Impressions and Perceptions of a Smartphone and Smartwatch Self-Management Tool for Patients With COPD: A Qualitative Study

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) often do not seek care until they experience an exacerbation. Improving self-management for these patients may increase health-related quality of life and reduce hospitalizations. Patients are willing to use wearable technology for real-time data reporting and perceive mobile technology as potentially helpful in COPD management, but there are many barriers to the uptake of these technologies.

OBJECTIVE

We aimed to understand patients' experiences using a wearable and mobile app and identify areas for improvement.

METHODS

We conducted semi-structured interviews as part of a larger prospective cohort study wherein patients used a wearable and app for 6 months. We asked which features patients found accessible, acceptable and useful.

RESULTS

We completed 26 interviews. We summarized our research findings into four main themes: (1) information, support and reassurance, (2) barriers to adoption, (3) impact on communication with health care providers, and (4) opportunities for improvement. Most patients found the feedback received through the app to be reassuring and useful. Some patients experienced technical difficulties with the app and found the wearable to be uncomfortable.

CONCLUSIONS

Patients found a wearable device and mobile application to be acceptable and useful for the management of COPD. We identified barriers to adoption and opportunities for improvement to the design of our app. Further research is needed to understand what people with COPD and their healthcare providers want and will use in a mobile app and wearable for COPD management.

Performance of a Wearable Ring in Controlled Hypoxia: A Prospective Observational Study

BACKGROUND

Over recent years, technological advances in wearables have allowed for continuous home monitoring of heart rate and oxygen saturation. These devices have primarily been used for sports and general wellness and may not be suitable for medical decision-making, especially in saturations below 90% and in patients with dark skin color. Wearable clinical-grade saturation of peripheral oxygen (SpO2) monitoring can be of great value to patients with chronic diseases, enabling them and their clinicians to better manage their condition with reliable real-time and trend data.

OBJECTIVE

This study aimed to determine the SpO2 accuracy of a wearable ring pulse oximeter compared with arterial oxygen saturation (SaO2) in a controlled hypoxia study based on the International Organization for Standardization (ISO) 80601-2-61:2019 standard over the range of 70%-100% SaO2 in volunteers with a broad range of skin color (Fitzpatrick I to VI) during nonmotion conditions. In parallel, accuracy was compared with a calibrated clinical-grade reference pulse oximeter (Masimo Radical-7). Acceptable medical device accuracy was defined as a maximum of 4% root mean square error (RMSE) per the ISO 80601-2-61 standard and a maximum of 3.5% RMSE per the US Food and Drug Administration guidance.

METHODS

We performed a single-center, blinded hypoxia study of the test device in 11 healthy volunteers at the Hypoxia Research Laboratory, University of California at San Francisco, under the direction of Philip Bickler, MD, PhD, and John Feiner, MD. Each volunteer was connected to a breathing apparatus for the administration of a hypoxic gas mixture. To facilitate frequent blood gas sampling, a radial arterial cannula was placed on either wrist of each participant. One test device was placed on the index finger and another test device was placed on the fingertip. SaO2 analysis was performed using an ABL-90 multi-wavelength oximeter.

RESULTS

For the 11 participants included in the analysis, there were 236, 258, and 313 SaO2-SpO2 data pairs for the test device placed on the finger, the test device placed on the fingertip, and the reference device, respectively. The RMSE of the test device for all participants was 2.1% for either finger or fingertip placement, while the Masimo Radical-7 reference pulse oximeter RMSE was 2.8%, exceeding the standard (4% or less) and the Food and Drug Administration guidance (3.5% or less). Accuracy of SaO2-SpO2 paired data from the 4 participants with dark skin in the study was separately analyzed for both test device placements and the reference device. The test and reference devices exceeded the minimum accuracy requirements for a medical device with RMSE at 1.8% (finger) and 1.6% (fingertip) and for the reference device at 2.9%.

CONCLUSIONS

The wearable ring meets an acceptable standard of accuracy for clinical-grade SpO2 under nonmotion conditions without regard to skin color.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05920278; https://clinicaltrials.gov/study/NCT05920278.

