Apple Watch Ultra 2

Sufyan Shahid, Minahil Iqbal, Humza Saeed, Sara Hira, Amna Batool, Salman Khalid, N. Tahirkheli

Background Electrocardiography (ECG) is the gold standard for the diagnosis of atrial fibrillation (AF). Recently, smartwatches like the Apple Watch have emerged as a promising, user-friendly device for rapid detection and diagnosis of AF, but the reliability and diagnostic accuracy still remain controversial. Objectives The purpose of this study was to perform a systematic review and diagnostic test accuracy meta-analysis evaluating the diagnostic performance of the Apple Watch ECG in detecting AF. Methods The literature search was conducted on PubMed, Embase, and Cochrane Library through April 2024 for studies comparing the diagnostic accuracy of Apple Watch to standard 12-lead ECG. Statistical analysis was performed using R Software version 4.4.0 and OpenMeta[Analyst]. Pooled analyses of sensitivity, specificity, and area under the receiver operating characteristic curve were determined along with their 95% CIs. The quality of studies was analyzed using the QUADAS-2 tool. Results The meta-analysis included 11 studies comprising 4,241 participants. Their mean age was 62.56 ± 3.92 years, and 28% of the patients were females. The pooled sensitivity and specificity of the Apple Watch for detecting AF were 94.8% (95% CI: 91.7% to 96.8%; I2 = 67%) and 95% (95% CI: 88.6% to 97.8%; I2 = 88%), respectively. The area under the receiver operating characteristic curve was 0.96 (95% CI: 0.92-0.97). Conclusions The Apple Watch ECG carries high accuracy in detecting atrial fibrillation, providing a convenient diagnostic option for patients.

Ben O'Grady, Rory Lambe, Maximus Baldwin, Tara Acheson, C. Doherty

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring.

Dominic J. Jaworski, Edward J. Park

This paper investigates the use of audio analysis alongside non-intrusive sensors to measure cardiorespiratory parameters for sleep stage classification. A ballistocardiogram (BCG) detected cardiorespiratory signals through vibrations on the mattress, while an Apple Watch Ultra 2 used optical sensors on the wrist for measurement. The cardiorespiratory signals were processed with non-linear methods and autocorrelation functions to create a dataset from the entire system, which was then trained in a long short-term memory (LSTM) model to classify sleep stages. The output was validated using a polysomnography (PSG) system, showing an agreement of 81% for REM and non-REM sleep stages. Good results were also obtained with audio and BCG, with approximately 76% sleep stage agreement, demonstrating a fully non-contact environment viable for sleep detection. Furthermore, incorporating audio features proved effective in evaluating sleep stages with a non-intrusive setup, ensuring minimum disturbance to participants’ natural sleep cycle.Clinical Relevance—Evaluating sleep quality is an inconvenient process for patients due to the extensive setup and discomfort of a clinical PSG system. A more convenient, minimal contact system that effectively assesses sleep quality without disrupting the patient’s natural sleep could provide clinicians with measurements that better reflect real-world sleep patterns. This would enable more accurate diagnoses of sleep disorders without inducing symptoms associated with the intensive clinical study experience.

Scott Dreisbach, M. Rhudy, Matthew F. Moran, Brandon Henriquez, Praveen Veerabhadrappa

Activity trackers monitor activity and health and can track activity progress within the chronic disease population. The purpose of this study was to validate the step counts of the Apple Watch and compare wrist, hip, and ankle-worn Actigraphs during overground and treadmill walking at different walking intensities using video monitoring of participants, with manual step counting as a reference.The Apple Watch and wrist, hip, and ankle-worn Actigraphs were used for this study. Participants walked on a set track on a flat, overground surface to mimic free-living conditions, with verbal instructions to walk at three stages (slow, moderate, and fast). Subsequently, participants completed a three-stage treadmill protocol using the calculated average velocity/stage from the overground walking. Both conditions were recorded using a Canon VIXIA HF500 camcorder to observe the steps.The Apple Watch had the lowest errors (absolute = –2.32, –8.95, and –20.00 steps; relative = –1%, –2% and –5%) during overground walking for all stages and on the treadmill protocol for the moderate and fast stages (absolute = –3.35 and –2.25 steps; relative = –1% and –1%).The Apple Watch is an extremely accurate and credible consumer device that can be used by the general population for activity monitoring during overground and treadmill walking. The study demonstrated the equivalence of step counts during overground and treadmill walking.

R. Tedeschi, F. Giorgi, V. Ricci, F. Vita, D. Donati