Assessment Task 1 - Literature Review WC 1869 Assignment Sample

Subject Name : Medical Science

Introduction

Type 1 diabetes mellitus (T1D) is an immune system illness that obliterates insulin-delivering pancreatic beta cells. The escalating prevalence of type 1 diabetes overall requires an examination into the ways utilised for taking care of the issue in various age classifications, especially the older. As indicated by Gregory et al. ( 2022), 8.4 million people overall were determined to have type 1 diabetes in 2021 alone. Furthermore, 19% of this populace was comprised of people matured 60 yrs or more. Furthermore, developments in persistent care have brought about superior life expectancies, prompting an expansion in a more aged populace in various nations, most quite Australia. Because of this segment shift, the general frequency of Type 1 diabetes among more established grown-ups has risen, offering unmistakable difficulties to the management. As per the Australian Institute of Health and Welfare (AIHW), almost 1 out of 5 Australians matured 80 to 84 had diabetes in 2021, which is almost multiple times higher than the predominance of the sickness among individuals under 40 (0.7%).

The way one manages type 1 diabetes contrasts by age because of different reasons. To start, changes in a singular's physiology as they age, like contrasts in insulin responsiveness or decrease in renal capability, can affect the control mechanism of glucose in the body. Second, additional age-related ailments, for example, limited movement and cardiovascular issues, could prompt worries in type 1 diabetes care (Van Duinkerken et al., 2020). Subsequently, exploring and acquiring a top-to-bottom comprehension of this theme is fundamental for medical care experts as well as faculty engaged with nursing, as medical caretakers assume a vital part in patient education, self-administration, and prescription adherence, at last guaranteeing quality patient care.

Research Aim

The aim of this literature review is to explore how the management of Type 1 diabetes differs among elderly individuals compared to other age groups and its impact on health outcomes and quality of life. Population: Elderly individuals (age 60 and above) with Type 1 diabetes, Intervention: Management and treatment strategies for Type 1 diabetes, Comparison: Other age groups with Type 1 diabetes (e.g., younger adults, children) and Outcome: Variations in glycemic control, complications, and quality of life.

Method

The search for literature with respect to this review was led by utilising a few information bases, including PubMed, Embase, and CINAHL, with an emphasis on articles in the range of 2015 and 2023. In addition to this, only papers that were published in the English language were included. The search terminologies utilised were: " Type 1 diabetes," "older," "geriatric," "diabetes management," "age-specific management," and "health outcomes." To guarantee extensiveness, both Medical Subject Headings (MeSH) terms and keywords were utilised. The search was restricted to English language distributions. The criteria for inclusion enveloped examinations that investigated Type 1 diabetes management explicitly in older people (60yrs or more), contrasted with other age gatherings, and evaluated well-being results and personal satisfaction as essential results.

The Critical Appraisal Skills Programme (CASP) instrument was used to evaluate the quality and systemic meticulousness of the chosen studies. The CASP instrument gives an organised structure to assessing the legitimacy, importance, and pertinence of exploration articles, empowering an orderly evaluation of the study design, technique, and likely inclinations. The quality evaluation process zeroed in on elements, for example, concentrating on the design of the study, size of the sample, approaches for the collection of data, statistical analysis and result interpretation. Studies with strong methodology, suitable statistical evaluations, and clear explanations of outcomes were given preference for inclusion in the review, providing a thorough and accurate analysis of the literature on the subject. The CASP tool guaranteed that the studies which demonstrated research of high quality were taken into consideration for the review, thereby upgrading the general authenticity and legitimacy of the review's discoveries.