Telemedicine With Wearable Technologies in Patients Undergoing Hematopoietic Cell Transplantation and Chimeric Antigen Receptor T-Cell Therapy (TEL-HEMATO Study): Prospective Noninterventional Single-Center Study

BACKGROUND

Patients with hematological malignancies receiving hematopoietic cell transplantation (HCT) or chimeric antigen receptor (CAR) T-cell therapy are at risk of developing serious clinical complications after discharge.

OBJECTIVE

The aim of the TEL-HEMATO study was to improve our telehealth platform for the follow-up of patients undergoing HCT or CAR T-cell therapy during the first 3 months after discharge with the addition of wearable devices.

METHODS

Eleven patients who received autologous (n=2) or allogeneic (n=5) HCT or CAR T-cell therapy (n=4) for hematological malignancies were screened from November 2022 to July 2023. Two patients discontinued the study after enrollment. The telehealth platform consisted of the daily collection of vital signs, physical symptoms, and quality of life assessment up to 3 months after hospital discharge. Each patient received a clinically validated smartwatch (ScanWatch) and a digital thermometer, and a dedicated smartphone app was used to collect these data. Daily revision of the data was performed through a web-based platform by a hematologist or a nurse specialized in HCT and CAR T-cell therapy.

RESULTS

Vital signs measured through ScanWatch were successfully collected with medium/high adherence: heart rate was recorded in 8/9 (89%) patients, oxygen saturation and daily steps were recorded in 9/9 (100%) patients, and sleeping hours were recorded in 7/9 (78%) patients. However, temperature recorded manually by the patients was associated with lower compliance, which was recorded in 5/9 (55%) patients. Overall, 5/9 (55%) patients reported clinical symptoms in the app. Quality of life assessment was completed by 8/9 (89%) patients at study enrollment, which decreased to 3/9 (33%) at the end of the third month. Usability was considered acceptable through ratings provided on the System Usability Scale. However, technological issues were reported by the patients.

CONCLUSIONS

While the addition of wearable devices to a telehealth clinical platform could have potentially synergic benefits for HCT and CAR T-cell therapy patient monitoring, noncomplete automation of the platform and the absence of a dedicated telemedicine team still represent major limitations to be overcome. This is especially true in our real-life setting where the target population generally comprises patients of older age with a low digital education level.

Flexible Wearable Electrochemical Sensors Based on AuNR/PEDOT:PSS for Simultaneous Monitoring of Levodopa and Uric Acid in Sweat

As a facile substitute for the invasive technique of blood testing, wearable electrochemical sensors exhibit high potential for the noninvasive and real-time monitoring of biomarkers in human sweat. However, owing to enzyme specificity, the simultaneous detection of multiple biomarkers by enzymatic analysis is challenging. Moreover, sweat accumulation under sensors causes sweat contamination, which hinders real-time biomarker detection from sweat. This study reports the design and fabrication of flexible wearable electrochemical sensors containing a composite comprising Au nanorods (AuNRs) and poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) for the nonenzymatic detection of levodopa (LD) and uric acid (UA) in sweat. Each sensor was integrated with a flexible three-electrode system and a microfluidic patch for sweat sampling. AuNRs immobilized by PEG-doped PEDOT:PSS showed excellent analytical performance for LD and UA at different potentials. Thus, the newly fabricated sensors could detect LD and UA over a broad detection range with high sensitivity and showed a low limit of detection for both species. On-body assessments confirmed the ability of these sensors to simultaneously detect LD and UA in real time. Therefore, this study could open new frontiers in the fabrication of wearable electrochemical sensors for the pharmacokinetic profile tracking of LD and gout management.

Efficacy and safety of tibial nerve stimulation using a wearable device for overactive bladder

OBJECTIVE

To evaluate the efficacy and safety of a wearable, smartphone-controlled, rechargeable transcutaneous tibial nerve stimulation (TTNS) device in patients with overactive bladder (OAB).

