The completeness, accuracy and impact on alerts, of wearable vital signs monitoring in hospitalised patients

The completeness, accuracy and impact on alerts, of wearable vital signs monitoring in hospitalised patients | 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 The completeness, accuracy and impact on alerts, of wearable vital signs monitoring in hospitalised patients Anthony Joseph Wilson, Alexander J Parker, Gareth B Kitchen, Andrew Martin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4976766/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Use of wearable vital signs sensors to monitor hospitalised patients is growing but uncertainty exists about completeness of data capture and accuracy of measurements. Implications for track and trigger systems are unclear. Methods In this observational study, adult inpatients with Covid-19 wore four wearable sensors recording heart rate/respiratory rate (HR/RR), oxygen saturation (SpO 2 ), axillary temperature and blood pressure (BP). Wearable vitals were paired with traditional vitals recorded concurrently. The accuracy of the wearable vitals was assessed using traditional vitals as the reference. National early warning (NEWS2) scores were calculated using wearable and traditional vitals. Results 48 patients were monitored for 204 days with the sensors. Median sensor wear was 3.9(IQR:1.7-5.9), 3.9(IQR:1.6-5.9) and 3.8(IQR:0.9-5.9) days for HR/RR, temperature and SpO 2 respectively. The BP cuff was worn for median 1.9(IQR:0.9-3.8) days in 33 patients. Length of hospital stay was 8(IQR:6-13) days. Completeness of data capture was 84% for HR/RR, 98% for temperature, 72% for SpO 2 and 36% for BP. There were 1632 HR, 1613 RR, 1411 temperature, 1294 SpO 2 and 51 BP wearable-traditional measurement pairs. 59.7% of HR pairs were within ±5bpm, 38.5% of RR pairs within ±3breaths/min, 24.4% of temperature pairs within ±0.3 o C, 32.9% of SpO 2 pairs within ±2% and 39.0% of BP pairs within ±10mmHg. Agreement between wearable and traditional RRs was poor at high RRs. 613 NEWS2 scores were calculated using wearable-traditional HR, RR, temperature and SpO 2 pairs. The median NEWS2 traditional was 1(IQR:1-2) and the median NEWS2 wearable was 4(IQR:3-6). Using traditional NEWS2 alerts as a reference, 86% (225/262) of wearable NEWS2 5+ alerts and 89% (82/92) of wearable NEWS2 7+ alerts were false positives. Conclusions Agreement between vital signs recorded by wearable sensors and concurrent traditional vitals is poor. Data from wearable sensors should not be used in existing track and trigger systems. Wearable sensors Vital signs monitoring Early warning scoring systems Covid-19 SARS-CoV-2 Figures Figure 1 Figure 2 Figure 3 Background Wearable vital signs sensors (WVSSs) are wireless, non-invasive devices worn by patients which permit near-continuous recording of heart rate (HR), respiratory rate (RR), oxygen saturations (SpO 2 ) and blood pressure (BP). In low resource settings and healthcare economies struggling with staff shortages, such sensors could automate repetitive manual vital signs measurements thereby freeing time to care. The near-continuous recording theoretically allows no deterioration to go unnoticed and permits advanced analytics to predict and detect important patient centred outcomes and potentially to personalise care. Patients and their relatives may feel a sense of security in knowing that they are always monitored. With these benefits in mind, there has been a growing interest 1 in the use of WVSSs to monitor patients in hospital but there remains uncertainty about the accuracy of the vital signs they record 2 , 3 . The evidence that they have a positive impact on outcomes for hospitalised patients is not established 4 . Numerous studies have assessed the accuracy of vitals recorded by WVSSs but many have focussed on short timescales or have compared WVSSs to critical care standards of monitoring, sometimes in optimised environments 3 . A true picture of their performance requires assessment in hospital wards which are subject to the challenges and limitations of such environments. Most hospitals use some form of track and trigger system to detect and respond to the deteriorating patient 5 . Yet clinical staff do not always follow track and trigger protocols 6 and WVSSs which could automate early warning score (EWS) completion could be valuable. However, studies which assess the impact of incorporating vital signs data from WVSSs into such systems are uncommon 7 . Instead, many authors have suggested developing new approaches to the deteriorating patient which can accommodate data from WVSSs. These have included incorporating an additional alerting step to prompt manual calculation of an EWS 8 , adjusting EWS systems to reflect wearable data 9 , 10 or the development of novel deterioration indices utilising wearable data 11 . A benefit of the EWS system is its simplicity and widespread adoption. New approaches add complexity. It is therefore prudent to assess what would happen if vital signs from WVSSs were simply used in existing EWS systems. In this context, we conducted the COSMIC-19 (continuous signs monitoring in Covid-19) study which evaluated a suite of WVSSs in hospitalised patients with Covid-19 in a real-world, ward environment. Our aim was to determine the completeness and accuracy of the data recorded by the WVSSs in comparison to traditional ward vital signs. We also sought to assess how National Early Warning Score 2 (NEWS2) scores 12 would differ if calculated using vitals obtained by WVSSs instead of concurrent traditional vitals measurements. Methods The COSMIC-19 study was registered with clinicaltrials.gov (registration: NCT04581031). It was approved by Yorkshire and the Humber – Bradford Leeds Research Ethics Committee, UK (reference 20/YH/0156). It was funded by the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, (project ID: 6239) with additional support from the Christie Hospital Charitable Fund. Symptomatic, adult inpatients (16 years or older) with suspected or confirmed Covid-19, who were suitable for escalation to critical care and receiving supplemental oxygen when screened were eligible for enrolment. Patients were recruited within 72 hours of hospital admission. We excluded patients unable to give informed consent, who were anticipated to die within 24 hours or those with a contraindication to wearing the WVSSs. Patients were recruited at Manchester Royal Infirmary, an academic, tertiary, metropolitan hospital in the UK with over 150,000 emergency department presentations annually. Wearable vital signs sensors We investigated four WVSSs which measured heart rate/respiratory rate (HR/RR), oxygen saturation (SpO 2 ), axillary temperature and systolic blood pressure (SBP). The four sensors were purchased from Isansys Ltd 13 and were used as part of the Isansys Patient Status Engine (PSE), a digital platform for vital signs capture and reporting which is a regulated medical device. Isansys Ltd had no input in the design or conduct of the research. The wearables are summarised in Table 1 . Participants wore the four WVSSs for up to 20 days or until they lost capacity to consent to continue or were discharged, whichever was earlier. They were free to discontinue any sensor at any time. Participants received usual care during monitoring with the WVSSs, including traditional vital signs measurements by ward staff. This included temperature measurements using a tympanic thermometer. The clinical team and participants were blinded to the WVSS measurements. The research team monitored the participants for WVSS disconnection via a remote dashboard. Each participant’s data was reviewed at least once every 24 hours. In the event of a disconnection, the researchers visited the participant to aid with wearable re-application or removal if they chose to discontinue a sensor. The research team maintained a log of all occasions when participants removed the sensors and their reasons for doing so. Data capture We recruited a convenience sample of 30–60 participants, informed by the number of WVSSs available for the research. No formal sample size calculations were performed. An electronic case report form (eCRF, DataTrial Ltd, Newcastle, UK) was used to capture demographics, diagnostic information, sensor application/removal and outcomes. Traditional vital signs were downloaded from the hospital electronic track and trigger system (Patientrack, Alcidion Ltd, Aus). Traditional vitals were filtered to exclude non-physiological values. Wearable vital signs were captured via the Isansys PSE. Error codes were removed from the wearable data but no vital signs measurements were excluded. Data analysis The completeness of vital sign capture was reported as the percentage of time for which a vital sign was recorded by a WVSS as a proportion of the total time for which the sensor was worn. The number of participants who removed each sensor prematurely and their reasons for doing was summarised. A Kaplan Meier analysis was conducted where the event of interest was a temporary gap in wearable sensor data of varying durations. Participants were censored if they permanently removed the WVSS for any reason. For each WVSS, the accuracy of the vital signs was assessed using traditional vital signs as a reference standard. For each traditional measurement, a corresponding wearable vital signs measurement was determined by taking the median of all wearable measurements within the preceding five minutes (for HR, RR, temperature and SpO 2 ) or 15 minutes (for SBP). These timescales and the approach were chosen to align with existing published work 7 , 14 – 19 . A Bland Altman analysis 20 was performed to determine the bias and 95% limits of agreement (LoA) for each vital. The repeated nature of measurements was accounted for using the methods described by Zou et al. 21 In keeping with previous literature 3 , we defined ± 5bpm, ±3breaths/min, ± 2%, ± 0.3 o C and ± 10mmHg a priori as clinically acceptable agreement between HR, RR, SpO 2 , temperature and SBP respectively. To assess how monitoring patients with WVSS would impact clinical alerts, a partial National Early Warning Score 12 (NEWS2) was calculated using wearable sensor measurements and compared to the NEWS2 calculated from traditional vitals. Both calculations did not include level of consciousness and air/oxygen scores. We assumed all participants were on SpO 2 scale 1. A modified Clarke Error Grid analysis 22 was also performed for each individual NEWS2 component. This quantifies how differences between corresponding wearable and traditional measurements would impact the NEWS2 component. Finally, to compare trends between successive wearable and traditional measurements, 4Q plots 23 were created for each vital sign. All analysis was conducted using R (version 3.6) 24 . Continuous variables were assessed for normality and are presented as mean (standard deviation) or median [interquartile range]. Categorical variables are presented as the population size and the percentage (of available data) for each class. Table 1 a summary of the wearable health monitor devices deployed in this study. BLE - Bluetooth low energy, BP = blood pressure, ECG = electrocardiogram, HR = heart rate, HRV = heart rate variability, pleth = plethysmography, PSE = patient status engine, RR = respiratory rate, SpO 2 = haemoglobin oxygen saturation. Feature Wearable Vital Signs Sensor HR/RR Isansys Lifetouch Sensor 13 Temperature Isansys Lifetemp Sensor 13 SpO 2 Nonin3150 WristOx 25 BP A&D TM2441 BP Monitor 26 Form Factor A small patch attached via standard ECG electrodes A small adhesive patch Wrist-worn module, pulse oximetry finger probe Cuff with attached servo unit. CE marked medical device Yes Yes Yes Yes Wear Location Precordium Axilla Wrist and fingertip Arm Frequency of vital sign measurement Every minute Every minute Every minute Hourly (6am-10pm), every two hours (10pm-6am) Battery Life 72 hours. 5 days. 48 hours. 2x AAA batteries. 2x AA batteries. Single Use? Yes Yes No No Data Synchronisation BLE to Samsung Galaxy Tablet. Subsequent data upload via WiFi/cellular from tablet to remote server (Isansys LifeGuard Server) Software Used Isansys PSE software. Metrics recorded Accelerometer (activity and posture), ECG (single lead), HR, HRV, RR. Temperature (skin - axilla) HR (pleth) SpO 2 (finger) Non-invasive BP Results Study participants Figure 1 summarises screening and recruitment. 179 eligible patients were identified and 48 took part. Most (43/48) were recruited between July 2020 and March 2021, during the first and second UK waves of the coronavirus pandemic. Five were recruited between July 2021 and February 2022, during the delta and omicron waves in the UK 27 . In 47 participants, SARS-CoV-2 infection was confirmed on nasopharyngeal swab (lateral flow or polymerase chain reaction), one participant had symptoms consistent with Covid-19 and high clinical suspicion of infection but without a positive screening result. Table 2 summarises the demographics and clinical characteristics of study participants, stratified according to whether they were admitted to critical care. 32/48 (66.7%) were male and 32/48 (66.7%) were from non-Caucasian ethnicities. Overall Not admitted to ICU Admitted to ICU N=48 n=37 n=11 Male sex 32 (66.7) 25 (67.6) 7 (63.6) Age 51.0 [40, 58] 51 [36, 60] 48 [42, 56] Ethnicity Arabic 3 (6.2) 1 (2.7) 2 (18.2) Indian/Pakistani 20 (41.7) 12 (32.4) 8 (72.7) Black 8 (16.7) 8 (21.6) 0 (0.0) Mixed 1 (2.1) 1 (2.7) 0 (0.0) Caucasian 16 (33.3) 15 (40.5) 1 (9.1) BMI kg/m 2 N=43 29.7 [27.0, 34.7] 30.2 [26.7, 34.0] 29.1 [28.2, 37.3] Type 2 diabetes mellitus 12 (25.0) 8 (21.6) 4 (36.4) Hypertension 12 (25.0) 8 (21.6) 4 (36.4) Ischaemic heart disease 2 (4.2) 2 (5.4) 0 (0.0) CKD (stage 2+) 4 (8.3) 2 (5.4) 2 (18.2) Heart failure (NYHA 3+) 1 (2.1) 1 (2.7) 0 (0.0) COPD 3 (6.2) 2 (5.4) 1 (9.1) Asthma 13 (27.1) 9 (24.3) 4 (36.4) Smoking status Current smoker 4 (8.3) 2 (5.4) 2 (18.2) Ex-smoker 9 (18.8) 7 (18.9) 2 (18.2) Never smoked 32 (66.7) 25 (67.6) 7 (63.6) Unknown 3 (6.2) 3 (8.1) 0 (0.0) Rockwood frailty score 1 15 (31.2) 11 (29.7) 4 (36.4) 2 17 (35.4) 15 (40.5) 2 (18.2) 3 14 (29.2) 10 (27.0) 4 (36.4) 4 2 (4.2) 1 (2.7) 1 (9.1) Table 2: demographics and clinical characteristics of patients recruited into the study. BMI = body mass index, CKD = chronic kidney disease classification, COPD = chronic obstructive pulmonary disease, ICU = intensive care unit, NYHA = New York Heart Association classification. Completeness of data capture from wearable sensors The median time from admission to wearable sensor application was 27 (IQR:22–46) hours. The median length of hospital stay for each participant was 8 (IQR:6–13) days. The total number of patient-days of sensor wear was 202, 200, 204 and 82 days for the HR/RR, temperature, SpO 2 and BP WVSSs respectively (Table 3 ). This represented a median duration of wear of approximately 4 days per participant for the HR/RR, temperature and SpO 2 sensors. The duration of wear for the BP cuff was median 1.9 days per participant. This was due to connectivity difficulties and because many participants either declined the sensor or requested early removal. The duration of wearable application was similar when calculated from the data recorded by each wearable or from the sensor log maintained by the research team (appendix 1). Table 3 duration of wear (first to last valid wearable vital signs measurement), completeness of data capture and reasons for wearable removal. The blood pressure cuff was not applied to 15 participants at their request. BP = non-invasive blood pressure, SpO 2 = oxygen saturations. Wearable Sensor (N = 48) HR/RR (LifeTouch) Temperature (LifeTemp) SpO 2 (Nonin PulseOx) BP (A&D TM2441) Duration of sensor wear (days/participant) 3.9 [1.7, 5.9] 3.9 [1.6, 5.9] 3.8 [0.9, 5.9] 1.9 [0.9–3.8] Overall completeness (%) of wearable sensor data 81.2 92.1 68.6 38.4 Completeness (%) per participant 83.8 [64.1, 95.7] 97.7 [79.7, 99.8] 72.3 [61.7, 87.2] 35.8 [16.3, 47.6] Reason for device removal Discharge from hospital 25 (52.1) 23 (47.9) 24 (50.0) 9 (18.8) Critical care, loss of capacity 4 (8.3) 4 (8.3) 3 (6.3) - Participant request 19 (39.6) 21 (43.8) 21 (43.8) 22 (45.8) Never applied - - - 15 (31.3) Other - - - 2 (4.2) Figure 2 summarises the results of the Kaplan Meier analysis of gaps of varying duration in wearable sensor data. The analysis was limited for SBP due to the intermittent nature of measurements (maximum frequency of wearable recordings: 1–2 hourly). The median survival time without data loss and the percentage of patients without data loss in 24 hours are summarised in appendix 2. Accuracy of wearable sensor measurements After creating pairs of wearable and traditional vital signs measurements (within the 5/15min epochs, see methods), there were 1633, 1614, 1412, 1294 and 59 pairs of HR, RR, temperature, SpO 2 and SBP measurements respectively. 