Assessing the Accuracy of Smartwatch-Based Estimation of Maximum Oxygen Uptake Using the Apple Watch Series 7: Validation Study.

BACKGROUND: Determining maximum oxygen uptake (VO2max) is essential for evaluating cardiorespiratory fitness. While laboratory-based testing is considered the gold standard, sports watches or fitness trackers offer a convenient alternative. However, despite the high number of wrist-worn devices, there is a lack of scientific validation for VO2max estimation outside the laboratory setting. OBJECTIVE: This study aims to compare the Apple Watch Series 7's performance against the gold standard in VO2max estimation and Apple's validation findings. METHODS: A total of 19 participants (7 female and 12 male), aged 18 to 63 (mean 28.42, SD 11.43) years were included in the validation study. VO2max for all participants was determined in a controlled laboratory environment using a metabolic gas analyzer. Thereby, they completed a graded exercise test on a cycle ergometer until reaching subjective exhaustion. This value was then compared with the estimated VO2max value from the Apple Watch, which was calculated after wearing the watch for at least 2 consecutive days and measured directly after an outdoor running test. RESULTS: The measured VO2max (mean 45.88, SD 9.42 mL/kg/minute) in the laboratory setting was significantly higher than the predicted VO2max (mean 41.37, SD 6.5 mL/kg/minute) from the Apple Watch (t18=2.51; P=.01) with a medium effect size (Hedges g=0.53). The Bland-Altman analysis revealed a good overall agreement between both measurements. However, the intraclass correlation coefficient ICC(2,1)=0.47 (95% CI 0.06-0.75) indicated poor reliability. The mean absolute percentage error between the predicted and the actual VO2max was 15.79%, while the root mean square error was 8.85 mL/kg/minute. The analysis further revealed higher accuracy when focusing on participants with good fitness levels (mean absolute percentage error=14.59%; root-mean-square error=7.22 ml/kg/minute; ICC(2,1)=0.60 95% CI 0.09-0.87). CONCLUSIONS: Similar to other smartwatches, the Apple Watch also overestimates or underestimates the VO2max in individuals with poor or excellent fitness levels, respectively. Assessing the accuracy and reliability of the Apple Watch's VO2max estimation is crucial for determining its suitability as an alternative to laboratory testing. The findings of this study will apprise researchers, physical training professionals, and end users of wearable technology, thereby enhancing the knowledge base and practical application of such devices in assessing cardiorespiratory fitness parameters.

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.

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF.

Background: Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases. Objective: DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats. Methods and Results: We performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as "AF," "SR" or "Cannot be concluded." In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012). Conclusions: This is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Dataset of acceleration signals recorded while performing activities of daily living.

Several research studies have investigated the human activity recognition (HAR) domain to detect and recognise patterns of daily human activities. However, the accurate and automatic assessment of activities of daily living (ADLs) through machine learning algorithms is still a challenge, especially due to limited availability of realistic datasets to train and test such algorithms. The dataset contains data from 52 participants in total (26 women, and 26 men). The data for these participants was collected in two phases: 33 participants initially, and 19 further participants later on. Participants performed up to 5 repetitions of 24 different ADLs. Firstly, we provide an annotated description of the dataset collected by wearing a wrist-worn measurement device, Empatica E4. Secondly, we describe the methodology of the data collection and the real context in which participants performed the selected activities. Finally, we present some examples of recent and relevant target applications where our dataset can be used, namely lifelogging, behavioural analysis and measurement device evaluation. The authors consider the dissemination of this dataset can highly benefit the research community, and specially those involved in the recognition of ADLs, and/or in the removal of cues that reveal identity.

Can wrist-worn devices and a smartphone application influence arm activity in children with unilateral cerebral palsy? A proof-of-concept study.

