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Resilience in Clinical Care: Getting a Grip on the Recovery Potential of Older Adults

Gijzel, Sanne M. W., Whitson, Heather E., van de Leemput, Ingrid A., Scheffer, Marten, van Asselt, Dieneke, Rector, Jerrald L., Olde Rikkert, Marcel G. M., Melis, René J. F.·2019·Journal of the American Geriatrics Society
DOI 10.1111/jgs.16149PMC PMC6916426
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Abstract

Background: Geriatricians are often confronted with unexpected health outcomes in older adults with complex multimorbidity. Aging researchers have recently called for a focus on physical resilience as a new approach to explaining such outcomes. Physical resilience, defined as the ability to resist functional decline or recover health following a stressor, is an emerging construct. Methods: Based on an outline of the state‐of‐the‐art in research on the measurement of physical resilience, this article describes what tests to predict resilience can already be used in clinical practice and which innovations are to be expected soon. Results: An older adult's recovery potential is currently predicted by static tests of physiological reserves. Although geriatric medicine typically adopts a multidisciplinary view of the patient and implicitly performs resilience management to a certain extent, clinical management of older adults can benefit from explicitly applying the dynamical concept of resilience. Two crucial leads for advancing our capacity to measure and manage the resilience of individual patients are advocated: first, performing multiple repeated measurements around a stressor can provide insight about the patient's dynamic responses to stressors; and, second, linking psychological and physiological subsystems, as proposed by network studies on resilience, can provide insight into dynamic interactions involved in a resilient response. Conclusion: A big challenge still lies ahead in translating the dynamical concept of resilience into clinical tools and guidelines. As a first step in bridging this gap, this article outlines what opportunities clinicians and researchers can already exploit to improve prediction, understanding, and management of resilience of older adults. J Am Geriatr Soc 67:2650–2657, 2019

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Figure 1 A, The recovery paradigm, as currently applied by most studies on predicting recovery potential. The measurements before the stressor (T0), after the stressor (T1), and in the future (T2) enable us to draw the green dashed line, which is an improvement over two time points (T0 and T2). However, the green line still does not capture the variable “real‐world” physiological responses of individuals, of which one example is represented by the blue solid line. B, The dynamical resilience paradigm allows the construction of more detailed trajectories of recovery from a stressor that provide insight in individual dynamic responses. This trajectory can be drawn if multiple repeated measurements (eg, T0‐T14) are performed. In this case, different characteristics of the response to a stressor can be quantified and used as measures of resilience. Figure 1B adapted from Hadley et al.4
Figure 1 A, The recovery paradigm, as currently applied by most studies on predicting recovery potential. The measurements before the stressor (T0), after the stressor (T1), and in the future (T2) enable us to draw the green dashed line, which is an improvement over two time points (T0 and T2). However, the green line still does not capture the variable “real‐world” physiological responses of individuals, of which one example is represented by the blue solid line. B, The dynamical resilience paradigm allows the construction of more detailed trajectories of recovery from a stressor that provide insight in individual dynamic responses. This trajectory can be drawn if multiple repeated measurements (eg, T0‐T14) are performed. In this case, different characteristics of the response to a stressor can be quantified and used as measures of resilience. Figure 1B adapted from Hadley et al.4CC BY-NC · PubMed Central
Figure 2 A, Each bodily system has its own level of resilience, depicted by the resilience landscape. When the ball (eg, the blood pressure system) lies in a deep well (has a high resilience), even a large perturbation will not push it over the tipping point to a different state (eg, syncope). While for a ball lying in a shallow well (low resilience), a small stressor (eg, orthostasis) is already sufficient to start the ball rolling. B, Each bodily system is, in turn, a network of subsystems with different levels of resilience. Since subsystems with low resilience are theorized to recover more slowly, they will also become more mutually dependent on each other, here illustrated by the on average stronger hypothetical links between blood pressure regulation subsystems. Hence, perturbations will spread more readily throughout the network, reflected by higher cross‐correlations between the dynamic fluctuations of physiological subsystems. This process diminishes the recovery potential of the person as a whole. Figure adapted from Scheffer et al.35
Figure 2 A, Each bodily system has its own level of resilience, depicted by the resilience landscape. When the ball (eg, the blood pressure system) lies in a deep well (has a high resilience), even a large perturbation will not push it over the tipping point to a different state (eg, syncope). While for a ball lying in a shallow well (low resilience), a small stressor (eg, orthostasis) is already sufficient to start the ball rolling. B, Each bodily system is, in turn, a network of subsystems with different levels of resilience. Since subsystems with low resilience are theorized to recover more slowly, they will also become more mutually dependent on each other, here illustrated by the on average stronger hypothetical links between blood pressure regulation subsystems. Hence, perturbations will spread more readily throughout the network, reflected by higher cross‐correlations between the dynamic fluctuations of physiological subsystems. This process diminishes the recovery potential of the person as a whole. Figure adapted from Scheffer et al.35CC BY-NC · PubMed Central

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Metadata

Authors
Gijzel, S. M. W., Whitson, H. E., van de Leemput, I. A., Scheffer, M., van Asselt, D., Rector, J. L., Olde Rikkert, M. G. M., & Melis, R. J. F.
Affiliations
  • Department of Geriatrics, Radboud Institute for Health Sciences Radboud University Medical Center Nijmegen The Netherlands
  • Department of Medicine Duke University School of Medicine Durham North Carolina
  • Department of Environmental Sciences Wageningen University Wageningen The Netherlands
ORCID
Gijzel, Sanne M. W.
Venue
Journal of the American Geriatrics Society
Publisher
Wiley
Published
2019-09-09
Volume
67
Issue
12
Pages
2650-2657
ISSN
  • 0002-8614
  • 1532-5415
DOI
10.1111/jgs.16149
PMC
PMC6916426
Cited by
92
References
7
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)

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