Burden of treatment for chronic illness: a concept analysis and review of the literature

Sav, Adem; King, Michelle Annette; Whitty, Jennifer A.; Kendall, Elizabeth; McMillan, Sara Sinclair; Kelly, Fiona; Hunter, Beth; Wheeler, Amanda

doi:10.1111/hex.12046

Cited by 275 publications

(294 citation statements)

References 46 publications

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“…increase medication use, the provision of additional services), resulting in more work for the patient and ultimately, greater treatment burden. Therapeutic interventions should assess treatment burden and adopt strategies to minimise the 'work' of being a patient, otherwise treatment burden can lead to treatment non-adherence, side effects or worsening or recurrence of symptoms, poor quality of life and ineffective use of finite health resources [10,11].…”

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confidence: 99%

“…Even within some of these studies, treatment burden has generally been included as one component within a multidimensional instrument, specifically designed to measure overall quality of life and treatment satisfaction [10]. Yet, chronic conditions rarely occur in isolation, with many people experiencing comorbidity of at least two or more conditions [14].…”

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confidence: 99%

“…In Australia, 20% of the population experience multiple chronic conditions [15]. Not surprisingly, there is evidence that treatment burden increases with co-morbidity [1], suggesting that the experiences of people with multiple and more complex conditions may be different from those people with only one condition [10]. There is clearly a need to ascertain the type and level of treatment burden experienced by people with co-morbidities.…”

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Treatment Burden and Chronic Illness: Who is at Most Risk?

Sav

Whitty

McMillan

et al. 2016

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Background: There is a need to ascertain the type and level of treatment burden experienced by people with co-morbidities. This is important to identify the characteristics of participants who are at most risk of treatment burden. Objective: The aim of this study is to identify the characteristics of participants who are at most risk of treatment burden. Methods: This cross-sectional study was part of a larger project and recruitment was conducted across four Australian regions: rural, semi-rural and metropolitan. Participants were asked about their treatment burden using an adapted version of a measure, which included the following five dimensions: medication, time and administrative, lifestyle change, social life and financial burden. Results: In total, 581 participants with various chronic health conditions reported a mean global treatment burden of 56.5 out of 150 (standard deviation = 34.5). Number of chronic conditions (β = .34, p < 0.01), age, (β = −.27, p < 0.01), the presence of an unpaid carer (β = .22, p < 0.001) and the presence of diabetes mellitus and other endocrine conditions (β = .13, p < 0.01) were significant predictors of overall treatment burden. For the five dimensions of treatment burden, social, medicine and administrative burden were predicted by the same cluster of variables: number of conditions, age, presence of an unpaid carer and diabetes. However, in addition to these variables, financial dimensions were also predicted by education level, ethnicity and health insurance. Educational level also influenced lifestyle burden. Conclusion: A substantial proportion of community-dwelling adults with chronic conditions have considerable levels of treatment burden. Specifically, health professionals should provide greater focus on managing overall treatment burden for persons who are of young age, have an endocrine condition or an unpaid carer, or a combination of these factors

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Treatment Burden and Chronic Illness: Who is at Most Risk?

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Whitty

McMillan

et al. 2016

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“…The sampling period here is 8 hours and hence, data is collected three times per day The studies mentioned above do not contain treatment. Hence, we augmented the model with a nonlinear effect of treatment which is known as treatment burden in social science [23]. More precisely, the following terms were introduced…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This constraint in the number of treatments is aimed at addressing the problem of treatment burden [23], which is the problem of decreasing effectiveness of treatment as the number of applied treatments increases.…”

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A robust MPC approach to the design of behavioural treatments

Bekiroğlu

Lagoa

Murphy

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-The objective of this paper is to provide some initial results on the application of control tools to the problem treatment design. Human behavior and reaction to treatment is complex and dependent on many unmeasurable external stimuli. Therefore, to the best of our knowledge, it cannot be described by simple models. Hence, one of the main messages in this paper is that, to design a treatment (controller) one cannot rely on exact models. More precisely, to be able to design effective treatments, we propose to use "simple" uncertain affine models whose response "covers" the most probable subject responses. So, we propose a simple model that contains two different types of uncertainties: one aimed at uncertainty of the dynamics and another aimed at approximating external perturbations that patients face in their daily life. With this model at hand, we design a robust model predictive controller, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. I. INTRODUCTIONAlthough, the use of control algorithms in treatment context is not new there is still limited use of the concept of feedback in the design of treatments especially in the area of behavioral sciences. In this paper, we present a preliminary attempt to apply some well-known concepts from robust controller design to the development of efficient and robust intervention for behavioural treatments.In the design of treatments in a behavioral context, one usually starts with data from several patients collected in a study. From this, one determines an approximate behavioral model to be used in the design of the control law. The first one is both variability in behavior and the way patients respond to treatment in study data leads to high inaccuracy of the models obtained. Moreover, in a practical setting a patient's behavior is always affected by external perturbations from events in daily life. All this leads to a large amount of uncertainty in the model and, hence, the need for feedback and, especially, robustness. Therefore, given the fact that one can increase the robustness of the overall treatment by using a control approach, the interest in using algorithms from control theory in a treatment context is growing. Moreover, the increasing use of portable devices enables the collection of information and delivery of the treatment in a timely manner and the possibility of automating treatments.

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Molecular and Materials Engineering for Delivery of Peptide Drugs to Treat Type 2 Diabetes

Varanko

Chilkoti

2019

Adv Healthcare Materials

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Type 2 diabetes is exploding globally. Despite numerous treatment options, nearly half of type 2 diabetics are unsuccessful at properly managing the disease, primarily due to a lack of patient compliance, driven by adverse side effects as well as complicated and frequent dosing schedules. Improving the delivery of type 2 diabetes drugs has the potential to increase patient compliance and thus, greatly enhance health outcomes and quality of life. This review focuses on molecular and materials engineering strategies that have been implemented to improve the delivery of peptide drugs to treat type 2 diabetes. Peptide drugs benefit from high potency and specificity but suffer from instability and short half‐lives that limit their utility as therapeutics and pose a significant delivery challenge. Several approaches have been developed to improve the availability and efficacy of antidiabetic peptides and proteins in vivo. These methods are reviewed herein and include devices, which sustain the release of peptides in long term, and molecular engineering strategies, which prolong circulation time and slow the release of therapeutic peptides. By optimizing the delivery of these peptides and proteins using these approaches, long‐term glucose control can be achieved in type 2 diabetes patients.

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Burden of treatment for chronic illness: a concept analysis and review of the literature

Abstract: Context Treatment burden, the burden associated with the treatment and management of chronic illness, has not yet been well articulated.

Cited by 275 publications

References 46 publications

Treatment Burden and Chronic Illness: Who is at Most Risk?

Treatment Burden and Chronic Illness: Who is at Most Risk?

A robust MPC approach to the design of behavioural treatments

Molecular and Materials Engineering for Delivery of Peptide Drugs to Treat Type 2 Diabetes

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