PATIENTS AND METHODS

This multicentre, prospective, single-blind, randomised clinical trial included eligible patients with OAB symptoms who were randomly assigned to the stimulation group or sham group. The primary efficacy outcome was change from baseline in voiding frequency/24 h after 4 weeks of treatment. The secondary efficacy outcomes included changes in bladder diary outcomes (urgency score/void, nocturia episodes/day, micturition volume/void, and incontinence episodes/day), questionnaires on Overactive Bladder Symptom Score (OABSS), Patient Perception of Bladder Condition (PPBC), and American Urological Association Symptom Index Quality of Life Score (AUA-SI-QoL) at baseline and after 4 weeks of treatment. Device-related adverse events (AEs) were also evaluated.

RESULTS

In the full analysis set (FAS), the mean (sd) change of voiding frequency/24 h in the stimulation group and sham group at 4 weeks were -3.5 (2.9) and -0.6 (2.4), respectively (P < 0.01). Similar results were obtained in the per-protocol set (PPS): -3.5 (2.9) vs -0.4 (2.3) (P < 0.01). In the FAS and PPS, micturition volume/void significantly improved at 4 weeks (P = 0.01 and P = 0.02). PPBC improvement almost reached significance in the FAS (P = 0.05), while it was significant in the PPS (P = 0.02). In the FAS and PPS, AUA-SI-QoL significantly improved at 4 weeks in the two groups (P < 0.01 and P < 0.01), whereas there were no significant differences in urgency score/void, nocturia episodes/day or OABSS between the groups. Also, no device-related serious AEs were reported.

CONCLUSIONS

The non-invasive neuromodulation technique using the novel ambulatory TTNS device is effective and safe for treating OAB. Its convenience and easy maintenance make it a new potential home-based treatment modality. Future studies are warranted to confirm its longer-term efficacy.

Highlighting Unseen Activity Through 48-Hour Continuous Measurement in Subacute Stroke Rehabilitation: Preliminary Cohort Study

BACKGROUND

Motor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities.

OBJECTIVE

This study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity.

METHODS

In this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients' daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks.

RESULTS

A significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=-0.41, P=.21) or 4 weeks post admission (ρ=-0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=-0.82, P=.002) and 4 weeks post admission (ρ=-0.73, P=.01).

CONCLUSIONS

Inpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings.

Understanding the Sociocultural Challenges and Opportunities for Affordable Wearables to Support Poststroke Upper-Limb Rehabilitation: Qualitative Study

Background

People who survive a stroke in many cases require upper-limb rehabilitation (ULR), which plays a vital role in stroke recovery practices. However, rehabilitation services in the Global South are often not affordable or easily accessible. For example, in Bangladesh, the access to and use of rehabilitation services is limited and influenced by cultural factors and patients' everyday lives. In addition, while wearable devices have been used to enhance ULR exercises to support self-directed home-based rehabilitation, this has primarily been applied in developed regions and is not common in many Global South countries due to potential costs and limited access to technology.

Objective

Our goal was to better understand physiotherapists', patients', and caregivers' experiences of rehabilitation in Bangladesh, existing rehabilitation practices, and how they differ from the rehabilitation approach in the United Kingdom. Understanding these differences and experiences would help to identify opportunities and requirements for developing affordable wearable devices that could support ULR in home settings.

Methods

We conducted an exploratory study with 14 participants representing key stakeholder groups. We interviewed physiotherapists and patients in Bangladesh to understand their approaches, rehabilitation experiences and challenges, and technology use in this context. We also interviewed UK physiotherapists to explore the similarities and differences between the 2 countries and identify specific contextual and design requirements for low-cost wearables for ULR. Overall, we remotely interviewed 8 physiotherapists (4 in the United Kingdom, 4 in Bangladesh), 3 ULR patients in Bangladesh, and 3 caregivers in Bangladesh. Participants were recruited through formal communications and personal contacts. Each interview was conducted via videoconference, except for 2 interviews, and audio was recorded with consent. A total of 10 hours of discussions were transcribed. The results were analyzed using thematic analysis.

