Interdisciplinary Team for MEDSReM-2 and Decision Support Through Pharmacology of Aging Principles

Abstract Hypertension is highly prevalent in older adults (74.5% in ≥60 years) with dire consequences, and adherence to hypertension medications is low (approximately 50%). With increased smartphone use among older adults (81% for 60-69 years, 62% for ≥70 years), technology innovations can improve medication adherence. This symposium highlights the efforts of an innovative interdisciplinary team of experts (clinical, cognitive aging, human factors, health technology) to develop and implement the Medication Education, Decision Support, Reminding, and Monitoring (MEDSReM) system to improve hypertension medication adherence for older adults. MEDSReM is a theory-based, integrated mobile application (app) and companion web portal that educates, supports missed dose decisions, reminds, monitors adherence, and incorporates blood pressure feedback. In this symposium, we describe the interdisciplinary development efforts. Insel et al. will present the theory-based intervention, technology translation, and advancement of the MEDSReM system. Lee et al. will describe the interdisciplinary team and describe the work by the decision support subteam that created the medication formulary and generated an algorithm to guide missed-dose decisions based on pharmacology of aging. Rogers et al. will discuss the education subteam’s development of educational information about hypertension, medications, and adherence for the MEDSReM system. Mitzner et al. will illustrate the instructional support sub-team’s efforts to ensure older adults can interact with both the smartphone app and online portal. Lastly, Hale et al. will describe the user testing subteam’s usability processes including the integration of blood pressure self-monitoring. These efforts will provide insights for other interdisciplinary teams developing technology interventions for older adults.

Download Full-text

Sustainable Infrastructure Project Planning

Green Technologies ◽

10.4018/978-1-60960-472-1.ch108 ◽

2011 ◽

pp. 95-110

Author(s):

Omar Mohd Faizal ◽

Bambang Trigunarsyah ◽

Johnny Wong

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Decision Support Systems ◽

Support System ◽

Support Systems ◽

Data Communication ◽

Team Members ◽

Infrastructure Project ◽

Collaborative Platform

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

Download Full-text

Delivering Interprofessional Education to Embed Interdisciplinary Collaboration in Effective Nutritional Care

10.1007/978-3-030-63892-4_12 ◽

2021 ◽

pp. 159-169

Author(s):

Julie Santy-Tomlinson ◽

Celia V. Laur ◽

Sumantra Ray

Keyword(s):

Older Adults ◽

Capacity Building ◽

Interprofessional Education ◽

Interdisciplinary Collaboration ◽

Interdisciplinary Team ◽

Evidence Based ◽

Nutritional Care ◽

Nutrition Care ◽

Interdisciplinary Approaches

AbstractPrevious and forthcoming chapters describe how to improve nutrition care with an emphasis on interdisciplinary approaches. Developing and improving the skills and knowledge of the interdisciplinary team through interprofessional education are essential for embedding evidence-based, collaborative, nutritional care. This capacity building in turn supports delivery of effective nutritional care for older adults.

Download Full-text

Overview of Nutrition Care in Geriatrics and Orthogeriatrics

10.1007/978-3-030-63892-4_1 ◽

2021 ◽

pp. 3-18

Author(s):

Ólöf G. Geirsdóttir ◽

Karen Hertz ◽

Julie Santy-Tomlinson ◽

Antony Johansen ◽

Jack J. Bell

Keyword(s):

Older Adults ◽

At Risk ◽

Primary Prevention ◽

Secondary Prevention ◽

Interdisciplinary Team ◽

Focal Points ◽

Community Settings ◽

Team Members ◽

Nutrition Care ◽

Care Approach

AbstractEngaging older adults, and all those who care for them, is pivotal to providing high-value nutrition care for older adults. Nurses and other interdisciplinary team members are essential to this process. The aim of this chapter is to provide an overview of the rationale and evidence for interdisciplinary and systematised nutrition care as an effective nutrition care approach for older adults with or at risk of malnutrition. This chapter also serves as a guide to detailed chapters across this book to provide focal points on different aspects of nutrition care that should be considered across primary prevention, acute care, rehabilitation, secondary prevention and community settings (Dreinhöfer et al., Injury 49(8):1393–1397, 2018).

