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Michael D. Garber

Researcher at Emory University

Publications -  17
Citations -  437

Michael D. Garber is an academic researcher from Emory University. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 5, co-authored 13 publications receiving 285 citations.

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Routine Assessment and Promotion of Physical Activity in Healthcare Settings: A Scientific Statement From the American Heart Association

TL;DR: The purpose of this statement is to provide a comprehensive review of the evidence on the feasibility, validity, and effectiveness of assessing and promoting physical activity in healthcare settings for adult patients to contribute to meeting the American Heart Association’s 2020 Impact Goals.
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Geographical Variation in Health-Related Physical Fitness and Body Composition among Chilean 8th Graders: A Nationally Representative Cross-Sectional Study

TL;DR: Prevalence of unhealthy CRF, MSF, and BMI is relatively high among Chilean 8th graders, especially in girls, when compared with global estimates, and Identification of geographical regions and municipalities with high prevalence of unhealthy physical fitness presents opportunity for targeted intervention.
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Comparing bicyclists who use smartphone apps to record rides with those who do not: Implications for representativeness and selection bias

TL;DR: To assess the extent to which app-using cyclists represent the broader cycling population to inform whether use of app-generated data in bike-infrastructure intervention research may bias effect estimates, a sample calculation illustrated how differences may induce selection bias in smartphone-data-based research on infrastructure and motor-vehicle-cyclist crash risk.
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Sampling and Sampling Frames in Big Data Epidemiology.

TL;DR: Three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample are identified: explicitly reconstruct the sampling frame, test the potential impacts of sampling using sensitivity analyses, and limit inference to sample.