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

Smartphone Photoplethysmography Pulse Rate Covaries With Stress and Anxiety During a Digital Acute Social Stressor

OBJECTIVE

Heart rate is a transdiagnostic correlate of affective states and the stress diathesis model of health. Although most psychophysiological research has been conducted in laboratory environments, recent technological advances have provided the opportunity to index pulse rate dynamics in real-world environments with commercially available mobile health and wearable photoplethysmography (PPG) sensors that allow for improved ecologically validity of psychophysiological research. Unfortunately, adoption of wearable devices is unevenly distributed across important demographic characteristics, including socioeconomic status, education, and age, making it difficult to collect pulse rate dynamics in diverse populations. Therefore, there is a need to democratize mobile health PPG research by harnessing more widely adopted smartphone-based PPG to both promote inclusivity and examine whether smartphone-based PPG can predict concurrent affective states.

METHODS

In the current preregistered study with open data and code, we examined the covariation of smartphone-based PPG and self-reported stress and anxiety during an online variant of the Trier Social Stress Test, as well as prospective relationships between PPG and future perceptions of stress and anxiety in a sample of 102 university students.

RESULTS

Smartphone-based PPG significantly covaries with self-reported stress and anxiety during acute digital social stressors. PPG pulse rate was significantly associated with concurrent self-reported stress and anxiety ( b = 0.44, p = .018) as well as prospective stress and anxiety at the subsequent time points, although the strength of this association diminished the farther away pulse rate got from self-reported stress and anxiety (lag 1 model: b = 0.42, p = .024; lag 2 model: b = 0.38, p = .044).

CONCLUSIONS

These findings indicate that PPG provides a proximal measure of the physiological correlates of stress and anxiety. Smartphone-based PPG can be used as an inclusive method for diverse populations to index pulse rate in remote digital study designs.

Commercial Smart Watches and Heart Rate Monitors: A Concurrent Validity Analysis

ABSTRACT

Montalvo, S, Martinez, A, Arias, S, Lozano, A, Gonzalez, MP, Dietze-Hermosa, MS, Boyea, BL, and Dorgo, S. Smartwatches and commercial heart rate monitors: a concurrent validity analysis. J Strength Cond Res 37(9): 1802-1808, 2023-The purpose of this study was to explore the concurrent validity of 2 commercial smartwatches (Apple Watch Series 6 and 7) against a clinical criterion device (12-lead electrocardiogram [ECG]) and a field criterion device (Polar H-10) during exercise. Twenty-four male collegiate football players and 20 recreationally active young adults (10 men and 10 women) were recruited and participated in a treadmill-based exercise session. The testing protocol included 3 minutes of standing still (resting), then walking at low intensity, jogging at a moderate intensity, running at a high intensity, and postexercise recovery. The intraclass correlation (ICC 2,k ), and Bland-Altman plot analyses showed a good validity of the Apple Watch Series 6 and Series 7 with increased error (bias) as jogging and running speed increased in the football and recreational athletes. The Apple Watch Series 6 and 7 are highly valid smartwatches at rest and different exercise intensities, with validity decreasing with increased running speed. Strength and conditioning professionals and athletes can confidently use the Apple Watch Series 6 and 7 when tracking heart rate; however, caution must be taken when running at moderate or higher speeds. The Polar H-10 can surrogate a clinical ECG for practical applications.

Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults

Importance

Advancements in technology, including mobile-based ecological momentary assessments (EMAs) and passive sensing, have immense potential to identify short-term suicide risk. However, the extent to which EMA and passive data, particularly in combination, have utility in detecting short-term risk in everyday life remains poorly understood.

Objective

To examine whether and what combinations of self-reported EMA and sensor-based assessments identify next-day suicidal ideation.

Design, Setting, and Participants

In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily and wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, including penalized generalized estimating equations and classification and regression trees (CARTs) with repeated 5-fold cross-validation, were used to optimize prediction of next-day suicidal ideation based on time-varying features from EMAs (affective, cognitive, behavioral risk factors) and sensor data (sleep, activity, heart rate). Young adult patients who visited an emergency department with recent suicidal ideation and/or suicide attempt were recruited. Identified via electronic health record screening, eligible individuals were contacted remotely to complete enrollment procedures. Participants (aged 18 to 25 years) completed 14 708 EMA observations (64.4% adherence) and wore a sensor wristband approximately half the time (55.6% adherence). Data were collected between June 2020 and July 2021. Statistical analysis was performed from January to March 2023.

Main Outcomes and Measures

The outcome was presence of next-day suicidal ideation.

Results

Among 102 enrolled participants, 83 (81.4%) were female; 6 (5.9%) were Asian, 5 (4.9%) were Black or African American, 9 (8.8%) were more than 1 race, and 76 (74.5%) were White; mean (SD) age was 20.9 (2.1) years. The best-performing model incorporated features from EMAs and showed good predictive accuracy (mean [SE] cross-validated area under the receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas the model that incorporated features from sensor data alone showed poor prediction (mean [SE] cross-validated AUC, 0.56 [0.02]). Sensor-based features did not improve prediction when combined with EMAs. Suicidal ideation-related features were the strongest predictors of next-day ideation. When suicidal ideation features were excluded, an alternative EMA model had acceptable predictive accuracy (mean [SE] cross-validated AUC, 0.76 [0.02]). Both EMA models included features at different timescales reflecting within-day, end-of-day, and time-varying cumulative effects.

