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Introduction

A chronic autoimmune condition called type 1 diabetes mellitus causes the pancreas' insulin-producing beta cells to die. People of various ages, especially those over 60, are affected by it. The management of diabetes in younger populations has received a great deal of attention (DiMeglio et al., 2018). Still, there is an increasing need to comprehend how Type 1 diabetes manifests differently in elderly individuals and the consequences for their well-being and quality of life. By filling up this information gap and illuminating the particular difficulties and opportunities related to diabetes care for the elderly, this study hopes to close the knowledge gap. Australia is not an exception to the trend of rising diabetes prevalence worldwide. 3,000 new cases of type 1 diabetes were detected in Australia in 2021 (Australian Institute of Health and Welfare [AIHW], 2023). Due to the country's aging demographic trend and developments in healthcare, a growing number of elderly in Australia are living with Type 1 diabetes. Concerns regarding how diabetes complications, management techniques, and quality of life differ by age group are raised by an alteration in the population's demographics (Harding et al., 2019).

Managing Type 1 diabetes in older persons presents a number of difficulties. For elderly patients, comorbidities, multiple medications, and age-related physiologic alterations impacting glucose regulation are prevalent issues. Their commitment to treatment regimens and self-care routines may also be impacted by their unique psychosocial and lifestyle traits. It is crucial to appreciate these complications in order to provide diabetic care that is specially customised to the requirements of this population (Powers et al., 2020). To ascertain the prevalence of problems associated with diabetes, changes in glycemic control, and general quality of life, this study compares the glycemic control of senior citizens (60 years and older) to younger age groups. Information that healthcare practitioners, policymakers, and researchers may use is crucial to enhancing the health outcomes and quality of life of older persons with diabetes.

Research Aim

To improve glycemic control, lower complications, and improve participants' quality of life, this project aims to find personalised diabetes care techniques for elderly individuals(aged 60 and above) with Type 1 diabetes. The primary research question is: "How does diabetes management among the elderly differ from other age groups, and what are the associated impacts on health outcomes and quality of life?"

Literature Review

The literature review sheds light on several facets of Type 1 diabetes management in various age groups. It emphasises the significance of customised care to match the individual requirements of varied populations. Numerous research studies have examined how glycemic management in people with Type 1 diabetes is affected by technological innovations such as closed-loop insulin delivery systems and continuous glucose monitoring (CGM). Laffel et al. (2020) reported that CGM considerably improved glycemic control in adolescents and young adults in the United States. Pratley et al. (2020) discovered a similar improvement in older people aged 60 and over. It is important to remember that Pratley et al.'s study did not only concentrate on the older population and had a relatively wide age range.

The advanced hybrid closed-loop (AHCL) system and sensor-augmented pump therapy were contrasted in New Zealand research by Collyns et al. (2021). The participants were people with Type 1 diabetes. Their results demonstrated how well AHCL worked to increase time in range (TIR), particularly in young people. Similarly, Kubilay et al. (2023) investigated the experiences of seniors utilising closed-loop insulin delivery, highlighting the potential advantages of this technology, such as enhanced sleep and decreased psychological stress.

Additionally, Ruissen et al. (2021) in the Netherlands looked into how external influences, such as the COVID-19 pandemic, affected the management of diabetes. Their research showed that those with Type 1 and Type 2 diabetes did not show a reduction in glycemic control during lockdowns but did have higher stress, weight gain, and limited exercise.

This demonstrates the adaptability and tenacity of people with diabetes in adversity. Wang et al. (2019) did a systematic review and meta-analysis of mobile health (mHealth) interventions in Australia. They concluded that mHealth therapies could enhance glycemic control in Type 1 diabetic patients. However, they also pointed out drawbacks such as modest sample sizes and possible bias in selection. The literature now in existence emphasises the potential advantages of technological development and the adaptability of people with Type 1 diabetes. However, it is crucial to determine whether these results can be directly applicable to the senior population and whether controlling diabetes in this age group presents particular difficulties and opportunities. Filling in this information gap will help provide older people with diabetes with more individualised and efficient care, ultimately enhancing their health and quality of life.

