Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Cardiac Health Assessment Using a Wearable Device Before and After Transcatheter Aortic Valve Implantation: Prospective Study.

Background: Due to aging of the population, the prevalence of aortic valve stenosis will increase drastically in upcoming years. Consequently, transcatheter aortic valve implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognoses is key, since TAVI is not without its risks. Currently, we are not able to adequately predict functional outcomes after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment. Objective: This study aimed to identify objective parameters (eg, change in heart rate) associated with improvement after TAVI for severe aortic stenosis from a wearable device. Methods: In total, 100 patients undergoing routine TAVI wore a Philips Health Watch device for 1 week before and after the procedure. Watch data were analyzed offline-before TAVI for 97 patients and after TAVI for 75 patients. Results: Parameters such as the total number of steps and activity time did not change, in contrast to improvements in the 6-minute walking test (6MWT) and physical limitation domain of the transformed WHOQOL-BREF questionnaire. Conclusions: These findings, in an older TAVI population, show that watch-based parameters, such as the number of steps, do not change after TAVI, unlike traditional 6MWT and QoL assessments. Basic wearable device parameters might be less appropriate for measuring treatment effects from TAVI.

Effect of Wearable Technology on Metabolic Control and the Quality of Life in Children and Adolescents with Type 1 Diabetes: A Systematic Review and Meta-Analysis

Background: Type 1 diabetes is one of the most common chronic diseases in children. Wearable technology (insulin pumps and continuous glucose monitoring devices) that makes diabetes management relatively simple, in addition to education and follow-ups, enhances the quality of life and health of individuals with diabetes. Aims: To evaluate the impact of wearable technology on metabolic management and the quality of life in children and adolescents with type 1 diabetes. Study Design: Systematic review and meta-analysis. Methods: The Preferred Reporting System for Systematic Reviews and Meta-Analyses was used to conduct a systematic review and meta-analysis. PubMed, Web of Science, MEDLINE, Cochrane Library, EBSCO, Ulakbim and Google Scholar were searched in July 2022 and July 2023 using predetermined keywords. The methodological quality of the studies was evaluated using the Joanna Briggs Institute's Critical Appraisal Checklists for randomized controlled experimental and cross-sectional studies. The meta-analysis method was used to pool the data. Results: Eleven studies published between 2011 and 2022 were included. The total sample size of the included studies was 1,853. The meta-analysis revealed that the decrease in hemoglobin A1C (HbA1c) level in those using wearable technology was statistically significant [mean difference (MD): -0.33, Z = 2.54, p = 0.01]. However, the technology had no effect on the quality of life [standardized mean difference (SMD): 0.44, Z = 1.72, p = 0.09]. The subgroup analyses revealed that the decrease in the HbA1c level occurred in the cross-sectional studies (MD: -0.49, Z = 2.54, p = 0.01) and the 12-19 (MD = 0.59, Z = 4.40, p < 0.001) and 4-18 age groups (MD: -0.31, Z = 2.56, p = 0.01). The subgroup analyses regarding the quality of life revealed that there was no difference according to the research design. However, the quality of life was higher in the wearable technology group than in the control group in the 8-12 and 4-18 age groups (SMD: 1.32, Z = 2.31, p = 0.02 and SMD: 1.00, Z = 5.76, p < 0.001, respectively). Conclusion: Wearable technology effectively reduces the HbA1c levels in children and adolescents with type 1 diabetes in some age groups. However, it does not affect the quality of life.

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).

Background: Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. objectives: The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. Methods: This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. Results: A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

Defining a systems framework for characterizing physical work demands with wearable sensors.

Measuring the physical demands of work is important in understanding the relationship between exposure to these job demands and their impact on the safety, health, and well-being of working people. However, work is changing and our knowledge of job demands should also evolve in anticipation of these changes. New opportunities exist for noninvasive long-term measures of physical demands through wearable motion sensors, including inertial measurement units, heart rate monitors, and muscle activity monitors. Inertial measurement units combine accelerometers, gyroscopes, and magnetometers to provide continuous measurement of a segment's motion and the ability to estimate orientation in 3-dimensional space. There is a need for a system-thinking perspective on how and when to apply these wearable sensors within the context of research and practice surrounding the measurement of physical job demands. In this paper, a framework is presented for measuring the physical work demands that can guide designers, researchers, and users to integrate and implement these advanced sensor technologies in a way that is relevant to the decision-making needs for physical demand assessment. We (i) present a literature review of the way physical demands are currently being measured, (ii) present a framework that extends the International Classification of Functioning to guide how technology can measure the facets of work, (iii) provide a background on wearable motion sensing, and (iv) define 3 categories of decision-making that influence the questions that we can ask and measures that are needed. By forming questions within these categories at each level of the framework, this approach encourages thinking about the systems-level problems inherent in the workplace and how they manifest at different scales. Applying this framework provides a systems approach to guide study designs and methodological approaches to study how work is changing and how it impacts worker safety, health, and well-being.