Conclusions and Relevance

In this prognostic study, self-reported risk factors showed utility in identifying near-term suicidal thoughts. Best-performing models required self-reported information, derived from EMAs, whereas sensor-based data had negligible predictive accuracy. These results may have implications for developing decision algorithms identifying near-term suicidal thoughts to guide risk monitoring and intervention delivery in everyday life.

Assessment of Noninvasive Oxygen Saturation in Patients With COPD During Pulmonary Rehabilitation: Smartwatch versus Pulse Oximeter

BACKGROUND

Patients with COPD can have hypoxemia; hence, monitoring peripheral SpO2 during pulmonary rehabilitation is recommended. This study aimed to examine the accuracy of SpO2 readings in patients with COPD as measured by wearable devices at rest and after physical exercise.

METHODS

Thirty-six participants with COPD (20 women), ages 52-89 years, participated in this cross-sectional study. Oxygen saturation was concurrently measured by using the Contec Pulse Oximeter CMS50D as a comparator, and the Apple Watch Series 7 and the Garmin Vivosmart 4 at rest and immediately after the 30-s sit-to-stand test and the 6-min walk test (6MWT).

RESULTS

For the Apple Watch, the root mean squared error showed a deviation of 3.5% at rest, 4.1% after the 30-s sit-to-stand test, and 3.9% after the 6MWT. The level of agreement was 2.8 ± 2.4 (7.6, -1.9) at rest, 3.1 ± 2.8 (8.6, -2.3) after the 30-s sit-to-stand test, and 2.8 ± 2.9 (8.6, -2.9) after the 6MWT. For the Garmin Vivosmart, the root mean squared error showed a deviation of 3.3% at rest, 6.1% after the 30-s sit-to-stand test, and 5.4% after the 6MWT. Level of agreement was 1.9 ± 2.7 (7.2, -3.3) at rest, 2.9 ± 5.4 (13.5, -7.7) after the 30-s sit-to-stand test, and 2.3 ± 5.0 (12.1, -7.4) after the 6MWT. The limits of agreement showed considerable measurement variance and a tendency for the devices to be less accurate at lower saturation levels.

CONCLUSIONS

The Apple Watch Series 7 and Garmin Vivosmart 4 overestimated SpO2 in participants with COPD when SpO2 was < 95% and underestimated oxygen saturation when saturation was > 95%. These findings suggest that wearable devices should not be used to monitor oxygen saturation during pulmonary rehabilitation.

Validation of Wearable Digital Devices for Heart Rate Measurement During Exercise Test in Patients With Coronary Artery Disease

OBJECTIVE

To assess the accuracy of recently commercialized wearable devices in heart rate (HR) measurement during cardiopulmonary exercise test (CPX) under gradual increase in exercise intensity, while wearable devices with HR monitors are reported to be less accurate in different exercise intensities.

METHODS

CPX was performed for patients with coronary artery disease (CAD). Twelve lead electrocardiograph (ECG) was the gold standard and Apple watch 7 (AW7), Galaxy watch 4 (GW4) and Bio Patch Mobicare 200 (MC200) were applied for comparison. Paired absolute difference (PAD), mean absolute percentage error (MAPE) and intraclass correlation coefficient (ICC) were evaluated for each device.

RESULTS

Forty-four participants with CAD were included. All the devices showed MAPE under 2% and ICC above 0.9 in rest, exercise and recovery phases (MC200=0.999, GW4=0.997, AW7=0.998). When comparing exercise and recovery phase, PAD of MC200 and AW7 in recovery phase were significantly bigger than PAD of exercise phase (p<0.05). Although not significant, PAD of GW4 tended to be bigger in recovery phase, too. Also, when stratified by HR 20, ICC of all the devices were highest under HR of 100, and ICC decreased as HR increased. However, except for ICC of GW4 at HR above 160 (=0.867), all ICCs exceeded 0.9 indicating excellent accuracy.

CONCLUSION

The HR measurement of the devices validated in this study shows a high concordance with the ECG device, so CAD patients may benefit from the devices during high-intensity exercise under conditions where HR is measured reliably.

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

The increase in the incidence of cardiovascular diseases worldwide raises concerns about the urgent need to increase definite measures for the self-determination of different parameters, especially those defining cardiac function. Heart rate variability (HRV) is a non-invasive method used to evaluate autonomic nervous system modulation on the cardiac sinus node, thus describing the oscillations between consecutive electrocardiogram R-R intervals. These fluctuations are undetectable except when using specialized devices, with ECG Holter monitoring considered the gold standard. HRV is considered an independent biomarker for measuring cardiovascular risk and for screening the occurrence of both acute and chronic heart diseases. Also, it can be an important predictive factor of frailty or neurocognitive disorders, like anxiety and depression. An increased HRV is correlated with rest, exercise, and good recovery, while a decreased HRV is an effect of stress or illness. Until now, ECG Holter monitoring has been considered the gold standard for determining HRV, but the recent decade has led to an accelerated development of technology using numerous devices that were created specifically for the pre-hospital self-monitoring of health statuses. The new generation of devices is based on the use of photoplethysmography, which involves the determination of blood changes at the level of blood vessels. These devices provide additional information about heart rate (HR), blood pressure (BP), peripheral oxygen saturation (SpO²), step counting, physical activity, and sleep monitoring. The most common devices that have this technique are smartwatches (used on a large scale) and chest strap monitors. Therefore, the use of technology and the self-monitoring of heart rate and heart rate variability can be an important first step in screening cardiovascular pathology and reducing the pressure on medical services in a hospital. The use of telemedicine can be an alternative, especially among elderly patients who are associated with walking disorders, frailty, or neurocognitive disorders.

