Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study

Concurrent and prospective associations between fitbit wearable-derived RDoC arousal and regulatory constructs and adolescent internalizing symptoms

BACKGROUND

Adolescence is characterized by alterations in biobehavioral functioning, during which individuals are at heightened risk for onset of psychopathology, particularly internalizing disorders. Researchers have proposed using digital technologies to index daily biobehavioral functioning, yet there is a dearth of research examining how wearable metrics are associated with mental health.

METHODS

We preregistered analyses using the Adolescent Brain Cognitive Development Study dataset using wearable data collection in 5,686 adolescents (123,862 person-days or 2,972,688 person-hours) to determine whether wearable indices of resting heart rate (RHR), step count, and sleep duration and variability in these measures were cross-sectionally associated with internalizing symptomatology. All models were also run controlling for age, sex, body mass index, socioeconomic status, and race. We then performed prospective analyses on a subset of this sample (n = 143) across 25 months that had Fitbit data available at baseline and follow-up in order to explore directionality of effects.

RESULTS

Cross-sectional analyses revealed a small, yet significant, effect size (R²  = .053) that higher RHR, lower step count and step count variability, and greater variability in sleep duration were associated with greater internalizing symptoms. Cross-lagged panel model analysis revealed that there were no prospective associations between wearable variables and internalizing symptoms (partial R²  = .026), but greater internalizing symptoms and higher RHR predicted lower step count 25 months later (partial R²  = .010), while higher RHR also predicted lower step count variability 25 months later (partial R²  = .008).

CONCLUSIONS

Findings indicate that wearable indices concurrently associate with internalizing symptoms during early adolescence, while a larger sample size is likely required to accurately assess prospective or directional effects between wearable indices and mental health. Future research should capitalize on the temporal resolution provided by wearable devices to determine the intensive longitudinal relations between biobehavioral risk factors and acute changes in mental health.

Accuracy of Heart Rate Measurement by the Fitbit Charge 2 During Wheelchair Activities in People With Spinal Cord Injury: Instrument Validation Study

Background

Heart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique.

Objective

The objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level.

Methods

The HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the <T5 (midthoracic and lower) group, T5-T1 (high-thoracic) group, and >T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise.

Results

With an overall MAPE_{all lesions} of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPE_{no SCI}=8.09%; MAPE_<T5=11.16%; MAPE_T1−T5=10.5%; and MAPE_>T1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPE_rest=6.5%, MAPE_activity=12.97%, and MAPE_strength=14.2%).

Conclusions

HR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution.

Accuracy of wrist-worn wearable devices for determining exercise intensity

Objective

As an indicator of exercise intensity, heart rate can be measured in a timely manner using wrist-worn devices. No study has attempted to estimate a target exercise intensity using wearable devices. The objective of the study was to evaluate the validity of prescribing exercise intensity using wrist-worn devices.

Methods

Thirty healthy subjects completed a maximal cardiopulmonary exercise test. Their heart rates were recorded using an electrocardiogram and two devices—Apple Watch Series 6 and Garmin Forerunner 945. Exercise intensity with the target heart rate was defined as resting heart rate + (maximal heart rate − resting heart rate) * n% ( n%: 40–60% for moderate-intensity exercise and 60–89% for vigorous-intensity exercise). Heart rate was analyzed at the lower and upper limits of each exercise intensity (HR40, HR60, and HR89). The mean absolute percentage error and concordance correlation coefficient were calculated, and Bland–Altman plots and scatterplots were constructed.

Results

Both devices showed a low mean absolute error (1.16–1.48 bpm for Apple and 1.35–2.25 for Garmin) and mean absolute percentage error (<1% for Apple and 1.16–1.39% for Garmin) in all intensities. A substantial correlation with electrocardiogram-measured heart rate was observed for moderate to vigorous intensity with concordance correlation coefficient > 0.95 for both devices, except that Garmin showed moderate correlation at the upper limit of vigorous activity with concordance correlation coefficient = 0.936. Moreover, Bland–Altman plots and scatterplots demonstrated a strong correlation without systematic error when the values obtained via the two devices were compared with electrocardiogram measurements.

Conclusions

Our findings indicate the high validity of exercise prescriptions based on the heart rate measured by the two devices. Additional research should explore other populations to confirm these findings.

Lessons from a multicenter clinical trial with an approved wearable electrocardiogram: issues and practical considerations

Although wearable electrocardiograms (ECGs) are being increasingly applied in clinical settings, validation methods have not been standardized. As an exploratory evaluation, we performed a multicenter clinical trial implementing an approved wearable patch ECG. Healthy male adults were enrolled in 2 study centers. The approved ECGs were deployed for 6 hours, and pulse rates were measured independently with conventional pulse oximetry at selected time points for correlation analyses. The transmission status of the data was evaluated by heart rates and classified into valid, invalid, and missing. A total of 55 subjects (40 in center 1 and 15 in center 2) completed the study. Overall, 77.40% of heart rates were within the valid range. Invalid and missing data accounted for 1.42% and 21.23%, respectively. There were significant differences in valid and missing data between centers. The proportion of missing data in center 1 (24.77%) was more than twice center 2 (11.77%). Heart rates measured by the wearable ECG and conventional pulse oximetry showed a poor correlation (intraclass correlation coefficient = 0.0454). In conclusion, we evaluated the multicenter feasibility of implementing wearable ECGs. The results suggest that systems to mitigate multicenter discrepancies and remove artifacts should be implemented prior to performing a clinical trial.

The Ava bracelet for collection of fertility and pregnancy data in free-living conditions: An exploratory validity and acceptability study

Objective

To evaluate the validity and acceptability of the Ava bracelet for collecting heart rate, sleep, mood, and physical activity data among reproductive-aged women (pregnant and nonpregnant) under free-living conditions.

Methods

Thirty-three participants wore the Ava bracelet on their non-dominant wrist and reported mood and physical activity in the Ava mobile application for seven nights. Criterion validity was determined by comparing the Ava bracelet heart rate and sleep duration measures to criterion measures from the Polar chest strap and ActiGraph GTX3 + accelerometer. Construct validity was determined by comparing self-report measures and the heart rate variability ratio collected in the Ava mobile application to previously validated measures. Acceptability was evaluated using the modified Acceptability of Health Apps among Adolescents Scale.