Results

Author, Year & Country of Origin	Aim/ Purpose of Study	Methodology	No. of Participants	Data Collection Procedure	Data Analysis	Key Findings	Limitations	CASP tool score
Laffel et al. (2020) & United States.	To determine the effect of continuous glucose monitoring (CGM) on glycemic control in adolescents and young adults with type 1 diabetes.	Randomised trial	153 individuals with type 1 diabetes aged between 14-24yrs	Sociodemographic details collected through medical records	Descriptive and inferential statistics	When continuous glucose monitoring was used instead of routine blood glucose monitoring, the hemoglobin A1c level was considerably lower after 26 weeks (adjusted difference, 0.37%). The most frequently reported negative outcomes in the CGM and BGM groups were severe hypoglycemia, hyperglycemia/ketosis, and diabetic ketoacidosis.	Individuals with type 1 diabetes and HbA1c levels beyond the qualifying range of 7.5% to 10.9% may not benefit from the findings. The intervention duration in the trial was quite brief (6 months). The report did not disclose specifics on the statistical methodologies employed for the analysis.	90%
Collyns et al. (2021) & New Zealand	To compare the AHCL system to sensor-augmented pump therapy with predictive low glucose management (SAP 1 PLGM) in individuals suffering from type 1 diabetes.	Dual-center, randomised, open-label, two-sequence crossover trial	60 individuals with type 1 diabetes between the age group of 7-80 years	Using the MiniMed Advanced Hybrid Closed-Loop (AHCL) system	Inferential statistical analysis	TIR favored AHCL versus SAP 1 PLGM by 12.5 6 8.5% (P 0.001), with greater progress overnight (18.8 6 12.9%, P 0.001). All age groups improved, with adolescents improving the most. Mean sensor glucose (SG) enhanced with AHCL but declined during PLGM. TIR was best whenever the algorithm set point was 5.6 mmol/L (100 mg/dL) versus 6.7 mmol/L (120 mg/dL), with no additional hypoglycemia.	The relatively short duration of the study, which does not allow for confirmation of sustained results or optimization of the AHCL system. The study participants were automated-insulin-delivery-naive and may not reflect the general population. The automated features of the MiniMed 670G system include conservative limitations prioritizing safety, such as the exclusion of automated bolus correction for hyperglycemia. The study did not assess psychosocial factors in detail, with methodologies, results, and discussions reported elsewhere (pending).	90%
Pratley et al. (2020) & United States	To determine the effectiveness of continuous glucose monitoring (CGM) over traditional blood glucose monitoring (BGM) in minimising hypoglycemia in older persons with type 1 diabetes	Randomised trial	203 adults with age greater than or equal to 60 yrs with type 1 diabetes	Patient-reported outcomes and cognitive tests were used to collect data.	Descriptive and inferential statistics	The percentage of time that participants' blood sugar levels were less than 70 mg/dL was the study's main finding. According to the findings, the time spent with hypoglycemia was statistically significantly less in the CGM group than in the conventional blood glucose monitoring (BGM) group. Secondary outcomes, including as other CGM measurements and HbA1c levels, also revealed statistically significant variations in favor of the CGM group. According to the study, using CGM for 6 months reduced hypoglycemia in older persons with type 1 diabetes by a tiny but statistically significant amount.	The first factor limiting the generalizability of the results is that the study cohort was made up of people receiving specialist diabetic care and having a relatively high socioeconomic position. Only six months made up the study's intervention period, which was rather brief. However, there was a 12-month extension phase during which the CGM group continued to use CGM, offering insights regarding longer-term use. It is unknown whether improved features of the CGM sensor would have led to a greater uptake of the device in this demographic, given that the study employed an older model. The absence of a system that halts insulin delivery from a pump when hypoglycemia is anticipated based on CGM readings would have had a bigger impact on lowering hypoglycemia	90%
Ruissen et al. (2021) & The Netherlands	To examine how people with both type 1 and type 2 diabetes are affected by stress levels, weight gain, exercise habits, and the COVID-19 pandemic and lockdown strategies.	Cohort study	435 participants, of which 280 had type 1 diabetes, and 155 had type 2 diabetes	Data was collected through an online questionnaire	Descriptive and inferential statistics	People with type 1 and type 2 diabetes who were subjected to lockdown procedures during the COVID-19 pandemic reported higher levels of stress, anxiety, weight gain, and limited exercise. Considering these modifications, no decline in glycemic control was seen. HbA1c readings in people with type 1 diabetes actually slightly decreased during the lockout.	Due to the lockdown's restrictions on access to medical facilities, the study relied on self-reported data, which could introduce bias and lead to an underestimating of weight changes and other variables. The lockdown period only lasted for a brief time (8–11 weeks) in comparison to the typical period of time used to evaluate glycemic control using HbA1c tests, which could have understated the effect of the lockout on glycemic control.	100%