Blue prescription: A pilot study of health benefits for oncological patients of a short program of activities involving the sea

Performing outdoor activities in blue spaces can help improve human health and mental well-being by reducing stress and promoting social relationships. The number of people surviving cancer has increased globally to experience this disease as a life-changing and chronic condition with physical and psychosocial symptoms that have negative impacts on their quality of life. While there has been a growth of programs in green spaces to meet the needs of cancer patients, such as follow-up post-treatment care, support groups and physical activity programs, very few studies have examined the effects of activities involving the sea for the health and well-being of oncology patients. This is the first study to evaluate whether different outdoor activities in blue spaces can benefit oncological patients' physical and mental health using smartwatches, sphygmomanometers and Profile of Mood States (POMS) questionnaires. We assessed changes in blood pressure, heart rate, sleep quality and mental health of 16 patients after twelve sessions of three different activities (walking, beach and snorkelling) and four sessions of a control activity. While no significant differences between activities were observed in terms of the data gathered by the smartwatches, a gradient of positive results for human mental health was observed towards exposure to a blue space, assessed through POMS questionnaires. Results show that exposure to blue spaces contributes to tension and anger reduction and improves the vigour mood state of oncology patients. No significant increases in patients' heart rate were recorded after the beach and snorkelling activities, with results similar to the control activity, suggesting that the contribution may be to participants' relaxation.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

The effect of the COVID-19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women

BACKGROUND

The SARS-CoV-2/COVID-19 pandemic has been associated with increased stress levels and higher alcohol use, including in pregnant and postpartum women. In the general population, alcohol use is associated with dysregulation in the autonomic nervous system (ANS), which is indexed by heart rate variability (HRV). The objectives of this study were to: (1) characterize changes in substance use during the SARS-CoV-2/COVID-19 pandemic via a baseline self-report survey followed by mobile ecological momentary assessment (mEMA) of substance use; and (2) examine the associations between momentary substance use and ambulatory HRV measures in pregnant and postpartum women.

METHODS

Pregnant and postpartum women were identified from the ENRICH-2 prospective cohort study. Participants were administered a baseline structured phone interview that included the Coronavirus Perinatal Experiences (COPE) survey and ascertained the prevalence of substance use. Over a 14-day period, momentary substance use was assessed three times daily, and HRV measurements were captured via wearable electronics. Associations between momentary substance use and HRV measures (root mean square of successive differences [RMSSD] and low frequency/high frequency [LF/HF] ratio) were examined using a mixed effects model that included within-subject (WS) and between-subject (BS) effects and adjusted for pregnancy status and participant age.

RESULTS

The sample included 49 pregnant and 22 postpartum women. From a combination of a baseline and 14-day mEMA surveys, 21.2% reported alcohol use, 16.9% reported marijuana use, and 8.5% reported nicotine use. WS effects for momentary alcohol use were associated with the RMSSD (β = -0.14; p = 0.005) and LF/HF ratio (β = 0.14; p = 0.01) when controlling for pregnancy status and maternal age. No significant associations were observed between HRV measures and instances of marijuana or nicotine use.

CONCLUSIONS

These findings highlight the negative effect of the SARS-CoV-2/COVID-19 pandemic on the psychological health of pregnant and postpartum women associated with substance use, and in turn, ANS dysregulation, which potentially puts some women at risk of developing a substance use disorder.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

METHODS

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

RESULTS

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

CONCLUSIONS

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation

BACKGROUND

Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated.

OBJECTIVE

The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings.

METHODS

Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set.

RESULTS

The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE.

CONCLUSIONS

In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.

Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study

BACKGROUND

Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy.

OBJECTIVE

The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance.

METHODS

During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of -2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT.

RESULTS

In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=-0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of -0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001).

CONCLUSIONS

Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies.

Study protocol for a pilot trial analysing the usability, validity and safety of an interventional health app programme for the structured prehabilitation of patients before major surgical interventions: the PROTEGO MAXIMA trial

INTRODUCTION

Major surgery is associated with a high risk for postoperative complications, leading to an increase in mortality and morbidity, particularly in frail patients with a reduced cardiopulmonary reserve. Prehabilitation, including aerobic exercise training, aims to improve patients' physical fitness before major surgery and reduce postoperative complications, length of hospital stay and costs. The purpose of the study is to assess the usability, validity and safety of an app-based endurance exercise software in accordance with the Medical Device Regulation using wrist-worn wearables to measure heart rate (HR) and distance.

METHODS AND ANALYSIS

The PROTEGO MAXIMA trial is a prospective, interventional study with patients undergoing major elective surgery, comprising three tasks. Tasks I and II aim to assess the usability of the app, using evaluation questionnaires and usability scenarios. In Task IIIa, patients will undergo a structured risk assessment by the Patronus App, which will be correlated with the occurrence of postoperative complications after 90 days (non-interventional). In Task IIIb, healthy students and patients will perform a supervised 6 min walking test and a 37 min interval training on a treadmill based on HR reserve, wearing standard ECG limb leads and two smartwatches, which will be driven by the test software. The aim of this task is to assess the accuracy of HR measurement by the wearables and the safety, using specific alarm settings of the devices and lab testing of the participants (interventional).