59.7% HR pairs were within ± 5bpm, 38.5% of RR pairs were within ± 3breaths/min, 24.4% of temperature pairs were within ± 0.3 o C, 32.9% of SpO 2 pairs were within ± 2% and 39.0% of SBP pairs were within ± 10mmHg. The correlation coefficients for each vital sign are displayed in Table 4 (see appendix 3 for scatterplots). Figure 3 displays the Bland Altman plots for HR, RR, temperature and SpO 2 stratified according to whether the participant was on a ward or in critical care. The corresponding plot for SBP is available in appendix 4. There was constant variation between wearable and traditional vital signs measurements across the measurement range for HR and SpO 2 . RR measurements showed a systematic difference at high mean RRs in critical care, with traditional RR measurements being higher than wearable measurements. A sensitivity analysis (appendix 5) identified that this difference was due to data from five participants who had higher traditional RR measurements than other participants. Temperature measurements also showed a systematic difference at low mean temperatures with traditional measurements being higher than wearable measurements. There were insufficient SBP measurement pairs to comment on the variation in measurements. Table 4 displays the bias and limits of agreement for each vital sign. Not all participants wore all sensors and only participants with at least two measurement pairs are included in this analysis. Table 4 Bland Altman metrics for each wearable vital sign measurement compared to traditional vital signs measurements as the reference standard. Bias = wearable - traditional (95% confidence interval), LoA = 95% limits of agreement (95% confidence interval). HR = heart rate, RR = respiratory rate, SBP = systolic blood pressure, SpO 2 = oxygen saturations, Temp = temperature. *any participants with only a single measurement pair for the vital sign concerned are excluded to enable calculation of the 95% confidence intervals. Vital sign HR (bpm) RR (/min) Temp ( o C) SpO 2 (%) SBP (mmHg) Participants* 46 46 47 43 10 Measurement pairs 1632 1613 1411 1294 51 Pairs/participant 24 [9,39] 24 [9,39] 26 [10,42] 24 [9,32] 5 [3,5] Correlation coefficient (Pearson r) 0.77 (0.75 to 0.79) 0.19 (0.15 to 0.24) 0.33 (0.28 to 0.38) 0.51 (0.47 to 0.55) 0.24 (-0.02 to 0.47) Bias -0.2 (0.3 to -0.7) -1.8 (-1.4 to -2.2) -1.0 (-1.0 to -1.1) -2.8 (-2.6 to -2.9) -0.7 (6.9 to -8.3) Upper LoA 21.3 (23.3 to 19.6) 14.2 (17.0 to 12.0) 1.7 (2.1 to 1.4) 3.5 (4.1 to 2.9) 37.6 (55.5 to 27.9) Lower LoA -21.6 (-19.9 to -23.7) -17.8 (-15.6 to -20.6) -3.8 (-3.5 to -4.2) -9.0 (-8.5 to -9.6) -39.0 (-29.2 to -56.8) Impact of wearable vital signs measurements on alerts Amongst the pairs of traditional and wearable vital signs measurements, there were only 31 instances when HR, RR, temperature, SpO 2 and SBP were simultaneously available to calculate a partial NEWS2 (see methods). Appendix 6 summarises the differences in partial NEWS2 scores calculated from these five vital signs and the impact on the rate of NEWS2 5 + or 7 + alerts by each method. This small number of NEWS2 scores reflects that many participants did not wear the BP cuff or chose to remove it early. In contrast, there were 613 instances when HR, RR, temperature and SpO 2 were simultaneously available to calculate a partial NEWS2. The median NEWS2 by traditional methods was 1 [IQR: 1–2] and by wearable methods was 4 [IQR: 3–6]. Table 5 summarises the number of times when a NEWS2 5 + or 7 + alert would have been generated by each method. At our institution, NEWS2 5 + would typically alert the ward based medical team, whilst 7 + would typically generate a critical care response. Table 5: confusion matrix for NEWS2 scores calculated from 4 vital signs (heart rate, respiratory rate, temperature and SpO 2 ) using paired traditional and wearable vital signs. A positive NEWS2 score is considered a score of 5+ or 7+ respectively. *On 11 occasions, vitals from wearable sensors identified a 5+ NEWS2 event at the same time as traditional measurements but also identified a 5+ NEWS2 event in the preceding 12 hours which was not detected by traditional measurements. We considered this to represent early detection of deterioration and therefore a true positive. $ Similarly, there were 5 instances of early detection of a 7+ NEWS2 event. NEWS2 = national early warning score 2. NEWS2 5+ (4 vitals, traditional) NEWS2 7+ (4 vitals, traditional) Positive Negative Positive Negative NEWS2 5+ (4 vitals, wearable) Positive 26 + 11* 225 Negative 6 345 NEWS2 7+ (4 vitals, wearable) Positive 5 + 5 $ 82 Negative 2 519 Appendix 7 displays the differences in NEWS2 component scores for each pair of vital signs measurements. The corresponding modified Clarke Error Grids for each vital sign are displayed in appendix 8. The greatest agreement in NEWS2 component scores was observed for HR (85.1%). For all other NEWS2 component scores agreement was less than 50%. Appendix 9 displays the 4Q plots which assess the correlation in differences between successive vital sign measurements by traditional and wearable techniques. There was a moderate correlation between HR measurements recorded on the ward (r = 0.39) and in critical care (r = 0.40), between SpO 2 measurements recorded in critical care (r = 0.35) and between SBP measurements recorded on the ward (r = 0.45). In all other cases there was little or no correlation. Discussion In this observational study of WVSSs in hospitalised patients with Covid-19 we monitored HR, RR, temperature and SpO 2 in 48 individuals for a median duration of 3.8–3.9 days. Non-invasive, intermittent, automated BP measurement was poorly tolerated and suffered technical challenges such that the median duration of monitoring was 1.9 days in 33 out of 48 participants. Our findings align with previous research suggesting that wearable patch/wrist-based vital signs sensors can achieve comprehensive data capture in an inpatient setting with modest (once daily) intervention to maintain the devices. Studies in which WVSSs have been validated against traditional vitals, in ward-based settings and for prolonged periods (> 48 hours) are uncommon but offer real world evidence about WVSS performance. Completeness of data capture by wearable sensors in such studies ranges from 76–96% for patch-based, chest HR/RR sensors 28 , 29 and from 50–68% for wrist worn pulse oximeters 30 , 31 . Our results are similar, and the variation between studies may be attributed to different patient populations and different levels of experience amongst patients, researchers and clinical teams in using and maintaining the sensors. Isolation measures due to Covid-19, may have also limited opportunities for sensor maintenance. Even the lowest rate of data capture in our study (SpO 2 , 68.6%) equates to continuous vitals for 16 out of every 24 hours, far exceeding what could be captured by traditional methods. Our survival analysis found that there would be no gaps in the data of over 4 hours duration for 87.9%, 81.5% and 97.5% of patients using the HR/RR, SpO 2 and temperature sensors in the first 24 hours respectively. As four hours is often the interval between nurse measured vital signs 32 it suggests that the time nursing staff would need to spend troubleshooting/reapplying such devices would be acceptable. In high-risk surgical populations in the Netherlands, Breteler and colleagues 18 , 19 found even greater durability of data capture with wearable sensors. Our results extend this finding to a UK setting with medical inpatients who are subject to isolation restrictions. In contrast to the chest and axillary patch sensors we acknowledge that the data capture of BP measurements in our study was poor. For cuff-based, BP sensors, data capture rates of 44–63% have been recorded 30 , 33 and our rates were lower than this due to technical difficulties and patient discomfort. This limits the conclusions which we can draw about the A&D TM2441 device as a useful wearable monitor but perhaps stresses the importance of considering if such devices are truly acceptable to patients as an automated, wearable technique. Inflation of a BP cuff can be uncomfortable, and it may be more tolerable when recorded by a nurse. We observed wide limits of agreement between traditional and wearable vital signs such that differences in only 59.7% of HR, 38.5% of RR, 32.9% of SpO 2 , 24.4% of temperature and 39.0% of SBP pairs were within pre-defined clinically acceptable limits which was lower than previous studies 34 – 36 . However, our findings agree with existing bias/LoA estimates where HR 15 , 16 , RR 16 and temperature 16 , 37 measurements have been validated in similar circumstances and where the repeated nature of measurements in the same patient is accounted for. It is difficult to comment on the validity of SBP measurements given the paucity of measurements in our study. In keeping with previous work 3 , we found that the patch-based, chest sensor tended to underestimate high RRs in critically ill patients who were transferred to the ICU. The patch-based, axillary temperature sensor also frequently recorded lower temperatures than a traditional tympanic thermometer. The reasons for imperfect agreement between wearable vitals and nurse recorded vitals are myriad. It is well recognised 38 that vital signs recorded by healthcare professionals are impacted by poor measurement technique, value bias in recording and a Hawthorne effect during measurement 39 . Arguably therefore, WVSSs may offer a truer reflection of a patient’s ongoing, non-observed physiological state. However, systematic errors in wearable vital signs measurements may also play an important role. In our study, the temperature differences we observed may be because estimates of core temperature recorded by an axillary skin sensor are different to estimates recorded by tympanic thermometers used at our institution. Similarly, RRs determined by a wearable sensor based on an algorithm utilising chest impedance and R-R variation may have been subject to systematic error in Covid-19 patients in whom a relative bradyarrhythmia has been observed 40 (potential for error due to Nyqist sampling limit) and potentially lower than expected chest wall excursion (potential for error due to smaller variation in chest impedance). Acknowledging these inherent differences, some authors have suggested that WVSS should not be compared to nurse recorded “spot” vitals 19 . We disagree, because the comparison serves to illustrate the impact that using WVSSs could have on existing patient deterioration alerting mechanisms in hospitals. In our study, NEWS2 scores derived from HR, RR, temperature and SpO 2 measurements were typically higher when calculated from WVSSs than traditional vitals. Differences in RR, SpO 2 and temperature NEWS2 component scores were responsible for most of this difference (appendix 7). Compared to NEWS2 scores generated by traditional vitals, 85.9% (225/262) of NEWS2 5 + and 89.1% (82/92) of NEWS2 7 + from the WVSSs were false positives. False negatives were rare, 1.7% (6/351) and 0.4% (2/521) for NEWS2 5 + and 7 + respectively. In keeping with our results, Weenk and colleagues found that two WVSS systems studied on surgical and medical wards 7 both returned higher modified early warning scores (MEWS) than traditional nurse recorded vital signs. Our findings suggest that vital signs obtained from WVSSs should not be used to directly replace traditional vital signs in track and trigger systems that utilise NEWS2. Compared to NEWS2 scores derived from concurrently recorded traditional vitals the majority of wearable NEWS2 alerts would be false alarms. We also highlight that more work may be needed to confirm the validity of wearable vital signs measurements in settings of illness (very high RR), where a patient’s physiology may be quite different from healthy volunteers. We propose that a different approach is needed to adopt WVSSs into care pathways for the deteriorating patient. This could include scheduled reviews of wearable sensor trends by healthcare providers 41 as opposed to automated alerts, re-development of EWS thresholds for specific use with wearable sensor data 9 , 10 or development of novel, broadly applicable deterioration indices using the granular data which wearable sensors provide or some of the additional metrics which they record. Examples include the presence or absence of micro-events 42 within the continuous data, trend information 43 and heart rate variability 44 . This work was a real-world clinical evaluation of WVSSs in a challenging clinical