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NUR 590 Evidence Based Practice Project Week 2 Assignment  

NUR 590 Evidence Based Practice Project Week 2 Assignment

NUR 590 Evidence Based Practice Project Week 2 Assignment

The occurrence of medication administration errors hinders effective attainment of quality and safe patient care and outcomes. Medication administration errors are significant safety issue in health care sector, especially when there are different crises affecting healthcare and quality outcomes. The susceptibility of patients to medication administration errors increase with reduced number of healthcare workers against an increase in demand for services due to several factors. Medication administration errors (MAEs) increase the length of stay for critically ill patients and cost of care.

Studies demonstrate that leveraging health information technologies that include barcode scanning and other interventions can reduce and prevent the occurrence of these events, especially among the critically ill patients. The purpose of this literature review is to offer a comparison of the articles that supports the evidence-based practice project of using health information technology to reduce the occurrence of medication administration errors among the critically ill patients. The review also identifies the methods used to search the literature and synthesizes it for effective understanding and use for the selected eight articles.

There is no question that nurses are on the frontline of healthcare on multiple levels. In the arena of pregnancy and postpartum care, nurses play a vital role in not only the assessment of mother and baby’s physical well-being, but mental and emotional well-being as well. Recent studies show that women who are at an increased risk for developing postpartum depression (PPD) can be identified prior to delivery and prior to developing the disorder (Mughal et al., 2022). Quite naturally, nurses are at in the perfect position to identify these women that may be at risk, recommend treatment or support, and maintain follow-up care. Currently, there are screening questionnaires such as the Edinburgh Postnatal Depression Scale (EPDS) that are commonly completed by women post-delivery, often during the newborn well-check appointments.

PICOT Statement

The use of health information technology can reduce and prevent the prevalence of medication administration errors (MAEs) among critically ill patients. The use of interventions like barcode scanning and electronic dispensation ensures that human errors that occur during medication dispensation are reduced or minimized, especially the critically-ill patients in different health settings.

PICOT Question for the Evidence-Based Practice Project

Among the critically ill patients (P), does the integration of health information technology (I) compared to conventional medication administration process (C), lead to a reduction in medication administration errors (O) during patient’s stay (T)?

Search Methods of the Literature

Effective search of articles comprises of using appropriate approaches and terms that align with the topic of interest. In this assignment, I employed different yet related strategies to search for the articles that support the EBP project. These included using institutional library to get databases of journals and their published peer-reviewed articles. I used terms like “peer review” and “scholarly works” about medication administration errors. Through these approaches, I obtained the articles that I used in providing this literature review as they support my EBP project. I also ensured that the article meet the criteria of being published within the last five years and are relevant to the nursing context and use.

Synthesis of Literature

Article 1

The first article is by Alotaibi and Federico (2012) who discuss the impacts of health information technology on patient safety. Through a review of present scientific evidence on the effects of health information technologies on improving patient safety, the authors demonstrate the effectiveness of these interventions in reducing medication administration errors. Their findings support the implementation of health information technology to reduce medication errors and mitigate adverse events while increasing compliance to established guidelines in nursing practice. The article supports the PICOT as it shows the interventions that can be used to improve quality care and enhance overall patient safety. The article also supports the PICOT by showing the time frame that facilities can use to attain the benefits of these interventions.

Article 2

The second article by Barakat and Franklin (2020) focuses on the effects of using barcode medication administration (BCMA) on nursing practice activity and workflow. The authors use a qualitative study design in two surgical wards at a large acute facility in London. Through observations, the authors found that BCMA increased the nursing workflow, patient verification and efficiencies in medication administration. The authors are emphatic that the use of barcode and other health information technologies can enhance care delivery by minimizing occurrence of medication administration errors. The article supports the PICOT as it addresses how nurse practitioners handling critically ill patients can use technology-based interventions to enhance workflow and increase efficiencies aimed at mitigating MAEs.

Article 3

The third article is by Alomari et al. (2020) which evaluates the effectiveness of nurse-based interventions in reducing medication errors in pediatric wards. The authors investigate the effects of using bundled interventions to reduce medication administration errors. The author also focused on enhancing nurses’ perspective of medication administration process. Using a quantitative research approach in their selected settings, the authors shows through phased action research that these interventions, including use of health information technology, can reduce medication errors by over 60%. The authors emphasize that these benefits are not impacted by tan increase in the number of patients and prescribed medications. The article supports the PICOT question and statement as it shows the duration and interventions that can be implemented by nursing staff and other professionals to reduce and prevent the occurrence of medication administration errors.

Article 4

The fourth study by Devin et al. (2020) focuses on the effects of health information technologies in reducing prescribing errors in hospitals. The authors also focus on behavioral change techniques linked to HIT implementation that can reduce occurrence of medication errors. using a qualitative approach in different settings, the authors show that HIT prescribing reduces medication errors, especially prescribing errors. The authors’ findings emphasize the need for providers to integrate different approaches to ensuring that medication errors do not occur during the entire medication process. The article supports the EBP PICOT statement as it integrates the use of health information technology as a critical intervention to reducing medication administration errors.