Methodology

Study Design:

A meta-analysis and systematic review methodology will be used for this secondary research. This methodology was used because it enables a thorough synthesis of previous investigations on treating Type 1 diabetes in seniors (those 60 years of age and older) in contrast to other age groups. Systematic reviews are renowned for offering an organised and objective summary of the currently available information, assisting in discovering trends, patterns, and knowledge gaps in the literature (Ahn & Kang, 2018).

Study Setting & Participants

The study encompasses an extensive evaluation of the body of knowledge on managing Type 1 diabetes among the elderly in numerous nations. Therefore, it is not geographically constrained. Persons with Type 1 diabetes 60 years of age and older and persons in younger age groups for comparison make up the study population. Since this secondary research analyses current studies rather than collecting primary data, the number of participants needed is not applicable. The information will come from published studies, systematic reviews, and meta-analyses that satisfy the inclusion requirements.

Data Collection

To acquire data for this secondary study, relevant articles must be gathered from reliable databases and sources. Published articles and systematic reviews covering the management of Type 1 diabetes in various age groups, focusing on elderly individuals, are the primary artifacts or evidence that needs to be gathered. The inclusion criteria will be established to identify studies that satisfy the study's objectives, such as studies that contrast Type 1 diabetes in older and younger age groups regarding glycemic control, challenges, and quality of life.

Electronic databases, including PubMed, Medline, and CINAHL, will be systematically searched using predetermined search phrases and Boolean operators to find pertinent articles (Bramer et al., 2018). Reference lists of relevant papers and pertinent reviews will also be checked to guarantee that all pertinent studies are included. To extract data, information from the chosen studies will be gathered, including research characteristics (such as author, year, and country), participant demographics, intervention specifics (if relevant), outcome measures (such as glycemic control and complications), and key findings. No new tools or instruments will be created for this secondary research; however, any current data collection tools or instruments utilised in the primary investigations will be referenced as necessary.

Data Analysis

To provide a thorough picture of the management of Type 1 diabetes in older adults compared to other age groups, pertinent data from the chosen studies will be synthesised. Key findings will be summarised, trends will be noted, and the overall calibre of the included research will be evaluated as part of this synthesis. A meta-analysis may be performed to quantitatively evaluate the pooled effect sizes if there is enough research with comparable results and techniques (Hansen et al., 2022). This would give a more reliable estimate of the effect of age on the results of diabetes care.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and the CASP (Critical Appraisal Skills Programme) tool for observational studies will be used to evaluate the quality of the included studies. This evaluation will assist in determining the quality and dependability of the data (Tran et al., 2021). The data analysis results will be presented concisely and organised, including tables, graphs, and narrative summaries, utilising statistical tools such as R or STATA (Mishra et al., 2019).

The importance of informed consent is first acknowledged. The typical requirement for informed permission from participants is irrelevant in this study because it involves the analysis of already-published data from other studies (Lewin et al., 2019). However, all data will be anonymised and de-identified to protect privacy and data use while complying with copyright and usage restrictions when retrieving and referencing published studies. This is done under the ethical standards established by the original researchers (Meyer, 2018). Another issue in the existing literature is publication bias. A thorough search strategy will be employed to encompass both published and unpublished studies to limit this risk and lessen the effect of publication bias on the meta-analysis (Nair, 2019).

Given the potential variances in methodological rigour, maintaining the calibre of included studies is crucial. This will be addressed by the implementation of a stringent quality evaluation procedure using accepted instruments, which will guarantee transparency and the detection of any potential biases in research design or execution (Johnson et al., 2020). Conflict of interest is also considered, and any possible conflicts found in the initial studies will be openly disclosed so that readers can evaluate how they might affect the presented findings (Boutron et al., 2019).

