anatomical injury including depression, resilience, pain catastrophizing, age, education level, race, socioeconomic status, and attitudes toward treatment can negatively affect pain and treatment outcomes. 4 – 13 Although nonmodifiable demographic factors cannot be directly affected with adjunctive
Aaron Sciascia, Jacob Waldecker, and Cale Jacobs
Meaghan Hindle, Katherine Aldinger, and Geoff Dover
sports rehabilitation professional programs and could improve rehabilitation outcomes. 8 Fear avoidance, and specifically pain catastrophizing when addressed, can improve rehabilitation outcomes in patients experiencing chronic pain. 9 The fear-avoidance model has helped explain the disconnect between
Kate N. Jochimsen, Margaret R. Pelton, Carl G. Mattacola, Laura J. Huston, Emily K. Reinke, Kurt P. Spindler, Christian Lattermann, and Cale A. Jacobs
been steadily increasing. In particular, pain catastrophizing is linked with increased disability, more intense pain, and more severe depression and anxiety in patients with chronic pain. 4 – 8 The Pain Catastrophizing Scale (PCS), a valid and reliable patient-reported survey, measures a patient
Sara Birch, Torben Bæk Hansen, Maiken Stilling, and Inger Mechlenburg
this have been investigated, and pain catastrophizing, defined as patients’ tendency to describe a pain experience in exaggerated terms, has been found to be an independent predictor of pain and reduced knee function after TKA ( Bierke & Petersen, 2017 ; Sullivan et al., 2011 ). However, the influence
Charles D.T. Macaulay
By Frederick O. Mueller and Robert C. Cantu. Published in 2019 by Carolina Academic Press , LLC, Durham, NC, USA Recent high-profile deaths in the football community make Frederick O. Mueller and Robert C. Cantu’s, Football Fatalities and Catastrophic Injuries, 1931–2016 a timely read (pp.1
Tara Edwards, Lew Hardy, Kieran Kingston, and Dan Gould
Structured in-depth interviews explored the catastrophic experiences of eight elite performers. Participants responded to questions concerning an event in which they felt they had experienced an uncharacteristic but very noticeable drop in their performance, a “performance catastrophe.” Inductive and deductive analyses were employed to provide a clear representation of the data. This paper reports on how the dimensions emerging from the hierarchical content analysis changed from prior to the catastrophic drop in performance, during the drop, and after the drop (in terms of any recovery). Two emerging higher order dimensions, “sudden, substantial drop in performance” and “performance continued to deteriorate” provide support for one of the fundamental underpinnings of the catastrophe model (Hardy, 1990, 1996a, 1996b); that is, performance decrements do not follow a smooth and continuous path. The paper examines the implications of the findings with respect to applied practice and future research.
Hardy and Fazey’s (1987) cusp catastrophe model of anxiety and performance has been criticized for being overly complex and difficult to test. The present paper attempts to clarify the model for researchers who are less familiar with its more subtle nuances; it then differentiates between the characteristics of cusp catastrophe models in general and the specific predictions of Hardy and Fazey’s cusp catastrophe model of anxiety and performance. For each prediction, methodological and statistical procedures are suggested whereby the prediction can be tested, and the available evidence that has used these procedures is then briefly reviewed. Some of the practical implications of the cusp catastrophe model for best practice are also discussed.
Lew Hardy, Tim Woodman, and Stephen Carrington
This paper examines Hardy’s (1990, 1996a) proposition that self-confidence might act as the bias factor in a butterfly catastrophe model of stress and performance. Male golfers (N = 8) participated in a golf tournament and reported their cognitive anxiety, somatic anxiety, and self-confidence prior to their tee shot on each hole. All anxiety, self-confidence, and performance scores were standardized within participants in order to control for individual differences. The data were then collapsed across participants and categorized into a high self-confidence condition and a low self-confidence condition by means of a median split. A series of two-way (Cognitive Anxiety × Somatic Anxiety) ANOVAs was conducted on each self-confidence condition in order to fag where the maximum Cognitive Anxiety × Somatic Anxiety interaction effect size lay along the somatic anxiety axis. These ANOVAs revealed that the maximum interaction effect size between cognitive and somatic anxiety was at a higher level of somatic anxiety for the high self-confidence condition than for the low self-confidence condition, thus supporting the moderating role of self-confidence in a catastrophe model framework.