Article 5

The fifth article by Zadvinski et al. (2018) explores the experience of nurses working with health information technology over time in their facilities. Using a longitudinal qualitative study design, the authors demonstrate the effects of nurses embracing technologies in a medical-surgical unit for a period of 18 months. The findings show that personal and organizational issues impact the adoption of HIT. The findings show that change of perception of these technologies is essential in attaining their intended benefits to the organization and patient safety goals. The article supports the PICOT statement as it shows that implementing health information technologies requires time for quality outcomes. Leveraging organizational policies and enhances the ability of nurses to adopt and use these technologies to enhance patient safety and quality outcomes.

Article 6

The study by Naidu and Alicia (2019) aims at evaluating the use of barcode medication administration and electronic medication administration records (e-MAR), outcomes, practice and policies and their effects on nurses in the medication administration duties in their nursing practice areas. Through an annotated literature review, the authors’ findings demonstrate that compliance to these interventions enhance patient safety and reduces reported medication administration errors. The use of these practices and policies also improves the efficiency of the BCMA system. The article is essential as it supports the PICOT by discussing the use of the health information technologies as interventions to reducing and preventing the prevalence of medication administration errors.

Article 7

The study by Jheeta and Franklin (2017) focuses on the how hospital electronic prescribing and medication administration system can enhance medication administration safety. Through an observational design, the authors show that implementation of these interventions encourages the occurrence of certain errors but also mitigates others. The implication is that using these interventions helps in reducing errors and enhancing patient safety. The article supports the PICOT statement as it emphasizes the need to use effective interventions and encourage their applications among all stakeholders in healthcare systems.

Article 8

The article by Härkänen et al. (2019) provides an analytical perspective of reported medication errors and their associated mortality in England and Wales for a period of nine years. The authors analyze medication errors in acute care that lead to death, identify the used drugs and describe the associated characteristics of the medication administration errors. Their findings show that most of deaths occurring due to medication administration errors happen in inpatients and among patients aged over 75 years with errors of omission being the most common form. The article supports the PICOT as it shows that medication errors occur due to several factors and should be addressed through integration of health information technologies.

Comparison of the Articles

The most prevalent issue in all these articles is the adoption of different health information technologies in mitigating and reducing the occurrence of medication administration errors (MAEs). Using an evidence-based practice approach, most of the articles demonstrate the effectiveness of having protocols and policies that supplement the use of technology in healthcare settings among healthcare workers. A majority of these articles use systematic reviews as they are considered the best level of evidence, especially in EBP projects. These reviews provide different interventions that healthcare providers can use to integrate health information technologies and reduce and prevent the occurrence of medication administration errors.

The main themes in these articles include use of medication administration processes, medication administration errors’ occurrence, the role of healthcare workers in embracing these interventions, and the effectiveness of the approaches to reduce medication administration errors. Some of the articles like the one by Jheeta and Franklin (2017) don not emphasize the effectiveness of health information technology in mitigating MAEs. However, the article emphasizes the need to have a multifaceted approach to the use of technologies to enhance care delivery. Each of the article has its unique shortcomings and areas not addressed. However, a common theme also emerges about the need to conduct further research to validate the outcomes and effectiveness of health information technology approaches to reduce and prevent medication administration errors, especially in acute care settings. Each of the article does not contain any controversy as the researchers complied with established guidelines to enhance validity and reliability.

Suggestions for Future Research

A majority of these articles recommend the need for further research on different aspects of the topic. The authors are categorical that while their studies offer evidence based on their research, it is imperative to conduct more studies on different aspects of these technologies to ascertain their overall effectiveness in addressing the issue under consideration (Alomari et al., 2020; Alotaibi & Federico, 2017). Gaps in effective research illustrating the interactions among various interventions and outcomes may require more approaches for better implementation of suggested approaches.

Conclusion

Medication administration errors (MAEs) remain a core concern in attaining better patient safety levels in different care settings. These events affect the quality of care and safety, especially for critically ill patients in hospitals who die for other causes other than their afflicted conditions. Therefore, stakeholders need interventions that leverage the best practices to reduce and prevent the occurrence of these events. The findings from these articles demonstrate the need for nurses and other healthcare workers to implement evidence-based practice interventions to reduce medication administration errors (MAEs). The selected articles show the need for enhance patient care and safety for better outcomes.

 References

Alomari, A., Sheppard-Law, S., Lewis, J. & Wilson, V. (2020). Effectiveness of Clinical Nurses’

interventions in reducing medication errors in a pediatric ward. The Journal of Clinical Nursing, 29(17-18): 3403-3413. https://doi.org/10.1111/jocn.15374

Alotaibi, Y. K. & Federico, F. (2017). The impact of health information technology on patient

safety. Saudi Medical Journal, 38(12):1173-1180. doi: 10.15537/smj.2017.12.20631

Barakat, S. & Franklin, B. D. (2020). An Evaluation of the Impact of Barcode Patient and

Medication Scanning on Nursing Workflow at a UK Teaching Hospital. Pharmacy (Basel), 8(3):148.  doi: 10.3390/pharmacy8030148

Devin, J., Cleary, B. J. & Cullinan, S. (2020). The impact of health information technology on

prescribing errors in hospitals: a systematic review and behavior change technique analysis. BMC Systematic Reviews, 9(275). https://doi.org/10.1186/s13643-020-01510-7

Jheeta, S. & Franklin, B. D. (2017). The impact of a hospital electronic prescribing and

medication administration system on medication administration safety: an observational study. BMC Health Services Research, 17(547). https://doi.org/10.1186/s12913-017-2462-2

Härkänen, M., Vehviläinen-Julkunen, K., Murrells, T., Rafferty, A. M., & Franklin, B. D.

