A Simplified Clinical Prediction Rule for Prognosticating Dependent Daily Living in Patients with Thoracic Spinal Cord Injury: A Multicenter Nationwide Japan Registry Study.

INTRODUCTION: Clinical prediction rule (CPR) using decision tree analysis is able to show the branching of the variables under consideration in a clear, hierarchical manner, including specific reference values, which can be used as classifiers in clinical practice. However, CPRs developed by decision tree analysis for predicting the degree of independent living of patients with thoracic spinal cord injury (SCI) are few. The purpose of this study was to develop a simplified CPR for prognosticating dependent daily living in patients with thoracic SCI. METHODS: We extracted data on patients with thoracic SCI from a national multicenter registry database, the Japan Rehabilitation Database (JRD). All patients with thoracic SCI who were hospitalized within 30 days after the injury onset were included. The independent living was categorized in the JRD as follows: independent socially, independent at home, needing care at home, independent at the facility, and needing care at the facility. These categories were used as the objective variables in classification and regression tree (CART) analysis. The CART algorithm was applied to develop the CPR for predicting whether patients with thoracic SCI achieve independent living at hospital discharge. RESULTS: Three hundred ten patients with thoracic SCI were included in the CART analysis. The CART model identified, in a hierarchical order, patient's age, residual function level, and the bathing sub-score of Functional Independence Measure as the top three factors with moderate classification accuracy and area under the curve. CONCLUSIONS: We developed a simplified, moderately accurate CPR for predicting whether patients with thoracic SCI achieve independent living at hospital discharge.

Early Initiation of Exoskeletal Robotic Gait Training Improves Functional Outcomes in the Patients with Stroke: A Retrospective Observational Study.

INTRODUCTION: The effect of early initiation of gait training using hybrid assistive limb (HAL) remains unclear. This observational study aimed to investigate whether early initiation of gait training using HAL improves functional outcomes in patients with stroke. METHODS: We retrospectively analyzed patients with acute stroke admitted to our facility. HAL was used for exoskeletal robotic gait training. Study participants were median split into an early group and a late group based on the days from stroke onset to initiation of gait training using HAL. The functional outcomes, defined by the Brunnstrom recovery stage (BRS), modified Rankin Scale (mRS), and Functional Independence Measure (FIM) at discharge, were compared using propensity score-matched analysis. RESULTS: We performed a propensity score-matched analysis in 63 patients with stroke (31 from the early group and 32 from the late group), and 17 pairs were matched. There were no significant differences in discharge in the BRS of the upper limb and finger in the post-matched cohort. On the other hand, the BRS of the lower limb in the early group was significantly higher than that in the late group. In addition, the mRS, but not FIM scores, was significantly better in the early group than that in the late group. CONCLUSIONS: In conclusion, early initiation of gait training using HAL might improve the motor function of the paralyzed lower limb and disability in patients with stroke.

Exploratory integrated analysis of circulating exosomal miRNA and tissue mRNA related to long-term physical activity for more than 25 years: a bioinformatics study.

Physical activity exerts various positive effects on both physical and mental health. Although the comprehensive expression profiles of each microRNA (miRNA) or messenger RNA (mRNA) related to physical activity have already been reported, the association between miRNA and mRNA remains unclear. Here, the integrated study was conducted to comprehensively explore the potential miRNA-mRNA relationships related to long-term physical activity over 25 years. Genome-wide public deposited mRNA expression data of adipose tissue (GSE20536) from six same-sex twin pairs (no information regarding gender) and of skeletal muscle tissue (GSE20319) from ten same-sex twin pairs (four female twin pairs) were used, and differentially expressed mRNAs (DEMs) related to discordant leisure-time physical activity for 30 years were identified using GEO2R. Overlapped mRNAs between DEMs and predicted possible target mRNAs, based on a previous study and TargetScan tool, were then identified and used as long-term physical activity-related mRNAs targeted by miRNAs. In adipose tissue, 36 mRNAs and 42 mRNAs were identified as upregulated or downregulated DEMs, respectively. Based on the results of the overlapped analysis between DEMs and predicted possible target mRNAs targeted by miRNAs, 15 upregulated mRNAs, including NDRG4, FAM13A, ST3GAL6, and AFF1, and 10 downregulated mRNAs, including RPL14, LBP, and GLRX, were identified. In muscle tissue, three downregulated mRNAs overlapped with the predicted target mRNAs targeted by miRNAs. Fifteen upregulated mRNAs in adipose tissue showed a tendency to enrich in "Cardiovascular" in GAD_DISEASE_CLASS category. Potential miRNA-mRNA relationships related to long-term physical activity over 25 years were identified through bioinformatics analysis.

