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Summary
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Sensi-steps uses patient-generated data from wearable sensors to track fall risks in older adults, especially post-stroke patients. This technology aims to improve personalized, preventative care through better decision-making.

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Area of Science:

  • Geriatric Medicine
  • Rehabilitation Technology
  • Digital Health

Background:

  • Falls are a significant risk for geriatric and post-stroke patients.
  • Current fall risk assessment methods may lack continuous, real-world data.
  • Integrating patient-generated data (PGD) into clinical workflows is challenging.

Purpose of the Study:

  • To introduce Sensi-steps, a novel application for fall risk prevention in geriatric and post-stroke populations.
  • To demonstrate the utility of patient-generated data (PGD) integrated with wearable sensors and clinical decision support.
  • To enhance shared decision-making among patients, caregivers, and healthcare providers.

Main Methods:

  • Utilized a wearable wrist device, pedometer, pressure, and proximity sensors, and a tablet for data collection.
  • Collected PGD through Timed Up and Go (TUG) tests and physiological data.
  • Integrated PGD into the Electronic Health Record (EHR) for clinical decision support.

Main Results:

  • Sensi-steps facilitates active tracking of fall risk factors.
  • The system enables a novel approach to managing PGD at the primary care nurse or Care Manager level.
  • The solution is designed for routine home use, potentially offering greater ease of use than existing tools.

Conclusions:

  • Sensi-steps offers a promising approach to personalized, preventative healthcare for fall risk.
  • The application has the potential to improve patient care and empower informed decision-making.
  • Clinicians can gain valuable insights into patient-generated, environmental, and lifestyle factors for tailored interventions.