Accuracy of vital sign monitoring using a photoplethysmography upper arm wearable device in postoperative patients: A prospective observational clinical validation study

Accuracy of vital sign monitoring using a photoplethysmography upper arm wearable device in postoperative patients: A prospective observational clinical validation study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Accuracy of vital sign monitoring using a photoplethysmography upper arm wearable device in postoperative patients: A prospective observational clinical validation study Noa Reijmers, Arthur Kootwijk, Eric E.C. Waal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7321520/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Vital sign monitoring in patients is essential for the early detection of deterioration of vital signs and timely medical intervention especially on general wards in hospitals. Traditionally performed manually and intermittently, wearable monitoring devices offer a promising alternative by automatically providing real-time, continuous data. In this prospective observational study, we aim to evaluate the accuracy of respiratory rate (RR), heart rate (HR), and peripheral oxygen saturation (SpO₂) measurements obtained from a photoplethysmography (PPG)-based upper arm wearable device viQtor® (smartQare, Eindhoven, The Netherlands), by simultaneously comparing its readings with standard monitoring equipment in the Post-Anesthesia Care Unit (PACU). Capnography was included as the gold-standard reference for RR. Agreement between the wearable and reference measurements were assessed using Bland-Altman analyses. Clinical accuracy was evaluated using Clarke Error Grid analyses. Vital sign data were collected from 42 postoperative patients (age: 65.5 years [IQR 37.4–74.7]; BMI: 24.1 kg/m 2 [IQR 21.7–26.9]) over a median duration of 14.0 hours. The Average Root Mean Square (ARMS) between the wearable device and the reference for RR was 2.85 BRPM, with a bias of -0.40 (95% LoA − 5.85 to 5.04); for HR 2.01 BPM, with a bias of 0.08 (95% LoA − 3.83 to 3.99); and for SpO 2 2.08%, with a bias of − 0.03 (95% LoA − 4.14 to 4.09). The viQtor® device demonstrated high accuracy for RR, HR, and SpO₂ in postoperative patients. Data availability was high across all three parameters, and patient satisfaction was excellent. These findings support its potential for continuous monitoring on general wards. Remote Patient Monitoring Wearable Device Photoplethysmography Clinical Deterioration Vital Signs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Vital signs, including respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO 2 ), are fundamental indicators of a patients’ physiological status and are crucial for early detection of clinical deterioration [ 1 – 4 ]. On general hospital wards however, these parameters are still typically measured manually and intermittently by nursing staff, most often at 8-hour intervals. This approach increases the risk of delayed recognition of clinical deterioration and imposes a substantial workload on clinical staff [ 5 – 7 ]. Wearable monitoring devices offer a promising solution by enabling continuous, non-invasive and wireless monitoring of vital signs [ 8 – 10 ]. Clinical studies have shown that continuous monitoring facilitates earlier intervention, lowers complication rates, reduces intensive care unit (ICU) admissions, shortens hospital stay, and decreases both nursing workload and overall healthcare costs [ 6 , 11 – 17 ]. Despite their potential, the clinical accuracy of wearable monitoring devices is insufficiently evaluated, especially for multi-parameter systems that include SpO₂. Most validation studies to date have focused solely on HR [ 18 – 20 ], or HR and RR [ 21 – 23 ], while SpO 2 is frequently not included in devices under investigation, despite evidence that up to 80% of desaturation episodes remain undetected with standard intermittent monitoring [ 7 ]. Only a limited number of studies have evaluated wearable medical devices capable of simultaneously measuring RR, HR, and SpO 2 in clinical settings [ 24 , 25 ]. One such study assessed a device that relied on multiple wired components which limits its usability in clinical environments, especially in lower-acuity settings such as general wards and home-based monitoring [ 25 ]. Another study evaluated a fully wireless wearable medical device, but data collection was restricted to a brief 40-minute monitoring period [ 24 ]. The goal of our study is to clinically validate the viQtor® wireless wearable medical device (smartQare, Eindhoven, The Netherlands) for continuous monitoring of RR, HR, and SpO 2 in postoperative patients. This population is particularly vulnerable to respiratory and hemodynamic complications and fluctuations in vital signs [ 26 – 28 ]. In contrast to previous studies, this study includes all three vital parameters and evaluates accuracy over extended monitoring periods, providing a more comprehensive and clinically relevant assessment of device performance. Additionally, we evaluate patient-reported satisfaction with the device worn on one of the upper arms. 2 Methods This prospective observational clinical validation study was conducted in the Post-Anesthesia Care Unit (PACU) in the University Medical Center Utrecht (UMCU), Utrecht, The Netherlands. The study was conducted following Good Clinical Practice and was performed in accordance with the ethical standards as laid down in the 2002 Declaration of Helsinki. Formal ethical approval was obtained from the Medical Research Ethics Committee of the UMCU, Utrecht, The Netherlands (METC 23–240). 2.1 Study population Included were adult patients (≥ 18 years) who were expected to be admitted to the PACU for at least 6 hours after elective major non-cardiac surgery. Excluded were patients with known skin hypersensitivity, allergic reactions to metals or plastics, tattoos at the intended sensor placement site, significant upper arm deformities or infections, compromised upper arm blood flow, presence of tremors or convulsions, or upper arm circumferences exceeding the device's fitting range (> 43cm). Patients who agreed to participate signed the informed consent form prior to surgery. 2.2 Study procedure Immediately after surgery at admission to the PACU, the viQtor® wearable device was applied on one of the patient's upper arms to start continuous measurement of RR, HR, and SpO 2 . Simultaneously, standard of care bedside monitoring was conducted using the Spacelab XPREZZON® 91393 (Spacelab Healthcare, Snoqualmie, USA). Patients continued to be monitored with both systems until the next morning, up to a maximum of 24 hours. Upon completion of the monitoring period or upon transfer to the general ward, participants were asked to complete a brief satisfaction survey. This survey included three statements assessed on a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree): I experienced the device as comfortable. I would be willing to wear the device again during a next hospital stay. I experienced skin reactions during or after wearing the armband with the device. 2.3 Investigational device (viQtor®) The investigational device, viQtor® is a CE-certified wearable monitoring system designed to be worn on the upper arm (Fig. 1 ). It uses photoplethysmography (PPG) technology to continuously monitor RR, HR, and SpO 2 . The device also provides non-vital parameters, including skin temperature, an activity index, and fall detection. However, these last three features were not evaluated in this study. viQtor® is reusable and equipped with a rechargeable battery, offering an average operational duration of 5 days per fully charged battery. For research purposes, data containing averaged one-minute values per parameter were stored on an SD card for off-line analysis. In clinical use, viQtor® can wirelessly transmit these data in intervals of 5 minutes via a secure mobile network, accessible through a web-based interface or integration into the electronic health record (EHR). These functionalities were not used or tested in this study. 2.4 Reference bedside monitor The XPREZZON® 91393 is a clinically validated bedside monitor equipped with cables and disposables to continuously track multiple vital signs, including RR, HR, SpO 2 , blood pressure, and body temperature. HR is derived from ECG and SpO 2 is measured using pulse oximetry at the fingertip. RR is typically measured using thoracic impedance pneumography. However, this method is not considered the gold-standard due to its susceptibility to inaccuracies caused by inaccurate ECG sticker placement, detachment and/or motion artifacts [ 29 ]. Therefore, to ensure reliable RR reference values, capnography was added as an additional measurement modality. The Smart Advanced Capnoline® H Plus EtCO 2 sampling line (Medtronic, Boulder, USA) was connected to the Spacelab monitor only during the initial hours of monitoring to minimize patient burden. Thoracic impedance pneumography remained available throughout the full monitoring period. 