JPP Student Journal Club Commentary: Linking Biology to the Environment: Novel Methods for Understanding Pediatric Obesity.
JPP Student Journal Club Commentary: Linking Biology to the Environment: Novel Methods for Understanding Pediatric Obesity.
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JPP 学生期刊俱乐部评论：将生物学与环境联系起来：了解儿童肥胖的新方法。
DOI：
10.1093/jpepsy/jsy001
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发表时间：
2018
影响因子：
3.6
通讯作者：
Black,MaureenM
中科院分区：
文献类型：
--
作者：
Armstrong,Bridget;Black,MaureenM
儿童肥胖是一个复杂的、看似短暂的公共健康问题，它促使人们进行研究，以确定病因和潜在的解决方案。肥胖相关行为和结果都受到社会生态模型多层次多因素动态过程的影响。环境与行为的关系是相互的、动态的，并由暂时依赖的反馈所主导。儿童心理学和公共卫生已经产生了大量关于肥胖预测因素的文献，儿童心理学侧重于个体社会决定因素，公共卫生侧重于环境决定因素。尽管呼吁整合，但只有有限的研究将环境和个体预测因素整合在一起（Black & Hager, 2013; McGuire, 2012）。Gartstein及其同事（Gartstein, Seamon, Thompson, & Lengua, 2018）在本期杂志上发表的这项研究成功地整合了个体生物数据和环境背景。理解和减轻肥胖的复杂性可能需要结合环境和个人数据。技术的进步提高了收集客观个人数据的能力。仅在儿科心理学领域的例子就包括加速度计（Cushing等人，2017）、生态瞬间评估（Heron, Everhart, McHale, & Smyth, 2017）、磁共振成像（Jensen, Duraccio, Carbine, & Kirwan, 2016）、连续血糖监测（Nelson, Aylward, & Rausch, 2011）和皮质醇采样（McCarthy等人，2011）。技术也改善了空间环境数据的收集和共享。空间数据包括政府资助的项目（如人口普查数据）、私人数据（如InfoUSA的商业邮件列表）和公开可用的专有数据（如谷歌街景；Odgers, Caspi, Bates, Sampson, & Moffitt, 2012）。空间数据通常通过数据存储库（例如https://www）公开提供。communitycommons。org/)，使个体健康指标（即个体皮质醇率）与“社区背景”相结合，例如Gartstein等人（2018）使用的皮质醇与犯罪率之间的相互作用。需要注意的是，不同来源的数据质量各不相同。例如，犯罪数据包括漏报（并非所有犯罪都被报道）和错误分类（并非所有犯罪都被准确报道，或者根本没有报道）的错误（Nolan, Haas, & Napier, 2011）。在理想条件下，高质量的空间数据应该是纵向的，包括数据收集方式和时间的描述，并且足够细，可以分析小的地理区域。研究长期的环境变化可以阐明健康的个人和环境决定因素的双向影响。由于Gartstein等人（2018）使用的空间数据仅限于2008年西雅图地区，因此作者无法使用他们收集的纵向皮质醇数据。如果纳入更多的空间和时间信息，使其与纵向个人层面的数据相匹配，这项研究将得到加强。未来可公开获得的特定时间纵向空间数据的数量和质量的增加，将使儿童心理学研究中丰富的个人数据与环境数据相结合。
Pediatric obesity is a complex, seemingly intransient public health concern that has spurred research to identify causes and potential solutions. Both obesityrelated behaviors and outcomes are affected by dynamic processes of many factors across multiple levels of the social ecological model. The environmental–behavioral relationship is reciprocal, dynamic, and dominated by temporally dependent feedback. Pediatric psychology and public health have generated substantial literature on predictors of obesity, with pediatric psychology focusing on individual social determinates and public health focusing on environmental determinants. Only limited research has integrated environment and individual predictors, despite calls for integration (Black & Hager, 2013; McGuire, 2012). The study by Gartstein and colleagues (Gartstein, Seamon, Thompson, & Lengua, 2018), published in this issue, successfully integrated individual biological data and environmental context. Comprehending and mitigating the complexity of obesity will likely require a combination of environmental and individual data. Advances in technology have enhanced the ability to gather objective individual-level data. Examples in pediatric psychology alone include accelerometers (Cushing et al., 2017), ecological momentary assessment (Heron, Everhart, McHale, & Smyth, 2017), magnetic resonance imaging (Jensen, Duraccio, Carbine, & Kirwan, 2016), continuous glucose monitoring (Nelson, Aylward, & Rausch, 2011), and cortisol sampling (McCarthy et al., 2011). Technology has also improved the collection and sharing of spatial environmental data. Spatial data include governmentfunded efforts (ie, census data), private data (ie, business mailing lists from InfoUSA), and publicly available proprietary data (ie, Google street view; Odgers, Caspi, Bates, Sampson, & Moffitt, 2012). Spatial data are often publicly available through data repositories (ie, https://www. communitycommons. org/), enabling the integration of individual indicators of health (ie, individual cortisol rates) with “community context,” such as the interaction between cortisol and crime rates used by Gartstein et al.(2018).A cautionary note is that data quality varies across sources. For example, crime data include errors of underreporting (not all crimes are reported) and misclassification (not all crimes are reported accurately, or at all)(Nolan, Haas, & Napier, 2011). Under ideal conditions, high-quality spatial data would be longitudinal, include descriptions of how and when data were collected, and be fine-grain enough to analyze small geographic areas. Examining environmental changes over time can elucidate the bidirectional effects of individual and environmental determinants of health. Because the spatial data used by Gartstein et al.(2018) were restricted to the Seattle area for 2008, the authors could not use the longitudinal cortisol data that they had collected. The study would have been strengthened by the inclusion of more spatial and temporal information to match their longitudinal individual-level data. Future increases in the amount and quality of publicly available time-specific, longitudinal spatial data will enable the integration of rich individual data from pediatric psychology studies with environmental data.
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DOI：
10.1111/j.1469-7610.2012.02565.x
发表时间：
2012-10
期刊：
Journal of child psychology and psychiatry, and allied disciplines
影响因子：
--
作者：
Odgers CL;Caspi A;Bates CJ;Sampson RJ;Moffitt TE
通讯作者：
Moffitt TE
DOI：
10.1056/nejmsa1603542
发表时间：
2017-05-11
期刊：
The New England journal of medicine
影响因子：
--
作者：
Coady SA;Mensah GA;Wagner EL;Goldfarb ME;Hitchcock DM;Giffen CA
通讯作者：
Giffen CA
儿科心理学培训建议：定义跨培训级别的核心能力。
影响因子：
3.6
作者：
Palermo,TonyaM;Janicke,DavidM;McQuaid,ElizabethL;Mullins,LarryL;Robins,PaulM;Wu,YelenaP
通讯作者：
Wu,YelenaP
影响因子：
3.6
作者：
Cushing, Christopher C.;Mitchell, Tarrah B.;Noser, Amy E.
通讯作者：
Noser, Amy E.
估计分类错误对统一犯罪报告“统计准确性”的影响
影响因子：
3.6
作者：
J. Nolan;S. Haas;Jessica S. Napier
通讯作者：
Jessica S. Napier