Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA.
Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA.
Soc Sci Med. 2021 Feb;271:112038. doi: 10.1016/j.socscimed.2018.11.018. Epub 2018 Nov 10.
Over the past seventy years, biomedical and epidemiological research has shown that regular physical activity (PA) is critical for physical and mental health. Despite this knowledge, physical inactivity is the fourth leading risk factor for global mortality, accounting for 9% (5.3 million) of premature deaths annually. We suggest this mismatch between knowing about the risks of PA and engaging in regular PA can be reconciled by focusing less on expected health benefits of PA and more on how people feel during PA. Specifically, in this position paper, we argue that affective response (feeling good versus bad) to PA is an intermediate phenotype that can explain significant variance in PA behavior and is, in turn, a function of genetic variability. In making this argument, we first review empirical evidence showing that affective response to PA predicts future physical activity behavior. Second, we systematically review research on single nucleotide morphisms (SNPs) that are associated with affective response to PA. Investigating affective response to PA as an intermediate phenotype will allow future researchers to move beyond asking "What SNPs are associated with PA?", and begin asking "How do these SNPs influence PA?", thus ultimately optimizing the translation of knowledge gained from genomic data to intervention development.
在过去的七十年中，生物医学和流行病学研究表明，有规律的身体活动（PA）对身心健康至关重要。尽管有了这些知识，但身体活动不足仍是全球第四大死亡风险因素，每年导致 9%（530 万人）的过早死亡。我们认为，这种对 PA 风险的了解与定期进行 PA 的行为之间的不匹配，可以通过减少关注 PA 的预期健康益处，更多地关注人们在 PA 期间的感受来解决。具体来说，在这份立场文件中，我们认为对 PA 的情感反应（感觉良好与感觉不佳）是一种中间表型，可以解释 PA 行为中的显著差异，而这反过来又是遗传变异性的函数。在提出这一论点时，我们首先回顾了表明对 PA 的情感反应预测未来身体活动行为的实证证据。其次，我们系统地回顾了与对 PA 的情感反应相关的单核苷酸多态性（SNP）的研究。将对 PA 的情感反应作为中间表型进行研究，将使未来的研究人员能够超越“哪些 SNP 与 PA 相关？”的问题，并开始问“这些 SNP 如何影响 PA？”，从而最终优化从基因组数据到干预措施开发的知识转化。
