In recent years, it has been suggested that obesity is a chronic disease58
that could benefit from the types of management strategies inherent in the Chronic Care Model, including support for self-management.59
Use of the Internet for counseling has been included in a list of model components that have shown promising results.13
Griffiths and colleagues have noted that, “Evaluation of the effectiveness of a health care intervention delivered by the Internet needs to include comparison with more traditional modes of delivery to answer the following question: What are the added benefits or disadvantages of Internet use that are particular to this mode of delivery?”60
This systematic review and meta-analysis addresses this question more broadly by considering computer-assisted delivery with regard to the treatment of overweight and obesity. Our meta-analysis indicates that the addition of computer-based interventions as a supplement to standard weight loss interventions produces significantly more weight loss, while the substitution of computer-based technology results in significantly less weight loss. This may not be surprising given that participants received additional tools in the addition studies, but the substitution findings of less weight loss raises questions about the effectiveness of the computer-based tools compared to non-computer-based modalities. It should also be noted that, while the addition studies demonstrated a statistically significant benefit favoring the use of computers, the magnitude of the difference (<1.5 kilograms) is unlikely to be clinically relevant in overweight and obese populations and its sustainability is questionable.
Eleven randomized trials met our inclusion criteria. While some might be concerned that the variations in the components and intensity of interventions across trials might have contributed to the mixed findings, the overall lack of heterogeneity in the results suggests that computer-based interventions will not produce substantially different results compared to non-computer-based interventions regardless of content, intensity, or delivery mechanism. That said, the predominantly white make up of participants in most included studies decreases the generalizability of the findings to minority populations. Similarly, there were relatively fewer men (33%) than women, and some of the studies excluded individuals with chronic conditions (e.g., diabetes).51,52
The requirement that participants in three studies have a computer and Internet connection at home51,53,57
and that participants in three studies make a monetary refundable deposit to participate in the study,44,50,55
further limits applicability to persons of lower socio-economic status.
Turning to the internal validity of the included trials, incomplete reporting greatly limited our ability to formally assess important potential sources of bias. That said, detection bias seems less likely based on the objective nature of the outcomes, and while performance bias is possible due to the challenges of blinding this intervention, it may have been mitigated by the fact that both study arms had to receive some intervention. On the other hand, conflicts of interest were identified and inadequate detail regarding randomization techniques and attrition precluded assessment of selection and attrition biases.
With regard to the review itself, every effort was made in the search, selection, data collection and analysis portions to reduce bias, including independent review of articles and use of a standard data extraction template. Authors were contacted for missing data, but one failed to respond. We imputed standard deviations using a formula that includes a correlation coefficient (R), which we set at 0.5.41
In trying various values of R, from 0.4 to 0.8, we found no substantial change in the summary estimate. We also performed a post hoc analysis based on the two methodological approaches we had identified during the study eligibility process—adding versus substituting a computer-based technology. Lastly, while selective reporting was difficult to assess due to lack of trial registration, the funnel plots did not suggest publication bias.
Studies have found that approximately 20% of overweight or obese adults are able to achieve and maintain long-term weight loss, defined as losing at least 10% of body weight and maintaining that loss for at least 12 months.48,49
Factors found to be associated with success include long-term behavior change (such as eating a diet that is low in calories and fat, and engaging in high levels of physical activity) and self-monitoring of weight (an explicit component of several studies included in this review).43,53,54,56,57
Computers might seem an ideal tool for reinforcing such factors because they incorporate potential advantages such as persistence, anonymity, scalability, ubiquity, and the abilities to manage large amounts of data and to utilize a variety of modalities.23
However, the findings of this meta-analysis suggest that the use of computers themselves adds little to the success of many weight loss interventions. The advent of numerous portable electronic devices (e.g., smart phones) and new applications, such as social networking sites, might add additional capabilities or effects, but we only found two qualifying studies43,45
that included such devices. This is not surprising given the time required to conduct and publish studies that include newer devices. Lag time between the conduct of such studies and their availability for inclusion in meta-analysis is a recognized limitation of the technique.61
As more studies are conducted that include such devices, a review of their effectiveness can be conducted.
In their 2010 article on “Motivational technologies to promote weight loss—From Internet to gadgets,”62
Svensson and Lagerros noted that, “modern technology may not curb the obesity epidemic alone – fighting against obesity requires a long-term and intense effort…. [in which] these tools may not replace, but rather serve as a complement to conventional healthcare.” This is congruent with the notion of utilizing the Chronic Care Model,59
with its consideration of multiple levels of focus and interventions to treat obesity. Indeed, it may be that computer-based interventions add value through other outcomes not assessed by this meta-analysis, such as patient and provider satisfaction, convenience, and cost-effectiveness but these are yet to be determined.