Type 2 Diabetics in the Elderly Population

Document Type:Thesis

Subject Area:Nursing

Document 1

Furthermore, the ailment is highly diverse among this population making it difficult to devise the most optimal way to manage it. Even though there is sufficient evidence linking old age and diabetes 2, this area has not received much attention as most clinical studies only focus on the younger working population. For this reason, healthcare providers are experiencing serious challenges in an attempt to find an effective treatment for this patient category. This research aims at identifying a standardised home health care that would adequately address the individual needs of elderly type 2 diabetes patients. Literature Review Background Unlike young people who have type 2 diabetes, specifying the treatment goals for the elderly patients is challenging due to the heterogeneous of the ageing process (Stellefson, Dipnarine & Stopka 2013).

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For this reason, health providers devised the Support Self-Management (SSM) model to help patients navigate daily decisions and events in a way that favours good health. Diabetes Self-management Support (DSMS) is the support required to implement and sustain coping skills and habits that are needed to self-manage the disease on an ongoing basis (Appelboom, Camacho, Abraham, Bruce, Dumont, Zacharia & Connolly, 2014). Furthermore, proper T2D management among the elderly can be improved using the chronic care model (CCM). In this model, a systematic approach is used to restructure medical care by creating a partnership between the community and health care. The model assumes that improvement in care requires an approach that incorporates provider, patient, and system level interventions (Gammon, Berntsen, Koricho, Sygna & Ruland 2015).

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As mentioned before, the risk of suffering from T2D increases with age which mean that older adults are at a higher risk than young individuals. Literature shows, that the population of the older adults is increasing at an alarming level. For example within a span of ten years i. e. between 2003 and 2013 the population of 65 and above people rose by 24. But at the same time, researchers are turning a blind eye to this area due to the assumption that understanding T2D among young people is essential as it has a direct effect on the economy. Thus caregivers are left with little information that they can base their decision upon, yet the disease is highly heterogeneous meaning that a slight mistake can be fatal (Chatterjee et al.

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Thus there is an urgent need for researchers to pay more attention to this area by involving the seniors in clinical studies and trials to find an optimal treatment that would offer appropriate guidance to caregivers. Sample and Location Heterogeneity of T2D among people in the age group 65 and above has been the main hurdle in finding a standard healthcare that can be suitable across the population. Furthermore, people living in rural areas are likely to suffer from T2D complications compared to those living in urban areas due to factors like health facilities accessibility among others. dependent and independent variable. Positive health outcome among the elderly T2D patients depends on educational intervention thus improved health is the dependent variable while the latter forms independent variable.

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Methodology & Design Research methodology/design entails formulation of an effective strategy to collect data and conduct the rest of the study’s activities. In this case, a quantitive approach of data collection would be the most appropirate because statastical data is needed to draw conclusions on the effects of education intervention on the diabetes knowledge in diabetese mellitus patients. Past research data, as well as clinical records, would provide valuable insights regarding health care management of T2D among the elderly. This study will employ three analytical techniques, regression analysis and evaluation using the EViews software. The first method is a full statistical process designed to estimate the relationship between the various variables. Data analysis is vital for integrating the data gathered from multiple sources.

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