EBP Research Sample Paper

ByLeon Kiprotich Leon Updated on

EBP Research Sample Paper

PICO Question

PICO question: In elderly patients, is patient monitoring using wearable devices compared to normal monitoring effective in reducing the number of patients falls?

Research Studies Analysis

Technology advancements infiltration into the healthcare sector has significantly improved healthcare outcomes. Wearable technology advancements are used in various sectors and majorly in the intensive care unit settings to closely monitor high-risk patients with major respiratory, cardiovascular, and neurological compromises. They aid in close patient monitoring. The significance of wearable devices in patient monitoring is of interest in preventing patients’ falls. Patient falls among geriatrics are a significant healthcare issue, and interventions to ensure adequate monitoring and reduced falls are essential.

Patient falls are unplanned often sudden descent to the floor and encompass incidences with or without injury. They are common, devastating, and avoidable complications during patient care, especially among elderly patients. The Agency for Healthcare Quality and Research estimates that between 700000-1000000 hospitalized patients fall each year (LeLaurin & Shorr, 2019). Patients fall due to various reasons. Poor vision, especially among the elderly, is a major cause of falls. Some medications cause side effects such as dizziness and body weaknesses and result in patient falls. As LeLaurin and Shorr (2019) further observe, patients are often weakened by their underlying conditions and fall when attempting to meet their needs, such as moving out of bed without assistance.

In addition, environmental hazards such as slippery floors and poor infrastructures such as lack of side rails, poor floor material, and lack of bedside rails significantly contribute to patient falls (LeLaurin & Shorr, 2019). Patient falls vary with intensity. Some falls result in no harm; mild ones result in twisting, bruising, and cuts, while major falls result in fractures, major internal organ damage, and sometimes, patient death. Patient falls result in prolonged hospital stays and increased healthcare costs. Patient falls lead to about $50billion in healthcare costs every year and other law-suit-related costs (Green et al., 2019). Patient falls are thus a major issue in nursing practice. Patient monitoring and assistance with activity performance is a nursing intervention. An intervention to enhance patient monitoring and prevent patient falls is thus essential.

Research Findings

Pang et al. (2019) carried out a systematic review that provides a high level of evidence (Level I) on wearable devices to prevent falls among patients of all ages. The study utilized credible articles from recognized databases such as CINAHL and MEDLINE. Analysis of the nine articles showed that wearable devices improve detection and correction of the patient near falls by a huge percentage (above 30%). These devices include gyroscopes, and their main location is the patients’ waists. The devices have high reliability and validity measures and are thus integral in preventing falls among elderly patients. The study also recommends the inclusion of other factors such as differentiation between actual and near falls and a provision for naturally occurring near falls, not necessarily associated with the patient’s situation. However, the study includes patients of all ages, and the main interest is the elderly population. In addition, the study is a systematic review, but it only utilizes nine research studies; hence usability and generalizability of the information on all patients are difficult.

Greene et al. (2019) conducted an observational study to determine the impact and importance of using wearable devices and digital fall risks evaluation tools to minimize falls among geriatric patients. It provides a strong level of evidence (level III). According to Greene et al. (2019), “Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning)” (p2). The study utilized data from the participants collected used a digital falls risk assessment protocol. Data used was from questionnaires regarding the risk for falls and data captured by the wearable technology devices. Using the digital falls risk assessment tool can help reduce outpatient and emergency department utilization secondary to falls in the elderly patients, as evidenced by the results (45% reduction in patient falls) (Greene et al., 2019). The study supports wearable devices such as gait sensors alongside digital evaluation tools to detect and prevent falls among elderly patients. However, the study limitation si the few participants (small sample size), which minimizes the validity of the information.

Hussain et al. (2018) evaluated the importance of utilizing wearable sensors in geriatric patients to determine the pattern of falls and prevent falls from occurring. The study is an expert opinion study (Level VII) developed after a patient falls briefing. It provides reliable data but a low level of evidence for the clinical significance of the study. However, findings from the study are important in informing future research studies and clinical decision-making. The study notes that many detectors detect fall incidents, but better outcomes would result from devices that detect the pattern and the method in which falls occur. Hussain et al.’s (2018) results show that fall detection alone is not enough to prevent future falls and must have support from other interventions such as close monitoring acting on the presenting pattern of falls.

Rajagopalan, Litvan, and Jung (2017) observe that most fall detection systems focus on physiological factors forgetting that causes of falls are multifactorial. According to this study, implementing wearable devices, patient monitoring systems, and other relevant tools requires information on all factors contributing to falls. The study is descriptive and thus presents a low level of evidence (level VI-single descriptive study). However, it analyzes the merits and demerits of common fall detection and prediction systems. It then provides helpful insights into workable systems such as the biomedical signal-based fall prediction system. The study, for example, explains that electromyography is integral in detecting the freezing of gait in Parkinson’s disease.  Rajagopalan, Litvan, and Jung (2017) also explain that wearable devices that detect changes in patients’ conditions that cause falls are an integral technological advancement in the healthcare sector. Thus, wearable devices should encompass more than physiological changes to the disease process, medications, and many other patient factors.

