Abstract

BackgroundRespondent 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, it has been possible to obtain survey data from users of mobile applications (apps) on a question-by-question basis. The author 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.MethodsPrimary 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.ResultsProvider 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.DiscussionAlthough 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.

Highlights

  • IntroductionHow to cite this article O’Reilly-Shah (2017), Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey

  • The explosion of smartphone technology (Rivera & Van der Meulen) that has accompanied the digital revolution brings opportunities for research into human behaviour at anHow to cite this article O’Reilly-Shah (2017), Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey

  • Overall growth in app availability and use has been accompanied by concomitant growth in the mobile health space (Akter & Ray, 2010; Liu et al, 2011; Ozdalga, Ozdalga & Ahuja, 2012). mHealth is an established MeSH entry term that broadly describes efforts in the area of mobile-based health information delivery, a reasonable argument can be made that it includes the collection of analytics and metadata from consumers of this information as well (HIMSS, 2012; National Library of Medicine, 2017)

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Summary

Introduction

How to cite this article O’Reilly-Shah (2017), Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey. Factors that are known to influence respondent fatigue include survey length, survey topic, question complexity, and open-ended question type. With the recent explosion of mobile smartphone technology, it has been possible to obtain survey data from users of mobile applications (apps) on a question-by-question basis. 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. Provider role and World Bank country income level were predictive of the rate of respondent fatigue for this in-app survey. 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

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