Digitally characterizing the dynamics of multiple health behavior change.
Digitally characterizing the dynamics of multiple health behavior change.
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DOI：
10.1037/hea0001057
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Hedeker D
中科院分区：
文献类型：
--
作者：
Spring B;Stump TK;Battalio SL;McFadden HG;Fidler Pfammatter A;Alshurafa N;Hedeker D
我们应用ORBIT模型以数字方式定义动态治疗途径，从而干预改善多种风险行为。我们假设，有效的干预可以提高目标健康行为的频率和一致性，并且两者都与自动性（习惯）和自我效能（自我调节）相关。通过位置尺度混合建模，我们比较了混合移动的干预与不针对更好的选择1（MBC 1）试验（n=204）中的每种行为时的效果。参与者有所有四种风险行为：低中等强度体力活动（MVPA）和水果和蔬菜消费（FV）;高饱和脂肪（FAT）和久坐不动的休闲屏幕时间（SED）。模型估计了平均值（位置）、受试者间方差和受试者内方差（尺度）。治疗与时间的相互作用表明，当治疗针对行为时，MVPA和FV的位置增加（Bs=1.68，0.61 ps<0.001），SED和FAT的位置减少（Bs=-2.01，-0.07，ps<0.05）。受试者内方差建模揭示了群体与时间的规模相互作用（taus=-0.19、-0.75、-0.17、-0.11 ps<.001）：当有目标时，所有行为都变得更加一致。在MBC 2试验（n=212）中，我们研究了三个目标行为的位置，规模，自我效能和自动性之间的相关性。对于SED，较高的规模（较低的一致性），而不是位置与较低的自我效能（r=-0.22，p=.014）和自动性（r=-0.23，p=.013）相关。对于FV和MVPA，较高的位置，而不是规模，与较高的自我效能（rs= 0.38，0.34，ps<0.001）和较高的自动性（rs= 0.46，0.42，ps<0.001）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收购复杂的饮食和活动行为。
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). 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 (n=204). Participants had all of four risk behaviors: low moderate-vigorous physical activity (MVPA) and fruit and vegetable consumption (FV); high saturated fat (FAT) and sedentary leisure screen time (SED). Models estimated the mean (location), between-subject variance, and within-subject variance (scale). Treatment by time interactions showed that location increased for MVPA and FV (Bs=1.68, .61 ps<.001) and decreased for SED and FAT (Bs=−2.01,−.07, ps<.05) more when treatments targeted the behavior. Within-subject variance modeling revealed group by time interactions for scale (taus=−.19, −.75, −.17, −.11 ps<.001): all behaviors grew more consistent when targeted. In the MBC2 trial (n=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 (r=−.22, p=.014) and automaticity (r=−.23, p=.013). For FV and MVPA, higher location, but not scale, correlated with higher self-efficacy (rs=.38, .34, ps<.001) and greater automaticity (rs=.46, .42, ps<.001). Location scale mixed modeling suggests that both habit and self-regulation changes probably accompany acquisition of complex diet and activity behaviors.
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DOI：
10.1111/j.1467-8721.2007.00534.x
发表时间：
2007-12-01
影响因子：
7.2
作者：
Baumeister, Roy F.;Vohs, Kathleen D.;Tice, Dianne M.
通讯作者：
Tice, Dianne M.
影响因子：
4.2
作者：
Abraham, Charles;Michie, Susan
通讯作者：
Michie, Susan
影响因子：
4.2
作者：
Czajkowski, Susan M.;Powell, Lynda H.;Charlson, Mary E.
通讯作者：
Charlson, Mary E.
影响因子：
8.9
作者：
Clark, Bronwyn K.;Sugiyama, Takemi;Owen, Neville
通讯作者：
Owen, Neville
影响因子：
7.6
作者：
Aarts, H;Dijksterhuis, A
通讯作者：
Dijksterhuis, A