This RUBIN-1_readme20240104.txt file was generated on 20240104 by Kaylee Alexander ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Cleaned Ping-level Wireless Sensor Data from the University of Utah from the CDC-funded Granular Modeling Project 2. Author Information Principal Investigator Contact Information Name: Michael Rubin, MD, PhD Institution: University of Utah/Salt Lake City Veterans Affairs Address: Division of Epidemiology, 295 Wakara Way, Salt Lake City, UT 81408 Email: michael.rubin@hsc.utah.edu ORCID: 0000-0002-2900-5548 Associate or Co-investigator Contact Information Name: Molly Leecaster, PhD Institution: University of Utah/Salt Lake City Veterans Affairs Address: Division of Epidemiology, 295 Wakara Way, Salt Lake City, UT 81408 Email: molly.leecaster@utah.edu Associate or Co-investigator Contact Information Name: Candace Haroldsen, MSPH Institution: University of Utah/Salt Lake City Veterans Affairs Address: Division of Epidemiology, 295 Wakara Way, Salt Lake City, UT 81408 Email: candace.haroldsen@hsc.utah.edu 3. Date of data collection (single date, range, approximate date) 2018 (specific dates removed for data privacy) 4. Geographic location of data collection (where was data collected?): University of Utah Hospital Acute Care Ward, Salt Lake City, Utah, USA 5. Information about funding sources that supported the collection of the data: Centers for Disease Control and Prevention (CDC) RFTOP #2015-006, "Granular Modeling–Simulating the Transmission of Healthcare Associated Infections in Hospitals" -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC BY NC - Allows others to use and share your data non-commercially and with attribution. 2. Links to publications that cite or use the data: NA 3. Links to other publicly accessible locations of the data: NA 4. Links/relationships to ancillary data sets: NA 5. Was data derived from another source? No 6. Recommended citation for the data: Rubin, Michael, Molly Leecaster, and Candace Haroldsen. 2024. "Cleaned Ping-level Wireless Sensor Data from the University of Utah from the CDC-funded Granular Modeling Project." The Hive: University of Utah Research Data Repository. www.doi.org/10.7278/S50d-twbh-955q. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: SensorData_Phase2.csv Short description: This dataset contains cleaned sensor pings of RFD reads between healthcare worker worn sensors and environmental sensors placed in facility. 2. Relationship between files: NA 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? No -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: The objective of using the wireless sensors was to improve understanding of the heterogeneity of healthcare worker (HCW) contact with patients and the physical environment in patients’ rooms. The framework and design were based on contact networks with a) nodes defined by HCW’s, rooms, and items in the room and b) edges defined by HCW’s in the room, near the bed, and touching items. Nodes had characteristics of HCW role and room number. Edges had characteristics of day, start time, and duration. Thus, patterns and heterogeneity could be understood within contexts of time, space, roles, and patient characteristics. At the University of Utah Hospital Cardiovascular ICU (CVICU), a 20-bed unit, we collected sensor data for 54 days. HCW contact with patients was measured using wireless sensors to capture time spent in patient rooms as well as time spent near the patient bed. HCW contact with the physical environment was measured using wireless sensors on the following items in patient rooms: door, sink, toilet, over-bed table, keyboard, vital signs monitor touchscreen, and cart. HCW’s clipped a sensor to their clothing or lanyard. A sensor sent radio signals that other sensors recorded. They record the signal strength received and the loss (difference) between sending and receiving signal strengths indicated the distance between them. The sensors also had an accelerometer; it sent a signal when vibration was detected. Non-accelerometer sensors sent signals at a pre-determined interval and recorded signals continuously. The signal included the sensor identifier and time stamp. Sensors were programmed with specifications tested during pilot work to balance collection of accurate data with battery and memory capacity. The specifications were sending strength, receiving filter, accelerometer sensitivity, and sending interval. There were three categories of sensors we programmed: 1) HCW sensors send & receive, 2) wall sensors send on an interval, and 3) item sensors send on vibration. Low sending strength was recorded within 2-3 feet, medium was recorded within 6 feet, and high was recorded within 10 feet. The filter was specified to limit memory use; the lower the value the lower the signal strength they recorded. Accelerometer sensitivity was set at a low value for items that vibrated a lot so that slight vibrations did not trigger a signal. Maintenance was performed to download data and charge batteries. This process resulted in clumps of data, many records among sensors in a short period of time. Maintenance also resulted in sporadic, but few, records during that time. For interpretation, these time periods were indicated in the data. 2. Methods for processing the data: Sensors were collected after deployment and downloaded to a local computer, creating hundreds of raw data files. Files were then combined into one dataset where they were cleaned to remove obvious unreliable data. Several metadata components were incorporated directly into this analysis dataset including: 1) timesync files used to calculate time of sensor event, 2) id dataset used to characterize each sensor and associated role (e.g. healthcare worker, near-bed sensor, sink, gel, etc.), 3) and event log files used to create indicator variables for whether a charging/swapping event took place during that particular sensor event (charging/swapping events could create noise in data that hinders its use/interpretation). 3. Instrument- or software-specific information needed to interpret the data: SAS 9.4 was used for importing and processing data. R 4.3.0 was used for validation and analysis. No special software is required for use of current dataset (csv format). 4. Standards and calibration information, if appropriate: Several pilot studies were done in order to calibrate the devices. 