Implementation into Practice
The Institute of Medicine set a goal that 90% of health care decisions should be
evidence-based by 2020. At best guess, less than 10% of decisions use best evidence.
Bridging the gap between research, findings, and practice implementation is one strategy to
meet this important goal. This assignment will help you to find gaps that may be used for
Use the following information to ensure successful completion of the assignment:
� Review the Agency for Healthcare Research and Quality (AHRQ) website to
complete the assignment.
� Doctoral learners are required to use APA style for their writing assignments.
� This assignment uses a rubric. Please Review the rubric prior to the beginning to
become familiar with the expectations for successful completion.
� Use at least 4 additional scholarly research sources published within the last 5
years. Provide citations and references for all sources used.
� You are required to submit this assignment to Turnitin and the similarity scores
cannot be more than 15%.
Select a practice from the AHRQ comparative effectiveness research site and write a
1,000 word paper that looks at a gap that exists between research findings and the
implementation of those findings in practice. Include the following:
- Discuss the practice.
- Assess to what extent the practice is being implemented.
- Evaluate the barriers to implementation into practice.
- Propose ways to overcome the barriers.
- Discuss the resources available on the selected site to inform translation.
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Implementation into Practice
Mobile Health Applications for Self-Management of Diabetes
Advancement in Health Information Technology has led to the use of programs installed
in mobile phones and tablets that aid in patient’s self-management of diabetes. Applications
assist patients in receiving diabetes health information and interpretation of measurements such
as blood glucose and glycosylated hemoglobin (A1C), body mass index (BMI) and blood
pressure. However, the use of mobile applications is known to be faced with challenges such as
illiteracy in use of applications, errors in data entry and lack of appropriate feedback (Scerri,
Garg, Garg, Scerri, Xuereb, & Tomaselli, 2015). The following paper discusses the practice of
mobile applications, the extent of the practice, barriers to practice and ways to overcome the
Description of the Practice
Mobile applications are installed on devices such as phones or tables. Some of the
applications contain educational information to the patient on self-management practices. The
information contained includes the recognition of symptoms of complications, instructions on
physical exercise and nutrition. Besides, the applications contain interpretive information on
measures and indicators such as blood glucose and A1C levels. Patients enter readings of their
blood glucose levels and A1C, and the applications provide information on whether measures are
within the normal range or not. The applications give indications when patients should seek
medical care or advice. Besides, the applications store data such as recorded blood glucose
levels. The applications provide warning information to the patient on aggravation of the
symptoms of diabetes. For instance, the applications calculate the BMI from data of body weight
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and height and thus indicate in cases where the BMI not within the normal range and requires
reduction (Kateraas & Medelius, 2015).
The Extent of implementation
Currently, there are over 160, 000 mobile applications for adding diabetes patients in the
management of the condition. The mobile applications are available for use to both the users of
android and apple operating systems of mobile devices. The increasing prevalence of diabetes
incidence rates contributes to an increase in mobile applications in patient self- management of
diabetes. The advancement in technology and specifically the growth of information technology
contributes to increased usage of the diabetes mobile applications. Over 50% of the mobile
applications for the management of diabetes are designed for managing blood glucose and A1C
levels (Alanzi, Bah, Jaber, Alshammari, & Alzahrani, 2016).
Barriers to Implementation into Practice
One of the barriers to the use of the mobile phone applications in managing diabetes is
the age of the patient. Diabetes is most prevalent among individuals aged 50 years and above.
However, persons aged above 50 years have limited interaction and use of mobile devices.
Therefore, this population group faces challenges in accessing the applications, installing onto
their mobile devices and comprehension of the applications in general. However, only a small
portion of the applications in the commercial market has such features such as images, graphs,
and depictions that are favorable for the use among the elderly (Alvarado, Kum, Coronado,
Foster, Ortega, & Lawley, 2017).
The mobile applications available utilize different colors, contrasting features, and other
display features. However, the patients who are mostly diagnosed with diabetes are elderly
patients above 50 years and have reduced the visual ability, color vision, and other visual
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impairments. Additionally, some patients are diagnosed with retinopathy as a complication of
diabetes. Therefore, visual impairments associated with diabetes limit the use of mobile
applications in the management of diabetes (Alvarado et al., 2017).