ETHICS AND DISSEMINATION

Ethical approval was granted by the Institutional Review Board of the University Hospital of Frankfurt and by the Federal Institute for Pharmaceuticals and Medical Products (BfArM, reference number 94.1.04-5660-13655) on 7 February 2022. The results from this study will be submitted to peer-reviewed journals and reported at suitable national and international meetings.

TRIAL REGISTRATION NUMBERS

European Database on Medical Devices (CIV-21-07-037311) and German Clinical Trial Registry (DRKS00026985).

A Call to Expand the Scope of Digital Phenotyping

Digital phenotyping refers to near-real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology using smartphones is often considered the gold standard by many for passive data collection within the field of digital phenotyping, which limits its applications mainly to adults or adolescents who use smartphones. However, other technologies, such as wearable devices, have evolved considerably in recent years to provide similar or better quality passive physiologic data of clinical relevance, thus expanding the potential of digital phenotyping applications to other patient populations. In this perspective, we argue for the continued expansion of digital phenotyping to include other potential gold standards in addition to smartphones and provide examples of currently excluded technologies and populations who may uniquely benefit from this technology.

An RDoC-based approach to adolescent self-injurious thoughts and behaviors: The interactive role of social affiliation and cardiac arousal

Recent theoretical models have posited that increases in self-injurious thoughts and behaviors (SITBs) during adolescence may be linked to failures in biological stress regulation in contexts of social stress. However, there is a lack of data examining this hypothesis during the transition to adolescence, a sensitive period of development characterized by changes across socioaffective and psychophysiological domains. Building on principles from developmental psychopathology and the RDoC framework, the present study used a longitudinal design in a sample of 147 adolescents to test whether interactions among experiences of social (i.e., parent and peer) conflict and cardiac arousal (i.e., resting heart rate) predicted adolescents' engagement in SITBs (i.e., nonsuicidal self-injury, NSSI; and suicidal ideation; SI) across 1-year follow-up. Prospective analyses revealed that adolescents experiencing a combination of greater peer, but not family, conflict and higher cardiac arousal at baseline showed significant longitudinal increases in NSSI. In contrast, social conflict did not interact with cardiac arousal to predict future SI. Findings indicate that greater peer-related interpersonal stress in adolescents may increase risk for future NSSI among youth with physiological vulnerabilities (i.e., higher resting heart rate) that may be markers of maladaptive stress responses. Future research should examine these processes at finer timescales to elucidate whether these factors are proximal predictors of within-day SITBs.

Association between Metabolic Syndrome Status and Daily Physical Activity Measured by a Wearable Device in Japanese Office Workers

(1) Background: This study examined the cross-sectional association between metabolic syndrome (MetS) status classified into three groups and daily physical activity (PA; step count and active minutes) using a wearable device in Japanese office workers. (2) Methods: This secondary analysis used data from 179 participants in the intervention group of a randomized controlled trial for 3 months. Individuals who had received an annual health check-up and had MetS or were at a high risk of MetS based on Japanese guidelines were asked to use a wearable device and answer questionnaires regarding their daily life for the entire study period. Multilevel mixed-effects logistic regression models adjusted for covariates associated with MetS and PA were used to estimate associations. A sensitivity analysis investigated the associations between MetS status and PA level according to the day of the week. (3) Results: Compared to those with no MetS, those with MetS were not significantly associated with PA, while those with pre-MetS were inversely associated with PA [step count Model 3: OR = 0.60; 95% CI: 0.36, 0.99; active minutes Model 3: OR = 0.62; 95% CI: 0.40, 0.96]. In the sensitivity analysis, day of the week was an effect modifier for both PA (p < 0.001). (4) Conclusions: Compared to those with no MetS, those with pre-MetS, but not MetS, showed significantly lower odds of reaching their daily recommended PA level. Our findings suggest that the day of the week could be a modifier for the association between MetS and PA. Further research with longer study periods and larger sample sizes are needed to confirm our results.

Validation of Oura ring energy expenditure and steps in laboratory and free-living

BACKGROUND

Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living.

METHODS

Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count.

RESULTS

Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits.

CONCLUSION

This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

Dynamic Loading-A New Marker for Abdominal Aneurysm Growth?

The growing possibilities of non-invasive heart rate and blood pressure measurement with mobile devices allow vital data to be continuously collected and used to assess patients' health status. When it comes to the risk assessment of abdominal aortic aneurysms (AAA), the continuous tracking of blood pressure and heart rate could enable a more patient-specific approach. The use of a load function and an energy function, with continuous blood pressure, heart rate, and aneurysm stiffness as input parameters, can quantify dynamic load on AAA. We hypothesise that these load functions correlate with aneurysm growth and outline a possible study procedure in which the hypothesis could be tested for validity. Subsequently, uncertainty quantification of input quantities and derived quantities is performed.

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA's characteristics will be key to developing preventive strategies. Many lives could be saved if SCA's early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician's computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

BACKGROUND

Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.

OBJECTIVE

This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.

METHODS

We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis.

RESULTS

Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.

CONCLUSIONS

Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

Examining motivation factors for African American students' use of consumer wearables

Purpose

This study was initiated to examine factors that motivate African American students who use wearable devices at an HBCU in Mississippi.

Method

We conducted a correlational research study on undergraduate and graduate students from a southern USA university. The stratified random sample comprised a total of 239 students. The responses of the students were analyzed using the Fisher exact test to determine whether or not there was a significant association between the categorical demographic variables (age, gender, ethnicity, and student classification) and the students' motivation for using a wearable device.