Results

Mean absolute percentage error was 11.4% for heart rate and 8.5% for sleep duration. There was no meaningful difference between the Ava bracelet, ActiGraph, and construct a measure of sleep quality. Compared to construct measures, Ava bracelet heart rate variability had a significant low negative correlation (r:−0.28), mood had a significant low positive correlation (r : 0.39), and physical activity level had a significant low (r_{level of physical activity}: 0.56) to moderate positive correlation (r_{MET−minutes/week}: 0.71). The acceptability of the Ava bracelet was high for fertility and low for pregnancy tracking.

Conclusion

Preliminary evidence suggests the Ava bracelet and mobile application estimates of sleep and heart rate are not equivalent to criterion measures in free-living conditions. Further research is needed to establish its utility for collecting prospective, subjective data throughout periods of preconception and pregnancy.

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Background

Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.

Objective

Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.

Methods

We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.

Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).

Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis

BACKGROUND

The COVID-19 pandemic has impacted multiple aspects of daily living, including behaviors associated with occupation, transportation, and health. It is unclear how these changes to daily living have impacted physical activity and sedentary behavior.

OBJECTIVE

In this study, we add to the growing body of research on the health impact of the COVID-19 pandemic by examining longitudinal changes in objectively measured daily physical activity and sedentary behavior among overweight or obese young adults participating in an ongoing weight loss trial in San Diego, California.

METHODS

Data were collected from 315 overweight or obese (BMI: range 25.0-39.9 kg/m²) participants aged from 18 to 35 years between November 1, 2019, and October 30, 2020, by using the Fitbit Charge 3 (Fitbit LLC). After conducting strict filtering to find valid data on consistent wear (>10 hours per day for ≥250 days), data from 97 participants were analyzed to detect multiple structural changes in time series of physical activity and sedentary behavior. An algorithm was designed to detect multiple structural changes. This allowed for the automatic identification and dating of these changes in linear regression models with CIs. The number of breakpoints in regression models was estimated by using the Bayesian information criterion and residual sum of squares; the optimal segmentation corresponded to the lowest Bayesian information criterion and residual sum of squares. To quantify the changes in each outcome during the periods identified, linear mixed effects analyses were conducted. In terms of key demographic characteristics, the 97 participants included in our analyses did not differ from the 210 participants who were excluded.

RESULTS

After the initiation of the shelter-in-place order in California on March 19, 2021, there were significant decreases in step counts (-2872 steps per day; 95% CI -2734 to -3010), light physical activity times (-41.9 minutes; 95% CI -39.5 to -44.3), and moderate-to-vigorous physical activity times (-12.2 minutes; 95% CI -10.6 to -13.8), as well as significant increases in sedentary behavior times (+52.8 minutes; 95% CI 47.0-58.5). The decreases were greater than the expected declines observed during winter holidays, and as of October 30, 2020, they have not returned to the levels observed prior to the initiation of shelter-in-place orders.

CONCLUSIONS

Among overweight or obese young adults, physical activity times decreased and sedentary behavior times increased concurrently with the implementation of COVID-19 mitigation strategies. The health conditions associated with a sedentary lifestyle may be additional, unintended results of the COVID-19 pandemic.

Using smartwatches to observe changes in activity during recovery from critical illness following COVID-19: a 1 year multi-centre observational study. (Preprint)

Background

As a sequela of the COVID-19 pandemic, a large cohort of critical illness survivors have had to recover in the context of ongoing societal restrictions.

Objective

We aimed to use smartwatches (Fitbit Charge 3; Fitbit LLC) to assess changes in the step counts and heart rates of critical care survivors following hospital admission with COVID-19, use these devices within a remote multidisciplinary team (MDT) setting to support patient recovery, and report on our experiences with this.

Methods

We conducted a prospective, multicenter observational trial in 8 UK critical care units. A total of 50 participants with moderate or severe lung injury resulting from confirmed COVID-19 were recruited at discharge from critical care and given a smartwatch (Fitbit Charge 3) between April and June 2020. The data collected included step counts and daily resting heart rates. A subgroup of the overall cohort at one site—the MDT site (n=19)—had their smartwatch data used to inform a regular MDT meeting. A patient feedback questionnaire and direct feedback from the MDT were used to report our experience. Participants who did not upload smartwatch data were excluded from analysis.

Results

Of the 50 participants recruited, 35 (70%) used and uploaded data from their smartwatch during the 1-year period. At the MDT site, 74% (14/19) of smartwatch users uploaded smartwatch data, whereas 68% (21/31) of smartwatch users at the control sites uploaded smartwatch data. For the overall cohort, we recorded an increase in mean step count from 4359 (SD 3488) steps per day in the first month following discharge to 7914 (SD 4146) steps per day at 1 year (P=.003). The mean resting heart rate decreased from 79 (SD 7) beats per minute in the first month to 69 (SD 4) beats per minute at 1 year following discharge (P<.001). The MDT subgroup’s mean step count increased more than that of the control group (176% increase vs 42% increase, respectively; +5474 steps vs +2181 steps, respectively; P=.04) over 1 year. Further, 71% (10/14) of smartwatch users at the MDT site and 48% (10/21) of those at the control sites strongly agreed that their Fitbit motivated them to recover, and 86% (12/14) and 48% (10/21), respectively, strongly agreed that they aimed to increase their activity levels over time.

Conclusions

This is the first study to use smartwatch data to report on the 1-year recovery of patients who survived a COVID-19 critical illness. This is also the first study to report on smartwatch use within a post–critical care MDT. Future work could explore the role of smartwatches as part of a randomized controlled trial to assess clinical and economic effectiveness.

International Registered Report Identifier (IRRID)

RR2-10.12968/ijtr.2020.0102

Induction of acute stress through an internet-delivered Trier Social Stress Test as assessed by photoplethysmography on a smartphone

Recent studies have demonstrated the feasibility of administering the Trier Social Stress Test (TSST) through the internet, with major implications for promoting inclusivity in research participation. However, online TSST studies to date are limited by a lack of control groups and the need for biological measures of stress reactivity that can be fully implemented online. Here, we test smartphone-based photoplethysmography as a measure of heart rate reactivity to an online variant of the TSST. Results demonstrate significant acceleration in heart rate and heightened self-reported stress and anxiety in the TSST condition relative to a placebo version of the TSST. The placebo condition led to a significant increase in self-reported stress and anxiety relative to baseline levels, but this increase was smaller in magnitude than that observed in the TSST condition. These findings highlight the potential for smartphone-based photoplethysmography in internet-delivered studies of cardiac reactivity and demonstrate that it is critical to utilize random assignment to a control or stressor condition when administering acute stress online.

