Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis.

BACKGROUND: Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. OBJECTIVE: This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. METHODS: We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland-Altman plot were used to compare the measurements of the 2 devices. RESULTS: Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. CONCLUSIONS: The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks.

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.

Comparison of Oura Smart Ring Against ActiGraph Accelerometer for Measurement of Physical Activity and Sedentary Time in a Free-Living Context.

Smart rings, such as the Oura ring, might have potential in health monitoring. To be able to identify optimal devices for healthcare settings, validity studies are needed. The aim of this study was to compare the Oura smart ring estimates of steps and sedentary time with data from the ActiGraph accelerometer in a free-living context. A cross-sectional observational study design was used. A convenience sample of healthy adults (n = 42) participated in the study and wore an Oura smart ring and an ActiGraph accelerometer on the non-dominant hand continuously for 1 week. The participants completed a background questionnaire and filled out a daily log about their sleeping times and times when they did not wear the devices. The median age of the participants (n = 42) was 32 years (range, 18-46 years). In total, 191 (61% of the potential) days were compared. The Oura ring overestimated the step counts compared with the ActiGraph. The mean difference was 1416 steps (95% confidence interval, 739-2093 steps). Daily sedentary time was also overestimated by the ring; the mean difference was 17 minutes (95% confidence interval, -2 to 37 minutes). The use of the ring in nursing interventions needs to be considered.

Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study.

BACKGROUND: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. OBJECTIVE: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. METHODS: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study. RESULTS: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2). CONCLUSIONS: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/17783.

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation.

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother's and her unborn baby's health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother's condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system's feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

Being 'A Google Mom' or securely monitored at home: Perceptions of remote monitoring in maternity care.

AIMS: To understand the perspectives of both healthcare professionals in maternity care and pregnant women with higher risk pregnancies about remote monitoring in maternity care. DESIGN: Qualitative descriptive design. METHODS: Individual and focus group interviews were conducted in public maternity care and in a level III hospital in Finland during April-May 2018. The sample consisted of healthcare professionals working in the primary care and at the hospital and hospitalized pregnant women. Altogether, 17 healthcare professionals and 4 pregnant women participated in the study. The data were analysed using inductive thematic network analysis. RESULTS: Many possibilities - and an equal number of concerns - were identified regarding remote monitoring in pregnancy, depending on the respondent's viewpoint from holistic to symptom-centred care. Healthcare staff had reservations about technology due to previous negative experiences and difficulties trusting technology. The pregnant women thought that monitoring would ease the staff's workload if the latter had enough technological skills. Remote monitoring could increase security in pregnancy care but create a feeling of false security if the women ignored their subjective symptoms. Face-to-face visits and the uniqueness of human contact were strongly favoured. Pregnant women wished to use monitoring as a confirmation of their subjective feelings. CONCLUSION: Remote monitoring could be used as a supplementary system in pregnancy care, although it could replace only some healthcare visits. Pregnant women identified more possibilities for remote monitoring compared with the staff members both in primary care and the hospital. IMPACT: A comprehensive understanding of pregnant women's and healthcare professionals' perceptions of remote monitoring in pregnancy was built to be able to develop new technologies in maternity care. In certain cases, remote monitoring would supplement traditional pregnancy follow-ups. Staff in primary and specialized care, and healthcare managers, should support teamwork to be able to understand different approaches to pregnancy care.

Feasibility of smart wristbands for continuous monitoring during pregnancy and one month after birth.

BACKGROUND: Smart wristbands enable the continuous monitoring of health parameters, for example, in maternity care. Understanding the feasibility and acceptability of these devices in an authentic context is essential. The aim of this study was to evaluate the feasibility of using a smart wristband to collect continuous activity, sleep and heart rate data from the beginning of the second trimester until one month postpartum. METHODS: The feasibility of a smart wristband was tested prospectively through pregnancy in nulliparous women (n = 20). The outcomes measured were the wear time of the device and the participants' experiences with the smart wristband. The data were collected from the wristbands, phone interviews, questionnaires, and electronic patient records. The quantitative data were analyzed with hierarchical linear mixed models for repeated measures, and qualitative data were analyzed using content analysis. RESULTS: Participants (n = 20) were recruited at a median of 12.9 weeks of gestation. They used the smart wristbands for an average of 182 days during the seven-month study period. The daily use of the devices was similar during the second (17.9 h, 95% CI 15.2 to 20.7) and third trimesters (16.7 h, 95% CI 13.8 to 19.5) but decreased during the postpartum period (14.4 h, 95% CI 11.4 to 17.4, p = 0.0079). Participants who could not wear smart wristbands at work used the device 300 min less per day than did those with no use limitations. Eight of the participants did not wear the devices or wore them only occasionally after giving birth. Nineteen participants reported that the smart wristband did not have any permanent effects on their behavior. Problems with charging and synchronizing the devices, perceiving the devices as uncomfortable, or viewing the data as unreliable, and the fear of scratching their babies with the devices were the main reasons for not using the smart wristbands. CONCLUSIONS: A smart wristband is a feasible tool for continuous monitoring during pregnancy. However, the daily use decreased after birth. The results of this study may support the planning of future studies and help with overcoming barriers related to the use of smart wristbands on pregnant women.