Results

We identified several sociocultural factors that affect ULR and should be taken into account when developing technologies for the home: the important role of family, who may influence the treatment based on social and cultural perceptions; the impact of gender norms and their influence on attitudes toward rehabilitation and physiotherapists; and differences in approach to rehabilitation between the United Kingdom and Bangladesh, with Bangladeshi physiotherapists focusing on individual movements that are necessary to build strength in the affected parts and their British counterparts favoring a more holistic approach. We propose practical considerations and design recommendations for developing ULR devices for low-resource settings.

Conclusions

Our work shows that while it is possible to build a low-cost wearable device, the difficulty lies in addressing sociotechnical challenges. When developing new health technologies, it is imperative to not only understand how well they could fit into patients', caregivers', and physiotherapists' everyday lives, but also how they may influence any potential tensions concerning culture, religion, and the characteristics of the local health care system.

A Human Friendly Self-Assembled Triboelectric Sensor for Multifunctional Tactile Sensing

Obtaining bioenergy from human movement is not only a prospective complementation to electrochemical power supply such as batteries in portable electronics but also a decipherable process for developing self-powered sensors that can simultaneously monitor the physiological movement. In this study, a low-cost, robust, and environmentally friendly triboelectric nanogenerator (TENG) was prepared with enhanced mechanical stability and tunneling conductivity on the base of cotton fabric. The as-designed TENG may produce energy sustainably by physical movements, and it can yield an amazing 417 V open-circuit voltage, 11.7 μA short-circuit current, and 237.60 mW/m2 excellent power density, showcasing its potential for efficient energy conversion in the single-electrode mode. Besides, such a design also shows real-time tactile perception ability toward human physiological signal and body motion where intelligent application of these environmental benign TENGs in sports and writing training were demonstrated, thus providing vital instruction for the creation of versatile and sustainable TENGs in the Internet of Things era.

Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset

Introduction

Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients.

Methods

We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation.

Results

Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones.

Discussion

We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.

"Notification! You May Have Cancer." Could Smartphones and Wearables Help Detect Cancer Early?

This viewpoint paper considers the authors' perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources.

Amphibious Multifunctional Hydrogel Flexible Haptic Sensor with Self-Compensation Mechanism

In recent years, hydrogel-based wearable flexible electronic devices have attracted much attention. However, hydrogel-based sensors are affected by structural fatigue, material aging, and water absorption and swelling, making stability and accuracy a major challenge. In this study, we present a DN-SPEZ dual-network hydrogel prepared using polyvinyl alcohol (PVA), sodium alginate (SA), ethylene glycol (EG), and ZnSO4 and propose a self-calibration compensation strategy. The strategy utilizes a metal salt solution to adjust the carrier concentration of the hydrogel to mitigate the resistance drift phenomenon to improve the stability and accuracy of hydrogel sensors in amphibious scenarios, such as land and water. The ExpGrow model was used to characterize the trend of the ∆R/R0 dynamic response curves of the hydrogels in the stress tests, and the average deviation of the fitted curves ϵ¯ was calculated to quantify the stability differences of different groups. The results showed that the stability of the uncompensated group was much lower than that of the compensated group utilizing LiCl, NaCl, KCl, MgCl2, and AlCl3 solutions (ϵ¯ in the uncompensated group in air was 276.158, 1.888, 2.971, 30.586, and 13.561 times higher than that of the compensated group in LiCl, NaCl, KCl, MgCl2, and AlCl3, respectively; ϵ¯ in the uncompensated group in seawater was 10.287 times, 1.008 times, 1.161 times, 4.986 times, 1.281 times, respectively, higher than that of the compensated group in LiCl, NaCl, KCl, MgCl2 and AlCl3). In addition, for the ranking of the compensation effect of different compensation solutions, the concentration of the compensation solution and the ionic radius and charge of the cation were found to be important factors in determining the compensation effect. Detection of events in amphibious environments such as swallowing, robotic arm grasping, Morse code, and finger-wrist bending was also performed in this study. This work provides a viable method for stability and accuracy enhancement of dual-network hydrogel sensors with strain and pressure sensing capabilities and offers solutions for sensor applications in both airborne and underwater amphibious environments.

