Valuing hypothetical health states is a demanding personal process, since it involves the psychological evaluation of hypothetical health states. It seems plausible that elderly individuals will value hypothetical health states differently than the general population. It is, however, important to understand the psychological division that oldest old subgroups construct between acceptable and unacceptable health states. This information can produce important evidence regarding well-being and disability conceptualization. To investigate how Dutch oldest old, conceptualize health-related quality of life health states when compared to well-being health states. In addition, we aim to compare subgroups, based on dependency classification. Ninety-nine elderly living in the Groningen, Hoogeveen and Veendam areas of the Netherlands participated in the study. Respondents were classified into three groups based on dependency levels. The respondents were asked to value hypothetical health states, a generic preference-based HRQoL and a well-being instrument, using a visual analog scale. All three groups ranked the same health states, from both questionnaires, below the average across the health states. The health-related quality of life health states was consistently ranked lower than the current well-being health states. Health state valuations performed by the oldest old indicate that conceptually, respondents view below average health-related and well-being health states as undesirable. The results indicated that the oldest old do view deficits in health-related health states as more important than deficits in well-being health states. Since the oldest old performed the valuations, focused interventions to improve below average health-related outcomes might be the most cost-effective way to increase oldest old well-being outcomes.
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http://dx.doi.org/10.2147/PPA.S193171 | DOI Listing |
Sci Rep
December 2024
School of Mechanical Engineering, Liaoning Engineering Vocational College, Tieling, 112008, Liaoning, People's Republic of China.
The paper proposes a multi-rigid-body system state identification method based on self-healing model in order to improve the accuracy and reliability of CNC machine tools. Firstly, considering the influence of the joint surface, the Lagrange method is used to establish the mechanical model of the multi-rigid-body system. We input acceleration information and use the second-order modulation function to complete the online real-time identification of the joint surface parameters, thereby establishing the self-healing mechanical model of the multi-rigid-body system.
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December 2024
Department of Biology, University of South Dakota, 414 East Clark Street, Vermillion, SD, 57069-2390, USA.
Psychological distress, including anxiety or mood disorders, emanates from the onset of chronic/unpredictable stressful events. Symptoms in the form of maladaptive behaviors are learned and difficult to treat. While the origin of stress-induced disorders seems to be where learning and stress intersect, this relationship and molecular pathways involved remain largely unresolved.
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December 2024
Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, 92697, USA.
Preserving the ability to vividly recall emotionally rich experiences contributes to quality of life in older adulthood. While prior works suggest that moderate-intensity physical activity (MPA) may bolster memory, it is unclear whether this extends to emotionally salient memories consolidated during sleep. In the current study, older adults (mean age = 72.
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December 2024
Division of Pulmonary and Critical Care, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095-1690, USA.
Electronic cigarettes (e-cigs) fundamentally differ from tobacco cigarettes in their generation of liquid-based aerosols. Investigating how e-cig aerosols behave when inhaled into the dynamic environment of the lung is important for understanding vaping-related exposure and toxicity. A ventilated artificial lung model was developed to replicate the ventilatory and environmental features of the human lung and study their impact on the characteristics of inhaled e-cig aerosols from simulated vaping scenarios.
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