Search

In the article version originally published online, Table 2 was switched in the publication process. The correct version of Table 2 is shown below. (Table presented.) Heat map of average correlations for supine-stand and sit-stand transitions, during initial response (0-30 seconds after standing up) and late response (30-175 seconds after standing up) * Significantly different (p

Objectives: The aim of the current study was to investigate the health-related quality of life (HRQol) of the family caregiver in MCI, explore possible determinants and study possible differences with mild dementia. Methods: This secondary data analysis included 145 persons with MCI and 154 persons with dementia and their family caregivers from two Dutch cohort studies. HRQoL was measured with the VAS of the EuroQol-5D-3L version. Regressions analyses were conducted to examine potential demographic and clinical determinants of the caregiver’s HRQoL. Results: The mean EQ5D-VAS in family caregivers of persons with MCI was 81.1 (SD 15.7), and did not significantly differ from family caregivers in mild dementia (81.9 (SD 13.0)). In MCI, patient measurements were not significantly associated with caregiver mean EQ5D-VAS. Concerning caregiver characteristics, being a spouse and a lower educational level were associated with a lower mean EQ5D-VAS (in a multiple linear regression model: unstandardized B −8.075, p = 0.013 and unstandardized B −6.162, p = 0.037 resp.). In mild dementia, the NPI item irritability showed an association with caregiver EQ5D-VAS in bivariate linear regression analyses. Conclusion: Results indicate that especially family caregiver characteristics seem to influence family caregiver HRQoL in MCI. Future research should include other potential determinants such as burden, coping strategies and relationship quality.

Clinical reasoning and research in modern geriatrics often prioritises the disease concept. This is understandable as it has brought impressive advances in medicine (e.g. antibiotics, vaccines, successful cancer treatment and many effective surgeries). However, so far the disease framework has not succeeded in getting us to root causes of many age-related chronic diseases (e.g. Alzheimer’s disease, diabetes, osteoarthritis). Moreover, in aging and disease constructs alone fail to explain the variability in illness presentations. Therefore, we propose to apply the underused illness concept in a new way by reconsidering the importance of common symptoms in the form of a dynamic network of symptoms as a complementary framework. We show that concepts and methods of complex system thinking now enable to fruitfully monitor and analyse the multiple interactions between symptoms in such in networks, offering new routes for prognosis and treatment. Moreover, close attention to the symptoms that bother older persons may also improve weighing the therapeutic objectives of well-being and survival and aligning treatment targets with the patients’ priorities.

Additional file 1. (1. Search strategy. 2. Flowchart. 3. Clusters and related factors of the model).

Sharing data offers opportunities to make research into older person care more efficient. However, this is not yet common practice in the Netherlands. To optimally utilize the potential of data sharing, insight into factors that promote the implementation of data sharing in older person care research is important. In the TOPICS-MDS project, research data from the National Program for Older Person Care (NPO) was collected, managed and reused on a large scale. The experiences of stakeholders involved in this project can teach us more about the needs researchers have when sharing their data. For this study, we interviewed 23 people involved in different ways in data sharing in TOPICS-MDS about their experiences in the data sharing process. Thematic analysis yielded four overarching themes, which we converted into the following lessons: those who want to promote data sharing must ensure 1) visibility of the added value of data sharing, 2) trust in the database, 3) transparency of the process and 4) communication. These lessons complement the results of previous research with concrete advice. Optimizing data sharing in older person research is both promising and challenging. It requires dedication and involvement from both the researcher and the research participant, and appreciation for both.

In this chapter, we review six quantitative approaches to examining resilience in the context of aging. We categorize these approaches based on the distinct statistical methods that are used to operationally define resilience: estimating “buffering” effects of hypothesized protective factors in the effect modification approach, scale construction in the psychometric approach, comparison of a profile of characteristics between groups based on predefined resilience responses in the a priori approach, data-driven subgroup identification based on resilience responses in the clustering approach, analyzing predictors of adversity-outcome residual values in the residual approach, and analyzing stressor-response patterns in high-density time-series in the complex system approach. We illustrate each of the methods with multiple examples from the literature and pay special attention to the theoretical and conceptual assumptions inherent to each approach about what resilience is and how its correlates can be identified. The approaches are not mutually exclusive. Researchers may choose to combine multiple approaches and may analyze the same data using multiple approaches to compare the findings between them. After reading this chapter, the reader will be familiar with commonly used quantitative approaches to resilience, their implicit and explicit theoretical assumptions, and their strengths and limitations.

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X211009382 for Hemodynamic and structural brain measures in high and low sedentary older adults by Carlijn M Maasakkers, Dick HJ Thijssen, Silvin P Knight, Louise Newman, John D O'Connor, Siobhan Scarlett, Daniel Carey, Anne Buckley, Jason P McMorrow, Caoilfhionn Ní Leidhin, Joanne Feeney, René JF Melis, Rose Anne Kenny, Jurgen AHR Claassen and Céline De Looze in Journal of Cerebral Blood Flow & Metabolism