Objective: To determine work activity patterns undertaken by ED consultants. Methods: A single observer time‐motion study of consultants rostered to clinical shifts: primarily administrative (Duty) or clinical (Resuscitation). Direct observation of 130 h was undertaken using purpose developed time‐stamping software. Primary outcome was task number and time spent in predetermined categories of activity. Comparisons occurred by role delineation, sex, weekday and time of day. Results: For each observed hour consultants performed 101 discrete tasks. A high proportion was spent multitasking; 77 min of overlapping activity in each hour of observation. Consultants spent 42% of each hour on communication, 35% on direct clinical care and 24% on computer use; only 9% was spent on non‐clinical tasks. Consultants spent little time (0.6%) accessing e‐resources. Duty consultants undertook more tasks than Resuscitation consultants, 111 versus 90, and more time was spent on communication (47% vs 35%) and computer use (32% vs 15%) with less on clinical care (29% vs 43%). Female consultants undertook 119 tasks per hour compared with 93 for male consultants; more time was spent on communication (51% vs 38%) and computer use (28% vs 22%). No difference in activity occurred by time of day or weekday. Conclusion: ED consultants have very high hourly task rates dominated by communication and clinical activities and frequently multitask. The activity is relatively constant throughout the week but is influenced by sex and role delineation. Appreciation of activity distribution might allow informed interventions to realign the workload or divert tasks to supporting resources.

The pulse arrival time (PAT), pre-ejection period (PEP) and pulse transit time (PTT) are calculated using on-body continuous wave radar (CWR), Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors for wearable continuous systolic blood pressure (SBP) measurements. The CWR and PPG sensors are placed on the sternum and left earlobe respectively. This paper presents a signal processing method based on wavelet transform and adaptive filtering to remove noise from CWR signals. Experimental data are collected from 43 subjects in various static postures and 26 subjects doing 6 different exercise tasks. Two mathematical models are used to calculate SBPs from PTTs/PATs. For 38 subjects participating in posture tasks, the best cumulative error percentage (CEP) is 92.28% and for 21 subjects participating in exercise tasks, the best CEP is 82.61%. The results show the proposed method is promising in estimating SBP using PTT. Additionally, removing PEP from PAT leads to improving results by around 9%. The CWR sensors present a low-power, continuous and potentially wearable system with minimal body contact to monitor aortic valve mechanical activities directly. Results of this study, of wearable radar sensors, demonstrate the potential superiority of CWR-based PEP extraction for various medical monitoring applications, including BP measurement.

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doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.