Abstract
Gaps in science exist to prevent treatable falls, such as those due to orthostatic hypotension, because of a myriad of issues related to its detection. Real-time motion data during activities such as the Sit to Stand (StS) maneuver, which can produce OH, are not captured by fall risk tools. And while Smartphones have been used to assess balance, sensing technology has not integrated the person’s symptoms with real time motion data. In our pilot using a Smartphone with tri-axial accelerometer and gyroscope sensors, motion data was generated and acceleration values were collected over a 30-second time period during the StS maneuver in 23 elderly residents of a 100 bed subacute rehabilitation unit in a nursing home. The real number acceleration values were squared in the X, Y and Z directions and summed together over each ten-second time interval. This summation of the X, Y, and Z direction squared acceleration values each represent a coefficient of excessive sway over the time interval. Six coefficients of excessive sway, three for each participant at three points in time were produced. Coefficients for each time interval were summed to produce a cumulative coefficient. Findings from four exemplars are presented which describe the clinical correlation of subjective and objective findings related to OH, lightheadedness, mean arterial and pulse pressure changes. Symptomatic OH was associated with the most dramatic change in pressures and highest coefficients of sway. Technology which integrates patient symptoms with objective findings is critical to advance the science in injurious falls prevention.