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	<title>aging populations &#8211; Science</title>
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	<title>aging populations &#8211; Science</title>
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		<title>Objective and Subjective Measures Differ in Rating Older Adults&#8217; Well-Being</title>
		<link>https://scienmag.com/objective-and-subjective-measures-differ-in-rating-older-adults-well-being/</link>
		
		<dc:creator><![CDATA[Beatrice Stafford]]></dc:creator>
		<pubDate>Thu, 03 Sep 2026 23:40:04 +0000</pubDate>
				<category><![CDATA[Social Science]]></category>
		<category><![CDATA[aging population policy challenges]]></category>
		<category><![CDATA[aging populations]]></category>
		<category><![CDATA[aging well-being assessment]]></category>
		<category><![CDATA[cross-method comparison in gerontology]]></category>
		<category><![CDATA[differences in well-being rankings among older adults]]></category>
		<category><![CDATA[differences in well-being ratings]]></category>
		<category><![CDATA[disparities in aging populations]]></category>
		<category><![CDATA[disparities in older adult populations]]></category>
		<category><![CDATA[gerontology research on quality of life]]></category>
		<category><![CDATA[indicators of social support and financial security in elderly]]></category>
		<category><![CDATA[limitations of single-score well-being metrics]]></category>
		<category><![CDATA[multidimensional aging well-being]]></category>
		<category><![CDATA[multidimensional health in older adults]]></category>
		<category><![CDATA[objective versus subjective well-being measures]]></category>
		<category><![CDATA[objective well-being assessment]]></category>
		<category><![CDATA[older adult well-being measurement]]></category>
		<category><![CDATA[policy implications for elderly support]]></category>
		<category><![CDATA[policy implications of well-being measurement]]></category>
		<category><![CDATA[preference-based well-being evaluation]]></category>
		<category><![CDATA[preference-based well-being measures]]></category>
		<category><![CDATA[social support and financial security in older adults]]></category>
		<category><![CDATA[subjective health perception in seniors]]></category>
		<category><![CDATA[subjective well-being evaluation]]></category>
		<category><![CDATA[well-being measurement methods]]></category>
		<guid isPermaLink="false">https://scienmag.com/objective-and-subjective-measures-differ-in-rating-older-adults-well-being/</guid>

					<description><![CDATA[When policymakers try to identify the most disadvantaged older adults, the answer they get depends surprisingly heavily on how they choose to measure disadvantage. That is the central conclusion of a new study published in Social Indicators Research by Veerle Van Loon and Koen Decancq of the University of Antwerp and KU Leuven, who compared [&#8230;]]]></description>
										<content:encoded><![CDATA[<p>When policymakers try to identify the most disadvantaged older adults, the answer they get depends surprisingly heavily on how they choose to measure disadvantage. That is the central conclusion of a new study published in <em>Social Indicators Research</em> by Veerle Van Loon and Koen Decancq of the University of Antwerp and KU Leuven, who compared three fundamentally different methods for assessing well-being in later life—objective, subjective, and preference-based—and found that they frequently point to completely different people as being the worst-off. In a sample of 813 adults aged 50 and older in Flanders, Belgium, fewer than seven percent of respondents landed in the bottom fifth of the well-being distribution under all three approaches at once.</p>
<p>The research tackles a problem that has grown more urgent as populations age. Measuring the well-being of older adults has long outgrown the narrow focus on disease and disability that defined early gerontology, and contemporary researchers now agree that well-being is a multidimensional concept encompassing health, social support, financial security, leisure, meaningful activity, and more. But agreeing that well-being has many dimensions is not the same as agreeing how to collapse those dimensions into a single score that can guide policy. Without such an aggregation, comparing two people—one thriving socially but struggling financially, the other the reverse—becomes impossible. And without comparison, there can be no monitoring of inequality, no evaluation of policies, and no principled allocation of scarce resources across populations.</p>
<p>The three aggregation strategies the researchers examined each rest on a different answer to a deceptively simple question: whose judgment should decide how much each dimension of life matters? The objective approach lets experts decide, often by simply giving every dimension equal weight. Classic examples in gerontology include the Rowe and Kahn model of successful aging and the CASP-19 quality-of-life scale, both of which impose a top-down definition of what a good later life looks like. Critics have long objected that this is paternalistic—the weights may not reflect what older people themselves actually care about. The subjective approach goes to the opposite extreme, simply asking people how satisfied they are with their lives and treating that self-report as the ultimate arbiter. It respects autonomy, but it carries well-known vulnerabilities: people use rating scales differently, some have &#8220;expensive tastes,&#8221; and many adapt their expectations downward in response to prolonged hardship, reporting contentment despite objectively poor circumstances. The preference-based approach, rooted in economic theory, tries to split the difference by empirically estimating how much weight older people themselves place on each life domain, in a way that is insulated from the distortions of adaptation and scale-use differences.</p>
