rural Burkina Faso: Observational Panel Study

BACKGROUND

Extreme weather, including heat and extreme rainfall, is projected to increase owing to climate change, which can have adverse impacts on human health. In particular, rural populations in sub-Saharan Africa are at risk because of a high burden of climate-sensitive diseases and low adaptive capacities. However, there is a lack of data on the regions that are anticipated to be most exposed to climate change. Improved public health surveillance is essential for better decision-making and health prioritization and to identify risk groups and suitable adaptation measures. Digital technologies such as consumer-grade wearable devices (wearables) may generate objective measurements to guide data-driven decision-making.

OBJECTIVE

The main objective of this observational study was to examine the impact of weather exposure on population health in rural Burkina Faso using wearables. Specifically, this study aimed to assess the relationship between individual daily activity (steps), sleep duration, and heart rate (HR), as estimated by wearables, and exposure to heat and heavy rainfall.

METHODS

Overall, 143 participants from the Nouna health and demographic surveillance system in Burkina Faso wore the Withings Pulse HR wearable 24/7 for 11 months. We collected continuous weather data using 5 weather stations throughout the study region. The heat index and wet-bulb globe temperature (WBGT) were calculated as measures of heat. We used linear mixed-effects models to quantify the relationship between exposure to heat and rainfall and the wearable parameters. Participants kept activity journals and completed a questionnaire on their perception of and adaptation to heat and other weather exposure.

RESULTS

Sleep duration decreased significantly (P<.001) with higher heat exposure, with approximately 15 minutes shorter sleep duration during heat stress nights with a heat index value of ≥25 °C. Many participants (55/137, 40.1%) reported that heat affected them the most at night. During the day, most participants (133/137, 97.1%) engaged in outdoor physical work such as farming, housework, or fetching water. During the rainy season, when WBGT was highest, daily activity was highest and increased when the daily maximum WBGT surpassed 30 °C during the rainiest month. In the hottest month, daily activity decreased per degree increase in WBGT for values >30 °C. Nighttime HR showed no significant correlation with heat exposure. Daytime HR data were insufficient for analysis. We found no negative health impact associated with heavy rainfall. With increasing rainfall, sleep duration increased, average nightly HR decreased, and activity decreased.

CONCLUSIONS

During the study period, participants were frequently exposed to heat and heavy rainfall. Heat was particularly associated with impaired sleep and daily activity. Essential tasks such as harvesting, fetching water, and caring for livestock expose this population to weather that likely has an adverse impact on their health. Further research is essential to guide interventions safeguarding vulnerable communities.

Remote electrical neuromodulation (REN) wearable device for adolescents with migraine: a real-world study of high-frequency abortive treatment suggests preventive effects

Introduction

Migraine is a chronic neurological disease manifesting as attacks of disabling head pain and associated symptoms. Remote electrical neuromodulation (REN) is a non-pharmacological, prescribed, wearable device (Nerivio®). This device has been certified by the FDA for the acute and/or preventive treatment of migraine with or without aura in patients 12 years of age or older. The device is affixed to the user's arm during 45-min treatment sessions and is operated using a smartphone app. This study (NCT05769322) aims to evaluate whether frequent use of REN for the acute treatment of migraine in adolescents resulted in a reduction in monthly migraine treatment days (MMTD), as previously demonstrated in adults through a dedicated prevention clinical trial (NCT04828707).

Methods

The study included real-world prospective data from adolescent patients who used REN on at least 10 days every 28-day month, following the REN migraine prevention guideline of an every-other-day pattern. Additional requirements were at least three REN treatment days in each of the two subsequent months. The number of MMTD was used as a proxy measure for the number of monthly migraine days (MMD). The change in MMTD from the first month, taken as a "baseline," to each of the following months was used to evaluate the presence and size of potential migraine preventive benefits of REN in adolescents.

Results

A total of 83 adolescents were eligible for analysis. The users were 15.9 ± 1.3 years of age (mean ± SD), and 89% of them were female. The results demonstrated a substantial month-to-month reduction in the mean (±SD) number of REN treatment days from 12.6 (±3.2) MMTD in the first month to 9.0 (±4.8) MMTD in the second month (p < 0.001), and a further decrease to 7.4 (±4.2) MMTD in the third month (p < 0.001). This indicates an accumulative reduction of 5.2 (±4.8) mean REN MMTD from the first month to the third month of consecutive REN treatment. The users also reported consistent 2-h acute pain responses in at least 50% of their treated attacks, with 61.9% of the users reported experiencing pain relief, 24.5% reported pain freedom, 67.4% indicated relief in functional disability, and 41.3% reported complete freedom from functional disability.

