Factors associated with clinically meaningful recovery in body function, activity, and participation after upper limb task-oriented training in people with stroke

Introduction:This study investigated upper extremity (UE) recovery predictors in post-stroke patients undergoing task-oriented training (TOT) rehabilitation. The retrospective analysis utilized data from a randomized controlled trial (NCT03019744) conducted at the Don Gnocchi Foundation hospitals in Italy between 2011 and 2015. The trial compared TOT alone with TOT combined with functional electrical stimulation (FES). Ethical approval was obtained, and all participants provided informed consent.Methods:Participants: Data were pooled from both arms of the original RCT (TOT + FES and TOT alone) as both groups received TOT, and no significant difference was found between them.Intervention: Participants received 25 sessions of TOT (45 minutes each, five days/week) as an adjunct to standard therapy. TOT focused on functional tasks with real-life objects and was progressively adapted to individual needs.Outcome Measures:Fugl-Meyer Assessment - Upper Extremity (FMA-UE): To assess impairment (ICF body function domain).15-item Action Research Arm Test (ARAT-15): To assess activity/performance (ICF activity domain).Quick version of the Disability of the Arm, Shoulder, and Hand questionnaire - 9 items (Q-DASH-9): To assess participation restrictions (ICF participation domain).Predictor Variables: Age, sex (i.e., male, female), dominance of the affected side (affected bodyside; i.e., dominant; non-dominant), chronicity (i.e., chronic; subacute), injury typology (i.e., ischemic; hemorrhagic), injury localization (i.e., cortical; sub-cortical), and baseline scores on the FMA-UE, ARAT-15, and Q-DASH-9.Data Analysis:Descriptive statistics were used to summarize data.Wilcoxon signed-rank test was used to compare pre- and post-intervention scores.Effect sizes were calculated using matched-pairs rank-biserial correlation.Participants were classified as "Responders" or "Non-responders" based on achieving minimally clinically important differences (MCID) in outcome measures.Stepwise binary logistic regression models were developed to identify predictors of responder status. A bidirectional approach was employed, starting with an empty model. Predictors were added (forward selection) or removed (backward elimination) one at a time based on whether their inclusion improved the Akaike information criterion (AIC). Model accuracy was assessed using McFadden’s pseudo-R2, Scaled Brier Score, Receiver Operating Characteristic curve (AUC), and Hosmer–Lemeshow test.Subgroup sensitivity