title stringlengths 1 1.19k | keywords stringlengths 0 668 | concept stringlengths 0 909 | paragraph stringlengths 0 61.8k | PMID stringlengths 10 11 |
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2.4. Outcome Measurement | PMC9964691 | |||
2.4.1. Discomfort Scores | NRS | For the assessment of discomfort scores, an 11-point numerical rating scale (NRS) was used. This scale was scored from 0 (without any discomfort) to 10 (extreme discomfort). The discomfort was scored three times, at T1, T4, and T7. Ferreira-Valente et al. (2011) claimed that this NRS can be used to determine specific i... | PMC9964691 | |
2.4.2. Patients’ Satisfaction | The global perceived effect (GPE) scale was selected to assess the participants’ satisfaction. The GPE is a 10-point scale ranging from −5 (worst symptom) to 0 (no change) to +5 (close to normal improvement). The intraclass correlation coefficient (ICC) of the GPE was between 0.90 and 0.99, which was assessed as excell... | PMC9964691 | ||
2.4.3. Trunk Muscle Fatigue | muscle fatigue | PAD, CONTRACTION | The skin of each participant was cleaned with an alcohol pad before the researcher attached one pair of surface electromyography (sEMG) electrodes (EL 503), with an electrical contact surface area of 1 cmThe electromyography data were recorded at 2000 samples per second using the Wireless Bipolar Cometa Mini Wave Plus ... | PMC9964691 |
2.5. Statistical Analysis | All analyses were performed using SPSS version 19.0 software (SPSS Inc., Chicago, IL, USA). Mean and standard deviation (SD) were used to assess the participants’ demographics. A Shapiro–Wilk test was performed to check the data distribution. A paired | PMC9964691 | ||
3. Results | The participants’ demographic characteristics are shown in | PMC9964691 | ||
3.1. Discomfort Score | Three groups of participants who sat for 60 min were evaluated for their back comfort using a discomfort score. As shown in As shown in Participants experienced the most back discomfort while sitting without a back pillow at all time points. Furthermore, when participants used the rubber back pillow, they experienced t... | PMC9964691 | ||
3.2. Participants’ Satisfaction | The participants’ satisfaction was measured twice while they sat for 60 min (as shown in When comparing the patients’ satisfaction within groups, the satisfaction of the control group decreased significantly after 60 min of sitting. However, within the rubber group, there was a statistically significant increase in sat... | PMC9964691 | ||
4. Discussion | muscle fatigue, fatigue, pain | CONTRACTION | The current study investigated the effect of using rubber pillows compared to foam pillows on TrA and IO muscle fatigue, discomfort scores, and participant satisfaction. Thirty healthy participants were asked to sit for 60 min on three consecutive days under three sitting conditions. The three conditions were (i) a con... | PMC9964691 |
5. Conclusions | trunk muscle fatigue, muscle fatigue | The current study was the first to compare the effects of foam and rubber pillows on TrA and IO muscle fatigue, discomfort scores, and patient satisfaction. The authors recommend that individuals who sit for prolonged periods during the day use back support to delay deep trunk muscle fatigue and reduce discomfort score... | PMC9964691 | |
Author Contributions | Conceptualization, R.P.; Methodology, T.C. and P.S.; Formal analysis, T.C. and P.S.; Investigation, P.S. and A.L.; Data curation, A.L.; Writing—original draft, T.C. and A.L.; Writing—review & editing, R.P. and T.C.; Project administration, R.P. All authors have read and agreed to the published version of the manuscript... | PMC9964691 | ||
Institutional Review Board Statement | Pain | This randomized crossover study was conducted at the Research Center in the Back, Neck, Other Joint Pain, and Human Performance (BNOJPH) laboratory at Khon Kaen University. The Khon Kaen University Ethics Committee (HE 632261, Khon Kaen, Thailand, 17 December 2020) approved the current study. | PMC9964691 | |
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC9964691 | ||
Data Availability Statement | The data will be available for anyone who wishes to access them for any purpose and contract should be made via the corresponding author (thiwch@kku.ac.th). | PMC9964691 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC9964691 | ||
References | Trunk muscle, Muscle fatigue, fatigue | Overview of the study.The assessment times for each outcome.General characteristics of 30 healthy participants.MaleFemaleHigh schoolBachelor’s degreeMaster’s degreeYesNoYesNoYesNoAbbreviations: SD, standard deviation; BMI, Body Mass Index.Comparison of discomfort scores in the back region within the group and between t... | PMC9964691 | |