Affective and Autonomic Reactivity During Parent-Child Interactions in Depressed and Non-Depressed Mothers and Their Adolescent Offspring

Depression presents risks that are profound and intergenerational, yet research on the association of depression with the physiological processes that might be associated with impaired mental and physical health has only recently been contextualized within the family environment. Participants in this multi-method case-control study were 180 mother-adolescent dyads (50% mothers with a history of depression treatment and current depressive symptoms). In order to examine the association between maternal depression and affective and autonomic reactivity amongst these mothers and their adolescent offspring we collected self-reported measures of positive and negative affect, as well as measures of cardiovascular and electrodermal autonomic activity, during mother-adolescent interaction tasks. Findings indicated that depressed mothers and their adolescent offspring exhibited greater self-reported negative affect reactivity during a problem-solving interaction and blunted (i.e., low) sympathetic activity as measured via skin conductance level across both interaction tasks. These effects remained significant after controlling for a range of potential covariates, including medication use, sex, age, adolescents own mental health symptoms, and behavior of the other interactant, along with correcting for multiple comparisons. Findings indicate that depressed mothers and their adolescent offspring both exhibit patterns of affect and physiology during interactions that are different from those of non-depressed mothers and their offspring, including increased negative affect reactivity during negative interactions and blunted sympathetic activity across both positive and negative interactions. These findings have potential implications for understanding the role of family processes in the intergenerational transmission of risk for depressive disorders.

Accuracy of Wrist-Worn Photoplethysmography Devices at Measuring Heart Rate in the Laboratory and During Free-Living Activities

This study compared heart rate (HR) measurements taken from two wrist-worn devices; the Empatica E4 and the Apple Watch Series 5, to that taken from a Polar H10 chest strap. Ten healthy adult volunteers took part in a laboratory validation study and performed a treadmill exercise protocol. A single-subject validity study was also conducted to evaluate the accuracy of continuous HR measurements obtained during free-living activities. The participant wore both wrist devices, as well as the Polar H10 for 12-hours, as she continued her habitual daily activities. The key findings of the laboratory study were that the Apple Watch was accurate at assessing HR compared to the Polar H10 with Mean Absolute Percentage Error (MAPE) values < 5% during treadmill exercise. The accuracy of the E4 however was generally poor with MAPE values > 15%. Findings from the single-subject validity study indicate that the Apple Watch produces accurate measurements of HR, whereas the E4 device overestimated HR, except for during the more strenuous activities undertaken where HR was underestimated.Clinical Relevance- The Apple Watch has acceptable accuracy in measuring HR during treadmill exercise and during free-living activities in healthy adult volunteers.

Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective

Digital phenotyping (DP) provides opportunities to study child and adolescent psychiatry from a novel perspective. DP combines objective data obtained from digital sensors with participant-generated "active data," in order to understand better an individual's behavior and environmental interactions. Although this new method has led to advances in adult psychiatry, its use in child psychiatry has been more limited. This review aims to demonstrate potential benefits of DP methodology and passive data collection by reviewing studies specifically in child and adolescent psychiatry. Twenty-six studies were identified that collected passive data from four different categories: accelerometer/actigraph data, physiological data, GPS data, and step count. Study topics ranged from the associations between manic symptomology and cardiac parameters to the role of daily emotions, sleep, and social interactions in treatment for pediatric anxiety. Reviewed studies highlighted the diverse ways in which objective data can augment naturalistic self-report methods in child and adolescent psychiatry to allow for more objective, ecologically valid, and temporally resolved conclusions. Though limitations exist-including a lack of participant adherence and device failure and misuse-DP technology may represent a new and effective method for understanding pediatric cognition, behavior, disease etiology, and treatment efficacy.

Challenges and Opportunities for Applying Wearable Technology to Sleep

Wearable technology has a history in sleep research dating back to the 1970s. Because modern wearable technology is relatively cheap and widely used by the general population, this represents an opportunity to leverage wearable devices to advance sleep medicine and research. However, there is a lack of published validation studies designed to quantify device performance against accepted gold standards, especially across different populations. Recommendations for conducting performance assessments and using wearable devices are now published with the goal of standardizing wearable device implementation and advancing the field.

Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

Wearable devices for tracking physical activity in the community after an acquired brain injury: A systematic review

OBJECTIVE

The application of wearable devices in individuals with acquired brain injury (ABI) resulting from stroke or traumatic brain injury (TBI) for monitoring physical activity (PA) has been relatively recent. The current systematic review aims to provide insights into the adaption of these devices, outcome metrics, and their transition from the laboratory to the community for PA monitoring of individuals with ABI.

LITERATURE SURVEY

The PubMed and Google Scholar databases were systematically reviewed using appropriate search terms. A total of 20 articles were reviewed from the past 15 years.

METHODOLOGY

Articles were classified into three categories - PA measurement studies, PA classification studies, and validation studies. The quality of studies was assessed using a quality appraisal checklist.

SYNTHESIS

It was found that the transition of wearable devices from in-lab to community-based studies in individuals with stroke has started but is not widespread. The transition of wearable devices in the community has not yet started for individuals with TBI. Accelerometer-based devices were more frequently chosen than pedometers and inertial measurement units. No consensus on a preferred wearable device (make or model) or wear location could be identified, though step count was the most common outcome metric. The accuracy and validity of most outcome metrics used in the community were not reported for many studies.

CONCLUSIONS

To facilitate future studies use wearable devices for PA measurement in the community, we recommend that researchers provide details on the accuracy and validity of the outcome metrics specific to the study environment. Once the accuracy and validity are established for a specific population, wearable devices and their derived outcomes can provide objective information on mobility impairment as well as the effect of rehabilitation in the community. This article is protected by copyright. All rights reserved.

Human-Centred Design Of A Digital Health Tool To Promote Effective Self-Care In Heart Failure Patients: Mixed Methods Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Modelling Human Activity using Smartphone Data

Background:

Over time, multichannel time series data were utilized for the purpose of modeling human activity. Instruments such as an accelerometer and gyroscope which had sensors embedded in them, recorded sensor data which were then utilized to record 6-axes, single dimensional convolution for the purpose of formulating a deep CNN. The resultant network achieved 94.79% activity recognition accuracy on raw sensor data, and 95.57% accuracy when Fast Fourier Transform (FFT) knowledge was added to the sensor data.

Objective:

This study helps to achieve an orderly report of daily Human activities for the overall balanced lifestyle of a healthy human being.

