Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention

Background Visual analogue scales (VASs) have been shown to be valid measurement instruments and a better alternative to Likert-type scales in Internet-based research, both empirically and theoretically [1,2]. Upsides include more differentiated responses, better measurement level, and less error. Their feasibility and properties in the context of eHealth, however, have not been examined so far. Objective The present study examined VASs in the context of a lifestyle study conducted online, measuring the impact of VASs on distributional properties and non-response. Method A sample of 446 participants with a mean age of 52.4 years (standard deviation (SD) = 12.1) took part in the study. The study was carried out as a randomized controlled trial, aimed at supporting participants over 8 weeks with an additional follow-up measurement. In addition to the randomized questionnaire, participants were further randomly assigned to either a Likert-type or VAS response scale version of the measures. Results Results showed that SDs were lower for items answered via VASs, 2P (Y ≥ 47 | n=55, P=.5) < .001. Means did not differ across versions. Participants in the VAS version showed lower dropout rates than participants in the Likert version, odds ratio = 0.75, 90% CI (0.58-0.98), P=.04. Number of missing values did not differ between questionnaire versions. Conclusions The VAS is shown to be a valid instrument in the eHealth context, offering advantages over Likert-type scales. The results of the study provide further support for the use of VASs in Internet-based research, extending the scope to senior samples in the health context. Trial Registration Clinicaltrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/6h88sLw2Y)

[1]  Rik Crutzen,et al.  Generating and predicting high quality action plans to facilitate physical activity and fruit and vegetable consumption: results from an experimental arm of a randomised controlled trial , 2016, BMC Public Health.

[2]  Julian Wienert,et al.  Effectiveness of a Web-Based Computer-Tailored Multiple-Lifestyle Intervention for People Interested in Reducing their Cardiovascular Risk: A Randomized Controlled Trial , 2016, Journal of medical Internet research.

[3]  R. Schwarzer,et al.  A Computerized Lifestyle Application to Promote Multiple Health Behaviors at the Workplace: Testing Its Behavioral and Psychological Effects , 2015, Journal of medical Internet research.

[4]  Julian Wienert,et al.  Designing a theory- and evidence-based tailored eHealth rehabilitation aftercare program in Germany and the Netherlands: study protocol , 2013, BMC Public Health.

[5]  S. Noar,et al.  A Meta-Analysis of Web-Delivered Tailored Health Behavior Change Interventions , 2013, Journal of health communication.

[6]  John E Hayes,et al.  Direct comparison of the generalized Visual Analog Scale (gVAS) and general Labeled Magnitude Scale (gLMS). , 2013, Food quality and preference.

[7]  Ulf-Dietrich Reips,et al.  Why Semantic Differentials in Web-Based Research Should Be Made from Visual Analogue Scales and Not from 5-Point Scales , 2012 .

[8]  P. O’Brien,et al.  Predictors of dropout in weight loss interventions: a systematic review of the literature , 2011, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[9]  Samuel D. Gosling,et al.  Advanced Methods for Conducting Online Behavioral Research , 2010 .

[10]  Ulf-Dietrich Reips,et al.  The methodology of Internet-based experiments , 2009 .

[11]  Ulf-Dietrich Reips,et al.  Interval-level measurement with visual analogue scales in Internet-based research: VAS Generator , 2008, Behavior research methods.

[12]  C. Abraham,et al.  A taxonomy of behavior change techniques used in interventions. , 2008, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[13]  Martin Voracek,et al.  Forced-response in online surveys: Bias from reactance and an increase in sex-specific dropout , 2007, J. Assoc. Inf. Sci. Technol..

[14]  Roger Tourangeau,et al.  Evaluating the Effectiveness of Visual Analog Scales , 2006 .

[15]  E. Diener,et al.  Handbook of Multimethod Measurement in Psychology , 2005 .

[16]  G. Eysenbach The Law of Attrition , 2005, Journal of medical Internet research.

[17]  P. Myles,et al.  The Linearity of the Visual Analogue Scale in Patients with Severe Acute Pain , 2005, Anaesthesia and intensive care.

[18]  R. Cork,et al.  A Comparison Of The Verbal Rating Scale And The Visual Analog Scale For Pain Assessment , 2003 .

[19]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[20]  T. Kuhlmann,et al.  A stitch in time saves nine: Things to consider when tailoring your online intervention , 2015 .

[21]  Ulf-Dietrich Reips,et al.  Design and Formatting in Internet-Based Research , 2010 .

[22]  Kevon R. Tucker-Seeley The Effects of Using Likert vs. Visual Analogue Scale Response Options on the Outcome of a Web-based Survey of 4th Through 12th Grade Students: Data from a Randomized Experiment , 2008 .

[23]  Roger Tourangeau,et al.  Evaluating the Effectiveness of Visual Analog Scales : A Web Experiment , 2006 .

[24]  Ulf-Dietrich Reips,et al.  Web-Based Methods , 2006 .

[25]  Ulf-Dietrich Reips,et al.  Mitarbeiterbefragungen per Internet oder Papier? : Der Einfluss von Anonymität, Freiwilligkeit und Alter auf das Antwortverhalten , 2004 .

[26]  Ulf-Dietrich Reips Standards for Internet-based experimenting. , 2002, Experimental psychology.

[27]  Michael Bosnjak,et al.  Participation in Non-Restricted Web-Surveys: A Typology and Explanatory Model for Item-Nonresponse , 2001 .

[28]  Stefan Schwarz,et al.  CGI versus JavaScript : a Web Experiment on the Reversed Hindsight Bias , 2001 .

[29]  Ulf-Dietrich Reips,et al.  A Brief History of Web Experimenting , 2000 .

[30]  A. C. Primavesi,et al.  ? The Pain Visual Analog Scale: Is It Linear or Nonlinear? , 2000 .