Health Signatures During COVID-19: A Precision Fitness Case Study

  1. Erin P. Pollet
  2. Aishwarya Sathish
  3. Zacharie Maloney
  4. Byron L. Long
  5. Jennifer Brethen
  6. Amina Ann Qutub

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Abstract

BACKGROUND

Stay-at-home orders have proven a controversial, while effective, method of SARS-CoV-2 containment. However objective measures of how the pandemic and stay-at-home orders are affecting the daily health of uninfected individuals have been lacking.

METHODS

We investigated the effect of pandemic-related events on 61 individuals in San Antonio, Texas whose daily activity and sleep data were recorded via wearable activity trackers from April 2019 to August 2020. We assessed changes in six fitness metrics (steps walked, resting heart rate, sedentary minutes, wake duration after sleep onset, rapid eye movement (REM) duration, total sleep duration). Cluster analysis and time-course analysis identified trends in activity before, after and during stay-at-home orders. Quantitative measures of activities were compared to survey responses.

RESULTS

Four behavior patterns during stay-at-home orders were identified. Most individuals suffered declines in healthy habits compared to their daily activity in 2019 and early 2020 (e.g., up to −60% steps walked). Inflection points corresponded with key dates relevant to SARS-CoV-2 including the first reported case in the U.S. (Feb 29) and city-wide stay-at-home orders (Mar 23). Pre-existing conditions (diabetes, asthma) were associated with a steeper than average decline in sleep quality during stay-at-home orders. Unexpectedly, we also identified a group of predominately male individuals who improved their daily fitness during stay-at-home orders.

CONCLUSIONS

Objective measures of daily activity indicated most individuals’ fitness suffered at the onset of stay-at-home orders and slowly returned towards baseline. For a subset of individuals, fitness quantitatively improved – better sleep, more exercise, lower resting heart rate – during stay-at-home orders.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.07.20245001: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variable125 adults (79 women, 46 men, age 56 ± 18.1 years) were recruited for a longitudinal study on daily behaviors and brain health between October 2017 and November 2019 (IRB Protocol 19-077R, Appendix C).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis was performed using the Python Stats package and Shrinkage Clustering in R38.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations of extrapolating this study across populations should also be noted. The cohort size was small and of a narrow demographic (61 people, age 56 ± 18.1 years, 54.8% retired, 100% in Texas). Subjects were specifically asked about stay-at-home orders; however, we cannot eliminate the possibility that unaccounted or combinatorial factors contributed to the observed activity changes. In summary, this work has the potential to guide policy decision-making by illuminating how stay-at-home orders during a pandemic quantitatively affect the daily health of a population. An exciting, unexpected conclusion from this work is that there exists a subset of individuals whose health quantitatively improves – better sleep, more exercise, lower resting heart rate – in the midst of a devastating pandemic and the accompanying restrictions imposed by stay-at-home orders. Future work remains to understand in detail what mitigation methods are most effective in combating a pandemic while encouraging optimal health.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.12.07.20245001v1 on medRxiv