2.5 Signal analysis Data from the viQtor® and reference systems were processed using Python 3.10. Signals were synchronized by maximizing signal correlation. Low quality data points from the viQtor® were automatically excluded based on its integrated quality index, which accounts for factors such as motion artifacts and low signal-to-noise ratios. For the reference ECG and capnography data, one-minute averages were computed to match the same time intervals of the viQtor® data. For SpO 2 and thoracic impedance pneumography, the reference monitor provided only one data point per minute, which was linearly interpolated to align with the exact time points of the viQtor® measurements. 2.6 Statistical analysis Statistical analysis was performed using Python 3.10. RR, HR and SpO 2 were evaluated using Bland-Altman analysis for repeated measurements [ 30 ]. The primary outcome is the Average Root Mean Square (ARMS), accompanied by the bias and 95% limits of agreement (LoA) between viQtor and the reference. To assess clinical acceptability, predefined ARMS thresholds were applied, derived from values commonly used in literature and international standards [ 31 , 32 ]: RR ARMS ≤ 3 breaths/min (BRPM); HR ARMS ≤ 3 beats/min (BPM); and SpO 2 ARMS ≤ 3%. Secondary outcomes included Clarke Error Grid analyses of RR and HR to evaluate the impact of measurement errors on clinical decision-making [ 33 ]. The Clarke Error Grid is a scatterplot-based method that categorizes data points into five regions (A-E) based on clinical relevance. Region A includes measurements within 20% of the reference. Region B includes measurements outside region A that would not lead to unnecessary treatment. Region C includes measurements that could result in unnecessary treatment. Region D represents potentially dangerous failures to detect a critical event (e.g. bradycardia, tachypnea), and region E reflects measurements where events are confusing (e.g., bradypnea with tachypnea). Thresholds for clinical relevance were based on the Modified Early Warning Score (MEWS) [ 34 ]. Additionally, viQtor® data availability was assessed by calculating average data loss. Finally, patient satisfaction surveys were analyzed descriptively. Supplementary materials include individual error plots and Bland-Altman analyses comparing RR measurements between viQtor ® and thoracic impedance pneumography, as well as capnography and thoracic impedance pneumography. 3 Results From January 2025 to May 2025, a total of 45 postoperative patients were initially included in the study. However, three patients were excluded due to incomplete data: two had no reference measurements available, and one had missing viQtor® data due to an incorrect start of the recording. The characteristics of the remaining 42 patients are summarized in Table 1 . In total, 522.6 h of vital sign monitoring with the viQtor® were available, with a median duration of 14.0 h per patient (range 2.7–22.3 h). Specifically, monitoring with the capnography sampling line was done for 341.3 h, with a median duration of 6.2 h per patient (range 2.4–19.9 h). Table 1 Patient characteristics (n = 42) Female, n (%) 22 (52.4) Age (years), median [IQR] 65.5 [37.4–74.7] BMI (kg/m 2 ), median [IQR] 24.1 [21.7–26.9] Surgical subspecialty, n (%) Neurosurgery 26 (61.9) Abdominal surgery 10 (23.8) Vascular surgery 5 (11.9) Head and neck surgery 1 (2.4) Comorbidities, n (%) Heart disease (ischemic, valvular, arrhythmias) 6 (14.3) Hypertension 3 (7.1) Peripheral vascular disease 2 (4.8) Cerebrovascular disease 1 (2.4) Lung disease (COPD, asthma, fibrosis) 8 (20.0) OSAS 1 (2.4) ASA physical status, median [IQR] 2.5 [ 2 – 3 ] Monitoring duration viQtor® (hours), median [IQR] 14.0 [5.7–18.4] Monitoring duration capnography (hours), median [IQR] 6.2 [5.1–9.6] ASA physical status, American Society of Anesthesiologists Physical Status Classification System; BMI, Body Mass Index; COPD, Chronic Obstructive Pulmonary Disease; IQR, Interquartile Range; kg, kilograms; m, meter; n, number of patients; OSAS, Obstructive Sleep Apnea Syndrome 3.1 Respiratory rate A total of 17,425 reference capnography-viQtor® RR measurement pairs were available for analysis. Data availability from the viQtor® was 95.4%. The overall ARMS was 2.85 BRPM, with a bias of − 0.40 BRPM and LoA of − 5.85 to 5.04 BRPM (Fig. 2 , Table 2 ). These results remained below the predefined acceptable threshold. Fig. S1 a (Supplement 1) shows the error plot of individual results. Table 2 Accuracy outcomes for all three vital signs measured by the viQtor® compared to the reference monitors. ARMS, Average Root Mean Square; HR, Heart Rate; LoA, Limits of Agreement; RR, Respiratory Rate; SpO 2 , peripheral oxygen saturation. Number of data pairs ARMS Bias Lower 95% LoA Upper 95% LoA RR (capnography reference) 17,425 2.85 -0.40 -5.85 5.04 HR 27,361 2.01 0.08 -3.83 3.99 SpO 2 26,842 2.08 -0.03 -4.14 4.09 3.2 Heart rate A total of 27,361 HR measurement pairs were available for analysis. Data availability from the viQtor® was 98.7%. The overall ARMS was 2.01 BRPM, with a bias of 0.08 BRPM and narrow LoA of − 3.83 to 3.99 BRPM (Fig. 3 , Table 2 ). These results remained below the predefined acceptable threshold. Fig. S1 b (Supplement 1) shows the error plot of individual results. 3.3 Oxygen saturation A total of 26,842 SpO 2 measurement pairs were available for analysis. Data availability from the viQtor® was 90.6%. The overall ARMS was 2.08%, with a bias of -0.03% and LoA of − 4.14 to 4.09% (Fig. 4 , Table 2 ). These results remained below the predefined acceptable threshold of ≤ 3%. Fig. S1 c (Supplement 1) shows the error plot of individual results. 3.4 Clarke Error Grid analysis The Clarke Error Grid analysis for RR and HR are presented in Fig. 5 , with the distribution of data pairs across regions A to E summarized in Table 3 . For RR, 98.4% of measurements fell within regions A or B, indicating that the device would support appropriate clinical decision-making in the vast majority of cases. Only 1.6% of RR values were located in regions C, D, or E, suggesting minimal risk of unnecessary interventions, missed treatments, or misinterpretation of critical conditions (e.g., confusing bradypnea with tachypnea). For HR, 100% of measurements were classified within region A or B, demonstrating excellent clinical accuracy of the wearable device. Table 3 Clarke Error Grid analysis outcomes for respiratory rate and heart rate. N, Number of measurement pairs. Region A, N (%) Region B, N (%) Region C, N (%) Region D, N (%) Region E, N (%) Respiratory rate 78.9 19.5 0.4 1.2 0.1 Heart rate 99.8 0.2 0 0 0 3.5 Example of vital sign trend data Figure 6 presents continuous vital sign data over a 22.3-hour period from one of the included postoperative patients, comparing the viQtor® with the reference monitor. To show the full 22-hour trends, pneumography was used as the reference for respiratory rate, as capnography was only available during the first 5 hours. The trends show a natural decline in both RR and HR during the initial hours following surgery, with lower values observed during the night. Notably, two sustained desaturation periods are visible in the SpO 2 trend, highlighting viQtor®’s ability to capture clinically relevant fluctuations. These episodes may have been triggered by routine nursing care activities such as washing or repositioning. 3.6 Patient satisfaction Patients responded very positively to the device. On the 5-point Likert scale survey (1 = strongly disagree, 5 = strongly agree), 98% rated the device as comfortable (n = 40, score 5; n = 1, score 4) and were willing to wear it again (n = 38, score 5; n = 3, score 4). Only one patient (2%) selected the lowest score for both statements, which may reflect a misunderstanding of the scale rather than actual dissatisfaction. No skin reactions were reported. 