Conclusion

The frequent use of REN among adolescents as an acute treatment for migraine attacks resulted in a decrease in the mean number of monthly treatment days in the subsequent months, suggesting that REN may have potential preventive benefits for migraine in this subpopulation.

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson's Disease through a Commercial Smartwatch

Daily steps could be a valuable indicator of real-world ambulation in Parkinson's disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland-Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2-5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann-Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84-0.90; SAM range: 7.8-9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD.

Use of a Mobile-Assisted Telehealth Regimen to Increase Exercise in Transplant Candidates: A Home-Based Prehabilitation Pilot and Feasibility Trial

INTRODUCTION

Physical fitness assessed by the Liver Frailty Index (LFI) and 6-minute walk test (6MWT) informs the prognosis of liver transplant candidates, although there are limited data on its reversibility after prehabilitation. On a home-based exercise trial, we aimed to improve LFI and 6MWT and to investigate trial feasibility and intervention adherence.

METHODS

Liver transplant candidates with cirrhosis wore a personal activity tracker and used Exercise and Liver FITness app for 14 weeks, including a 2-week technology acclimation run-in. The 12-week intervention consisted of Exercise and Liver FITness app plus personal activity tracker and 15-/30-minute weekly calls with a physical activity coach aiming to complete ≥2 video-training sessions/week, or ≥500 step/d baseline increase for ≥8 weeks. We defined feasibility as ≥66% of subjects engaging in the intervention phase and adherence as ≥50% subjects meeting training end point.

RESULTS

Thirty-one patients (61 ± 7 years, 71% female, model for end-stage liver disease 17 ± 5, ∼33% frail) consented and 21 (68%) started the intervention. In the 15 subjects who completed the study, LFI improved from 3.84 ± 0.71 to 3.47 ± 0.90 ( P = 0.03) and 6MWT from 318 ± 73 to 358 ± 64 m ( P = 0.005). Attrition reasons included death (n = 4) and surgery (n = 2). There was 57% adherence, better for videos than for walking, although daily steps significantly increased (3,508 vs baseline: 1,260) during best performance week. One adverse event was attributed to the intervention.

DISCUSSION

Our clinical trial meaningfully improved LFI by 0.4 and 6MWT by 41 m and met feasibility/adherence goals. In-training daily step increase supported physical self-efficacy and intervention uptake, but maintenance remained a challenge despite counseling.

Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study

Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children (N = 10) aged 7-10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states-calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.

A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring

Early prevention and detection of respiratory disease have attracted extensive attention due to the significant increase in people with respiratory issues. Restraining the spread and relieving the symptom of this disease is essential. However, the traditional auscultation technique demands a high-level medical skill, and computational respiratory sound analysis approaches have limits in constrained locations. A wearable auscultation device is required to real-time monitor respiratory system health and provides consumers with ease. In this work, we developed a Respiratory Sound Diagnosis Processor Unit (RSDPU) based on Long Short-Term Memory (LSTM). The experiments and analyses were conducted on feature extraction and abnormality diagnosis algorithm of respiratory sound, and Dynamic Normalization Mapping (DNM) was proposed to better utilize quantization bits and lessen overfitting. Furthermore, we developed the hardware implementation of RSDPU including a corrector to filter diagnosis noise. We presented the FPGA prototyping verification and layout of the RSDPU for power and area evaluation. Experimental results demonstrated that RSDPU achieved an abnormality diagnosis accuracy of 81.4 %, an area of 1.57 × 1.76 mm under the SMIC 130 nm process, and power consumption of 381.8 μW, which met the requirements of high accuracy, low power consumption, and small area.

Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract

BACKGROUND

Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints.

OBJECTIVE

This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints.

METHODS

The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews.

RESULTS

A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal.

CONCLUSIONS

A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/47119.

