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Evidence-Based Practice Project Proposal Research Design Comparison

NUR 550 Evidence-Based Practice Project Proposal Research Design Comparison

Evidence-Based Practice Project Proposal Research Design Comparison

Translational Research Graphic Organizer Template

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T1 Research

 

T2 Research

 

T3 Research

Quantitative

Research

Observations (Similarities/Differences)
Methodology Entails testing effects of and use of scientific research outcomes in clinical settings. It ascertains the efficacy of knowledge generated from scientific discoveries on human physiology (Fort et al., 2017).

It also entails proof of concept and use of healthy volunteers to ascertain the efficacy of the drugs or knowledge produced.

 

 

 

 

 

Researchers utilize controlled settings in trying new diagnostic interventions.

Through the approach they develop evidence-based uses and guidelines in clinical practice (Felege et al., 2016).

Research explores the use of the guidelines in general population.

The model uses diffusion research to translate the guidelines into practice to benefit populations.

Quantitative research emphasizes objective measurements and statistical, and numerical analysis of the collected data (Deistung et al., 2017). Both translational and quantitative researches utilize human subjects and clinical settings or studies.

Quantitative studies focus on diagnosis, interventions and treatment protocols of diseases while translational research fills the gap between practice and scientific discoveries (Parajuli et al., 2018).

 

Goals The goal is to yield knowledge on human physiology and the potential for intervention (Rubio et al., 2016).

 

 

 

 

 

To offer information about the efficacy of the interventions in clinical environment

To ascertain the efficacy of interventions and treatment.

To utilize information and knowledge obtained to provide health services

To attain information on the efficacy of the interventions in practical settings

Dissemination and execution of the research recommendations.

To develop guidelines based on outcomes as patient routine practices (Felege et al., 2016).

The goal of quantitative research is to test certain hypotheses, look at the cause and effect scenarios in research, and make predictions (Felege et al., 2016). Quantitative research uses controlled settings to test proved hypotheses that helps in idea exploration as well as lays the foundation for more studies, particularly translational research.

Translational research focuses on developing clinical and scientific results to improve patient outcomes and community health (Rubio et al., 2016).

The three translation research studies are related as they are used systematically in offering new treatment interventions in populations

Data Collection Data is collected from the observational studies, clinical trials and case studies, and phase I and II trials (Surkis et al., 2016).

 

 

 

 

 

Data is collected through synthesizing of evidence, developed guidelines and phase III trials. Data is collected through diffusion research, phase IV trials and dissemination research. Data is collected through surveys, experiments, and observation, and content analysis (Deistung et al., 2017). Data comes from questionnaires and manipulating pre-existing data  A common data collection approach used in these studies is observation. The method is utilized in both T1 and T2 and quantitative. In both research approaches, data is quantified into outcomes for interpretation (Parajuli et al., 2018). The two research models use data collection approaches that include interview, and surveys.

 

 

References

Bouhassira, D., & Attal, N. (2016). Translational neuropathic pain research: a clinical

perspective. Neuroscience, 338, 27-35.

Deistung, A., Schweser, F., & Reichenbach, J. R. (2017). Overview of quantitative susceptibility

Evidence-Based Practice Project Proposal Research Design Comparison
Evidence-Based Practice Project Proposal Research Design Comparison

mapping. NMR in Biomedicine, 30(4), e3569.

Fort, D. G., Herr, T. M., Shaw, P. L., Gutzman, K. E. & Starren, J. B. (2017). Mapping the

evolving definitions of translational research. Journal of Clinical and Translational Science, 1(1), 60-66.

Rubio, D. M., Robinson, G. F. W. B., Gillian, V. A., Primack, B. A., Switzer, G. E., Seltzer, D.

L., & Kapoor, W. N. (2016). Characterization of Investigators’ Approach to Translational Research: A Qualitative Study. CTS Journal, 7(6), 441-446.

Surkis, A., Hogle, J. A., DiazGranados, D., Hunt, J. D., Mazmanian, P. E., Connors, E.,

Westaby, K., Whipple, E. C., Adamus, T., Mueller, M. & Aphinyanaphongs, Y. (2016). Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach. Journal of Translational Medicine, 14(235).

Parajuli, S. B., Bhattarai, P., & Heera, K. C. (2018). Translational research: Current status, challenges and future strategies in Nepal. Nepalese Heart Journal, 15(2), 3-8.

Felege, C., Hahn, E., & Hunter, C. (2016). Bench, bedside, curbside, and home: Translational research to include transformative change using educational research. Journal of Research Practice, 12(2), P1.

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.

Comparison 1: Translational Research vs. Qualitative Research

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

 

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.

 

 

 

 

 

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.

 

 

 

 

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 count was done for six different treadmill speeds. The generated fall detection and alerts were analyzed to determine their effectiveness in reducing fall risks among the patients.