Purpose | myocardial and cerebrovascular infarction, hypotension, hypotensive, Hypotension, acute kidney injury | ADVERSE EVENTS, EVENTS, HYPOTENSIVE | Intraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of puls... | PMC10061960 |
Methods | esophageal or lung resection, hypotensive | ADVERSE EVENTS, EVENTS, HYPOTENSIVE | Thirty four patients undergoing esophageal or lung resection were randomized into 2 groups -“machine learning algorithm” (AcumenIQ) and “conventional pulse contour analysis” (Flotrac). Analyzed variables were occurrence, severity and duration of hypotensive events (defined as a period of at least one minute of MAP belo... | PMC10061960 |
Results | hypotensive, hypotension, AUT | EVENTS, HYPOTENSIVE | Patients in the AcumenIQ group had significantly lower area below the hypotensive threshold (AUT, 2 vs 16.7 mmHg x minutes) and time-weighted AUT (TWA, 0.01 vs 0.08 mmHg). Also, there were less patients with hypotensive events and cumulative duration of hypotension in the AcumenIQ group. No significant difference betwe... | PMC10061960 |
Conclusions | hypotensive | EVENTS, HYPOTENSIVE | Hemodynamic optimization guided by machine learning algorithm leads to a significant decrease in number and duration of hypotensive events compared to traditional goal directed therapy using pulse-contour analysis hemodynamic monitoring in patients undergoing major thoracic procedures. Further, larger studies are neede... | PMC10061960 |
Trial registration | Date of first registration: 14/11/2022Registration number: 04729481-3a96-4763-a9d5-23fc45fb722d | PMC10061960 | ||
Keywords | PMC10061960 | |||
Introduction | cerebral and myocardial infarction, myocardial infarction, IOH includes systolic blood pressure, hypovolemia, cerebrovascular insult, IOH, hypotension, postoperative organ dysfunction, Hypotension, acute kidney injury | MYOCARDIAL INFARCTION, POSTOPERATIVE COMPLICATIONS, ADVERSE EVENTS, HEMODYNAMIC INSTABILITY, COMPLICATIONS, EDWARDS | In Europe, approximately 20 million major surgical procedures are performed annually. Up to 5% of these patients will die and 10–15% will develop adverse events that could have been prevented in more than 33% of cases [Intraoperative hypotension (IOH) may increase mortality and morbidity rates in the postoperative peri... | PMC10061960 |
Patients, materials and methods | PMC10061960 | |||
Participants and group allocation | heart failure, heart defects, coin toss, valvular anomalies | HEART FAILURE, PERSISTENT ATRIAL FIBRILLATION, HEART | By design this study is a prospective, randomized, single blinded study.Participants are patients over 18 years of age which were scheduled for elective major thoracic procedure (lung resection, pleurectomy or resection of the esophagus) with planned thoracotomy and intraoperative period of one lung ventilation with pl... | PMC10061960 |
Study protocol | PMC10061960 | |||
Hemodynamic measurements | tumor | STERILE, TUMOR, INFILTRATION, DECUBITUS | In the operating room and before induction of anesthesia, arterial and central venous lines were placed after sterile skin preparation and local anesthetic infiltration, zeroed to account for hydrostatic pressure differences between the pressure transducer and the heart, and monitoring was initiated.Following hemodynam... | PMC10061960 |
Anesthesia protocol and hemodynamics related interventions | Induction of anesthesia was performed using propofol (1–1.5 mg/kg), sufentanil (0.1–0.5 mcg/kg) and rocuronium bromide (0.6 mg/kg). After intubation, placement of Robert Shaw tube was verified capnographically and the correct position of endobronchial lumen was verified using a flexible fiber bronchoscope. Anesthesia w... | PMC10061960 | ||
Endpoints | hypotension, AUT | HYPOTENSIVE EPISODE | Primary endpoint is time weighted average of area spent under 65 mmHg for MAP per patient (AUT - depth of hypotension below a MAP of 65 mmHg × time spent below a MAP of 65 mm Hg divided by procedure duration in minutes) of AUT (TWA-AUT).Secondary endpoints are AUT, number of hypotensive episodes (defined as MAP < 65 mm... | PMC10061960 |
Discussion | blood loss, hypotension, AUT, hypotensive, Hypotension | BLOOD LOSS, EVENTS, HYPOTENSIVE EPISODE, HYPOTENSIVE | In the studied cohort, use of machine learning algorithm guided hemodynamic optimization (HPI) significantly reduced the number and duration of intraoperative hypotensive episodes, as well as AUT and the primary outcome—TWA of AUT.These results are in agreement with results obtained by Wijnberge et al. [A recent study ... | PMC10061960 |