"But for the blind spot": Accuracy and diagnostic performance of smart watch cardiac features in pediatric patients.

BACKGROUND: The Apple Watch™ (AW) offers heart rate (HR) tracking by photoplethysmography (PPG) and single-lead electrocardiographic (ECG) recordings. The accuracy of AW-HR and diagnostic performance of AW-ECGs among children during both sinus rhythm and arrhythmias have not been explored. OBJECTIVE: The purposes of this study were to assess the accuracy of AW-HR measurements compared to gold standard modalities in children during sinus rhythm and arrhythmias and to identify non-sinus rhythms using AW-ECGs. METHODS: Subjects ≤18 years wore an AW during (1) telemetry admission, (2) electrophysiological study (EPS), or (3) exercise stress test (EST). AW-HRs were compared to gold standard modality values. Recorded AW-ECGs were reviewed by 3 blinded pediatric electrophysiologists. RESULTS: Eighty subjects (median age 13 years; interquartile range 1.0-16.0 years; 50% female) wore AW (telemetry 41% [n = 33]; EPS 34% [n = 27]; EST 25% [n = 20]). A total of 1090 AW-HR measurements were compared to time-synchronized gold standard modality HR values. Intraclass correlation coefficient (ICC) was high 0.99 (0.98-0.99) for AW-HR during sinus rhythm compared to gold standard modalities. ICC was poor comparing AW-HR to gold standard modality HR in tachyarrhythmias (ICC 0.24-0.27) due to systematic undercounting of AW-HR values. A total of 126 AW-ECGs were reviewed. Identification of non-sinus rhythm by AW-ECG showed sensitivity of 89%-96% and specificity of 78%-87%. CONCLUSIONS: We found high levels of agreement for AW-HR values with gold standard modalities during sinus rhythm and poor agreement during tachyarrhythmias, likely due to hemodynamic effects of tachyarrhythmias on PPG-based measurements. AW-ECGs had good sensitivity and moderate specificity in identification of non-sinus rhythm in children.

A non-invasive wearable stress patch for real-time cortisol monitoring using a pseudoknot-assisted aptamer.

Stress is part of everyone's life and is exacerbated by traumatic events such as pandemics, disasters, violence, lifestyle changes, and health disorders. Chronic stress has many detrimental health effects and can even be life-threatening. Long-term stress monitoring outside of a hospital is often accomplished by measuring heart rate variability. While easy to measure, this digital biomarker has low specificity, greatly limiting its utility. To address this shortcoming, we report a non-invasive, wearable biomolecular sensor to monitor cortisol levels in sweat. Cortisol is a neuroendocrine hormone that regulates homeostasis as part of the stress pathway. Cortisol is detected using an electrochemical sensor functionalized with a pseudoknot-assisted aptamer and a flexible microfluidic sweat sampling system. The skin-worn microfluidic sampler provides rapid sweat collection while separating old and new sweat. The conformation-switching aptamer provides high specificity towards cortisol while being regenerable, allowing it to monitor temporal changes continuously. The aptamer was engineered to add a pseudoknot, restricting it to only two states, thus minimizing the background signal and enabling high sensitivity. An electrochemical pH sensor allows pH-corrected amperometric measurements. Device operation was demonstrated invitro with a broad linear dynamic range (1 pM - 1 µM) covering the physiological range and a sub-picomolar (0.2 pM) limit of detection in sweat. Real-time, on-body measurements were collected from human subjects using an induced stress protocol, demonstrating in-situ signal regeneration and the ability to detect dynamic cortisol fluctuations continuously for up to 90 min. The reported device has the potential to improve prognosis and enable personalized treatments.

A wearable cardiac ultrasound imager.

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness5-11, and existing wearable cardiac devices can only capture signals on the skin12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.

Effects of Healthcare Technologies on the Promotion of Physical Activities in Older Persons: A Systematic Review.