4 Discussion 4.1 Principal findings This study evaluated the performance of the upper arm PPG-based wearable device (viQtor®) for continuous monitoring of RR, HR, and SpO 2 in a cohort of postoperative patients (ASA physical status median 2.5 [IQR 2–3]) with some having cardiopulmonary comorbidities, such as cardiac arrhythmias treated with cardiac pacemakers, and COPD (Table 1 ). This diversity ensured a broad spectrum of patients and comorbidities, contributing to a robust and clinically relevant validation process. The device showed high agreement with gold-standard reference methods for RR and HR and remained well within the acceptability threshold for SpO 2 compared to the reference pulse oximeter. Data availability was high across all vital signs, and patients found the device comfortable and were willing to wear it again. Agreement for RR was high when compared to the gold-standard capnography, with an ARMS ≤ 3 BRPM. In contrast, comparison with thoracic impedance pneumography yielded substantially lower agreement (ARMS = 4.98 BRPM; Fig S2 a, Supplement 2), highlighting the impact of an adequate reference method. While impedance pneumography is widely used for continuous RR monitoring in PACU settings, it is prone to inaccurate measurements due to motion artifacts, ECG sticker detachment, and speech interference [ 29 ]. A direct comparison between capnography and thoracic impedance pneumography yielded unacceptable agreement (ARMS = 5.39 BRPM; Fig. S2 b, Supplement 2), emphasizing the limitations of impedance-based RR monitoring and the need for robust reference methods in validation studies. These findings also reflect the current challenge of accurately measuring RR in clinical practice. HR measurements from the viQtor® wearable showed high agreement with the reference monitor and excellent clinical accuracy. However, one outlier patient had an ARMS of 10 BPM (Fig. S1 b, Supplement 1) which was attributed to periods of erroneously high HR values caused by poor PPG signal quality. This was likely due to low perfusion at the sensor site, possibly resulting from the patient lying on the arm where the device was worn. SpO 2 measurements from the viQtor® wearable also showed high agreement with the reference pulse oximeter, even though the reference was not a gold-standard. In the example data shown in Fig. 6 , the viQtor® device reported slightly lower SpO₂ values compared to the reference, which measured prolonged readings of 100% saturation. This discrepancy does not reflect overall results, as the pooled Bland-Altman analysis confirmed the absence of systematic bias between the two devices (Fig. 4 ). In contrast to previous validation studies, which focused on fewer vital signs or brief monitoring periods (30 to 40 minutes) [ 18 – 25 ], our study continuously evaluated three key parameters (RR, HR, and SpO 2 ) over a median period of 14 hours per patient, providing a more robust assessment of device performance throughout the PACU stay. Similarly, Breteler et al. recently conducted a validation study of a multi-parameter wearable (Checkpoint Cardio system) with a median monitoring duration of 26 hours in surgical wards. They reported a respiratory rate bias of 1.5 BRPM (LoA − 3.7 to 7.5), HR bias of 0.0 BPM (LoA − 3.5 to 3.4), and SpO 2 bias of 0.4% (LoA − 3.1 to 4.0) [ 35 ], which are comparable to our results. However, the Checkpoint Cardio system is considerably more complex and intrusive, consisting of multiple wired components. Qualitative studies have shown that cumbersome or intrusive devices reduce acceptance among both patients and nurses, hindering clinical implementation [ 36 – 38 ]. In our study, 98% of patients rated the viQtor® as comfortable and expressed willingness to wear it again. Comparative data remain limited, particularly for upper arm worn devices. For example, Lockhorst et al. reported positive experiences in 69% of 191 patients using an adhesive patch sensor [ 37 ]. The higher satisfaction in our study may reflect design advantages of the viQtor®, which uses a soft, elastic arm band instead of adhesives. This design facilitates easy removal, repositioning, and minimizes the risk of skin irritation, features that support prolonged patient use and ease of use by nurses. 4.2 Limitations Several limitations should be considered. Although the study captured important variations in vital signs, the full physiological range was not represented, which may limit the generalizability of the observed device performance. Additionally, patient mobility was relatively low during monitoring in the PACU, whereas patients in general wards or ambulatory settings typically exhibit higher levels of physical activity. The viQtor® device incorporates a signal quality index that excludes segments with poor signal quality, which may be caused by motion artifacts. While this feature improves the accuracy and reliability of reported values, it may reduce data continuity in highly mobile populations. Nevertheless, given the high data availability observed in this study (95.4% for RR, 98.7% for HR, and 90.6% for SpO₂), it is reasonable to assume the device would still outperform standard intermittent monitoring practices in terms of data frequency and the potential for earlier detection of clinical deterioration, even under motion conditions. Furthermore, SpO₂ measurements were compared against a pulse oximeter rather than arterial blood gas analysis (the gold-standard for oxygen saturation) which limits the robustness of the validation. However, given that the viQtor device met the acceptable accuracy threshold in this comparison, it is reasonable that it would also meet this threshold when validated against the gold-standard. 4.3 Future directions Continuous remote vital sign monitoring has the potential to improve patient monitoring (and subsequently patient outcomes) and to reduce clinical workload, especially on general wards where high-risk patients may deteriorate between intermittent checks [ 39 ]. Several studies have reported significant reductions in ICU admissions [ 15 ], complication rates [ 40 ], length of stay [ 13 , 15 ], and nurse workload [ 17 ]. Despite these promising findings, robust evidence remains limited. To advance the field, future research should evaluate comprehensive implementation strategies that integrate continuous monitoring with deterioration detection algorithms, response protocols, and outcome measures reflecting the full clinical pathway [ 39 , 41 ]. A prospective implementation study is currently underway in a surgical ward in the Netherlands [ 42 ]. Similar studies across diverse ward settings and patient populations are needed to optimize continuous monitoring strategies and clinical workflows. Attention should be given to minimizing alarm burden through context-sensitive alerting [ 43 , 44 ] or trend-based assessments without real-time alarms [ 45 ]. 5 Conclusions The PPG-based, upper arm–worn viQtor® device demonstrated high accuracy in measuring RR and HR compared to gold-standard references and met the acceptability threshold for SpO 2 compared to a pulse oximeter. These results support viQtor®’s ability to accurately and continuously monitor postoperative patients at possible risk of clinical deterioration. Data availability was consistently high across all three parameters, and patient satisfaction was excellent. Together, these findings show the potential of the viQtor® device for continuous monitoring on general wards. Declarations Data availability All data generated and analyzed during this study will be made available by the corresponding author on reasonable request (after anonymization). Acknowledgements We gratefully acknowledge the support and collaboration of the PACU staff, and special thanks to Sylvia van Rossum. We also thank all study participants for their valuable contribution. Funding The authors declare that no specific funding or grants were received for the preparation of this manuscript. Authors and affiliations Department of Technical Medicine, Delft University of Technology, Leiden University Medical Center, Erasmus University Medical Center, Rotterdam, The Netherlands Noa Reijmers Department of Computerization, Automation, and Medical Technology (iMED), OLVG, Amsterdam, The Netherlands Arthur van Kootwijk Department of Anesthesiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands Eric E.C. de Waal Contributions NR: Writing – Original Draft. AvK: Data collection, Writing - Review & Editing. EdW: Conceptualization, Data collection, Data analysis, Writing - Review & Editing, Supervision. All authors have read and approved the final manuscript. Corresponding author Correspondence to Eric E.C. de Waal. Ethics declarations Ethical approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of each of the Medical Research Ethics Committees of the University Medical Center Utrecht (METC 23-240). Consent to participate Informed consent was obtained from all individual participants included in the study. Competing interests NR was employed part-time by the company during the study. The remaining authors declare no competing interests. References Buist M, Bernard S, Nguyen TV, Moore G, Anderson J. Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. 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Clinical evaluation of a smart wristband for monitoring oxygen saturation, pulse rate, and respiratory rate. J Clin Monit Comput. 2025;39:451–7. https://doi.org/10.1007/s10877-024-01229-z . van Melzen R, Haveman ME, Schuurmann RCL, van Amsterdam K, El Moumni M, Tabak M, et al. Validity and Reliability of Wearable Sensors for Continuous Postoperative Vital Signs Monitoring in Patients Recovering from Trauma Surgery. Sens (Basel). 2024;24:6379. https://doi.org/10.3390/s24196379 . Buitelaar DR, Balm AJ, Antonini N, van Tinteren H, Huitink JM. Cardiovascular and respiratory complications after major head and neck surgery. Head Neck. 2006;28:595–602. https://doi.org/10.1002/hed.20374 . Mathew JT, D'Souza GA, Kilpadi AB. Respiratory complications in postoperative patients. J Assoc Physicians India. 