Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach.

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.

Cow characteristics associated with the variation in number of contacts between dairy cows.

In modern freestall barns where large groups of cows are housed together, the behavior displayed by herd mates can influence the welfare and production of other individuals. Therefore, understanding social interactions in groups of dairy cows is important to enhance herd management and optimize the outcomes of both animal health and welfare in the future. Many factors can affect the number of social contacts in a group. This study aimed to identify which characteristics of a cow are associated with the number of contacts it has with other group members in 2 different functional areas (feeding and resting area) to increase our understanding of the social behavior of dairy cows. Inside 2 herds housed in freestall barns with around 200 lactating cows each, cow positions were recorded with an ultra-wideband real-time location system collecting all cows' positions every second over 2 wk. Using the positioning data of the cows, we quantified the number of contacts between them, assuming that cows spending time in proximity to one another (within a distance of 2.5 m for at least 10 min per day) were interacting socially. We documented in which barn areas these interactions occurred and used linear mixed models to investigate if lactation stage, parity, breed, pregnancy status, estrus, udder health, and claw health affect the number of contacts. We found variation in the number of contacts a cow had between individuals in both functional areas. Cows in later lactation had more contacts in the feeding area than cows in early lactation. Furthermore, in one herd, higher parity cows had fewer contacts in the feeding area than first parity cows, and in the other herd, cows in third parity or higher had more contacts in the resting area. This study indicates that cow characteristics such as parity and days in milk are associated with the number of contacts a cow has daily to its herd mates and provides useful information for further research on social interactions of dairy cows.

Factors Affecting the Implementation, Use, and Adoption of Real-Time Location System Technology for Persons Living With Cognitive Disabilities in Long-term Care Homes: Systematic Review.

BACKGROUND: As the aging population continues to grow, the number of adults living with dementia or other cognitive disabilities in residential long-term care homes is expected to increase. Technologies such as real-time locating systems (RTLS) are being investigated for their potential to improve the health and safety of residents and the quality of care and efficiency of long-term care facilities. OBJECTIVE: The aim of this study is to identify factors that affect the implementation, adoption, and use of RTLS for use with persons living with dementia or other cognitive disabilities in long-term care homes. METHODS: We conducted a systematic review of the peer-reviewed English language literature indexed in MEDLINE, Embase, PsycINFO, and CINAHL from inception up to and including May 5, 2020. Search strategies included keywords and subject headings related to cognitive disability, residential long-term care settings, and RTLS. Study characteristics, methodologies, and data were extracted and analyzed using constant comparative techniques. RESULTS: A total of 12 publications were included in the review. Most studies were conducted in the Netherlands (7/12, 58%) and used a descriptive qualitative study design. We identified 3 themes from our analysis of the studies: barriers to implementation, enablers of implementation, and agency and context. Barriers to implementation included lack of motivation for engagement; technology ecosystem and infrastructure challenges; and myths, stories, and shared understanding. Enablers of implementation included understanding local workflows, policies, and technologies; usability and user-centered design; communication with providers; and establishing policies, frameworks, governance, and evaluation. Agency and context were examined from the perspective of residents, family members, care providers, and the long-term care organizations. CONCLUSIONS: There is a striking lack of evidence to justify the use of RTLS to improve the lives of residents and care providers in long-term care settings. More research related to RTLS use with cognitively impaired residents is required; this research should include longitudinal evaluation of end-to-end implementations that are developed using scientific theory and rigorous analysis of the functionality, efficiency, and effectiveness of these systems. Future research is required on the ethics of monitoring residents using RTLS and its impact on the privacy of residents and health care workers.

Measuring Sensitivity and Precision of Real-Time Location Systems (RTLS): Definition, Protocol and Demonstration for Clinical Relevance.

The ability of a Real Time Location System (RTLS) to provide correct information in a clinical environment is an important consideration in evaluating the effectiveness of the technology. While past efforts describe how well the technology performed in a lab environment, the performance of such technology has not been specifically defined or evaluated in a practice setting involving workflow and movement. Clinical environments pose complexity owing to various layouts and various movements. Further, RTL systems are not equipped to provide true negative information (where an entity is not located). Hence, this study defined sensitivity and precision in this context, and developed a simulation protocol to serve as a systematic testing framework using actors in a clinical environment. The protocol was used to measure the sensitivity and precision of an RTL system in the emergency department space of a quaternary care medical center. The overall sensitivity and precision were determined to be 84 and 93% respectively. These varied for patient rooms, staff area, hallway and other rooms.

