Abstract: We consider the framework of non-stationary Online Convex Optimization where a learner seeks to control its \emph{dynamic regret} against an \emph{arbitrary} sequence of comparators. When the loss functions are strongly convex or exp-concave, we demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a principled way of controlling dynamic regret in terms of \emph{path variation} $V_T$ \emph{of the comparator sequence}. Specifically, we show that SA algorithms enjoy $\tilde O(\sqrt{TV_T} \vee \log T)$ and $\tilde O(\sqrt{dTV_T} \vee d\log T)$ dynamic regret for strongly convex and exp-concave losses respectively \emph{without} apriori knowledge of $V_T$, thus answering an open question in \cite{zhang2018dynamic}. The versatility of the principled approach is further demonstrated by the novel results in the setting of learning against bounded linear predictors and online regression with Gaussian kernels.