4.7 Article

Affective response to physical activity as an intermediate phenotype

Journal

SOCIAL SCIENCE & MEDICINE
Volume 271, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.socscimed.2018.11.018

Keywords

Affect; Exercise; Intermediate phenotype; Physical activity; Motivation; Mechanism

Funding

  1. National Institutes of Health, United States
  2. National Heart, Lung, and Blood Institute [1F31HL140817, T32 HL076134]
  3. National Cancer Institute [5F31CA206245, R21CA224609]

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Regular physical activity is crucial for physical and mental health, yet physical inactivity is a major risk factor for global mortality. Focusing on individuals' emotional responses during physical activity can help explain variations in behavior and improve the development of interventions.
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.

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