Edgren Robert, Baretta Dario, Inauen Jennifer
Department of Health Psychology and Behavioral Medicine, University of Bern, Bern, Switzerland.
Appl Psychol Health Well Being. 2025 Feb 25;17(1):e12612. doi: 10.1111/aphw.12612. Epub 2024 Oct 25.
Habits are cue-behavior associations learned through repetition that are assumed to be relatively stable. Thereby, unhealthy habits can pose a health risk due to facilitating relapse. In the absence of research on habit decay in daily life, we aimed to investigate how habit decreases over time when trying to degrade a habit and whether this differs by four health-risk behaviors (sedentary behavior, unhealthy snacking, alcohol consumption, and smoking). This 91-day intensive longitudinal study included four parallel non-randomized groups (one per behavior; N = 194). Habit strength was measured daily with the Self-Report Behavioral Automaticity Index (11,805 observations) and modelled over time with constant, linear, quadratic, cubic, asymptotic, and logistic models. Person-specific modelling revealed asymptotic and logistic models as the most common best-fitting models (54% of the sample). The time for habit decay to stabilize ranged from 1 to 65 days. Multilevel modelling indicated substantial between-person heterogeneity and suggested initial habit strength but not the decay process to vary by behavioral group. Findings suggest that habit decay when trying to degrade a habit typically follows a decelerating negative trend but that it is a highly idiosyncratic process. Recommendations include emphasizing the role of person-specific modelling and data visualization in habit research.
习惯是通过重复学习形成的线索 - 行为关联,被认为相对稳定。因此,不健康的习惯由于容易导致复发而会带来健康风险。鉴于缺乏关于日常生活中习惯消退的研究,我们旨在探究在试图戒除一种习惯时,习惯强度如何随时间下降,以及这在四种健康风险行为(久坐行为、不健康零食摄入、饮酒和吸烟)上是否存在差异。这项为期91天的密集纵向研究包括四个平行的非随机组(每种行为一组;N = 194)。每天使用自我报告行为自动化指数测量习惯强度(共11,805次观察),并使用常数、线性、二次、三次、渐近和逻辑模型对随时间变化的情况进行建模。针对个体的建模显示,渐近模型和逻辑模型是最常见的最佳拟合模型(占样本的54%)。习惯消退趋于稳定的时间范围为1至65天。多层次建模表明个体间存在显著差异,并显示初始习惯强度因行为组而异,但习惯消退过程并无差异。研究结果表明,在试图戒除一种习惯时,习惯消退通常遵循负向减速趋势,但这是一个高度因人而异的过程。建议包括强调个体建模和数据可视化在习惯研究中的作用。