setting. The duration of monitoring is similar to the longest periods of monitoring in previous wearable studies. As monitoring was continued into the ICU for some patients, we were also able to assess sensor performance in critically ill patients with more extreme physiology. Furthermore, we studied the clinical implications of adopting wearable sensors both in terms of maintenance requirements to prevent large gaps in data and the implications of using the wearable data in NEWS2 calculations. We acknowledge several limitations. Firstly, we studied a population with Covid-19 in which there was a preponderance of Black and Asian participants and a high rate of admission to critical care. 45 This may limit the generalisability of our findings to wider populations of inpatients. Our ward staff were blinded to wearable sensor data and not involved in sensor maintenance. Taken together with the fact that we studied a group at high risk of deterioration, with significant isolation and infection control demands, this may mean that future, deployment of wearable sensors in broader inpatient groups could achieve better data capture. Secondly, we note that some of our participants did not wear the devices for a long time, especially the blood pressure cuff. Further qualitative work is needed to understand the acceptability of wearable devices to differing patient groups as this may aid their optimal deployment. Finally, the precise time that traditional vital signs measurements were taken was not recorded, reflecting that there may be a delay between measurements and recording in the electronic patient record by our nursing staff. Whilst this could impact our validation results, the guidance at our institution is that recording of vital signs should be performed promptly, making it reasonable to assume that measurements were made in the preceding 5 minutes. Conclusion Wearable vital signs sensors have potential to improve the care of patients in hospital but the best way to deploy them in existing healthcare systems remains unclear. In this study, 48 inpatients with Covid-19 wore four wearable sensors for a median of almost four days. The BP cuff was poorly tolerated and suffered technical difficulties. Completeness of data capture from the other three sensors was in keeping with previous work and a survival analysis found no gaps in data collection of over 4 hours in over 80% of patients in the first 24 hours. Compared to nurse recorded vital signs, the validity of data capture by the wearable sensors was poor. Less than 60% of HR measurements, 40% of RR and SpO 2 measurements and 25% of temperature measurements fell within pre-defined clinically acceptable limits. There were systematic differences in measurements at high RRs and low temperatures. As a result, NEWS2 alerts from wearable sensor data were frequently false positives when compared to NEWS2 alerts from traditional vitals. As it stands, vital signs from wearable sensors cannot directly replace traditional vital signs measurements in existing track and trigger systems. Declarations Ethics approval and consent to participate The COSMIC-19 study was registered with clinicaltrials.gov (registration: NCT04581031, registered 4 th May 2020). It was approved by Yorkshire and the Humber – Bradford Leeds Research Ethics Committee, UK (reference 20/YH/0156). All participants gave written consent to participate. Consent for publication Not applicable. Availability of data and materials The COSMIC-19 dataset is available on request subject to appropriate information governance and data sharing agreements. Competing interests Prof Thistlethwaite declares that she is the director of the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, which provided funding for this work. All other authors have no competing interests to declare. Funding Funded by the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, (project ID: 6239) with additional support from the Christie Hospital Charitable Fund. Authors contributions Anthony Wilson - conceptualisation, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualisation, writing – original draft, writing – review & editing Alexander Parker - data curation, writing - review & editing Gareth Kitchen - conceptualisation, methodology, project administration, writing – review & editing Andrew Martin – conceptualisation Lukas Hughes-Noehrer - writing – review & editing Mahesh Nirmalan - supervision, writing – review & editing Niels Peek - methodology, supervision, writing – review & editing Glen Martin - supervision, writing – review & editing Fiona Thistlethwaite - conceptualisation, funding acquisition, project administration, resources, supervision, writing – review & editing Acknowledgements The authors acknowledge Manchester University NHS Foundation Trust’s Clinical Data Science Unit who supported extraction of traditional vital signs measurements from the electronic patient record. Authors’ information Not applicable References Michard F, Sessler DI. Ward monitoring 3.0. Br J Anaesth [Internet] Elsevier; 2018 [cited 2018 Nov 4]; 121 : 999–1001 Available from: http://www.ncbi.nlm.nih.gov/pubmed/30336872 Patel V, Orchanian-Cheff A, Wu R. Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review. JMIR Mhealth Uhealth [Internet] JMIR Mhealth Uhealth; 2021 [cited 2024 Apr 23]; 9 Available from: https://pubmed.ncbi.nlm.nih.gov/34406121/ Chan PY, Ryan NP, Chen D, McNeil J, Hopper I. Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis. Anaesthesia [Internet] Anaesthesia; 2022 [cited 2024 Apr 23]; 77 : 1268–80 Available from: https://pubmed.ncbi.nlm.nih.gov/35947876/ Areia C, Biggs C, Santos M, et al. The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis. Crit Care [Internet] BioMed Central; 2021 [cited 2021 Oct 24]; 25 : 1–17 Available from: https://ccforum.biomedcentral.com/articles/10.1186/s13054-021-03766-4 Gao H, McDonnell A, Harrison DA, et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward. Intensive Care Med [Internet] Springer; 2007 [cited 2022 Feb 21]; 33 : 667–79 Available from: https://link.springer.com/article/10.1007/s00134-007-0532-3 Eddahchouri Y, Koeneman M, Plokker M, et al. Low compliance to a vital sign safety protocol on general hospital wards: A retrospective cohort study. Int J Nurs Stud [Internet] Int J Nurs Stud; 2021 [cited 2024 Jul 3]; 115 Available from: https://pubmed.ncbi.nlm.nih.gov/33517078/ Weenk M, Koeneman M, van de Belt TH, Engelen LJLPG, van Goor H, Bredie SJH. Wireless and continuous monitoring of vital signs in patients at the general ward. Resuscitation [Internet] European Resuscitation Council, American Heart Association, Inc., and International Liaison Committee on Resuscitation.~Published by Elsevier Ireland Ltd; 2019 [cited 2021 Nov 17]; 136 : 47–53 Available from: https://doi.org/10.1016/j.resuscitation.2019.01.017 Stellpflug C, Pierson L, Roloff D, et al. Continuous Physiological Monitoring Improves Patient Outcomes. Am J Nurs [Internet] Am J Nurs; 2021 [cited 2021 Nov 28]; 121 : 40–6 Available from: https://pubmed.ncbi.nlm.nih.gov/33755624/ van Ede ES, Scheerhoorn J, Bonomi AG, Buise MP, Bouwman RA, Nienhuijs SW. Continuous remote monitoring in post-bariatric surgery patients: development of an early warning protocol. Surg Obes Relat Dis [Internet] Surg Obes Relat Dis; 2022 [cited 2024 Jul 3]; 18 : 1298–303 Available from: https://pubmed.ncbi.nlm.nih.gov/35850957/ van der Stam JA, Mestrom EHJ, Nienhuijs SW, et al. A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients. European Journal of Surgical Oncology W.B. Saunders; 2023; 49 : 278–84 Watkinson PJ, Pimentel MA, Clifton L, et al. Early detection of physiological deterioration in post-surgical patients using wearable technology combined with an integrated monitoring system: a pre- and post-interventional study. medRxiv [Internet] Cold Spring Harbor Laboratory Press; 2020 [cited 2021 Nov 17]; 2020.12.01.20240770 Available from: https://www.medrxiv.org/content/10.1101/2020.12.01.20240770v1 National Early Warning Score (NEWS) 2 | RCP London [Internet]. [cited 2021 Jun 6]. Available from: https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2 Welcome to Isansys [Internet]. [cited 2023 Jun 24]. Available from: https://www.isansys.com/ Van Der Stam JA, Mestrom EHJ, Scheerhoorn J, et al. Accuracy of vital parameters measured by a wearable patch following major abdominal cancer surgery. European Journal of Surgical Oncology [Internet] Elsevier; 2022 [cited 2022 Apr 14]; 48 : 917–23 Available from: http://www.ejso.com/article/S0748798321007848/fulltext Cheng SM, Chan JJI, Tan CW, Lu E, Sultana R, Sng BL. Use of wireless respiratory rate sensor monitoring during opioid patient-controlled analgesia after gynaecological surgery: A prospective cohort study. Indian J Anaesth [Internet] Wolters Kluwer Medknow Publications; 2021 [cited 2022 Apr 18]; 65 : 146–52 Available from: https://journals.lww.com/ijaweb/Fulltext/2021/02000/Use_of_wireless_respiratory_rate_sensor_monitoring.10.aspx Downey C, Ng S, Jayne D, Wong D. Reliability of a wearable wireless patch for continuous remote monitoring of vital signs in patients recovering from major surgery: A clinical validation study from the TRaCINg trial. BMJ Open [Internet] British Medical Journal Publishing Group; 2019 [cited 2021 Nov 17]; 9 : e031150 Available from: https://bmjopen.bmj.com/content/9/8/e031150 Kant N, Peters GM, Voorthuis BJ, et al. Continuous vital sign monitoring using a wearable patch sensor in obese patients: a validation study in a clinical setting. J Clin Monit Comput [Internet] [cited 2022 Apr 14]; Available from: https://doi.org/10.1007/s10877-021-00785-y Breteler MJM, Huizinga E, Van Loon K, et al. Reliability of wireless monitoring using a wearable patch sensor in high-risk surgical patients at a step-down unit in the Netherlands: a clinical validation study. BMJ Open [Internet] BMJ Open; 2018 [cited 2022 Feb 21]; 8 : e020162 Available from: https://pubmed.ncbi.nlm.nih.gov/29487076/ Breteler MJMM, KleinJan EJ, Dohmen DAJJ, et al. Vital Signs Monitoring with Wearable Sensors in High-risk Surgical PatientsA Clinical Validation Study. Anesthesiology [Internet] American Society of Anesthesiologists; 2020 [cited 2020 Jan 21]; 132 : 424–39 Available from: http://journals.lww.com/10.1097/ALN.0000000000003029 Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res [Internet] Sage PublicationsSage CA: Thousand Oaks, CA; 1999 [cited 2022 Jan 3]; 8 : 135–60 Available from: https://journals.sagepub.com/doi/abs/10.1177/096228029900800204 Zou GY. Confidence interval estimation for the Bland-Altman limits of agreement with multiple observations per individual. Stat Methods Med Res [Internet] Stat Methods Med Res; 2013 [cited 2022 Jun 7]; 22 : 630–42 Available from: https://pubmed.ncbi.nlm.nih.gov/21705434/ Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL. Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose. Diabetes Care [Internet] American Diabetes Association; 1987 [cited 2021 Dec 28]; 10 : 622–8 Available from: http://diabetesjournals.org/care/article-pdf/10/5/622/515265/10-5-622.pdf Montenij LJ, Buhre WF, Jansen JR, Kruitwagen CL, De Waal EE. Methodology of method comparison studies evaluating the validity of cardiac output monitors: a stepwise approach and checklist. BJA: British Journal of Anaesthesia [Internet] Oxford Academic; 2016 [cited 2021 Nov 27]; 116 : 750–8 Available from: https://academic.oup.com/bja/article/116/6/750/2566462 Team RC. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2019. Available from: http://www.r-project.org/ WristOx2® Model 3150 with USB - Nonin [Internet]. [cited 2024 Apr 23]. Available from: https://www.nonin.com/products/3150-usb/ TM-2441 | Professional | Medical | Products | A&D [Internet]. [cited 2024 Apr 23]. Available from: https://www.aandd.jp/products/medical/professional/2441.html Cases in the UK | Coronavirus in the UK [Internet]. [cited 2023 Jun 24]. Available from: https://coronavirus.data.gov.uk/details/cases Haahr-Raunkjaer C, Skovbye M, Rasmussen SM, et al. Agreement between standard and continuous wireless vital sign measurements after major abdominal surgery: a clinical comparison study. Physiol Meas [Internet] Physiol Meas; 2022 [cited 2023 Apr 11]; 43 Available from: https://pubmed.ncbi.nlm.nih.gov/36322987/ Elvekjaer M, Aasvang EK, Olsen RM, et al. Physiological abnormalities in patients admitted with acute exacerbation of COPD: an observational study with continuous monitoring. J Clin Monit Comput [Internet] Springer; 2020 [cited 2021 Nov 18]; 34 : 1051–60 Available from: https://link.springer.com/article/10.1007/s10877-019-00415-8 Mølgaard J, Rasmussen SS, Eiberg J, Sørensen HBD, Meyhoff CS, Aasvang EK. Continuous wireless pre- and postoperative vital sign monitoring reveal new, severe desaturations after vascular surgery. Acta Anaesthesiol Scand [Internet] John Wiley & Sons, Ltd; 2023 [cited 2023 Apr 12]; 67 : 19–28 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/aas.14158 Rasmussen PS, Aasvang EK, Olsen RM, et al. Continuous peripheral perfusion index in patients admitted to hospital wards – An observational study. Acta Anaesthesiol Scand [Internet] John Wiley & Sons, Ltd; 2021 [cited 2024 Apr 23]; 65 : 257–65 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/aas.13711 Dall’Ora C, Griffiths P, Hope J, et al. How long do nursing staff take to measure and record patients’ vital signs observations in hospital? A time-and-motion study. Int J Nurs Stud [Internet] Elsevier; 2021 [cited 2024 Apr 23]; 118 Available from: /pmc/articles/PMC8249906/ Haahr-Raunkjaer C, Mølgaard J, Elvekjaer M, et al. Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients. Acta Anaesthesiol Scand John Wiley and Sons Inc; 2022; Weenk M, van Goor H, Frietman B, et al. Continuous Monitoring of Vital Signs Using Wearable Devices on the General Ward: Pilot Study. JMIR Mhealth Uhealth [Internet] JMIR Mhealth Uhealth; 2017 [cited 2021 Nov 23]; 5 Available from: https://pubmed.ncbi.nlm.nih.gov/28679490/ Lee PJ. Clinical evaluation of a novel respiratory rate monitor. J Clin Monit Comput [Internet] Springer Netherlands; 2016 [cited 2023 Feb 20]; 30 : 175–83 Available from: https://link.springer.com/article/10.1007/s10877-015-9697-4 Liu Y, Liu C, Gao M, et al. Evaluation of a wearable wireless device with artificial intelligence, iThermonitor WT705, for continuous temperature monitoring for patients in surgical wards: a prospective comparative study. BMJ Open [Internet] 2020 [cited 2022 Apr 15]; 10 : 39474 Available from: http://bmjopen.bmj.com/ Zsuzsanna Balla H, Theodorsson E, Ström JO. Evaluation of commercial, wireless dermal thermometers for surrogate measurements of core temperature. Scand J Clin Lab Invest [Internet] Scand J Clin Lab Invest; 2019 [cited 2023 Mar 28]; 79 : 1–6 Available from: https://pubmed.ncbi.nlm.nih.gov/30882250/ Kallioinen N, Hill A, Christofidis MJ, Horswill MS, Watson MO. Quantitative systematic review: Sources of inaccuracy in manually measured adult respiratory rate data. J Adv Nurs [Internet] Wiley-Blackwell; 2021 [cited 2024 Apr 23]; 77 : 98 Available from: /pmc/articles/PMC7756810/ Kallioinen N, Hill A, Christofidis MJ, Horswill MS, Watson MO. Quantitative systematic review: Sources of inaccuracy in manually measured adult respiratory rate data. J Adv Nurs [Internet] Wiley-Blackwell; 2021 [cited 2024 May 14]; 77 : 98 Available from: /pmc/articles/PMC7756810/ Jung L-Y, Kim J-M, Ryu S, Lee C-S. Relative bradycardia in patients with COVID-19. International Journal of Arrhythmia 2022 23:1 [Internet] BioMed Central; 2022 [cited 2024 Apr 23]; 23 : 1–5 Available from: https://arrhythmia.biomedcentral.com/articles/10.1186/s42444-022-00073-z 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 Public Library of Science; 2022; 17 Duus CL, Aasvang EK, Olsen RM, et al. Continuous vital sign monitoring after major abdominal surgery—Quantification of micro events. Acta Anaesthesiol Scand [Internet] 2018 [cited 2021 Nov 20]; 62 : 1200–8 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/aas.13173 van Rossum MC, Vlaskamp LB, Posthuma LM, et al. Adaptive threshold-based alarm strategies for continuous vital signs monitoring. J Clin Monit Comput [Internet] Springer Science and Business Media B.V.; 2021 [cited 2022 Apr 14]; 1–11 Available from: https://link.springer.com/article/10.1007/s10877-021-00666-4 Jansen C, Chatterjee DA, Thomsen KL, et al. Significant reduction in heart rate variability is a feature of acute decompensation of cirrhosis and predicts 90-day mortality. Aliment Pharmacol Ther [Internet] Blackwell Publishing Ltd; 2019 [cited 2021 Nov 28]; 50 : 568–79 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/apt.15365 Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ [Internet] British Medical Journal Publishing Group; 2020 [cited 2023 Aug 14]; 369 Available from: https://www.bmj.com/content/369/bmj.m1985 Additional Declarations Competing interest reported. Prof Thistlethwaite declares that she is the director of the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, which provided funding for this work. All other authors have no competing interests to declare. Supplementary Files SDCWilsonValidationofWearableSensorsBMC.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Oct, 2024 Reviews received at journal 23 Oct, 2024 Reviews received at journal 21 Oct, 2024 Reviewers agreed at journal 15 Oct, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviews received at journal 09 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers invited by journal 01 Sep, 2024 Editor invited by journal 30 Aug, 2024 Editor assigned by journal 28 Aug, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 26 Aug, 2024 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. 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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-4976766","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359412065,"identity":"0cac8232-bc6c-41df-8537-f651250ef8cb","order_by":0,"name":"Anthony Joseph Wilson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYHACNgjFzNwAJG0Y2IEUM0hAAqcOZpgWRpCWNAaeY0RrYQBrOUxYC//s/mMPPu44zGBwnLH5w8cd56N55BvYHhf8YUic2YBdi8Sdw+yGM88AtRxmbJOceeZ2bg8bA7vxzDaGxNm43HUjmU2at+0wgxlQCzNv2+3c/WwMQJEGhsR5OHTII2lp/szbdg5kC5s0zx/cWgyQtDQAGQegWthwO8zwRrKZ5My2dB57sF/akoFaEtuAeiWMcXlf7kbiM4mPbdZykv2HD3/42GaX28N8+BjQYTayMw7g8j8YNPMgccARhDsioaCOkIJRMApGwSgYyQAAH2hUaU1HL04AAAAASUVORK5CYII=","orcid":"","institution":"The University of Manchester","correspondingAuthor":true,"prefix":"","firstName":"Anthony","middleName":"Joseph","lastName":"Wilson","suffix":""},{"id":359412066,"identity":"2212bc85-60a7-4b0f-b647-69ed5be0f2e3","order_by":1,"name":"Alexander J Parker","email":"","orcid":"","institution":"Manchester University NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"J","lastName":"Parker","suffix":""},{"id":359412067,"identity":"43759db3-ef91-42dd-b62a-5f2d965a4f52","order_by":2,"name":"Gareth B Kitchen","email":"","orcid":"","institution":"The University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Gareth","middleName":"B","lastName":"Kitchen","suffix":""},{"id":359412068,"identity":"1c127189-7f55-4ff7-bc1a-2ef49fd298b5","order_by":3,"name":"Andrew Martin","email":"","orcid":"","institution":"Manchester University NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Martin","suffix":""},{"id":359412069,"identity":"20dd30ac-2942-422c-bd84-06a82728a39a","order_by":4,"name":"Lukas Hughes-Noehrer","email":"","orcid":"","institution":"The University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Hughes-Noehrer","suffix":""},{"id":359412070,"identity":"5df231a4-65cc-46ad-8e96-80411ebc5474","order_by":5,"name":"Mahesh Nirmalan","email":"","orcid":"","institution":"Manchester University NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Mahesh","middleName":"","lastName":"Nirmalan","suffix":""},{"id":359412071,"identity":"89ec0677-dd99-45e2-8c3d-a8292d528d80","order_by":6,"name":"Niels Peek","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Niels","middleName":"","lastName":"Peek","suffix":""},{"id":359412072,"identity":"0019a624-7f23-4e2d-999b-d4c4ff9af992","order_by":7,"name":"Glen Martin","email":"","orcid":"","institution":"The University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Glen","middleName":"","lastName":"Martin","suffix":""},{"id":359412073,"identity":"5c9c12b3-7367-4bca-ba4b-22c5206d25d9","order_by":8,"name":"Fiona Thistlethwaite","email":"","orcid":"","institution":"The Christie NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Fiona","middleName":"","lastName":"Thistlethwaite","suffix":""}],"badges":[],"createdAt":"2024-08-26 09:17:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4976766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4976766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66888859,"identity":"45d49dcc-951c-44e2-a337-7084fcfc8b95","added_by":"auto","created_at":"2024-10-17 14:13:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003escreening and recruitment to the study\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4976766/v1/0ef5ac1178599b0f4c2acb4a.png"},{"id":66888858,"identity":"22836a79-ff01-4a27-839f-2a4908a06f0c","added_by":"auto","created_at":"2024-10-17 14:13:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":832959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan Meier analysis of gaps of varying duration in wearable sensor data.\u0026nbsp; HR/RR = heart rate/respiratory rate, SpO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e = oxygen saturation, SBP = systolic blood pressure, Temp = temperature.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4976766/v1/8726f314c672ad863be1e582.png"},{"id":66889094,"identity":"5cd63497-e2bf-4b1c-8d49-f5f80ca72ab0","added_by":"auto","created_at":"2024-10-17 14:21:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1741866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBland Altman plots corrected for repeated measures. HR = heart rate, RR = respiratory rate, SpO2 = oxygen saturations, Temp = temperature\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4976766/v1/3555237346998520012a81c9.png"},{"id":66890350,"identity":"7e68517c-2da9-40dc-8fe0-723828506a1f","added_by":"auto","created_at":"2024-10-17 14:29:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4179510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4976766/v1/12f51473-7be0-4b44-914f-a64283544504.pdf"},{"id":66890349,"identity":"f8246c9f-25eb-4ca7-ad45-45acfae08d74","added_by":"auto","created_at":"2024-10-17 14:29:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1879252,"visible":true,"origin":"","legend":"","description":"","filename":"SDCWilsonValidationofWearableSensorsBMC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4976766/v1/c2e5a012a17577cd99db3e4e.pdf"}],"financialInterests":"Competing interest reported. Prof Thistlethwaite declares that she is the director of the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, which provided funding for this work. All other authors have no competing interests to declare.","formattedTitle":"The completeness, accuracy and impact on alerts, of wearable vital signs monitoring in hospitalised patients","fulltext":[{"header":"Background","content":"\u003cp\u003eWearable vital signs sensors (WVSSs) are wireless, non-invasive devices worn by patients which permit near-continuous recording of heart rate (HR), respiratory rate (RR), oxygen saturations (SpO\u003csub\u003e2\u003c/sub\u003e) and blood pressure (BP). In low resource settings and healthcare economies struggling with staff shortages, such sensors could automate repetitive manual vital signs measurements thereby freeing time to care. The near-continuous recording theoretically allows no deterioration to go unnoticed and permits advanced analytics to predict and detect important patient centred outcomes and potentially to personalise care. Patients and their relatives may feel a sense of security in knowing that they are always monitored. With these benefits in mind, there has been a growing interest\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e in the use of WVSSs to monitor patients in hospital but there remains uncertainty about the accuracy of the vital signs they record\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The evidence that they have a positive impact on outcomes for hospitalised patients is not established\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous studies have assessed the accuracy of vitals recorded by WVSSs but many have focussed on short timescales or have compared WVSSs to critical care standards of monitoring, sometimes in optimised environments\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A true picture of their performance requires assessment in hospital wards which are subject to the challenges and limitations of such environments.\u003c/p\u003e \u003cp\u003eMost hospitals use some form of track and trigger system to detect and respond to the deteriorating patient\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Yet clinical staff do not always follow track and trigger protocols\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and WVSSs which could automate early warning score (EWS) completion could be valuable. However, studies which assess the impact of incorporating vital signs data from WVSSs into such systems are uncommon\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Instead, many authors have suggested developing new approaches to the deteriorating patient which can accommodate data from WVSSs. These have included incorporating an additional alerting step to prompt manual calculation of an EWS\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, adjusting EWS systems to reflect wearable data\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e or the development of novel deterioration indices utilising wearable data\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A benefit of the EWS system is its simplicity and widespread adoption. New approaches add complexity. It is therefore prudent to assess what would happen if vital signs from WVSSs were simply used in existing EWS systems.\u003c/p\u003e \u003cp\u003eIn this context, we conducted the COSMIC-19 (continuous signs monitoring in Covid-19) study which evaluated a suite of WVSSs in hospitalised patients with Covid-19 in a real-world, ward environment. Our aim was to determine the completeness and accuracy of the data recorded by the WVSSs in comparison to traditional ward vital signs. We also sought to assess how National Early Warning Score 2 (NEWS2) scores\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e would differ if calculated using vitals obtained by WVSSs instead of concurrent traditional vitals measurements.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003eThe COSMIC-19 study was registered with clinicaltrials.gov (registration: NCT04581031). It was approved by Yorkshire and the Humber – Bradford Leeds Research Ethics Committee, UK (reference 20/YH/0156). It was funded by the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, (project ID: 6239) with additional support from the Christie Hospital Charitable Fund.\u003c/p\u003e\u003cp\u003eSymptomatic, adult inpatients (16 years or older) with suspected or confirmed Covid-19, who were suitable for escalation to critical care and receiving supplemental oxygen when screened were eligible for enrolment. Patients were recruited within 72 hours of hospital admission. We excluded patients unable to give informed consent, who were anticipated to die within 24 hours or those with a contraindication to wearing the WVSSs. Patients were recruited at Manchester Royal Infirmary, an academic, tertiary, metropolitan hospital in the UK with over 150,000 emergency department presentations annually.\u003c/p\u003e\u003cp\u003eWearable vital signs sensors\u003c/p\u003e\u003cp\u003eWe investigated four WVSSs which measured heart rate/respiratory rate (HR/RR), oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e), axillary temperature and systolic blood pressure (SBP). The four sensors were purchased from Isansys Ltd\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and were used as part of the Isansys Patient Status Engine (PSE), a digital platform for vital signs capture and reporting which is a regulated medical device. Isansys Ltd had no input in the design or conduct of the research. The wearables are summarised in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eParticipants wore the four WVSSs for up to 20 days or until they lost capacity to consent to continue or were discharged, whichever was earlier. They were free to discontinue any sensor at any time. Participants received usual care during monitoring with the WVSSs, including traditional vital signs measurements by ward staff. This included temperature measurements using a tympanic thermometer. The clinical team and participants were blinded to the WVSS measurements.\u003c/p\u003e\u003cp\u003eThe research team monitored the participants for WVSS disconnection via a remote dashboard. Each participant’s data was reviewed at least once every 24 hours. In the event of a disconnection, the researchers visited the participant to aid with wearable re-application or removal if they chose to discontinue a sensor. The research team maintained a log of all occasions when participants removed the sensors and their reasons for doing so.\u003c/p\u003e\u003cp\u003eData capture\u003c/p\u003e\u003cp\u003eWe recruited a convenience sample of 30–60 participants, informed by the number of WVSSs available for the research. No formal sample size calculations were performed.