Common practices in detecting psychological early warning signals may lead to incorrect results

The past few years have seen a rapid growth in research on early warning signals (EWSs) in psychological systems. Whereas early studies showed that EWSs are associated with sudden changes in clinical change trajectories, later findings showed that EWSs may not be general and have low predictive power. In this study, we demonstrate that two common practices in psychological EWS studies are not warranted by theories and may lead to false-negative or false-positive results, explaining the mixed findings in the literature. These two practices are (1) using loosely-defined time windows for early warning indicators and (2) using different variables for detecting transitions and calculating early warning indicators. We first review the theoretical background of EWSs and current research practices for EWS studies. Two simulation studies with different types of system changes are used to demonstrate the possible consequences of the two practices. In Study 1, we show that when the time window for early warning indicators is not strictly before the transition, the transition process itself and the system dynamics after the transition may confound the result. In Study 2, we show that when the transition and early warning indicators are measured from different variables in the same system, the predictive relationship may not exist. Based on our findings, we provide suggestions for future EWS studies in terms of theory construction, study design, and data analysis

simlandr:Simulation-Based Landscape Construction for Dynamical Systems

We present the simlandr package for R, which provides a set of tools for constructing potential landscapes for dynamic systems using Monte Carlo simulation. Potential landscapes can be used to quantify the stability of system states. While the canonical form of a potential function is defined for gradient systems, generalized potential functions can also be defined for non-gradient dynamical systems. Our method is based on the potential landscape definition by Wang, Xu, and Wang (2008), and can be used for a large variety of models. Using two multistable dynamical systems as examples, we illustrate how simlandr can be used for model simulation, landscape construction, and barrier height calculation

Anticipating Injuries and Health Problems in Elite Soccer Players Using Dynamic Complexity

BACKGROUND/AIM: Injuries and health problems of soccer players may appear abruptly and are often unexpected. However, hypotheses from complex systems theory suggest that these events can be preceded by certain Early Warning Signals (EWSs).1 We tested whether injuries and health problems can be anticipated with a specific type of EWS, that is, an increase in dynamic complexity (DC).2METHODS:Over two competitive seasons, we collected psychological and physiological self-reports (i.e., self-efficacy, motivation, mood, rating of own performance, enjoyment, and recovery) and data from heart rate sensors on every training and match day from 14 youth soccer players. We recorded time-loss injuries daily and players filled in the Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H2) once a week. We then calculated the DC of the self-reports and sensor data in a seven-day window to test for increased variability and complexity over time before injuries and health problems.RESULTS:Players experienced 5.6 injuries and 8.4 health problems on average across two seasons (range=1-18 and range=2-26, respectively). Results showed that increases in DC could often anticipate the occurrence of injuries and health problems. In 55% and 37% of the players DC increased up to five days before injuries and health problems, respectively (SD=39% and SD=25%, Min=0% and Min=0%, Max=100% and Max=83%).CONCLUSIONS:Results of this study suggest that EWSs can be used for real-time anticipation of injuries and health problems in daily soccer practice. Future research should test for the robustness of these results within and between individuals and perform sensitivity and specificity tests. In addition, finding out how warning signals can be communicated to soccer players and staff is an interesting avenue.REFERENCES1. Den Hartigh RJR, Meerhoff LRA, Van Yperen NW, et al. Resilience in Sports: A Multidisciplinary, Dynamic, and Personalized Perspective. Int Rev Sport Exerc Psychol. 2022. doi:https://doi.org/10.1080/1750984X.2022.20397492. Olthof M, Hasselman F, Strunk G, et al. Critical Fluctuations as an Early-Warning Signal for Sudden Gains and Losses in Patients Receiving Psychotherapy for Mood Disorders. Clin Psychol Sci. 2020;8(1):25-35. doi:10.1177/2167702619865969<br/

Anticipating Injuries and Health Problems in Elite Soccer Players Using Dynamic Complexity