Effects of Non-Face-to-Face and Noncontact Interventions on Knee Pain and Physical Activity in Older Adults With Knee Osteoarthritis: A Systematic Review and Meta-Analysis.

Increased physical activity is an effective treatment for knee osteoarthritis that causes knee pain. However, due to the coronavirus disease 2019, noncontact and non-face-to-face interventions have increased, but the quality of evidence supporting their effectiveness remains unclear. The purpose of the study was to assess the quality of evidence of the effects of non-face-to-face and noncontact interventions on knee pain and physical activity in older adults with knee osteoarthritis. A meta-analysis was conducted to determine the effects of different intervention methods (education and exercise). The Cochrane Central Register of Controlled Trials, PubMed, Cumulative Index to Nursing and Allied Health Literature, and Physiotherapy Evidence Database were systematically searched. Four randomized controlled trials were included in the analysis. The meta-analysis demonstrated that the educational intervention group was significantly effective, although supportive evidence was low quality. Educational intervention may be effective, but the effects need to be confirmed by higher quality clinical trials.

Developing a Clinical Prediction Rule for Gait Independence at Discharge in Patients with Stroke: A Decision-Tree Algorithm Analysis.

OBJECTIVES: To develop a clinical prediction rule (CPR) for gait independence at discharge in patients with stroke, using the decision-tree algorithm and to investigate the usefulness of CPR at admission to the rehabilitation ward. MATERIALS AND METHODS: We included 181 subjects with stroke during the postacute phase. The Chi-squared automatic interaction detection analysis method with 10-fold cross-validation was used to develop two CPRs; CPR 1 using easily obtainable data available at admission; CPR 2 using easily obtainable data available 1 month after admission, for prediction of gait independence at discharge. RESULTS: The degree of independence of toileting was extracted as a first node in the development of two CPRs to predict gait independence at discharge. CPR 1 included the presence of delirium. CPR 2 included problem-solving abilities. The accuracy and area under the curve of CPR 1 were 84.5% and 0.911, respectively; those of CPR 2 were 89.0% and 0.958, respectively. CONCLUSIONS: Toileting independence is a key factor in predicting the gait independence for the discharge of patients with stroke during the postacute phase. Early intervention, during the acute phase, for delirium and cognitive decline, as well as for toileting, increases the possibility of gait independence at discharge.

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.

BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatment. The current study aimed to develop a clinical prediction rule using machine learning to identify the characteristics of patients with stroke who can the achieve minimal clinically important difference of the Fugl-Meyer Upper Extremity Evaluation (FMA-UE) after single-joint hybrid assistive limb (HAL-SJ) rehabilitation. METHODS: This study included 71 patients with subacute stroke who received HAL-SJ rehabilitation. The chi-square automatic interaction detector (CHAID) model was applied to predict improvement in upper limb motor function. Based the analysis using CHAID, age, sex, days from stroke onset to the initiation of HAL-SJ rehabilitation, and upper limb motor and cognitive functions were used as independent variables. Improvement in upper limb motor function was determined based on the minimal clinically important difference of the FMA-UE, which was used as a dependent variable. RESULTS: According to the CHAID model, the FMA-UE score during the initiation of HAL-SJ rehabilitation was the most significant predictive factor for patients who are likely to respond to the intervention. Interestingly, this therapy was more effective in patients with moderate upper limb motor dysfunction and early initiation of HAL-SJ rehabilitation. The accuracy of the CHAID model was 0.89 (95% confidence interval: 0.81-0.96). CONCLUSION: We developed a clinical prediction rule for identifying the characteristics of patients with stroke whose upper limb motor function can improve with HAL-SJ rehabilitation.