Using Real-Time Locating Systems to Optimize Endoscope Use at a Large Academic Medical Center.

Massachusetts General Hospital (MGH) manages a large inventory of surgical equipment which must be delivered to operating rooms on-time, efficiently, and according to a set of quality standards and regulatory guidelines. In recent years, flexible scope management has become a topic of interest for many hospitals, as they face pressure to reduce costs, prevent infections that can result from mismanagement, and are under increased regulatory oversight. This work conducted at MGH proposes a novel method for surgical equipment management in a hospital. The proposed solution uses a real-time locating system to track flexible scopes, a semantic reasoning engine to determine the state of each scope, and a user interface to inform staff about necessary interventions to avoid scope expirations while maximizing efficiency. This study aimed to accomplish three primary goals. First, the study sought to improve the hospital's compliance to quality standards in order to reduce risks of infection due to expired scopes. Second, the study aimed to improve the cost-efficiency of scope disinfecting processes through more efficient inventory management. Finally, the study served as an opportunity for the hospital to establish best practices for working with the newly installed real-time locating system. The system proposed in this work was implemented at MGH on a subset of the hospital's flexible scopes. The study results demonstrated a quality compliance increase from 88.9% to 94.5%. The study also showed an estimated $17,350 annual cost savings due to more efficient scope management. Finally, the study demonstrated the feasibility, increase in regulatory compliance, and cost savings that would make this technology valuable when scaled across the hospital to other types of scopes and medical devices.

A Framework for an Intelligent and Personalized Fire Evacuation Management System.

Many research studies have focused on fire evacuation planning. However, because of the uncertainties in fire development, there is no perfect solution. This research proposes a fire evacuation management framework which takes advantage of an information-rich building information modeling (BIM) model and a Bluetooth low energy (BLE)-based indoor real-time location system (RTLS) to dynamically push personalized evacuation route recommendations and turn-by-turn guidance to the smartphone of a building occupant. The risk score (RS) for each possible route is evaluated as a weighted summation of risk level index values of all risk factors for all segments along the route, and the route with the lowest RS is recommended to the evacuee. The system will automatically re-evaluate all routes every 2 s based on the most updated information, and the evacuee will be notified if a new and safer route becomes available. A case study with two testing scenarios was conducted for a commercial office building in Tianjin, China, in order to verify this framework.

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting.

This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.

A Pilot of Data-Driven Modeling to Assess Potential for Improved Efficiency in an Academic Breast-Imaging Center.

Patient satisfaction and department efficiency are central pillars in defining quality in medicine. Patient satisfaction is often linked to wait times. We describe a novel method to study workflow and simulate solutions to improve efficiency, thereby decreasing wait times and adding value. We implemented a real-time location system (RTLS) in our academic breast-imaging department to study workflow, including measuring patient wait time, quantifying equipment utilization, and identifying bottlenecks. Then, using discrete event simulation (DES), we modeled solutions with changes in staffing and equipment. Nine hundred and ninety-nine patient encounters were tracked over a 10-week period. The RTLS system recorded 551,512 raw staff and patient time stamps, which were analyzed to produce 17,042 staff and/or patient encounter time stamps. Mean patient wait time was 27 min. The digital breast tomosynthesis (DBT) unit had the highest utilization rate and was identified as a bottleneck. DES predicts a 19.2% reduction in patient length of stay with replacement of a full field digital mammogram (FFDM) unit by a DBT unit and the addition of technologists. Through integration of RTLS with discrete event simulation testing, we created a model based on real-time data to accurately assess patient wait times and patient progress through an appointment, evaluate patient staff-interaction, identify system bottlenecks, and quantitate potential solutions. This quality improvement initiative has important implications, potentially allowing data-driven decisions for staff hiring, equipment purchases, and department layout.

Robotic direct reading device with spatial, temporal, and PID sensors for laboratory VOC exposure assessment.

