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      Profile of Caregiving Activities and Association With Physical Health Among Dementia Spousal Caregivers

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          Abstract

          Background and Objectives

          This study aims to identify patterns of caregiving intensity and assess associations between caregiving intensity and multidimensional physical health indicators and health behaviors among spousal caregivers of persons with Alzheimer’s disease and related dementia.

          Research Design and Methods

          Using data from 152 spousal caregivers aged 65 and older, the intensity of their caregiving experience was measured as the number and frequency of health- and medical-related helping activities for their care recipient. Multidimensional health indicators included self-reported fatigue, sleep disturbance, physical functioning, pain interference, general health, and the number of chronic conditions from the electronic health records. Self-reported health promotion behaviors were assessed as health responsibility, physical activity, nutrition, interpersonal relations, and stress management.

          Results

          Two distinct caregiving intensity patterns, high-intensity (37.5%) and low-intensity (62.5%) caregiving, were identified with cluster analysis. Caregivers in the high-intensity caregiving cluster reported feeling more tired ( t = 2.25, p < .05), experiencing more sleep disturbance ( t = 3.06, p < .01), and performing less physical activity ( t = 2.05, p < .05) compared with caregivers in the low-intensity group.

          Discussion and Implications

          Future studies are needed to develop effective interventions to address caregiving intensity and its consequences on the health of spousal caregivers of persons with dementia.

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          Most cited references37

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          The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008.

          Patient-reported outcomes (PROs) are essential when evaluating many new treatments in health care; yet, current measures have been limited by a lack of precision, standardization, and comparability of scores across studies and diseases. The Patient-Reported Outcomes Measurement Information System (PROMIS) provides item banks that offer the potential for efficient (minimizes item number without compromising reliability), flexible (enables optional use of interchangeable items), and precise (has minimal error in estimate) measurement of commonly studied PROs. We report results from the first large-scale testing of PROMIS items. Fourteen item pools were tested in the U.S. general population and clinical groups using an online panel and clinic recruitment. A scale-setting subsample was created reflecting demographics proportional to the 2000 U.S. census. Using item-response theory (graded response model), 11 item banks were calibrated on a sample of 21,133, measuring components of self-reported physical, mental, and social health, along with a 10-item Global Health Scale. Short forms from each bank were developed and compared with the overall bank and with other well-validated and widely accepted ("legacy") measures. All item banks demonstrated good reliability across most of the score distributions. Construct validity was supported by moderate to strong correlations with legacy measures. PROMIS item banks and their short forms provide evidence that they are reliable and precise measures of generic symptoms and functional reports comparable to legacy instruments. Further testing will continue to validate and test PROMIS items and banks in diverse clinical populations. Copyright © 2010 Elsevier Inc. All rights reserved.
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            Caregiving and the Stress Process: An Overview of Concepts and Their Measures

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              The AD8: a brief informant interview to detect dementia.

              Brief measures that accurately discriminate normal cognitive aging from very mild dementia are lacking. Cognitive tests often are insensitive to very mild dementia. Informant-based measures may be more sensitive in detecting early dementia. To identify informant-reported clinical variables that differentiate cognitively normal individuals from those with very mild dementia. A 55-item battery of informant queries regarding an individual's cognitive status was derived from a semistructured interview and a consensus panel of dementia experts. The battery was evaluated with informants for 189 consecutive participants of a longitudinal study of memory and aging and compared with an independently obtained Clinical Dementia Rating (CDR) score for the participant. Multiple regression and receiver operator characteristic curves assessed subsets of the items to discriminate between CDR 0 (no dementia) and CDR 0.5 (very mild dementia). The final version (AD8) querying memory, orientation, judgment, and function was administered to an additional sample of 112 CDR 0 and 68 CDR 0.5 participants. Using a cut-off of two items endorsed, the area under the curve was 0.834, suggesting good to excellent discrimination, sensitivity was 74%, and specificity was 86% (prevalence of 0.38 for very mild dementia). Inclusion of 56 additional individuals with mild to severe dementia (increasing dementia prevalence to 0.53) increased sensitivity to 85%. The AD8 is a brief, sensitive measure that reliably differentiates between nondemented and demented individuals. Use of the AD8 in conjunction with a brief assessment of the participant could improve diagnostic accuracy in general practice.
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                Author and article information

                Contributors
                Role: Decision Editor
                Journal
                Innov Aging
                Innov Aging
                innovateage
                Innovation in Aging
                Oxford University Press (US )
                2399-5300
                2024
                15 February 2024
                15 February 2024
                : 8
                : 3
                : igae017
                Affiliations
                Department of Family and Community Medicine, Saint Louis University School of Medicine , Saint Louis, Missouri, USA
                Center for Applied Health Research, Baylor Scott & White Research Institute , Temple, Texas, USA
                Center for Gerontology, Virginia Tech , Blacksburg, Virginia, USA
                Center for Applied Health Research, Baylor Scott & White Research Institute , Temple, Texas, USA
                Department of Medicine, Baylor College of Medicine , Houston, Texas, USA
                Department of Biostatistics, School of Public Health, Yale University , New Haven, Connecticut, USA
                Section of Geriatrics, Department of Internal Medicine, School of Medicine, Yale University , New Haven, Connecticut, USA
                Department of Medicine, Baylor College of Medicine , Houston, Texas, USA
                Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center , Houston, Texas, USA
                Author notes
                Address correspondence to: Jinmyoung Cho, PhD. E-mail: jinmyoung.cho@ 123456health.slu.edu
                Author information
                https://orcid.org/0000-0002-5836-2368
                https://orcid.org/0000-0001-7685-8175
                Article
                igae017
                10.1093/geroni/igae017
                10960627
                38524243
                a42981af-6bd1-4e29-85db-4136a019c184
                © The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 September 2023
                : 22 January 2024
                : 23 March 2024
                Page count
                Pages: 9
                Funding
                Funded by: National Institute on Aging, DOI 10.13039/100000049;
                Award ID: 1R21AG072395-01
                Funded by: Yale Claude D. Pepper Older Americans Independence Center;
                Award ID: P30AG021342
                Funded by: Yale Alzheimer’s Disease Research Center;
                Award ID: P30AG066508
                Funded by: Office of Research and Development, Houston Health Services Research and Development Center for Innovations in Quality, Effectiveness and Safety;
                Award ID: CIN 13-413
                Funded by: Michael E. DeBakey VA Medical Center;
                Funded by: NIA-VA Mentored Physician-Scientist Award in Alzheimer’s Disease and Related Dementias;
                Award ID: IK2HX003163-01
                Categories
                Original Research Article
                AcademicSubjects/SOC02600

                cluster analysis,intensity of caregiving,multidimensional health indicators

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