Methods:

Interfacing is done using Arduino Uno, Raspberry-Pi 3, heart rate sensor and accelerometer ADXL345 to generate real time values of day-to-day human activities such as walking, sleeping, climbing upstairs/downstairs and so on. Initially, the heart pulse of our four tested individuals is recorded and tabulated to depict and draw conclusions all the way from “Low BP” to “Heavy Exercise”. The convolution neural network is initially trained with an online human activity dataset and tested using our real time generated values which are sent to the MAC OS using a Bluetooth interface.

Results:

We obtain graphical representations of the amount of each activity performed by the test set of individuals, and in turn conclusions which suggest increase or decrease in the consistency of certain activities to the users, depicted through our developed iOS application, “Fitnesse”.

Conclusion:

The result of this works is used to improve the daily health routines and the overall lifestyle of distressed patients.

Feasibility, acceptability and validation of wearable devices for climate change and health research in the low-resource contexts of Burkina Faso and Kenya: Study protocol

As the epidemiological transition progresses throughout sub-Saharan Africa, life lived with diseases is an increasingly important part of a population's burden of disease. The burden of disease of climate-sensitive health outcomes is projected to increase considerably within the next decades. Objectively measured, reliable population health data is still limited and is primarily based on perceived illness from recall. Technological advances like non-invasive, consumer-grade wearable devices may play a vital role in alleviating this data gap and in obtaining insights on the disease burden in vulnerable populations, such as heat stress on human cardiovascular response. The overall goal of this study is to investigate whether consumer-grade wearable devices are an acceptable, feasible and valid means to generate data on the individual level in low-resource contexts. Three hundred individuals are recruited from the two study locations in the Nouna health and demographic surveillance system (HDSS), Burkina Faso, and the Siaya HDSS, Kenya. Participants complete a structured questionnaire that comprises question items on acceptability and feasibility under the supervision of trained data collectors. Validity will be evaluated by comparing consumer-grade wearable devices to research-grade devices. Furthermore, we will collect demographic data as well as the data generated by wearable devices. This study will provide insights into the usage of consumer-grade wearable devices to measure individual vital signs in low-resource contexts, such as Burkina Faso and Kenya. Vital signs comprising activity (steps), sleep (duration, quality) and heart rate (hr) are important measures to gain insights on individual behavior and activity patterns in low-resource contexts. These vital signs may be associated with weather variables-as we gather them from weather stations that we have setup as part of this study to cover the whole Nouna and Siaya HDSSs-in order to explore changes in behavior and other variables, such as activity, sleep, hr, during extreme weather events like heat stress exposure. Furthermore, wearable data could be linked to health outcomes and weather events. As a result, consumer-grade wearables may serve as a supporting technology for generating reliable measurements in low-resource contexts and investigating key links between weather occurrences and health outcomes. Thus, wearable devices may provide insights to better inform mitigation and adaptation interventions in these low-resource settings that are direly faced by climate change-induced changes, such as extreme weather events.

An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health

Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.

A Pilot Study of Blood Pressure Monitoring After Cardiac Surgery Using a Wearable, Non-invasive Sensor

Background: Continuous blood pressure (BP) measurement in intensive care units is based on arterial line (AL) transducers, sometimes associated with clinical complications. Our objective was to evaluate continuous BP measurements obtained from a non-invasive, wireless photoplethysmography (PPG)-based device using two distinct configurations (wristwatch and chest-patch monitors) compared to an AL. Methods: In this prospective evaluation study, comparison of the PPG-based devices to the AL was conducted in 10 patients immediately following cardiac surgery. Pulse rate (PR), systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) were recorded using both the AL and the PPG-based devices simultaneously for an average of 432 ± 290 min starting immediately after cardiac surgery. Bland-Altman plots and Pearson's correlations were used to assess the accuracy and degree of agreement between techniques. Results: A total of ~4,000 data points were included in the final analysis. AL measurements for PR, SBP, DBP and MAP were significantly (p < 0.001) and strongly correlated with both the wristwatch (r = 0.99, r = 0.94, r = 0.93 and r = 0.96, respectively) and the chest-patch (r = 0.99, r = 0.95, r = 0.93 and r = 0.95, respectively) monitors. Both configurations showed a marginal bias of <1 mmHg for BP measurements and <1 beat/min for PR [95% limits of agreement -3,3 beat/min; BP measurements: (-6)-(-10), 6-10 mmHg] compared to AL measurements. Conclusion: The PPG-based devices offer a high level of accuracy for cardiac-related parameters compared to an AL in post-cardiac surgery patients. Such devices could provide advanced monitoring capabilities in a variety of clinical settings, including immediate post-operative and intensive care unit settings. Clinical Trial Registration:www.clinicaltrials.gov, NCT03603860.

Smart wearable devices in cardiovascular care: where we are and how to move forward

Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.

Demonstrating the Applicability of Smartwatches in PM2.5 Health Impact Assessment

Smartwatches are being increasingly used in research to monitor heart rate (HR). However, it is debatable whether the data from smartwatches are of high enough quality to be applied in assessing the health impacts of air pollutants. The objective of this study was to assess whether smartwatches are useful complements to certified medical devices for assessing PM_2.5 health impacts. Smartwatches and medical devices were used to measure HR for 7 and 2 days consecutively, respectively, for 49 subjects in 2020 in Taiwan. Their associations with PM_2.5 from low-cost sensing devices were assessed. Good correlations in HR were found between smartwatches and certified medical devices (r_s > 0.6, except for exercise, commuting, and worshipping). The health damage coefficients obtained from smartwatches (0.282% increase per 10 μg/m³ increase in PM_2.5) showed the same direction, with a difference of only 8.74% in magnitude compared to those obtained from certified medical devices. Additionally, with large sample sizes, the health impacts during high-intensity activities were assessed. Our work demonstrates that smartwatches are useful complements to certified medical devices in PM_2.5 health assessment, which can be replicated in developing countries.

Symptom Monitoring in Children With Life-Threatening Illness: A Feasibility Study Using mHealth

Children with life-threatening illness (C-LTI) experience considerable symptom distress. Mobile technology may offer opportunities to better obtain symptom data that will lead to better symptom management. A mixed-methods study was conducted to explore the feasibility of monitoring and visualizing symptoms using 2 mobile health devices in C-LTI. Participants engaged with the Apple Watch 56% and recorded in the study app 63% of their study days. Our findings showed feasibility of using mobile technology for monitoring symptoms and further explored opportunities to visualize these data showing symptom occurrences, patterns, and trajectories in C-LTI.

Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study

Background

When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP.

Objective

We aimed to study the association of habitual physical activity with home BP.

Methods

Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure.