Wang et al. (2019) & Australia	To assess the impact of mobile health (mHealth) treatments on the management of type 1 diabetes, particularly with regard to glycemic control as shown by glycated hemoglobin (HbA1c) levels.	Systematic Review and Meta-Analysis	602 participants	Data collection through systematic literature review	Inferential statistics	The glycemic control of type 1 diabetic patients was found to be improved by mobile health interventions. The usage of a mobile application, adult age, and an increased intervention duration were all linked to lower HbA1c values.	Small sample sizes in some studies. Chances of selection bias Different characteris- tics can lead to heterogeneity and influence the reliability of the results.	100%
Kubilay et al. (2023) & Australia	To investigate the life experiences of older people with type 1 diabetes utilising closed-loop automated administration of insulin	Randomized, open-label, two-stage crossover trial	21 adults aged 60 years and above with type 1 diabetes	Data collection through semi-structured interviews	Thematic analysis	When older persons without frailty or significant cognitive impairment used closed-loop therapy, their lived experiences were largely favourable, with better sleep, less psychological stress from diabetes, and lower glucose levels. However, there were also disconnects between realities and expectations, usability problems, and financial obstacles to maintaining access to closed-loop therapy.	The study's assumptions were made using a first-generation closed-loop system. Therefore, it might not be applicable to nations with other types of healthcare delivery systems. Future generations of automated delivery systems for insulin are anticipated to overcome the usability difficulties that have been discovered.	100%
Briganti et al. (2018) & Australia	To compare the efficacy as well as the safety of CSII (continuous subcutaneous insulin infusion) in older persons with type 1 diabetes to CSII in younger adults and CSII in older adults (MDI).	Retrospective review of patients	293 patients with type 1 diabetes	Examining the electronic health records of people with type 1 diabetes who attended systematic patient education and started on CSII or MDI between 2000 and 2016.	Descriptive statistics	With similar results to those shown in younger individuals using CSII, CSII can be utilised efficiently and safely in older patients with type 1 diabetes. In elderly individuals, CSII was linked to better glycemic outcomes than MDI.	The study's limitations include the use of an observational and retrospective methodology, the possibility of bias in the selection of participants, and the limited sample size of earlier studies on the effectiveness of CSII in older patients with type 1 diabetes.	100%
Trawley et al.(2022) & Australia	To evaluate the glucose profiles of older people with type 1 diabetes using sensor-augmented pump therapy and contrasting them with clinical target guidelines for older people based on consensus CGM data.	Older Adult Closed Loop (ORACL) trial.	30 individuals with 60 years of age or older with type 1 diabetes	Data collection through clinical assessments	Descriptive and inferential statistics	The participants exceeded the minimal consensus guidelines for the time in range by a significant amount, but they fell short of the current strict hypoglycemia criteria for all older persons. Predictive low alert usage was linked to decreased hypoglycemia.	Small sample size Lack of consistency with respect to the effect of predictive alerts across all CGM products Short observation period Findings might not be applicable to broader groups	100%
Chakrabarti et al. (2022)	To examine how closed-loop insulin delivery affects older persons with type 1 diabetes' glycemia (blood glucose levels) during sleep and their quality of sleep.	Randomized, crossover trial	Older adults with type 1 diabetes	Data collected through real-time patient data and questionnaire	Descriptive and inferential statistics	With closed-loop therapy, the daily sleep quality measurements were worse. During monitored sleep with closed-loop therapy, there were 30% more system alarms. For older persons, first-generation closed-loop treatment improves some sleep quality metrics but has significant glycemic benefits during sleep. When developing closed-loop technologies, sleep quality deserves consideration and research.	Small sample size Short observation period Findings might not be applicable to broader groups	100%
McCarthy & Grey (2018) & United States	To examine patterns of glycemic control factors and diabetes self-management behaviours across the adult life span.	Cross-sectional analysis	7,153 adults with T1D	Questionnaire and medical record review used for data collection	Descriptive and inferential statistics	Different factors were associated with higher odds of hemoglobin A1c ≥7 in each developmental stage group.	Lack of diversity in the sample No inclusion of variables	100%