This study aimed to explore the effects of health technologies on the promotion of health through physical activities of older persons. Following PRISMA guidelines, a systematic review of relevant articles published prior to 2020 was conducted from selected indices such as COCHRANE, PubMed, Science Direct, Proquest, including the use of hand search procedure. Twenty-seven articles were analyzed with significant findings influential to older people nursing: types of health technologies used for promoting physical activity; effects of technology use in promoting physical activity of older person care; and aspects that need to be considered in technology use among older persons. Characteristics of technologies were accuracy, usefulness, reliability, comfort, safety, and relevancy. Most technologies promoting physical activities for older people were wearable technologies that use artificial intelligence. Altogether, these technologies influenced overall healthcare behaviors of older persons. With healthcare technology efficiencies, proficiencies, and dependencies, technology-based healthcare have served older people well. Most technologies for older people care, such as wearables, reliably produce characteristics enhancing dependency and accuracy of bio-behavioral information influencing physical activities of older persons. Health technologies foster the values of physical activities among older persons thereby promoting healthy living.

Validation of the Perception Neuron system for full-body motion capture.

Recent advancements in Inertial Measurement Units (IMUs) offers the possibility of its use as a cost effective and portable alternative to traditional optoelectronic motion capture systems in analyzing biomechanical performance. One such commercially available IMU is the Perception Neuron motion capture system (PNS). The accuracy of the PNS had been tested and was reported to be a valid method for assessing the upper body range of motion to within 5° RMSE. However, testing of the PNS was limited to upper body motion involving functional movement within a single plane. Therefore, the purpose of this study is to further validate the Perception Neuron system with reference to a conventional optoelectronic motion capture system (VICON) through the use of dynamic movements (e.g., walking, jogging and a multi-articular sports movement with object manipulation) and to determine its feasibility through full-body kinematic analysis. Validation was evaluated using Pearson's R correlation, RMSE and Bland-Altman estimates. Present findings suggest that the PNS performed well against the VICON motion analysis system with most joint angles reporting a RMSE of < 4° and strong average Pearson's R correlation of 0.85, with the exception of the shoulder abduction/adduction where RMSE was larger and Pearson's R correlation at a moderate level. Bland-Altman analysis revealed that most joint angles across the different movements had a mean bias of less than 10°, except for the shoulder abduction/adduction and elbow flexion/extension measurements. It was concluded that the PNS may not be the best substitute for traditional motion analysis technology if there is a need to replicate raw joint angles. However, there was adequate sensitivity to measure changes in joint angles and would be suitable when normalized joint angles are compared and the focus of analysis is to identify changes in movement patterns.

Highly efficient patterning technique for silver nanowire electrodes by electrospray deposition and its application to self-powered triboelectric tactile sensor.

A patterned transparent electrode is a crucial component of state-of-the-art wearable devices and optoelectronic devices. However, most of the patterning methods using silver nanowires (AgNWs), which is one of the outstanding candidate materials for the transparent electrode, wasted a large amount of unused AgNWs during the patterning process. Here, we report a highly efficient patterning of AgNWs using electrospray deposition with grounded electrolyte solution (EDGE). During electrospray deposition, a patterned electrolyte solution collector attracted AgNWs by strong electrostatic attraction and selectively deposited them only on the patterned collector, minimizing AgNW deposited elsewhere. The enhanced patterning efficiency was verified through a comparison between the EDGE and conventional process by numerical simulation and experimental validation. As a result, despite the same electrospray deposition conditions for both cases except for the existence of the electrolyte solution collector, the coverage ratio of AgNWs fabricated by the EDGE process was at least six times higher than that of AgNWs produced by the conventional process. Furthermore, the EDGE process provided high design flexibility in terms of not only the material of the substrate, including a polymer and a ceramic but also the shape of the substrate, including a 2D flat and 3D curved surface. As an application of the EDGE process, a self-powered touch sensor exploiting the triboelectric effect was demonstrated. Thus, the EDGE process would be utilized in further application in wearable or implantable devices in the field of biomedicine, intelligent robots, and human-machine interface.

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology.

Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings.

Real-time quantitation of thyroidal radioiodine uptake in thyroid disease with monitoring by a collar detection device.