Results

Students used wearables for one main reason, to help them increase their awareness of their health status because they understand the importance of tracking their health metrics to boost their health status and reduce risk factors for developing chronic diseases. Students also demonstrated that they understand the value of tracking health information, such as heart rate and blood pressure, as a way to reduce the prevalence and impact of risk factors that can lead to chronic diseases.

Conclusions

Wearables enable individuals to regularly observe, measure, and record their physical status and physiological measures, and facilitate medication adherence by enabling individuals to maintain their prescribed medication regimen adequately. The data collected and stored through these wearables can provide data that will be useful for medical personnel in their treatment of patients and in developing strategies for prevention and intervention for the larger community.

Feasibility of Using an Armband Optical Heart Rate Sensor in Naturalistic Environment

Consumer-grade heart rate (HR) sensors including chest straps, wrist-worn watches and rings have become very popular in recent years for tracking individual physiological state, training for sports and even measuring stress levels and emotional changes. While the majority of these consumer sensors are not medical devices, they can still offer insights for consumers and researchers if used correctly taking into account their limitations. Multiple previous studies have been done using a large variety of consumer sensors including Polar® devices, Apple® watches, and Fitbit® wrist bands. The vast majority of prior studies have been done in laboratory settings where collecting data is relatively straightforward. However, using consumer sensors in naturalistic settings that present significant challenges, including noise artefacts and missing data, has not been as extensively investigated. Additionally, the majority of prior studies focused on wrist-worn optical HR sensors. Arm-worn sensors have not been extensively investigated either. In the present study, we validate HR measurements obtained with an arm-worn optical sensor (Polar OH1) against those obtained with a chest-strap electrical sensor (Polar H10) from 16 participants over a 2-week study period in naturalistic settings. We also investigated the impact of physical activity measured with 3-D accelerometers embedded in the H10 chest strap and OH1 armband sensors on the agreement between the two sensors. Overall, we find that the arm-worn optical Polar OH1 sensor provides a good estimate of HR (Pearson r = 0.90, p <0.01). Filtering the signal that corresponds to physical activity further improves the HR estimates but only slightly (Pearson r = 0.91, p <0.01). Based on these preliminary findings, we conclude that the arm-worn Polar OH1 sensor provides usable HR measurements in daily living conditions, with some caveats discussed in the paper.

In-ear infrasonic hemodynography with a digital health device for cardiovascular monitoring using the human audiome

Human bodily mechanisms and functions produce low-frequency vibrations. Our ability to perceive these vibrations is limited by our range of hearing. However, in-ear infrasonic hemodynography (IH) can measure low-frequency vibrations (<20 Hz) created by vital organs as an acoustic waveform. This is captured using a technology that can be embedded into wearable devices such as in-ear headphones. IH can acquire sound signals that travel within arteries, fluids, bones, and muscles in proximity to the ear canal, allowing for measurements of an individual's unique audiome. We describe the heart rate and heart rhythm results obtained in time-series analysis of the in-ear IH data taken simultaneously with ECG recordings in two dedicated clinical studies. We demonstrate a high correlation (r = 0.99) between IH and ECG acquired interbeat interval and heart rate measurements and show that IH can continuously monitor physiological changes in heart rate induced by various breathing exercises. We also show that IH can differentiate between atrial fibrillation and sinus rhythm with performance similar to ECG. The results represent a demonstration of IH capabilities to deliver accurate heart rate and heart rhythm measurements comparable to ECG, in a wearable form factor. The development of IH shows promise for monitoring acoustic imprints of the human body that will enable new real-time applications in cardiovascular health that are continuous and noninvasive.

Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis

BACKGROUND

Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS).

METHODS

Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender.

RESULTS

Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods.

CONCLUSIONS

Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity.

Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study

BACKGROUND

Wearable device technology has recently been involved in the healthcare industry substantially. India is the world's third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries.

METHODS

This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above.

RESULTS

A total of 495 responses were obtained. In all, 50.3% were aged between 25-50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032).

CONCLUSIONS

This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.

Evaluation of physical health status beyond daily step count using a wearable activity sensor

Physical health status defines an individual's ability to perform normal activities of daily living and is usually assessed in clinical settings by questionnaires and/or by validated tests, e.g. timed walk tests. These measurements have relatively low information content and are usually limited in frequency. Wearable sensors, such as activity monitors, enable remote measurement of parameters associated with physical activity but have not been widely explored beyond measurement of daily step count. Here we report on results from a cohort of 22 individuals with Pulmonary Arterial Hypertension (PAH) who were provided with a Fitbit activity monitor (Fitbit Charge HR^®) between two clinic visits (18.4 ± 12.2 weeks). At each clinical visit, a maximum of 26 measurements were recorded (19 categorical and 7 continuous). From analysis of the minute-to-minute step rate and heart rate we derive several metrics associated with physical activity and cardiovascular function. These metrics are used to identify subgroups within the cohort and to compare to clinical parameters. Several Fitbit metrics are strongly correlated to continuous clinical parameters. Using a thresholding approach, we show that many Fitbit metrics result in statistically significant differences in clinical parameters between subgroups, including those associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests. These results highlight the fact that daily step count is only one of many metrics that can be derived from activity monitors.

How is the usability of commercial activity monitors perceived by older adults and by researchers? A cross-sectional evaluation of community-living individuals

OBJECTIVES

Using commercial activity monitors may advance research with older adults. However, usability for the older population is not sufficiently established. This study aims at evaluating the usability of three wrist-worn monitors for older adults. In addition, we report on usability (including data management) for research.