This study evaluated a novel robotic direct reading method that used a real-time location system to measure the spatial-concentration distribution of volatile organic compounds (VOCs) in a chemistry laboratory. The CEMWIP II is a custom-made sensor that measures VOCs, temperature, humidity, and location, sending data wirelessly in real time to a remote location for display and storage. In this study, the CEMWIP II device was mounted on a robotic platform to create a CEMWIP II-mobile platform. The autonomous mobile platform was released from a corner of the room and allowed to travel randomly along an open floor with the goal of characterizing the spatial distribution of VOCs and identifying their sources in the laboratory. The experiment consisted of 12 runs made of permutations of four corner release sites and four beaker locations, with two beakers containing water and two containing the solvent acetone. The autonomous mobile platform was tasked with locating the two beakers of acetone. The sensor had a detection limit of 100 ppb and the confidence of detecting a source within a 1.46 m2 area was p = 0.0005 by ANOVA. The CEMWIP II-mobile platform was able to measure the spatial distribution of VOCs within a laboratory that were associated with open solvent containers.

An automated approach to measuring child movement and location in the early childhood classroom.

Children's movement is an important issue in child development and outcome in early childhood research, intervention, and practice. Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.

Technical note: Evaluation of an ear-attached real-time location monitoring system.

Position tracking of cows within the barn environment allows for determining behavioral patterns and activities. Such data might be used for detection of estrus and disease. A newly marketed real-time location monitoring system (Smartbow, Smartbow GmbH, Weibern, Austria) was tested in this study. Cow location was continuously monitored with the Smartbow tags mounted on the cow's ear, which sends low-frequency signals to receivers further transmitting the information to a server. Through incoming data, the server triangulates the location of the cow within the barn environment in real time. The validation of the system was carried out in 4 steps. The first 2 steps served as static testing steps (tags and 1 cow positioned at 30 reference points), and steps 3 and 4 were dynamic steps with cows moving in the barn environment. For 48 h, locations of 15 cows were confirmed each hour by laser measurements performed by a team (step 3) or 1 observer (step 4). Interobserver variability was 0.83 m (range: 0.05 to 2.87 m), and intraobserver variability had a range of 0.02 to 0.31 m. In the 4 validation steps, the mean distance between observer laser measurements and Smartbow was between 1.22 and 1.80 m. Step 4, with 334 observations, resulted in a mean distance difference of 1.22 m (standard error = 1.32 m). Data can be used for development of algorithms to detect sick cows with changed behavioral patterns. Data may also be used to monitor cow responses to physical environment, potentially improving facility design. Time budgets in proximity to important barn features (i.e., feed bunk and water trough) and distances traveled can be calculated and used to identify cows in need of caretaker's attention and identify the cow's exact location in the barn.

Development of the chemical exposure monitor with indoor positioning (CEMWIP) for workplace VOC surveys.

The purpose of this article was to research and develop a direct-reading exposure assessment method that combined a real-time location system with a wireless direct-reading personal chemical sensor. The personal chemical sensor was a photoionization device for detecting volatile organic compounds. The combined system was calibrated and tested against the same four standard gas concentrations and calibrated at one standard location and tested at four locations that included the standard locations. Data were wirelessly collected from the chemical sensor every 1.4 sec, for volatile organic compounds concentration, location, temperature, humidity, and time. Regression analysis of the photo-ionization device voltage response against calibration gases showed the chemical sensor had a limit of detection of 0.2 ppm. The real-time location system was accurate to 13 cm ± 6 cm (standard deviation) in an open area and to 57 cm ± 31 cm in a closed room where the radio frequency has to penetrate drywall-finished walls. The streaming data were collected and graphically displayed as a three-dimensional hazard map for assessment of peak exposure with location. A real-time personal exposure assessment device with indoor positioning was practical and provided new knowledge on direct reading exposure assessment methods.

Pinpointing pigs: performance and challenges of an ultra-wideband real-time location system for tracking growing-finishing pigs under practical conditions.