Innovative Material-Based Wearable Non-Invasive Electrochemical Sweat Sensors towards Biomedical Applications

Sweat is an accessible biofluid that provides useful physiological information about the body's biomolecular state and systemic health. Wearable sensors possess various advantageous features, such as lightweight design, wireless connectivity, and compatibility with human skin, that make them suitable for continuous monitoring. Wearable electrochemical sweat sensors can diagnose diseases and monitor health conditions by detecting biomedical signal changes in sweat. This paper discusses the state-of-the-art research in the field of wearable sweat sensors and the materials used in their construction. It covers biomarkers present in sweat, sensing modalities, techniques for sweat collection, and ways to power these sensors. Innovative materials are categorized into three subcategories: sweat collection, sweat detection, and self-powering. These include substrates for sensor fabrication, analyte detection electrodes, absorbent patches, microfluidic devices, and self-powered devices. This paper concludes by forecasting future research trends and prospects in material-based wearable non-invasive sweat sensors.

Perceptions of Wearable Health Tools Post the COVID-19 Emergency in Low-Income Latin Communities: Qualitative Study

BACKGROUND

Mobile health (mHealth) wearable devices are increasingly being adopted by individuals to help manage and monitor physiological signals. However, the current state of wearables does not consider the needs of racially minoritized low-socioeconomic status (SES) communities regarding usability, accessibility, and price. This is a critical issue that necessitates immediate attention and resolution.

OBJECTIVE

This study's aims were 3-fold, to (1) understand how members of minoritized low-SES communities perceive current mHealth wearable devices, (2) identify the barriers and facilitators toward adoption, and (3) articulate design requirements for future wearable devices to enable equitable access for these communities.

METHODS

We performed semistructured interviews with low-SES Hispanic or Latine adults (N=19) from 2 metropolitan cities in the Midwest and West Coast of the United States. Participants were asked questions about how they perceive wearables, what are the current benefits and barriers toward use, and what features they would like to see in future wearable devices. Common themes were identified and analyzed through an exploratory qualitative approach.

RESULTS

Through qualitative analysis, we identified 4 main themes. Participants' perceptions of wearable devices were strongly influenced by their COVID-19 experiences. Hence, the first theme was related to the impact of COVID-19 on the community, and how this resulted in a significant increase in interest in wearables. The second theme highlights the challenges faced in obtaining adequate health resources and how this further motivated participants' interest in health wearables. The third theme focuses on a general distrust in health care infrastructure and systems and how these challenges are motivating a need for wearables. Lastly, participants emphasized the pressing need for community-driven design of wearable technologies.

CONCLUSIONS

The findings from this study reveal that participants from underserved communities are showing emerging interest in using health wearables due to the COVID-19 pandemic and health care access issues. Yet, the needs of these individuals have been excluded from the design and development of current devices.

Accuracy of the Apple Watch Series 4 and Fitbit Versa for Assessing Energy Expenditure and Heart Rate of Wheelchair Users During Treadmill Wheelchair Propulsion: Cross-sectional Study

BACKGROUND

The Apple Watch (AW) Series 1 provides energy expenditure (EE) for wheelchair users but was found to be inaccurate with an error of approximately 30%, and the corresponding error for heart rate (HR) provided by the Fitbit Charge 2 was approximately 10% to 20%. Improved accuracy of estimated EE and HR is expected with newer editions of these smart watches (SWs).

OBJECTIVE

This study aims to assess the accuracy of the AW Series 4 (wheelchair-specific setting) and the Fitbit Versa (treadmill running mode) for estimating EE and HR during wheelchair propulsion at different intensities.