DESIGN

Data were collected cross-sectionally. Between-person of three activity monitor type (Apple Watch 3, Fitbit Charge 4, Polar A370) were made.

SETTING

The activity monitors were worn in normal daily life in an urban community in Germany. The period of wear was 2 weeks.

PARTICIPANTS

Using convenience sampling, we recruited N=27 healthy older adults (≥60 years old) who were not already habitual users of activity monitors.

OUTCOMES

To evaluate usability from the participant perspective, we used the System Usability Scale (SUS) as well as a study-specific qualitative checklist. Assessment further comprised age, highest academic degree, computer proficiency and affinity for technology interaction. Usability from the researchers' perspective was assessed using quantitative data management markers and a study-specific qualitative check-list.

RESULTS

There was no significant difference between monitors in the SUS. Female gender was associated with higher SUS usability ratings. Qualitative participant-usability reports revealed distinctive shortcomings, for example, in terms of battery life and display readability. Usability for researchers came with problems in data management, such as completeness of the data download.

CONCLUSION

The usability of the monitors compared in this work differed qualitatively. Yet, the overall usability ratings by participants were comparable. Conversely, from the researchers' perspective, there were crucial differences in data management and usability that should be considered when making monitor choices for future studies.

Assessing mental workload with wearable devices - Reliability and applicability of heart rate and motion measurements

Wearable devices are increasingly used for assessing physiological data. Industry 4.0 aims to achieve the real-time assessment of the workers' condition to adapt processes including the current mental workload. Mental workload can be assessed via physiological data. This paper researches the potential of wearable devices for mental workload assessment by utilizing heart rate and motion data collected with a smartwatch. A laboratory study was conducted with four levels of mental workload, ranging from none to high and during sitting and stepping activities. When sitting, a difference in the heart rate and motion data from the smartwatch was only found between no mental workload and any mental workload task. For the stepping condition, differences were found for the movement data. Based on these results, wearable devices could be useful in the future for detecting whether a mental demanding task is currently performed during low levels of physical activity.

LifeSnaps, a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral and psychological patterns due to challenges in collecting and releasing such datasets, including waning user engagement or privacy considerations. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n = 71 participants. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71 M rows of data. The participants contributed their data through validated surveys, ecological momentary assessments, and a Fitbit Sense smartwatch and consented to make these data available to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data will open novel research opportunities and potential applications in multiple disciplines.

Systematic Review of Fitbit Charge 2 Validation Studies for Exercise Tracking

Context

There are research-grade devices that have been validated to measure either heart rate (HR) by electrocardiography (ECG) with a Polar chest strap, or step count with ACTiGraph accelerometer. However, wearable activity trackers that measure HR and steps concurrently have been tested against research-grade accelerometers and HR monitors with conflicting results. This review examines validation studies of the Fitbit Charge 2 (FBC2) for accuracy in measuring HR and step count and evaluates the device's reliability for use by researchers and clinicians.

Design

This registered review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The robvis (risk-of-bias visualization) tool was used to assess the strength of each considered article.

Eligibility Criteria

Eligible articles published between 2018 and 2019 were identified using PubMed, CINHAL, Embase, Cochran, and World of Science databases and hand-searches. All articles were HR and/or step count validation studies for the FBC2 in adult ambulatory populations.

Study Selection

Eight articles were examined in accordance with the eligibility criteria alignment and agreement among the authors and research librarian.

Main Outcome Measures

Concordance correlation coefficients (CCC) were used to measure agreement between the tracker and criterion devices. Mean absolute percent error (MAPE) was used to average the individual absolute percent errors.

Results

Studies that measured CCC found agreement between the FBC2 and criterion devices ranged between 26% and 92% for HR monitoring, decreasing in accuracy as exercise intensity increased. Inversely, CCC increased from 38% to 99% for step count when exercise intensity increased. HR error between MAPE was 9.21% to 68% and showed more error as exercise intensity increased. Step measurement error MAPE was 12% for healthy persons aged 24-72 years but was reported at 46% in an older population with heart failure.

Conclusions

Relative agreement with criterion and low-to-moderate MAPE were consistent in most studies reviewed and support validation of the FBC2 to accurately measure HR at low or moderate exercise intensities. However, more investigation controlling testing and measurement congruency is needed to validate step capabilities. The literature supports the validity of the FBC2 to accurately monitor HR, but for step count is inconclusive so the device may not be suitable for recommended use in all populations.

The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.

The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review

BACKGROUND

An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts.

OBJECTIVE

This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment.

METHODS

A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch.

RESULTS

A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep.

CONCLUSIONS

The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.

Measurement of Heart Rate Using the Withings ScanWatch Device During Free-living Activities: Validation Study

Background

Wrist-worn devices that incorporate photoplethysmography (PPG) sensing represent an exciting means of measuring heart rate (HR). A number of studies have evaluated the accuracy of HR measurements produced by these devices in controlled laboratory environments. However, it is also important to establish the accuracy of measurements produced by these devices outside the laboratory, in real-world, consumer use conditions.

Objective

This study sought to examine the accuracy of HR measurements produced by the Withings ScanWatch during free-living activities.

Methods

A sample of convenience of 7 participants volunteered (3 male and 4 female; mean age 64, SD 10 years; mean height 164, SD 4 cm; mean weight 77, SD 16 kg) to take part in this real-world validation study. Participants were instructed to wear the ScanWatch for a 12-hour period on their nondominant wrist as they went about their day-to-day activities. A Polar H10 heart rate sensor was used as the criterion measure of HR. Participants used a study diary to document activities undertaken during the 12-hour study period. These activities were classified according to the 11 following domains: desk work, eat or drink, exercise, gardening, household activities, self-care, shopping, sitting, sleep, travel, and walking. Validity was assessed using the Bland-Altman analysis, concordance correlation coefficient (CCC), and mean absolute percentage error (MAPE).

