Understanding attrition from international Internet health interventions: a step towards global eHealth
Understanding attrition from international Internet health interventions: a step towards global eHealth
Worldwide automated Internet health interventions have the potential to greatly reduce health disparities. High attrition from automated Internet interventions is ubiquitous, and presents a challenge in the evaluation of their effectiveness. Our objective was to evaluate variables hypothesized to be related to attrition, by modeling predictors of attrition in a secondary data analysis of two cohorts of an international, dual language (English and Spanish) Internet smoking cessation intervention. The two cohorts were identical except for the approach to follow-up (FU): one cohort employed only fully automated FU (n = 16 430), while the other cohort also used 'live' contact conditional upon initial non-response (n = 1000). Attrition rates were 48.1 and 10.8% for the automated FU and live FU cohorts, respectively. Significant attrition predictors in the automated FU cohort included higher levels of nicotine dependency, lower education, lower quitting confidence and receiving more contact emails. Participants' younger age was the sole predictor of attrition in the live FU cohort. While research on large-scale deployment of Internet interventions is at an early stage, this study demonstrates that differences in attrition from trials on this scale are (i) systematic and predictable and (ii) can largely be eliminated by live FU efforts. In fully automated trials, targeting the predictors we identify may reduce attrition, a necessary precursor to effective behavioral Internet interventions that can be accessed globally.
442-452
Geraghty, A.W.
2c6549fe-9868-4806-b65a-21881c1930af
Torres, L.D.
64127d7b-6698-4b93-be19-21dad54d0148
Leykin, Y.
3748bbca-3e30-42a4-a470-01f46c222227
Perez-Stable, E.J.
e27d8136-c8f1-4796-841c-eee395a36671
Munoz, R.F.
110b4a43-c55d-445a-8513-db15a66133fa
September 2013
Geraghty, A.W.
2c6549fe-9868-4806-b65a-21881c1930af
Torres, L.D.
64127d7b-6698-4b93-be19-21dad54d0148
Leykin, Y.
3748bbca-3e30-42a4-a470-01f46c222227
Perez-Stable, E.J.
e27d8136-c8f1-4796-841c-eee395a36671
Munoz, R.F.
110b4a43-c55d-445a-8513-db15a66133fa
Geraghty, A.W., Torres, L.D., Leykin, Y., Perez-Stable, E.J. and Munoz, R.F.
(2013)
Understanding attrition from international Internet health interventions: a step towards global eHealth.
Health Promotion International, 28 (3), .
(doi:10.1093/heapro/das029).
(PMID:22786673)
Abstract
Worldwide automated Internet health interventions have the potential to greatly reduce health disparities. High attrition from automated Internet interventions is ubiquitous, and presents a challenge in the evaluation of their effectiveness. Our objective was to evaluate variables hypothesized to be related to attrition, by modeling predictors of attrition in a secondary data analysis of two cohorts of an international, dual language (English and Spanish) Internet smoking cessation intervention. The two cohorts were identical except for the approach to follow-up (FU): one cohort employed only fully automated FU (n = 16 430), while the other cohort also used 'live' contact conditional upon initial non-response (n = 1000). Attrition rates were 48.1 and 10.8% for the automated FU and live FU cohorts, respectively. Significant attrition predictors in the automated FU cohort included higher levels of nicotine dependency, lower education, lower quitting confidence and receiving more contact emails. Participants' younger age was the sole predictor of attrition in the live FU cohort. While research on large-scale deployment of Internet interventions is at an early stage, this study demonstrates that differences in attrition from trials on this scale are (i) systematic and predictable and (ii) can largely be eliminated by live FU efforts. In fully automated trials, targeting the predictors we identify may reduce attrition, a necessary precursor to effective behavioral Internet interventions that can be accessed globally.
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e-pub ahead of print date: 10 July 2012
Published date: September 2013
Organisations:
Primary Care & Population Sciences
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Local EPrints ID: 363489
URI: http://eprints.soton.ac.uk/id/eprint/363489
ISSN: 0957-4824
PURE UUID: 0e7f476a-0c36-409f-9aa7-f2d32fdbba6a
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Date deposited: 25 Mar 2014 14:26
Last modified: 15 Mar 2024 03:36
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Author:
L.D. Torres
Author:
Y. Leykin
Author:
E.J. Perez-Stable
Author:
R.F. Munoz
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