What's the healthiest day?: Circaseptan (weekly) rhythms in healthy considerations

Am J Prev Med. 2014 Jul;47(1):73-6. doi: 10.1016/j.amepre.2014.02.003. Epub 2014 Apr 18.

Abstract

Background: Biological clocks govern numerous aspects of human health, including weekly clocks-called circaseptan rhythms-that typically include early-week spikes for many illnesses.

Purpose: To determine whether contemplations for healthy behaviors also follow circaseptan rhythms.

Methods: We assessed healthy contemplations by monitoring Google search queries (2005-2012) in the U.S. that included the word healthy and were Google classified as health-related (e.g., healthy diet). A wavelet analysis was used in 2013 to isolate the circaseptan rhythm, with the resulting series compared by estimating ratios of relative query volume (healthy versus all queries) each day (e.g., (Monday-Wednesday)/Wednesday).

Results: Healthy searches peaked on Monday and Tuesday, thereafter declining until rebounding modestly on Sunday. Monday and Tuesday were statistically indistinguishable (t=1.22, p=0.22), but their combined mean had 30% (99% CI=29, 32) more healthy queries than the combined mean for Wednesday-Sunday. Monday and Tuesday query volume was 3% (99% CI=2, 5) greater than Wednesday, 15% (99% CI=13, 17) greater than Thursday, 49% (99% CI=46, 52) greater than Friday, 80% (99% CI=76, 84) greater than Saturday, and 29% (99% CI=27, 31) greater than Sunday. We explored media-based (priming) motivations for these patterns and they were consistently rejected.

Conclusions: Just as many illnesses have a weekly clock, so do healthy considerations. Discovery of these rhythms opens the door for a new agenda in preventive medicine, including implications for hypothesis development, research strategies to further explore these rhythms, and interventions to exploit daily cycles in healthy considerations.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biological Clocks / physiology*
  • Circadian Rhythm / physiology*
  • Health Behavior*
  • Humans
  • Internet
  • Search Engine / statistics & numerical data*
  • Time Factors
  • United States
  • Wavelet Analysis