key: cord-0982578-xf7d9zsu authors: Li, Xinxia; Hu, Chenghong; Meng, Airong; Guo, Yanmei; Chen, Yang; Dang, Rongqing title: Heart rate variability and heart rate monitoring of nurses using PPG and ECG signals during working condition: A pilot study date: 2022-01-19 journal: Health Sci Rep DOI: 10.1002/hsr2.477 sha: ed879228a6faf35b39a1d99cb2dd31098e7a2177 doc_id: 982578 cord_uid: xf7d9zsu The use of wearable photoplethysmography (PPG) technology for estimating heart rate (HR) and HR variability (HRV) in the health care system is gaining attention in recent years. However, the performance of these devices remains questionable in their ability to collect data in real working conditions for long‐term monitoring. The present study aimed to examine the data collected from nurses during working hours by PPG and electrocardiography (ECG) devices. Twenty‐two nurses underwent a 60‐minute work protocol during the normal working conditions while wearing a PPG device and an ECG device. HR, low‐frequency component (LF) and high‐frequency component (HF), LF/HF ratio, and percent LF distribution in total spectral power, and steps were examined. Pearson's correlation analysis and Bland‐Altman method was performed to examine the relationships between the two devices based on HR and HRV indices. The results found strong positive correlations between HR estimates of both devices, and moderate correlations between LF/HF ratio and percent LF indices estimates, respectively. Moreover, the Bland‐Altman analysis showed a small mean bias in general between the captured data of both devices. This pilot study suggested that the PPG device appears to demonstrate good overall reliability in measuring HR, LF/HF ratio, and percent LF. A further large‐scale study is required to investigate the feasibility and practicality for HR and HRV analysis in nurses during real working conditions using PPG devices. Nursing is perceived as a strenuous job, in which nurses regularly suffered from serious musculoskeletal disorders and psychological disturbances, such as back pain and stress-related problems. 1 In particular, during the COVID-19 pandemic, nurses have experienced increasing pressure, anxiety, exhaustion, isolation, and ongoing emotional trauma. 2, 3 The high level of workplace stress is becoming a major and serious problem for nurses, hospitals, and related institutions, and it significantly affects their quality of life, and the quality of care service they provide. 4 Investigations have been conducted on workplace stress of nurses to identify their stressors, stress management intervention, and outcomes. 1, 5, 6 Nevertheless, those studies mainly assessed the stress level of the subjects by questionnaires, and the examined activities were strongly tied to their perceived stress. [7] [8] [9] If the activities could be tied to quantitative measures, it would provide a better grasp of the stress level of the nurses. Monitoring workplace stress for nurses objectively and efficiently, and providing proper intervention when necessary should be the main focus in making healthcare policy for nurses. Heart rate (HR), blood pressure, and heart rate variability (HRV) are often regarded as physiological monitoring markers in stress assessment. 10 In recent years, the quantitative analysis of HRV has been proposed as a possible way to study the effects of work-related stresses on cardiovascular autonomic regulation in high stress conditions. [11] [12] [13] HRV analysis was suggested to provide an objective measurement of stress in nurses group in theory. 11 Although electrocardiography (ECG) device can reliably measure HR and HRV signals, 8 ECG electrode pad and chest strap often lead to skin irritation and discomfort at contact areas, especially on female subjects. 9, 14 Thus, it has been difficult to measure stress for a long period of time in real working conditions. As an emerging technology in recent years, photoplethysmography (PPG) wearable device is becoming a popular technology for continuous HR monitoring. Evidence showed that HR can be estimated reliably from the PPG method. 15, 16 Compared to an ECG device with an electrode pad and chest strap, PPG wrist-worn device is a more convenient and less intrusive measurement technique. 15 Many smartwatches and wristbands contain PPG sensors, and their designs are getting more compact and user-friendly. 9 However, these devices mainly provide HR measurement but not detailed HRV analysis. Most importantly, the performance of PPG sensors remains questionable with respect to data collection in actual working conditions due to motion artifacts. 17 Getting accurate and detailed HRV measurements from PPG wearable devices remains a challenging problem. Even with the popularity and merit of PPG wearable devices, research work on the validation of PPG in HRV monitoring has not been thoroughly conducted. Therefore, the present study aimed to investigate the data collected by a PPG device and an ECG device and to evaluate the PPG device's ability against the ECG device to accurately collect HR and HRV measurements for nurses under normal working conditions. In particular, the objectives of this pilot study are to analyze and compare: (a) the HR estimates; (b) LF/HF ratio estimates; and (c) percent LF estimates between the collected PPG and ECG signals. A convenience sampling method was used to recruit the participants. Inclusion criteria were as follows: registered nurse, on active duty, has no significant medical condition that may potentially influence HRV measurements, and is willing to volunteer to take part in this study. Twenty-two healthy nurses working at intensive care unit (ICU) and neurosurgery intensive care unit (neuro-ICU) at a public hospital in signal, were recorded during the experiment. LF is usually regarded as a marker of fluctuations in either sympathetic of sympathetic plus vagal activity, while HF is a marker of vagal activity. 