The Use of Apple Smartwatches to Obtain Vital Signs Readings in Dental Surgical Patients

doi:10.21203/rs.3.rs-4334674/v1

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The Use of Apple Smartwatches to Obtain Vital Signs Readings in Dental Surgical Patients

https://doi.org/10.21203/rs.3.rs-4334674/v1

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Introduction: Wearable devices, such as Apple smartwatches, have been widely used for medical and healthcare purposes. Although they provide a wide array of applications, such as heart rate and oxygen saturation determination, only a few studies have demonstrated the reliability and accuracy of these devices. Hence, this study aimed to assess the reliability of the Apple smartwatch series 8 (ASWs8) in determining heart rate and oxygen saturation against conventional monitoring devices (CMD).

Methods: This cross-section study encompassed a cohort of 52 patients from King Abdulaziz University Dental Hospital in Jeddah, Saudi Arabia. We collected heart rate and oxygen saturation data via the ASWs8 and CMD. Subsequently, the mean measurement of each parameter was compared and evaluated for any significant differences.

Results: The ASWs8 yielded a mean heart rate measurement of 83.04 ± 13.4 BPM, while CMD produced 82.81 ± 13.5 BPM. The mean difference stood at -0.23 ± 0.7 BPM, with limits of agreement ranging from -25 to 25%. No significant difference emerged in the determination of heart rate (Cronbach α= 0.999, p= 0.311). Additionally, the ASWs8 recorded an average oxygen saturation measurement of 97.10 ± 1.6%, compared to CMD’s 98.23 ± 1.0%. This resulted in a mean difference of 0.74% and limits of agreement spanning from -3 to 1%. Once again, the analysis unveiled that no statistically significant difference existed in the obtained oxygen saturation levels (Cronbach α= 0.735, p= 0.094).

Conclusion: In this study, we have uncovered compelling evidence that the ASWs8 stands as a reliable and accurate device. It exhibits strong concordance with the CMD, rendering it suitable for continuous vital signs, particularly for heart rate and oxygen saturation. Furthermore, this device can play a pivotal role in clinical management, facilitating the early detection of abnormal physiological parameters among pre-operative patients, thereby potentially reducing hospital admissions.

Health sciences/Health care/Health services

Biological sciences/Physiology

Health sciences/Health care

Physical sciences/Nanoscience and technology

Vital signs monitoring plays an important role in determining abnormal physiological parameters in a pre-operative patient and identifying delayed recovery or adverse effects in a post-operative patient. ^1,2 It also plays a pivotal role in identifying patients’ risk for serious adverse events, allowing healthcare providers to mitigate risk and take the necessary actions to prevent adverse events. Furthermore, assessing a healthcare facility’s ability to respond effectively and provide escalated care to patients has been a key factor in evaluating hospital quality. While vital signs monitoring significantly impact clinical decision-making, influencing whether a patient is hospitalized or discharged, it has been deemed labor-intensive. This process also entails result interpretation and analysis, which not only increases its cost but can often lead to inaccuracies. ¹

In addition, infrequent traditional vital signs monitoring can pose risks by potentially leaving abnormal vital signs undetected. ² Until recently, certain technological advancements have led to the development of medical devices that enable convenient, continuous, and real-time monitoring of multiple parameters. These devices can be used at home or while on the go, providing up-to-date healthcare status information. ^3–5 Such medical devices, also known as wearables, are compact and intelligent devices that leverage wireless sensor networks (WSNs). WSNs consist of many nodes, including sensors, microcontrollers, memory, radio transceivers, and power supply. The sensor, a detection chip, captures data from the patient’s body. The microcontroller handles data processing tasks, such as information compression and controlling other sensor node functions. The memory stores the collected physiological data. The radio transceiver facilitates communication among nodes, enabling wireless transmission and reception of the physiological data. Lastly, the power supply, typically battery-based and with a lifespan of several months, energizes the sensor node. All these nodes are co-located to establish robust intercommunication, forming a wireless body area network (WBAN), which can determine several physiological parameters and provide real-time results. ³

