The influence maximization problem (IMP) as classically formulated is based on the strong assumption that "chosen" nodes always adopt the new product. In this paper we propose a new influence maximization problem, referred to as the "Link-based Influence Maximization Problem" (LIM), which differs from IMP in that the decision variable of the spreader has changed from choosing an optimal seed to selecting an optimal node to influence in order to maximize the spread. Based on our proof that LIM is NP-hard with a monotonic increasing and submodular target function, we propose a greedy algorithm, GLIM, for optimizing LIM and use numerical simulation to explore the performance in terms of spread and computation time in different network types. The results indicate that the performance of LIM varies across network types. We illustrate LIM by applying it in the context of a Dutch national health promotion program for prevention of youth obesity within a network of Dutch schools. GLIM is seen to outperform the other methods in all network types at the cost of a higher computation time. These results suggests that GLIM may be utilized to increase the effectiveness of health promotion programs.