The goal of the literature review was to look into the subtle changes in Type 1 diabetes management between senior people and people of various ages, as well as how these variations affect health outcomes and quality of life. The review included a wide range of publications that were taken from databases including PubMed, Embase, and CINAHL, and it covered the years 2015 to 2023. The research covered a wide range of examinations into many aspects of managing Type 1 diabetes in people of various ages.

Insights from the studies

Laffel et al. (2020) revealed through a randomised trial that the employment of continuous glucose monitoring (CGM) substantially augmented glycemic control among adolescents and young adults with Type 1 diabetes compared to traditional blood glucose monitoring. In a parallel vein, Pratley et al. (2020) found that older individuals (60 and above) also reaped benefits from CGM, experiencing reduced instances of hypoglycemia. The study by Kubilay et al. (2023) explored the experiences of elderly individuals utilising closed-loop automated insulin delivery systems. While participants reported positive outcomes such as improved sleep, reduced diabetes-related psychological stress, and better glucose management, challenges in usability and financial accessibility were also noted.

Wang et al.'s (2019) systematic review and meta-analysis highlighted the effectiveness of mobile health treatments in improving glycemic control in Type 1 diabetes patients. Notably, older age, longer intervention durations, and the usage of mobile applications were associated with lower HbA1c values. Effect of COVID-19 Pandemic: Briganti et al. (2018) investigated the effects of the COVID-19 pandemic and related lockdown measures on people with Type 1 and Type 2 diabetes. None of the Type 1 diabetes patients' glycemic control significantly worsened despite higher stress levels, weight gain, and decreased exercise.

Collyns et al. (2021) conducted a comparative study showcasing improved time in range (TIR) with an advanced hybrid closed-loop system in comparison to sensor-augmented pump therapy, particularly during the night. McCarthy & Grey (2018) explored age-related patterns of glycemic control factors and diabetes self-management behaviours, revealing varying associations with elevated HbA1c levels across different age groups.

Despite these valuable insights, each study bore its own set of limitations, such as small sample sizes, short observation windows, and potential biases. However, this compilation of research contributions elucidated critical components of Type 1 diabetes management among the elderly, underlining the necessity of tailored care that accounts for physiological aging, technological advancements like CGM and closed-loop systems, and external influences like the COVID-19 pandemic.

Discussion

Findings and Themes

The compilation of articles selected for this literature review revealed a wealth of insights into the nuanced management of Type 1 diabetes among elderly individuals in comparison to other age groups. A recurring theme that emerged from the studies was the profound impact of technological advancements, specifically continuous glucose monitoring (CGM) and closed-loop insulin delivery systems. Laffel et al. (2020) and Pratley et al. (2020) illuminated the advantages of CGM, highlighting its potential to substantially enhance glycemic control and minimise hypoglycemia instances across different age spectrums. Notably, both adolescents and young adults, as well as older individuals, experienced improved outcomes. Furthermore, Kubilay et al. (2023) offered deeper insights into the realm of closed-loop systems, uncovering their potential to not only ameliorate glycemic control but also enhance sleep quality and alleviate psychological stress associated with diabetes management.

Another prominent theme woven through the studies was the impact of external factors on diabetes management outcomes. Briganti et al. (2018) provided a poignant example by delving into the effects of the COVID-19 pandemic and related lockdown measures on diabetes management. Despite heightened stress, weight gain, and limited physical activity during lockdowns, individuals with Type 1 diabetes maintained stable glycemic control. This finding underscores the adaptability of diabetes management strategies even in challenging circumstances. Furthermore, Wang et al. (2019) highlighted the positive influence of mobile health interventions on glycemic control, suggesting that technology-driven approaches hold promise in empowering patients across diverse age groups to proactively manage their condition.

Clinical Recommendations and Future Research

The collective findings of these studies hold important implications for clinical practice and suggest intriguing avenues for future research. Healthcare practitioners should consider integrating CGM and closed-loop insulin delivery systems into the treatment plans of both young adults and elderly individuals with Type 1 diabetes. The demonstrated efficacy of these technologies in enhancing glycemic control and reducing hypoglycemic episodes underscores their potential to offer substantial benefits across various age brackets (Ware & Hovorka, 2022). Given the positive impact of mobile health interventions on glycemic outcomes, clinicians could encourage patients to leverage diabetes management apps and tools to facilitate self-monitoring and foster patient engagement (Ashrafzadeh, & Hamdy, 2019).

Looking ahead, future research endeavours should prioritise investigating the long-term effects of CGM and closed-loop systems, particularly among the elderly population. Comprehensive assessments of the interactions between age-related physiological changes and diabetes management technologies are essential to inform tailored treatment approaches (Limbert et al., 2022). Furthermore, more research needs to be done on the psychosocial dimensions identified by Kubilay et al. (2023) and Chakrabarti et al. (2022). The psychological and emotional dynamics at play when incorporating new technology into the daily lives of elderly people with Type 1 diabetes could be revealed through in-depth studies (Abdoli et al., 2019).

Limitations

It is important to recognise the review's inherent limitations even when its conclusions provide insightful information. Even while the search method was thorough, it concentrated on articles written in English, which might have prevented it from finding relevant materials published in other languages. The generalizability of the results to larger populations may also be constrained by the variety of sample populations used across the research. Furthermore, the extremely brief observation times in certain research might not accurately reflect the long-lasting benefits of interventions over longer times. For a more thorough understanding of the intricacies of Type 1 diabetes management among elderly people, these constraints highlight the need for a multidimensional research approach that spans languages, cultures, and longer time periods.