Results

We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI.

Conclusions

In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.

Identifying Mobile Sensing Indicators of Stress-Resilience

Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility without the perceived stigma of formal mental health assessments. In this work, we created a system to find indicators of resilience using passive wearable sensors and smartphone-delivered ecological momentary assessment (EMA). This system identified indicators of resilience during a medical internship, the high stress first-year of a residency program. We then created density estimation approaches to predict these indicators before mental health changes occurred, and validated whether the predicted indicators were also associated with resilience. Our system identified resilience indicators associated with physical activity (step count), sleeping behavior, reduced heart rate, increased mood, and reduced mood variability. Density estimation models were able to replicate a subset of the associations between sleeping behavior, heart rate, and resilience. To the best of our knowledge, this work provides the first methodology to identify and predict indicators of resilience using passive sensing and EMA. Researchers studying resident mental health can apply this approach to design resilience-building interventions and prevent mental health symptom development.

Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study

BACKGROUND

Cardiovascular diseases are a leading cause of death worldwide and result in significant economic costs to health care systems. The prevalence of cardiovascular conditions that require monitoring is expected to increase as the average age of the global population continues to rise. Although an accurate cardiac assessment can be performed at medical centers, frequent visits for assessment are not feasible for most people, especially those with limited mobility. Monitoring of vital signs at home is becoming an increasingly desirable, accessible, and practical alternative. As wearable devices are not the ideal solution for everyone, it is necessary to develop parallel and complementary approaches.

OBJECTIVE

This research aims to develop a zero-effort, unobtrusive, cost-effective, and portable option for home-based ambient heart rate monitoring.

METHODS

The prototype seat cushion uses load cells to acquire a user's ballistocardiogram (BCG). The analog signal from the load cells is amplified and filtered by a signal-conditioning circuit before being digitally recorded. A pilot study with 20 participants was conducted to analyze the prototype's ability to capture the BCG during five real-world tasks: sitting still, watching a video on a computer screen, reading, using a computer, and having a conversation. A novel algorithm based on the continuous wavelet transform was developed to extract the heart rate by detecting the largest amplitude values (J-peaks) in the BCG signal.

RESULTS

The pilot study data showed that the BCG signals from all five tasks had sufficiently large portions to extract heart rate. The continuous wavelet transform-based algorithm for J-peak detection demonstrated an overall accuracy of 91.4% compared with electrocardiography. Excluding three outliers that had significantly noisy BCG data, the algorithm achieved 94.6% accuracy, which was aligned with that of wearable devices.

CONCLUSIONS

This study suggests that BCG acquired through a seat cushion is a viable alternative to wearable technologies. The prototype seat cushion presented in this study is an example of a relatively accessible, affordable, portable, and unobtrusive zero-effort approach to achieve frequent home-based ambient heart rate monitoring.

Feasibility of a Waistband-Type Wireless Wearable Electrocardiogram Monitoring System Based on a Textile Electrode: Development and Usability Study

BACKGROUND

Electrocardiogram (ECG) monitoring in daily life is essential for effective management of cardiovascular disease, a leading cause of death. Wearable ECG measurement systems in the form of clothing have been proposed to replace Holter monitors used for clinical ECG monitoring; however, they have limitations in daily use because they compress the upper body and, in doing so, cause discomfort during wear.

OBJECTIVE

The purpose of this study was to develop a wireless wearable ECG monitoring system that includes a textile ECG electrode that can be applied to the lining of pants and can be used in the same way that existing lower clothing is worn, without compression to the upper body.

METHODS

A textile electrode with stretchable characteristics was fabricated by knitting a conductive yarn together with polyester-polyurethane fiber, which was then coated with silver compound; an ECG electrode was developed by placing it on an elastic band in a modified limb lead configuration. In addition, a system with analog-to-digital conversion, wireless communication, and a smartphone app was developed, allowing users to be able to check and store their own ECGs in real time. A signal processing algorithm was also developed to remove noise from the obtained signal and to calculate the heart rate. To evaluate the ECG and heart rate measurement performance of the developed module, a comparative evaluation with a commercial device was performed. ECGs were measured for 5 minutes each in standing, sitting, and lying positions; the mean absolute percentage errors of heart rates measured with both systems were then compared.

RESULTS

The system was developed in the form of a belt buckle with a size of 53 × 45 × 12 mm (width × height × depth) and a weight of 23 g. In a qualitative evaluation, it was confirmed that the P-QRS-T waveform was clearly observed in ECGs obtained with the wearable system. From the results of the heart rate estimation, the developed system could track changes in heart rate as calculated by a commercial ECG measuring device; in addition, the mean absolute percentage errors of heart rates were 1.80%, 2.84%, and 2.48% in the standing, sitting, and lying positions, respectively.

CONCLUSIONS

The developed system was able to effectively measure ECG and calculate heart rate simply through being worn as existing clothing without upper body pressure. It is anticipated that general usability can be secured through further evaluation under more diverse conditions.

A Breathing Sonification System to Reduce Stress During the COVID-19 Pandemic

Since sound and music are powerful forces and drivers of human behavior and physiology, we propose the use of sonification to activate healthy breathing patterns in participants to induce relaxation. Sonification is often used in the context of biofeedback as it can represent an informational, non-invasive and real-time stimulus to monitor, motivate or modify human behavior. The first goal of this study is the proposal and evaluation of a distance-based biofeedback system using a tempo- and phase-aligned sonification strategy to adapt breathing patterns and induce states of relaxation. A second goal is the evaluation of several sonification stimuli on 18 participants that were recruited online and of which we analyzed psychometric and behavioral data using, respectively questionnaires and respiration rate and ratio. Sonification stimuli consisted of filtered noise mimicking a breathing sound, nature environmental sounds and a musical phrase. Preliminary results indicated the nature stimulus as most pleasant and as leading to the most prominent decrease of respiration rate. The noise sonification had the most beneficial effect on respiration ratio. While further research is needed to generalize these findings, this study and its methodological underpinnings suggest the potential of the proposed biofeedback system to perform ecologically valid experiments at participants' homes during the COVID-19 pandemic.

