In　this　article,　we　focus　on　the　estimation　of　varying-coefficient　mixed　effects　models　for　longitudinal　and　sparse　functional　response　data,　by　using　the　generalized　least　squares　method　coupling　a　modified　local　kernel　smoothing　technique.　This　approach　provides　a　useful　framework　that　simultaneously　takes　into　account　the　within-subject　covariance　and　all　observation　information　in　the　estimation　to　improve　efficiency.　We　establish　both　uniform　consistency　and　pointwise　asymptotic　normality　for　the　proposed　estimators　of　varying-coefficient　functions.　Numerical　studies　are　carried　out　to　illustrate　the　finite　sample　performance　of　the　proposed　procedure.　An　application　to　the　white　matter　tract　dataset　obtained　from　Alzheimer＇s　Disease　Neuroimaging　Initiative　study　is　also　provided.