Acute pain intensity monitoring with the classification of multiple physiological parameters.

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

Can Sleep Quality Attributes be Predicted from Physical Activity in Everyday Settings?

Sleep is crucial for physical, mental, and emotional well-being. Physical activity and sleep are known to be interrelated; however, limited research has been performed to investigate their interactions in long-term. Conventional studies have presented sleep quality prediction, focusing on a single sleep quality aspect, such as sleep efficiency. In addition, the relationship between daily physical activity and sleep quality has yet to be explored, despite physical activities being utilized in previous studies for sleep quality prediction. In this paper, we develop an Extreme Gradient boosting method to predict sleep duration, sleep efficiency, and deep sleep based on users' daily activity information collected from wearable devices. Our model is trained and tested using data collected with an OURA ring from 34 pregnant mothers for six months under free-living conditions. Our finding shows an accuracy of 90.58%, 95.38%, and 91.45% for sleep duration, efficiency, and deep sleep, respectively. Moreover, we assess the contribution of each physical activity parameter to the prediction results using the Shapley Additive Explanations method. Our results indicate that sedentary time is the most influential parameter for sleep duration prediction, while the inactive time feature (e.g., resting or lying down) has a strong negative relationship with sleep efficiency, and the pregnancy week is the most critical parameter for deep sleep prediction.

Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study.

BACKGROUND: Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. OBJECTIVE: The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period and identify the important objective physiological parameters in loneliness detection. METHODS: We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks post partum. Responses to this questionnaire and background information of the participants were collected through our customized cross-platform mobile app. We leveraged participants' smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores≥12) and nonloneliness (scores<12). We developed decision tree and gradient-boosting models to predict loneliness. We evaluated the models by using leave-one-participant-out cross-validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. RESULTS: The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1-scores of 0.897 and 0.872, respectively. Our results also show that loneliness is highly associated with activity intensity and activity distribution during the day. In addition, resting heart rate (HR) and resting HR variability (HRV) were correlated with loneliness. CONCLUSIONS: Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1-score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being through early detection of loneliness.

Comparing prenatal and postpartum stress among women with previous adverse pregnancy outcomes and normal obstetric histories: A longitudinal cohort study.

OBJECTIVE: The aim of this study was to compare subjectively and objectively measured stress during pregnancy and the three months postpartum in women with previous adverse pregnancy outcomes and women with normal obstetric histories. METHODS: We recruited two cohorts in southwestern Finland for this longitudinal study: (1) pregnant women (n = 32) with histories of preterm births or late miscarriages January-December 2019 and (2) pregnant women (n = 30) with histories of full-term births October 2019-March 2020. We continuously measured heart rate variability (HRV) using a smartwatch from 12 to 15 weeks of pregnancy until three months postpartum, and subjective stress was assessed with a smartphone application. RESULTS: We recruited the women in both cohorts at a median of 14.2 weeks of pregnancy. The women with previous adverse pregnancy outcomes delivered earlier and more often through Caesarean section compared with the women with normal obstetric histories. We found differences in subjective stress between the cohorts in pregnancy weeks 29 and 34. The cohort of women with previous adverse pregnancy outcomes had a higher root mean square of successive differences between normal heartbeats (RMSSD), a well-known HRV parameter, compared with the other cohort in pregnancy weeks 26 (64.9 vs 55.0, p = 0.04) and 32 (63.0 vs 52.3, p = 0.04). Subjective stress did not correlate with HRV parameters. CONCLUSIONS: Women with previous adverse pregnancy outcomes do not suffer from stress in subsequent pregnancies more than women with normal obstetric histories. Healthcare professionals need to be aware that interindividual variation in stress during pregnancy is considerable.

PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network.

Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to collect various vital signs, including heart rate and heart rate variability. The signal is highly susceptible to motion artifacts, which is inevitable in health monitoring and may lead to inaccurate decision-making. Studies in the literature proposed time series analysis, signal decomposition, and machine learning methods to reconstruct PPG signals or reduce noise. However, they are limited to short-term noisy signals or to noise caused by certain physical activities. In this paper, we propose a deep convolutional generative adversarial network (GAN) method to reconstruct distorted PPG signals. Our method exploits the temporal information extracted from the corrupted signal and preceding data to perform PPG reconstruction. The model is trained and tested using data collected by smartwatches in a home-based health monitoring application. We evaluate the proposed GAN method in comparison to three state-of-the-art PPG reconstruction methods. The evaluation includes noisy PPG signals with different durations and SNR values. The proposed method outperforms the other methods by obtaining the least error rates. The results indicate that the proposed method is effective for improving PPG signal quality to produce reliable heart rate and heart rate variability.

Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context.

BACKGROUND: Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV. OBJECTIVE: The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period. METHODS: A total of 58 pregnant women were monitored using an Internet of Things-based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. RESULTS: HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004). CONCLUSIONS: Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.

Personal Pain Sensitivity Prediction from Ultra-short-term Resting Heart Rate Variability.