Download Full-text

Sustainable Infrastructure Project Planning

Rethinking Sustainable Development - Advances in Environmental Engineering and Green Technologies ◽

10.4018/978-1-61692-022-7.ch018 ◽

2011 ◽

pp. 255-270

Author(s):

Omar Mohd Faizal ◽

Bambang Trigunarsyah ◽

Johnny Wong

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Decision Support Systems ◽

Support System ◽

Support Systems ◽

Data Communication ◽

Team Members ◽

Infrastructure Project ◽

Collaborative Platform

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

Download Full-text

Decision-making profile in older adults: the influence of cognitive impairment, premorbid intelligence and depressive symptoms

International Psychogeriatrics ◽

10.1017/s1041610219001029 ◽

2019 ◽

Vol 32 (6) ◽

pp. 697-703

Author(s):

Marina Maria Biella ◽

Alaise Silva Santos de Siqueira ◽

Marcus Kiiti Borges ◽

Elyse Soares Ribeiro ◽

Regina Miksian Magaldi ◽

...

Keyword(s):

Older Adults ◽

Decision Making ◽

Logistic Regression ◽

Cognitive Impairment ◽

Depressive Symptoms ◽

Cognitive Aging ◽

Multinomial Logistic Regression ◽

Cognitive Disorders ◽

Cross Sectional ◽

Premorbid Intelligence

ABSTRACTObjective:Decision-making (DM) is a component of executive functioning. DM is essential to make proper decisions regarding important life and health issues. DM can be impaired in cognitive disorders among older adults, but current literature is scarce. The aim of this study was to evaluate the DM profile in participants with and without cognitive impairment.Design:Cross-sectional analysis of a cohort study on cognitive aging.Participants:143 older adults.Setting:University-based memory clinic.Methods:Patients comprised three groups after inclusion and exclusion criteria: healthy controls (n=29), mild cognitive impairment (n=81) and dementia (n=33). Participants were evaluated using an extensive neuropsychological protocol. DM profile was evaluated by the Melbourne Decision Making Questionnaire. Multinomial logistic regression was used to evaluate associations between age, sex, educational level, estimated intelligence quotient (IQ), cognitive disorders, depressive or anxiety symptoms, and the DM profiles.Results:The most prevalent DM profile was the vigilant type, having a prevalence of 64.3%. The vigilant profile also predominated in all three groups. The multinomial logistic regression showed that the avoidance profile (i.e. buck-passing) was associated with a greater presence of dementia (p=0.046) and depressive symptoms (p=0.024), but with less anxious symptoms (p=0.047). The procrastination profile was also associated with depressive symptoms (p=0.048). Finally, the hypervigilant profile was associated with a lower pre-morbid IQ (p=0.007).Conclusion:Older adults with cognitive impairment tended to make more unfavorable choices and have a more dysfunctional DM profile compared to healthy elders.

Download Full-text

The Design Of The Spk In Fulfilling The Production Target Of Vegetable Availability In Lut Tawar Sub-District Uses The SAW Method

International Journal of Management Science and Information Technology ◽

10.35870/ijmsit.v1i1.230 ◽

2021 ◽

Vol 1 (1) ◽

pp. 21

Author(s):

Rayuwati Rayuwati ◽

Yelli Novelia Putri

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Food Availability ◽

Support System ◽

Technology Development ◽

Vegetable Intake ◽

Simple Additive Weighting ◽

Production Target

Efforts to meet the availability of vegetables in fulfilling vegetable intake, especially for people in Lutawar sub-district, Central Aceh district, community agriculture requires a technology development that supports the calculation of production at each harvest and can also determine the fulfillment of community food availability. In this case, the fulfillment of production targets for the availability of vegetables will be very effective if it is calculated using a decision support system application, using the Simple Additive weighting (SAW) method.The process of collecting and analyzing data is carried out systematically and logically using a survey, which is to collect as much data as possible about the vegetables found in Lut Tawar Subdistrict, which is a decision support for the quality of the research. Then analyze vegetables in meeting production targets and food availability. Application of the Simple Additive Weighting (SAW) method into a decision support system to create a decision support system for food availability that is assessed from its production, including 4 criteria used to make an assessment, C1: Productivity, C2: Harvest Area, C3: Area Planting, C4: Population, then decision making assigns weight to each criterion. The types of vegetables as an alternative in this study are as follows

Download Full-text

Decisions from experience: adaptive information search and choice in younger and older adults

10.32920/14638635 ◽

2021 ◽

Author(s):

Julia Spaniol ◽

Pete Wegier

Keyword(s):