Heart rate detection by Fitbit ChargeHR™ : A validation study versus portable polysomnography

Consumer “Smartbands” can collect physiological parameters, such as heart rate (HR), continuously across the sleep–wake cycle. Nevertheless, the quality of HR data detected by such devices and their place in the research and clinical field is debatable, as they are rarely rigorously validated. The objective of the present study was to investigate the reliability of pulse photoplethysmographic detection by the Fitbit ChargeHR^™ (FBCHR, Fitbit Inc.) in a natural setting of continuous recording across vigilance states. To fulfil this aim, concurrent portable polysomnographic (pPSG) and the Fitbit’s photoplethysmographic data were collected from a group of 25 healthy young adults, for ≥12 hr. The pPSG‐derived HR was automatically computed and visually verified for each 1‐min epoch, while the FBCHR HR measurements were downloaded from the application programming interface provided by the manufacturer. The FBCHR was generally accurate in estimating the HR, with a mean (SD) difference of −0.66 (0.04) beats/min (bpm) versus the pPSG‐derived HR reference, and an overall Pearson’s correlation coefficient (r) of 0.93 (average per participant r = 0.85 ± 0.11), regardless of vigilance state. The correlation coefficients were larger during all sleep phases (rapid eye movement, r = 0.9662; N1, r = 0.9918; N2, r = 0.9793; N3, r = 0.9849) than in wakefulness (r = 0.8432). Moreover, the correlation coefficient was lower for HRs of >100 bpm (r = 0.374) than for HRs of <100 bpm (r = 0.84). Consistently, Bland–Altman analysis supports the overall higher accuracy in the detection of HR during sleep. The relatively high accuracy of FBCHR pulse rate detection during sleep makes this device suitable for sleep‐related research applications in healthy participants, under free‐living conditions.

Repeatability and accuracy of fingertip pulse oximeters for measurement of hemoglobin oxygen saturation in arterial blood and pulse rate in anesthetized dogs breathing 100% oxygen

OBJECTIVE

To evaluate the repeatability and accuracy of fingertip pulse oximeters (FPO) for measurement of hemoglobin oxygen saturation in arterial blood and pulse rate (PR) in anesthetized dogs breathing 100% O₂.

ANIMALS

29 healthy client-owned anesthetized dogs undergoing various surgical procedures.

PROCEDURES

In randomized order, each of 7 FPOs or a reference pulse oximeter (PO) was applied to the tongue of each intubated anesthetized dog breathing 100% O₂. Duplicate measurements of oxygen saturation (Spo₂) and PR were obtained within 60 seconds of applying an FPO or PO. A nonparametric version of Bland-Altman analysis was used. Coefficient of repeatability was the interval between the 5th and 95th percentiles of the differences between duplicate measurements. Bias was the median difference, and the limits of agreement were the 5th and 95th percentiles of the differences between each FPO and the PO. Acceptable values for the coefficient of repeatability of Spo₂ were ≤ 6%. Agreements were accepted if the limits of agreement had an absolute difference of ≤ ± 3% in Spo₂ and relative difference of ≤ ± 10% in PR.

RESULTS

Coefficient of repeatability for Spo₂ was acceptable for 5 FPOs, but the limits of agreement for Spo₂ were unacceptable for all FPOs. The limits of agreement for PR were acceptable for 2 FPOs.

CONCLUSIONS AND CLINICAL RELEVANCE

Results suggested that some FPOs may be suitable for accurately monitoring PRs of healthy anesthetized dogs breathing 100% O₂, but mild underestimation of Spo₂ was common.

An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.

Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability

Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.

Validity of the Polar Vantage M watch when measuring heart rate at different exercise intensities

BACKGROUND

The use of wrist worn wearable fitness trackers has been growing rapidly over the last decade. The growing popularity can be partly attributed to the improvements in technology, making activity trackers more affordable, comfortable and convenient for use in different fitness and environmental applications. Fitness trackers typically monitor activity level, track steps, distance, heart rate (HR), sleep, peripheral capillary oxygen saturation and more, as the technology continuously is advancing. In terms of measuring HR, photoplethysmography (PPG) is a relatively new technology utilised in wearables. PPG estimates HR through an optical technique that monitors changes in blood volume beneath the skin. With these new products becoming available it is important that the validity of these devices be evaluated. Therefore, the aim of this study was to assess the validity of the Polar Vantage M (PVM) watch to measure HR compared to medical grade ECG on a healthy population during a range of treadmill exercise intensities.

METHODS

A total of 30 healthy participants (n = 17 males, n = 13 females) were recruited for this study. The validity of the PVM watch to measure HR was compared against the gold standard 5-lead ECG. The study was conducted on 2 separate testing days with 24-48 h between sessions. Participants completed the Bruce Treadmill Protocol, and HR was measured every 30 s. Validation of the PVM watch in comparison to the ECG was measured with an Intraclass Correlation Coefficient (ICC) and associated 95% confidence intervals (CI) and levels of agreement were identified with Bland-Altman plots with 90% limits of agreement. Linear regression analysis was performed to calculate the value of r 2 computing the variation of HR obtained by the PVM watch and ECG.

RESULTS

In total, 30 participants completed the protocol, with data from 28 participants utilised for statistical analysis (16 males, 14 females, 26.10 ± 3.39 years, height 52.36 m ± 7.40 cm, mass 73.59 ± 11.90 kg). A strong and significant correlation was found between the PVM watch and ECG, demonstrating good criterion validity (p < 0.05, r 2 = 0.87). Good validity was seen for day 1 and day 2 for stage 0 (ICC = 0.83; 95% CI [0.63-0.92], ICC = 0.74; 95% CI [0.37-0.88]), stage 1 (ICC = 0.78; 95% CI [0.52-0.90], ICC = 0.88; 95% CI [0.74-0.95]), and stage 2 (ICC = 0.88; 95% CI [0.73-0.94], ICC = 0.80; 95% CI [0.40-0.92]). Poor validity was demonstrated on day 1 and day 2 for stages 3-5 (ICC < 0.50).

CONCLUSION

This study revealed that the PVM watch had a strong correlation with the ECG throughout the entire Bruce Protocol, however the level of agreement (LoA) becomes widely dispersed as exercise intensities increased. Due to the large LoA between the ECG and PVM watch, it is not advisable to use this device in clinical populations in which accurate HR measures are essential for patient safety; however, the watch maybe used in settings where less accurate HR is not critical to an individual's safety while exercising.

Psychobiological markers of allostatic load in depressed and nondepressed mothers and their adolescent offspring

BACKGROUND

A substantial body of research has emerged suggesting that depression is strongly linked to poor physical health outcomes, which may be partly due to increased allostatic load across stress response systems. Interestingly, health risks associated with depression are also borne by the offspring of depressed persons. Our aim was to simultaneously investigate whether maternal depression is associated not only with increased allostatic load across cardiac control, inflammation, cellular aging, but also if this is transmitted to adolescent children, possibly increasing the risk for early onset of psychiatric conditions and disease in these offspring.