(2019). Medication administration errors and mortality: Incidents reported in England and Wales between 2007 ̶ 2016. Research in Social and Administrative Pharmacy, 15(7), 858-863. https://doi.org/10.1016/j.sapharm.2018.11.010

Naidu, M.  and Alicia, Y.L.Y. (2019). Impact of Bar-Code Medication Administration and

Electronic Medication Administration Record System in Clinical Practice for an Effective Medication Administration Process. Health, 11, 511-526. https://doi.org/10.4236/health.2019.115044

Zadvinskis, I. M., Smith, J. G., & Yen, P. Y. (2018). Nurses’ experience with health information

technology: Longitudinal qualitative study. JMIR medical informatics, 6(2), e38. doi: 10.2196/medinform.8734

Week 2 Assignment

Evidence-Based Practice Proposal – Section A: Organizational Culture and Readiness Assessment and Section B: Proposal/Problem Statement and Literature Review

In order to formulate your evidence-based practice (EBP), you need to assess your organization. In this assignment, you will be responsible for setting the stage for EBP. This assignment is conducted in two parts: an organizational cultural and readiness assessment and the proposal/problem statement and literature review, which you completed in NUR-550.

Section A: Organizational Culture and Readiness Assessment

It is essential to understand the culture of the organization in order to begin assessing its readiness for EBP implementation. Select an appropriate organizational culture survey tool and use this instrument to assess the organization’s readiness.

Develop an analysis of 250 words from the results of the survey, addressing your organization’s readiness level, possible project barriers and facilitators, and how to integrate clinical inquiry, providing strategies that strengthen the organization’s weaker areas.

Make sure to include the rationale for the survey category scores that were significantly high and low, incorporating details or examples. Explain how to integrate clinical inquiry into the organization.

Submit a summary of your results. The actual survey results do not need to be included.

Section B: Proposal/Problem Statement and Literature Review

In NUR-550, you developed a PICOT statement and literature review for a population quality initiative. In 500-750 words, include the following:

Refine your PICOT into a proposal or problem statement.

Provide a summary of the research you conducted to support your PICOT, including subjects, methods, key findings, and limitations.

General Guidelines:

You are required to cite three to five sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the assignment criteria and nursing content.

Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to LopesWrite. Please refer to the directions in the Student Success Center.

Also Read: NUR 590 Evidence Based Practice Project Week 8 Discussion

Note: After submitting the assignment, you will receive feedback from the instructor. Use this feedback to make revisions for your final paper submission. This will be a continuous process throughout the course for each section.

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: NUR 590 Evidence Based Practice Project Week 2 Assignment  

ADDITIONAL INSTRUCTIONS FOR THE CLASS

Discussion Questions (DQ)

Initial responses to the DQ should address all components of the questions asked, include a minimum of one scholarly source, and be at least 250 words.

Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source.

One or two sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words.

I encourage you to incorporate the readings from the week (as applicable) into your responses.

Weekly Participation

Your initial responses to the mandatory DQ do not count toward participation and are graded separately.

In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies.

Participation posts do not require a scholarly source/citation (unless you cite someone else’s work).

Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week.

APA Format and Writing Quality

Familiarize yourself with APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required).

Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation.

I highly recommend using the APA Publication Manual, 6th edition.

Use of Direct Quotes

I discourage overutilization of direct quotes in DQs and assignments at the Masters’ level and deduct points accordingly.

As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content.

It is best to paraphrase content and cite your source.

LopesWrite Policy

For assignments that need to be submitted to LopesWrite, please be sure you have received your report and Similarity Index (SI) percentage BEFORE you do a “final submit” to me.

Once you have received your report, please review it. This report will show you grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra few minutes to review instead of getting counted off for these mistakes.

Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own?

Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for tips on improving your paper and SI score.

Late Policy

The university’s policy on late assignments is 10% penalty PER DAY LATE. This also applies to late DQ replies.

Please communicate with me if you anticipate having to submit an assignment late. I am happy to be flexible, with advance notice. We may be able to work out an extension based on extenuating circumstances.

If you do not communicate with me before submitting an assignment late, the GCU late policy will be in effect.

I do not accept assignments that are two or more weeks late unless we have worked out an extension.

As per policy, no assignments are accepted after the last day of class. Any assignment submitted after midnight on the last day of class will not be accepted for grading.

Communication

Communication is so very important. There are multiple ways to communicate with me:

Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class.

Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours.

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