<p>The study&#8217;s methodological innovation was to compare all three approaches on exactly the same dimensions for the same individuals, removing a major weakness of earlier comparative research. The six dimensions were health, social relations, income, leisure, engagement in meaningful activities, and religion, each coded into four levels from worst to best. Combining the levels across six dimensions produced 4,096 possible &#8220;well-being states,&#8221; described by six-letter codes—a structure reminiscent of the EQ-5D health-state classifications used in health economics. Under the objective approach, each level received a score from 1 to 4, summed across dimensions, so a state of all-worst levels scored 6 and all-best levels scored 24. Under the subjective approach, respondents read a personalized vignette describing their own life across the six dimensions, generated from their earlier survey answers, and rated their satisfaction with that life on an 11-point scale. For the preference-based approach, the researchers ran a factorial survey experiment: each respondent evaluated seven randomly composed vignettes depicting different combinations of the six dimensions, and a multilevel regression model converted those evaluations into relative importance weights for every dimension level.</p>
<p>The experiment revealed what older Flemish adults actually prioritize. Health commanded the largest weight, followed by social relations and income; leisure and engagement mattered less, and religion—despite being included because qualitative research flags it as salient for some populations—carried essentially no weight, or even a slightly negative one, for most respondents. The weights were also nonlinear: moving from moderately severe to mild health problems mattered more than moving from mild to no problems, and an income rise from 1,500 to 2,700 euros counted for more than subsequent gains up to 5,000 euros. Because the dimension levels in the vignettes varied randomly, these weights could be interpreted causally, a significant advantage over observational estimates based on regressing life satisfaction on circumstances.</p>
<p>When the researchers ranked the same 813 respondents using each approach, the results diverged dramatically. The Spearman rank correlation between the objective and subjective rankings was a mere 0.27, indicating that knowing where someone stands on the expert-weighted index tells you almost nothing about where they place themselves in life satisfaction. The preference-based approach aligned far more closely with the objective one, with a correlation of 0.85, yet even here 44 respondents crossed the critical 20th-percentile threshold in opposite directions under the two methods. The subjective and preference-based rankings correlated at just 0.34. Defining the worst-off as the bottom 20 percent under each method, fully 35.4 percent of the sample fell into that zone under at least one approach, but only 6.7 percent were identified consistently by all three. The subjective approach was the biggest outlier, sharing less than nine percent of its worst-off group with either other method and singling out a distinct 10 percent of respondents whom neither of the other approaches flagged.</p>
<p>More revealing still were the sociodemographic profiles of the different worst-off groups. On basics like age, gender, and household size, the three methods agreed broadly. But on education, employment, and health they split in a patterned way. The objective and preference-based approaches both identified worst-off groups dominated by people with lower educational attainment and greater physical disadvantage: tertiary education accounted for only about 30 percent of their worst-off groups, versus 52.4 percent in the full sample, and disability prevalence ran at 47 to 57 percent. The subjective approach told the opposite story. Its worst-off group was half tertiary-educated—matching the sample average, with essentially no educational gradient—contained a higher share of people still employed, and showed the highest depression scores of the three groups, averaging 4.2 on the CES-D scale compared with roughly 3.3 for the other two.</p>
<p>The researchers interpret this asymmetry through the lens of well-established theories of aging. Processes of adaptation and selective optimization, described by Paul and Margret Baltes and elaborated in socioemotional selectivity theory, mean that people recalibrate their aspirations and evaluative standards as they age. Older adults with declining health or limited resources may nonetheless report high life satisfaction—the well-documented &#8220;well-being paradox&#8221;—while those still grappling with psychological distress, often in midlife rather than old age, rate their lives poorly. The subjective approach, in other words, drifted away from structural conditions and toward mental health, identifying a group whose disadvantage lies in depression rather than material deprivation. From a social-justice standpoint, this is a serious concern: a policy that allocated resources purely on self-reported satisfaction might systematically bypass people facing the most adverse objective circumstances simply because they had lowered their expectations.</p>
