This　article　is　focused　on　the　problem　to　measure　and　test　the　conditional　mean　dependence　of　a　response　variable　on　a　predictor　variable.　A　local　influence　detection　approach　is　developed　combining　with　the　martingale　difference　divergence　(MDD)　metric,　and　an　efficient　wild　bootstrap　implementation　is　given.　The　obtained　new　metric　of　the　conditional　mean　dependence　holds　the　merits　of　MDD,　while　it　is　more　sensitive　than　the　original　one,　and　leads　to　a　powerful　test　to　nonlinear　relationships.　It　is　shown　by　simulations　that　the　proposed　test　can　achieve　higher　power　for　general　conditional　mean　dependence　relationships　even　in　high-dimensional　settings.　Theoretical　asymptotic　properties　of　the　local　influence　test　statistic　are　given,　and　a　real　data　analysis　is　also　presented　for　further　illustration.　The　localization　idea　could　be　combined　with　other　conditional　mean　dependence　metrics.