Internet-Based Behavioral Interventions for Obesity: An Updated Systematic Review
Gian Mauro Manzoni 1, 2, *, Francesco Pagnini 2, Stefania Corti 1, Enrico Molinari 1, 3, Gianluca Castelnuovo 1, 3
1 Istituto Auxologico Italiano IRCCS, Psychology Research Laboratory, Ospedale San Giuseppe, Verbania, Italy
2 Department of Psychology, University of Bergamo, Italy
3 Department of Psychology, Catholic University of Milan, Italy
The objective of this systematic review is to update a previous systematic review on the effectiveness of internet-based interventions for weight loss and weight loss maintenance in overweight and obese people with new or additional studies. A literature search from 2008 to March 2010 was conducted. Studies were eligible for inclusion if: participants were adults with a body mass index ≤ 25, at least one study arm involved an internet-based intervention and the primary aims were weight loss or maintenance. Eight additional studies over the eighteen included in the previous review met the inclusion criteria. Data were extracted on sample characteristics, attrition, weight loss, duration of treatment and maintenance of weight loss. Effect sizes (Hedges g) and relative 95% confidence intervals were calculated for all two-way comparisons within each study. No attempt was made to pool the data in a meta-analysis because of the great heterogeneity of designs among studies. An examination of effect sizes show that the higher significant effects pertain studies that found a superiority of behavioral internet-based programs enhanced by features such as tailored feedback on self-monitoring of weight, eating and activity over education only internet-based interventions. However, control groups are very different among studies and this heterogeneity probably accounts for much of the variance in effect sizes. Hence, questions still remain as to the effectiveness of web-based interventions in achieving weight loss or maintenance. Implications for further research include using a “real” control group in order to make meta-analysis possible and developing multi-factorial design in order to separate components of interventions and identify which of them or patterns of them are keys to success.
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