• S Fabbrini, G Casati, and D Bonaiuti.
• Geriatric Institute "P. Redaelli", Milan, Italy - gloria.casati@gmail.com.
• Eur J Phys Rehabil Med. 2014 Oct 1; 50 (5): 505-14.

BackgroundConstraint Induced Movement Therapy (CIMT) in hemiplegic patient is an efficacious method for upper limb rehabilitation.AimTo verify the applicability of CIMT in post stroke inpatients and to verify the predictive value of some clinical and functional independent variables on the outcomes, in order to identify a population of subjects to which this technique can be more effective.DesignNon-controlled clinical study.SettingSeven Italian, non-experimental, Rehabilitation Departments.Materials And MethodsAll post-stroke inpatients (subacute and chronic), consecutively admitted in 7 Rehabilitation Departments, were screened according to our inclusion criteria. The eligible population was assessed according to selected clinical and functional variables at the baseline, and it was evaluated with Wolf Motor Function Test (WMFT-FAS and WMFT-PTT) and Motor Activity Log (MAL-AOU and MAL-QOM), before treatment (T0), after treatment (T1) and at a 3-month follow up (T2). Patients underwent 2 weeks of CIMT from T0.ResultsOf the 600 inpatients screened, 44 were admitted in the CIMT protocol and were evaluated at T0 and T1; just 24 completed the assessment at T2. At the end of treatment NIHSS and Motricity Index (MI) were predictive factors of MAL scores, while Geriatric Depression Scale (GDS), Modified Barthel Inedx (MBI) and MI were predictive factors of WMFT scores. At the follow up NIHSS and GDS remained predictive factors of MAL scores, and MBI was predictive of WMFT FAS score.ConclusionCIMT efficacy did not result to be related to patient's age, mild cognitive deficit, time since stroke. Depression and pinch ability are the main predictors of motor recovery. Despite the demonstrated efficacy, CIMT feasibility still needs to be demonstrated, considering the low percentage (6.5%) of eligibility among all stroke inpatients.Clinical Rehabilitation ImpactCIMT requires high costs and resources; therefore it is valuable to know the predictive factors which help select the eligible patients. It is then useful to recognize the risk factors of developing Learned Non Use after stroke.

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