A study investigating perceptions of nurses about wearable devices to predict risk of nurse burnout

Yuceel, Adviye I

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https://hdl.handle.net/2142/122186 Copy

Description

Title

A study investigating perceptions of nurses about wearable devices to predict risk of nurse burnout

Author(s)

Yuceel, Adviye I

Issue Date

2023-12-08

Director of Research (if dissertation) or Advisor (if thesis)

Wooldridge, Abigail R

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Industrial Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Wearable sensors
• Work system integration
• Usability
• Nurses

Abstract

Burnout is a global public health issue, and in recent years, many have called to proactively monitor and address health care worker burnout, particularly nurses, who comprise the largest group of health workers in the United States. Current measures of burnout impede proactive risk prediction – wearable sensors could be an alternative approach to survey-based assessment. As this approach would require the wearing of sensors by nurses throughout their shifts, perceptions of nurses about such wearable sensors to incorporate in the design of such a system is an integral part of the solution for it to be effective and sustainable. In response, the overall objective of this thesis is to identify characteristics, including of the device and broader work system, nurses prefer and need from such burnout risk prediction systems in order for seamless integration of such devices into their work routines. We collected survey and interview data about a specific wearable device to do so: survey and interview regarding a specific wearable device. In our first objective, we aimed to measure the usability of a wearable device, the Empatica E4, to investigate the aspects of such a device nurses were satisfied and dissatisfied with. Eighteen nurses from two hospitals wore the Empatica E4 wristband during working shifts for a period of four weeks. Afterwards, they completed a survey including the System Usability Scale and open-ended usability questions regarding their experience. We then conducted exploratory semi-constructed interviews to identify the characteristics nurses would prefer in preventative wearable risk prediction devices, including implementation characteristics such as who would have access to such data, at what level, and with what purpose. We interviewed four nurses from one hospital regarding characteristics they would ask from wearable devices for seamless integration of such devices into their workflow. We conducted inductive thematic analysis on the open-ended survey questions the interviews with a consensus-based discussion to define dimensions of usability of the Empatica E4 that participants viewed to be positive, negative, or asked to be improved. We then integrated these two data streams to draw conclusions and make recommendations. The Empatica E4 received an overall SUS score of 85.1, receiving a B letter grade for usability, an adjective rating of “Excellent”, and an acceptability range of “Acceptable”. We defined 4 usability dimensions each with its respective positive, negative, and improvement aspect: aesthetics, comfort, functionality, and workflow integration. We found that the participants were most dissatisfied with the comfort of the Empatica E4 and made the most frequent improvement suggestions for the Empatica E4 regarding the comfort dimension, so we proposed that the highest priority usability dimension that would need to be addressed in the case of the Empatica E4 being an element of a nurse burnout risk prediction system to be comfort. All interview participants highlighted the importance of data privacy as well as ethics regarding the use of collected data by the hospital. All participants stated being uncomfortable with the hospital having access to individual level identifiable data. We recommend increasing the comfort of the Empatica E4. Based on our interview data, we identified three priorities for future work developing the risk prediction system: 1) privacy of data, 2) ethics regarding usage of data, 3) preferred work system integration design characteristics to be implemented within the wearable device. Across both data streams, participants emphasized ensuring good workflow integration, functionality, aesthetics, and comfort of wearable devices.

Graduation Semester

2023-12

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Adviye Yuceel

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