Transition of Carbon Nanotube Sheets from Hydrophobicity to Hydrophilicity by Facile Electrochemical Wetting

The present study delves into the transformative effects of electrochemical oxidation on the hydrophobic-to-hydrophilic transition of carbon nanotube (CNT) sheets. The paper elucidates the inherent advantages of CNT sheets, such as high electrical conductivity and mechanical strength, and contrasts them with the limitations posed by their hydrophobic nature. A comprehensive investigation is conducted to demonstrate the efficacy of electrochemical oxidation treatment in modifying the surface properties of CNT sheets, thereby making them hydrophilic. The study reveals that the treatment not only is cost-effective and time-efficient compared to traditional plasma treatment methods but also results in a significant decrease in water contact angle. Mechanistic insights into the hydrophilic transition are provided, emphasizing the role of oxygen-containing functional groups introduced during the electrochemical oxidation process.

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Background

In sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable.

Methods

Patients with SCD (≥18 years) who were admitted for a vaso-occlusive crisis were enrolled at a single academic center. Participants were instructed to report daily pain scores (0-10) in a mobile app (Nanbar) and to continuously wear an Apple Watch up to 30 days after discharge. Data included heart rate (in rest, average and variability) and step count. Demographics, SCD genotype, and details of hospitalization including pain scores reported to nurses, were extracted from electronic medical records. Physiological data from the wearable were associated with pain scores to fit 3 different machine learning classification models. The performance of the machine learning models was evaluated using: accuracy, F1, root-mean-square error and area under the receiver-operating curve.

Results

Between April and June 2022, 19 patients (74% HbSS genotype) were included in this study and followed for a median time of 28 days [IQR 22-34], yielding a dataset of 2,395 pain data points. Ten participants were enrolled while hospitalized for VOC. The metrics of the best performing model, the random forest model, were micro-averaged accuracy of 92%, micro-averaged F1-score of 0.63, root-mean-square error of 1.1, and area under the receiving operating characteristic curve of 0.9.

Conclusion

Our random forest model accurately predicts high pain scores during admission for VOC and after discharge. The Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of pain scores.

Optical Integration in Wearable, Implantable and Swallowable Healthcare Devices

Recent advances in materials and semiconductor technologies have led to extensive research on optical integration in wearable, implantable, and swallowable health devices. These optical systems utilize the properties of light─intensity, wavelength, polarization, and phase─to monitor and potentially intervene in various biological events. The potential of these devices is greatly enhanced through the use of multifunctional optical materials, adaptable integration processes, advanced optical sensing principles, and optimized artificial intelligence algorithms. This synergy creates many possibilities for clinical applications. This Perspective discusses key opportunities, challenges, and future directions, particularly with respect to sensing modalities, multifunctionality, and the integration of miniaturized optoelectronic devices. We present fundamental insights and illustrative examples of such devices in wearable, implantable, and swallowable forms. The constant pursuit of innovation and the dedicated approach to critical challenges are poised to influence diverse fields.

Electrochemically Oxidized Carbon Nanotube Sheets for High-Performance and Flexible-Film Supercapacitors

The development of flexible, high-performance supercapacitors has been a focal point in energy storage research. While carbon nanotube (CNT) sheets offer promising mechanical and electrical properties, their low electrical double-layer capacitance significantly limits their practicability. Herein, we introduce a novel approach to address this challenge via the electrochemical oxidation treatment of CNT sheets stacked on a polyethylene terephthalate substrate. This introduces oxygen-containing functional groups onto the CNT surface, thereby dramatically enhancing the pseudocapacitive effect and improving ion adsorption. Consequently, using the material in a two-electrode system increased the capacitance by 54 times compared to pristine CNT. The results of electrochemical performance characterization, including cyclic voltammograms, galvanostatic charge/discharge curves, and capacitance retention testing data, confirm the efficacy of the electrochemical oxidation approach. Furthermore, the mechanical flexibility of the electrochemically wetted CNT sheets was validated through resistance and discharge retention testing under repetitive bending (98% capacitance retention after 1000 bending cycles). The results demonstrate that electrochemically wetted CNT sheets retain their intrinsic mechanical and electrical properties while significantly enhancing the electrochemical performance (0.59 mF/cm2 or 97.8 F/g). This work represents a significant advancement in the development of flexible, high-performance supercapacitors with potential applicability to wearable electronics, flexible displays, and next-generation energy storage solutions.