Ethical reporting and transparency throughout the study process will be respected as long as findings and interpretations are appropriately portrayed based on the data offered in the chosen studies. Data or interpretation conflicts or uncertainties will be acknowledged and transparently disclosed (Nowell et al., 2017). The research process, including search tactics, inclusion/exclusion criteria, data extraction techniques, and statistical analysis procedures, will also be meticulously documented to improve transparency and reproducibility. The research protocol and supporting materials will be available for replication and outside review (Synder et al., 2019).

Significance

The value of this proposed study rests in its potential to improve our knowledge of Type 1 diabetes management in older people (60 years of age and older), as compared with various age groups. As the world's population ages, more older individuals are developing diabetes. However, there is a severe knowledge gap regarding how diabetes care might be customised to match the unique requirements of this group. This study seeks to close that gap by methodically analysing the body of prior research to offer insightful explanations of the specific difficulties and opportunities in controlling Type 1 diabetes in the elderly.

This study's expected contribution will take many different forms. In the beginning, it will summarise and synthesise the present body of research, providing a thorough analysis of the variations in glycemic control, challenges, and quality of life between older and younger people with Type 1 diabetes. This knowledge will be crucial for researchers, policymakers, and healthcare professionals as they optimise diabetes treatment regimens for older individuals, enhancing their health and quality of life. The results of this study also have an opportunity to inspire the creation of evidence-based recommendations and interventions designed especially for seniors with Type 1 diabetes. Healthcare practitioners can deliver more individualised and effective care, improving glucose control and reducing complications by better grasping the unique circumstances influencing this population, leading to improved health outcomes.

References

Ahn, E., & Kang, H. (2018). Introduction to systematic review and meta-analysis. Korean Journal of Anesthesiology, 71 (2), 103-112. https://doi.org/10.4097/kjae.2018.71.2.103

Australian Institute of Health and Welfare [AIHW]. (2023). Diabetes: Australian facts. https://www.aihw.gov.au/reports/diabetes/diabetes/contents/summary

Boutron, I., Page, M. J., Higgins, J. P., Altman, D. G., Lundh, A., Hróbjartsson, A., & Cochrane Bias Methods Group. (2019). Considering bias and conflicts of interest among the included studies. Cochrane Handbook for Systematic Reviews of Interventions , 177-204. https://doi.org/10.1002/9781119536604.ch7

Bramer, W. M., De Jonge, G. B., Rethlefsen, M. L., Mast, F., & Kleijnen, J. (2018). A systematic approach to searching: An efficient and complete method to develop literature searches. Journal of the Medical Library Association: JMLA, 106 (4), 531. https://doi.org/10.5195/jmla.2018.283

Collyns, O. J., Meier, R. A., Betts, Z. L., Chan, D. S., Frampton, C., Frewen, C. M., & de Bock, M. I. (2021). Improved glycemic outcomes with Medtronic MiniMed advanced hybrid closed-loop delivery: Results from a randomized crossover trial comparing automated insulin delivery with predictive low glucose suspend in people with type 1 diabetes. Diabetes Care 44 (4), 969-975. https://doi.org/10.2337/dc20-2250

DiMeglio, L. A., Evans-Molina, C., & Oram, R. A. (2018). Type 1 diabetes. The Lancet, 391 (10138), 2449-2462. https://doi.org/10.1016/S0140-6736(18)31320-5

Hansen, C., Steinmetz, H., & Block, J. (2022). How to conduct a meta-analysis in eight steps: A practical guide. Management Review Quarterly , 1-19. https://doi.org/10.1007/s11301-021-00247-4

Harding, J. L., Pavkov, M. E., Magliano, D. J., Shaw, J. E., & Gregg, E. W. (2019). Global trends in diabetes complications: A review of current evidence. Diabetologia, 62 , 3-16. https://doi.org/10.1007/s00125-018-4711-2

