Abstract
Diabetes mellitus is a prevalent chronic disease that results in many morbidities and mortalities. The management of diabetes involves maintaining proper glycemic control, eating healthy diets, regular exercise, and medication adherence. However, inadequate self-management efforts are responsible for the high rates of morbidities attributed to the disease. Therefore, there is a need to take advantage of technological advancements to improve diabetes self-care. The purpose of this paper is to use evidence-based strategies to determine the impact of mobile technology (mHealth) on lifestyle changes in diabetic patients. A literature search was conducted on the Google Scholar database using the keywords mobile technology, lifestyle changes, and diabetes mellitus. The search was limited to articles published between 2014 and 2018. 5 primary research articles were reviewed. Mobile phone technology led to positive improvements in the lifestyles of patients with diabetes mellitus as measured by HbA1c levels and other secondary outcomes. However, the differences were not statistically significant. These findings indicate that nurses should incorporate mobile technologies in the provision of diabetes care and education to ensure optimal outcomes. Additionally, future studies should focus on augmenting the benefits of mHealth technologies in diabetic populations.
Introduction
Diabetes is a longstanding metabolic disorder attributed to difficulties in the utilization of glucose. Diabetes is the 7th leading cause of morbidities and mortalities in the United States. The American Diabetes Association (2018) reports that approximately 30.3 million people in the United States suffered from diabetes in 2015. Out of this number, 7.2 million people were undiagnosed. About 1.25 million children and adults had insulin-dependent diabetes, whereas 12 million older adults had either type 1 or type 2 diabetes. It is also estimated that 1.5 million new diagnoses are made annually. Several debilitating consequences are associated with diabetes, including retinopathy, diabetic foot ulcers, cardiovascular disease, kidney damage, and nerve problems (Martin, Albers, Pop-Busui, & DCCT/EDiC Research Group, 2014). These complications lower the quality of life of diabetic patients and increase medical costs significantly. These facts point towards the gravity of diabetes and the need to ensure that affected populations manage the condition by maintaining optimal blood sugar levels.
Advances in technology have revolutionized health care over the last few centuries. For example, the adoption of electronic health records has improved how data is captured and stored, thus ensuring patient safety and improving overall health outcomes. The use of health trackers, sensors, and wearable devices helps physicians to keep an eye on patients following discharge (Bian et al., 2017). A similar concept is employed in mobile health technology (mHealth), which uses mobile devices to gather medical data and transmit information to health workers, investigators, and patients (Fatehi, Gray, & Russell, 2017). It is also possible to attain real-time monitoring of patients’ vital signs and provide care through mobile telemedicine. Additional mHealth constituents for diabetes care include health management, physical activity, healthy eating, medication dosage information, as well as symptoms of hyperglycemia and hypoglycemia.
The nursing practice strives to ensure optimum patient outcomes through care, education, and advocacy strategies. Self-care is a useful aspect of the management of diabetes. Patients are responsible for their own glycemic control when away from the hospital. Measures to promote glycemic control include blood sugar monitoring, eating balanced meals, and regular physical activity. Nurses attempt to empower patients towards diabetes self-care through educational endeavors. However, adverse outcomes continue to be reported in diabetic patients. It is hypothesized that the use of mHealth technology will provide patients with opportunities to self-manage their conditions effectively from the comfort of their homes. Consequently, incorporating technology (through mHealth technology) into diabetes self-care regimens will enhance communication between patients and their healthcare providers, thus facilitating the timely identification of potential problems before they progress into serious diabetic complications.
The purpose of this paper is to illustrate the use of evidence-based processes to determine the impact of mobile technology on lifestyle changes of diabetic patients in outpatient settings.
Methods
The Google Scholar database was used to search for scholarly works on the research topic. The keywords used were “mobile technology,” “lifestyle changes,” and “diabetes mellitus.” 22,400 articles were retrieved. The search was narrowed by excluding patents and restricting the results to papers published between 2014 and 2018. Consequently, 11,100 articles were retrieved and sorted according to their relevance. The “advanced search” option was selected to limit further exploration by looking for articles that contained the keywords anywhere in the paper. About 10,200 results were recovered. Suitable articles were chosen by determining the relevance of the study from the wording of the titles and abstracts. Systematic reviews, meta-analyses, and reviews were excluded from the study. Randomized controlled trials and other forms of primary research reports were included in the analysis. In total, 5 publications were reviewed and included in the paper.
Literature Review/Discussion
Initial reports of mobile technology for the management of diabetes are positive. However, there is limited information regarding the efficiency of mHealth solutions for diabetes care. This section reviews five primary research articles to determine the effectiveness of mHealth solutions in lifestyle modifications and overall diabetes care.
Four randomized controlled trials (Arora, Peters, Burner, Lam, & Menchine, 2014; Holmen et al., 2014; Block et al. 2015; Wayne, Perez, Kaplan, & Ritvo, 2015) and a mixed-method observational cohort study (Nundy et al., 2014) were evaluated. Arora et al. (2014) appraised a mHealth intervention that involved sending health-related text messages (on a daily basis for 6 months) to emergency department patients of low socioeconomic standing. These messages were aimed at prompting alterations in HbA1c levels, medication compliance, self-efficacy, execution of self-care tasks, knowledge of diabetes, patient satisfaction, and quality of life. The intervention lowered HbA1c levels and enhanced the secondary outcomes in the treatment group. The major shortcomings were the inability to determine the most effective text message and the sample selection process, which limited the generalizability of the findings.