BACKGROUND/AIM: Injuries and health problems of soccer players may appear abruptly and are often unexpected. However, hypotheses from complex systems theory suggest that these events can be preceded by certain Early Warning Signals (EWSs).1 We tested whether injuries and health problems can be anticipated with a specific type of EWS, that is, an increase in dynamic complexity (DC).2METHODS:Over two competitive seasons, we collected psychological and physiological self-reports (i.e., self-efficacy, motivation, mood, rating of own performance, enjoyment, and recovery) and data from heart rate sensors on every training and match day from 14 youth soccer players. We recorded time-loss injuries daily and players filled in the Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H2) once a week. We then calculated the DC of the self-reports and sensor data in a seven-day window to test for increased variability and complexity over time before injuries and health problems.RESULTS:Players experienced 5.6 injuries and 8.4 health problems on average across two seasons (range=1-18 and range=2-26, respectively). Results showed that increases in DC could often anticipate the occurrence of injuries and health problems. In 55% and 37% of the players DC increased up to five days before injuries and health problems, respectively (SD=39% and SD=25%, Min=0% and Min=0%, Max=100% and Max=83%).CONCLUSIONS:Results of this study suggest that EWSs can be used for real-time anticipation of injuries and health problems in daily soccer practice. Future research should test for the robustness of these results within and between individuals and perform sensitivity and specificity tests. In addition, finding out how warning signals can be communicated to soccer players and staff is an interesting avenue.REFERENCES1. Den Hartigh RJR, Meerhoff LRA, Van Yperen NW, et al. Resilience in Sports: A Multidisciplinary, Dynamic, and Personalized Perspective. Int Rev Sport Exerc Psychol. 2022. doi:https://doi.org/10.1080/1750984X.2022.20397492. Olthof M, Hasselman F, Strunk G, et al. Critical Fluctuations as an Early-Warning Signal for Sudden Gains and Losses in Patients Receiving Psychotherapy for Mood Disorders. Clin Psychol Sci. 2020;8(1):25-35. doi:10.1177/2167702619865969<br/

From metaphor to computation:Constructing the potential landscape for multivariate psychological formal models

For psychological formal models, the stability of different phases is an important property for understanding individual differences and change processes. Many researchers use landscapes as a metaphor to illustrate the concept of stability, but so far there is no method to quantify the stability of a system’s phases. We here propose a method to construct the potential landscape for multivariate psychological models. This method is based on the generalized potential function defined by Wang et al. (2008) and Monte Carlo simulation. Based on potential landscapes we define three different types of stability for psychological phases: absolute stability, relative stability, and geometric stability. The panic disorder model by Robinaugh et al. (2019) is used as an example, to demonstrate how the method can be used to quantify the stability of states and phases, illustrate the influence of model parameters, and guide model modifications. An R package, simlandr, was developed to provide an implementation of the method

Anticipating Injuries and Health Problems in Elite Soccer Players Using Dynamic Complexity

BACKGROUND/AIM: Injuries and health problems of soccer players may appear abruptly and are often unexpected. However, hypotheses from complex systems theory suggest that these events can be preceded by certain Early Warning Signals (EWSs).1 We tested whether injuries and health problems can be anticipated with a specific type of EWS, that is, an increase in dynamic complexity (DC).2METHODS:Over two competitive seasons, we collected psychological and physiological self-reports (i.e., self-efficacy, motivation, mood, rating of own performance, enjoyment, and recovery) and data from heart rate sensors on every training and match day from 14 youth soccer players. We recorded time-loss injuries daily and players filled in the Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H2) once a week. We then calculated the DC of the self-reports and sensor data in a seven-day window to test for increased variability and complexity over time before injuries and health problems.RESULTS:Players experienced 5.6 injuries and 8.4 health problems on average across two seasons (range=1-18 and range=2-26, respectively). Results showed that increases in DC could often anticipate the occurrence of injuries and health problems. In 55% and 37% of the players DC increased up to five days before injuries and health problems, respectively (SD=39% and SD=25%, Min=0% and Min=0%, Max=100% and Max=83%).CONCLUSIONS:Results of this study suggest that EWSs can be used for real-time anticipation of injuries and health problems in daily soccer practice. Future research should test for the robustness of these results within and between individuals and perform sensitivity and specificity tests. In addition, finding out how warning signals can be communicated to soccer players and staff is an interesting avenue.REFERENCES1. Den Hartigh RJR, Meerhoff LRA, Van Yperen NW, et al. Resilience in Sports: A Multidisciplinary, Dynamic, and Personalized Perspective. Int Rev Sport Exerc Psychol. 2022. doi:https://doi.org/10.1080/1750984X.2022.20397492. Olthof M, Hasselman F, Strunk G, et al. Critical Fluctuations as an Early-Warning Signal for Sudden Gains and Losses in Patients Receiving Psychotherapy for Mood Disorders. Clin Psychol Sci. 2020;8(1):25-35. doi:10.1177/2167702619865969<br/

Studying Behaviour Change Mechanisms under Complexity

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.Peer reviewe