Real-Time Location Systems (RTLSs) are promising precision livestock farming tools and have been employed in behavioural studies across various farm animal species. However, their application in research with fattening pigs is so far unexplored. The implementation of these systems has great potential to gain insight into pigs' spatial behaviour such as the use of functional areas and pigs' proximity to each other as indicators for social relationships. The aim of this study was therefore to validate the accuracy, precision, and data quality of the commercial Noldus Information Technology BV TrackLab system. We conducted different measurement sets: first, we performed static measurements in 12 pens at four different locations in each pen at three heights each using a single ultra-wideband tag (UWB). We recorded unfiltered x- and y-coordinates at 1 Hz. We repeated these measurements with six tags aligned in a 2 × 3 grid with varied spacing to test interference between the tags. We also tested dynamic performance by moving the tags along the centre line of the pens. Finally, we measured the data quality with 55 growing pigs in six pens, including the identification of location 'jumps' from the inside to the outside of the pen. Each pen housed ten animals fitted with a UWB tag attached to their farm ear tag. We collected data for 10 days and analysed seven 24-h periods of raw and filtered data. The mean accuracy of the RTLS measurements was 0.53 m (precision: 0.14 m) for single and 0.46 m (precision: 0.07 m) for grouped tags. Accuracy improved with increasing measurement height for single tags but less clearly for grouped tags (P [height single] = 0.01; P [height grouped] = 0.22). When tags were fitted to animals, 63.3% of the filtered data was lost and 21.8% of the filtered location estimates were outside the pens. Altogether, the TrackLab system was capable of fairly accurate and precise assignment of the functional areas where individual animals were located, but was insufficient for the analysis of social relationships. Furthermore, the frequent occurrence of gaps in signal transmission and the overall high data loss rates presented significant limitations. Additionally, the challenging hardware requirements for attaching sensors to the animals underline the need for further technological advances in RTLS for the application with growing-finishing pigs.

Measuring nursing movements using real time tracking data at the Royal Wolverhampton Hospital Trust.

BACKGROUND: Real time location systems (RTLS) are increasingly used in healthcare with applications that include contract tracing and staffing. However, their potential to provide organizational insights requires staff compliance with the system. MATERIALS AND METHODS: Our goal is to assess how many nurses are using the RTLS correctly (i.e. complying to the system). We collect RTLS data on the movements of nurses at the Royal Wolverhampton NHS Trust. We identify the number of RTLS active nurses and compare it to what expected from the nurses' rotas. RESULTS: We find that a significant number of nurses appear not to be active from the RTLS data. For approximately 15% of the active users, RTLS records below 10 movements per day. Nevertheless, most of the active users have daily RTLS times consistent with the average shift length. CONCLUSION: Applications of RTLS data may need to account for imperfect compliance of staff to the system.

Real-time location systems technology in the care of older adults with cognitive impairment living in residential care: A scoping review.

Introduction: There has been growing interest in using real-time location systems (RTLS) in residential care settings. This technology has clinical applications for locating residents within a care unit and as a nurse call system, and can also be used to gather information about movement, location, and activity over time. RTLS thus provides health data to track markers of health and wellbeing and augment healthcare decisions. To date, no reviews have examined the potential use of RTLS data in caring for older adults with cognitive impairment living in a residential care setting. Objective: This scoping review aims to explore the use of data from real-time locating systems (RTLS) technology to inform clinical measures and augment healthcare decision-making in the care of older adults with cognitive impairment who live in residential care settings. Methods: Embase (Ovid), CINAHL (EBSCO), APA PsycINFO (Ovid) and IEEE Xplore databases were searched for published English-language articles that reported the results of studies that investigated RTLS technologies in persons aged 50 years or older with cognitive impairment who were living in a residential care setting. Included studies were summarized, compared and synthesized according to the study outcomes. Results: A total of 27 studies were included. RTLS data were used to assess activity levels, characterization of wandering, cognition, social interaction, and to monitor a resident's health and wellbeing. These RTLS-based measures were not consistently validated against clinical measurements or clinically important outcomes, and no studies have examined their effectiveness or impact on decision-making. Conclusion: This scoping review describes how data from RTLS technology has been used to support clinical care of older adults with dementia. Research efforts have progressed from using the data to track activity levels to, most recently, using the data to inform clinical decision-making and as a predictor of delirium. Future studies are needed to validate RTLS-based health indices and examine how these indices can be used to inform decision-making.