According to Abidoye et al (2011), some of the known applications of wireless sensors in healthcare may include cardiovascular diseases, asthma, cancer detection, diabetes, and artificial retina. ³ Cardiovascular diseases, including stroke, heart attack, heart failure, and coronary artery diseases (CADs), account for approximately 30% of all global deaths, making them one of the leading causes of mortality. In fact, approximately half of the patients typically die either before reaching a healthcare facility or within an hour of symptom onset. WBAN technology, with its continuous detection and transmission of physiological data from the control unit to a medical server through a personal server, can enable real-time monitoring helping to prevent potential delays in healthcare provision. WBAN technology serves various critical roles beyond cardiovascular diseases. It proves invaluable for asthma patients by continuously monitoring airborne allergens, providing real-time data to both physicians and patients. ³ Simultaneously, achieving accurate glucose level monitoring with less invasiveness, such as the case with using WBAN, is increasingly recognized as a more effective approach to managing diabetes. ³

With the aid of technology, wearable devices have evolved to become capable of monitoring parameters and storing data storage for future retrieval. When used for healthcare monitoring, these devices alleviate stress for healthcare providers, reduce the risk of medical error, lighten the workload, enhance the efficiency of hospital staff, and contribute to improved patient comfort. ^3,6 In general, wearables have effectively addressed the demand for enhancing the connection between humans and computers. They have helped students in promoting physical activity, leading to healthier habits. Athletes have befitted from them by tracking energy expenditures, while individuals struggling with obesity have found them valuable in monitoring weight loss progress. Additionally, wearables have played a role in enhancing internal communication among industrial workers. ^5,6

Moreover, wearable devices come in various forms, including smartwatches, glucose sensors, fitness trackers, smart bracelets, and other various types tailored to specific healthcare needs. Among these, smartwatches have gained significant popularity, with the Apple Watch being a prominent product in the market. ^7,8 A smartwatch is a compact device with computational power and can be worn like a conventional wristwatch. A smartwatch does not display the time but also collects and stores extensive, seamlessly transmitting it to other smart devices via short-range wireless connections. ⁹ With their specification and design, smartwatches offer non-invasive, unobtrusive, continuous, and portable healthcare monitoring, enabling users to effortlessly track their health and fitness without disrupting their daily routines.⁸ However, it’s crucial to note that validation studies supporting the accuracy and reliability of smartwatches in determining various physiological parameters, especially vital signs, are currently lacking. Thus, there is an urgent need for such validation. ^1,6

Furthermore, when conventional methods are employed for vital signs monitoring, patients are obligated to visit a healthcare facility solely for the purpose of continuous physiological parameter tracking. In such instances, patients may be required to stay in a hospital, incurring monitoring their status. ⁸ Beyond addressing this concern, smartwatches have the potential to enhance user motivation and encourage a healthier lifestyle due to the continuous availability of personal health data reflection.¹⁰

In this study, we aim to assess the reliability of Apple smartwatches, with a focus on the Apple smartwatch series 8, in monitoring vital signs among pre-operative patients at a dental clinic. The specific objective is to investigate the comparability of heart rate and oxygen saturation readings obtained from the Apple smartwatch series 8 (ASWs8) with those obtained from a conventional monitoring device (CMD).

(The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.)

This is a cross-sectional study carried out at King AbdulAziz University Dental Hospital in Jeddah, Saudi Arabia. The study involved 52 patients, comprising 32 males and 20 females, who sought treatment at outpatient dental clinics between September 2022 and April 2023 during the academic year. Ethical approval was obtained following institute guidelines and conducted in adherence to the Declaration of Helsinki. Demographic data, including age and gender, were collected and each patient’s ASA status was assessed.