Möller et al. (2021) describe the modern prevention of falls among the elderly using modern technology. Technology has infiltrated all areas of healthcare, and the goals of healthcare technology are to prevent patient falls, improve their physical activity, and improve healthcare patient satisfaction. The study had three interventions: snubblometer, mobile apps, and a web-based educational program on patients’ fall prevention. The study provided a strong level of evidence (Level 1- randomized clinical trial). Möller et al. (2021) also note that effective preventive measures can reduce 30-60% falls.  Falls in older adults lead to more significant health problems compared to the rest of the population. Thus, interventions such as the MoTFall (Modern Technological against falls) are integral in ensuring geriatrics safety. Healthcare providers are encouraged to help patients prevent even when at home through specific interventions. The snubblometer in the MoTFall project is a wearable device with high sensitivity and specificity in detecting and preventing falls among geriatrics and effective intervention to enhance patient monitoring.

Research Analysis and Synthesis

From the research regarding wearable devices, there is much information related to the topic. 100% of the studies support the use of wearable devices in preventing falls among geriatric patients. The studies highlight benefits such as decreased fall prevalence among the elderly. The studies also note that wearable devices do not exclude patient monitoring but provide efficient patient handling with minimal errors (Khanuja et al., 2018). According to Rajagopalan et al. (2017), wearable devices should not rely on physiological factors alone; they involve other factors such as biomedical changes in the patients. The patient health dynamics are also important in the utilization of wearable devices.

Moller et al. (2021), Rajagopalan et al. (2017), and Hussein et al. (2018) all contend that that wearable devices may not be a reliable tool alone in detecting and preventing falls. Other supporting interventions include training healthcare workers on wearable devices use, introducing smartphone apps in patient monitoring, and a holistic approach in developing fall prediction and prevention among elderly patients. These supporting interventions improve the efficacy and efficiency of wearable technology. Wearable devices such as gyroscopes should thus be used in conjunction with other interventions to improve their efficacy. Some wearable technologies only detect falls and do not help in preventing patient falls. However, some motion and gait sensors detect actual and near falls (Pang et al., 2019). Some devices also note the fall patterns and thus inform interventions to break the cycle and prevent the falls before they occur (LeLaurin & Shorr, 2019).

Conclusion

Arguably, for best results, the wearable devices of choice should be lightweight, detecting changes in gait and motion, actual and near falls, and keeping patterns of falls to ensure effective break of the fall patterns. In addition, the use of such technology must be supported by other interventions, such as effective nurse training on their use. From the above, wearable devices such as motion detectors and gait sensors are formidable tools in decreasing patient falls, improving physical activity, and improving patient satisfaction. Thus, healthcare institutions should embrace wearable technology in the prevention of patient falls.

References

  • Greene, B. R., McManus, K., Redmond, S. J., Caulfield, B., & Quinn, C. C. (2019). Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors. NPJ Digital Medicine, 2(1), 1-7. https://doi.org/10.1038/s41746-019-0204-z
  • Hussain, F., Ehatisham-ul-Haq, M., Azam, M. A., & Khalid, A. (2018, October). Elderly assistance using wearable sensors by detecting falls and recognizing fall patterns. Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers (pp. 770-777). https://doi.org/10.1145/3267305.3274129
  • Khanuja, K., Joki, J., Bachmann, G., & Cuccurullo, S. (2018). Gait and balance in the aging population: Fall prevention using innovation and technology. Maturitas, 110, 51-56. https://doi.org/10.1016/j.maturitas.2018.01.021
  • LeLaurin, J. H., & Shorr, R. I. (2019). Preventing falls in hospitalized patients: state of the science. Clinics in Geriatric Medicine, 35(2), 273-283. https://doi.org/10.1016/j.cger.2019.01.007
  • Möller U, O., Fänge A, M., & Hansson E, E. (2021). Modern technology against falls–A description of the MoTFall project. Health Informatics Journal, 27(2), 14604582211011514. https://doi.org/10.1177/14604582211011514
  • Pang, I., Okubo, Y., Sturnieks, D., Lord, S. R., & Brodie, M. A. (2019). Detection of near falls using wearable devices: a systematic review. Journal of Geriatric Physical Therapy, 42(1), 48-56. Doi: 10.1519/JPT.0000000000000181
  • Rajagopalan, R., Litvan, I., & Jung, T. P. (2017). Fall prediction and prevention systems: recent trends, challenges, and future research directions. Sensors, 17(11), 2509. https://doi.org/10.3390/s17112509