| Literature DB >> 35186312 |
Anthony Fitzdonald Davies1,2, Patrick Hill3, Daniel Fay4, Annily Dee1, Cosima Locher5,2,6.
Abstract
We propose a theory known as the Hyland model to help conceptualise Fibromyalgia within a complex adaptive control system. A fundamental assumption is that symptom generating mechanisms are causally connected, forming a network that has emergent properties. An illness narrative has been developed which has a 'goodness of fit' with the lived experience of those with Fibromyalgia. The theory guides management within the clinical setting and incorporates current evidence-based therapeutic strategies, within a multi-modal intervention described as 'Body Reprogramming'. This intervention focuses on non-pharmacological and lifestyle-based considerations. The theoretical framework also helps explain why modest therapeutic effects are gained from current pharmacological options.Entities:
Keywords: Body Reprogramming; Fibromyalgia; Hyland model; adaptive network system; central sensitivity syndrome; functional disorder; stop signals
Year: 2020 PMID: 35186312 PMCID: PMC8851147 DOI: 10.1177/2055102920971494
Source DB: PubMed Journal: Health Psychol Open ISSN: 2055-1029
Figure 1.Evolution of functional disorder symptom clusters utilising adaptive network theory. (a) Each node represents a symptom cluster relating to an individual control system. (b) Increased level of adaptive activity in a specific node when the control system is challenged. (c) Represents modulation of activation level of other control systems (nodes) via connective pathways. (d) Increased strength in casual connections between nodes with chronic activation. (e) Increased activation of secondary nodes further activate other nodal connectivity. (f) Continued activation results in reduced differentiation between symptom clusters (nodes) resulting in more widespread symptomology.
Figure 2.Reprinted from Melidis et al. (2018), with permission from Elsevier. Creation of an adaptive network algorithm for functional disorders from a machine learning analysis of symptoms. Each node in the graph corresponds to a cluster with the size of the nodes scaling according to the number of symptoms contained in each cluster. The edges connecting the nodes represent the connections between the clusters. Their size varies with respect to the strength of each connection, showing the value difference between in-coming and out-going connection for each edge. (1) Fatigue/cognitive – 9 symptoms. (2) Hypothalamic-pituitary-adrenal – 11 symptoms. (3) Limbic system – 7 symptoms. (4) Atopy (IgE) – 5 symptoms. (5) Central sensitisation – 8 symptoms. (6) Gastric – 6 symptoms. (7) Frequent urination – 1 symptom. (8) Mood – 4 symptoms. (9) Micro-capillary – 2 symptoms. (10) Tinnitus – 1 symptom. (11) Small nerve fibre – 7 symptoms.
Figure 3.Development of biological and psychological stop signals into stop programs.
Figure 4.Pictorial representation of the evolution of stop signals to facilitate an explanation of the Hyland model.
Figure 5.Adaptation of Lippitt model for influencing complex change processes.