Study limitations | hypotension | SECONDARY, MYOCARDIAL INFARCTION | Since surgical manipulation (such as heart, lung or major vascular structure compression leading to preload drop) in thoracic procedures will result in rapid hemodynamic changes that cannot be predicted by an algorithm, there were concerns that use of HPI to guide hemodynamic optimization in such procedures is futile. ... | PMC10061960 |
Acknowledgements | N/A | PMC10061960 | ||
Authors’ contributions | JoP - data acquisition | AŠ - study concept and design, data acquisition, statistical analysis, manuscript preparation, ISJ - data acquisition, manuscript preparation, JM - data acquisition, HA - data acquisition, IB - data acquisition, JoP - data acquisition, JaP - final manuscript draft supervision. The author(s) read and approved the final ... | PMC10061960 | |
Funding | None received | PMC10061960 | ||
Availability of data and materials | ’ | The datasets generated and/or analyzed during the current study are not publicly available due to authors’ fear that they will be used without consent but are available from the corresponding author on reasonable request after the manuscript has been published. | PMC10061960 | |
Declarations | PMC10061960 | |||
Ethics approval and consent to participate | Methods employed adhere to the relevant ethical guidelines and study protocol was approved by the University Hospital Dubrava ethics committee under ID 2022/1807–03 and ClinicalTrials.gov under ID NCT05615168.Informed consent was obtained in the anesthesia preparation room by all participants. | PMC10061960 | ||
Consent for publication | Not applicable. | PMC10061960 | ||
Competing interests | The authors declare no competing interests. | PMC10061960 | ||
References | PMC10061960 | |||
Key Points | PMC9982698 | |||
Question | Can natural language processing (NLP) be used to measure clinical trial outcomes? | PMC9982698 | ||
Findings | In this diagnostic study evaluating the performance, feasibility, and power implications of using deep-learning NLP to measure the outcome of documented goals-of-care discussions in a 2512-patient pragmatic trial, NLP-screened human abstraction measured the outcome with 92.6% sensitivity, substantial savings in abstrac... | PMC9982698 | ||
Meaning | The findings suggest that NLP may facilitate measurement of previously inaccessible outcomes in clinical trials and that incorporation of misclassification-adjusted power calculations into the design of studies using NLP may be beneficial. | PMC9982698 | ||
Importance | Many clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes efficiently, but ignoring NLP-related misclassification may lead to underpowered ... | PMC9982698 | ||
Objective | To evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a communication intervention. | PMC9982698 | ||
Design, Setting, and Participants | This diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened human abstraction (manual verification of NLP-positive records), and (3) conventional manual abstraction. The study includ... | PMC9982698 | ||
Main Outcomes and Measures | Main outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-of-care discussions. Performance of NLP was evaluated with receiver operating characteristic (ROC) curves and precision-... | PMC9982698 | ||
Results | A total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learning NLP trained on a separate training data set identified patients with documented goals-of-care discussions with moderat... | PMC9982698 | ||
Conclusions and Relevance | In this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified power loss from NLP-related misclassification, suggesting that incorporation of this approach into the design of studies u... | PMC9982698 | ||
Introduction | Natural language processing (NLP) of free-text electronic health records (EHRs) presents rich opportunities for measuring outcomes that would otherwise require costly, laborious medical record abstraction.Researchers in the fields of palliative care and serious illness communication have shown interest in using NLPIn t... | PMC9982698 | ||
Methods | chronic life-limiting illness, ADRD, Alzheimer disease, dementias | ALZHEIMER DISEASE | This diagnostic study was conducted to inform selection of an outcome measurement strategy for a pragmatic randomized clinical trial of a communication-priming intervention for hospitalized patients (Project to Improve Communication About Serious Illness—Hospital Study: Pragmatic Trial 1 [PICSI-H Trial 1]).The trial en... | PMC9982698 |