Inclusion criteria encompassed individuals classified as ASA I & II, aged 18 years and above, and possessing an Apple Smartwatch Series 8. Since heart rate and oxygen saturation ranges differ between adults and children, we exclusively included adult individuals. Similarly, patients with conditions such as hypertension and cardiovascular or respiratory diseases exhibit distinct heart rate and oxygen saturation levels. Therefore, we opted to include only ASA I and II patients. Those not meeting these criteria were excluded from the study. All eligible patients were provided with and signed informed consent before participating. Subsequently, we categorized patients into three age groups: <25 years old, 25–30 years old, and > 30 years old. The age of the patients ranged from 24 to 54 years, with a mean age of 27.40 ± 5.4 years.

Heart rate (HR) and oxygen saturation measurements were obtained using an Apple Smartwatch Series 8 (ASWs8) and a conventional monitoring device (CMD). The specific CMD utilized for this study was the Dynascope Model DS-7100, classified as type DS-7101L Class I. Both devices, the ASWs8 and CMD, were employed to measure HR and oxygen saturation on both the right and left hands.

The procedure was accomplished as follows: Upon patients’ arrival at the clinic, they were allowed a 5-minute rest period. Subsequently, the ASWs8 was placed on one hand, and the CMD was applied to the other hand to record HR and oxygen saturation readings. After another 5-minute resting period for the patient, the devices were switched between the hands, and HR and oxygen saturation readings were recorded.

Data were collected using a nonprobability convenience sampling technique and calculated at a significance criterion of α = 0.05 and power of 0.80 with a margin of error of 5% and confidence interval of 95%. The results were analyzed using IBM SPSS version 23 (IBM Corp., Armonk, N.Y., USA). A simple descriptive statistic was used to define the characteristics of the study variables through the form of counts and percentages for the categorical and nominal variables while continuous variables are presented by mean and standard deviations. Reliability analysis was used with a model of Alpha (Cronbach) to study the properties of measurement scales, the items that compose the scales, and the average inter-item correlation. A re-analysis was also made with the same test but separated by age, gender, and ASA type. Also, a Linear Regression was used to estimate the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable. Lastly, a conventional p-value < 0.05 was the criteria to reject the null hypothesis.

Out of the 52 patients included in the study, 32 were male, comprising 61.5% of the total patient population. This left 20 females, accounting for the remaining 38.5% of the included patients. Additionally, in terms of ASA classification, 32 out of the 52 patients were classified as ASA I, representing a dominant proportion of 61.5%. This corresponded to a 38.5% representation of ASA II patients, which translates to a cohort of twenty individuals.

Furthermore, the study cohort was stratified into three age groups: those aged < 25 years, those aged 25–30 years, and those aged > 30 years. The < 25 age group comprised 16 patients (30.8%), the 25–30 age group included 25 patients (48.1%), and the > 30 age group encompassed 11 patients (21.2%). The mean age within the cohort was 27.40 ± 5.4 years, with the youngest individuals being 24 years old and the oldest 54 years old. These details are summarized in Table 1.

Table 1

Demographic data of patients (N = 52).
Demographics	Type	Count	Percentage, %
Gender	Male	32	61.5
Gender	Female	20	38.5
ASA	I	32	61.5
ASA	II	20	38.5
Age	< 25	16	30.8
	25–30	25	48.1
	> 30	11	21.2
	Mean ± SD	27.40 ± 5.4
	Minimum	24
	Maximum	54

This research, as detailed in Table 2, provides insights into heart rate and oxygen saturation levels. In the case of heart rate measurements, the mean ± standard deviation (SD) obtained from the right hand was 83.75 ± 13.8 BPM, with a range spanning from 52.0 to 119.0 BPM. Conversely, measurements from the left hand yielded a mean of 82.33 ± 13.3 BPM, with a range of 53.0 and 115.0 BPM. Regarding oxygen saturation, data from the right hand exhibited a mean ± SD of 97.06 ± 2.0%, with values ranging between 92.0% and 100.0%. Meanwhile, measurements from the left hand revealed a mean ± SD of 97.13 ± 2.1%, spanning from 91.0 and 100.0%.

