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

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

Assist Technol., 2022 · DOI: 10.1080/10400435.2020.1862938 · Published: July 4, 2022

Spinal Cord InjuryAssistive TechnologyRehabilitation

Simple Explanation

This study uses a mobile app called AW-Shift© to help wheelchair users with spinal cord injuries prevent pressure injuries. The app provides a live pressure map and reminders to shift weight, and the study visualizes how users interact with these features. The visualizations help understand when and how often users engage with the app's features, such as checking the pressure map or responding to weight shift reminders.

Study Duration
7 days
Participants
6 adults with SCI
Evidence Level
Not specified

Key Findings

  • 1
    Participants used the app differently: some used it throughout the day for spot checks, while others primarily used it in the morning.
  • 2
    Adherence to weight shift reminders was generally low, suggesting the live pressure map may be more useful to users.
  • 3
    Global pressure alerts were the most frequent type of alert, and disconnection alerts were common for all participants.

Research Summary

The study used circular frequency plots to visualize how individuals with SCI interacted with a mobile pressure mapping app (AW-Shift©) over seven days. The visualizations revealed different usage patterns, with some participants using the app for regular spot checks and others for morning wheelchair setup. Participant adherence to weight shift reminders was low, suggesting that the live pressure map feature might be more useful. The study also identified challenges with system disconnections.

Practical Implications

Personalized Interventions

Tailor mHealth interventions to individual needs and preferences, considering factors like technology aptitude, health literacy, and cognitive function.

System Improvement

Improve hardware design to prevent disconnections and develop a closed-loop system for automatic weight shift detection.

Clinical Integration

Use visualizations in clinical practice to provide clinicians with a concise overview of patient adherence to weight shift routines.

Study Limitations

  • 1
    Small sample size
  • 2
    Lack of continuous pressure data
  • 3
    Inaccurate self-reporting of weight shifts

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