Revolutionizing Diabetes Management: The Power of mHealth - Essay Sample

Introduction

Diabetes is one of the chronic diseases that require optimal control interventions. The condition is currently a health problem facing both low-income and high-income countries. It is difficult to manage diabetes and hence the need for high levels of health numeracy and literacy besides reliable self- monitoring tools. Mobile technologies (mHealth) has revolutionized the management of diabetes, especially in developing countries, where it is a challenge for a majority of the population to maintain frequent contacts with healthcare practitioners. A research article Appropriation of Mobile Health for Diabetes Self-Management: Lessons from Two Qualitative Studies by Rossmann et al. (2019) critically evaluates the effectiveness of using self-monitoring technologies in managing diabetes. The study produced significant evidence on the essence and roles of mHealth applications. However, it has limitations related to sample size, replicability, and generalizability of the findings. The researchers, therefore, should have addressed these issues to enhance the reliability of the study in decision-making.

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The authors are qualified to write about this topic considering their outstanding educational credentials. In this perspective, all the authors have Ph.D. qualifications, which suggests that they can competently undertake proper and reliable research. While the subject requires knowledge in information technology, it is undeniable that one should have an in-depth understanding of diabetes to write about the topic. In this case, the authors are experts in the fields of media and ICT. The study thus is subjective in that the researchers' judgment and interpretations on the subject matter are from a technology point of view. The study should have incorporated researchers with a thorough understanding of diabetes and its management interventions. Also, physicians have an in-depth knowledge of clinical outcomes and self-care behaviors among people diagnosed with diabetes.

Research by Arsand et al. (2012) incorporated clinicians and experts in the fields of telemedicine and information technology. The difference in the composition of the researchers influenced the results. Arsand et al. (2012), for instance, found that an app should have considerable health benefits to qualify as a mHealth technology. Conversely, Rossmann et al. (2019) noted that many diabetic patients use other beneficial platforms, including traditional health information forums, and websites, lifestyle, and messaging apps. The authors, therefore, did not ascertain whether such tools have considerable health outcomes to the patients. Also, Arsand et al. (2012) focused on clinical factors such as technologies that support diabetes management by transferring blood glucose data to physicians. Rossmann et al. (2019), on the other hand, studied a wide range of tools in the mobile media spectrum. These elements are clear indications that the subjectivity underlying the study shaped the research findings.

Rossmann et al. (2019) noted significant results, but some limitations arise from the research design and methodology. First, a sample size of 21 respondents in Singapore and 16 participants in Germany is too small for the study. This aspect implies that it is difficult to generalize the outcome of the research in a larger population that have different characteristics. Also, the study is not representative of the national population since it is limited in scope. Secondly, it was a prerequisite that all the respondents had a diabetic condition to participate in the study, and for this reason, the findings have limited generalizability. Precisely, the research limits the generalizability of the results from a larger non-diabetic population that have an adequate understanding of mHealth technologies. However, Yingling, Allen, Litchman, Colicchio, and Gibson (2019) conducted a similar study in a low-income population (n=21) and attained fair results, although the results were different from those of Rossmann et al. (2019). The study concluded that mHealth is an acceptable technology among low-income adults (Yingling et al., 2019).

While the study had a small sample size, the researchers achieved a significant variance in the characteristics of the population. The recruitment of the interviewees, according to Rossmann et al. (2019), considered demographic factors such as age, gender, forms of therapy, type of diabetes, and the period since diagnosis. With such a broad cross-section of respondents having different characteristics, it is no doubt that the researchers gained more insights into the use of mHealth platforms in managing diabetes. Such as sample size is also suitable for thematic analysis and the estimated response rate. Besides, it is no doubt that the use of semi-structured interviews enhanced the validity of the study. This approach enables the researchers to utilize predetermined questions and thus provides comparable, reliable qualitative data.

However, Rossmann et al. (2019) did not assess the eligibility of mHealth technologies and thus lowered the validity of the research findings. According to Ye, Khan, Boren, Simoes, and Kim (2018), there are many diabetes self-management tools, but it is necessary to assess their eligibility. Ye et al. (2018) found that only 173 out of 1050 mHealth applications in online sites were eligible and useful for managing diabetes. The research further indicates that a significant proportion of mHealth applications do not follow the guidelines of the American Association of Diabetes Educators (AADE7) (Ye et al., 2018). A significant proportion of these technologies do not support critical features such as reducing risk, healthy coping, and problem-solving, which are crucial in managing diabetes (Ye et al., 2018).