Results

Across all activity domains, the ScanWatch measured HR with MAPE values <10%, except for the shopping activity domain (MAPE=10.8%). The activity domains that were more sedentary in nature (eg, desk work, eat or drink, and sitting) produced the most accurate HR measurements with a small mean bias and MAPE values <5%. Moderate to strong correlations (CCC=0.526-0.783) were observed between devices for all activity domains, except during the walking activity domain, which demonstrated a weak correlation (CCC=0.164) between devices.

Conclusions

The results of this study show that the ScanWatch measures HR with a degree of accuracy that is acceptable for general consumer use; however, it would not be suitable in circumstances where more accurate measurements of HR are required, such as in health care or in clinical trials. Overall, the ScanWatch was less accurate at measuring HR during ambulatory activities (eg, walking, gardening, and household activities) compared to more sedentary activities (eg, desk work, eat or drink, and sitting). Further larger-scale studies examining this device in different populations and during different activities are required.

RP3MES: A Key to Minimize Infection Spreading

Healthcare facilities, especially in highly populated countries like India where patient to doctor ratio is very high, are under a huge burden. Thus, Remote Patient Physiological Parameter Monitoring using Embedded System (RP3MES) becomes essential to monitor a large number of people admitted in hospitals and also patients afflicted with infectious diseases. The design for RP3MES addresses the key issues of portability, cost-effectiveness, low power consumption, user-friendliness, high accuracy and remote communication to facilitate vital parameter(s), like heart rate and body temperature, measurements and emergency notification, keeping in mind, the health of the caregiver(s). ARM Cortex M3 embedded processor and low-cost sensors are used to achieve the cost-effectiveness and low power consumption. Alarming unit intimidates a remote caregiver regarding their patient’s health condition. The accuracy of the system measured data is 99.4% compared with the gold standard, which has been verified using Lin’s Concordance Correlation Coefficient and Bland–Altman analysis. A comparison of our system with other commercially available ones is also presented here. The proposed system has wireless connectivity which minimizes infection transmission among family members and caregivers of the patients. It may also reduce the burden on healthcare staffs in hospitals.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.

Virtual reality exergaming improves affect during physical activity and reduces subsequent food consumption in inactive adults

An individual's affective (i.e. emotional) response to exercise may play an important role in post-exercise eating behaviour for some individuals. Taking advantage of advances in fully immersive virtual reality (VR) technology, this study aimed to: a) examine whether VR exergaming can improve the psychological response to exercise in inactive adults, and b) assess the extent to which this improvement reduces post-exercise appetite and eating behaviour. In a cross-over study, 34 adults not meeting the World Health Organisation's physical activity recommendations completed two exercise sessions on a stationary bike; one while engaging in a VR exergame and one without VR. Monitoring enabled heart rate, energy expenditure, and duration across conditions to be closely matched. The Physical Activity Enjoyment Scale, Feeling Scale, Felt Arousal Scale and Borg's Ratings of Perceived Exertion were measured to capture the affective responses to exercise. Appetite and eating behaviour were evaluated using visual-analogue scales, a computerised food preference task, and intake at a post-exercise buffet meal. Cycling in VR elicited greater exercise enjoyment (p < 0.001, η2p = 0.62), pleasure (p < 0.001, η2p = 0.47), and activation (p < 0.001, η2p = 0.55). VR exergaming did not alter perceived physical exertion (p = 0.64), perceived appetite (p = 0.68), and preference for energy dense (p = 0.78) or sweet/savoury foods (p = 0.90) compared to standard exercise. However, it did result in a mean 12% reduction in post-exercise food intake (mean difference: 105.9 kcal; p < 0.01; η2p = 0.20) and a decrease in relative food intake (p < 0.01; η2p = 0.20), although inter-individual differences in response to VR exergaming were observed. The integration of VR in a cycling workout improves the affective experience of physical activity for inactive adults and reduces subsequent food intake. Virtual reality technology shows potential as an adjunct tool to support adults in weight management programmes become more active, especially for those individuals who are prone to eat in excess after physical activity.

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

BACKGROUND

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

OBJECTIVE

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

METHODS

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.

RESULTS

We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.

CONCLUSIONS

High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.

Accuracy of a New Pulse Oximetry in Detection of Arterial Oxygen Saturation and Heart Rate Measurements: The SOMBRERO Study