SISTINE 3.0: Wearable System for Remote Functional Assessment of Phlebopathics

Phlebopathic diseases are chronic conditions that impact the health status and affect functional capacity. We developed SISTINE 3.0, a wearable system for remote monitoring of patients, and the aim of the study is to evaluate whether it can detect differences between participants with phlebology and healthy ones. Twelve patients and five healthy subjects performed a 3-metres Timed-Up-Go wearing SISTINE 3.0 system. The results support the system's potential to discriminate participants, especially based on the linear walking and turning angular velocity.

Wearable Electrochemical Glove-Based Analytical Device (eGAD) for the Detection of Methamphetamine Employing Silver Nanoparticles

Illicit drug misuse has become a widespread issue that requires continuous drug monitoring and diagnosis. Wearable electrochemical drug detection devices possess the potential to function as potent screening instruments in the possession of law enforcement personnel, aiding in the fight against drug trafficking and facilitating forensic investigations conducted on site. These wearable sensors are promising alternatives to traditional detection methods. In this study, we present a novel wearable electrochemical glove-based analytical device (eGAD) designed especially for detecting the club drug, methamphetamine. To develop this sensor, we immobilized meth aptamer onto silver nanoparticle (AgNPs)-modified electrodes that were printed onto latex gloves. The characteristics of AgNPs, including their shape, size and purity were analysed using FTIR, SEM and UV vis spectrometry, confirming the successful synthesis. The developed sensor shows a 0.1 µg/mL limit of detection and 0.3 µg/mL limit of quantification with a linear concentration range of about 0.01-5 µg/mL and recovery percentages of approximately 102 and 103%, respectively. To demonstrate its applicability, we tested the developed wearable sensor by spiking various alcoholic and non-alcoholic drink samples. We found that the sensor remains effective for 60 days, making it a practical option with a reasonable shelf-life. The developed sensor offers several advantages, including its affordability, ease of handling and high sensitivity and selectivity. Its portable nature makes it an ideal tool for rapid detection of METH in beverages too.

Nurses' Roles in mHealth App Development: Scoping Review

BACKGROUND

Although mobile health (mHealth) apps for both health consumers and health care providers are increasingly common, their implementation is frequently unsuccessful when there is a misalignment between the needs of the user and the app's functionality. Nurses are well positioned to help address this challenge. However, nurses' engagement in mHealth app development remains unclear.

OBJECTIVE

This scoping review aims to determine the extent of the evidence of the role of nurses in app development, delineate developmental phases in which nurses are involved, and to characterize the type of mHealth apps nurses are involved in developing.

METHODS

We conducted a scoping review following the 6-stage methodology. We searched 14 databases to identify publications on the role of nurses in mHealth app development and hand searched the reference lists of relevant publications. Two independent researchers performed all screening and data extraction, and a third reviewer resolved any discrepancies. Data were synthesized and grouped by the Software Development Life Cycle phase, and the app functionality was described using the IMS Institute for Healthcare Informatics functionality scoring system.

RESULTS

The screening process resulted in 157 publications being included in our analysis. Nurses were involved in mHealth app development across all stages of the Software Development Life Cycle but most frequently participated in design and prototyping, requirements gathering, and testing. Nurses most often played the role of evaluators, followed by subject matter experts. Nurses infrequently participated in software development or planning, and participation as patient advocates, research experts, or nurse informaticists was rare.

CONCLUSIONS

Although nurses were represented throughout the preimplementation development process, nurses' involvement was concentrated in specific phases and roles.

A Portable and a Scalable Multi-Channel Wireless Recording System for Wearable Electromyometrial Imaging

Electromyometrial imaging (EMMI) technology has emerged as one of the promising technology that can be used for non-invasive pregnancy risk stratification and for preventing complications due to pre-term birth. Current EMMI systems are bulky and require a tethered connection to desktop instrumentation, as a result, the system cannot be used in non-clinical and ambulatory settings. In this article, we propose an approach for designing a scalable, portable wireless EMMI recording system that can be used for in-home and remote monitoring. The wearable system uses a non-equilibrium differential electrode multiplexing approach to enhance signal acquisition bandwidth and to reduce the artifacts due to electrode drifts, amplifier 1/f noise, and bio-potential amplifier saturation. A combination of active shielding, a passive filter network, and a high-end instrumentation amplifier ensures sufficient input dynamic range ([Formula: see text]) such that the system can simultaneously acquire different bio-potential signals like maternal electrocardiogram (ECG) in addition to the EMMI electromyogram (EMG) signals. We show that the switching artifacts and the channel cross-talk introduced due to non-equilibrium sampling can be reduced using a compensation technique. This enables the system to be potentially scaled to a large number of channels without significantly increasing the system power dissipation. We demonstrate the feasibility of the proposed approach in a clinical setting using an 8-channel battery-powered prototype which dissipates less than 8 μW per channel for a signal bandwidth of 1 KHz.

Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch

STUDY OBJECTIVES

Although wrist-worn consumer wearables are widely used for home sleep monitoring, few have been validated. It is unclear whether consumer wearables could be an alternative to Actiwatch. This study aimed to establish and validate an automatic sleep staging system (ASSS) utilizing photoplethysmography and acceleration data collected from a wrist-worn wearable device.

METHODS

Seventy-five participants from a community population underwent overnight polysomnography (PSG) while wearing a smartwatch (MT2511) and Actiwatch Spectrum Plus (Philips Respironics, Inc; Murrysville, PA, USA). Photoplethysmography and acceleration data collected from the smartwatches were utilized to build a 4-stage (wake, light sleep, deep sleep, and rapid eye movement [REM] sleep) classifier, which was validated against PSG. The performance of the sleep/wake classifier was compared with Actiwatch. All analyses were conducted separately for participants with PSG sleep efficiency (SE) ≥ 80% and SE < 80%.

RESULTS

The 4-stage classifier and PSG showed fair overall epoch-by-epoch agreement (kappa, 0.55; 95% confidence interval, 0.52 to 0.57). The deep sleep and REM times were comparable between ASSS and PSG, while ASSS underestimated the wake time and overestimated the light sleep time among participants with SE < 80%. Moreover, ASSS underestimated sleep-onset latency and wake after sleep onset and overestimated total sleep time and SE among participants with SE < 80%, while all were comparable among participants with SE ≥ 80%. The biases were smaller for ASSS than for Actiwatch.

CONCLUSIONS

Our photoplethysmography- and acceleration-based ASSS was reliable for participants with SE ≥ 80% and had a smaller bias than Actiwatch among those with SE < 80%. Thus, ASSS may be a promising alternative to Actiwatch.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Validation of Sleep Healthcare System; URL: https://clinicaltrials.gov/study/NCT04252482; Identifier: NCT04252482.

CITATION

Liu P-K, Ting N, Chiu H-C, et al. Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch. J Clin Sleep Med. 2023;19(10):1797-1810.

Electrochemical Affinity Biosensors: Pervasive Devices with Exciting Alliances and Horizons Ahead

Electrochemical affinity biosensors are evolving at breakneck speed, strengthening and colonizing more and more niches and drawing unimaginable roadmaps that increasingly make them protagonists of our daily lives. They achieve this by combining their intrinsic attributes with those acquired by leveraging the significant advances that occurred in (nano)materials technology, bio(nano)materials and nature-inspired receptors, gene editing and amplification technologies, and signal detection and processing techniques. The aim of this Perspective is to provide, with the support of recent representative and illustrative literature, an updated and critical view of the repertoire of opportunities, innovations, and applications offered by electrochemical affinity biosensors fueled by the key alliances indicated. In addition, the imminent challenges that these biodevices must face and the new directions in which they are envisioned as key players are discussed.

A Novel Sensor-Based Application for Home-Based Rehabilitation Can Objectively Measure Postoperative Outcomes following Anterior Cruciate Ligament Reconstruction

