Diabetes and Mobile Health Technology

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):__________________________
Database Searched Keyword(s) Used Limits Used # retrieved # reviewed # used Notes
Google Scholar Mobile technology, lifestyle changes, and diabetes mellitus. No limits 22,400 articles 0 0 Additional search strategies were applied.
Google Scholar Mobile technology, lifestyle changes, and diabetes mellitus. Publications between 2014 and 2018 11,100 articles 0 0 Additional search strategies were applied.
Google Scholar Mobile technology, lifestyle changes, and diabetes mellitus. Articles containing the keywords anywhere in the paper. 10,200 articles 30 5 Abstracts of the articles were read to exclude qualitative studies and reviews.

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.

Authors (year) Evidence Type (study design) Setting, Sample, Sample Size Variables and Outcome Measures Results/ Recommendations Limitations
Arora, Peters, Burner, Lam, & Menchine (2014). Level II evidence. A randomized controlled trial. 128 low-income adult patients with poorly managed type 2 diabetes were selected from an urban, public emergency department. The intervention (independent variable) encompassed 2 daily text messages of generic care reminders (TExT-MED) for 6 months.
The main outcome was alterations in HbA1c levels, whereas the secondary outcomes consisted of modifications in medication
compliance, self-efficacy, execution of self-care tasks, knowledge of diabetes, patient satisfaction, and quality of life.
The intervention group recorded a 1.05% reduction in HbA1c levels as opposed to a 0.6% decline in the controls. Secondary outcomes were also enhanced in the intervention group with the most notable one being self-reported medication compliance. Larger effects were observed in Spanish speakers. 93.6% of participants reported that they enjoyed the approach. It was concluded that TExT-MED did not yield a statistically significant change in HbA1c. However, it led to improvements in other secondary measures. Subjects were enlisted
from one ED, thereby reducing the generalizability of the findings. Medication compliance was assessed by self-report, thus introducing the likelihood of recall bias. The high participant satisfaction with the program could be attributed to social desirability bias. Also, the study did not assess the most effective types of messages with the biggest impact. Approximately 30% of subjects were lost to follow-up.
Nundy et al. (2014). Evidence level III.
A mixed methods observational cohort study.
The setting was a working-class, metropolitan African American society. Participants belonged to the University of Chicago Health Plan (UCHP). 14 subjects were interviewed to ascertain novel behavioral concepts shaped by the intervention. The independent variable was a theory-driven, mobile phone-based treatment that mixed preset text messaging and distant nursing. The dependent variables were diabetes self-care areas such as medication adherence, exercise, blood sugar checking, foot care, and healthy diets. Other outcome measures included health beliefs, self-efficacy, and social support. The intervention led to improvements in 5 realms of self-care: medication adherence, blood sugar checking, physical activity, foot care, and healthy meals. There were significant improvements in at least one measure of self-efficacy. The subjects reported that the program changed their knowledge, outlooks, and ownership. It was concluded that behaviorally propelled mobile health intermediations can tackle numerous behavioral paths linked with lasting behavior change. The observational cohort approach lacked a control group, which constrained the capacity to ascertain causality. The sample was fairly well-educated, which reduced the generalizability of the findings to susceptible health populations. There was no longstanding follow-up, which was needed to confirm that the intervention led to lasting behavior change.
Holmen et al. (2014). Level II evidence
A randomized controlled trial.
The sample included individuals (aged 18 and older) with type 2 diabetes and HbA1c levels equal to or greater than 7.1%. A total of 151 subjects living in their homes in Norway were enrolled in the study. The independent variable was telephone health psychotherapy by a diabetes specialist nurse. The outcome measures were HbA1c levels, self-management, and quality of life with respect to health. Other outcomes included signs of depression and lifestyle changes as marked by dietary habits and physical activity. HbA1c levels reduced in all groups after 1 year. However, the difference was not statistically significant. There was no difference in the secondary outcomes between groups. Subjects aged 63 years and older reported a higher use of the app than their younger counterparts. Health counseling increased the self-management skills. The study concluded that age was not a hindrance to the use of technology. However, additional studies were needed to corroborate this observation. New and enhanced varieties of mobile phones were introduced into the market during the follow-up period. Consequently, there was low use of the mobile phones provided to the subjects.
Block et al. (2015). Level II evidence.
A randomized controlled trial.
339 subjects were recruited from the Palo Alto Medical Foundation (PAMF) in Northern California. The subjects’ blood sugar levels and HbA1c levels were in the prediabetic range. The treatment involved delivering the Alive-PD diabetes program through the Internet, the Web, computerized phone calls, and mobile phone. The main outcomes were modifications in fasting glucose and HbA1c after 6 months. Other outcome measures encompassed adjustments in body weight, waist circumference, body mass index (BMI), the proportion of triglyceride and high-density lipoprotein cholesterol (TG/HDL), and Framingham diabetes risk tally. There was a significant reduction in fasting glucose, HbA1c, and body weight in the Alive-PD subjects than controls following the intervention. There were substantial reductions in the secondary outcome measures in the Alive-PD enrollees than the control group after 6 months. The Framingham 8-year diabetes risk dropped from 16% to 11% in the treatment group compared to the control. It was concluded that the program has immense potential for scalability and should be modified to reach approximately 86 million American adults at risk of diabetes. Human support should be integrated into the system to maximize the impact of the intervention. The automation of the Alive-PD program was a weakness because some people required human interaction and support for maximal responses. The delivery of the intervention via email, Internet, and mobile phone restricted access to people who lacked Internet access or those who lack knowledge of technology. Additionally, the randomized trial lasted only 6 months instead of the recommended 1 year due to funding limitations. Finally, the subjects were mainly non-Hispanic whites and fairly well-educated, which limited the generalizability of the findings to less educated persons and ethnic minority groups.
Wayne, Perez, Kaplan, & Ritvo. (2015). Level II evidence.
A randomized controlled trial.
138 patients were recruited from two primary care health centers in Toronto, Canada. The subjects had type 2 diabetes and HbA1c levels of 56.3 mmol/mol and higher. The main outcome was alterations in HbA1c over 6 months. Secondary outcomes comprised body weight, body mass index (BMI), waist circumference, contentment with life, depression, apprehension, and quality of life. A reduction in HbA1c levels was observed in both groups. However, the difference was not statistically significant. Nonetheless, it was noted that the treatment group achieved a faster HbA1c drop. The intervention group reported substantial reductions in weight and waist circumference compared to controls, whereas the control group did not. Improvements in disposition, contentment with life, and quality of life were observed in both groups. Health coaching enhanced glycemic control and mental wellbeing in people of low socioeconomic standing with and without mobile technology. The connectivity presented by mobile technology improved acceptance and adherence to health behaviors. Subjects were motivated to take part, which initiated potential biases and restricted the applicability of the findings to the entire population. The control group was given health coach support devoid of mobile scrutiny as opposed to conventional care. Consequently, there was a likelihood of bias with the coaches using extra effort to prompt behavior modifications in the control group.