Pain is a subjective experience with interpersonal perception sensitivity differences. Pain sensitivity is of scientific and clinical interest, as it is a risk factor for several pain conditions. Resting heart rate variability (HRV) is a potential pain sensitivity measure reflecting the parasympathetic tone and baroreflex function, but it remains unclear how well the prediction can achieve. This work investigated the relationship between different ultra-short-term HRV features and various pain sensitivity representations from heat and electrical pain tests. From leave-subject-out cross-validated results, we found that HRV can better predict a composite pain sensitivity score built from different tests and measures than a single measure in terms of the agreement between predictions and observations. Heat pain sensitivity was more possibly predicted than electrical pain. SDNN, RMSSD and LF better predicted the composite pain sensitivity score than other feature combinations, consis-tent with pain's physical and emotional attributes. It should be emphasized that the validity is probably limited within HRV at the resting state rather than an arbitrary measurement. This work implies a potential pain sensitivity prediction possibility that may be worth further validation.

A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability.

BACKGROUND: Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions. OBJECTIVE: We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor. METHODS: We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods. RESULTS: We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances. CONCLUSION: The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.

Pregnant women's daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology.

BACKGROUND: Technology enables the continuous monitoring of personal health parameter data during pregnancy regardless of the disruption of normal daily life patterns. Our research group has established a project investigating the usefulness of an Internet of Things-based system and smartwatch technology for monitoring women during pregnancy to explore variations in stress, physical activity and sleep. The aim of this study was to examine daily patterns of well-being in pregnant women before and during the national stay-at-home restrictions related to the COVID-19 pandemic in Finland. METHODS: A longitudinal cohort study design was used to monitor pregnant women in their everyday settings. Two cohorts of pregnant women were recruited. In the first wave in January-December 2019, pregnant women with histories of preterm births (gestational weeks 22-36) or late miscarriages (gestational weeks 12-21); and in the second wave between October 2019 and March 2020, pregnant women with histories of full-term births (gestational weeks 37-42) and no pregnancy losses were recruited. The final sample size for this study was 38 pregnant women. The participants continuously used the Samsung Gear Sport smartwatch and their heart rate variability, and physical activity and sleep data were collected. Subjective stress, activity and sleep reports were collected using a smartphone application developed for this study. Data between February 12 to April 8, 2020 were included to cover four-week periods before and during the national stay-at-home restrictions. Hierarchical linear mixed models were exploited to analyze the trends in the outcome variables. RESULTS: The pandemic-related restrictions were associated with changes in heart rate variability: the standard deviation of all normal inter-beat intervals (p = 0.034), low-frequency power (p = 0.040) and the low-frequency/high-frequency ratio (p = 0.013) increased compared with the weeks before the restrictions. Women's subjectively evaluated stress levels also increased significantly. Physical activity decreased when the restrictions were set and as pregnancy proceeded. The total sleep time also decreased as pregnancy proceeded, but pandemic-related restrictions were not associated with sleep. Daily rhythms changed in that the participants overall started to sleep later and woke up later. CONCLUSIONS: The findings showed that Finnish pregnant women coped well with the pandemic-related restrictions and lockdown environment in terms of stress, physical activity and sleep.

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study.

BACKGROUND: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects. OBJECTIVE: The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients. METHODS: The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. RESULTS: Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier. CONCLUSIONS: We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/17783.

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography.

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.

Continuous 7-Month Internet of Things-Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective Observational Feasibility Study.

BACKGROUND: Monitoring during pregnancy is vital to ensure the mother's and infant's health. Remote continuous monitoring provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments. OBJECTIVE: This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity, sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an Internet of Things (IoT)-based monitoring system. METHODS: This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed with a smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to 42 was 203 days and 28 days in the postpartum period. RESULTS: Valid physical activity data were available for a median of 144 (range 13-188) days (75% of possible monitoring days), and valid sleep data were available for a median of 137 (range 0-184) days (72% of possible monitoring days) per participant during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54% of possible monitoring days) of valid physical activity data and 16 (range 0-27) days (57% of possible monitoring days) of valid sleep data were available. Physical activity decreased from the second trimester to the third trimester by a mean of 1793 (95% CI 1039-2548) steps per day (P<.001). The decrease continued by a mean of 1339 (95% CI 474-2205) steps to the postpartum period (P=.004). Sleep during pregnancy also decreased from the second trimester to the third trimester by a mean of 20 minutes (95% CI -0.7 to 42 minutes; P=.06) and sleep time shortened an additional 1 hour (95% CI 39 minutes to 1.5 hours) after delivery (P<.001). The mean resting heart rate increased toward the third trimester and returned to the early pregnancy level during the postpartum period. CONCLUSIONS: The smart wristband with IoT technology was a feasible system for collecting representative data on continuous variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled appointments and thus may help target and tailor pregnancy follow-up.

Sleep Tracking of a Commercially Available Smart Ring and Smartwatch Against Medical-Grade Actigraphy in Everyday Settings: Instrument Validation Study.

BACKGROUND: Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. OBJECTIVE: This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. METHODS: We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. RESULTS: We found significant correlations between the ring's and actigraphy's TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups. CONCLUSIONS: In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.