Table 2

Comparison of the means of heart rate and oxygen saturation obtained using ASWs8 and CMD.
	N	Min	Max	Mean	SD
ASWs8 heart rate right hand, BPM	52	52.0	119.0	83.75	13.8
ASWs8 heart rate left hand, BPM	52	53.0	115.0	82.33	13.3
CMD heart rate right hand, BPM	52	51.0	116.0	82.17	13.6
CMD heart rate left hand, BPM	52	52.0	117.0	83.44	13.8
ASWs8 heart rate, BPM	52	52.5	116.5	83.04	13.4
CMD heart rate, BPM	52	51.5	115.5	82.81	13.5
Heart rate difference	52	-1.50	1.50	-0.23	0.7
Heart rate mean	52	52.0	116.0	82.92	13.4
ASWs8 oxygen saturation right hand, %	52	92.0	100.0	97.06	2.0
ASWs8 oxygen saturation left hand, %	52	91.0	100.0	97.13	2.1
CMD oxygen saturation right hand, %	52	95.0	100.0	98.29	1.2
CMD oxygen saturation left hand, %	52	96.0	100.0	98.17	1.1
ASWs8 oxygen saturation, %	52	92.5	100.0	97.10	1.6
CMD oxygen saturation, %	52	96.5	100.0	98.23	1.0
Oxygen saturation difference	52	-1.50	2.50	0.74	1.1
Oxygen saturation mean	52	95.5	100.0	97.82	1.0
Abbreviations: ASWs8: Apple smartwatch series 8, CMD: conventional monitoring device, SD: standard deviation, Min: minimum, Max: maximum

Furthermore, for the assessment of vital signs using conventional monitoring devices, heart rate data from the right hand demonstrated a mean ± SD of 82.17 ± 13.6 BPM, with readings varying from 51.0 to 116.0 BPM. Measurements from the left hand indicated a mean ± SD of 83.44 ± 13.8 BPM, ranging from 52.0 to 117.0 BPM. In terms of oxygen saturation, values from the right hand showed a mean ± SD of 98.29 ± 1.2%, with a range of 95.0 and 100.0%. On the other hand, oxygen saturation data from the left hand yielded a mean ± SD of 98.17 ± 1.1%, with values spanning from 96.0 and 100.0%.

To assess the reliability of ASWs8 in deterring heart rate and oxygen saturation compared to CMD, we refer to the data presented in Table 2. The overall mean heart rate obtained through ASWs was 83.04 ± 13.4, CMD yielded a mean of 82.81 ± 13.5, showcasing a negligible overall difference of -0.23, with the minimum and maximum deviations being − 1.50 and 1.50, respectively.

Turning to oxygen saturation measurements, ASWs8 produced an overall mean of 97.10 ± 1.6, whereas CMD provided a mean of 98.23 ± 1.0. The combined mean of these measures was 97.82 ± 1.0, indicating an overall difference of 0.74. The range of differences observed spanned from − 1.50 to 2.50. These findings illuminate the comparative performance of ASWs8 and CMD in assessing heart rate and oxygen saturation levels.

As presented in Tables 3 and 4, the reliability and proportionality bias tests demonstrate that the ASWs8 and CMD exhibit no statistically significant differences in determining heart rates (Cronbach α = 0.999, p = 0.311). Similarly, the analysis reveals that there are no significant differences between ASWs8 and CMD in determining oxygen saturation (Cronbach α = 0.735, p = 0.094).

Table 3

Data for reliability test.
Reliability Statistics	Cronbach's Alpha	N of Items
Hearth rate	0.999	2
Oxygen saturation	0.735	2

Table 4

Data for the proportionality bias test.
ANOVA		Sum of Squares	df	Mean Square	F	p-value
Hearth rate^a	Regression	0.568	1	0.568	1.045	0.311
	Residual	27.163	50	0.543
	Total	27.731	51
Oxygen saturation^b	Regression	3.189	1	3.189	2.941	0.094
	Residual	46.622	43	1.084
	Total	49.811	44
^aDependent variable = Hearth rate difference, Predictors = Hearth rate mean. ^bDependent variable = Oxygen saturation difference, Predictors = Oxygen saturation mean.