METHODS

A preregistered, case-control study of 180 low-income mothers (50% mothers depressed, 50% mothers nondepressed) and their adolescent offspring was conducted to determine how depressed mothers and their adolescent offspring systematically differ in terms of autonomic, sympathetic, and parasympathetic cardiac control; inflammation; cellular aging; and behavioral health in offspring, which are indicators suggestive of higher allostatic load.

RESULTS

Findings indicate that depressed mothers and their adolescent offspring differ in terms of comorbid mental and physical health risk profiles that are suggestive of higher allostatic load. Findings indicate that depressed mothers exhibit elevated resting heart rate and decreased heart rate variability, and adolescent offspring of depressed mothers exhibit greater mental health symptoms, elevated heart rate, and accelerated biological aging (shorter telomeres). These effects persisted after controlling for a range of potential covariates, including medication use, sex, age, and adolescents' own mental health symptoms.

CONCLUSIONS

Findings indicate that maternal depression is associated with increased allostatic load in depressed women and their adolescent children, possibly increasing risk for early onset of psychiatric conditions and disease in these offspring. Future research is needed to delineate why some biological systems are more impacted than others and to explore how findings might inform preventative programs targeted at adolescent offspring of depressed mothers.

Effects of aerobic walking on cognitive function in patients with schizophrenia: A randomized controlled trial

Cognitive deficits, which are core manifestations in schizophrenia and exhibit a limited response to antipsychotic treatment, contribute to poor treatment outcomes and functional disability. Evidence on the effect of aerobic walking (AW) and exercise intensity on cognitive function in patients with schizophrenia is lacking. In total, 79 patients with schizophrenia were recruited for a 12-week randomized control trial and allocated to the treatment-as-usual (TAU, n = 38) and treatment-as-usual plus AW (TAW, n = 39) groups. The TAW participants joined a supervised 12-week AW program consisting of 30-min sessions five times per week while wearing a Fitbit Charge 2 device. Cognitive function was evaluated using the Brief Assessment of Cognition in Schizophrenia. After randomization, 67 (34 TAU and 33 TAW) participants joined the 12-week trial and were included in the intention-to-treat analysis. Multivariate general linear model repeated measures analysis revealed no significant time × group interaction effect on cognitive function changes between the TAU and TAW groups and a marginally significant group effect on verbal fluency (p = 0.09). The interaction effect of time and treatment group on verbal fluency (p = 0.05) was marginally significant between the high and low AW intensity groups, whereas a significant group effect on attention and processing speed (p = 0.04) was observed. Supervised 12-week AW of moderate intensity may have potential cognitive benefits for patients with schizophrenia.

Rapid, user-friendly, and inexpensive detection of azidothymidine

Strict adherence to highly active antiretroviral therapy (HAART) is very important to improve the quality of life for HIV-positive patients to reduce new infections and determine treatment success. Azidothymidine (AZT) is an antiretroviral drug commonly used in HAART treatment. In this research, an "add, mix, and measure" assay was developed to detect AZT within minutes. Three different probes designed to release fluorophores when samples containing AZT are added were synthesized and characterized. The limit of detection to AZT in simulated urine samples was determined to be 4 μM in 5 min for one of the probes. This simple and rapid point-of-care test could potentially be used by clinicians and health care workers to monitor the presence of AZT in low resource settings.

Wrist-Worn Activity Trackers in Laboratory and Free-Living Settings for Patients With Chronic Pain: Criterion Validity Study

BACKGROUND

Physical activity is evidently a crucial part of the rehabilitation process for patients with chronic pain. Modern wrist-worn activity tracking devices seemingly have a great potential to provide objective feedback and assist in the adoption of healthy physical activity behavior by supplying data of energy expenditure expressed as metabolic equivalent of task units (MET). However, no studies of any wrist-worn activity tracking devices' have examined criterion validity in estimating energy expenditure, heart rate, or step count in patients with chronic pain.

OBJECTIVE

The aim was to determine the criterion validity of wrist-worn activity tracking devices for estimations of energy expenditure, heart rate, and step count in a controlled laboratory setting and free-living settings for patients with chronic pain.

METHODS

In this combined laboratory and field validation study, energy expenditure, heart rate, and step count were simultaneously estimated by a wrist-worn activity tracker (Fitbit Versa), indirect calorimetry (Jaeger Oxycon Pro), and a research-grade hip-worn accelerometer (ActiGraph GT3X) during treadmill walking at 3 speeds (3.0 km/h, 4.5 km/h, and 6.0 km/h) in the laboratory setting. Energy expenditure and step count were also estimated by the wrist-worn activity tracker in free-living settings for 72 hours. The criterion validity of each measure was determined using intraclass and Spearman correlation, Bland-Altman plots, and mean absolute percentage error. An analysis of variance was used to determine whether there were any significant systematic differences between estimations.

RESULTS

A total of 42 patients (age: 25-66 years; male: 10/42, 24%; female: 32/42, 76%), living with chronic pain (duration, in years: mean 9, SD 6.72) were included. At baseline, their mean pain intensity was 3.5 (SD 1.1) out of 6 (Multidimensional Pain Inventory, Swedish version). Results showed that the wrist-worn activity tracking device (Fitbit Versa) systematically overestimated energy expenditure when compared to the criterion standard (Jaeger Oxycon Pro) and the relative criterion standard (ActiGraph GT3X). Poor agreement and poor correlation were shown between Fitbit Versa and both Jaeger Oxycon Pro and ActiGraph GT3X for estimated energy expenditure at all treadmill speeds. Estimations of heart rate demonstrated poor to fair agreement during laboratory-based treadmill walks. For step count, the wrist-worn devices showed fair agreement and fair correlation at most treadmill speeds. In free-living settings; however, the agreement for step count between the wrist-worn device and waist-worn accelerometer was good, and the correlation was excellent.

CONCLUSIONS

The wrist-worn device systematically overestimated energy expenditure and showed poor agreement and correlation compared to the criterion standard (Jaeger Oxycon Pro) and the relative criterion standard (ActiGraph GT3X), which needs to be considered when used clinically. Step count measured with a wrist-worn device, however, seemed to be a valid estimation, suggesting that future guidelines could include such variables in this group with chronic pain.

Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network

Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.