Holmen et al. (2014) sought to determine whether using a mobile phone-based self-management scheme would improve HbA1c levels within 1 year. Other outcome events were self-management and quality of life. The intervention group received telephone health advice from a diabetes specialist nurse during the initial 16 weeks. The mediation reduced HbA1c levels, though the reduction was not statistically significant. A notable observation in this investigation was that older subjects reported more use of mobile technology than younger participants. The main shortcoming was the release of better phone models during the study period, which limited the use of the phones given to the participants.
Nundy et al. (2014) also examined the effect of a preset text messaging system and remote nursing on self-efficacy, medication adherence, exercise, diet, and foot care in 14 subjects. The intervention led to improvements in the outcome measures thus indicating the potential of mHealth in fostering longstanding behavior modifications. However, the study lacked a control group and long-term follow-up. The generalizability of the findings was limited by using a fairly well-educated sample.
Block et al. (2015) and Wayne et al. (2015) involved 339 and 138 patients respectively to determine the impact of mHealth on HbA1c levels, BMI, weight, and waist circumferences. However, Block et al. (2015) also checked the proportion of triglyceride/ high-density lipoprotein cholesterol (TG/HDL), and Framingham diabetes risk scores, whereas Wayne et al. (2015) monitored contentment with life, depression, apprehension, and quality of life. The interventions led to positive outcomes in these measures.
Overall, all the studies examined changes in HbA1c levels as the main outcome measure, which showed the importance of this parameter in ascertaining glycemic control over time. The main secondary outcomes included body weight, BMI, self-efficacy, medication adherence, and the overall quality of life. The mobile-based interventions led to reductions in the primary measure and improvements in the other outcomes. These observations underpin the importance of incorporating mobile interventions into diabetes care and management. The positive benefits of mHealth interventions were attributed to the ease of use of mobile phones. These observations add to the body of literature in the furtherance of mHealth as an accessible, feasible, and cost-effective strategy of promoting public health in diabetes populations.
Implications for Practice and Conclusion
The positive effect of mobile technology in the management of diabetes indicates that mobile technologies are accepted by the population. Therefore, nurses and other healthcare workers should incorporate these technologies into their day-to-day treatment, education, and counseling of diabetic patients. The overall strength found in the literature was that mobile technology-enhanced primary and secondary measures in diabetes, even though the impact was not statistically significant. The prevalent weakness was the use of small samples with specific attributes (knowledge levels, race, and setting) that limited the generalizability of the findings to the population. A major literature gap identified in the review relates to ways of ensuring that mHealth technologies realize statistically significant differences in diabetic populations.
The use of culturally concordant messaging when reaching out to patients as reported by Arora et al. (2014) implies that healthcare providers should consider customizing mHealth interventions to match the needs of their clients. Future studies should conduct more rigorous investigations that focus on similar interventions on a larger scale to pave way for comprehensive implementation of mobile technology-assisted disease management. Further studies are also needed to determine ways of augmenting the positive effects of mHealth on diabetic populations.
Overall, was concluded that mHealth technologies were beneficial in enhancing glycemic control among diabetic patients, which was vital to the effective management of the disease. Additionally, mHealth interventions improved other disease management processes such as self-efficiency and medication compliance. Positive modifications in the lifestyles of the patients were also noted as marked by decreased body mass indices, body weights, and waist circumferences. However, additional studies should be conducted to maximize the benefits of mHealth technologies.
References
American Diabetes Association. (2018). Statistics about diabetes. Web.
Arora, S., Peters, A. L., Burner, E., Lam, C. N., & Menchine, M. (2014). Trial to examine text message–based mHealth in emergency department patients with diabetes (TExT-MED): A randomized controlled trial. Annals of Emergency Medicine, 63(6), 745-754.
Bian, R. R., Piatt, G. A., Sen, A., Plegue, M. A., De Michele, M. L., Hafez, D.,… Richardson, C. R. (2017). The effect of technology-mediated diabetes prevention interventions on weight: A meta-analysis. Journal of Medical Internet Research, 19(3), e76.
Block, G., Azar, K. M., Romanelli, R. J., Block, T. J., Hopkins, D., Carpenter, H. A.,… Block, C. H. (2015). Diabetes prevention and weight loss with a fully automated behavioral intervention by email, web, and mobile phone: a randomized controlled trial among persons with prediabetes. Journal of Medical Internet Research, 17(10), e240.
Fatehi, F., Gray, L. C., & Russell, A. W. (2017). Mobile health (mHealth) for diabetes care: Opportunities and challenges. Diabetes Technology & Therapeutics, 19(1), 1-3.
Holmen, H., Torbjørnsen, A., Wahl, A. K., Jenum, A. K., Småstuen, M. C., Årsand, E., & Ribu, L. (2014). A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: One-year results from the Norwegian randomized controlled trial RENEWING HEALTH. JMIR mHealth and uHealth, 2(4), e57.
Martin, C. L., Albers, J. W., Pop-Busui, R., & DCCT/EDiC Research Group. (2014). Neuropathy and related findings in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care, 37(1), 31-38.
Nundy, S., Mishra, A., Hogan, P., Lee, S. M., Solomon, M. C., & Peek, M. E. (2014). How do mobile phone diabetes programs drive behavior change? Evidence from a mixed methods observational cohort study. The Diabetes Educator, 40(6), 806-819.
Wayne, N., Perez, D. F., Kaplan, D. M., & Ritvo, P. (2015). Health coaching reduces HbA1c in type 2 diabetic patients from a lower-socioeconomic status community: A randomized controlled trial. Journal of Medical Internet Research, 17(10), e224.
Appendices
- Informatics EBP Paper: Database Search Form
- Student Name: ______________________
- Initial search date(s):__________________________
Individual Evidence Summary Table
The purpose of this literature review is to determine the impact of mobile technology on lifestyle and behavior changes in the management of diabetes.