To further elucidate the relationship between the mean values obtained through ASWs8 and CMD, we conducted a statistical analysis of the mean difference using 0 as the test value (Table 5). Results also affirm that the ASWs8 and CMD exhibit no statistically significant differences in their determinations of both the heart rate and oxygen saturation.

Table 5

One sample t-test with a test value of 0.
Dependent Variable		Unstandardized Coefficients		Standardized Coefficients	t	p-value
Dependent Variable		B	Std. Error	Beta	t	p-value
Heart rate difference	(Constant)	-0.882	0.645	0.143	-1.367	0.178
Heart rate difference	Mean	0.008	0.008	0.143	1.022	0.311
Oxygen saturation difference	(Constant)	26.245	14.869	-0.253	1.765	0.085
Oxygen saturation difference	Mean	-0.261	0.152	-0.253	-1.715	0.094

We also employed Bland-Altman plots as a visual tool to illustrate the concordance between measurements obtained with ASWs8 and CMD. Remarkably, the mean differences between the two devices consistently fell within the upper and lower confidence intervals, aligning closely with the zero axis. These graphical representations, showcased in Figs. 1 and 2 for heart rate and oxygen saturation measurements, respectively, underscore the remarkable similarity in readings obtained from both devices.

A further analysis was conducted, categorizing the data by age, gender, and ASA type (Table 6–7). In general, the results indicate that ASWs8 and CMD exhibit no statistically significant differences in the determination of both heart rate and oxygen saturation, particularly within the male population, as evidenced by p-values of 0.771 for heart rate and 0.657 for oxygen saturation. Similarly, when examining different age groups, no statistically significant differences emerged. Specifically, for the ≤ 25 age group, the corresponding p-values were 0.110 for heart rate and 0.541 for oxygen saturation. In the 25–30 age group, p-values of 0.528 and 0.968 were recorded for heart rate and oxygen saturation, respectively. Lastly, within a general ASA type I population, the analysis showed no significant differences, with corresponding p-values of 0.065 for heart rate and 0.779 for oxygen saturation.

Table 6

Estimation of parameters using heart rate difference as the dependent variable.
Parameter	B	S.E.	95% C.I.		p-value
Parameter	B	S.E.	Lower Bound	Upper Bound	p-value
Intercept	-0.076	0.306	-0.692	0.541	0.806
Gender = Male	0.067	0.230	-0.395	0.530	0.771
Age ≤ 25	0.499	0.307	-0.118	1.117	0.110
Age = 25–30	-0.164	0.258	-0.682	0.355	0.528
ASA = I	-0.441	0.233	-0.910	0.028	0.065

Table 7

Estimation of parameters using oxygen saturation difference as the dependent variable.
Parameter	B	S.E.	95% C.I.		p-value
Parameter	B	S.E.	Lower Bound	Upper Bound	p-value
Intercept	1.032	0.621	-0.223	2.287	0.104
Gender = Male	-0.184	0.412	-1.016	0.648	0.657
Age ≤ 25	-0.328	0.531	-1.401	0.746	0.541
Age = 25–30	0.019	0.460	-0.912	0.949	0.968
ASA = I	-0.117	0.416	-0.958	0.723	0.779

Errors in healthcare provision often stem from the absence of timely and comprehensive information, resulting in incorrect clinical diagnoses. To ensure safety and avert potential fatalities, healthcare providers should have seamless access to precise and reliable patient data. Thus, physiological parameters play an important role in clinical decision-making. ³ Nowadays, a wide variety of wearables and automatic devices that are capable of monitoring and tracking different physiological parameters have been widely available for healthcare and medical purposes. ⁵ Technological advances have allowed users to effortlessly collect their information, such as time of sleep, heartbeat, number of daily steps, number of movements, etc. ⁴ Patients, whether located in urban or rural areas, can utilize such devices with or without the internet. This not only alleviates stress and reduces the workload of healthcare providers but also minimizes errors through continuous monitoring, lowers healthcare service costs, and enhances patient comfort. ³ With these benefits as well as their potential benefits, the market for wearable devices has been steadily expanding. Although these devices can offer quicker and more convenient vital sign measurements, concerns about their accuracy persist. ¹ Only a limited number of studies have addressed the validity and reliability of wearable devices such as smartwatches. ⁶