1999;47:1086–8. Thompson JS, Baxter BT, Allison JG, Johnson FE, Lee KK, Park WY. Temporal patterns of postoperative complications. Arch Surg. 2003;138:596–602. https://doi.org/10.1001/archsurg.138.6.596 . discussion – 3. Bawua LK, Miaskowski C, Hu X, Rodway GW, Pelter MM. A review of the literature on the accuracy, strengths, and limitations of visual, thoracic impedance, and electrocardiographic methods used to measure respiratory rate in hospitalized patients. Ann Noninvasive Electrocardiol. 2021;26:e12885. https://doi.org/10.1111/anec.12885 . Lu MJ, Zhong WH, Liu YX, Miao HZ, Li YC, Ji MH. Sample Size for Assessing Agreement between Two Methods of Measurement by Bland-Altman Method. Int J Biostat. 2016;12. https://doi.org/10.1515/ijb-2015-0039 . :/j/jjb.2016.12.issue-2 . Standardization IOf. ISO 80601-2-61:2017. Medical electrical equipment – Part 2–61: Particular requirements for basic safety and essential performance of pulse oximeter equipment. Geneva: ISO; 2017. Administration USFaD. Pulse Oximeters - Premarket Notification Submissions [510(k)s]: Guidance for Industry and Food and Drug Administration Staff. 2013. Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL. Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care. 1987;10:622–8. https://doi.org/10.2337/diacare.10.5.622 . Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified Early Warning Score in medical admissions. QJM. 2001;94:521–6. https://doi.org/10.1093/qjmed/94.10.521 . Breteler MJM, Leigard E, Hartung LC, Welch JR, Brealey DA, Fritsch SJ, et al. Reliability of an all-in-one wearable sensor for continuous vital signs monitoring in high-risk patients: the NIGHTINGALE clinical validation study. J Clin Monit Comput. 2025. https://doi.org/10.1007/s10877-025-01279-x . Leenen JPL, Dijkman EM, van Dijk JD, van Westreenen HL, Kalkman C, Schoonhoven L, et al. Feasibility of continuous monitoring of vital signs in surgical patients on a general ward: an observational cohort study. BMJ Open. 2021;11:e042735. https://doi.org/10.1136/bmjopen-2020-042735 . Lockhorst EW, van Noordenne M, Klouwens L, Govaert KM, de Bruijn E, Gobardhan PD, et al. Monitoring Vital Signs With Continuous Monitoring After Major Gastrointestinal Surgical Procedures: The Patient, Nurse and Physician Perspective. J Eval Clin Pract. 2025;31:e70099. https://doi.org/10.1111/jep.70099 . Van Melzen R, Haveman ME, Schuurmann RCL, Struys M, de Vries JPM. Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting. Sens (Basel). 2023;23:6736. https://doi.org/10.3390/s23156736 . Khanna AK, Flick M, Saugel B. Continuous vital sign monitoring of patients recovering from surgery on general wards: a narrative review. Br J Anaesth. 2025;134:501–9. https://doi.org/10.1016/j.bja.2024.10.045 . Verrillo SC, Cvach M, Hudson KW, Winters BD. Using Continuous Vital Sign Monitoring to Detect Early Deterioration in Adult Postoperative Inpatients. J Nurs Care Qual. 2019;34:107–13. https://doi.org/10.1097/NCQ.0000000000000350 . Bowles T, Trentino KM, Lloyd A, Trentino L, Jones G, Murray K, et al. Outcomes in patients receiving continuous monitoring of vital signs on general wards: A systematic review and meta-analysis of randomised controlled trials. Digit Health. 2024;10:20552076241288826. https://doi.org/10.1177/20552076241288826 . Jerry EE, Bouwman AR, Nienhuijs SW. Remote Monitoring by ViQtor Upon Implementation on a Surgical Department (REQUEST-Trial): Protocol for a Prospective Implementation Study. JMIR Res Protoc. 2025;14:e70707. https://doi.org/10.2196/70707 . Haahr-Raunkjaer C, Molgaard J, Elvekjaer M, Rasmussen SM, Achiam MP, Jorgensen LN, et al. Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients. Acta Anaesthesiol Scand. 2022;66:552–62. https://doi.org/10.1111/aas.14048 . Anusic N, Gulluoglu A, Ekrami E, Mascha EJ, Li S, Coffeng R, et al. Continuous vital sign monitoring on surgical wards: The COSMOS pilot. J Clin Anesth. 2024;99:111661. https://doi.org/10.1016/j.jclinane.2024.111661 . Leenen JPL, Rasing HJM, van Dijk JD, Kalkman CJ, Schoonhoven L, Patijn GA. Feasibility of wireless continuous monitoring of vital signs without using alarms on a general surgical ward: A mixed methods study. PLoS ONE. 2022;17:e0265435. https://doi.org/10.1371/journal.pone.0265435 . Additional Declarations No competing interests reported. Supplementary Files Supplement1Errorplotsindividualpatients.docx Supplement2Thoracicimpedancepneumographycomparisons.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 09 Aug, 2025 Submission checks completed at journal 09 Aug, 2025 First submitted to journal 07 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7321520","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502306945,"identity":"cb2a6dbd-dfd9-4693-abc2-e0631a7cf019","order_by":0,"name":"Noa Reijmers","email":"","orcid":"","institution":"Delft University of Technology, Leiden University Medical Center, Erasmus University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Noa","middleName":"","lastName":"Reijmers","suffix":""},{"id":502306946,"identity":"47f18835-51ac-49d8-afe7-a661e480e712","order_by":1,"name":"Arthur Kootwijk","email":"","orcid":"","institution":"OLVG","correspondingAuthor":false,"prefix":"","firstName":"Arthur","middleName":"","lastName":"Kootwijk","suffix":""},{"id":502306947,"identity":"7efcb33e-d76a-47bf-a5a7-72df94ff5cbd","order_by":2,"name":"Eric E.C. Waal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYPACZhDBeOADjM/YACTYCWthODgDRLKBtRjABPFrOcxDjBZ+BuYDDD8YrOUMjh9+cNi2bZu8wf3ehw9+7vjDYI5Di2QDWwJjD0O6scGZNIPDuW23DTccYzc27D1jwGDZjF2LwQEeA6BphxO3HUgAa0kwOMbGJs3YZsBgcBi7FvsD/B9AWuq3nX/+4bAlRAv7b3xaDBh4QN48nGB2I8fgMCPUFmZ8WiQOsxkc7DFIN9x/403BwZ5ztw1nHktjluxtM+bBpYW/vfnhgx8V1vKS/ekbH/wouy3Pd/gY44efbXLAMGzArgfoqgNA52ECHuzqR8EoGAWjYBQQAwBjxVr7hkf8yQAAAABJRU5ErkJggg==","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":true,"prefix":"","firstName":"Eric","middleName":"E.C.","lastName":"Waal","suffix":""}],"badges":[],"createdAt":"2025-08-07 19:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7321520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7321520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89986773,"identity":"f258d47f-0c7f-414e-8bf2-d254e8d73590","added_by":"auto","created_at":"2025-08-27 06:56:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244856,"visible":true,"origin":"","legend":"\u003cp\u003eThe\u003cstrong\u003e \u003c/strong\u003eviQtorÒ wearable medical device (smartQare, Eindhoven, The Netherlands). The wearable sensor, attached to the upper arm, measures RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e using PPG signals. Additional features include skin temperature, activity index, and fall detection, which were not evaluated in this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/c325ff0f440427175fac193e.png"},{"id":89988434,"identity":"7bbf1ac2-0595-4c2b-86fb-a03efaccc88b","added_by":"auto","created_at":"2025-08-27 07:04:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98723,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plot from the pooled analysis comparing viQtorÒ respiratory rate measurements to the capnography reference, with color indicating the number of measurement pairs (white = low, black = high). The solid black line represents the bias and the dashed red line the limits of agreement\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/493bc66f87a7c63fe9fd8cd8.png"},{"id":89986777,"identity":"e96721ca-3e68-4c2b-862c-c0a84cbce263","added_by":"auto","created_at":"2025-08-27 06:56:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59906,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plot from the pooled analysis comparing viQtorÒ heart rate measurements to the ECG-derived reference, with color indicating the number of measurement pairs (white = low, black = high). The solid black line represents the bias and the dashed red line the limits of agreement\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/9d59a6461285ddba4698c20f.png"},{"id":89986785,"identity":"0d8a2af5-60ad-4094-99b3-d308b8737286","added_by":"auto","created_at":"2025-08-27 06:56:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83564,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plot from the pooled analysis comparing viQtorÒ SpO\u003csub\u003e2\u003c/sub\u003e measurements to the reference pulse oximeter, with color indicating the number of measurement pairs (white = low, black = high). The solid black line represents the bias and the dashed red line the limits of agreement\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/d94fa8e25e41ba9381ffecc7.png"},{"id":89986789,"identity":"8fb4555d-5145-45f7-8d50-da6e27403db9","added_by":"auto","created_at":"2025-08-27 06:56:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171582,"visible":true,"origin":"","legend":"\u003cp\u003eClarke Error Grid analysis of (a): respiratory rate (capnography reference) and (b): heart rate measurements, with color indicating the number of measurement pairs (white = low, black = high). Region A includes measurements within 20% of the reference. Region B includes measurements outside region A that would not lead to unnecessary treatment. Region C includes measurements that could result in unnecessary treatment. Region D represents potentially dangerous failures to detect a critical event (e.g. bradycardia, bradypnea), and region E reflects measurements where events are confusing (e.g., bradypnea with tachypnea).