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. 2022 Jul 4;34(4):444-453.
doi: 10.1080/10400435.2020.1862938. Epub 2021 Mar 11.

Visualization of user interactions with a pressure mapping mobile application for wheelchair users at risk for pressure injuries

Brianna M Goodwin  1   2 , Christine M Olney  3 , John E Ferguson  3   4 , Andrew H Hansen  3   4 , Byron Eddy  3   4 , Gary Goldish  3   4 , Melissa M Morrow  1   2 , Tamara L Vos-Draper  4   5
Affiliations

Visualization of user interactions with a pressure mapping mobile application for wheelchair users at risk for pressure injuries

Brianna M Goodwin et al. Assist Technol. .

Abstract

Pressure injuries for individuals with spinal cord injuries (SCI) are correlated with mortality and are a leading cause for rehospitalizations. The Assisted Weight Shift (AW-Shift©) is a mobile pressure mapping application designed to provide users with a live map view and reminders to perform weight shifts. Novel visualization techniques were used to understand daily distributions of user interaction wit h AW-Shift©. The date and time of system interactions were recorded for six participants with SCI over 7 days (five males/one female, five manual users/one power user, 55.3 ± 17.3 years old, 10.6 ± 6.5 years since injury). Circular frequency plots were created to visualize the time and frequency participants brought the app to the foreground of their phone and received alerts and reminders to complete weight shifts. While some participants used the system regularly throughout the day, others primarily used it before 8am; highlighting the system's importance for regular spot checks and morning wheelchair setup. Participant adherence to weight shift reminders was low suggesting the live pressure map may be more useful. Circular frequency plots can be used by clinicians to more easily review large amounts of patient data. Future work will investigate raw pressure mat data and create a closed-loop weight shift detection algorithm.

Keywords: data visualization; interface pressure mapping; mobile application; pressure injury prevention; spinal cord injury; wheelchair seating; wheelchairs.

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Figures

Figure 1.
Figure 1.
The Assisted Weight Shift system; comprised of (a) an integrated battery and Raspberry Pi to pull pressure data via USB from the (b) commercially available pressure mat in a custom cover to fit on a wheelchair cushion. The pressure data is transmitted via Bluetooth to an app on a smart phone for (c) user visualization and interactions. (d) Data is transmitted through multiple steps form the pressure mat to a Clinician Review Interface, where the data were downloaded.
Figure 2.
Figure 2.
The number of times each participant brought the app to the foreground of their phone. The shaded grey regions indicate when notifications were originally set to not go off (10pm – 8 am).
Figure 3.
Figure 3.
The day and time participants were prompted to complete weight shifts (left panel) and their responses to the completing the weight shifts prompts. The shaded grey diagonal lines indicate a completed weight shift, solid grey indicates an ignored weight shift, and black indicates a snoozed weight shift (right panel). The percent of completed weight shifts for each day is also indicated.
Figure 4.
Figure 4.
The time of day participants are alerted of a global pressure alert, local pressure alert, or a disconnection of the system (left panel). The type of alerts participants receive each day (right panel).
Figure 5.
Figure 5.
The number of times participants interacted (solid grey) or did not interact (grey diagonal lines) with the app after receiving an alert (left panel). The percentage indicates the percentage of time users interacted with the app after receiving an alert each day. The number of times participants changed their settings (solid grey) or did not change settings (solid black) within 2 minutes of receiving an alert (right panel). The percentage indicates the percentage of times users changed settings within 2 minutes of receiving an alert each day.
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