Wearable Devices For Fall Prediction, Detection, And Prevention Assignment

Wearable Devices For Fall Prediction, Detection, And Prevention Assignment

One of the biggest health risks for senior individuals around the world is falling. In Australia, Canada, and the United Kingdom, the rate of hospital admission for falls among seniors 60 years of age and older ranges between 1.6 and 3.0 per 10,000 individuals yearly (Baig et al., 2019). Injuries like hip fractures, subdural hematomas, deep tissue traumas, and brain injuries happen after one in ten falls among older persons. Falls can result in psychological distress as well as social repercussions in addition to physical harm. It is common knowledge that fear of falling and post-fall anxiety syndrome are unfavorable outcomes of falls. Self-imposed functional restrictions might emerge from a loss of self-confidence that makes it unsafe to ambulate (Tanwar et al., 2022). Given the rising population of elderly persons, predicting and assessing fall risks in older adults aged 60 years and above is crucial. It is now possible to create fall prevention systems that are more effective and optimized due to developments in the fields of sensors, cameras, and communication. Knowing and evaluating the factors that cause falls, foreseeing potential falls, and ultimately preventing falls are all part of the fall prevention process. Applying the research methods, I identified evidence-based research that evaluated the intervention in question (Melnyk & Fineout-Overholt, 2018)Wearable Devices For Fall Prediction, Detection, And Prevention Assignment.

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The use of smart wearable devices has been employed in the detection and prevention of falls in older adults both at home and in hospitals (Silva de Lima et al., 2017). According to a study by Baig et al. (2019) which aimed at reviewing various wearable devices for fall detection and prevention, the footwear-based SmartStep device was more effective than other wrist-worn and waist-worn devices. SmartStep is a footwear-based wearable device with insole sensors, a 3D accelerometer, and a 3D gyroscope integrated into a CC2540 system on a chip that is embedded in an insole. The effectiveness of this device on fall detection and prevention was compared with the performance of a wrist-worn sensor. The findings of the study reveal that the footwear-based device was more comfortable and more effective in fall detection and prevention than the wrist-worn and waist-worn devices (Baig et al., 2019)Wearable Devices For Fall Prediction, Detection, And Prevention Assignment.

These findings are supported by another study conducted by Tanwar et al. (2022). According to this study, in order to identify a suitable system for detecting and preventing falls in older adults, possible risk factors for falls must be considered first. Environment, demographic, behavior, and biology are the major common risk factors for falls in older adults. In this review, various forms of fall detection devices such as wearable devices, camera-based devices, and ambiance devices were compared. Findings indicate that wearable devices were more sensitive and effective in detecting falls compared to camera-based and ambiance devices (Tanwar et al., 2022). In addition, wearable devices were less costly and easy to configure thereby suitable for use by older adults. Further, there are no visuals in wearable devices, therefore, offering some privacy to the target client. Overall, wearable smart devices tested in a sample population achieved an accuracy of 99.0%, 100% sensitivity, and 97.9% specificity (Tanwar et al., 2022)Wearable Devices For Fall Prediction, Detection, And Prevention Assignment.

Fall detection systems are very important since they detect falls and send alerts to caregivers. This enables the older adults to get urgent help in order to avoid the after-effects of falls which might be severe (Silva de Lima et al., 2017). These devices also support elderly persons to live independently. Generally, wearable smart devices for fall detection and prevention are effective, low cost, offer privacy, and are easy to use therefore for use by older adults both at home and in hospital. In general, through critical appraisal of evidence, the above three studies which utilized systemic review study design were identified and considered to obtain the best evidence-based practice to apply in the clinical setting (Fineout-Overholt et al., 2010)Wearable Devices For Fall Prediction, Detection, And Prevention Assignment.

References

Baig, M. M., Afifi, S., GholamHosseini, H., & Mirza, F. (2019). A systematic review of wearable sensors and IoT-based monitoring applications for older adults – a focus on ageing population and independent living. Journal of Medical Systems, 43(8). https://doi.org/10.1007/s10916-019-1365-7

Fineout-Overholt, E., Melnyk, B. M., Stillwell, S. B., & Williamson, K. M. (2010). Evidence-based practice step by step: Critical appraisal of the evidence: Part I. AJN, American Journal of Nursing, 110(7), 47-52. https://doi.org/10.1097/01.naj.0000383935.22721.9c

Melnyk, B. M., & Fineout-Overholt, E. (2018). Evidence-based practice in nursing & healthcare: A guide to best practice (4th ed.). Philadelphia, PA: Wolters Kluwer.

Silva de Lima, A. L., Evers, L. J., Hahn, T., Bataille, L., Hamilton, J. L., Little, M. A., Okuma, Y., Bloem, B. R., & Faber, M. J. (2017). Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: A systematic review. Journal of Neurology, 264(8), 1642-1654. https://doi.org/10.1007/s00415-017-8424-0

Tanwar, R., Nandal, N., Zamani, M., & Manaf, A. A. (2022). Pathway of trends and technologies in fall detection: A systematic review. Healthcare, 10(1), 172. https://doi.org/10.3390/healthcare10010172 Wearable Devices For Fall Prediction, Detection, And Prevention Assignment

To Prepare:

Reflect on the four peer-reviewed articles you selected in Module 2 and the four systematic reviews (or other filtered high- level evidence) you selected in Module 3.
Reflect on the four peer-reviewed articles you selected in Module 2 and analyzed in Module 3.
Review and download the Critical Appraisal Tool Worksheet Template provided in the Resources.
The Assignment (Evidence-Based Project)Wearable Devices For Fall Prediction, Detection, And Prevention Assignment

Part 3A: Critical Appraisal of Research

Conduct a critical appraisal of the four peer-reviewed articles you selected by completing the Evaluation Table within the Critical Appraisal Tool Worksheet Template. Choose a total of four peer- reviewed articles that you selected related to your clinical topic of interest in Module 2 and Module 3.

Note: You can choose any combination of articles from Modules 2 and 3 for your Critical Appraisal. For example, you may choose two unfiltered research articles from Module 2 and two filtered research articles (systematic reviews) from Module 3 or one article from Module 2 and three articles from Module 3. You can choose any combination of articles from the prior Module Assignments as long as both modules and types of studies are represented.

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Part 3B: Critical Appraisal of Research

Based on your appraisal, in a 1-2-page critical appraisal, suggest a best practice that emerges from the research you reviewed. Briefly explain the best practice, justifying your proposal with APA citations of the research Wearable Devices For Fall Prediction, Detection, And Prevention Assignment