NLP Development and Model Training | We collected a training data set of 4642 EHR notes from 150 participants in a previous pilot trial of a similar patient- and clinician-facing communication-priming intervention at the same study hospitals (Project to Improve Communication About Serious Illness—Pilot Study [PICSI-P]) (eTable 1 in We used the training da... | PMC9982698 | ||
Training, Prediction, and Validation of the Natural Language Processing (NLP) Model | EHR indicates electronic health record; GOC, goals of care; PICSI-H Trial 1, Project to Improve Communication About Serious Illness—Hospital Study: Pragmatic Trial (Trial 1) | PMC9982698 | ||
Trial Data Set and Validation Sample | Following conclusion of the PICSI-H Trial 1 outcome assessment period, we used automated database queries to collect all EHR notes authored by attending and trainee physicians, subintern medical students, nurse practitioners, and physician assistants between the date of randomization and 30 days thereafter. This yielde... | PMC9982698 | ||
Statistical Analysis | PMC9982698 | |||
Evaluating NLP Performance | ADRD | Following model training and manual abstraction of the validation sample, we used the trained BERT NLP model to predict the probability of documented goals-of-care discussions in all 2.64 million EHR passages (44 324 notes) of the PICSI-H Trial 1 data set. To characterize the expected performance of BERT NLP in the tri... | PMC9982698 | |
Results | Between April 23, 2020, and March 26, 2021, PICSI-H Trial 1 enrolled 2512 patients, whose baseline characteristics are presented in eTable 1 in | PMC9982698 | ||
Abstraction and Composition of Training and Validation Data Sets | In manual abstraction of 4642 EHR notes in the training data set, 340 notes (7%; belonging to 34 of 150 patients [23%]) contained documented goals-of-care discussions (eTable 2 in In manual abstraction of 2840 EHR notes in the validation sample, 268 notes (9%; belonging to 54 of 159 patients [34%]) contained documented... | PMC9982698 | ||
BERT NLP Performance in the Validation Sample | In comparing BERT NLP predictions with manual abstraction in the validation sample, BERT NLP demonstrated note- and patient-level areas under the ROC curve of 0.962 and 0.924, respectively ( | PMC9982698 | ||
Performance of BERT Natural Language Processing in Classifying 30-Day Documented Goals-of-Care Discussions at Note and Patient Levels in a 159-Patient, 2480-Note Internal Validation Sample | Representative values of sensitivity, specificity, positive and negative predictive values, and F1 scores from these curves are presented in the Table. B, Dotted lines indicate nondiscriminating classifiers. AUC indicates area under the receiver operating characteristic curve; AUPRC, area under the precision-recall cur... | PMC9982698 | ||
Performance Metrics for BERT NLP in Classifying 30-Day Documented Goals-of-Care Discussions at Note and Patient Levels in a 159-Patient, 2480-Note Internal Validation Sample | Abbreviations: AUC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; NLP, natural language processing; NPV, negative predictive value; PPV, positive predictive value.Performance metrics are shown at observed discrimination thresholds with sensitivities closest to pre... | PMC9982698 | ||
Power Estimates With and Without Misclassification | In conventional power analysis (which assumes no misclassification), we calculated that the trial (N = 2512; 1:1 allocation; assumed | PMC9982698 | ||
Detectable Risk Difference Over Classifier Performance | Assumptions: n1 = 1256; n2 = 1256; p1 = 0.335; power = 0.8; and 2-sided α = .05. An interactive 3-D plot is available at | PMC9982698 | ||
Comparison of Outcome Measurement Strategies | fatigue | We evaluated 3 strategies for measuring the primary outcome: manual human abstraction, BERT NLP alone, and BERT NLP–screened human abstraction. The first approach, manual human abstraction, is the de facto gold standard and would power the study to detect a risk difference of 5.4%. Our experience suggested that individ... | PMC9982698 | |
Discussion | In this diagnostic study, we examined the novel use of deep-learning–based NLP to measure a complex outcome from unstructured EHR text in a large pragmatic clinical trial. We also demonstrated and validated the use of statistical methods to quantitatively assess the effects of NLP-related misclassification on study pow... | PMC9982698 | ||