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/420d3a3e4f6d4d22d4fe02ca.png"},{"id":89988435,"identity":"04e092dc-ae4a-4511-b01a-a27dff0cf4ad","added_by":"auto","created_at":"2025-08-27 07:04:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":519509,"visible":true,"origin":"","legend":"\u003cp\u003eExample vital sign trend data from the viQtorÒ (blue) and reference monitor (red) during 22.3 hours of continuous monitoring, demonstrating variability in vital sign values. From top to bottom, the graphs show respiratory rate (pneumography reference), heart rate, and SpO\u003csub\u003e2 \u003c/sub\u003emeasurements. Pneumography was used as the reference in this illustration because capnography was not available for the full monitoring period.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/459a377e140f5b57a1da9772.png"},{"id":89990419,"identity":"1e74990e-f540-4788-b1e7-0ec294196e2f","added_by":"auto","created_at":"2025-08-27 07:12:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1962687,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/97618409-13aa-4bd1-b119-d8be9928d2aa.pdf"},{"id":89986775,"identity":"22b90366-667f-4548-9460-21afb859fcb0","added_by":"auto","created_at":"2025-08-27 06:56:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":991002,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1Errorplotsindividualpatients.docx","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/368d1bb050d6e6a76e96e3b1.docx"},{"id":89986780,"identity":"b1197a37-57b4-4f7e-9a63-2a27bf1d64ed","added_by":"auto","created_at":"2025-08-27 06:56:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":475421,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2Thoracicimpedancepneumographycomparisons.docx","url":"https://assets-eu.researchsquare.com/files/rs-7321520/v1/66e5dfaa5ee3cec84de0a0e0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accuracy of vital sign monitoring using a photoplethysmography upper arm wearable device in postoperative patients: A prospective observational clinical validation study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eVital signs, including respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e), are fundamental indicators of a patients\u0026rsquo; physiological status and are crucial for early detection of clinical deterioration [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. On general hospital wards however, these parameters are still typically measured manually and intermittently by nursing staff, most often at 8-hour intervals. This approach increases the risk of delayed recognition of clinical deterioration and imposes a substantial workload on clinical staff [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWearable monitoring devices offer a promising solution by enabling continuous, non-invasive and wireless monitoring of vital signs [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Clinical studies have shown that continuous monitoring facilitates earlier intervention, lowers complication rates, reduces intensive care unit (ICU) admissions, shortens hospital stay, and decreases both nursing workload and overall healthcare costs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite their potential, the clinical accuracy of wearable monitoring devices is insufficiently evaluated, especially for multi-parameter systems that include SpO₂. Most validation studies to date have focused solely on HR [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], or HR and RR [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while SpO\u003csub\u003e2\u003c/sub\u003e is frequently not included in devices under investigation, despite evidence that up to 80% of desaturation episodes remain undetected with standard intermittent monitoring [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOnly a limited number of studies have evaluated wearable medical devices capable of simultaneously measuring RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e in clinical settings [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. One such study assessed a device that relied on multiple wired components which limits its usability in clinical environments, especially in lower-acuity settings such as general wards and home-based monitoring [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Another study evaluated a fully wireless wearable medical device, but data collection was restricted to a brief 40-minute monitoring period [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe goal of our study is to clinically validate the viQtor\u0026reg; wireless wearable medical device (smartQare, Eindhoven, The Netherlands) for continuous monitoring of RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e in postoperative patients. This population is particularly vulnerable to respiratory and hemodynamic complications and fluctuations in vital signs [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast to previous studies, this study includes all three vital parameters and evaluates accuracy over extended monitoring periods, providing a more comprehensive and clinically relevant assessment of device performance. Additionally, we evaluate patient-reported satisfaction with the device worn on one of the upper arms.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eThis prospective observational clinical validation study was conducted in the Post-Anesthesia Care Unit (PACU) in the University Medical Center Utrecht (UMCU), Utrecht, The Netherlands. The study was conducted following Good Clinical Practice and was performed in accordance with the ethical standards as laid down in the 2002 Declaration of Helsinki. Formal ethical approval was obtained from the Medical Research Ethics Committee of the UMCU, Utrecht, The Netherlands (METC 23\u0026ndash;240).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eIncluded were adult patients (\u0026ge;\u0026thinsp;18 years) who were expected to be admitted to the PACU for at least 6 hours after elective major non-cardiac surgery. Excluded were patients with known skin hypersensitivity, allergic reactions to metals or plastics, tattoos at the intended sensor placement site, significant upper arm deformities or infections, compromised upper arm blood flow, presence of tremors or convulsions, or upper arm circumferences exceeding the device's fitting range (\u0026gt;\u0026thinsp;43cm). Patients who agreed to participate signed the informed consent form prior to surgery.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study procedure\u003c/h2\u003e\u003cp\u003eImmediately after surgery at admission to the PACU, the viQtor\u0026reg; wearable device was applied on one of the patient's upper arms to start continuous measurement of RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e. Simultaneously, standard of care bedside monitoring was conducted using the Spacelab XPREZZON\u0026reg; 91393 (Spacelab Healthcare, Snoqualmie, USA). Patients continued to be monitored with both systems until the next morning, up to a maximum of 24 hours.\u003c/p\u003e\u003cp\u003e Upon completion of the monitoring period or upon transfer to the general ward, participants were asked to complete a brief satisfaction survey. This survey included three statements assessed on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 2\u0026thinsp;=\u0026thinsp;disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 4\u0026thinsp;=\u0026thinsp;agree, 5\u0026thinsp;=\u0026thinsp;strongly agree):\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI experienced the device as comfortable.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI would be willing to wear the device again during a next hospital stay.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eI experienced skin reactions during or after wearing the armband with the device.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Investigational device (viQtor\u0026reg;)\u003c/h2\u003e\u003cp\u003eThe investigational device, viQtor\u0026reg; is a CE-certified wearable monitoring system designed to be worn on the upper arm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It uses photoplethysmography (PPG) technology to continuously monitor RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e. The device also provides non-vital parameters, including skin temperature, an activity index, and fall detection. However, these last three features were not evaluated in this study. viQtor\u0026reg; is reusable and equipped with a rechargeable battery, offering an average operational duration of 5 days per fully charged battery.