Wearable devices like ASWs8 provide precise heart rate measurements by capturing blood flow in the wrist and calculating it as heartbeats per minute. For many years, determining heart rate has remained a crucial parameter for both medical diagnoses and daily health monitoring. ¹¹ Alongside blood pressure and electrocardiography (ECG), heart rate serves as a potential predictor of a heart attack. ¹² In a prior study, researchers developed an application wherein patients with cardiac diseases were monitored using various sensors. This research combined a universal computation with the aid of digital health advancements, enabling the continuous monitoring of high-risk cardiac patients through sensors and smartphones. The analyses generated by smartphones served as real-time ECG, offering critical insights into whether the patient required external support and assistance. Depending on the assessed risk, the smartphone would automatically send alerts to the designated healthcare provider and/or ambulance. Additionally, patients received personalized exercise recommendations based on environmental information. In conclusion, the authors effectively processed sensor data using smartphones, enabling uninterrupted monitoring without the need for hospitalization. ¹³ Previously, the U.S. FDA approved ECG features in Apple smartwatches, starting with series 4. Such features allow safe tracking of heart rhythm, offering advantages in managing patients with a variety of conditions, including COVID-19. ¹⁴

This study revealed that the reliability of ASWs8 in determining heart rate has similar results to that obtained with the CMD without any statistically significant differences. Moreover, the mean differences almost fell within the zero axis, which implies that these differences might not be significant enough to show reliability differences. The results of the current study agreed with some prior studies conducted to compare the reliability of smartwatches with standard vital signs recording methods. Spaccorotella et al. (2022) verified the accuracy of the Apple smartwatch Series 6 against a standard pulse oximeter using data obtained from 257 adult patients, with a mean age of 64 years old. Results revealed that the two devices have good concordance with a bias of -0.11 only, and a range of -5.84 and 5.63%. Thus, the reliability of the two methods was found to be clinically insignificant. ¹⁵ Meanwhile, Pipek et al (2021) conducted a study comparing heart rate measurements obtained from an Apple smartwatch series 6 and a conventional oximeter in a sample of 100 patients. Their findings indicated no significant differences between the two devices in determining these parameters, with a mean difference of 0% and limits of agreement ranging from − 8 to 8%. ⁴ Similarly, Seshadri et al. (2020) assessed the reliability of the Apple smartwatch series 4 in measuring heart rate by comparing its readings to telemetry in a controlled setting. They included fifty post-operative patients with atrial fibrillation in their study. The results revealed that the mean heart rate readings obtained from the Apple smartwatch were 88 ± 14, ranging from 57 to 125 BPM, while telemetry recorded rates of 86 ± 15, ranging from 54 to 137 BPM. Concordance was employed to evaluate the agreement between the two devices, revealing less-than-optimal agreement for patients with atrial fibrillation. As a result, further evaluation was deemed necessary. ¹⁶