Evidence-Based Approach to Palpitations

Palpitations are a common presenting symptom in primary care, yet their cause can be difficult to diagnose due to their intermittent and sometimes infrequent nature. All patients presenting with a chief complaint of palpitations should undergo a detailed history, physical examination, and electrocardiogram (ECG). This alone can yield a probable diagnosis. Limited laboratory testing, ambulatory ECG monitoring, and cardiology referral are sometimes indicated. This article reviews current data and guidelines on how to evaluate palpitations in the primary care setting.

BWDAT: A research tool for analyzing the consumption of VOD content at home

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BWDAT is a reliable tool that facilitates the study of viewing experience on VOD platforms.

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Collects users’ physiological data and users’ interactions with Netflix interface.

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Non-intrusive and easy to use, successfully used in long-term projects with more than 200 users.

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Includes a graphical display of the viewing sessions to help researchers visualize the data.

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Includes an automatic report generator and data exporter for multiple platforms.

Introduction

New approaches to the study of the binge-watching phenomenon require new technology, leading to the development of a non-intrusive and low-cost analytical research software that facilitates a holistic understanding of binge-watching in an uncontrolled environment remotely (e.g., the home). BWDAT was developed to allow the collection of three types of data: users’ physiological data gathered from a smartwatch, users’ interactions from video-on-demand interfaces, and self-reported data. This tool offers the possibility to generate automatic data analysis reports, facilitating researchers’ data analysis tasks.

Methods

Two trial studies and a long-term study were used to evaluate the design and the technical implementation of the BWDAT tool. The metrics used were the BWDAT smartwatch’s App data coverage of the viewing sessions, and the data’s reliability of the viewer’s interactions with the Netflix interface, collected by the BWDAT Chrome Extension.

Results

High percentages of data coverage and content coverage were verified in the sessions collecting the smartwatch’s data. The reporting system developed proved to be useful in the collection and synchronization of physiological and users’ interaction data with Netflix interface, both generated in uncontrolled environments. Furthermore, the BWDAT tool facilitated the analysis of a large amount of nuanced data.

Conclusion

The results obtained confirm the reliability, accuracy, and usability of BWDAT. This tool has the potential to help researchers shed new light on the field of media and audience studies, and in particular on binge-watching.

Multilevel mobile health approach to improve cardiovascular health in resource-limited communities with Step It Up: a randomised controlled trial protocol targeting physical activity

INTRODUCTION

Although physical activity (PA) reduces cardiovascular disease (CVD) risk, physical inactivity remains a pressing public health concern, especially among African American (AA) women in the USA. PA interventions focused on AA women living in resource-limited communities with scarce PA infrastructure are needed. Mobile health (mHealth) technology can increase access to PA interventions. We describe the development of a clinical protocol for a multilevel, community-based, mHealth PA intervention for AA women.

METHODS AND ANALYSIS

An mHealth intervention targeting AA women living in resource-limited Washington, DC communities was developed based on the socioecological framework for PA. Over 6 months, we will use a Sequential Multi-Assignment, Randomized Trial approach to compare the effects on PA of location-based remote messaging (named 'tailored-to-place') to standard remote messaging in an mHealth intervention. Participants will be randomised to a remote messaging intervention for 3 months, at which point the intervention strategy will adapt based on individuals' PA levels. Those who do not meet the PA goal will be rerandomised to more intensive treatment. Participants will be followed for another 3 months to determine the contribution of each mHealth intervention to PA level. This protocol will use novel statistical approaches to account for the adaptive strategy. Finally, effects of PA changes on CVD risk biomarkers will be characterised.

ETHICS AND DISSEMINATION

This protocol has been developed in partnership with a Washington, DC-area community advisory board to ensure feasibility and acceptability to community members. The National Institutes of Health Intramural IRB approved this research and the National Heart, Lung, and Blood Institute provided funding. Once published, results of this work will be disseminated to community members through presentations at community advisory board meetings and our quarterly newsletter.

TRIAL REGISTRATION NUMBER

NCT03288207.

Validation of Garmin Fenix 3 HR Fitness Tracker Biomechanics and Metabolics (VO2max)

The purpose of this study was to determine the validity of the Garmin fēnix® 3 HR fitness tracker. Methods: A total of 34 healthy recreational runners participated in biomechanical or metabolic testing. Biomechanics participants completed three running conditions (flat, incline, and decline) at a self-selected running pace, on an instrumented treadmill while running biomechanics were tracked using a motion capture system. Variables extracted were compared with data collected by the Garmin fēnix 3 HR (worn on the wrist) that was paired with a chest heart rate monitor and a Garmin Foot Pod (worn on the shoe). Metabolic testing involved two separate tests; a graded exercise test to exhaustion utilizing a metabolic cart and treadmill, and a 15-min submaximal outdoor track session while wearing the Garmin. 2 × 3 analysis of variances with post hoc t tests, mean absolute percentage errors, Pearson’s correlation (R), and a t test were used to determine validity. Results: The fēnix kinematics had a mean absolute percentage errors of 9.44%, 0.21%, 26.38%, and 5.77% for stride length, run cadence, vertical oscillation, and ground contact time, respectively. The fēnix overestimated (p < .05) VO2max with a mean absolute percentage error of 8.05% and an R value of .917. Conclusion: The Garmin fēnix 3 HR appears to produce a valid measure of run cadence and ground contact time during running, while it overestimated vertical oscillation in every condition (p < .05) and should be used with caution when determining stride length. The fēnix appears to produce a valid VO2max estimate and may be used when more accurate methods are not available.

Present and Future of Digital Health in Diabetes and Metabolic Disease

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

Validity and Reliability of Physiological Data in Applied Settings Measured by Wearable Technology: A Rapid Systematic Review

The purpose of this review was to evaluate the current state of the literature and to identify the types of study designs, wearable devices, statistical tests, and exercise modes used in validation and reliability studies conducted in applied settings/outdoor environments. This was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We identified nine articles that fit our inclusion criteria, eight of which tested for validity and one tested for reliability. The studies tested 28 different devices with exercise modalities of running, walking, cycling, and hiking. While there were no universally common analytical techniques used to measure accuracy or validity, correlative measures were used in 88% of studies, mean absolute percentage error (MAPE) in 75%, and Bland–Altman plots in 63%. Intra-class correlation was used to determine reliability. There were not any universally common thresholds to determine validity, however, of the studies that used MAPE and correlation, there were only five devices that had a MAPE of < 10% and a correlation value of > 0.7. Overall, the current review establishes the need for greater testing in applied settings when validating wearables. Researchers should seek to incorporate multiple intensities, populations, and modalities into their study designs while utilizing appropriate analytical techniques to measure and determine validity and reliability.