19 In this experiment, the following frequency domain parameters were analyzed: LF/HF ratio, and percent LF distribution in total spectral power (ie, LF/[LF + HF]*100). The spectral components were calculated as absolutes units (ms 2 ). The ECG device (myBeat-WHS-1, Union Tool Co. Ltd., Japan) was used to provide criterion measures of HR and HRV. The device was fixed to the chest around the epigastrium by a dedicated chest strap with electrodes. The ECG data were recorded at 1000 Hz. The HR, LF (0.04-0.15 Hz), HF (0.15-0.4 Hz), body surface temperature, and triaxial acceleration were recorded during the experiment. LF and HF components were calculated as normalized units (nu) with this ECG device. The HRV-based stress indices, LF/HF ratio, and percent LF, were also calculated. The detailed ECG processing and HRV analysis used in this device is explained in Ota et al. 20 The experiments were performed at a public hospital in the Inner Mongolia Autonomous Region of China. Information about the study was presented to the hospital management and the head nurses. Approval was obtained to conduct the experiment. The head nurses explained the main goals and relevant information of the experiment to the participating nurses. All participants signed the consent form and had the right to withdraw at any time. All personal information is protected according to the protocol approved by the ethics committee. To compare the HR and HRV indices between PPG and ECG signals, all participants underwent a 60-minute work protocol during normal working conditions while wearing a PPG device and an ECG device. In particular, the PPG device was worn on the upper arm, while the ECG device was fixed to the chest at the same time during the experiment. HR and HRV data were recorded concurrently and continuously throughout the experiment. One of the authors provided training on how to use the device to the nurses, retrieved the devices, and transferred the data to a computer after the experiment. The recorded PPG and ECG signals were retrieved using software provided by the manufacturers respectively. HR data were checked for integrity and then averaged at 1-minute intervals for analysis. 21 The HRV parameters were analyzed in the frequency domain. Although the two devices use different units for LF and HF measurements, the HRV parameters being examined, LF/HF ratio, and percent LF are both ratios, so the difference in the unit does not influence the result. LF and HF were averaged at 5-minute intervals for further analysis. 22 Pearson correlation analysis was performed to determine the strength of the relationship between the two devices. Bland-Altman method was used on aggregate data of HR and stress values to assess the agreement between the two devices for HR and HRV measurements Moreover, the PPG and ECG signals of the day and night shifts were further analyzed using Bland-Altman to assess the agreement more specifically due to the following considerations: (a) previous studies showed that shift schedule significantly affects the overall physical and mental health of nurses. Shift time is also often regarded as an influence factor of nurses' workplace stress. 23 The tasks and shifts of each participant can be found in the supplemental material. worked in night shift. There was no significant difference in age, BMI, and steps between the two shifts. Analyzed PPG and ECG data were simultaneously collected from these 22 subjects. There were no missing data. The correlation coefficient, mean HR, and mean difference (bias) are presented in Table 1 and Table 1 ). Results of LF/HF data pair analyses were presented in Table 2 and Figure 2 . The results showed that there was a moderate positive correlation for aggregate data, day and night shift data respectively (r = 0.577, P < .0001; r = 0.617, P < .0001, and r = 0.464, P < .0001 of agreement between the two methods was found for HRV parameters. This study examined the HR, LF/HF ratio, and percent LF measurements collected by PPG and ECG devices. The HR aggregate dataset indicated a strong positive correlation and a very small mean bias between the captured data of the two types of devices. These findings agreed with previous laboratory studies, 9, 16 which reported similar correlations and a high degree of agreement of HR derived from PPG and ECG devices. The present study showed that HR measurements from the two devices used in this experiment are in a high degree of agreement for nurses under real working conditions. Considering the differences in workloads between shifts, we further Abbreviations: CI, confidence interval; ECG, electrocardiography; HF, high frequency; LF, low frequency; LoA, limits of agreement; n, per 5-minute data pairs; PPG, photoplethysmography; SD, standard deviation. *** P < .001. analyzed the HR for day and night shifts respectively to examine the degree of agreement between PPG and ECG signal by the two devices more specifically. Similarly, in both shifts, the HR measurements obtained from the PPG device were strongly correlated to ECG measurements with a very small mean bias. Both devices used power spectral density analysis to identify and remove the motion artifacts from the raw data to calculate the HRV parameters. 9 28 and mechanical factors such as motion artifacts 17 and sampling devices. 18, 22 Overall, in conjunction with existing self-evaluation mechanisms, PPG wearable devices that are capable of collecting HRV parameters in real working conditions will contribute to the stress management of nurses to reliably assess their conditions, and to evaluate the effectiveness of interventions. In this study, we examined the validity of the HR and HRV parame- The authors would like to thank all hospital leaders for their helpful support and all volunteers for their participation. The authors declare no conflicts of interest. Conceptualization: Xinxia Li. Data Curation: Chenghong Hu. Formal Analysis: Chenghong Hu. Funding Acquisition, Xinxia Li. Investigation: Yang Chen, Rongqing Dang. Project Administration: Xinxia Li. Resources: Airong Meng, Yanmei Guo. Supervision: Xinxia Li. Writing-Original Draft Preparation: Chenghong Hu. Writing-Review and Editing: Xinxia Li, Chenghong Hu. All authors have read and agreed to the published version of the manuscript. Chenghong Hu had full access to all of the data in the study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis. Xinxia Li affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant registered) have been explained. 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