5. Environmental/experimental conditions: This study was performed in vivo in an acute care hospital 6. Describe any quality-assurance procedures performed on the data: Initial data processing steps: Import raw sensor data files Join time sync file to calculate sensor date time Join IDs file to identify sensor role and location Assign event categories (EventCat) Contact between HCWs Item use recorded by HCW: sink, soap, gel, door, table, toilet, keyboard, touchscreen, cart, ventilator Patient room location recorded by HCW (room and near-bed) Combine all sensor files into one dataset Define sublocation for gel/soap/sink in HA using ‘placement’ (provides location along hallway wrt patient room numbers) Define limitingEvent: Charge for times when sensors could have been taken away, charged, and replaced Restrict for times when the patient or family requested no one enter the room Define location as Patient Room #, HA (hallway), NS (nursing station), or unknown (Contacts) Data cleaning steps: Delete HCW IDs that never interact with environmental sensors Delete HCW IDs that either never record signals or are never recorded by others Delete Reboot > 0 [occurred in Sensor Lab Office] Delete IsSync > 0 [occurred in Sensor Lab Office] Delete Ego_global=ego_local [occurred in Sensor Lab Office] Delete Battery < 3.9 [too low for reliable sending/recording] Keep: Ego roles: nurse (na), nurse assistant (cn), physician (ph), physical therapist (pt), respiratory therapist (rt), study personnel (sa) Remove if rssi<15 Keep only event category pings: Delete sensor events that occurred while sensors were in transport, indicated by: rssi>=30 and signal internal <= every 20 seconds for 5+minutes. Advanced data cleaning steps (beyond Phase 1): Patient Room Wall Sensor Data: Delete patient room wall sensor pings with RSSI < 17 Delete pings if not other ping in patient room within +/- 1 minute (removed single pings) Delete pings between ego/alter pairs (person and wall sensors) < 20 seconds apart Delete pings if no pings from a different wall sensor in patient room within +/- 1 minute Delete pings if they come from rooms 2+ doors away within 25 seconds Created room events by aggregating pings: maximum gap between pings from a room is 1 minute. Delete room events < 2 minutes or > 60 minutes. If room events from different rooms overlapped in time, the one with the longer duration was kept (this aligned with the maximum RSSI) Kept pings from wall sensors if within Room Event to retain the placement and ping information. Patient Room Item Sensor Data: Deleted pings not associated with ego (person) and shift in Room Event data Deleted pings not in location and timeframe of Room Events Outside of Room Item Sensor Data (gel, sink, soap): Deleted pings not associated with ego (person) and shift in Room Event data Deleted pings that occured during a Room Event HCW to HCW Sensor Data (Contact): Deleted pings not associated with ego (person) and alter (person) and shift in Room Event data 7. People involved with sample collection, processing, analysis and/or submission: Candace Haroldsen, MSPH (Data Manager); Kristina Stratford (Study Coordinator), Tavis Huber (Study Coordinator) ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: SensorData_Phase2.csv ----------------------------------------- 1. Number of variables: 18 2. Number of cases/rows: 1,448,105 3. Variable List A. Name: row number Description: integer 1-n; unique identifier of row B. Name: ego_role Description: indicator of mote vs accelerometer VALUES: M=mote A=accelerometer C. Name: ego_location Description: type of healthcare worker or if environmental sensor, location of that sensor. VALUES: nu=nurse, rt=respiratory therapist, pt=physical therapist, ph=physician, sa=sampler, cn=certified nurse assistant. For locations: NS=nurses station, HA=hallway, other locations are numeric and correspond to a patient room. D. Name: ego_subloc Description: 3 letter identifier for hcw. Used in conjunction with ego, will identify a unique HCW. Non-HCW sensors identified by name of item, e.g., wall, table, sink. E. Name: alter_role Description: indicator of mote vs accelerometer VALUES: M=mote A=accelerometer F. Name: alter_location Description: type of healthcare worker or if environmental sensor, location of that sensor. VALUES: nu=nurse, rt=respiratory therapist, pt=physical therapist, ph=physician, sa=sampler, cn=certified nurse assistant. For locations: NS=nurses station, hall#### = hallway with nearby room numbers, other locations are numeric and correspond to a patient room. G. Name: alter_subloc Description: 3 letter identifier for hcw. Used in conjunction with ego, will identify a unique HCW. Non-HCW sensors identified by name of item, e.g., wall, table, sink. H. Name: alter_placement Description: letter indicating placement within the subloc. b for second gel dispenser within patient room. For wall, C = cabinet, W = window, B = nearbed I. Name: ego Description: id number of sensor J. Name: alter Description: id number of sensor K. Name: rssi Description: strength of signal recorded by alter sensor L. Name: download date Description: date sensor data was downloaded M. Name: SensorTime Description: time of sensor event N. Name: EventCat Description: type of event VALUES: Cart=sensor on cart triggered, Contact=hcw to hcw event, Door=sensor on door triggered, Gel=sensor on gel triggered, Keyboard=sensor on keyboard triggered, Near-bed=sensor signal from wall of patient room behind bed, Room=sensor signal from patient room wall, Sink=sensor on sink triggered, Soap=sensor on soap triggered, Table=sensor on table triggered, Toilet=sensor on toilet triggered, Touchscreen=sensor on touchscreen triggered. O. Name: location Description: location of sensing event VALUES: NS=nurses station, HA=hall, unknown=hcw contact event - all locations unknown, the rest are integers corresponding to a room number. P. Name: SwapRestrict Description: sensors were swapped out and/or recharged during that sensing event or patient/family requested no one come into the room. Could indicate unreliable record. Q. Name: newStudyDay Description: sequential day numbering, from 1, on the first day of microbiology data collection. R. Name: RoomEventNum Description: Unique number identifying how pings were aggregated in to a Room Event. These were then disaggregated to pings. 4. Missing data codes: NA 5. Specialized formats of other abbreviations used: NA