Hernando et al. (2018) validated the reliability of the Apple smartwatch in determining heart rate variability among twenty healthy individuals during their relaxation and mental stress. They employed a validated heart rate monitor, specifically the Polar H7 chest band, as a reference. Their findings revealed strong reliability between the two devices, with a correlation coefficient exceeding 0.90. ¹⁷ On the contrary, Khushhal et al. (2017) evaluated the reliability and validity of the Apple smartwatch in determining heart rate during and after various physical activities, including walking, jogging, and running. Their study involved twenty-nine healthy males with a mean age of 31.4 ± 7.4. The criteria for evaluation were set using a Polar S810i monitor. The results revealed a trend where device reliability decreased with increasing physical activity intensity, displaying a stronger correlation during milder activities such as walking and a varying, though generally less reliable, correlation during higher-intensity activities like jogging and running. A similar trend was observed in terms of device validity with accuracy decreasing as exercise intensity increased. ¹⁸ In addition, Hahnen et al. (2020) conducted a related study in which they compared heart rate readings from a smartwatch (brand: Everlast) to a standard device. Their findings indicated an average absolute difference of 6.5 ± 9.2 BPM, demonstrating that the Everlast smartwatch met accuracy standards for heart rate measurements. ⁵ On a different note, Scherbina et al. (2017) assessed the accuracy of seven commercially available wearable devices, including the Apple smartwatch, in determining heart rate. The results revealed that the Apple Watch achieved the lowest overall error, with an accuracy rate of 2.0%. The minimum and maximum error values were found to be 1.2 and 2.8%, respectively. ⁶

When comparing the reliability of heart rate measurement with wearable devices, it’s important to note that the assessment of oxygen saturation is a relatively newer and less explored field. Oxygen saturation, like any other vital sign, serves as an indicator of a patient’s respiratory adequacy, making it a critical parameter for continuous monitored, especially for those with respiratory issues. Traditionally, pulse oximeters have been the go-to method for measuring oxygen saturation. These devices employ a probe that is typically placed on either the finger or ear. They utilize light-emitting diodes (LEDs) to gauge oxygen saturation in the blood by shining infrared light through the tissue, measuring the absorbed light’s corresponding saturation value. ^11,15

More recently, wearable devices with sensors and applications capable of continuous oxygen saturation measurement have emerged. ¹⁹ According to Barros et al. (2021), the Oxitone 100 M ⁸ is the first FDA-approved wearable health monitor, that features a pulse sensor positioned on the upper part of the ulnar wrist bone. However, measuring oxygen saturation on the wrist imposes certain challenges. Data collection on the wrist is area-specific, akin to the radial artery, and even slight variations in measurement placement can significantly impact results. ¹⁴ Even with these possible drawbacks, the availability and accessibility of wearable devices, such as the Apple smartwatch, can still allow monitoring oxygen saturation at home. During the COVID-19 pandemic, there has been a surge in the popularity of home-based vital signs monitoring, particularly for oxygen saturation. This is due to the presentation of hypoxia, panting with difficulty in breathing, and the potential need for intubation among COVID-19 patients. In times when healthcare facilities are overwhelmed, wearable devices have played a pivotal role in monitoring oxygen saturation, serving as an indicator for hospitalization or early intubation. Home-based healthcare monitoring has also contributed to reducing the spread of COVID-19 contagion brought by suspected or infected individuals. ¹⁴

In this study, the reliability of ASWs8 in measuring oxygen saturation was also validated. The mean oxygen saturation obtained with ASWs8 was 97.10 ± 1.6, while with CMD, it was 98.23 ± 1.0. This resulted in a mean difference of 0.74%, with a range of -1.50 and 2.50%. The results revealed no significant difference between the measurements obtained from ASWs8 and CMD. These findings aligned with those of Spaccarotella et al. (2022), who found a strong correlation between Apple Watch Series 6 and conventional devices in determining oxygen saturation among 257 adult patients, with a bias of -0.23% and limits of -3.49 and 3.04%. The authors concluded that the Apple smartwatch can provide accurate results for oxygen saturation in both healthy and cardiovascular patients. ¹⁵