Limitations | Our study has several important limitations. First, our model was trained and validated on data from a single health system, and its performance may not generalize to other systems. Future external validation and efforts to improve explainability of deep learning models | PMC9982698 | ||
Conclusions | In this diagnostic study evaluating the use of deep-learning NLP to measure EHR-documented goals-of-care discussions, we measured the primary outcome of a large pragmatic clinical trial using NLP-screened human abstraction with acceptable sensitivity and substantial savings in abstractor-hours. Our experience demonstra... | PMC9982698 | ||
2. Materials and Methods | PMC10456951 | |||
2.2. Functional Validation of the CPAP-AirFlife™ Flow Generator | MSA, HIT | This section provides a comprehensive overview of the tests conducted to validate the flow generator, a crucial component responsible for controlling flow and oxygen concentrations in the device. The testing process involved meticulously examining the flow generator valve performance, oxygen concentration, flow rates, ... | PMC10456951 | |
2.3. Clinical Trial Design | To ascertain the clinical efficacy of the device, a rigorous randomized non-inferiority and randomized crossover clinical trial was conducted at Servicios Especiales de Salud (S.E.S) Hospital Universitario de Caldas, situated in Manizales, Caldas, Colombia. The testing procedures took place within a specially adapted h... | PMC10456951 | ||
2.3.1. Outcomes | The main focus of this study was to assess the primary outcome variable, SpO | PMC10456951 | ||
2.3.2. Eligibility Criteria for Participants | PATHOLOGY | Inclusion criteria: healthy adult volunteers aged 18 years or older.Exclusion criteria: individuals with any form of respiratory pathology or other underlying comorbidities. | PMC10456951 | |
2.3.3. Interventions | The study included a sample of 19 healthy adult volunteers, who were divided into two groups. Group 1 initiated with conventional CPAP ( | PMC10456951 | ||
2.3.4. Sample Size | The objective of this experimental validation is not to establish the superiority of CPAP-AirFlife™ over a conventional CPAP delivery device (such as the General Electric model R860 ventilator) but rather to demonstrate that it is not inferior or of lower quality than the latter. Based on the null hypothesis Ho, which ... | PMC10456951 | ||
2.3.5. Randomization | The randomization process for determining the starting order of each CPAP technology (conventional vs. CPAP-AirFlife™) was conducted using a simple randomization method. This method ensured that each patient had an equal probability of beginning with either option. To maintain the concealment of randomization, sequenti... | PMC10456951 | ||
2.4. Statistical Methods | For this study, continuous data such as age were reported as means and standard deviations following a normal distribution based on the Shapiro–Wilk test. These data were compared using Student’s Here, | PMC10456951 | ||
3. Results and Discussion | PMC10456951 | |||
4. Conclusions | The study findings demonstrate that the low-cost CPAP-AirFlife™ CPAP device incorporating aerosol control headgear is not inferior to conventional equipment across various efficacy measures. These measures include hemoglobin oxygen saturation levels, exhaled PCOOne significant advantage of the CPAP-AirFlife™ device is ... | PMC10456951 | ||
Author Contributions | Conceptualization, H.A.T., L.P.-H., J.A.H.-C., J.F.E.-S. and O.J.-R.; methodology, H.A.T., L.P.-H., J.A.H.-C., J.F.E.-S., O.J.-R. and O.D.A.-O.; software, H.A.T., L.P.-H., J.A.H.-C. and J.F.E.-S.; validation, H.A.T., L.P.-H., J.A.H.-C., J.F.E.-S., O.J.-R. and O.D.A.-O.; formal analysis, H.A.T., L.P.-H., J.A.H.-C., J.F.... | PMC10456951 | ||
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki, and approved by the bioethics Committee of UNIVERSIDAD AUTONOMA DE MANIZALES for the project 754-118 approved on 11 August 2020. | PMC10456951 | ||
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC10456951 | ||
Data Availability Statement | The data collected in this research are available when be required. | PMC10456951 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10456951 | ||
References | (Configuration for validation of the CPAP-AirFlife™ flow generator.CPAP-AirFlife™ System flow generator valves.Comparison between conventional (Variation of OClinical trial protocol for healthy patients.Variations in the flow and pressure parameters.Variations in Oxygen (OClinical and sociodemographic characteristics.*... | PMC10456951 | ||