\u003c/p\u003e\u003cp\u003eAn electronic case report form (eCRF, DataTrial Ltd, Newcastle, UK) was used to capture demographics, diagnostic information, sensor application/removal and outcomes. Traditional vital signs were downloaded from the hospital electronic track and trigger system (Patientrack, Alcidion Ltd, Aus). Traditional vitals were filtered to exclude non-physiological values. Wearable vital signs were captured via the Isansys PSE. Error codes were removed from the wearable data but no vital signs measurements were excluded.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe completeness of vital sign capture was reported as the percentage of time for which a vital sign was recorded by a WVSS as a proportion of the total time for which the sensor was worn. The number of participants who removed each sensor prematurely and their reasons for doing was summarised. A Kaplan Meier analysis was conducted where the event of interest was a temporary gap in wearable sensor data of varying durations. Participants were censored if they permanently removed the WVSS for any reason.\u003c/p\u003e\u003cp\u003eFor each WVSS, the accuracy of the vital signs was assessed using traditional vital signs as a reference standard. For each traditional measurement, a corresponding wearable vital signs measurement was determined by taking the median of all wearable measurements within the preceding five minutes (for HR, RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e) or 15 minutes (for SBP). These timescales and the approach were chosen to align with existing published work\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A Bland Altman analysis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e was performed to determine the bias and 95% limits of agreement (LoA) for each vital. The repeated nature of measurements was accounted for using the methods described by Zou et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e In keeping with previous literature\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, we defined ± 5bpm, ±3breaths/min, ± 2%, ± 0.3\u003csup\u003eo\u003c/sup\u003eC and ± 10mmHg a priori as clinically acceptable agreement between HR, RR, SpO\u003csub\u003e2\u003c/sub\u003e, temperature and SBP respectively.\u003c/p\u003e\u003cp\u003eTo assess how monitoring patients with WVSS would impact clinical alerts, a partial National Early Warning Score\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e (NEWS2) was calculated using wearable sensor measurements and compared to the NEWS2 calculated from traditional vitals. Both calculations did not include level of consciousness and air/oxygen scores. We assumed all participants were on SpO\u003csub\u003e2\u003c/sub\u003e scale 1. A modified Clarke Error Grid analysis\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e was also performed for each individual NEWS2 component. This quantifies how differences between corresponding wearable and traditional measurements would impact the NEWS2 component. Finally, to compare trends between successive wearable and traditional measurements, 4Q plots\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e were created for each vital sign.\u003c/p\u003e\u003cp\u003eAll analysis was conducted using R (version 3.6)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Continuous variables were assessed for normality and are presented as mean (standard deviation) or median [interquartile range]. Categorical variables are presented as the population size and the percentage (of available data) for each class.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" 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\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\u003ea summary of the wearable health monitor devices deployed in this study. BLE - Bluetooth low energy, BP = blood pressure, ECG = electrocardiogram, HR = heart rate, HRV = heart rate variability, pleth = plethysmography, PSE = patient status engine, RR = respiratory rate, SpO\u003csub\u003e2\u003c/sub\u003e = haemoglobin oxygen saturation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWearable Vital Signs Sensor\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR/RR\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eIsansys Lifetouch Sensor\u003c/b\u003e\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eIsansys Lifetemp Sensor\u003c/b\u003e\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSpO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eNonin3150 WristOx\u003c/b\u003e\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eA\u0026amp;D TM2441 BP Monitor\u003c/b\u003e\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForm Factor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA small patch attached via standard ECG electrodes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA small adhesive patch\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWrist-worn module, pulse oximetry finger probe\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCuff with attached servo unit.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCE marked medical device\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWear Location\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecordium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxilla\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWrist and fingertip\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of vital sign measurement\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvery minute\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvery minute\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvery minute\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHourly (6am-10pm), every two hours (10pm-6am)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBattery Life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 hours.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 days.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 hours. 2x AAA batteries.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2x AA batteries.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle Use?\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Synchronisation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBLE to Samsung Galaxy Tablet. Subsequent data upload via WiFi/cellular from tablet to remote server (Isansys LifeGuard Server)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoftware Used\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eIsansys PSE software.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrics recorded\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccelerometer (activity and posture), ECG (single lead), HR, HRV, RR.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemperature (skin - axilla)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (pleth)\u003c/p\u003e \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (finger)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-invasive BP\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eStudy participants\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarises screening and recruitment. 179 eligible patients were identified and 48 took part. Most (43/48) were recruited between July 2020 and March 2021, during the first and second UK waves of the coronavirus pandemic. Five were recruited between July 2021 and February 2022, during the delta and omicron waves in the UK\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In 47 participants, SARS-CoV-2 infection was confirmed on nasopharyngeal swab (lateral flow or polymerase chain reaction), one participant had symptoms consistent with Covid-19 and high clinical suspicion of infection but without a positive screening result.\u003c/p\u003e\n\u003cp\u003eTable 2 summarises the demographics and clinical characteristics of study participants, stratified according to whether they were admitted to critical care. 32/48 (66.7%) were male and 32/48 (66.7%) were from non-Caucasian ethnicities.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"551\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot admitted to ICU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmitted to ICU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en=37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en=11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e32 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e25 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e7 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e51.0 [40, 58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e51 [36, 60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e48 [42, 56]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Arabic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Indian/Pakistani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e20 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e12 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e8 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e8 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Mixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Caucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e16 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e15 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eN=43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e29.7 [27.0, 34.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e30.2 [26.7, 34.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e29.1 [28.2, 37.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e8 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e8 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eIschaemic heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e2 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCKD (stage 2+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e2 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHeart failure (NYHA 3+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e2 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e9 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Current smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e2 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Ex-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e7 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Never smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e32 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e25 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e7 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e3 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eRockwood frailty score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e11 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e15 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e10 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTable 2: demographics and clinical characteristics of patients recruited into the study. \u0026nbsp;BMI = body mass index, CKD = chronic kidney disease classification, COPD = chronic obstructive pulmonary disease, ICU = intensive care unit, NYHA = New York Heart Association classification.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCompleteness of data capture from wearable sensors\u003c/p\u003e\n\u003cp\u003eThe median time from admission to wearable sensor application was 27 (IQR:22\u0026ndash;46) hours. The median length of hospital stay for each participant was 8 (IQR:6\u0026ndash;13) days. The total number of patient-days of sensor wear was 202, 200, 204 and 82 days for the HR/RR, temperature, SpO\u003csub\u003e2\u003c/sub\u003e and BP WVSSs respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This represented a median duration of wear of approximately 4 days per participant for the HR/RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003esensors. The duration of wear for the BP cuff was median 1.9 days per participant. This was due to connectivity difficulties and because many participants either declined the sensor or requested early removal. The duration of wearable application was similar when calculated from the data recorded by each wearable or from the sensor log maintained by the research team (appendix 1).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eduration of wear (first to last valid wearable vital signs measurement), completeness of data capture and reasons for wearable removal. The blood pressure cuff was not applied to 15 participants at their request. BP\u0026thinsp;=\u0026thinsp;non-invasive blood pressure, SpO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;oxygen saturations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWearable Sensor (N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR/RR\u003c/p\u003e\n \u003cp\u003e(LifeTouch)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003cp\u003e(LifeTemp)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(Nonin PulseOx)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003cp\u003e(A\u0026amp;D TM2441)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of sensor wear (days/participant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003cp\u003e[1.7, 5.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003cp\u003e[1.6, 5.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003cp\u003e[0.9, 5.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e[0.9\u0026ndash;3.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall completeness (%) of wearable sensor data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompleteness (%) per participant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.8\u003c/p\u003e\n \u003cp\u003e[64.1, 95.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.7\u003c/p\u003e\n \u003cp\u003e[79.7, 99.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.3\u003c/p\u003e\n \u003cp\u003e[61.7, 87.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003cp\u003e[16.3, 47.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReason for device removal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDischarge from hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical care, loss of capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipant request\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the results of the Kaplan Meier analysis of gaps of varying duration in wearable sensor data. The analysis was limited for SBP due to the intermittent nature of measurements (maximum frequency of wearable recordings: 1\u0026ndash;2 hourly). The median survival time without data loss and the percentage of patients without data loss in 24 hours are summarised in appendix 2.