<p>The authors are careful about the limits of their findings. The data were collected online in May 2020, at the tail end of the first COVID-19 wave, a period that reshaped priorities—research elsewhere found that health and social relationships gained salience during the pandemic—and it is unclear whether those shifts are temporary. The sample came from a non-probability online panel, underrepresenting the very old, migrants, and lower-educated adults, so the results are descriptive rather than population-representative. The preference weights were estimated as a single homogeneous set, which cannot capture the fact that religion may matter enormously to some individuals even if the average respondent ignores it. And because the subjective analysis lacked anchoring vignettes to correct for scale-use heterogeneity, some of the divergence attributed to adaptation may partly reflect differences in how people use rating scales.</p>
<p>Even with those caveats, the message for policy is stark. Choosing an aggregation method is not a technical detail to be settled by convenience; it is a normative decision about whose perspective on the good life should prevail—experts&#8217;, the individual&#8217;s own felt experience, or the individual&#8217;s stated priorities. The study shows that these choices are not innocuous: they produce different lists of who needs help most, with different educational, occupational, and health profiles, and therefore different implications for who receives support. The authors argue that no single approach is definitively correct, and that the real demand is transparency—making explicit the assumptions behind whatever measure a government or agency adopts when it claims to identify the most vulnerable older adults.</p>
<div class="scienmag-article-metadata"><strong>Subject of Research:</strong> Comparing objective, subjective, and preference-based methods for measuring well-being and identifying the worst-off among adults aged 50 and older in Flanders, Belgium</p>
<p><strong>Article Title:</strong> Who Are the Worst-Off? Objective, Subjective, and Preference-Based Approaches to Measure Older Adults&#8217; Well-Being</p>
<p><strong>Article References:</strong> Van Loon, V., &amp; Decancq, K. (2026). Who Are the Worst-Off? Objective, Subjective, and Preference-Based Approaches to Measure Older Adults’ Well-Being. <em>Social Indicators Research, 184</em>(2), Article 36. <a href="https://doi.org/10.1007/s11205-026-03926-5" target="_blank" rel="noopener noreferrer">https://doi.org/10.1007/s11205-026-03926-5</a></p>
<p><strong>Image Credits:</strong> AI Generated</p>
<p><strong>DOI:</strong> <a href="https://doi.org/10.1007/s11205-026-03926-5" target="_blank" rel="noopener noreferrer">10.1007/s11205-026-03926-5</a></p>
<p><strong>Keywords:</strong> multidimensional well-being, older adults, life satisfaction, preference elicitation, factorial survey, successful aging, worst-off identification, adaptation, well-being paradox, aggregation methods</p>
</div>
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		<post-id xmlns="com-wordpress:feed-additions:1">186839</post-id>	</item>
		<item>
		<title>Functional Support Base Diminishes with Age</title>
		<link>https://scienmag.com/functional-support-base-diminishes-with-age/</link>
		
		<dc:creator><![CDATA[Denise Maddox]]></dc:creator>
		<pubDate>Mon, 27 Oct 2025 11:48:29 +0000</pubDate>
				<category><![CDATA[Technology and Engineering]]></category>
		<category><![CDATA[aging populations]]></category>
		<category><![CDATA[biomechanical factors in aging]]></category>
		<category><![CDATA[fall prevention in older adults]]></category>
		<category><![CDATA[fall-related injuries statistics]]></category>
		<category><![CDATA[functional base of support]]></category>
		<category><![CDATA[gait analysis technologies]]></category>
		<category><![CDATA[human locomotion research]]></category>
		<category><![CDATA[impact of aging on physical capabilities]]></category>
		<category><![CDATA[independence and health in aging populations]]></category>
		<category><![CDATA[interventions for elderly mobility]]></category>
		<category><![CDATA[mobility enhancement strategies]]></category>
		<category><![CDATA[quality of life and safety in elders]]></category>
		<guid isPermaLink="false">https://scienmag.com/functional-support-base-diminishes-with-age/</guid>

					<description><![CDATA[Slightly shifting the focus of research towards aging populations, a new study uncovers a significant relationship between age and the size of the functional base of support in human locomotion. The functional base of support, a critical biomechanical factor, refers to the area of space beneath a person that is bounded by their points of [&#8230;]]]></description>
										<content:encoded><![CDATA[<p>Slightly shifting the focus of research towards aging populations, a new study uncovers a significant relationship between age and the size of the functional base of support in human locomotion. The functional base of support, a critical biomechanical factor, refers to the area of space beneath a person that is bounded by their points of contact with the ground during activities such as standing, walking, or running. As our population continues to age, understanding how certain physical capabilities change accordingly has become vital for developing strategies to enhance mobility and reduce the risk of falls in older adults.</p>
<p>A central theme of this investigation highlights how diminished physical faculties can adversely impact an individual&#8217;s overall safety and quality of life. Fall-related injuries are a concerning issue within elderly households, with statistics indicating that they pose a significant threat to their independence and health. Therefore, by analyzing the size of the functional base of support among varying age groups, researchers aim not only to inform the scientific community but also to influence practical interventions that might mitigate these risks.</p>