A qualitative review by Muralidharan, Ranjani, Anjana, Allender, and Mohan (2017) corroborate with the findings of Rossmann et al. (2019) that diabetic patients use mHealth technologies besides a broad spectrum of mobile applications. Muralidharan et al. (2017) noted that 29% of research in low-income countries points out that diabetic patients use mHealth technologies, primarily for insulin optimization and for medication reminders. Also, 48% of the studies show that a significant proportion of diabetic patients use short message service technology (SMS) as their primary intervention in managing diabetes. About 23% of the research further indicates that clinicians utilize mHealth technologies to support added health outcomes.

Yingling et al. (2019) support the study by Rossmann et al. (2019) that technical, financial, and temporal restrictions are the main barriers to the use of mHealth technologies. Yingling et al. (2019) found that the usage of mHealth tools has high acceptability and feasibility in low-income populations. These self-monitoring tools were found to be effective in improving self-care behaviors and clinical outcomes among diabetic patients (Yingling et al., 2019).

Rossmann et al. (2019) argued that physicians are reluctant to recommend using mHealth platforms. However, Quinn et al. (2018) differed that mHealth apps are useful tools since it allows clinicians to provide a randomized assignment for diabetic patients to follow. The article by Rossmann et al. (2019), therefore, does not consider the fact that diabetic patients can follow physician's assignments without further recommendations. Shan, Sarkar, Martin (2019) noted that mHealth apps increase behavioral outcomes since it enhances frequent contact between doctors and diabetic patients through timely dissemination of health information.

Conclusion

Conclusively, mobile health technologies play essential roles in managing diabetes. The article gives more insights into the benefits of mHealth tools, although the research was subjective. However, many patients use multiple platforms, some of which do not comply with the standards of evidence-based guidelines. The future mobile health technologies, therefore, should incorporate AADE7 provisions to enhance the efficiency of diabetes management. However, future research should assess the usefulness of mHealth tools and whether their design follows the AADE7 guidelines.

References

Arsand, E., Froisland, D. H., Skrovseth, S. O., Chomutare, T., Tatara, N., Hartvigsen, G., &

Tufano, J. T. (2012). Mobile Health Applications to Assist Patients with Diabetes: Lessons Learned and Design Implications. Journal of Diabetes Science and Technology, 6(5), 1197-1206. DOI: 10.1177/193229681200600525

Muralidharan, S., Ranjani, H., Anjana, R. M., Allender, S., & Mohan, V. (2017). Mobile health technology in the prevention and management of type 2 diabetes. Indian journal of endocrinology and metabolism, 21(2), 334. DOI: 10.4103/ijem.ijem_407_16

Quinn, C. C., Swasey, K. K., Torain, J. M., Shardell, M. D., Terrin, M. L., Barr, E. A., & Gruber-Baldini, A. L. (2018). An mHealth Diabetes Intervention for Glucose Control: Health Care Utilization Analysis. JMIR mHealth and uHealth, 6(10), e10776. DOI: 10.2196/10776

Rossmann, C., Riesmeyer, C., Brew-Sam, N., Karnowski, V., Joeckel, S., Chib, A., & Ling, R. (2019). Appropriation of Mobile Health for Diabetes Self-Management: Lessons From Two Qualitative Studies. JMIR Diabetes, 4(1), e10271. DOI: 10.2196/10271

Shan, R., Sarkar, S., & Martin, S. S. (2019). Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia, 62(6), 877-887. DOI: 10.1007/s00125-019-4864-7

Ye, Q., Khan, U., Boren, S. A., Simoes, E. J., & Kim, M. S. (2018). An Analysis of Diabetes Mobile Applications Features Compared to AADE7: Addressing Self-Management Behaviors in People With Diabetes. Journal of Diabetes Science and Technology, 12(4), 808-816. DOI: 10.1177/1932296818754907

Yingling, L., Allen, N. A., Litchman, M. L., Colicchio, V., & Gibson, B. S. (2019). An Evaluation of Digital Health Tools for Diabetes Self-Management in Hispanic Adults: Exploratory Study. JMIR Diabetes, 4(3), e12936. DOI: 10.2196/12936