摘要：Public health research and practice have been guided by a cognitive, rational paradigm where inputs produce linear, predictable changes in outputs. However, the conceptual and statistical assumptions underlying this paradigm may be flawed. In particular, this perspective does not adequately account for nonlinear and quantum influences on human behavior. We propose that health behavior change is better understood through the lens of chaos theory and complex adaptive systems. Key relevant principles include that behavior change (1) is often a quantum event; (2) can resemble a chaotic process that is sensitive to initial conditions, highly variable, and difficult to predict; and (3) occurs within a complex adaptive system with multiple components, where results are often greater than the sum of their parts. The understanding and modification of behavior within public health research and practice generally has been guided by a linear, reductionistic paradigm. That is, we assume small inputs produce proportionally small outputs and that the whole equals the simple sum of its parts. Across the dominant theoretical models used by researchers and practitioners, the key determinants of behavior typically involve some variation of knowledge, attitude, belief, self-efficacy, and intention. 1 , 2 Change is usually conceptualized as rational and as a deterministic process in which individuals obtain information, consider pros and cons, make a behavioral decision, and then plan a course of action. An implicit assumption within this perspective is that the change process is largely under conscious control. Consistent with this perspective, our public health statistical models have almost exclusively assumed a linear relationship between psychosocial predictors and behavior (change); that is, greater increases in knowledge, attitudes, and intentions will lead to greater (and proportional) changes in behavior. 3 In other words, small inputs create small outputs. The conceptual and statistical assumptions underlying this rational, linear paradigm may be seriously flawed and might limit our ability to both explain and modify health behaviors. In particular, such a perspective fails to account for nonlinear, quantum influences on human thought and action. 3 The limitations of a rational-linear conceptualization of behavior change may, in addition to measurement error, explain in part the modest proportion of behavioral variance accounted for in the literature (typically 10%–20% and rarely higher than 50%). 4 – 13 We propose that our conceptual and statistical understanding of the behavior change process can be informed by nonlinear paradigms, most notably chaos theory and complex adaptive systems. Key principles from these perspectives relevant to understanding health behavior change are that it (1) is often a quantum event rather than a linear one; (2) can resemble a chaotic process that is sensitive to initial conditions, highly variable, and difficult to predict; and (3) occurs within a complex adaptive system that involves multiple component parts that interact in a nonlinear fashion, and the results of their interaction are often greater than the sum of their parts. A key statistical implication that flows from this conceptualization is that patterns of change can be mathematically modeled. However, such patterns usually involve nonlinear terms and multiple levels of interaction.