<p>Conducted by a team of experts including Sloot, Gerhardy, and Mombaur, this research employed a combination of advanced gait analysis technologies including motion capture systems and force plates to accurately measure the support area of participants. When juxtaposed with preceding studies, which often relied on subjective assessments or simpler metrics such as overall strength, this work provides a more in-depth exploration of the physiological changes that accompany aging.</p>
<p>Throughout the course of this study, researchers analyzed the functional base of support in distinct cohorts corresponding to different age brackets, ranging from youthful individuals to the elderly. A notable finding underscores the progressive reduction in support size as age increases. This decline could be attributed to a series of factors encompassing musculoskeletal degradation, reduced balance capabilities, and neurological changes that collectively impact physical stability.</p>
<p>In particular, the team observed a marked decrease in older adults, who showed a sharp contrast to their younger counterparts. The implications of such observations extend beyond mere academic interest, offering valuable insights into the age-related biomechanical changes that predispose older adults to falls. By investigating these parameters, the researchers illuminated pathways to potential interventions or rehabilitation methods that could enhance balance and support.</p>
<p>Additionally, the study delves into the relationship between physical activity levels and the size of the functional base of support. Findings suggest that older individuals who engage in regular physical activity exhibit a larger support area compared to their sedentary peers. This relationship highlights the crucial role that sustained physical fitness can play in mitigating age-related declines in functional capabilities.</p>
<p>Moreover, variations in support size among individuals were not solely attributable to age but also influenced by external factors such as environmental conditions. Researchers noted how uneven surfaces or inclement weather distinctly alter an individual’s balance and, consequently, their base of support. These discoveries emphasize the necessity for adaptive strategies that account for both intrinsic and extrinsic factors during the design of fall prevention programs.</p>
<p>As the study suggests, fostering an improved understanding of how the functional base of support interacts with various age-related factors could bear significant implications for public health policies. For instance, community-level programs could be crafted to promote safe physical activities among older adults, ultimately enhancing their functional capabilities and aiding in fall prevention initiatives.</p>
<p>Recognizing that balance and mobility are multifaceted constructs, the researchers advocate for further exploration into this domain. Future studies could benefit from longitudinal approaches, tracing the transitions in the functional base of support over time as individuals age. Such methodologies could facilitate robust data collection and foster a more nuanced understanding of aging.</p>
<p>In conclusion, the findings derived from this investigation not only underscore the complexities associated with aging but also spark a vital dialogue regarding the necessity of innovative health solutions targeting older adults. As societies evolve and populations age, assuring physical wellbeing for all demographics is paramount. The idea that enhancing the functional base of support can serve as a protective factor against falls presents a promising avenue for further research and practical application.</p>
<p>In light of this significant work, it becomes apparent that scientists, healthcare providers, and policymakers must unite in efforts to emphasize the importance of mobility-focused interventions. By doing so, we may pave the way for future generations of older adults to experience greater independence, safety, and enhancements in their quality of life.</p>
<p>The outcome of this research serves as a clarion call for collective action in the realm of elder care, compelling a re-evaluation of how we perceive and address the challenges facing aging individuals. Cross-disciplinary collaboration, encompassing biomechanics, physical therapy, and gerontology, will be indispensable as we confront these pressing issues.</p>
<p>Strengthening the bridge between scientific research and practical health solutions ensures that we are well-equipped to support our aging population. Continued dissemination and interpretation of studies like these will not only enrich academic discourse but also resonate with communities striving to nurture healthful, active, and fulfilling lives as they age.</p>
<p>In summary, the exploration of the functional base of support in relation to aging sheds light on critical considerations necessary for improving the quality of life for older adults. By further dissecting these intricacies, society can better navigate the geriatric landscape, crafting informed, balanced responses that champion the health and safety of older individuals.</p>
<hr />
<p><strong>Subject of Research</strong>: The relationship between age and the size of the functional base of support in human locomotion.</p>
<p><strong>Article Title</strong>: The size of the functional base of support decreases with age.</p>
<p><strong>Article References</strong>:</p>
<p class="c-bibliographic-information__citation">Sloot, L.H., Gerhardy, T., Mombaur, K. <i>et al.</i> The size of the functional base of support decreases with age.<br />
                    <i>Sci Rep</i> <b>15</b>, 37351 (2025). https://doi.org/10.1038/s41598-025-22630-x</p>
<p><strong>Image Credits</strong>: AI Generated</p>
<p><strong>DOI</strong>: 10.1038/s41598-025-22630-x</p>
<p><strong>Keywords</strong>: aging, functional base of support, falls, mobility, physical activity, biomechanics, elder care.</p>
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