Impact of educational level on dropout and appreciation of eHealth interventions: example of seven RCTs

Abstract

Objective: High dropout rates are a major issue in computer-tailored (CT) eHealth interventions. The aim of this study is to assess if people with a low educational level dropout more frequently and to what extent this depends on appreciation of the program. Method: Seven longitudinal CT eHealth intervention trials were used to investigate dropout rates among participants with different educational levels and to pool data regarding program appreciation. Regression analysis was used to assess whether program appreciation predicted dropout at follow-up. Results: Among the seven studies, five found a higher dropout rate among participants with a lower educational level. In two studies, there was no significant difference with regard to dropout among different educated participants. Two of the seven studies showed that participants with a lower education appreciated the interventions significantly better than high educated participants. Appreciation of the interventions did not predict dropout at follow-up in any of the seven studies. Conclusion: As appreciation does not seem to be related to high dropout rates, future research must try to identify alternative explanations.