METHODS

Data from 20 manual wheelchair users (male: n=11, female: n=9; body mass: mean 75, SD 19 kg) and 20 people without a disability (male: n=11, female: n=9; body mass: mean 75, SD 11 kg) were included. Three 4-minute wheelchair propulsion stages at increasing speed were performed on 3 separate test days (0.5%, 2.5%, or 5% incline), while EE and HR were collected by criterion devices and the AW or Fitbit. The mean absolute percentage error (MAPE) was used to indicate the absolute agreement between the criterion device and SWs for EE and HR. Additionally, linear mixed model analyses assessed the effect of exercise intensity, sex, and group on the SW error. Interclass correlation coefficients were used to assess relative agreement between criterion devices and SWs.

RESULTS

The AW underestimated EE with MAPEs of 29.2% (SD 22%) in wheelchair users and 30% (SD 12%) in people without a disability. The Fitbit overestimated EE with MAPEs of 73.9% (SD 7%) in wheelchair users and 44.7% (SD 38%) in people without a disability. Both SWs underestimated HR. The device error for EE and HR increased with intensity for both SWs (all comparisons: P<.001), and the only significant difference between groups was found for HR in the AW (-5.27 beats/min for wheelchair users; P=.02). There was a significant effect of sex on the estimation error in EE, with worse accuracy for the AW (-0.69 kcal/min; P<.001) and better accuracy for the Fitbit (-2.08 kcal/min; P<.001) in female participants. For HR, sex differences were found only for the AW, with a smaller error in female participants (5.23 beats/min; P=.02). Interclass correlation coefficients showed poor to moderate relative agreement for both SWs apart from 2 stage-incline combinations (AW: 0.12-0.57 for EE and 0.11-0.86 for HR; Fitbit: 0.06-0.85 for EE and 0.03-0.29 for HR).

CONCLUSIONS

Neither the AW nor Fitbit were sufficiently accurate for estimating EE or HR during wheelchair propulsion. The AW underestimated EE and the Fitbit overestimated EE, and both SWs underestimated HR. Caution is hence required when using SWs as a tool for training intensity regulation and energy balance or imbalance in wheelchair users.

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study

BACKGROUND

Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life.

OBJECTIVE

We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test.

METHODS

This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT.

RESULTS

In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024.

CONCLUSIONS

The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/55452.

Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis

BACKGROUND

Implementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems-based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken.

OBJECTIVE

We aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations.

METHODS

A mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed.

RESULTS

A total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology).

CONCLUSIONS

The recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of "virtual wards."

TRIAL REGISTRATION

ClinicalTrials.gov NCT05321004; https://www.clinicaltrials.gov/study/NCT05321004.

Digital Interventions to Understand and Mitigate Stress Response: Protocol for Process and Content Evaluation of a Cohort Study

BACKGROUND

Staffing and resource shortages, especially during the COVID-19 pandemic, have increased stress levels among health care workers. Many health care workers have reported feeling unable to maintain the quality of care expected within their profession, which, at times, may lead to moral distress and moral injury. Currently, interventions for moral distress and moral injury are limited.

OBJECTIVE

This study has the following aims: (1) to characterize and reduce stress and moral distress related to decision-making in morally complex situations using a virtual reality (VR) scenario and a didactic intervention; (2) to identify features contributing to mental health outcomes using wearable, physiological, and self-reported questionnaire data; and (3) to create a personal digital phenotype profile that characterizes stress and moral distress at the individual level.

METHODS

This will be a single cohort, pre- and posttest study of 100 nursing professionals in Ontario, Canada. Participants will undergo a VR simulation that requires them to make morally complex decisions related to patient care, which will be administered before and after an educational video on techniques to mitigate distress. During the VR session, participants will complete questionnaires measuring their distress and moral distress, and physiological data (electrocardiogram, electrodermal activity, plethysmography, and respiration) will be collected to assess their stress response. In a subsequent 12-week follow-up period, participants will complete regular assessments measuring clinical outcomes, including distress, moral distress, anxiety, depression, and loneliness. A wearable device will also be used to collect continuous data for 2 weeks before, throughout, and for 12 weeks after the VR session. A pre-post comparison will be conducted to analyze the effects of the VR intervention, and machine learning will be used to create a personal digital phenotype profile for each participant using the physiological, wearable, and self-reported data. Finally, thematic analysis of post-VR debriefing sessions and exit interviews will examine reoccurring codes and overarching themes expressed across participants' experiences.