Radioactive iodine (RAI) is safe and effective in most patients with hyperthyroidism but not all individuals are cured by the first dose, and most develop post-RAI hypothyroidism. Postoperative RAI therapy for remnant ablation is successful in 80-90% of thyroid cancer patients and sometimes induces remission of nonresectable cervical and/or distant metastatic disease but the effective tumor dose is usually not precisely known and must be moderated to avoid short- and long-term adverse effects on other tissues. The Collar Therapy Indicator (COTI) is a radiation detection device embedded in a cloth collar secured around the patient's neck and connected to a recording and data transmission box. In previously published experience, the data can be collected at multiple time points, reflecting local cervical RAI exposure and correlating well with conventional methods. We evaluated the real-time uptake of RAI in patients with hyperthyroid Graves' disease and thyroid cancer. We performed a pilot feasibility prospective study. Data were analyzed using R© (version 4.0.3, The R Foundation for Statistical Computing, 2020), and Python (version 3.6, Matplotlib version 3.0.3). The COTI was able to provide a quantitative temporal pattern of uptake within the thyroid in persons with Graves' disease and lateralized the remnant tissue in persons with thyroid cancer. The study has demonstrated that the portable collar radiation detection device outside of a healthcare facility is accurate and feasible for use after administration of RAI for diagnostic studies and therapy to provide a complete collection of fractional target radioactivity data compared to that traditionally acquired with clinic-based measurements at one or two time-points.Clinical Trials Registration NCT03517579, DOR 5/7/2018.

Evaluation of the Accuracy of Cuffless Blood Pressure Measurement Devices: Challenges and Proposals.

Several novel cuffless wearable devices and smartphone applications claiming that they can measure blood pressure (BP) are appearing on the market. These technologies are very attractive and promising, with increasing interest among health care professionals for their potential use. Moreover, they are becoming popular among patients with hypertension and healthy people. However, at the present time, there are serious issues about BP measurement accuracy of cuffless devices and the 2021 European Society of Hypertension Guidelines on BP measurement do not recommend them for clinical use. Cuffless devices have special validation issues, which have been recently recognized. It is important to note that the 2018 Universal Standard for the validation of automated BP measurement devices developed by the American Association for the Advancement of Medical Instrumentation, the European Society of Hypertension, and the International Organization for Standardization is inappropriate for the validation of cuffless devices. Unfortunately, there is an increasing number of publications presenting data on the accuracy of novel cuffless BP measurement devices, with inadequate methodology and potentially misleading conclusions. The objective of this review is to facilitate understanding of the capabilities and limitations of emerging cuffless BP measurement devices. First, the potential and the types of these devices are described. Then, the unique challenges in evaluating the BP measurement accuracy of cuffless devices are explained. Studies from the literature and computer simulations are employed to illustrate these challenges. Finally, proposals are given on how to evaluate cuffless devices including presenting and interpreting relevant study results.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-Kids study.

BACKGROUND: Wearable technologies play an important role in measuring physical activity (PA) and promoting health. Standardized validation indices (i.e., accuracy, bias, and precision) compare performance of step counting wearable technologies in young people. PURPOSE: To produce a catalog of validity indices for step counting wearable technologies assessed during different treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]), wear locations (waist, wrist/arm, thigh, and ankle), and age groups (children, 6-12 years; adolescents, 13-17 years; young adults, 18-20 years). METHODS: One hundred seventeen individuals (13.1 ± 4.2 years, 50.4% female) participated in this cross-sectional study and completed 5-min treadmill bouts (0.8 km/h to 8.0 km/h) while wearing eight devices (Waist: Actical, ActiGraph GT3X+, NL-1000, SW-200; Wrist: ActiGraph GT3X+; Arm: SenseWear; Thigh: activPAL; Ankle: StepWatch). Directly observed steps served as the criterion measure. Accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV) were computed. RESULTS: Five of the eight tested wearable technologies (i.e., Actical, waist-worn ActiGraph GT3X+, activPAL, StepWatch, and SW-200) performed at < 5% MAPE over the range of normal speeds. More generally, waist (MAPE = 4%), thigh (4%) and ankle (5%) locations displayed higher accuracy than the wrist location (23%) at normal speeds. On average, all wearable technologies displayed the lowest accuracy across slow speeds (MAPE = 50.1 ± 35.5%), and the highest accuracy across normal speeds (MAPE = 15.9 ± 21.7%). Speed and wear location had a significant effect on accuracy and bias (P < 0.001), but not on precision (P > 0.05). Age did not have any effect (P > 0.05). CONCLUSIONS: Standardized validation indices focused on accuracy, bias, and precision were cataloged by speed, wear location, and age group to serve as important reference points when selecting and/or evaluating device performance in young people moving forward. Reduced performance can be expected at very slow walking speeds (0.8 to 3.2 km/h) for all devices. Ankle-worn and thigh-worn devices demonstrated the highest accuracy. Speed and wear location had a significant effect on accuracy and bias, but not precision. TRIAL REGISTRATION: Clinicaltrials.gov NCT01989104 . Registered November 14, 2013.

