Marilyn Rantz1, Lorraine J Phillips2, Colleen Galambos3, Kari Lane2, Gregory L Alexander2, Laurel Despins2, Richelle J Koopman4, Marjorie Skubic5, Lanis Hicks6, Steven Miller2, Andy Craver2, Bradford H Harris5, Chelsea B Deroche7. 1. Sinclair School of Nursing, University of Missouri, Columbia, MO. Electronic address: rantzm@missouri.edu. 2. Sinclair School of Nursing, University of Missouri, Columbia, MO. 3. School of Social Work, University of Missouri, Columbia, MO. 4. Department of Family and Community Medicine, School of Medicine, University of Missouri, Columbia, MO. 5. Electrical and Computer Engineering, University of Missouri, Columbia, MO. 6. Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO. 7. Biostatistics & Research Design Unit, Health Management & Informatics, School of Medicine, University of Missouri, Columbia, MO.
Abstract
OBJECTIVES: Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. DESIGN: Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) orcomparison group (n=85) receiving usual care. METHODS:Intervention participants lived with the sensor system an average of one year. MEASUREMENTS: Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns. RESULTS: The randomized comparison group functionally declined more rapidly than the intervention group. Walking speed and several measures from GaitRite, velocity, step length left and right, stride length left and right, and the fall risk measure of functional ambulation profile (FAP) all had clinically significant changes. The walking speed increase (worse) and velocity decline (worse) of 0.073 m/s for comparison group exceeded 0.05 m/s, a value considered to be a minimum clinically important difference. No differences were measured in health care costs. CONCLUSIONS: These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.
RCT Entities:
OBJECTIVES: Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. DESIGN: Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) or comparison group (n=85) receiving usual care. METHODS: Intervention participants lived with the sensor system an average of one year. MEASUREMENTS: Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns. RESULTS: The randomized comparison group functionally declined more rapidly than the intervention group. Walking speed and several measures from GaitRite, velocity, step length left and right, stride length left and right, and the fall risk measure of functional ambulation profile (FAP) all had clinically significant changes. The walking speed increase (worse) and velocity decline (worse) of 0.073 m/s for comparison group exceeded 0.05 m/s, a value considered to be a minimum clinically important difference. No differences were measured in health care costs. CONCLUSIONS: These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.
Authors: Marilyn Rantz; Kari Lane; Lorraine J Phillips; Laurel A Despins; Colleen Galambos; Gregory L Alexander; Richelle J Koopman; Lanis Hicks; Marjorie Skubic; Steven J Miller Journal: Nurs Outlook Date: 2015-09-08 Impact factor: 3.250
Authors: Marilyn Rantz; Lori L Popejoy; Colleen Galambos; Lorraine J Phillips; Kari R Lane; Karen Dorman Marek; Lanis Hicks; Katy Musterman; Jessica Back; Steven J Miller; Bin Ge Journal: Nurs Outlook Date: 2014-02-22 Impact factor: 3.250