Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey
- Subject Areas
- Anesthesiology and Pain Management, Global Health, Human-Computer Interaction, Statistics
- surveys, mobile applications, respondent fatigue, mHealth, smartphones, sugammadex, anesthesiology, LMIC, survey design, in-app surveys
- © 2017 O'Reilly-Shah
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey. PeerJ Preprints 5:e2939v1 https://doi.org/10.7287/peerj.preprints.2939v1
Background: Respondent fatigue, also known as survey fatigue, is a common problem in the collection of survey data. Factors that are known to influence respondent fatigue include survey length, survey topic, question complexity, and open-ended question type. There is a great deal of interest in understanding the drivers of physician survey responsiveness due to the value of information received from these practitioners. With the recent explosion of mobile smartphone technology 7, it has been possible to obtain survey data from users of mobile applications (apps) on a question-by-question basis. We obtained basic demographic survey data as well as survey data related to an anesthesiology-specific drug called sugammadex and leveraged nonresponse rates to examine factors that influenced respondent fatigue.
Methods: Primary data were collected between December 2015 and February 2017. Surveys and in-app analytics were collected from global users of a mobile anesthesia calculator app. Key independent variables were user country, healthcare provider role, rating of importance of the app to personal practice, length of time in practice, and frequency of app use. Key dependent variable was the metric of respondent fatigue.
Results: Provider role and World Bank country income level were predictive of the rate of respondent fatigue for this in-app survey. Importance of the app to the provider and length of time in practice were moderately associated with fatigue. Frequency of app use was not associated. This study focused on a survey with a topic closely related to the subject area of the app. Respondent fatigue rates will likely change dramatically if the topic does not align closely,
Discussion: Although apps may serve as powerful platforms for data collection, responses rates to in-app surveys may differ on the basis of important respondent characteristics. Studies should be carefully designed to mitigate fatigue as well as powered with the understanding of the respondent characteristics that may have higher rates of respondent fatigue.
This is a submission to PeerJ for review.