Johnson, J. L., Adkins, D., & Chauvin, S. (2020). A review of the quality indicators of rigor in qualitative research. American Journal of Pharmaceutical Education, 84 (1). https://doi.org/10.5688/ajpe7120

Kubilay, E., Trawley, S., Ward, G. M., Fourlanos, S., Grills, C. A., Lee, M. H., & McAuley, S. A. (2023). Lived experience of older adults with type 1 diabetes using closed‐loop automated insulin delivery in a randomised trial. Diabetic Medicine, 40 (4), e15020. https://doi.org/10.1111/dme.15020

Laffel, L. M., Kanapka, L. G., Beck, R. W., Bergamo, K., Clements, M. A., Criego, A., & Miller, K. M. (2020). Effect of continuous glucose monitoring on glycemic control in adolescents and young adults with type 1 diabetes: A randomized clinical trial. Jama 323 (23), 2388-2396. https://doi.org/10.1001/jama.2020.6940

Lewin, S., Glenton, C., Lawrie, T. A., Downe, S., Finlayson, K. W., Rosenbaum, S., & Tunçalp, Ö. (2019). Qualitative Evidence Synthesis (QES) for Guidelines: Paper 2–Using qualitative evidence synthesis findings to inform evidence-to-decision frameworks and recommendations. Health Research Policy and Systems, 17 (1), 1-18. https://doi.org/10.1186/s12961-019-0468-4

Meyer, M. N. (2018). Practical tips for ethical data sharing. Advances in Methods and Practices in Psychological Science, 1 (1), 131-144. https://doi.org/10.1177/2515245917747656

Mishra, P., Pandey, C. M., Singh, U., Keshri, A., & Sabaretnam, M. (2019). Selection of appropriate statistical methods for data analysis. Annals of Cardiac Anaesthesia, 22 (3), 297. https://doi.org/10.4103/aca.ACA_248_18

Nair, A. S. (2019). Publication bias - Importance of studies with negative results! Indian Journal of Anaesthesia 63 (6), 505-507. https://doi.org/10.4103/ija.IJA_142_19

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16 (1), 1609406917733847. https://doi.org/10.1177/16094069177338

Powers, M. A., Bardsley, J. K., Cypress, M., Funnell, M. M., Harms, D., Hess-Fischl, A., & Uelmen, S. (2020). Diabetes self-management education and support in adults with type 2 diabetes: A consensus report of the American Diabetes Association, the Association of Diabetes Care & Education Specialists, the Academy of Nutrition and Dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American Pharmacists Association. Journal of the American Pharmacists Association, 60 (6), e1-e18. https://doi.org/10.1016/j.japh.2020.04.018

Pratley, R. E., Kanapka, L. G., Rickels, M. R., Ahmann, A., Aleppo, G., Beck, R., & Miller, K. M. (2020). Effect of continuous glucose monitoring on hypoglycemia in older adults with type 1 diabetes: A randomized clinical trial. Jama 323 (23), 2397-2406. https://doi.org/10.1001/jama.2020.6928

Ruissen, M. M., Regeer, H., Landstra, C. P., Schroijen, M., Jazet, I., Nijhoff, M. F., & de Koning, E. J. (2021). Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic. BMJ Open Diabetes Research and Care (1), e002035. http://dx.doi.org/10.1136/bmjdrc-2020-002035

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104 , 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039

Tran, L., Tam, D. N. H., Elshafay, A., Dang, T., Hirayama, K., & Huy, N. T. (2021). Quality assessment tools used in systematic reviews of in vitro studies: A systematic review. BMC Medical Research Methodology, 21 (1), 101. https://doi.org/10.1186/s12874-021-01295-w

Wang, X., Shu, W., Du, J., Du, M., Wang, P., Xue, M., & Hou, L. (2019). Mobile health in the management of type 1 diabetes: A systematic review and meta-analysis. BMC Endocrine Disorders, 19 (1), 1-10. https://doi.org/10.1186/s12902-019-0347-6

See our related work: Epidemiology in Times of Covid 19

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