In order to successfully implement individualized patient rehabilitation and home-based rehabilitation programs, the rehabilitation process should be objectifiable, monitorable and comprehensible. For this purpose, objective measurements are required in addition to subjective measurement tools. Thus, the aim of this prospective, single-center clinical trial is the clinical validation of an objective, digital medical device (DMD) during the rehabilitation after anterior cruciate ligament reconstruction (ACLR) with regards to an internationally accepted measurement tool. Sixty-seven patients planned for primary ACLR (70:30% male-female, aged 25 years [21-32], IKDC-SKF 47 [31-60], Tegner Activity Scale 6 [4-7], Lysholm Score 57 [42-72]) were included and received physical therapy and the DMD after surgery. For clinical validation, combined measures of range of motion (ROM), coordination, strength and agility were assessed using the DMD in addition to patient-reported outcome measures (PROMs) at three and six months after ACLR. Significant correlations were detected for ROM (rs = 0.36-0.46, p < 0.025) and strength/agility via the single-leg vertical jump (rs = 0.43, p = 0.011) and side hop test (rs = 0.37, p = 0.042), as well as for coordination via the Y-Balance test (rs = 0.58, p ≤ 0.0001) regarding the IKDC-SKF at three months. Additionally, DMD test results for coordination, strength and agility (Y-Balance test (rs = 0.50, p = 0.008), side hop test (rs = 0.54, p = 0.004) and single-leg vertical jump (rs = 0.44, p = 0.018)) correlate significantly with the IKDC-SKF at six months. No adverse events related to the use of the sensor-based application were reported. These findings confirm the clinical validity of a DMD to objectively quantify knee joint function for the first time. This will have further implications for clinical and therapeutic decision making, quality control and monitoring of rehabilitation measures as well as scientific research.

Flexible Self-Powered Energy Systems Based on H2 O/Ni2+ Intercalated Nix V2 O5 ⋅ nH2 O

The development of portable electronic devices has created greater demands for multifunctional energy integration systems. Self-powered systems have gained widespread interest because they can collect and storage renewable environmental energy and provide stable electricity to electronic devices. Herein, we developed a flexible self-charging energy system, involving textile-based zinc-ion hybrid (ZIHC) and triboelectric nanogenerator (TENG), which demonstrates wearable, compatibility, lightweight and can quickly harvest and store energy. Nix V2 O5  ⋅ nH2 O (NVO) loaded on carbon cloth (CC) with Ni2+ /H2 O ions intercalated as the cathode was assembled with activated CC to form a ZIHC, which has a voltage range of 2.0 V and capacitance value of 267.1 mF cm-2 as well as good charge and discharge rates and excellent cycling stability. At the same time, the NVO/CC can be assembled with PDMS to form a TENG achieving a maximum instantaneous power of 18.5 mW cm-2 . The device can be flexibly worn over the body to continuously harvest and store biomechanical energy and charge the electronic wristwatch successfully. This work demonstrates great convenience and promising practical applications as sustainable flexible energy system for portable electronic devices.

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study

BACKGROUND

Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers' health-related quality of life (HRQOL), their effectiveness for caregivers remains poorly understood.

OBJECTIVE

The primary objective of this study is to evaluate the dose-response relationship of a fully automated JITAI-based self-management intervention involving personalized mobile app notifications targeted at decreasing the level of caregiver strain, anxiety, and depression. The secondary objective is to investigate whether the effectiveness of this mobile health intervention was moderated by the caregiver group. We also explored whether the effectiveness of this intervention was moderated by (1) previous HRQOL measures, (2) the number of weeks in the study, (3) step count, and (4) minutes of sleep.

METHODS

We examined 36 caregivers from 3 disease groups (10 from spinal cord injury, 11 from Huntington disease, and 25 from allogeneic hematopoietic cell transplantation) in the intervention arm of a larger randomized controlled trial (subjects in the other arm received no prompts from the mobile app) designed to examine the acceptability and feasibility of this intensive type of trial design. A series of multivariate linear models implementing a weighted and centered least squares estimator were used to assess the JITAI efficacy and effect.

RESULTS

We found preliminary support for a positive dose-response relationship between the number of administered JITAI messages and JITAI efficacy in improving caregiver strain, anxiety, and depression; while most of these associations did not meet conventional levels of significance, there was a significant association between high-frequency JITAI and caregiver strain. Specifically, administering 5-6 messages per week as opposed to no messages resulted in a significant decrease in the HRQOL score of caregiver strain with an estimate of -6.31 (95% CI -11.76 to -0.12; P=.046). In addition, we found that the caregiver groups and the participants' levels of depression in the previous week moderated JITAI efficacy.

CONCLUSIONS

This study provides preliminary evidence to support the effectiveness of the self-management JITAI and offers practical guidance for designing future personalized JITAI strategies for diverse caregiver groups.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591.

Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study

BACKGROUND

Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome.

OBJECTIVE

This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws.

METHODS

A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home.

RESULTS

In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life.

CONCLUSIONS

On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition.