\u003c/p\u003e\u003cp\u003eFor research purposes, data containing averaged one-minute values per parameter were stored on an SD card for off-line analysis. In clinical use, viQtor\u0026reg; can wirelessly transmit these data in intervals of 5 minutes via a secure mobile network, accessible through a web-based interface or integration into the electronic health record (EHR). These functionalities were not used or tested in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Reference bedside monitor\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe XPREZZON\u0026reg; 91393 is a clinically validated bedside monitor equipped with cables and disposables to continuously track multiple vital signs, including RR, HR, SpO\u003csub\u003e2\u003c/sub\u003e, blood pressure, and body temperature. HR is derived from ECG and SpO\u003csub\u003e2\u003c/sub\u003e is measured using pulse oximetry at the fingertip. RR is typically measured using thoracic impedance pneumography. However, this method is not considered the gold-standard due to its susceptibility to inaccuracies caused by inaccurate ECG sticker placement, detachment and/or motion artifacts [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, to ensure reliable RR reference values, capnography was added as an additional measurement modality. The Smart Advanced Capnoline\u0026reg; H Plus EtCO\u003csub\u003e2\u003c/sub\u003e sampling line (Medtronic, Boulder, USA) was connected to the Spacelab monitor only during the initial hours of monitoring to minimize patient burden. Thoracic impedance pneumography remained available throughout the full monitoring period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Signal analysis\u003c/h2\u003e\u003cp\u003eData from the viQtor\u0026reg; and reference systems were processed using Python 3.10. Signals were synchronized by maximizing signal correlation. Low quality data points from the viQtor\u0026reg; were automatically excluded based on its integrated quality index, which accounts for factors such as motion artifacts and low signal-to-noise ratios. For the reference ECG and capnography data, one-minute averages were computed to match the same time intervals of the viQtor\u0026reg; data. For SpO\u003csub\u003e2\u003c/sub\u003e and thoracic impedance pneumography, the reference monitor provided only one data point per minute, which was linearly interpolated to align with the exact time points of the viQtor\u0026reg; measurements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using Python 3.10. RR, HR and SpO\u003csub\u003e2\u003c/sub\u003e were evaluated using Bland-Altman analysis for repeated measurements [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The primary outcome is the Average Root Mean Square (ARMS), accompanied by the bias and 95% limits of agreement (LoA) between viQtor and the reference. To assess clinical acceptability, predefined ARMS thresholds were applied, derived from values commonly used in literature and international standards [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]: RR ARMS\u0026thinsp;\u0026le;\u0026thinsp;3 breaths/min (BRPM); HR ARMS\u0026thinsp;\u0026le;\u0026thinsp;3 beats/min (BPM); and SpO\u003csub\u003e2\u003c/sub\u003e ARMS\u0026thinsp;\u0026le;\u0026thinsp;3%. Secondary outcomes included Clarke Error Grid analyses of RR and HR to evaluate the impact of measurement errors on clinical decision-making [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The Clarke Error Grid is a scatterplot-based method that categorizes data points into five regions (A-E) based on clinical relevance. Region A includes measurements within 20% of the reference. Region B includes measurements outside region A that would not lead to unnecessary treatment. Region C includes measurements that could result in unnecessary treatment. Region D represents potentially dangerous failures to detect a critical event (e.g. bradycardia, tachypnea), and region E reflects measurements where events are confusing (e.g., bradypnea with tachypnea). Thresholds for clinical relevance were based on the Modified Early Warning Score (MEWS) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, viQtor\u0026reg; data availability was assessed by calculating average data loss. Finally, patient satisfaction surveys were analyzed descriptively. Supplementary materials include individual error plots and Bland-Altman analyses comparing RR measurements between viQtor \u0026reg; and thoracic impedance pneumography, as well as capnography and thoracic impedance pneumography.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eFrom January 2025 to May 2025, a total of 45 postoperative patients were initially included in the study. However, three patients were excluded due to incomplete data: two had no reference measurements available, and one had missing viQtor\u0026reg; data due to an incorrect start of the recording. The characteristics of the remaining 42 patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In total, 522.6 h of vital sign monitoring with the viQtor\u0026reg; were available, with a median duration of 14.0 h per patient (range 2.7\u0026ndash;22.3 h). Specifically, monitoring with the capnography sampling line was done for 341.3 h, with a median duration of 6.2 h per patient (range 2.4\u0026ndash;19.9 h).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (52.4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.5 [37.4\u0026ndash;74.7]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.1 [21.7\u0026ndash;26.9]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical subspecialty, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurosurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (61.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (23.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (11.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead and neck surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease (ischemic, valvular, arrhythmias)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (14.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral vascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung disease (COPD, asthma, fibrosis)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOSAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA physical status, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonitoring duration viQtor\u0026reg; (hours), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 [5.7\u0026ndash;18.4]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonitoring duration capnography (hours), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2 [5.1\u0026ndash;9.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASA physical status, American Society of Anesthesiologists Physical Status Classification System; BMI, Body Mass Index; COPD, Chronic Obstructive Pulmonary Disease; IQR, Interquartile Range; kg, kilograms; m, meter; n, number of patients; OSAS, Obstructive Sleep Apnea Syndrome\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Respiratory rate\u003c/h2\u003e\u003cp\u003eA total of 17,425 reference capnography-viQtor\u0026reg; RR measurement pairs were available for analysis. Data availability from the viQtor\u0026reg; was 95.4%. The overall ARMS was 2.85 BRPM, with a bias of \u0026minus;\u0026thinsp;0.40 BRPM and LoA of \u0026minus;\u0026thinsp;5.85 to 5.04 BRPM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results remained below the predefined acceptable threshold. Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea (Supplement 1) shows the error plot of individual results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAccuracy outcomes for all three vital signs measured by the viQtor\u0026reg; compared to the reference monitors. ARMS, Average Root Mean Square; HR, Heart Rate; LoA, Limits of Agreement; RR, Respiratory Rate; SpO\u003csub\u003e2\u003c/sub\u003e, peripheral oxygen saturation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of data pairs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBias\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower 95% LoA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper 95% LoA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRR\u003c/b\u003e (capnography reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17,425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27,361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26,842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Heart rate\u003c/h2\u003e\u003cp\u003eA total of 27,361 HR measurement pairs were available for analysis. Data availability from the viQtor\u0026reg; was 98.7%. The overall ARMS was 2.01 BRPM, with a bias of 0.08 BRPM and narrow LoA of \u0026minus;\u0026thinsp;3.83 to 3.99 BRPM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results remained below the predefined acceptable threshold. Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb (Supplement 1) shows the error plot of individual results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Oxygen saturation\u003c/h2\u003e\u003cp\u003eA total of 26,842 SpO\u003csub\u003e2\u003c/sub\u003e measurement pairs were available for analysis. Data availability from the viQtor\u0026reg; was 90.6%. The overall ARMS was 2.08%, with a bias of -0.03% and LoA of \u0026minus;\u0026thinsp;4.14 to 4.09% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results remained below the predefined acceptable threshold of \u0026le;\u0026thinsp;3%. Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec (Supplement 1) shows the error plot of individual results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Clarke Error Grid analysis\u003c/h2\u003e\u003cp\u003eThe Clarke Error Grid analysis for RR and HR are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with the distribution of data pairs across regions A to E summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For RR, 98.4% of measurements fell within regions A or B, indicating that the device would support appropriate clinical decision-making in the vast majority of cases. Only 1.6% of RR values were located in regions C, D, or E, suggesting minimal risk of unnecessary interventions, missed treatments, or misinterpretation of critical conditions (e.g., confusing bradypnea with tachypnea). For HR, 100% of measurements were classified within region A or B, demonstrating excellent clinical accuracy of the wearable device.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClarke Error Grid analysis outcomes for respiratory rate and heart rate. N, Number of measurement pairs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegion A,\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegion B,\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRegion C,\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegion D,\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRegion E,\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiratory rate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeart rate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Example of vital sign trend data\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents continuous vital sign data over a 22.3-hour period from one of the included postoperative patients, comparing the viQtor\u0026reg; with the reference monitor. To show the full 22-hour trends, pneumography was used as the reference for respiratory rate, as capnography was only available during the first 5 hours. The trends show a natural decline in both RR and HR during the initial hours following surgery, with lower values observed during the night. Notably, two sustained desaturation periods are visible in the SpO\u003csub\u003e2\u003c/sub\u003e trend, highlighting viQtor\u0026reg;\u0026rsquo;s ability to capture clinically relevant fluctuations. These episodes may have been triggered by routine nursing care activities such as washing or repositioning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Patient satisfaction\u003c/h2\u003e\u003cp\u003ePatients responded very positively to the device. On the 5-point Likert scale survey (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree), 98% rated the device as comfortable (n\u0026thinsp;=\u0026thinsp;40, score 5; n\u0026thinsp;=\u0026thinsp;1, score 4) and were willing to wear it again (n\u0026thinsp;=\u0026thinsp;38, score 5; n\u0026thinsp;=\u0026thinsp;3, score 4). Only one patient (2%) selected the lowest score for both statements, which may reflect a misunderstanding of the scale rather than actual dissatisfaction. No skin reactions were reported.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Principal findings\u003c/h2\u003e\u003cp\u003eThis study evaluated the performance of the upper arm PPG-based wearable device (viQtor\u0026reg;) for continuous monitoring of RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e in a cohort of postoperative patients (ASA physical status median 2.5 [IQR 2\u0026ndash;3]) with some having cardiopulmonary comorbidities, such as cardiac arrhythmias treated with cardiac pacemakers, and COPD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This diversity ensured a broad spectrum of patients and comorbidities, contributing to a robust and clinically relevant validation process.\u003c/p\u003e\u003cp\u003eThe device showed high agreement with gold-standard reference methods for RR and HR and remained well within the acceptability threshold for SpO\u003csub\u003e2\u003c/sub\u003e compared to the reference pulse oximeter. Data availability was high across all vital signs, and patients found the device comfortable and were willing to wear it again.\u003c/p\u003e\u003cp\u003eAgreement for RR was high when compared to the gold-standard capnography, with an ARMS\u0026thinsp;\u0026le;\u0026thinsp;3 BRPM. In contrast, comparison with thoracic impedance pneumography yielded substantially lower agreement (ARMS\u0026thinsp;=\u0026thinsp;4.98 BRPM; Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea, Supplement 2), highlighting the impact of an adequate reference method. While impedance pneumography is widely used for continuous RR monitoring in PACU settings, it is prone to inaccurate measurements due to motion artifacts, ECG sticker detachment, and speech interference [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A direct comparison between capnography and thoracic impedance pneumography yielded unacceptable agreement (ARMS\u0026thinsp;=\u0026thinsp;5.39 BRPM; Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb, Supplement 2), emphasizing the limitations of impedance-based RR monitoring and the need for robust reference methods in validation studies. These findings also reflect the current challenge of accurately measuring RR in clinical practice.\u003c/p\u003e\u003cp\u003eHR measurements from the viQtor\u0026reg; wearable showed high agreement with the reference monitor and excellent clinical accuracy. However, one outlier patient had an ARMS of 10 BPM (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb, Supplement 1) which was attributed to periods of erroneously high HR values caused by poor PPG signal quality. This was likely due to low perfusion at the sensor site, possibly resulting from the patient lying on the arm where the device was worn.\u003c/p\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e measurements from the viQtor\u0026reg; wearable also showed high agreement with the reference pulse oximeter, even though the reference was not a gold-standard. In the example data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the viQtor\u0026reg; device reported slightly lower SpO₂ values compared to the reference, which measured prolonged readings of 100% saturation. This discrepancy does not reflect overall results, as the pooled Bland-Altman analysis confirmed the absence of systematic bias between the two devices (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to previous validation studies, which focused on fewer vital signs or brief monitoring periods (30 to 40 minutes) [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], our study continuously evaluated three key parameters (RR, HR, and SpO\u003csub\u003e2\u003c/sub\u003e) over a median period of 14 hours per patient, providing a more robust assessment of device performance throughout the PACU stay. Similarly, Breteler et al. recently conducted a validation study of a multi-parameter wearable (Checkpoint Cardio system) with a median monitoring duration of 26 hours in surgical wards. They reported a respiratory rate bias of 1.5 BRPM (LoA \u0026minus;\u0026thinsp;3.7 to 7.5), HR bias of 0.0 BPM (LoA \u0026minus;\u0026thinsp;3.5 to 3.4), and SpO\u003csub\u003e2\u003c/sub\u003e bias of 0.4% (LoA \u0026minus;\u0026thinsp;3.1 to 4.0) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which are comparable to our results. However, the Checkpoint Cardio system is considerably more complex and intrusive, consisting of multiple wired components. Qualitative studies have shown that cumbersome or intrusive devices reduce acceptance among both patients and nurses, hindering clinical implementation [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, 98% of patients rated the viQtor\u0026reg; as comfortable and expressed willingness to wear it again. Comparative data remain limited, particularly for upper arm worn devices. For example, Lockhorst et al. reported positive experiences in 69% of 191 patients using an adhesive patch sensor [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The higher satisfaction in our study may reflect design advantages of the viQtor\u0026reg;, which uses a soft, elastic arm band instead of adhesives. This design facilitates easy removal, repositioning, and minimizes the risk of skin irritation, features that support prolonged patient use and ease of use by nurses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Limitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be considered. Although the study captured important variations in vital signs, the full physiological range was not represented, which may limit the generalizability of the observed device performance. Additionally, patient mobility was relatively low during monitoring in the PACU, whereas patients in general wards or ambulatory settings typically exhibit higher levels of physical activity. The viQtor\u0026reg; device incorporates a signal quality index that excludes segments with poor signal quality, which may be caused by motion artifacts. While this feature improves the accuracy and reliability of reported values, it may reduce data continuity in highly mobile populations. Nevertheless, given the high data availability observed in this study (95.4% for RR, 98.7% for HR, and 90.6% for SpO₂), it is reasonable to assume the device would still outperform standard intermittent monitoring practices in terms of data frequency and the potential for earlier detection of clinical deterioration, even under motion conditions.