\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eAccuracy of wearable sensor measurements\u003c/p\u003e\n\u003cp\u003eAfter creating pairs of wearable and traditional vital signs measurements (within the 5/15min epochs, see methods), there were 1633, 1614, 1412, 1294 and 59 pairs of HR, RR, temperature, SpO\u003csub\u003e2\u003c/sub\u003e and SBP measurements respectively. 59.7% HR pairs were within \u0026plusmn;\u0026thinsp;5bpm, 38.5% of RR pairs were within \u0026plusmn;\u0026thinsp;3breaths/min, 24.4% of temperature pairs were within \u0026plusmn;\u0026thinsp;0.3\u003csup\u003eo\u003c/sup\u003eC, 32.9% of SpO\u003csub\u003e2\u003c/sub\u003e pairs were within \u0026plusmn;\u0026thinsp;2% and 39.0% of SBP pairs were within \u0026plusmn;\u0026thinsp;10mmHg. The correlation coefficients for each vital sign are displayed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (see appendix 3 for scatterplots).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays the Bland Altman plots for HR, RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e stratified according to whether the participant was on a ward or in critical care. The corresponding plot for SBP is available in appendix 4. There was constant variation between wearable and traditional vital signs measurements across the measurement range for HR and SpO\u003csub\u003e2\u003c/sub\u003e. RR measurements showed a systematic difference at high mean RRs in critical care, with traditional RR measurements being higher than wearable measurements. A sensitivity analysis (appendix 5) identified that this difference was due to data from five participants who had higher traditional RR measurements than other participants. Temperature measurements also showed a systematic difference at low mean temperatures with traditional measurements being higher than wearable measurements. There were insufficient SBP measurement pairs to comment on the variation in measurements. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays the bias and limits of agreement for each vital sign. Not all participants wore all sensors and only participants with at least two measurement pairs are included in this analysis.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBland Altman metrics for each wearable vital sign measurement compared to traditional vital signs measurements as the reference standard. Bias\u0026thinsp;=\u0026thinsp;wearable - traditional (95% confidence interval), LoA\u0026thinsp;=\u0026thinsp;95% limits of agreement (95% confidence interval). HR\u0026thinsp;=\u0026thinsp;heart rate, RR\u0026thinsp;=\u0026thinsp;respiratory rate, SBP\u0026thinsp;=\u0026thinsp;systolic blood pressure, SpO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;oxygen saturations, Temp\u0026thinsp;=\u0026thinsp;temperature. *any participants with only a single measurement pair for the vital sign concerned are excluded to enable calculation of the 95% confidence intervals.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eVital sign\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (bpm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRR (/min)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeasurement pairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePairs/participant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 [9,39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 [9,39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 [10,42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 [9,32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 [3,5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelation coefficient (Pearson r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e(0.75 to 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003cp\u003e(0.15 to 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e(0.28 to 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003cp\u003e(0.47 to 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(-0.02 to 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(0.3 to -0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003cp\u003e(-1.4 to -2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003cp\u003e(-1.0 to -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.8\u003c/p\u003e\n \u003cp\u003e(-2.6 to -2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003cp\u003e(6.9 to -8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper LoA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003cp\u003e(23.3 to 19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003cp\u003e(17.0 to 12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e(2.1 to 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e(4.1 to 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003cp\u003e(55.5 to 27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower LoA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.6\u003c/p\u003e\n \u003cp\u003e(-19.9 to -23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.8\u003c/p\u003e\n \u003cp\u003e(-15.6 to -20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.8\u003c/p\u003e\n \u003cp\u003e(-3.5 to -4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003cp\u003e(-8.5 to -9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-39.0\u003c/p\u003e\n \u003cp\u003e(-29.2 to -56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eImpact of wearable vital signs measurements on alerts\u003c/p\u003e\n\u003cp\u003eAmongst the pairs of traditional and wearable vital signs measurements, there were only 31 instances when HR, RR, temperature, SpO\u003csub\u003e2\u003c/sub\u003e and SBP were simultaneously available to calculate a partial NEWS2 (see methods). Appendix 6 summarises the differences in partial NEWS2 scores calculated from these five vital signs and the impact on the rate of NEWS2 5\u0026thinsp;+\u0026thinsp;or 7\u0026thinsp;+\u0026thinsp;alerts by each method. This small number of NEWS2 scores reflects that many participants did not wear the BP cuff or chose to remove it early.\u003c/p\u003e\n\u003cp\u003eIn contrast, there were 613 instances when HR, RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e were simultaneously available to calculate a partial NEWS2. The median NEWS2 by traditional methods was 1 [IQR: 1\u0026ndash;2] and by wearable methods was 4 [IQR: 3\u0026ndash;6]. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003esummarises the number of times when a NEWS2 5\u0026thinsp;+\u0026thinsp;or 7\u0026thinsp;+\u0026thinsp;alert would have been generated by each method. At our institution, NEWS2 5\u0026thinsp;+\u0026thinsp;would typically alert the ward based medical team, whilst 7\u0026thinsp;+\u0026thinsp;would typically generate a critical care response.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTable 5: confusion matrix for NEWS2 scores calculated from 4 vital signs (heart rate, respiratory rate, temperature and SpO\u003csub\u003e2\u003c/sub\u003e) using paired traditional and wearable vital signs. A positive NEWS2 score is considered a score of 5+ or 7+ respectively. *On 11 occasions, vitals from wearable sensors identified a 5+ NEWS2 event at the same time as traditional measurements but also identified a 5+ NEWS2 event in the preceding 12 hours which was not detected by traditional measurements. \u0026nbsp;We considered this to represent early detection of deterioration and therefore a true positive. \u003csup\u003e$\u003c/sup\u003eSimilarly, there were 5 instances of early detection of a 7+ NEWS2 event. \u0026nbsp;NEWS2 = national early warning score 2.\u003c/em\u003e\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNEWS2 5+\u003c/p\u003e\n \u003cp\u003e(4 vitals, traditional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNEWS2 7+\u003c/p\u003e\n \u003cp\u003e(4 vitals, traditional)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEWS2 5+\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(4 vitals, wearable)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;+\u0026thinsp;11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEWS2 7+\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(4 vitals, wearable)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026thinsp;+\u0026thinsp;5\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAppendix 7 displays the differences in NEWS2 component scores for each pair of vital signs measurements. The corresponding modified Clarke Error Grids for each vital sign are displayed in appendix 8. The greatest agreement in NEWS2 component scores was observed for HR (85.1%). \u0026nbsp;For all other NEWS2 component scores agreement was less than 50%. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAppendix 9 displays the 4Q plots which assess the correlation in differences between successive vital sign measurements by traditional and wearable techniques. There was a moderate correlation between HR measurements recorded on the ward (r = 0.39) and in critical care (r = 0.40), between SpO\u003csub\u003e2\u003c/sub\u003e measurements recorded in critical care (r = 0.35) and between SBP measurements recorded on the ward (r = 0.45). In all other cases there was little or no correlation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this observational study of WVSSs in hospitalised patients with Covid-19 we monitored HR, RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e in 48 individuals for a median duration of 3.8\u0026ndash;3.9 days. Non-invasive, intermittent, automated BP measurement was poorly tolerated and suffered technical challenges such that the median duration of monitoring was 1.9 days in 33 out of 48 participants.\u003c/p\u003e \u003cp\u003eOur findings align with previous research suggesting that wearable patch/wrist-based vital signs sensors can achieve comprehensive data capture in an inpatient setting with modest (once daily) intervention to maintain the devices. Studies in which WVSSs have been validated against traditional vitals, in ward-based settings and for prolonged periods (\u0026gt;\u0026thinsp;48 hours) are uncommon but offer real world evidence about WVSS performance. Completeness of data capture by wearable sensors in such studies ranges from 76\u0026ndash;96% for patch-based, chest HR/RR sensors\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and from 50\u0026ndash;68% for wrist worn pulse oximeters\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our results are similar, and the variation between studies may be attributed to different patient populations and different levels of experience amongst patients, researchers and clinical teams in using and maintaining the sensors. Isolation measures due to Covid-19, may have also limited opportunities for sensor maintenance. Even the lowest rate of data capture in our study (SpO\u003csub\u003e2\u003c/sub\u003e, 68.6%) equates to continuous vitals for 16 out of every 24 hours, far exceeding what could be captured by traditional methods.\u003c/p\u003e \u003cp\u003eOur survival analysis found that there would be no gaps in the data of over 4 hours duration for 87.9%, 81.5% and 97.5% of patients using the HR/RR, SpO\u003csub\u003e2\u003c/sub\u003e and temperature sensors in the first 24 hours respectively. As four hours is often the interval between nurse measured vital signs\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e it suggests that the time nursing staff would need to spend troubleshooting/reapplying such devices would be acceptable. In high-risk surgical populations in the Netherlands, Breteler and colleagues\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e found even greater durability of data capture with wearable sensors. Our results extend this finding to a UK setting with medical inpatients who are subject to isolation restrictions.\u003c/p\u003e \u003cp\u003eIn contrast to the chest and axillary patch sensors we acknowledge that the data capture of BP measurements in our study was poor. For cuff-based, BP sensors, data capture rates of 44\u0026ndash;63% have been recorded\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and our rates were lower than this due to technical difficulties and patient discomfort. This limits the conclusions which we can draw about the A\u0026amp;D TM2441 device as a useful wearable monitor but perhaps stresses the importance of considering if such devices are truly acceptable to patients as an automated, wearable technique. Inflation of a BP cuff can be uncomfortable, and it may be more tolerable when recorded by a nurse.\u003c/p\u003e \u003cp\u003eWe observed wide limits of agreement between traditional and wearable vital signs such that differences in only 59.7% of HR, 38.5% of RR, 32.9% of SpO\u003csub\u003e2\u003c/sub\u003e, 24.4% of temperature and 39.0% of SBP pairs were within pre-defined clinically acceptable limits which was lower than previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. However, our findings agree with existing bias/LoA estimates where HR\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, RR\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and temperature\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e measurements have been validated in similar circumstances and where the repeated nature of measurements in the same patient is accounted for. It is difficult to comment on the validity of SBP measurements given the paucity of measurements in our study. In keeping with previous work\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, we found that the patch-based, chest sensor tended to underestimate high RRs in critically ill patients who were transferred to the ICU. The patch-based, axillary temperature sensor also frequently recorded lower temperatures than a traditional tympanic thermometer.\u003c/p\u003e \u003cp\u003eThe reasons for imperfect agreement between wearable vitals and nurse recorded vitals are myriad. It is well recognised\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e that vital signs recorded by healthcare professionals are impacted by poor measurement technique, value bias in recording and a Hawthorne effect during measurement\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Arguably therefore, WVSSs may offer a truer reflection of a patient\u0026rsquo;s ongoing, non-observed physiological state. However, systematic errors in wearable vital signs measurements may also play an important role. In our study, the temperature differences we observed may be because estimates of core temperature recorded by an axillary skin sensor are different to estimates recorded by tympanic thermometers used at our institution. Similarly, RRs determined by a wearable sensor based on an algorithm utilising chest impedance and R-R variation may have been subject to systematic error in Covid-19 patients in whom a relative bradyarrhythmia has been observed\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (potential for error due to Nyqist sampling limit) and potentially lower than expected chest wall excursion (potential for error due to smaller variation in chest impedance). Acknowledging these inherent differences, some authors have suggested that WVSS should not be compared to nurse recorded \u0026ldquo;spot\u0026rdquo; vitals\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. We disagree, because the comparison serves to illustrate the impact that using WVSSs could have on existing patient deterioration alerting mechanisms in hospitals.