Older Adults ◽

Decision Making ◽

Information Search ◽

Cognitive Aging ◽

Age Groups ◽

Younger Adults ◽

Association Learning ◽

Desirable Outcome ◽

Dopamine Hypothesis ◽

Risky Option

In real-world decision making, choice outcomes, and their probabilities are often not known a priori but must be learned from experience. The dopamine hypothesis of cognitive aging predicts that component processes of experience-based decision making (information search and stimulus–reward association learning) decline with age. Many existing studies in this domain have used complex neuropsychological tasks that are not optimal for testing predictions about specific cognitive processes. Here we used an experimental sampling paradigm with real monetary payoffs that provided separate measures of information search and choice for gains and losses. Compared with younger adults, older adults sought less information about uncertain risky options. However, like younger adults, older participants also showed evidence of adaptive decision making. When the desirable outcome of the risky option was rare (p = 0.10 or 0.20), both age groups engaged in more information search and made fewer risky choices, compared with when the desirable outcome of the risky option was frequent (p = 0.80 or 0.90). Furthermore, loss options elicited more sampling and greater modulation of risk taking, compared with gain options. Overall, these findings support predictions of the dopamine hypothesis of cognitive aging, but they also highlight the need for additional research into the interaction of age and valence (gain vs. loss) on experience-based choice.

Download Full-text

Motivation and the Social Context of Aging and Decision Making

10.1093/oso/9780197528976.003.0011 ◽

2021 ◽

pp. 234-251

Author(s):

JoNell Strough ◽

Kelly Smith

Keyword(s):

Older Adults ◽

Decision Making ◽

Cognitive Aging ◽

Life Experience ◽

Future Research ◽

Cognitive Resources ◽

Adult Age ◽

Age Related ◽

The Social ◽

Motivational Influences

This chapter focuses on the role of motivation for understanding adult age differences in decision making. It begins by reviewing research on cognitive aging and decision making. It then reviews research that highlights older adults’ motivation to selectively allocate cognitive resources when making decisions and their motivation to draw upon life experience and emotion regulation strategies. Next, because age-related shifts in motivation accentuate a focus on meaningful social relationships, the chapter considers social aspects of aging and decision making. It discusses age-related differences in selection of social network members and considers factors that may motivate older adults to involve others in the decision-making process. It suggests how research on motivation and aging and decision making could be used to inform decision-making interventions. After suggesting directions for future research, it concludes by highlighting the implications of research on aging and decision making for understanding contextual and motivational influences on cognitive aging.

Download Full-text

What older adults and their caregivers need for making better health-related decisions at home: a participatory mixed methods protocol

BMJ Open ◽

10.1136/bmjopen-2020-039102 ◽

2020 ◽

Vol 10 (11) ◽

pp. e039102

Author(s):

Claudia Lai ◽

Paul Holyoke ◽

Karine V Plourde ◽

Simon Décary ◽

France Légaré

Keyword(s):

Older Adults ◽

Decision Making ◽

Decision Support ◽

Mixed Methods ◽

Home Care ◽

Social Care ◽

Phase 1 ◽

Care Providers ◽

Health And Social Care ◽

Health Related

IntroductionShared decision making is an interpersonal process whereby healthcare providers collaborate with and support patients in decision-making. Older adults receiving home care need support with decision-making. We will explore what older adults receiving home care and their caregivers need for making better health-related decisions.Methods and analysisThis two-phase sequential exploratory mixed methods study will be conducted in a pan-Canadian healthcare organisation, SE Health. First, we will create a participant advisory group to advise us throughout the research process. In phase 1 (qualitative), we will recruit a convenience sample of 15–30 older adults and caregivers receiving home care to participate in open-ended semi-structured interviews. Phase 1 participants will be invited to share what health-related decisions they face at home and what they need for making better decisions. In phase 2 (quantitative), interdisciplinary health and social care providers will be invited to answer a web-based survey to share their views on the decisional needs of older adults and their caregivers. The survey will include questions informed by findings from qualitative interviews in phase 1, and a workbook for assessing decisional needs based on the Ottawa Decision Support Framework. Finally, qualitative and quantitative results will be triangulated (by methods, investigator, theory and source) to develop a comprehensive understanding of decision-making needs from the perspective of older adults, caregivers and health and social care providers. We will use the quality of mixed methods studies in health services research guidelines and the Checklist for Reporting the Results of Internet E-Surveys checklist.Ethics and disseminationEthics approval was obtained from the research ethics boards at Southlake Regional Health Centre and Université Laval. This study will inform the design of decision support interventions. Further dissemination plans include summary briefs for study participants, tailored reports for home care decision makers and policy makers, and peer-reviewed publications.Trial registration numberNCT04327830.

Download Full-text