\u003c/p\u003e\u003cp\u003eFurthermore, SpO₂ measurements were compared against a pulse oximeter rather than arterial blood gas analysis (the gold-standard for oxygen saturation) which limits the robustness of the validation. However, given that the viQtor device met the acceptable accuracy threshold in this comparison, it is reasonable that it would also meet this threshold when validated against the gold-standard.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Future directions\u003c/h2\u003e\u003cp\u003eContinuous remote vital sign monitoring has the potential to improve patient monitoring (and subsequently patient outcomes) and to reduce clinical workload, especially on general wards where high-risk patients may deteriorate between intermittent checks [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Several studies have reported significant reductions in ICU admissions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], complication rates [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], length of stay [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and nurse workload [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite these promising findings, robust evidence remains limited.\u003c/p\u003e\u003cp\u003eTo advance the field, future research should evaluate comprehensive implementation strategies that integrate continuous monitoring with deterioration detection algorithms, response protocols, and outcome measures reflecting the full clinical pathway [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A prospective implementation study is currently underway in a surgical ward in the Netherlands [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similar studies across diverse ward settings and patient populations are needed to optimize continuous monitoring strategies and clinical workflows. Attention should be given to minimizing alarm burden through context-sensitive alerting [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] or trend-based assessments without real-time alarms [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe PPG-based, upper arm\u0026ndash;worn viQtor\u0026reg; device demonstrated high accuracy in measuring RR and HR compared to gold-standard references and met the acceptability threshold for SpO\u003csub\u003e2\u003c/sub\u003e compared to a pulse oximeter. These results support viQtor\u0026reg;\u0026rsquo;s ability to accurately and continuously monitor postoperative patients at possible risk of clinical deterioration. Data availability was consistently high across all three parameters, and patient satisfaction was excellent. Together, these findings show the potential of the viQtor\u0026reg; device for continuous monitoring on general wards.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study will be made available by the corresponding author on reasonable request (after anonymization).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the support and collaboration of the PACU staff, and special thanks to Sylvia van Rossum. We also thank all study participants for their valuable contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no specific funding or grants were received for the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Technical Medicine, Delft University of Technology, Leiden University Medical Center, Erasmus University Medical Center, Rotterdam, The Netherlands\u003c/p\u003e\n\u003cp\u003eNoa Reijmers\u003c/p\u003e\n\u003cp\u003eDepartment of Computerization, Automation, and Medical Technology (iMED), OLVG, Amsterdam, The Netherlands\u003c/p\u003e\n\u003cp\u003eArthur van Kootwijk\u003c/p\u003e\n\u003cp\u003eDepartment of Anesthesiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands\u003c/p\u003e\n\u003cp\u003eEric E.C. de Waal\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNR: Writing \u0026ndash; Original Draft. AvK: Data collection, Writing - Review \u0026amp; Editing. EdW: Conceptualization, Data collection, Data analysis, Writing - Review \u0026amp; Editing, Supervision.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Eric E.C. de Waal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of each of the Medical Research Ethics Committees of the University Medical Center Utrecht (METC 23-240).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNR was employed part-time by the company during the study. The remaining authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBuist M, Bernard S, Nguyen TV, Moore G, Anderson J. Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. 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PLoS ONE. 2022;17:e0265435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0265435\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0265435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Remote Patient Monitoring, Wearable Device, Photoplethysmography, Clinical Deterioration, Vital Signs","lastPublishedDoi":"10.21203/rs.3.rs-7321520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7321520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVital sign monitoring in patients is essential for the early detection of deterioration of vital signs and timely medical intervention especially on general wards in hospitals. Traditionally performed manually and intermittently, wearable monitoring devices offer a promising alternative by automatically providing real-time, continuous data. In this prospective observational study, we aim to evaluate the accuracy of respiratory rate (RR), heart rate (HR), and peripheral oxygen saturation (SpO₂) measurements obtained from a photoplethysmography (PPG)-based upper arm wearable device viQtor\u0026reg; (smartQare, Eindhoven, The Netherlands), by simultaneously comparing its readings with standard monitoring equipment in the Post-Anesthesia Care Unit (PACU). Capnography was included as the gold-standard reference for RR. Agreement between the wearable and reference measurements were assessed using Bland-Altman analyses. Clinical accuracy was evaluated using Clarke Error Grid analyses. Vital sign data were collected from 42 postoperative patients (age: 65.5 years [IQR 37.4\u0026ndash;74.7]; BMI: 24.1 kg/m\u003csup\u003e2\u003c/sup\u003e [IQR 21.7\u0026ndash;26.9]) over a median duration of 14.0 hours. The Average Root Mean Square (ARMS) between the wearable device and the reference for RR was 2.85 BRPM, with a bias of -0.40 (95% LoA \u0026minus;\u0026thinsp;5.85 to 5.04); for HR 2.01 BPM, with a bias of 0.08 (95% LoA \u0026minus;\u0026thinsp;3.83 to 3.99); and for SpO\u003csub\u003e2\u003c/sub\u003e 2.08%, with a bias of \u0026minus;\u0026thinsp;0.03 (95% LoA \u0026minus;\u0026thinsp;4.14 to 4.09). The viQtor\u0026reg; device demonstrated high accuracy for RR, HR, and SpO₂ in postoperative patients. Data availability was high across all three parameters, and patient satisfaction was excellent. These findings support its potential for continuous monitoring on general wards.\u003c/p\u003e","manuscriptTitle":"Accuracy of vital sign monitoring using a photoplethysmography upper arm wearable device in postoperative patients: A prospective observational clinical validation study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:56:06","doi":"10.21203/rs.3.rs-7321520/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-27T21:48:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T05:59:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47810534381548275205173584248236378241","date":"2025-08-26T03:23:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-18T12:42:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-09T09:09:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-09T09:09:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Clinical Monitoring and Computing","date":"2025-08-07T19:09:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5edf71d0-b62b-4417-8142-456ac4335b3f","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-05T15:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:56:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7321520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7321520","identity":"rs-7321520","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}