\u003c/p\u003e \u003cp\u003eIn our study, NEWS2 scores derived from HR, RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e measurements were typically higher when calculated from WVSSs than traditional vitals. Differences in RR, SpO\u003csub\u003e2\u003c/sub\u003e and temperature NEWS2 component scores were responsible for most of this difference (appendix 7). Compared to NEWS2 scores generated by traditional vitals, 85.9% (225/262) of NEWS2 5\u0026thinsp;+\u0026thinsp;and 89.1% (82/92) of NEWS2 7\u0026thinsp;+\u0026thinsp;from the WVSSs were false positives. False negatives were rare, 1.7% (6/351) and 0.4% (2/521) for NEWS2 5\u0026thinsp;+\u0026thinsp;and 7\u0026thinsp;+\u0026thinsp;respectively. In keeping with our results, Weenk and colleagues found that two WVSS systems studied on surgical and medical wards\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e both returned higher modified early warning scores (MEWS) than traditional nurse recorded vital signs.\u003c/p\u003e \u003cp\u003eOur findings suggest that vital signs obtained from WVSSs should not be used to directly replace traditional vital signs in track and trigger systems that utilise NEWS2. Compared to NEWS2 scores derived from concurrently recorded traditional vitals the majority of wearable NEWS2 alerts would be false alarms. We also highlight that more work may be needed to confirm the validity of wearable vital signs measurements in settings of illness (very high RR), where a patient\u0026rsquo;s physiology may be quite different from healthy volunteers.\u003c/p\u003e \u003cp\u003eWe propose that a different approach is needed to adopt WVSSs into care pathways for the deteriorating patient. This could include scheduled reviews of wearable sensor trends by healthcare providers\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e as opposed to automated alerts, re-development of EWS thresholds for specific use with wearable sensor data\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e or development of novel, broadly applicable deterioration indices using the granular data which wearable sensors provide or some of the additional metrics which they record. Examples include the presence or absence of micro-events\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e within the continuous data, trend information\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and heart rate variability\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis work was a real-world clinical evaluation of WVSSs in a challenging clinical setting. The duration of monitoring is similar to the longest periods of monitoring in previous wearable studies. As monitoring was continued into the ICU for some patients, we were also able to assess sensor performance in critically ill patients with more extreme physiology. Furthermore, we studied the clinical implications of adopting wearable sensors both in terms of maintenance requirements to prevent large gaps in data and the implications of using the wearable data in NEWS2 calculations.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations. Firstly, we studied a population with Covid-19 in which there was a preponderance of Black and Asian participants and a high rate of admission to critical care.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e This may limit the generalisability of our findings to wider populations of inpatients. Our ward staff were blinded to wearable sensor data and not involved in sensor maintenance. Taken together with the fact that we studied a group at high risk of deterioration, with significant isolation and infection control demands, this may mean that future, deployment of wearable sensors in broader inpatient groups could achieve better data capture.\u003c/p\u003e \u003cp\u003eSecondly, we note that some of our participants did not wear the devices for a long time, especially the blood pressure cuff. Further qualitative work is needed to understand the acceptability of wearable devices to differing patient groups as this may aid their optimal deployment. Finally, the precise time that traditional vital signs measurements were taken was not recorded, reflecting that there may be a delay between measurements and recording in the electronic patient record by our nursing staff. Whilst this could impact our validation results, the guidance at our institution is that recording of vital signs should be performed promptly, making it reasonable to assume that measurements were made in the preceding 5 minutes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWearable vital signs sensors have potential to improve the care of patients in hospital but the best way to deploy them in existing healthcare systems remains unclear. In this study, 48 inpatients with Covid-19 wore four wearable sensors for a median of almost four days. The BP cuff was poorly tolerated and suffered technical difficulties. Completeness of data capture from the other three sensors was in keeping with previous work and a survival analysis found no gaps in data collection of over 4 hours in over 80% of patients in the first 24 hours. Compared to nurse recorded vital signs, the validity of data capture by the wearable sensors was poor. Less than 60% of HR measurements, 40% of RR and SpO\u003csub\u003e2\u003c/sub\u003e measurements and 25% of temperature measurements fell within pre-defined clinically acceptable limits. There were systematic differences in measurements at high RRs and low temperatures. As a result, NEWS2 alerts from wearable sensor data were frequently false positives when compared to NEWS2 alerts from traditional vitals. As it stands, vital signs from wearable sensors cannot directly replace traditional vital signs measurements in existing track and trigger systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe COSMIC-19 study was registered with clinicaltrials.gov (registration: NCT04581031, registered 4\u003csup\u003eth\u003c/sup\u003e May 2020). It was approved by Yorkshire and the Humber \u0026ndash; Bradford Leeds Research Ethics Committee, UK (reference 20/YH/0156). All participants gave written consent to participate.\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe COSMIC-19 dataset is available on request subject to appropriate information governance and data sharing agreements.\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eProf Thistlethwaite declares that she is the director of the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, which provided funding for this work. All other authors have no competing interests to declare.\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFunded by the Innovate Manchester Advanced Therapy Centre Hub, Innovate UK, (project ID: 6239) with additional support from the Christie Hospital Charitable Fund.\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eAuthors contributions\u003c/u\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAnthony Wilson - conceptualisation, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualisation, writing \u0026ndash; original draft, writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eAlexander Parker - data curation, writing - review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eGareth Kitchen - conceptualisation, methodology, project administration, writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eAndrew Martin \u0026ndash; conceptualisation\u003c/li\u003e\n\u003cli\u003eLukas Hughes-Noehrer - writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eMahesh Nirmalan - supervision, writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eNiels Peek - methodology, supervision, writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eGlen Martin - supervision, writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003cli\u003eFiona Thistlethwaite - conceptualisation, funding acquisition, project administration, resources, supervision, writing \u0026ndash; review \u0026amp; editing\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge Manchester University NHS Foundation Trust\u0026rsquo;s Clinical Data Science Unit who supported extraction of traditional vital signs measurements from the electronic patient record.\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eAuthors\u0026rsquo; information\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cu\u003e\u003cbr\u003e \u003c/u\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMichard F, Sessler DI. Ward monitoring 3.0. \u003cem\u003eBr J Anaesth\u003c/em\u003e [Internet] Elsevier; 2018 [cited 2018 Nov 4]; \u003cstrong\u003e121\u003c/strong\u003e: 999\u0026ndash;1001 Available from: http://www.ncbi.nlm.nih.gov/pubmed/30336872\u003c/li\u003e\n\u003cli\u003ePatel V, Orchanian-Cheff A, Wu R. Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e [Internet] JMIR Mhealth Uhealth; 2021 [cited 2024 Apr 23]; \u003cstrong\u003e9\u003c/strong\u003e Available from: https://pubmed.ncbi.nlm.nih.gov/34406121/\u003c/li\u003e\n\u003cli\u003eChan PY, Ryan NP, Chen D, McNeil J, Hopper I. Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis. \u003cem\u003eAnaesthesia\u003c/em\u003e [Internet] Anaesthesia; 2022 [cited 2024 Apr 23]; \u003cstrong\u003e77\u003c/strong\u003e: 1268\u0026ndash;80 Available from: https://pubmed.ncbi.nlm.nih.gov/35947876/\u003c/li\u003e\n\u003cli\u003eAreia C, Biggs C, Santos M, et al. The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis. \u003cem\u003eCrit Care\u003c/em\u003e [Internet] BioMed Central; 2021 [cited 2021 Oct 24]; \u003cstrong\u003e25\u003c/strong\u003e: 1\u0026ndash;17 Available from: https://ccforum.biomedcentral.com/articles/10.1186/s13054-021-03766-4\u003c/li\u003e\n\u003cli\u003eGao H, McDonnell A, Harrison DA, et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward. \u003cem\u003eIntensive Care Med\u003c/em\u003e [Internet] Springer; 2007 [cited 2022 Feb 21]; \u003cstrong\u003e33\u003c/strong\u003e: 667\u0026ndash;79 Available from: https://link.springer.com/article/10.1007/s00134-007-0532-3\u003c/li\u003e\n\u003cli\u003eEddahchouri Y, Koeneman M, Plokker M, et al. Low compliance to a vital sign safety protocol on general hospital wards: A retrospective cohort study. \u003cem\u003eInt J Nurs Stud\u003c/em\u003e [Internet] Int J Nurs Stud; 2021 [cited 2024 Jul 3]; \u003cstrong\u003e115\u003c/strong\u003e Available from: https://pubmed.ncbi.nlm.nih.gov/33517078/\u003c/li\u003e\n\u003cli\u003eWeenk M, Koeneman M, van de Belt TH, Engelen LJLPG, van Goor H, Bredie SJH. Wireless and continuous monitoring of vital signs in patients at the general ward. \u003cem\u003eResuscitation\u003c/em\u003e [Internet] European Resuscitation Council, American Heart Association, Inc., and International Liaison Committee on Resuscitation.~Published by Elsevier Ireland Ltd; 2019 [cited 2021 Nov 17]; \u003cstrong\u003e136\u003c/strong\u003e: 47\u0026ndash;53 Available from: https://doi.org/10.1016/j.resuscitation.2019.01.017\u003c/li\u003e\n\u003cli\u003eStellpflug C, Pierson L, Roloff D, et al. Continuous Physiological Monitoring Improves Patient Outcomes. \u003cem\u003eAm J Nurs\u003c/em\u003e [Internet] Am J Nurs; 2021 [cited 2021 Nov 28]; \u003cstrong\u003e121\u003c/strong\u003e: 40\u0026ndash;6 Available from: https://pubmed.ncbi.nlm.nih.gov/33755624/\u003c/li\u003e\n\u003cli\u003evan Ede ES, Scheerhoorn J, Bonomi AG, Buise MP, Bouwman RA, Nienhuijs SW. Continuous remote monitoring in post-bariatric surgery patients: development of an early warning protocol. \u003cem\u003eSurg Obes Relat Dis\u003c/em\u003e [Internet] Surg Obes Relat Dis; 2022 [cited 2024 Jul 3]; \u003cstrong\u003e18\u003c/strong\u003e: 1298\u0026ndash;303 Available from: https://pubmed.ncbi.nlm.nih.gov/35850957/\u003c/li\u003e\n\u003cli\u003evan der Stam JA, Mestrom EHJ, Nienhuijs SW, et al. A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients. \u003cem\u003eEuropean Journal of Surgical Oncology\u003c/em\u003e W.B. Saunders; 2023; \u003cstrong\u003e49\u003c/strong\u003e: 278\u0026ndash;84 \u003c/li\u003e\n\u003cli\u003eWatkinson PJ, Pimentel MA, Clifton L, et al. Early detection of physiological deterioration in post-surgical patients using wearable technology combined with an integrated monitoring system: a pre- and post-interventional study. \u003cem\u003emedRxiv\u003c/em\u003e [Internet] Cold Spring Harbor Laboratory Press; 2020 [cited 2021 Nov 17]; 2020.12.01.20240770 Available from: https://www.medrxiv.org/content/10.1101/2020.12.01.20240770v1\u003c/li\u003e\n\u003cli\u003eNational Early Warning Score (NEWS) 2 | RCP London [Internet]. [cited 2021 Jun 6]. Available from: https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2\u003c/li\u003e\n\u003cli\u003eWelcome to Isansys [Internet]. [cited 2023 Jun 24]. Available from: https://www.isansys.com/\u003c/li\u003e\n\u003cli\u003eVan Der Stam JA, Mestrom EHJ, Scheerhoorn J, et al. Accuracy of vital parameters measured by a wearable patch following major abdominal cancer surgery. \u003cem\u003eEuropean Journal of Surgical Oncology\u003c/em\u003e [Internet] Elsevier; 2022 [cited 2022 Apr 14]; \u003cstrong\u003e48\u003c/strong\u003e: 917\u0026ndash;23 Available from: http://www.ejso.com/article/S0748798321007848/fulltext\u003c/li\u003e\n\u003cli\u003eCheng SM, Chan JJI, Tan CW, Lu E, Sultana R, Sng BL. Use of wireless respiratory rate sensor monitoring during opioid patient-controlled analgesia after gynaecological surgery: A prospective cohort study. \u003cem\u003eIndian J Anaesth\u003c/em\u003e [Internet] Wolters Kluwer Medknow Publications; 2021 [cited 2022 Apr 18]; \u003cstrong\u003e65\u003c/strong\u003e: 146\u0026ndash;52 Available from: https://journals.lww.com/ijaweb/Fulltext/2021/02000/Use_of_wireless_respiratory_rate_sensor_monitoring.10.aspx\u003c/li\u003e\n\u003cli\u003eDowney C, Ng S, Jayne D, Wong D. Reliability of a wearable wireless patch for continuous remote monitoring of vital signs in patients recovering from major surgery: A clinical validation study from the TRaCINg trial. \u003cem\u003eBMJ Open\u003c/em\u003e [Internet] British Medical Journal Publishing Group; 2019 [cited 2021 Nov 17]; \u003cstrong\u003e9\u003c/strong\u003e: e031150 Available from: https://bmjopen.bmj.com/content/9/8/e031150\u003c/li\u003e\n\u003cli\u003eKant N, Peters GM, Voorthuis BJ, et al. Continuous vital sign monitoring using a wearable patch sensor in obese patients: a validation study in a clinical setting. \u003cem\u003eJ Clin Monit Comput\u003c/em\u003e [Internet] [cited 2022 Apr 14]; Available from: https://doi.org/10.1007/s10877-021-00785-y\u003c/li\u003e\n\u003cli\u003eBreteler MJM, Huizinga E, Van Loon K, et al. Reliability of wireless monitoring using a wearable patch sensor in high-risk surgical patients at a step-down unit in the Netherlands: a clinical validation study. \u003cem\u003eBMJ Open\u003c/em\u003e [Internet] BMJ Open; 2018 [cited 2022 Feb 21]; \u003cstrong\u003e8\u003c/strong\u003e: e020162 Available from: https://pubmed.ncbi.nlm.nih.gov/29487076/\u003c/li\u003e\n\u003cli\u003eBreteler MJMM, KleinJan EJ, Dohmen DAJJ, et al. Vital Signs Monitoring with Wearable Sensors in High-risk Surgical PatientsA Clinical Validation Study. \u003cem\u003eAnesthesiology\u003c/em\u003e [Internet] American Society of Anesthesiologists; 2020 [cited 2020 Jan 21]; \u003cstrong\u003e132\u003c/strong\u003e: 424\u0026ndash;39 Available from: http://journals.lww.com/10.1097/ALN.0000000000003029\u003c/li\u003e\n\u003cli\u003eBland JM, Altman DG. Measuring agreement in method comparison studies. \u003cem\u003eStat Methods Med Res\u003c/em\u003e [Internet] Sage PublicationsSage CA: Thousand Oaks, CA; 1999 [cited 2022 Jan 3]; \u003cstrong\u003e8\u003c/strong\u003e: 135\u0026ndash;60 Available from: https://journals.sagepub.com/doi/abs/10.1177/096228029900800204\u003c/li\u003e\n\u003cli\u003eZou GY. Confidence interval estimation for the Bland-Altman limits of agreement with multiple observations per individual. \u003cem\u003eStat Methods Med Res\u003c/em\u003e [Internet] Stat Methods Med Res; 2013 [cited 2022 Jun 7]; \u003cstrong\u003e22\u003c/strong\u003e: 630\u0026ndash;42 Available from: https://pubmed.ncbi.nlm.nih.gov/21705434/\u003c/li\u003e\n\u003cli\u003eClarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL. Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose. \u003cem\u003eDiabetes Care\u003c/em\u003e [Internet] American Diabetes Association; 1987 [cited 2021 Dec 28]; \u003cstrong\u003e10\u003c/strong\u003e: 622\u0026ndash;8 Available from: http://diabetesjournals.org/care/article-pdf/10/5/622/515265/10-5-622.pdf\u003c/li\u003e\n\u003cli\u003eMontenij LJ, Buhre WF, Jansen JR, Kruitwagen CL, De Waal EE. Methodology of method comparison studies evaluating the validity of cardiac output monitors: a stepwise approach and checklist. \u003cem\u003eBJA: British Journal of Anaesthesia\u003c/em\u003e [Internet] Oxford Academic; 2016 [cited 2021 Nov 27]; \u003cstrong\u003e116\u003c/strong\u003e: 750\u0026ndash;8 Available from: https://academic.oup.com/bja/article/116/6/750/2566462\u003c/li\u003e\n\u003cli\u003eTeam RC. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2019. Available from: http://www.r-project.org/\u003c/li\u003e\n\u003cli\u003eWristOx2\u0026reg; Model 3150 with USB - Nonin [Internet]. [cited 2024 Apr 23]. Available from: https://www.nonin.com/products/3150-usb/\u003c/li\u003e\n\u003cli\u003eTM-2441 | Professional | Medical | Products | A\u0026amp;D [Internet]. [cited 2024 Apr 23]. Available from: https://www.aandd.jp/products/medical/professional/2441.html\u003c/li\u003e\n\u003cli\u003eCases in the UK | Coronavirus in the UK [Internet]. [cited 2023 Jun 24]. Available from: https://coronavirus.data.gov.uk/details/cases\u003c/li\u003e\n\u003cli\u003eHaahr-Raunkjaer C, Skovbye M, Rasmussen SM, et al. Agreement between standard and continuous wireless vital sign measurements after major abdominal surgery: a clinical comparison study. \u003cem\u003ePhysiol Meas\u003c/em\u003e [Internet] Physiol Meas; 2022 [cited 2023 Apr 11]; \u003cstrong\u003e43\u003c/strong\u003e Available from: https://pubmed.ncbi.nlm.nih.gov/36322987/\u003c/li\u003e\n\u003cli\u003eElvekjaer M, Aasvang EK, Olsen RM, et al. Physiological abnormalities in patients admitted with acute exacerbation of COPD: an observational study with continuous monitoring. \u003cem\u003eJ Clin Monit Comput\u003c/em\u003e [Internet] Springer; 2020 [cited 2021 Nov 18]; \u003cstrong\u003e34\u003c/strong\u003e: 1051\u0026ndash;60 Available from: https://link.springer.com/article/10.1007/s10877-019-00415-8\u003c/li\u003e\n\u003cli\u003eM\u0026oslash;lgaard J, Rasmussen SS, Eiberg J, S\u0026oslash;rensen HBD, Meyhoff CS, Aasvang EK. Continuous wireless pre- and postoperative vital sign monitoring reveal new, severe desaturations after vascular surgery. \u003cem\u003eActa Anaesthesiol Scand\u003c/em\u003e [Internet] John Wiley \u0026amp; Sons, Ltd; 2023 [cited 2023 Apr 12]; \u003cstrong\u003e67\u003c/strong\u003e: 19\u0026ndash;28 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/aas.14158\u003c/li\u003e\n\u003cli\u003eRasmussen PS, Aasvang EK, Olsen RM, et al. Continuous peripheral perfusion index in patients admitted to hospital wards \u0026ndash; An observational study. \u003cem\u003eActa Anaesthesiol Scand\u003c/em\u003e [Internet] John Wiley \u0026amp; Sons, Ltd; 2021 [cited 2024 Apr 23]; \u003cstrong\u003e65\u003c/strong\u003e: 257\u0026ndash;65 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/aas.13711\u003c/li\u003e\n\u003cli\u003eDall\u0026rsquo;Ora C, Griffiths P, Hope J, et al. How long do nursing staff take to measure and record patients\u0026rsquo; vital signs observations in hospital? A time-and-motion study. \u003cem\u003eInt J Nurs Stud\u003c/em\u003e [Internet] Elsevier; 2021 [cited 2024 Apr 23]; \u003cstrong\u003e118\u003c/strong\u003e Available from: /pmc/articles/PMC8249906/\u003c/li\u003e\n\u003cli\u003eHaahr-Raunkjaer C, M\u0026oslash;lgaard J, Elvekjaer M, et al. Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients. \u003cem\u003eActa Anaesthesiol Scand\u003c/em\u003e John Wiley and Sons Inc; 2022; \u003c/li\u003e\n\u003cli\u003eWeenk M, van Goor H, Frietman B, et al. Continuous Monitoring of Vital Signs Using Wearable Devices on the General Ward: Pilot Study. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e [Internet] JMIR Mhealth Uhealth; 2017 [cited 2021 Nov 23]; \u003cstrong\u003e5\u003c/strong\u003e Available from: https://pubmed.ncbi.nlm.nih.gov/28679490/\u003c/li\u003e\n\u003cli\u003eLee PJ. Clinical evaluation of a novel respiratory rate monitor. \u003cem\u003eJ Clin Monit Comput\u003c/em\u003e [Internet] Springer Netherlands; 2016 [cited 2023 Feb 20]; \u003cstrong\u003e30\u003c/strong\u003e: 175\u0026ndash;83 Available from: https://link.springer.com/article/10.1007/s10877-015-9697-4\u003c/li\u003e\n\u003cli\u003eLiu Y, Liu C, Gao M, et al. Evaluation of a wearable wireless device with artificial intelligence, iThermonitor WT705, for continuous temperature monitoring for patients in surgical wards: a prospective comparative study. \u003cem\u003eBMJ Open\u003c/em\u003e [Internet] 2020 [cited 2022 Apr 15]; \u003cstrong\u003e10\u003c/strong\u003e: 39474 Available from: http://bmjopen.bmj.com/\u003c/li\u003e\n\u003cli\u003eZsuzsanna Balla H, Theodorsson E, Str\u0026ouml;m JO. Evaluation of commercial, wireless dermal thermometers for surrogate measurements of core temperature. \u003cem\u003eScand J Clin Lab Invest\u003c/em\u003e [Internet] Scand J Clin Lab Invest; 2019 [cited 2023 Mar 28]; \u003cstrong\u003e79\u003c/strong\u003e: 1\u0026ndash;6 Available from: https://pubmed.ncbi.nlm.nih.gov/30882250/\u003c/li\u003e\n\u003cli\u003eKallioinen N, Hill A, Christofidis MJ, Horswill MS, Watson MO. Quantitative systematic review: Sources of inaccuracy in manually measured adult respiratory rate data. \u003cem\u003eJ Adv Nurs\u003c/em\u003e [Internet] Wiley-Blackwell; 2021 [cited 2024 Apr 23]; \u003cstrong\u003e77\u003c/strong\u003e: 98 Available from: /pmc/articles/PMC7756810/\u003c/li\u003e\n\u003cli\u003eKallioinen N, Hill A, Christofidis MJ, Horswill MS, Watson MO. Quantitative systematic review: Sources of inaccuracy in manually measured adult respiratory rate data. \u003cem\u003eJ Adv Nurs\u003c/em\u003e [Internet] Wiley-Blackwell; 2021 [cited 2024 May 14]; \u003cstrong\u003e77\u003c/strong\u003e: 98 Available from: /pmc/articles/PMC7756810/\u003c/li\u003e\n\u003cli\u003eJung L-Y, Kim J-M, Ryu S, Lee C-S. Relative bradycardia in patients with COVID-19. \u003cem\u003eInternational Journal of Arrhythmia 2022 23:1\u003c/em\u003e [Internet] BioMed Central; 2022 [cited 2024 Apr 23]; \u003cstrong\u003e23\u003c/strong\u003e: 1\u0026ndash;5 Available from: https://arrhythmia.biomedcentral.com/articles/10.1186/s42444-022-00073-z\u003c/li\u003e\n\u003cli\u003eLeenen 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. \u003cem\u003ePLoS One\u003c/em\u003e Public Library of Science; 2022; \u003cstrong\u003e17\u003c/strong\u003e \u003c/li\u003e\n\u003cli\u003eDuus CL, Aasvang EK, Olsen RM, et al. Continuous vital sign monitoring after major abdominal surgery\u0026mdash;Quantification of micro events. \u003cem\u003eActa Anaesthesiol Scand\u003c/em\u003e [Internet] 2018 [cited 2021 Nov 20]; \u003cstrong\u003e62\u003c/strong\u003e: 1200\u0026ndash;8 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/aas.13173\u003c/li\u003e\n\u003cli\u003evan Rossum MC, Vlaskamp LB, Posthuma LM, et al. Adaptive threshold-based alarm strategies for continuous vital signs monitoring. \u003cem\u003eJ Clin Monit Comput\u003c/em\u003e [Internet] Springer Science and Business Media B.V.; 2021 [cited 2022 Apr 14]; 1\u0026ndash;11 Available from: https://link.springer.com/article/10.1007/s10877-021-00666-4\u003c/li\u003e\n\u003cli\u003eJansen C, Chatterjee DA, Thomsen KL, et al. Significant reduction in heart rate variability is a feature of acute decompensation of cirrhosis and predicts 90-day mortality. \u003cem\u003eAliment Pharmacol Ther\u003c/em\u003e [Internet] Blackwell Publishing Ltd; 2019 [cited 2021 Nov 28]; \u003cstrong\u003e50\u003c/strong\u003e: 568\u0026ndash;79 Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/apt.15365\u003c/li\u003e\n\u003cli\u003eDocherty AB, Harrison EM, Green CA, et al. Features of 20\u0026thinsp;133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. \u003cem\u003eBMJ\u003c/em\u003e [Internet] British Medical Journal Publishing Group; 2020 [cited 2023 Aug 14]; \u003cstrong\u003e369\u003c/strong\u003e Available from: https://www.bmj.com/content/369/bmj.m1985\u003c/li\u003e\n\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":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wearable sensors, Vital signs monitoring, Early warning scoring systems, Covid-19, SARS-CoV-2","lastPublishedDoi":"10.21203/rs.3.rs-4976766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4976766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eUse of wearable vital signs sensors to monitor hospitalised patients is growing but uncertainty exists about completeness of data capture and accuracy of measurements.\u0026nbsp; Implications for track and trigger systems are unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this observational study, adult inpatients with Covid-19 wore four wearable sensors recording heart rate/respiratory rate (HR/RR), oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e), axillary temperature and blood pressure (BP). Wearable vitals were paired with traditional vitals recorded concurrently. The accuracy of the wearable vitals was assessed using traditional vitals as the reference.\u0026nbsp; National early warning (NEWS2) scores were calculated using wearable and traditional vitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e48 patients were monitored for 204 days with the sensors.\u0026nbsp; Median sensor wear was 3.9(IQR:1.7-5.9), 3.9(IQR:1.6-5.9) and 3.8(IQR:0.9-5.9) days for HR/RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e respectively. The BP cuff was worn for median 1.9(IQR:0.9-3.8) days in 33 patients. Length of hospital stay was 8(IQR:6-13) days. Completeness of data capture was 84% for HR/RR, 98% for temperature, 72% for SpO\u003csub\u003e2\u003c/sub\u003e and 36% for BP.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There were 1632 HR, 1613 RR, 1411 temperature, 1294 SpO\u003csub\u003e2\u003c/sub\u003e and 51 BP wearable-traditional measurement pairs. 59.7% of HR pairs were within ±5bpm, 38.5% of RR pairs within ±3breaths/min, 24.4% of temperature pairs within ±0.3\u003csup\u003eo\u003c/sup\u003eC, 32.9% of SpO\u003csub\u003e2\u003c/sub\u003e pairs within ±2% and 39.0% of BP pairs within ±10mmHg. Agreement between wearable and traditional RRs was poor at high RRs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;613 NEWS2 scores were calculated using wearable-traditional HR, RR, temperature and SpO\u003csub\u003e2\u003c/sub\u003e pairs.\u0026nbsp; The median NEWS2\u003csub\u003etraditional\u003c/sub\u003e was 1(IQR:1-2) and the median NEWS2\u003csub\u003ewearable\u003c/sub\u003e was 4(IQR:3-6).\u0026nbsp; Using traditional NEWS2 alerts as a reference, 86% (225/262) of wearable NEWS2 5+ alerts and 89% (82/92) of wearable NEWS2 7+ alerts were false positives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgreement between vital signs recorded by wearable sensors and concurrent traditional vitals is poor. Data from wearable sensors should not be used in existing track and trigger systems.\u003c/p\u003e","manuscriptTitle":"The completeness, accuracy and impact on alerts, of wearable vital signs monitoring in hospitalised patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 14:13:15","doi":"10.21203/rs.3.rs-4976766/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-31T16:21:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-24T03:40:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-21T15:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146192655583866702931230438978953143918","date":"2024-10-15T20:41:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101439069620669048859380903870911643478","date":"2024-10-14T08:28:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T16:46:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29123845659407109124366039084236679396","date":"2024-09-03T13:25:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-01T10:17:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-30T14:07:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-28T23:05:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-28T23:04:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2024-08-26T09:15:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b20d37f-a2b3-4eda-a088-433f1fb4afb0","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-13T16:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 14:13:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4976766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4976766","identity":"rs-4976766","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}