Further, diabetes management mobile applications require the patient to enter data on
some measurements, for example, blood glucose level manually. The manual data entry is
susceptible to errors. Notably, some values entered are critical and form the basis for furthered
management decisions. For instance, the value entered for blood glucose level may be used as
the basis for calculating the dosages for anti-diabetic medication. Therefore, the manual entry of
the data may adversely affect the use of mobile applications by diabetes patients. Lack of
consideration of individual factors limits the use of mobile applications in self-management of
diabetes. Several available applications serve same or similar functions. Individual factors form
the basis for the suitability of specific mobile applications to various patients. For instance, the
use of specific applications is determined by demographic data and information on the patient,
for instance, the age of the patients utilizing the mobile applications. Finally, the lack of diverse
data on the impacts of the mobile applications is a barrier to their use. Only a few studies have
endeavored to establish the benefits and the side effects of the use of the mobile application by
diabetes patients. There is no extensive evidence on the impact of the use of the applications to
indicators such as symptomatic management and improvement in the quality of life (Goyal,
Morita, Lewis, Yu, Seto, & Cafazzo, 2016).
Ways to Overcome the Barriers
Clinical evaluation of the patients is critical to establish examples of the best-suited
applications for use by the specific patient. The age of the patient and the level of education of
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the patient should be used as the basis for advising the patient on the most appropriate mobile
applications for use.
Further, collaboration between the manufactures of applications, such as Apple and the
health care system is another way to overcome the barriers. The mobile application manufacturer
should make the applications based on the advice received from the clinicians. Clinicians’ advice
should emphasize on the need for flexibility of the applications to various patient factors such as
age and background. This step ensures that the applications can adequately serve the various
patients. Further, patients should be trained in the use of the applications by health informaticists
to increase the ease of use and benefits of the applications. Extensive research is required on the
impact of the use of the applications. Research studies should focus on the effectiveness of the
mobile applications towards the management of diabetes symptoms, prevention of complications
and improvement of the quality of life (Zapirain, Díez, Sainz de Abajo, & Coronado, 2016).
Resources Available to Inform Translation
To begin with, some published studies document the challenges facing the use of the
mobile applications. The information forms the basis for the necessary changes to improve
utilization of the mobile applications. The data points on limitations concerning the usability,
acceptability, and flexibility of mobile applications. Further, the Agency for Healthcare Research
and Quality has set the standards on the use of Health Information Technology for clinical
purposes. The guidelines concern the safety and confidentiality of the patients’ data stored in
technology devices and maintaining patients’ safety. The applications for the management of
patients’ data should ensure the safety and privacy of the data and minimize the access of the
data by other persons (Borrero, Vasques, & Vargas, 2015).
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In conclusion, the use of mobile applications in the management of diabetes is one of the
practices widely used as part of health information technology. However, the practice faces
several barriers. Notably, research on the practice will form the basis for the evidence-based use
of mobile applications in managing diabetes. There are available resources for the use in
translating evidence not the practice of use of mobile applications in managing diabetes.
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Alanzi, T. M., Bah, S., Jaber, F., Alshammari, S., & Alzahrani, S. (2016, March). Evaluation of a
Mobile Social Networking Application for Glycaemic Control and Diabetes Knowledge
in Patients with Type 2 Diabetes: A Randomized Controlled Trial Using WhatsApp. In
Qatar Foundation Annual Research Conference Proceedings (Vol. 2016, No. 1, p.
HBPP2533). Qatar: HBKU Press.
Alvarado, M. M., Kum, H. C., Coronado, K. G., Foster, M. J., Ortega, P., & Lawley, M. A.
(2017). Barriers to remote health interventions for type 2 diabetes: a systematic review
and proposed classification scheme. Journal of medical Internet research, 19(2).
Borrero, A. F., Vasques, J., & Vargas, R. (2015). Implementation of a Mobile Application to
Promote Self-care in Elder Diabetic Patients. In VI Latin American Congress on
Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 (pp.
797-800). Springer, Cham.
Garcia-Zapirain, B., de la Torre Díez, I., Sainz de Abajo, B., & López-Coronado, M. (2016).
Development, technical, and user evaluation of a web mobile application for self-control
of diabetes. Te
Goyal, S., Morita, P., Lewis, G. F., Yu, C., Seto, E., & Cafazzo, J. A. (2016). The systematic
design of a behavioural mobile health application for the self-management of type 2
diabetes. Canadian Journal of diabetes, 40(1), 95-104.
Kateraas, E. D., & Medelius, P. J. (2015). U.S. Patent No. 8,936,552. Washington, DC: U.S.
Patent and Trademark Office.
Scerri, S., Garg, L., Garg, R., Scerri, C., Xuereb, P., & Tomaselli, G. (2015, December).
Understanding Human-Device Interaction patterns within the context of mobile nutrition.
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In Recent Advances in Engineering & Computational Sciences (RAECS), 2015 2nd
International Conference on (pp. 1-7). IEEE.