Shortwave infrared diffuse optical wearable probe for quantification of water and lipid content in emulsion phantoms using deep learning

Significance

The shortwave infrared (SWIR, ∼900 to 2000 nm) holds promise for label-free measurements of water and lipid content in thick tissue, owed to the chromophore-specific absorption features and low scattering in this range. In vivo water and lipid estimations have potential applications including the monitoring of hydration, volume status, edema, body composition, weight loss, and cancer. To the best of our knowledge, there are currently no point-of-care or wearable devices available that exploit the SWIR wavelength range, limiting clinical and at-home translation of this technology.

Aim

To design and fabricate a diffuse optical wearable SWIR probe for water and lipid quantification in tissue.

Approach

Simulations were first performed to confirm the theoretical advantage of SWIR wavelengths over near infrared (NIR). The probe was then fabricated, consisting of light emitting diodes at three wavelengths (980, 1200, 1300 nm) and four source-detector (S-D) separations (7, 10, 13, 16 mm). In vitro validation was then performed on emulsion phantoms containing varying concentrations of water, lipid, and deuterium oxide (D2O). A deep neural network was developed as the inverse model for quantity estimation.

Results

Simulations indicated that SWIR wavelengths could reduce theoretical water and lipid extraction errors from ∼6% to ∼1% when compared to NIR wavelengths. The SWIR probe had good signal-to-noise ratio (>32  dB up to 10 mm S-D) and low drift (<1.1% up to 10 mm S-D). Quantification error in emulsion phantoms was 2.1±1.1% for water and -1.2±1.5% for lipid. Water estimation during a D2O dilution experiment had an error of 3.1±3.7%.

Conclusions

This diffuse optical SWIR probe was able to quantify water and lipid contents in vitro with good accuracy, opening the door to human investigations.

Magnetic Properties of Amorphous Ta/CoFeB/MgO/Ta Thin Films on Deformable Substrates with Magnetic Field Angle and Tensile Strain

Recently, the application of cobalt iron boron (CoFeB) thin films in magnetic sensors has been widely studied owing to their high magnetic moment, anisotropy, and stability. However, most of these studies were conducted on rigid silicon substrates. For diverse applications of magnetic and angle sensors, it is important to explore the properties of ferromagnetic thin films grown on nonrigid deformable substrates. In this study, representative deformable substrates (polyimide (PI), polyethylene naphthalate (PEN), and polydimethylsiloxane (PDMS)), which can be bent or stretched, were used to assess the in-plane magnetic field angle-dependent properties of amorphous Ta/CoFeB/MgO/Ta thin films grown on deformable substrates. The effects of substrate roughness, tensile stress, deformable substrate characteristics, and sputtering on magnetic properties, such as the coercive field (Hc), remanence over saturation magnetization (Mr/Ms), and biaxial characteristics, were investigated. This study presents an unconventional foundation for exploring deformable magnetic sensors capable of detecting magnetic field angles.

Microfluidic-Integrated Multimodal Wearable Hybrid Patch for Wireless and Continuous Physiological Monitoring

Despite extensive advances in wearable monitoring systems, most designs focus on the detection of physical parameters or metabolites and do not consider the integration of microfluidic channels, miniaturization, and multimodality. In this study, a combination of multimodal (biochemical and electrophysiological) biosensing and microfluidic channel-integrated patch-based wireless systems is designed and fabricated using flexible materials for improved wearability, ease of operation, and real-time and continuous monitoring. The reduced graphene oxide-based microfluidic channel-integrated glucose biosensor exhibits a good sensitivity of 19.97 (44.56 without fluidic channels) μA mM-1 cm-2 within physiological levels (10 μM-0.4 mM) with good long-term and bending stability. All the sensors in the patch are initially validated using sauna gown sweat-based on-body and real-time tests with five separate individuals who perspired three times each. Multimodal glucose and electrocardiogram (ECG) sensing, along with their real-time adjustment based on sweat pH and temperature fluctuations, optimize sensing accuracy. Laser-burned hierarchical MXene-polyvinylidene fluoride-based conductive carbon nanofiber-based dry ECG electrodes exhibit low skin contact impedance (40.5 kΩ cm2) and high-quality electrophysiological signals (signal-to-noise ratios = 23.4-32.8 dB). The developed system is utilized to accurately and wirelessly monitor the sweat glucose and ECG of a human subject engaged in physical exercise in real time.