Early diagnosis and continuous monitoring of respiratory failure (RF) in the course of the most prevalent chronic cardio-vascular (CVD) and respiratory diseases (CRD) are a clinical, unresolved problem because wearable, non-invasive, and user-friendly medical devices, which could grant reliable measures of the oxygen saturation (SpO2) and heart rate (HR) in real-life during daily activities are still lacking. In this study, we investigated the agreement between a new medical wrist-worn device (BrOxy M) and a reference, medical pulseoximeter (Nellcor PM 1000N). Twelve healthy volunteers (aged 20−51 years, 84% males, 33% with black skin, obtaining, during the controlled hypoxia test, the simultaneous registration of 219 data pairs, homogeneously deployed in the levels of Sat.O2 97%, 92%, 87%, 82% [ISO 80601-2-61:2017 standard (paragraph EE.3)]) were included. The paired T test 0 and the Bland-Altman plot were performed to assess bias and accuracy. SpO2 and HR readings by the two devices resulted significantly correlated (r = 0.91 and 0.96, p < 0.001, respectively). Analyses excluded the presence of proportional bias. For SpO2, the mean bias was −0.18% and the accuracy (ARMS) was 2.7%. For HR the mean bias was 0.25 bpm and the ARMS3.7 bpm. The sensitivity to detect SpO2 ≤ 94% was 94.4%. The agreement between BrOxy M and the reference pulse oximeter was “substantial” (for SpO2 cut-off 94% and 90%, k = 0.79 and k = 0.80, respectively). We conclude that BrOxy M demonstrated accuracy, reliability and consistency in measuring SpO2 and HR, being fully comparable with a reference medical pulseoxymeter, with no adverse effects. As a wearable device, Broxy M can measure continually SpO2 and HR in everyday life, helping in detecting and following up CVD and CRD subjects.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Background

Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap.

Methods

We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm.

Results

Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms.

Discussion

In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

Remote investigation and assessment of vital signs (RIA-VS)-proof of concept for contactless estimation of blood pressure, pulse, respiratory rate, and oxygen saturation in patients with suspicion of COVID-19

BACKGROUND

Vital signs are critical in assessing the severity and prognosis of infections, for example, COVID-19, influenza, sepsis, and pneumonia. This study aimed to evaluate a new method for rapid camera-based non-contact measurement of heart rate, blood oxygen saturation, respiratory rate, and blood pressure.

METHODS

Consecutive adult patients attending a hospital emergency department for suspected COVID-19 infection were invited to participate. Vital signs measured with a new camera-based method were compared to the corresponding standard reference methods. The camera device observed the patient's face for 30 s from ∼1 m.

RESULTS

Between 1 April and 1 October 2020, 214 subjects were included in the trial, 131 female (61%) and 83 male (39%). The mean age was 44 years (range 18-81 years). The new camera-based device's vital signs measurements were, on average, very close to the gold standard but the random variation was larger than the reference methods.

CONCLUSIONS

The principle of contactless measurement of blood pressure, pulse, respiratory rate, and oxygen saturation works, which is very promising. However, technical improvements to the equipment used in this study to reduce its random variability is required before clinical implementation. This will likely be a game changer once this is sorted out.

CLINICAL TRIAL REGISTRATION

Universal Trial Number (UTN) U1111-1251-4114 and the ClinicalTrials.gov Identifier NCT04383457.

Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study

BACKGROUND

Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed.

OBJECTIVE

The objective of this study was to compare the Polar Vantage watch (Polar Electro Oy) and Oura ring (generation 2; Ōura Health Oy) activity trackers to research-based instruments for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration in free-living adults.

METHODS

A total of 21 participants wore 2 consumer-based activity trackers (Polar watch and Oura ring), an ActiGraph accelerometer (ActiGraph LLC), and an Actiheart accelerometer and heart rate monitor (CamNtech Ltd) and completed a sleep diary for up to 7 days. We assessed Polar watch and Oura ring validity and comparability for measuring physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analyzed repeated measures correlations, Bland-Altman plots, and mean absolute percentage errors.

RESULTS

The Polar watch and Oura ring values strongly correlated (P<.001) with the ActiGraph values for steps (Polar: r=0.75, 95% CI 0.54-0.92; Oura: r=0.77, 95% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar: r=0.76, 95% CI 0.62-0.88; Oura: r=0.70, 95% CI 0.49-0.82), and total energy expenditure (Polar: r=0.69, 95% CI 0.48-0.88; Oura: r=0.70, 95% CI 0.51-0.83) and strongly or very strongly correlated (P<.001) with the sleep diary-derived sleep durations (Polar: r=0.74, 95% CI 0.56-0.88; Oura: r=0.82, 95% CI 0.68-0.91). Oura ring-derived resting heart rates had a very strong correlation (P<.001) with the Actiheart-derived resting heart rates (r=0.9, 95% CI 0.85-0.96). However, the mean absolute percentage error was high for all variables except Oura ring-derived sleep duration (10%) and resting heart rate (3%), which the Oura ring underreported on average by 1 beat per minute.

CONCLUSIONS

The Oura ring can potentially be used as an alternative to the Actiheart to measure resting heart rate. As for sleep duration, the Polar watch and Oura ring can potentially be used as replacements for a manual sleep diary, depending on the acceptable error. Neither the Polar watch nor the Oura ring can replace the ActiGraph when it comes to measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but they may be used as additional sources of physical activity measures in some settings. On average, the Polar Vantage watch reported higher outputs compared to those reported by the Oura ring for steps, moderate-to-vigorous physical activity, and total energy expenditure.

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Purpose

A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity.

Patients and Methods

Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM's traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30.

Results

According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers' sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool.

Conclusion

Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands' data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.

A Scalable Risk Scoring System for COVID-19 Inpatients Based on Consumer-grade Wearables: Statistical Analysis and Model Development

Background

To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards.

Objective

In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches).

Methods

Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland.

Results

First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation.

Conclusions

Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system.

Trial Registration

Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834

Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health

Background: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. Methods: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. Results: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. Conclusions: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention.

Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis

BACKGROUND

Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.

OBJECTIVE

The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.

METHODS

We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.

RESULTS

A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively).

CONCLUSIONS

Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study

Background

Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research.

Objective

This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2.

Methods

Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed.

Results

A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm).

Conclusions

Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.