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Using multiple imputations to accommodate time-outs in online interventions.

Susan M Shortreed1, Andy Bogart, Jennifer B McClure

  • 1Group Heatlh Research Institute, Biostatistics Unit, Seattle, WA, United States. shortreed.s@ghc.org.

Journal of Medical Internet Research
|November 23, 2013
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Summary
This summary is machine-generated.

Multiple imputations accurately estimate online intervention engagement by accounting for timed-out page views. This method reveals no significant difference in engagement between prescriptive and motivational content tones.

Keywords:
Internetautomatic time-outbehavioral researchengagementmultiple imputationsonline interventionssmoking cessationtime spent onlineutilization

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Area of Science:

  • Digital Health
  • Health Behavior Research
  • Statistical Methodology

Background:

  • Estimating user exposure duration to online interventions is crucial for engagement analysis.
  • Standard time-out features for inactivity can introduce bias in exposure duration calculations.
  • Accurate measurement of online intervention engagement is vital for understanding user interaction and program effectiveness.

Purpose of the Study:

  • To introduce multiple imputation as a method for accurately estimating time spent on webpages with prolonged inactivity or time-outs.
  • To demonstrate how multiple imputation improves the analysis of online intervention engagement.
  • To compare the impact of different time-out handling methods on study conclusions.

Main Methods:

  • Utilized data from the Q(2) randomized smoking cessation trial, focusing on time-out events.
  • Applied multiple imputation techniques to create five complete datasets, estimating time spent on timed-out pages.
  • Calculated standard errors using Rubin's formulas to account for imputation variability and compared results with traditional methods (exclusion or arbitrary assignment).

Main Results:

  • Over 10% of page views (683/6592) resulted in time-outs.
  • Excluding time-outs showed no difference in engagement between content tones (ratio 0.87).
  • Assigning 30 minutes to time-outs suggested less engagement with motivational content (ratio 0.86), while multiple imputation indicated no significant difference (ratio 0.87).

Conclusions:

  • The method used to handle time-outs significantly influences conclusions about online intervention engagement.
  • Multiple imputation offers a standardized approach to treat time spent on timed-out pages as missing data.
  • This methodology enhances the accuracy of engagement metrics in online health interventions.