The Use of Objective Data to Assess Activity and Participation Prior to and Following Critical Illness

Abstract

This thesis is composed of five distinct chapters that investigate the feasibility of using smartphone data to define activity and participation in patients who have suffered critical illness. Specifically the thesis investigates the current state of play with regards to outcome measurement following critical illness; explores how others have used objective activity data to monitor survivors of critical illness;, investigates the ownership of smartphones and the data available on these devices amongst Intensive Care Unit (ICU) survivors; demonstrates the accuracy of the step data collected by a smartphone; and explores the use of a dedicated smartphone app to monitor patient activity and participation. The thesis comprises 6 distinct manuscripts. Survival of critical illness frequently leads to the development of Post Intensive Care Syndrome (PICS). PICS is the new or worsening of cognitive, psychological and/or physical function that persists following hospital discharge in patients who have suffered critical illness. While PICS leads to disability its measurement is challenging, often relying on surveys that lack construct validity in survivors of critical illness (Chapter 1.1). Objective measurement of activity following critical illness has been investigated using wearable devices, but not smartphones, that are becoming ubiquitous in modern day life (Chapter 1.2). We investigated smartphone ownership amongst ICU survivors and were able to obtain step and location data from a minority of these phones using manual techniques (Chapter 2.2). These patients were followed up (Chapter 2.3) at 3 and 6 months following ICU discharge. This study demonstrates that the step data collected from smartphones appears to be accurate when compared to a dedicated wearable device. We were able to show that the location data obtained from smartphones could be used to demonstrate elements of physical participation such as time spent at home, distance traveled and the extent of travel in activity spaces (Chapter 2.4). However, the manual extraction of these data was time consuming, relying on the use of obtaining Global Position System (GPS) data from screenshots of patients’ phones. In 2015 Google Maps released a function that allowed the download of a single file that contained an individual’s whole location history. We explored the availability of this data in patients admitted under general medicine (Chapter 3.2). Although the data extraction was more efficient and the analysis could be automated, the data were only available on a minority of devices. Due to the inefficiencies associated with smartphone data extraction, we examined the smartphone user ability in patients receiving renal dialysis (Chapter 4.2). This demonstrated that the majority of smartphone owning patients do have the user ability, or immediate access to assistance to install a smartphone app. The development of a custom-built smartphone app in collaboration with the Software Engineering department of the University of Adelaide allowed the testing of a smartphone app to collect Step and GPS data in patients undergoing Cardiothoracic Surgery. The data generated by the app showed great promise in being able to monitor patient’s recovery remotely and link this back to the same pre-morbid metrics, However, the technical failures of the app provide excellent learning opportunities for future studies.