Spring Bonnie, Stump Tammy K, Battalio Samuel L, McFadden H Gene, Fidler Pfammatter Angela, Alshurafa Nabil, Hedeker Donald
Department of Preventive Medicine.
Department of Public Health Sciences.
Health Psychol. 2021 Dec;40(12):897-908. doi: 10.1037/hea0001057. Epub 2021 Feb 11.
We applied the ORBIT model to digitally define dynamic treatment pathways whereby intervention improves multiple risk behaviors. We hypothesized that effective intervention improves the frequency and consistency of targeted health behaviors and that both correlate with automaticity (habit) and self-efficacy (self-regulation).
Study 1: Via location scale mixed modeling we compared effects when hybrid mobile intervention did versus did not target each behavior in the Make Better Choices 1 (MBC1) trial ( = 204). Participants had all of four risk behaviors: low moderate-vigorous physical activity (MVPA) and fruit and vegetable consumption (FV), and high saturated fat (FAT) and sedentary leisure screen time (SED). Models estimated the mean (location), between-subjects variance, and within-subject variance (scale).
Treatment by time interactions showed that location increased for MVPA and FV (s = 1.68, .61; s < .001) and decreased for SED and FAT (s = -2.01, -.07; s < .05) more when treatments targeted the behavior. Within-subject variance modeling revealed group by time interactions for scale (taus = -.19, -.75, -.17, -.11; s < .001), indicating that all behaviors grew more consistent when targeted.
Study 2: In the MBC2 trial ( = 212) we examined correlations between location, scale, self-efficacy, and automaticity for the three targeted behaviors.
For SED, higher scale (less consistency) but not location correlated with lower self-efficacy ( = -.22, = .014) and automaticity ( = -.23, = .013). For FV and MVPA, higher location, but not scale, correlated with higher self-efficacy (s = .38, .34, s < .001) and greater automaticity (s = .46, .42, s < .001).
Location scale mixed modeling suggests that both habit and self-regulation changes probably accompany acquisition of complex diet and activity behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
我们应用ORBIT模型以数字方式定义动态治疗路径，通过干预改善多种风险行为。我们假设有效的干预会提高目标健康行为的频率和一致性，且这两者都与自动性（习惯）和自我效能感（自我调节）相关。
研究1：通过位置尺度混合模型，我们在“做出更好选择1（MBC1）”试验（n = 204）中比较了混合移动干预针对与未针对每种行为时的效果。参与者存在所有四种风险行为：低中高强度身体活动（MVPA）和果蔬摄入量低（FV），以及高饱和脂肪摄入量（FAT）和久坐休闲屏幕时间长（SED）。模型估计了均值（位置）、受试者间方差和受试者内方差（尺度）。
治疗与时间的交互作用表明，当治疗针对相关行为时，MVPA和FV的位置增加（s = 1.68，.61；p <.001），而SED和FAT的位置减少（s = -2.01，-.07；p <.05）。受试者内方差建模揭示了组与时间在尺度上的交互作用（taus = -.19，-.75，-.17，-.11；p <.001），表明所有行为在被针对时变得更加一致。
研究2：在“做出更好选择2（MBC2）”试验（n = 212）中，我们研究了三种目标行为的位置、尺度、自我效能感和自动性之间的相关性。
对于SED，更高的尺度（更低的一致性）而非位置与更低的自我效能感（r = -.22，p =.014）和自动性（r = -.23，p =.013）相关。对于FV和MVPA，更高的位置而非尺度与更高的自我效能感（p =.38，.34，p <.001）和更高的自动性（p =.46，.42，p <.001）相关。
位置尺度混合模型表明，习惯和自我调节的变化可能伴随着复杂饮食和活动行为的养成。（PsycInfo数据库记录（c）2022美国心理学会，保留所有权利）
