The selected nursing problem of focus in my project is falls among hospitalized patients aged 65 years and above. Elderly patients have the highest fall rates as compared to other patient populations. Statistics show that at least 300000 older people suffer from hip fractures annually in the USA. More than 95% of these fractures are attributable to falling sideways. Besides fractures, falls result in premature mortalities, prolonged hospitalizations, poor quality of life, and increased care costs. Health technologies have proven effective in detecting, reducing, and preventing patient falls. For example, the use of automated fall detection systems and sensors have been shown to enhance early detection, prevention, and minimization of falls among hospitalized patients. Therefore, my project examines the use of the technology to improve fall rates among hospitalized elderly patients aged 65 years and above.
| Criteria | Peer-Reviewed Translational Article and Permalink/Working Link:
Rahme, M., Folkeard, P., & Scollie, S. (2021). Evaluating the accuracy of step tracking and fall detection in the Starkey Livio artificial intelligence hearing aids: A pilot study. American Journal of Audiology, 30(1), 182–189. https://doi.org/10.1044/2020_AJA-20-00105
Translational Research Type: T2
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Peer-Reviewed Traditional Article and Permalink/Working Link:
Coahran, M., Hillier, L. M., Bussel, L. V., Black, E., Churchyard, R., Gutmanis, I., Ioannou, Y., Michael, K., Ross, T., & Mihailidis, A. (2018). Automated fall detection technology in inpatient geriatric psychiatry: Nurses’ perceptions and lessons learned. Canadian Journal on Aging / La Revue Canadienne Du Vieillissement, 37(3), 245. 10.1017/S0714980818000181 Traditional Qualitative Research Type: Qualitative study |
Observations (Similarities/Differences) |
| Methodology | This study was pilot research to examine the effectiveness of an automated fall detection system in fall detection and detecting fall maneuvers. The adopted technology was Starkey Livio Artificial Intelligence hearing aids and tracking step count. The participants wore the system, a Sportline pedometer, and Fitbit Charge 3 concurrently during treadmill and real-world walking conditions. Fall detection and alert were assessed by falling maneuvers of the activities of daily living.
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This study was a qualitative study that examined the perceptions of nurses with the HELPER system and lessoned learned from its ability to prevent and reduce patient falls. The study was conducted following a pilot test where nurses were interviewed about their perceptions of the HELPER technology. The nurses were from two geriatric units in Ontario, Canada. Data was analyzed using qualitative naturalistic inquiry approach. | The studies differ on their designs. The study by Rahme et al. (2021) adopted quantitative methods while that by Coahran et al. (2018) adopted qualitative methods. They also differ based on the technologies that were examined for effectiveness in fall prevention and detection. Coahran et al. (2018) utilized qualitative methods of data collection and analysis while Rahme et al. (2021) used quantitative approaches to data collection and analysis. They both focused on the effectiveness of automated technologies in fall detection and prevention. |
| Goals | The primary aim of this research was to examine the effectiveness and efficacy of Starkey Livio Artificial Intelligence hearing aids in tracking step count. The secondary aim was to investigate the accuracy of the fall detection and alert system of Livio hearing aids in detecting fall maneuvers.
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The goal of this study was to obtain the perceptions of nurses with their use of the HELPER system. The study also aimed to identify lessons learned from the technology use in preventing falls in two geriatric units caring patients aged between 60 and 90 years. | The two studies are similar in that they examined the effectiveness of health technologies in fall detection, notification, and prevention. They differ based on the technologies that were being investigated for their effectiveness. |
| Data Collection | Data on patient’s real-world health condition was obtained through a 5-day period. Step co
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