scholarly journals The Effect of Prolonged Inpatient Rehabilitation Therapy in Subacute Stroke Patients

2012 ◽  
Vol 36 (1) ◽  
pp. 16 ◽  
Author(s):  
Jong Hwa Lee ◽  
Sang Beom Kim ◽  
Kyeong Woo Lee ◽  
Ji Yeong Lee
Keyword(s):  
Inpatient Rehabilitation ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
Subacute Stroke

Abstract 2303: Therapeutic Intensity and Functional Gains of Stroke Patients during Inpatient Rehabilitation.

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Hua Wang ◽  
Michelle Camicia ◽  
Joseph Terdiman ◽  
Murali K Mannava ◽  
M E Sandel
Keyword(s):  
Functional Outcomes ◽  
Inpatient Rehabilitation ◽  
Functional Improvement ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
The Mean ◽  
Total Therapy ◽  
Therapeutic Intensity ◽  
Functional Gain ◽  
Functional Gains

Objectives: To study the effects of therapeutic intensity on functional gains of stroke patients in inpatient rehabilitation. Design: A retrospective cohort study. Setting: An inpatient rehabilitation hospital (IRH) in northern California. Participants: Three hundred and sixty stroke patients discharged from the IRH in 2007. Interventions: Average number of minutes of rehabilitation therapy per day, including physical therapy (PT), occupation therapy (OT), speech language therapy (SLT), and total treatment. Main Outcome Measures: Functional gain measured by the Functional Independence Measure (FIM TM ), including activities of daily living (ADL), mobility, cognition, and total FIM TM scores. Results: The study sample had a mean age of 64.8 years (SD=13.8), and was 57.4% male, and 61.4% White. About three quarter of the patients had an ischemic stroke; 61.4% had one or more significant comorbid conditions. Median IRH length-of-stay (LOS) was 20 days. The mean total therapy time was 190.3 minutes per day (PT 114.0, OT 42.8, and SLT 33.8). The mean total functional gain was 26.0 (ADL 9.1, mobility 11.4, and cognition 6.2). A longer therapeutic duration per day was significantly associated with functional improvement (r=0.20, p<.001). However, patients who received total therapy time of less than 3 hours per day showed significantly lower total functional gain than those treated 3 hours or longer. There was no significant difference in total functional gain between patients treated 3-3.5 hours and over 3.5 hours per day. Intensity of PT, OT, and SLT in hours per day of treatment time was also significantly associated with corresponding sub-scale functional improvements. Figure 1 presents age and gender adjusted therapeutic intensity and FIM TM Gain. Multiple linear regression analyses showed that young age, hemorrhagic stroke, earlier admission to IRH, and longer IRH stay were independent predictors of functional improvement. Conclusions: The study demonstrated a significant relationship between therapeutic intensity and functional gain during IRH stay and provides evidence of treatment intensity thresholds for optimal functional outcomes for stroke patients in inpatient rehabilitation. Key Words: Stroke, rehabilitation therapy, intensity, functional outcomes.

scholarly journals Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study

2019 ◽  
Author(s):  
Christian Giang ◽  
Elvira Pirondini ◽  
Nawal Kinany ◽  
Camilla Pierella ◽  
Alessandro Panarese ◽  
...  
Keyword(s):  
Motor Impairment ◽  
Motor Deficits ◽  
Pilot Test ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
Model Based ◽  
Link Type ◽  
Upper Limb Exoskeleton ◽  
Subacute Stroke ◽  
Motor Improvement

AbstractBackgroundIn the past years, robotic systems have become increasingly popular in both upper and lower limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients’ individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches.MethodsHere, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients’ motor improvement for a series of point-to-point reaching movements in different directions and comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper limb exoskeleton and tested it with seventeen healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test.ResultsThe experiments illustrated the model’s capability to differentiate distinct motor improvement progressions among subjects and subtasks. The model suggested personalized training schedules based on motor improvement estimations for each movement in different directions. Patients’ motor performances were retained when training movements were reintroduced at a later stage.ConclusionsOur results demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing auspicious results for the applicability in clinical settings.Trial registrationThis study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).

scholarly journals Blinding in electric current stimulation in subacute neglect patients with current densities of 0.8 A/m²: A cross-over pilot study

2020 ◽  
Author(s):  
Anna Gorsler ◽  
Ulrike Grittner ◽  
Nadine Külzow ◽  
Torsten Rackoll
Keyword(s):  
Standard Care ◽  
Inpatient Rehabilitation ◽  
Stroke Patients ◽  
Sham Stimulation ◽  
Brain Volume Loss ◽  
Stroke Population ◽  
Subacute Stroke ◽  
Age Related ◽  
Current Densities ◽  
Improve Standard

Abstract Objective Transcranial direct current stimulation (tDCS) is a promising adjuvant technique to improve standard care neglect therapy in patients suffering from stroke. Current densities in tDCS are modeled by tissue distribution in the brain. Therefore, we hypothesized that higher current densities are needed in aged stroke population to counteract age related brain volume loss. Here it is still unresolved whether blinding of participants can be achieved. Our aim was to test whether stroke patients with left-sided hemineglect are able to differentiate beyond chance active tDCS from sham stimulation at a current density of 0.8 A/m². Therefore, we investigated 12 early subacute stroke patients with left-sided hemineglect in a cross-over design with two stimulation settings (active/sham stimulation in randomized order). Stimulation was performed simultaneous to standard care neglect therapy with 0.8 A/m² and progress of neglect symptomatology was monitored during inpatient rehabilitation.Results Our sample exhibited higher odds of correct guessing an active tDCS condition compared to wrongly judge an active tDCS condition as sham stimulation (Odds ratio 10.00, 95%CI: 0.65 - 154.40, p = 0.099). Therefore, we must question the feasibility of blinding success in studies with current densities of 0.8 A/m². Assessment in multisession protocols still warrants further investigation.

scholarly journals Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

2021 ◽  
Vol 11 (11) ◽  
pp. 1080
Author(s):  
Jong Taek Lee ◽  
Eunhee Park ◽  
Tae-Du Jung
Keyword(s):  
Inpatient Rehabilitation ◽  
Video Data ◽  
Stroke Patients ◽  
Patient Centered ◽  
Time Asymmetry ◽  
Rehabilitation Therapy ◽  
Deep Model ◽  
Swing Time ◽  
3D Cnn

The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk action cases were collected from 206 stroke patients receiving inpatient rehabilitation therapy (63.24 ± 14.36 years old). As ground truth, the dependence in ambulation was assessed and labeled using the functional ambulatory categories (FACs) and Berg balance scale (BBS). The dependent ambulation was defined as a FAC score less than 4 or a BBS score less than 45. We extracted patient-centered video and patient-centered pose of the target from the tracked target’s posture keypoint location information. Then, the extracted patient-centered video was input in the 3D-CNN, and the extracted patient-centered pose was used to measure swing time asymmetry. Finally, we evaluated the classification of dependence in ambulation using video data via fivefold cross-validation. When training the 3D-CNN based on FACs and BBS, the model performed with 86.3% accuracy, 87.4% precision, 94.0% recall, and 90.5% F1 score. When the 3D-CNN based on FACs and BBS was combined with swing time asymmetry, the model exhibited improved performance (88.7% accuracy, 89.1% precision, 95.7% recall, and 92.2% F1 score). The proposed framework for dependence in ambulation can be useful, as it alerts clinicians or caregivers when stroke patients with dependent ambulatory move alone without assistance. In addition, monitoring dependence in ambulation can facilitate the design of individualized rehabilitation strategies for stroke patients with impaired mobility and balance function.

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