 

 

 

 

 

Data for this research was collected through interviews conducted with nurses working in the unit. The interviews were conducted over two days by a trained research associate who did not participate in the pilot implementation. The interviews were recorded digitally and transcribed. The data collection approaches in the studies differ. Coahran et al. (2018) utilized interviews that were digitally recorded and transcribed. Rahme et al. (2021) utilized quantitative methods of data collection based on the observed and physiological changes with activity.

 

Comparison 2: Translational Research vs. Quantitative Research

            Criteria Peer-Reviewed Translational Article and Permalink/Working Link:

Lumetzberger, J., Münzer, T., & Kampel, M. (2021). Non-obtrusive 3d body tracking for automated mobility assessment in independently living older persons: Results of a pilot trial. EAI Endorsed Transactions on Pervasive Health and Technology, 7(26), e4–e4. https://doi.org/10.4108/eai.4-3-2021.168863

Translational Research Type: T2

Peer-Reviewed Traditional Article and Permalink/Working Link:

Nemeth, B., van der Kaaij, M., Nelissen, R., van Wijnen, J.-K., Drost, K., & Blauw, G. J. (2022). Prevention of hip fractures in older adults residing in long-term care facilities with a hip airbag: A retrospective pilot study. BMC Geriatrics, 22(1), 547. https://doi.org/10.1186/s12877-022-03221-1

Traditional Quantitative Research Type: Retrospective quantitative study

 

Observations (Similarities/Differences)
Methodology The study was a pilot investigation of the effectiveness of real time data and mobility assessments in fall detection and prevention. The intervention entailed automatic tracking and detection of movements for the study participants using Orbbec Astra 3d camera. A field trial for the intervention was done for a 10-month period in the private homes of 20 generally healthy older adults. 20 study participants were enrolled and assessed following their use of automated trackers for parameters such as movement patterns, size, and height. Data was expressed as standard deviation and means. Linear regression analysis was done to determine the association of manual physical therapy with machine-based gait data.

 

 

 

This study was a retrospective pilot study that involved 969 participants residing in 11 long-term facilities for the older patients. The researchers utilized intervention that entails the application of 45 WOLK-hip airbags for fall and fracture detection and prevention. The inclusion criteria included physically active participants with pelvic circumference of 90-125 cm. The exclusion criteria included participants who continuously removed hip airbag for themselves and those depending on wheelchair for mobility.

 

The two studies focused on the effect of technology use in improving gait, physical activity, and falls among the elderly. They differed based on the study designs. While the study by Nemeth et al., (2022) was a retrospective quantitative research, the one by Lumetzberger et al., (2021) was a pilot study on the use of 3D technology in patient monitoring and assessment of fall risk. The two studies support that health technologies are feasible for use in fall detection and prevention.

 

 

Goals The goal of this study was to assess mobility of the older persons using real time data and comparing it with the mobility assessment of physiotherapists.

 

 

 

 

The aim of this study was to evaluate the effect of introducing WOLK hip airbag on the incidence of hip fractures. The secondary aim was to evaluate the occurrences of falls and pelvic fractures among the participants.

 

The two studies differ based on their goals. The study by Lumetzberger et al., (2021) examined the effectiveness of using real-time data on gait studies and fall rates while Nemeth et al., (2022) investigated the effect of airbags on fall rates and fractures among those at risk.

 

Data Collection A trained physical therapist conducted gait study tests to each of the study subjects. They collected data on the participants’ ability to perform three repetitive tasks to assess for possible mobility changes. At the same time, an automated tracker measured test duration and gait velocity for use in comparing both data.

 

 

 

Data on hip, falls, and pelvic fractures were collected from electronic incidence reports for the participants. The demographic data were extracted electronically from patient records and summarized for median of the study period.

 

The studies differ on the approaches to data collection. The study by Nemeth et al., (2022) utilized electronic data of the participants to determine the effectiveness of the intervention. On the other hand, Lumetzberger et al., (2021) focused mainly on the physiological changes that occurred with the delivery of the intervention to the participants. Both approaches to data collection were quantitative.

Conclusion

In summary, the reviewed studies show that automated technologies and systems are effective in fall detection, notification, and prevention. They also reduce the risk and rate of injuries due to falls, including fractures. Evidence obtained from translational and traditional sources of evidence support technology use in fall prevention. Therefore, it should be considered for use in healthcare and nursing practice.

References

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. https://doi.org/10.1017/S0714980818000181

Lumetzberger, J., Münzer, T., & Kampel, M. (2021). Non-obtrusive 3d body tracking for automated mobility assessment in independently living older persons: Results of a pilot trial. EAI Endorsed Transactions on Pervasive Health and Technology, 7(26), e4–e4. https://doi.org/10.4108/eai.4-3-2021.168863

Nemeth, B., van der Kaaij, M., Nelissen, R., van Wijnen, J.-K., Drost, K., & Blauw, G. J. (2022). Prevention of hip fractures in older adults residing in long-term care facilities with a hip airbag: A retrospective pilot study. BMC Geriatrics, 22(1), 547. https://doi.org/10.1186/s12877-022-03221-1

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