Conclusion

In conclusion, this review of the literature explores the complex management of Type 1 diabetes in the elderly, highlighting unique difficulties and approaches in comparison to other age groups. The analysis of the data highlights how external variables, technological advancements, and physiological changes interact to influence the quality of life and outcomes of diabetes care. Significantly, the adoption of closed-loop insulin administration systems and continuous glucose monitoring (CGM) emerges as a key theme across age spectrums. These developments show their potential to improve glucose control, reduce hypoglycemia, and handle the psychological stress related to managing diabetes. The review also illuminates the flexibility of diabetes care approaches in the face of external difficulties, as demonstrated by the stable glycemic control of Type 1 diabetes patients in the midst of the COVID-19 pandemic.

Clinical consequences place a strong emphasis on the incorporation of CGM, closed-loop systems, and mobile health interventions into treatment regimens for both older and younger cohorts. The learned insights propose ways to enhance self-monitoring and patient participation. Future research directions stress the necessity for thorough studies of these technologies throughout time, taking aging-related physiological changes and psychological factors into account. The review admits its shortcomings, which include skewed wording and a range of sample characteristics, even if it provides useful insights. Because of this, a thorough understanding of Type 1 diabetes management in the elderly requires an all-encompassing research strategy that crosses boundaries and extends observation times.

References

Abdoli, S., Hessler, D., Vora, A., Smither, B., & Stuckey, H. (2019). CE: original research: experiences of diabetes burnout: A qualitative study among people with type 1 diabetes. AJN The American Journal of Nursing, 119(12), 22-31. https://doi.org/10.1097/01.NAJ.0000615776.64043.be

Ashrafzadeh, S., & Hamdy, O. (2019). Patient-driven diabetes care of the future in the technology era. Cell Metabolism, 29(3), 564-575. https://www.cell.com/cell-metabolism/pdf/S1550-4131(18)30570-9.pdf

Australian Institute of Health and Welfare. (2023). Diabetes: Australian facts. https://www.aihw.gov.au/reports/diabetes/diabetes-australian-facts/contents/how-common-is-diabetes/type-1-diabetes

Briganti, E. M., Summers, J. C., Fitzgerald, Z. A., Lambers, L. N., & Cohen, N. D. (2018). Continuous subcutaneous insulin infusion can be used effectively and safely in older patients with type 1 diabetes: long-term follow-up. Diabetes Technology & Therapeutics, 20(11), 783-786. https://doi.org/10.1089/dia.2018.0215

Chakrabarti, A., Trawley, S., Kubilay, E., Mohammad Alipoor, A., Vogrin, S., Fourlanos, S., & McAuley, S. A. (2022). Closed-loop insulin delivery effects on glycemia during sleep and sleep quality in older adults with type 1 diabetes: Results from the ORACL trial. Diabetes Technology & Therapeutics, 24(9), 666-671. https://doi.org/10.1089/dia.2022.0110

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

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

Limbert, C., Tinti, D., Malik, F., Kosteria, I., Messer, L., Jalaludin, M. Y., & Marcovecchio, M. L. (2022). ISPAD Clinical Practice Consensus Guidelines 2022: The delivery of ambulatory diabetes care to children and adolescents with diabetes. Pediatric Diabetes, 23(8), 1243-1269. https://www.researchgate.net/profile/Paul-Benitez-Aguirre/publication/366427945

McCarthy, M. M., & Grey, M. (2018). Type 1 diabetes self-management from emerging adulthood through older adulthood. Diabetes Care, 41(8), 1608-1614. https://doi.org/10.2337/dc17-2597

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, 9(1), e002035. http://dx.doi.org/10.1136/bmjdrc-2020-002035

Trawley, S., Ward, G. M., Vogrin, S., Colman, P. G., Fourlanos, S., Grills, C. A., & McAuley, S. A. (2022). Glucose profiles of older adults with type 1 diabetes using sensor-augmented pump therapy in Australia: Pre-randomisation results from the ORACL study. The Lancet Healthy Longevity, 3(12), e839-e848. https://doi.org/10.1016/S2666-7568(22)00266-5

Van Duinkerken, E., Snoek, F. J., & De Wit, M. (2020). The cognitive and psychological effects of living with type 1 diabetes: A narrative review. Diabetic Medicine, 37(4), 555-563. https://doi.org/10.1111/dme.14216

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

Ware, J., & Hovorka, R. (2022). Closed-loop insulin delivery: update on the state of the field and emerging technologies. Expert Review of Medical Devices, 19(11), 859-875. https://doi.org/10.1080/17434440.2022.2142556

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