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Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a Bridge in a Group-based Intervention

机译:识别有助于久坐行为改变的社交网络条件:在基于团体的干预中成为桥梁的好处

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摘要

Using data from one of the first trials to try to leverage social networks as a mechanism for obesity intervention, we examined which social network conditions amplified behavior change. Data were collected as part of a community-based healthy lifestyle intervention in Nashville, USA, between June 2014 and July 2017. Adults randomized to the intervention arm were assigned to a small group of 10 participants that met in person for 12 weekly sessions. Intervention small group social networks were measured three times; sedentary behavior was measured by accelerometry at baseline and 12 months. Multivariate hidden Markov models classified people into distinct social network trajectories over time, based on the structure of the emergent network and where the individual was embedded. A multilevel regression analysis assessed the relationship between network trajectory and sedentary behavior (N = 261). Being a person that connected clusters of intervention participants at any point during the intervention predicted an average reduction of 31.3 min/day of sedentary behavior at 12 months, versus being isolated [95% CI: (−61.4, −1.07), = 0.04]. Certain social network conditions may make it easier to reduce adult sedentary behavior in group-based interventions. While further research will be necessary to establish causality, the implications for intervention design are discussed.
机译:使用来自第一个试验之一的数据尝试利用社交网络作为肥胖症干预机制,我们研究了哪些社交网络条件会加剧行为改变。在2014年6月至2017年7月之间,这些数据是在美国纳什维尔进行的一项基于社区的健康生活方式干预措施的一部分。随机分为干预组的成年人被分配给一个由10人组成的小组,每人每周见面12次。干预小组社交网络进行了三次测量。在基线和12个月时通过加速度计测量久坐行为。多元隐马尔可夫模型会根据新兴网络的结构以及个人的嵌入位置,将人们随时间推移分为不同的社交网络轨迹。多级回归分析评估了网络轨迹与久坐行为之间的关系(N = 261)。作为一个在干预过程中的任何时候都连接了干预参与者群体的人,与孤立时相比,预计在12个月时平均久坐行为平均减少31.3分钟/天[95%CI:(-61.4,-1.07),= 0.04] 。在基于小组的干预措施中,某些社交网络条件可能会更容易减少成年人久坐的行为。虽然需要进一步的研究来确定因果关系,但对干预设计的意义进行了讨论。

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