RESULTS

The study was funded in 2022 and received research ethics board approval in April 2023. The study is ongoing.

CONCLUSIONS

It is expected that the VR scenario will elicit stress and moral distress. Additionally, the didactic intervention is anticipated to improve understanding of and decrease feelings of stress and moral distress. Models of digital phenotypes developed and integrated with wearables could allow for the prediction of risk and the assessment of treatment responses in individuals experiencing moral distress in real-time and naturalistic contexts. This paradigm could also be used in other populations prone to moral distress and injury, such as military and public safety personnel.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05923398; https://clinicaltrials.gov/study/NCT05923398.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/54180.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography

Objective.Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising.Approach.In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs).Main results.We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape.Significance.In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Laughter in everyday life: an event-based experience sampling method study using wrist-worn wearables

Laughter is a universal, nonverbal vocal expression of broad significance for humans. Interestingly, rather little is known about how often we laugh and how laughter is associated with our personality. In a large, event-based, experience sampling method study (N = 52; k = 9,261 assessments) using wrist-worn wearables and a physical analogue scale, we analyzed belly laughs and fit of laughter events in participants' everyday life for 4 weeks. Additionally, we assessed associations with laughter frequency such as personality, happiness, life satisfaction, gelotophobia (i.e., fear of being laughed at), and cheerfulness. Validating our new measurement approach (i.e., wearables, physical analogue scale), laughter events elicited higher happiness ratings compared to reference assessments, as expected. On average, participants reported 2.5 belly laughs per day and on every fourth day a fit of laughter. As expected, participants who were happier and more satisfied with their life laughed more frequently than unhappier, unsatisfied participants. Women and younger participants laughed significantly more than men and older participants. Regarding personality, laughter frequency was positively associated with openness and conscientiousness. No significant association was found for gelotophobia, and results for cheerfulness and related concepts were mixed. By using state-of-the-art statistical methods (i.e., recurrent event regression) for the event-based, multi-level data on laughter, we could replicate past results on laughing.

Delineating cognitive resilience using fractal regulation: Cross-sectional and longitudinal evidence from the Rush Memory and Aging Project

INTRODUCTION

Degradation of fractal patterns in actigraphy independently predicts dementia risk. Such observations motivated the study to understand the role of fractal regulation in the context of neuropathologies.

METHODS

We examined associations of fractal regulation with neuropathologies and longitudinal cognitive changes in 533 older participants who were followed annually with actigraphy and cognitive assessments until death with brain autopsy performed. Two measures for fractal patterns were extracted from actigraphy, namely, α1 (representing the fractal regulation at time scales of <90 min) and α2 (for time scales 2 to 10 h).

RESULTS

We found that larger α1 was associated with lower burdens of Lewy body disease or cerebrovascular disease pathologies; both α1 and α2 were associated with cognitive decline. They explained an additional significant portion of the variance in the rate of cognitive decline above and beyond neuropathologies.

DISCUSSION

Fractal patterns may be used as a biomarker for cognitive resilience against dementia-related neuropathologies.

Recent Research on Preparation and Application of Smart Joule Heating Fabrics

Multifunctional wearable heaters have attracted much attention for their effective applications in personal thermal management and medical therapy. Compared to passive heating, Joule heating offers significant advantages in terms of reusability, reliable temperature control, and versatile coupling. Joule-heated fabrics make wearable electronics smarter. This review critically discusses recent advances in Joule-heated smart fabrics, focusing on various fabrication strategies based on material-structure synergy. Specifically, various applicable conductive materials with Joule heating effect are first summarized. Subsequently, different preparation methods for Joule heating fabrics are compared, and then their various applications in smart clothing, healthcare, and visual indication are discussed. Finally, the challenges faced in developing these smart Joule heating fabrics and their possible solutions are discussed.

Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated.

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.