Similarly, Littell et al. (2022) assessed the accuracy of Apple Watch Series 6 against a conventional pulse oximeter in 84 pediatric cardiology patients, averaging 7.2 years old. Using the traditional oximeter as the reference, the median oxygen saturation was obtained at 98%, ranging from 78 to 100%. Comparing the two devices, the average absolute difference was only 2%m with a correlation coefficient of 0.76 and limits of agreement between − 7% and 5%. The study concluded that the Apple smartwatch series 6 is accurate enough for measuring oxygen saturation in the pediatric population. ²⁰ In the same year, Rafl et al. (2022) compared the reliability of Apple smartwatch series 6 against a conventional pulse oximeter (Masimo Radical − 7) in detecting hypoxemia among twenty-four healthy participants. Their oxygen saturation measurements were taken at 30-second intervals. After obtaining 642 individual data points, the bias or mean difference was computed to be 0% (95% CI: -0.2 to 0.3) for the entire range, 1.2% (95% CI: 0.7 to 1.7) for the < 90% oxygen saturation range, and − 0.3% (95% CI: -0.6 to 0.1) for the 90–100% oxygen saturation range. These relatively low biases suggested that the Apple smartwatch 6 can reliably and accurately detect hypoxemia or any reduction in blood oxygen saturation. ²¹

Pipek et al. (2021) assessed the oxygen saturation of sixty-one patients, including those with interstitial lung disease. The average readings were 94.4% with a conventional oximeter and 95.9% with an Apple watch. The study found a correlation coefficient of 0.81 and limits of agreement between − 2.7% and 4.1%, concluding that the Apple Watch Series 6 is reliable for measuring oxygen saturation in patients with lung diseases. ⁵ Moreover, Hearn et al. (2021) compared arterial oxygen saturation measurements using four different wearable devices, including the Apple smartwatch Series 6 and the FDA-approved Oxitone 100M, ⁸ against a traditional pulse oximeter as the reference. The study involved ten healthy volunteers (6 male, 4 female) with a mean age of 27 ± 6 years. The oxygen saturation was measured at different activity intensities, such as stationary, slight body motion, and moderate body motion. The results showed no significant differences between the readings from the Apple smartwatch and Oxitone 100M ⁸ compared to the reference pulse oximeter. suggests that the Apple smartwatch has high validity after passing reliability tests against both a traditional reference and an FDA-approved device. ²²

In summary, validating the reliability and accuracy of ASWs8 offers substantial benefits, including its non-invasive nature, user-friendly interface, and widespread availability. For these reasons, this device serves as an excellent alternative that facilitates reliable, frequent, and uninterrupted health monitoring, potentially leading to enhanced healthcare outcomes and cost reduction. ²³ In addition, individuals generally favor smartwatches due to their comfortable and sleek design, offering prolonged usability. ¹⁴

Although ASWs8 presents promising features and this study provides novel and valuable insights and directions, it’s crucial to acknowledge certain potential. First, Apple smartwatches are often considered expensive compared to other digital devices. Previous research has highlighted that the general population’s familiarity with using such devices is limited, with more substantial knowledge primarily confined to healthcare professionals. Consequently, it becomes imperative for users of wearables to receive adequate training in terms of both device operation and data analysis. ²⁴ This knowledge, especially as it pertains to data analysis is important to prevent unnecessary anxiety and apprehension. Moreover, it’s worth noting that most of the studies conducted so far have taken place in a controlled environment with stable temperatures. This implies that the applicability of ASWs8 might not be consistent under varying temperature conditions. ¹⁵ Additionally, wearable devices may exhibit variability in performance under conditions involving minimal body movement and variation in data collection sites. We only included healthy patients, but we needed someone who is not healthy like CVS or resp. diseases when determining these levels. Are they functional? That is a limitation because we excluded them.

This study effectively validates the reliability of the Apple Smartwatch Series 8 in measuring heart rate and oxygen saturation, comparing it to a conventional monitoring device. ASWs8 exhibits strong concordance with CMD for heart rate measurements. Similarly, ASWs8 showcases comparable reliability to CMD for oxygen saturation measurements. It has the potential to significantly contribute to clinical management by promptly identifying abnormal physiological parameters in pre-operative patients, potentially reducing hospital admissions.

Author Contribution

SA and AB Conception , wrote the main manuscript textMA, RA, MN Analysis of dataOB, MB,AA Acquisition of data

Data Availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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The Use of Apple Smartwatches to Obtain Vital Signs Readings in Dental Surgical Patients

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