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Uleman JF, Quax R, Melis RJF, Hoekstra AG, Olde Rikkert MGM. The need for systems thinking to advance Alzheimer's disease research. Psychiatry Res 2024; 333:115741. [PMID: 38277813 DOI: 10.1016/j.psychres.2024.115741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/08/2023] [Accepted: 01/12/2024] [Indexed: 01/28/2024]
Abstract
Despite extensive research efforts to mechanistically understand late-onset Alzheimer's disease (LOAD) and other complex mental health disorders, curative treatments remain elusive. We emphasize the multiscale multicausality inherent to LOAD, highlighting the interplay between interconnected pathophysiological processes and risk factors. Systems thinking methods, such as causal loop diagrams and systems dynamic models, offer powerful means to capture and study this complexity. Recent studies developed and validated a causal loop diagram and system dynamics model using multiple longitudinal data sets, enabling the simulation of personalized interventions on various modifiable risk factors in LOAD. The results indicate that targeting factors like sleep disturbance and depressive symptoms could be promising and yield synergistic benefits. Furthermore, personalized interventions showed significant potential, with top-ranked intervention strategies differing significantly across individuals. We argue that systems thinking approaches can open new prospects for multifactorial precision medicine. In future research, systems thinking may also guide structured, model-driven data collection on the multiple interactions in LOAD's complex multicausality, facilitating theory development and possibly resulting in effective prevention and treatment options.
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Affiliation(s)
- Jeroen F Uleman
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - Rick Quax
- Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | - René J F Melis
- Department of Geriatric Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Alfons G Hoekstra
- Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | - Marcel G M Olde Rikkert
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
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Hasselman F. Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development. Dev Psychopathol 2023; 35:2186-2198. [PMID: 37814420 DOI: 10.1017/s0954579423001281] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.
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Affiliation(s)
- Fred Hasselman
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
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Heino MTJ, Proverbio D, Marchand G, Resnicow K, Hankonen N. Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation. Health Psychol Rev 2023; 17:655-672. [PMID: 36420691 PMCID: PMC10261543 DOI: 10.1080/17437199.2022.2146598] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022]
Abstract
Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.
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Affiliation(s)
| | | | | | - Kenneth Resnicow
- School of Public Health, University of Michigan. Rogel Cancer Center University of Michigan
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Hasselman F, den Uil L, Koordeman R, de Looff P, Otten R. The geometry of synchronization: quantifying the coupling direction of physiological signals of stress between individuals using inter-system recurrence networks. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1289983. [PMID: 38020243 PMCID: PMC10646523 DOI: 10.3389/fnetp.2023.1289983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023]
Abstract
In the study of synchronization dynamics between interacting systems, several techniques are available to estimate coupling strength and coupling direction. Currently, there is no general 'best' method that will perform well in most contexts. Inter-system recurrence networks (IRN) combine auto-recurrence and cross-recurrence matrices to create a graph that represents interacting networks. The method is appealing because it is based on cross-recurrence quantification analysis, a well-developed method for studying synchronization between 2 systems, which can be expanded in the IRN framework to include N > 2 interacting networks. In this study we examine whether IRN can be used to analyze coupling dynamics between physiological variables (acceleration, blood volume pressure, electrodermal activity, heart rate and skin temperature) observed in a client in residential care with severe to profound intellectual disabilities (SPID) and their professional caregiver. Based on the cross-clustering coefficients of the IRN conclusions about the coupling direction (client or caregiver drives the interaction) can be drawn, however, deciding between bi-directional coupling or no coupling remains a challenge. Constructing the full IRN, based on the multivariate time series of five coupled processes, reveals the existence of potential feedback loops. Further study is needed to be able to determine dynamics of coupling between the different layers.
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Affiliation(s)
- Fred Hasselman
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
| | - Luciënne den Uil
- Department of Research and Development, Pluryn, Nijmegen, Netherlands
- Fivoor Science and Treatment Innovation, Den Dolder, Netherlands
| | - Renske Koordeman
- Department of Research and Development, Pluryn, Nijmegen, Netherlands
| | - Peter de Looff
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
- Fivoor Science and Treatment Innovation, Den Dolder, Netherlands
- Department of Developmental Psychology, Tilburg University, Tilburg, Netherlands
- Specialized Forensic Care, De Borg National Expert Center, Den Dolder, Netherlands
| | - Roy Otten
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
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Hill Y, Den Hartigh RJR. Resilience in sports through the lens of dynamic network structures. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1190355. [PMID: 37275962 PMCID: PMC10235604 DOI: 10.3389/fnetp.2023.1190355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/12/2023] [Indexed: 06/07/2023]
Affiliation(s)
- Yannick Hill
- Department of Human Movement Sciences, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
- Institute of Brain and Behaviour Amsterdam, Amsterdam, Netherlands
- Lyda Hill Institute for Human Resilience, Colorado Springs, CO, United States
| | - Ruud J. R. Den Hartigh
- Department of Psychology, Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, Netherlands
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Zimatore G, Gallotta MC, Campanella M, Skarzynski PH, Maulucci G, Serantoni C, De Spirito M, Curzi D, Guidetti L, Baldari C, Hatzopoulos S. Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912719. [PMID: 36232025 PMCID: PMC9564658 DOI: 10.3390/ijerph191912719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 05/03/2023]
Abstract
Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.
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Affiliation(s)
- Giovanna Zimatore
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
- IMM-CNR, 40129 Bologna, Italy
- Correspondence: (G.Z.); (G.M.)
| | - Maria Chiara Gallotta
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, 00185 Roma, Italy
| | - Matteo Campanella
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
| | - Piotr H. Skarzynski
- Department of Teleaudiology and Screening, World Hearing Center, Institute of Physiology and Pathology of Hearing, 02-042 Warsaw, Poland
- Heart Failure and Cardiac Rehabilitation Department, Faculty of Medicine, Medical University of Warsaw, 03-042 Warsaw, Poland
- Institute of Sensory Organs, 05-830 Warsaw, Poland
| | - Giuseppe Maulucci
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Correspondence: (G.Z.); (G.M.)
| | - Cassandra Serantoni
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Marco De Spirito
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Davide Curzi
- Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
| | - Laura Guidetti
- Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
| | - Carlo Baldari
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
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