NIHR CLAHRC Wessex. School of Health Sciences, University of Southampton, Building 67, University Road, Southampton, SO17 1BJ, UK.
BMC Public Health. 2019 Aug 28;19(1):1178. doi: 10.1186/s12889-019-7467-9.
Obesity is a key risk factor for developing a long-term condition and a leading cause of mortality globally. The limited evidence associated with interventions that currently target obesity-related behaviours demand new approaches to tackle this problem. Given the evidence that social ties are implicated in the gaining and reduction of weight, the use of social networks in interventions is potentially a novel and useful means of tackling this health issue. There is a specific gap in the literature regarding what and how social network properties and processes together with environmental and individual factors influence the adoption of positive and negative obesity-related behaviours in adults.
To address this gap in developing an integrated and holistic conceptual approach, a critical interpretative synthesis was undertaken following a line of argument synthesis as an analytical strategy.
Twenty-four studies were included. The data-driven themes meso-micro network processes, contextual and individual factors, and types of ties and properties were identified individually as components and causes of different health scenarios. Nevertheless, these drivers do not act on their own. As a consequence, developing multi-agent coalitions considering cross-level influences between the data-driven themes are two mechanisms that are created to understand more in-depth how social networks and the environment influence the adoption of obesity-related behaviours. These two new constructs point to a dynamic multilevel set of influences between multiple constructs, developing scenarios where positive and negative health results are identified.
This critical interpretative synthesis offers a new means of exploring the application of social network properties and mechanisms in the 'obesity' field. The synthesizing argument created during the analysis process might be considered by health policy-makers, who might need to contemplate the wider open system of socially connected individuals and harness these forces to design new interventions where social networks and other contextual and individual factors operate together in a complex multilevel environment influencing obesity-related behaviours and practices.
肥胖是导致长期疾病和全球死亡的主要风险因素。目前针对肥胖相关行为的干预措施所依据的证据有限,因此需要新的方法来解决这个问题。鉴于社会关系与体重增加和减轻有关的证据,在干预措施中利用社交网络可能是解决这一健康问题的新的、有用的方法。关于社会网络属性和过程以及环境和个体因素如何共同影响成年人采取积极和消极的肥胖相关行为,文献中存在特定的空白。
为了解决这一空白,开发了一种综合和整体的概念方法,采用论证综合作为分析策略,进行了批判性解释性综合。
纳入了 24 项研究。单独确定了数据驱动的主题中观微观网络过程、背景和个体因素以及不同类型的联系和属性,作为不同健康情景的组成部分和原因。然而,这些驱动因素并不是孤立起作用的。因此,考虑到数据驱动主题之间的跨层次影响,开发多代理联盟是理解社交网络和环境如何影响肥胖相关行为的采用的两种机制。这两个新的构建指向一个多水平的动态影响,多个构建之间形成了积极和消极的健康结果。
这种批判性解释性综合提供了一种新的方法来探索社交网络属性和机制在“肥胖”领域的应用。分析过程中创建的综合论点可以为卫生政策制定者所考虑,他们可能需要考虑到更广泛的社会联系个体的开放系统,并利用这些力量来设计新的干预措施,在这种复杂的多层次环境中,社交网络和其他背景和个体因素共同运作,影响肥胖相关行为和实践。