The feasibility and validity of using a real time location system (RTLS) to measure bedside contact time.

Background: The association between the nurse-to-patient ratio and patient outcomes has been extensively investigated. Real time location systems have the potential capability of measuring the actual amount of bedside contact patients receive. Aims: This study aimed to determine the feasibility and accuracy of real time location systems as a measure of the amount of contact time that nurses spent in the patients' bed space. Methods: An exploratory, observational, feasibility study was designed to compare the accuracy of data collection between manual observation performed by a researcher and real time location systems data capture capability. Four nurses participated in the study, which took place in 2019 on two hospital wards. They were observed by a researcher while carrying out their work activities for a total of 230 minutes. The amount of time the nurses spent in the patients' bed space was recorded in 10-minute blocks of time and the real time location systems data were extracted for the same nurse at the time of observation. Data were then analysed for the level of agreement between the observed and the real time location systems measured data, descriptively and graphically using a kernel density and a scatter plot. Results: The difference (in minutes) between researcher observed and real time location systems measured data for the 23, 10-minute observation blocks ranged from zero (complete agreement) to 5 minutes. The mean difference between the researcher observed and real time location systems time in the patients' bed space was one minute (10% of the time). On average, real time location systems measured time in the bed space was longer than the researcher observed time. Conclusions: There were good levels of agreement between researcher observation and real time location systems data of the time nurses spend at the bedside. This study confirms that it is feasible to use real time location systems as an accurate measure of the amount of time nurses spend at the patients' bedside.

Real-time location and inpatient care systems based on passive RFID.

RFID technology meets identification and tracking requirements in healthcare environments with potential to speed up and increase reliability of involved processes. Due to this, high expectations for this integration have emerged, but hospital and medical centers interested in adoption of RFID technology require prior knowledge on how to squeeze RFID capabilities, real expectations and current challenges. In this paper, we show our lab tested solutions in two specific healthcare scenarios. On the one hand, we analyze the case of a medical equipment tracking system for healthcare facilities enabling both real-time location and theft prevention. Worth-noting aspects such as possible EMI interferences, technology selection and management of RFID data from hospital information system are analyzed. Lab testing of system reliability based on passive UHF RFID is provided for this case. On the other hand, we analyze and provide a solution for care and control of patients in a hospital based on passive HF RFID with the result of a fully functional demonstrator. Our prototype squeezes RFID features in order to provide a backup data source from patient's wristband. It also provides an offline working mode aiming to increase application reliability under network fail down and therefore, improving patient's safety. Considerations regarding lessons learned and challenges faced are exposed.

Contact Tracing in Healthcare Settings During the COVID-19 Pandemic Using Bluetooth Low Energy and Artificial Intelligence-A Viewpoint.

The COVID-19 pandemic has inflicted great damage with effects that will likely linger for a long time. This crisis has highlighted the importance of contact tracing in healthcare settings because hospitalized patients are among the high risk for complications and death. Moreover, effective contact tracing schemes are not yet available in healthcare settings. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. One potential solution that addresses all of these elements leverages an indoor real-time location system based on Bluetooth Low Energy and artificial intelligence.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare.

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Movement at work: A comparison of real time location system, accelerometer and observational data from an office work environment.

Office workers can spend significant periods of time being stationary whilst at work, with potentially serious health consequences. The development of effective health interventions could be aided by a greater understanding of the location and environmental context in which this stationary behaviour occurs. Real time location systems (RTLS) potentially offer the opportunity to gather this much needed information, but they have not been extensively trialled in office workplaces, nor rigorously compared against more familiar devices such as accelerometers. The aim of this paper was to determine whether an RTLS can measure and spatially locate the non-stationary and stationary behaviours of adults working in an office work environment. Data collected from a series of comparison studies undertaken in a commercial office building suggests that RTLS can measure the velocity at which people are moving and locate them, when stationary, with an accuracy of 0.668 m (SD 0.389). This opens up significant opportunities to further understand how people move within buildings, the indoor physical environmental influences on that movement, and the development of effective interventions to help people to move more whilst at work.