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Updated: Sep 30, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
A Methodological Approach to Retaining Accelerometers and Associated Data in Community-Based Studies
Gregory Knell1, Deborah Salvo1, Kerem Shuval1
1Knell, Kohl, and Gabriel are with the Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX. Salvo is with the Prevention Research Center at the Brown School, Washington University, St. Louis, MO. Shuval is with the Department of Epidemiology, School of Public Health, University of Haifa, Haifa, Israel, and the American Cancer Society, Atlanta, GA. Durand is with the Department of Health Promotion and Behavioral Science at the School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX.
Abstract:
Recent technological advances allow for field-based data collection of accelerometers in community-based studies. Mail-based administration can markedly reduce the cost and logistic challenges and burden associated with in-person data collection. It necessitates, however, other resources, such as phone calls and mailed reminder prompts, to increase protocol compliance and data recovery. Additionally, lost accelerometers can impact the study's budget and its internal validity due to missing data. In this article, we present an applied methodological approach used to define thresholds (or cutoff points) at which pursuing unreturned accelerometers is a worthwhile versus futile pursuit. This methodological approach was designed, specifically, to maximize scalability across multiple sectors. We used data from an on-going study that administered accelerometers through the mail to illustrate and encourage investigators to replicate the approach for use in their own studies. In heterogeneous study samples, investigators might consider repeating this approach by study-relevant strata to refine thresholds and improve the return percentages of data collection instruments, minimize the potential missing data, and optimize study staff time and resources.
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