Aim: To determine whether a wrist-worn triaxial accelerometer-based device and software (including smartphone application), incorporating feedback, is feasible, acceptable, and can lead to increased affected upper limb use during everyday activities in children with unilateral cerebral palsy (UCP). Methods: Study design: Mixed methods proof of concept study. Participants: Children aged 8-18 years with UCP; age-matched typically developing controls ("Buddies"), therapists. Intervention: Baseline (2 weeks): devices recorded arm activity. Active feedback (6 weeks): devices also gave vibratory prompts if affected arm activity fell below pre-set personalised thresholds (UCP group only; control group continued as per Baseline). Final 2 weeks: as baseline. Both groups accessed a smartphone application providing feedback on relative arm motion throughout the study. Assessment and analysis: ABILHAND-Kids questionnaires and MACS classifications captured baseline participant characteristics (UCP group). Accelerometer data was used to calculate relative arm activity (signal vector magnitude) corrected for time worn/day, and trends in relative arm activity examined using single case experimental design (both groups). In-depth interviews with families, "Buddies" and therapists assessed feasibility and acceptability of implementation. A framework approach was used for qualitative data analysis. Results: We recruited 19 participants with UCP; 19 buddies; and 7 therapists. Five participants (two with UCP) did not complete the study. Baseline mean (stdev) ABILHAND-Kids score of children with UCP who completed the study was 65.7 (16.2); modal MACS score was II.Qualitative analysis demonstrated acceptability and feasibility of the approach. Active therapist input for this group was minimal. Therapists appreciated the potential for summary patient data to inform management. Arm activity in children with UCP increased in the hour following a prompt (mean effect size z = 0.261) for the non-dominant hand, and the dominant hand (z = 0.247). However, a significant increase in affected arm activity between baseline and intervention periods was not demonstrated. Discussion: Children with UCP were prepared to wear the wristband devices for prolonged periods. Whilst arm activity increased bilaterally in the hour following a prompt, increases were not sustained. Delivery of the study during the COVID-19 pandemic may have negatively influenced findings. Technological challenges occurred but could be overcome. Future testing should incorporate structured therapy input.

Effect of Missing Inter-Beat Interval Data on Heart Rate Variability Analysis Using Wrist-Worn Wearables.

Most of the wrist-worn devices on the market provide a continuous heart rate measurement function using photoplethysmography, but have not yet provided a function to measure the continuous heart rate variability (HRV) using beat-to-beat pulse interval. The reason for such is the difficulty of measuring a continuous pulse interval during movement using a wearable device because of the nature of photoplethysmography, which is susceptible to motion noise. This study investigated the effect of missing heart beat interval data on the HRV analysis in cases where pulse interval cannot be measured because of movement noise. First, we performed simulations by randomly removing data from the RR interval of the electrocardiogram measured from 39 subjects and observed the changes of the relative and normalized errors for the HRV parameters according to the total length of the missing heart beat interval data. Second, we measured the pulse interval from 20 subjects using a wrist-worn device for 24 h and observed the error value for the missing pulse interval data caused by the movement during actual daily life. The experimental results showed that mean NN and RMSSD were the most robust for the missing heart beat interval data among all the parameters in the time and frequency domains. Most of the pulse interval data could not be obtained during daily life. In other words, the sample number was too small for spectral analysis because of the long missing duration. Therefore, the frequency domain parameters often could not be calculated, except for the sleep state with little motion. The errors of the HRV parameters were proportional to the missing data duration in the presence of missing heart beat interval data. Based on the results of this study, the maximum missing duration for acceptable errors for each parameter is recommended for use when the HRV analysis is performed on a wrist-worn device.

Multi-Biosignal Analysis for Epileptic Seizure Monitoring.

Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient's physician. Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist-worn device and which in combination produce a very low false positive rate. Stage II looks for a specific pattern created by these three biosignals. For the patients whose seizures cannot be detected by Stage II, Stage III detects seizures using limited-channel electroencephalogram (EEG) monitoring with at most three electrodes. Out of 10 patients, Stage I recognized all 11 seizures from seven patients, Stage II detected all 10 seizures from six patients and Stage III detected all of the seizures of two out of the three patients it analyzed.