Introduction | chronic stroke, stroke, death, Stroke | ADVERSE EFFECTS, STROKE, STROKE | Stroke is a leading cause of death and long-term disabilityRobot-assisted therapy is an effective intervention for promoting upper limb function in stroke survivors since robot-assisted therapy provides patients with intense, repetitive practice which is considered a key element for motor trainingMirror therapy is an e... | PMC10558527 |
Methods | PMC10558527 | |||
Study design and participants | post-stroke, spasticity, cognitive impairment, chronic stroke, stroke, peripheral polyneuropathy, dementia, neurological diseases, aphasia | STROKE, NEUROLOGICAL DISEASE | This study was a single-blinded, randomized controlled trial to investigate whether using mirror therapy as a priming strategy would augment therapeutic effects of robot-assisted therapy on motor function, daily function, and self-efficacy in chronic stroke survivors (ClinicalTrials.gov Identifier: NCT03917511, registe... | PMC10558527 |
Intervention protocol | upper extremity impairment | Participants were stratified into four strata based on the lesion side (left and right) and the initial upper extremity impairment levels (Fugl-Meyer Assessment for Upper Extremity score < 35 and ≥ 36) | PMC10558527 | |
Mirror therapy protocol | hand movements | A mirror was placed in the participants’ midsagittal plane to create a visual illusion of a paretic limb by using the mirror reflection of the non-paretic arm. The paretic arm was placed behind a mirror without being seen by the participant. During the 20-min mirror therapy, a robotic hand was attached to the paretic h... | PMC10558527 | |
Robot-assisted therapy protocol | hand movements | RECRUITMENT | The 40-min robot-assisted therapy consisted of 10-min active-assisted training and 30-min interactive training using the Hand of Hope (HOH) robotic hand system (Rehab-Robotics Co. Ltd, Hongkong, China). HOH is an exoskeleton type of robot with 2 surface electromyography (EMG) sensors that detect the level of motor unit... | PMC10558527 |
Outcome measurements | stroke | STROKE | We used clinical assessments to examine three domains of therapeutic effects of sequential combination of mirror therapy and robot-assisted therapy: (1) motor function, (2) independence in daily function, and (3) self-efficacy. Clinical assessments included Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Wolf Motor... | PMC10558527 |
Domain of motor function | upper extremity motor ability, stroke | STROKE | Fugl-Meyer Assessment for Upper Extremity (FMA-UE): The FMA-UE includes 33 items assessing movements, reflexes, and coordination of upper limbs. Each item is measured on a 3-point ordinal scale and the total score ranges from 0 to 66Wolf Motor Function Test (WMFT): The WMFT assesses upper extremity motor ability by mea... | PMC10558527 |
Domain of independence in daily function | Nottingham Extended Activities of Daily Living Scale (NEADL): The NEADL is a measure of independence in 4 areas of daily life, including mobility, kitchen, domestic, and leisure activities. It includes 22 items, and each item is measured on a 4-point scale. The total score ranges from 0 to 66 and a higher score indicat... | PMC10558527 | ||
Domain of self-efficacy | stroke | STROKE | The stroke self-efficacy questionnaires (SSEQ): The SSEQ measures an individual's confidence in relation to functional performance and self-management after stroke. It includes 13 items, and each item is rated on a 10-point scale from 0 (not at all confident) to 10 (very confident). The reliability and validity of the ... | PMC10558527 |
Statistical analysis | The | PMC10558527 | ||
Results | PMC10558527 | |||
Daily function | Results of NEADL showed no statistically significant interaction effect between group and intervention ( | PMC10558527 | ||
Self-efficacy | Results of SSEQ showed no statistically significant interaction effect between group and intervention (Results of DLSES showed no statistically significant interaction effect between group and intervention ( | PMC10558527 | ||
Discussion | post-stroke, chronic stroke, stroke, self-efficacy post-stroke, left hemisphere damage, hand movements, hemispheric lesions, right hemisphere damage, MR | STROKE | In this study, we used a randomized controlled trial to examine whether mirror therapy would augment therapeutic effects of robot-assisted therapy on motor function, daily function, and self-efficacy in chronic stroke survivors. Based on the timeline of stroke recovery framework proposed by SRRR group, chronic stroke i... | PMC10558527 |
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