When users struggle on the Web they employ extreme adaptations to tackle problematic situations, namely coping strategies. If we are able to automatically detect such situations we can provide the means to bypass or pre-empt them. However, isolating these coping strategies is a challenging task: coping occurs seldom and when it happens, coping is not always overtly manifested. Therefore, in order to identify the coping strategies employed by users in situ longitudinal observations have to be conducted, which are resource intensive. We propose a more economical method that transfers the coping strategies employed by groups of users that cope frequently and overtly, such as people with disabilities, to broader populations. To do so, we first identify the coping strategies employed by people with disabilities; then we code these strategies and convert them into coping detection algorithms that are injected into web pages. Remote longitudinal studies are run with broader populations to measure the detection rate of the algorithms. Based on participants’ feedback we iteratively modify the algorithms to adjust them to the coping strategies users employ. We illustrate this method with a case study that transfers the strategies employed by visually disabled users to able-bodied users. We discover that different populations do not only face the same problems, but also exhibit similar strategies to tackle them.