Interventions Using Wearable Physical Activity Trackers Among Adults With Cardiometabolic Conditions: A Systematic Review and Meta-analysis.

Importance: Wearable physical activity (PA) trackers, such as accelerometers, fitness trackers, and pedometers, are accessible technologies that may encourage increased PA levels in line with current recommendations. However, whether their use is associated with improvements in PA levels in participants who experience 1 or more cardiometabolic conditions, such as diabetes, prediabetes, obesity, and cardiovascular disease, is unknown. Objective: To assess the association of interventions using wearable PA trackers (accelerometers, fitness trackers, and pedometers) with PA levels and other health outcomes in adults with cardiometabolic conditions. Data Sources: For this systematic review and meta-analysis, searches of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO were performed from January 1, 2000, until December 31, 2020, with no language restriction. A combination of Medical Subject Heading terms and text words of diabetes, obesity, cardiovascular disease, pedometers, accelerometers, and Fitbits were used. Study Selection: Randomized clinical trials or cluster randomized clinical trials that evaluated the use of wearable PA trackers, such as pedometers, accelerometers, or fitness trackers, were included. Trials were excluded if they assessed the trackers only as measuring tools of PA before and after another intervention, they required participants to be hospitalized, assessors were not blinded to the trackers, or they used a tracker to measure the effect of a pharmacological treatment on PA among individuals. Data Extraction and Synthesis: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. A random-effects model was used for the meta-analysis. Main Outcomes and Measures: The primary outcome was mean difference in PA levels. When the scale was different across studies, standardized mean differences were calculated. Heterogeneity was quantified using the I2 statistic and explored using mixed-effects metaregression. Results: A total of 38 randomized clinical trials with 4203 participants were eligible in the systematic review; 29 trials evaluated pedometers, and 9 evaluated accelerometers or fitness trackers. Four studies did not provide amenable outcome data, leaving 34 trials (3793 participants) for the meta-analysis. Intervention vs comparator analysis showed a significant association of wearable tracker use with increased PA levels overall (standardized mean difference, 0.72; 95% CI, 0.46-0.97; I2 = 88%; 95% CI, 84.3%-90.8%; P < .001) in studies with short to medium follow-up for median of 15 (range, 12-52) weeks. Multivariable metaregression showed an association between increased PA levels and interventions that involved face-to-face consultations with facilitators (23 studies; ß = -0.04; 95% CI, -0.11 to -0.01), included men (23 studies; ß = 0.48; 95% CI, 0.01-0.96), and assessed pedometer-based interventions (26 studies; ß = 0.20; 95% CI, 0.02-0.32). Conclusions and Relevance: In this systematic review and meta-analysis, interventions that combined wearable activity trackers with health professional consultations were associated with significant improvements in PA levels among people with cardiometabolic conditions.

Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass.

Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.

Accuracy and precision of wrist-worn actigraphy for measuring steps taken during over-ground and treadmill walking in adults with Parkinson's disease.

INTRODUCTION: Step counts represent a straight-forward method of measuring physical activity in adults with Parkinson's disease (PD). The present study examined the absolute and relative accuracy and precision of a wrist-worn research-grade accelerometer (i.e., ActiGraph GT3X+) for measuring step counts during over-ground and treadmill walking in adults with PD and controls without PD. METHODS: Participants (PD: n = 29; controls: n = 31) wore two ActiGraph GT3X + accelerometers, one on each wrist, and completed an over-ground walking bout followed by a treadmill walking bout at the same speed. Step counts were measured manually using a hand-held tally counter. Accuracy and precision were based on absolute and relative metrics. RESULTS: The ActiGraph GT3X + underestimated step counts in both participants with PD (4.7-11% error) and controls without PD (8.8-17% error), with a greater discrepancy in controls. The ActiGraph GT3X + provided more accurate and precise estimates of step counts when placed on the more affected wrist and non-dominant wrist for participants with PD and controls, respectively, and was more accurate and precise during over-ground walking compared with treadmill walking for both groups. CONCLUSIONS: Our results suggest that placement of the device (i.e., dominant vs. non-dominant), type of activity (i.e., over-ground vs. treadmill walking), and presence of clinical conditions may impact the accuracy and precision of data when using the research-grade ActiGraph GT3X + accelerometer for measuring step counts.