Clinical Features for Identifying the Possibility of Toileting Independence after Convalescent Inpatient Rehabilitation in Severe Stroke Patients: A Decision Tree Analysis Based on a Nationwide Japan Rehabilitation Database.

BACKGROUND AND PURPOSE: In severe stroke patients, considerable concern should be given to toileting activity in rehabilitative support. Recently, the application of artificial intelligence, including machine learning (ML), has expanded into the stroke medical field, which could clarify the factors affecting toileting independence in severe stroke patients. This study aimed to identify the factors affecting toileting independence in severe stroke patients using ML. METHODS: We used the Japan Rehabilitation Database from 2005 to 2015 to investigate data from 2292 severe stroke patients. We performed the chi-squared automatic interaction detection (CHAID) algorithm with various explanatory variables. RESULTS: The CHAID model identified modified Rankin scale (mRS) score as the first discriminator. Among those with an mRS score ≤4, the next discriminator was age (score ≤72, 73-80, or >80). Among those with an mRS score > 4, the next discriminator was also age (score ≤57, 58-72, 73-80, or >80). Interestingly, some patients achieved toileting independence, although this study focused on severe stroke patients. In branches based on age, the percentage of the patients who achieved toileting independence at discharge decreased progressively with age. CONCLUSION: We identified the influential factors, including reference values, for achieving toileting independence in convalescent severe stroke patients.

Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge.

BACKGROUND AND PURPOSE: Physical environmental factors are generally likely to become barriers for discharge to home of wheelchair users, compared with non-wheelchair users. However, the importance of environmental factors has not been investigated adequately. Application of machine learning technology might efficiently identify the most influential factors, although it is not easy to interpret and integrate various information including individual and environmental factors in clinical stroke rehabilitation. This study aimed to identify the influential factors affecting home discharge in the stroke patients who use a wheelchair after discharge by using machine learning technology. METHODS: This study used the rehabilitation database of our facility, which includes all stroke patients admitted into the convalescence rehabilitation ward. The chi-squared automatic interaction detection (CHAID) algorithm was used to develop a model to classify wheelchair-using stroke patients discharged to home or not-to-home. RESULTS: Among the variables, including basic information, motor functional factor, activities of daily living ability factor, and environmental factors, the CHAID model identified house renovation and the existence of sloping roads around the house as the first and second discriminators for home discharge. CONCLUSIONS: Our present results could scientifically clarify that the clinician need to focus on the physical environmental factors for achieving home discharge in the patients who use a wheelchair after discharge.

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method. METHOD: This was a cohort study on data from the maintained database of all patients with stroke who were admitted to the convalescence rehabilitation ward of our facility. In total, 1125 stroke patients were investigated. We developed three classification and regression tree (CART) models to identify the possibility of home discharge after inpatient rehabilitation. RESULTS: Among three models, CART model incorporating basic information, functional factor, and environmental factor variables achieved the highest accuracy for identification of home discharge. This model identified FIM dressing of the upper body (score of ≤2 or >2) as the first single discriminator for home discharge. Performing house renovation was associated with a high possibility of home discharge even in patients with stroke who had a poor FIM score in the ability to dress the upper body (≤2) at admission into the convalescence rehabilitation ward. Interestingly, many patients who performed house renovation have achieved home discharge regardless of the degree of lower limb paralysis. CONCLUSION: We identified the influential factors for realizing home discharge using the decision tree algorithm, including environmental factors, in patients with convalescent stroke.

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.

OBJECTIVES: Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients. MATERIALS AND METHODS: This was a secondary analysis based on the data of 481 stroke patients from the database of our institution. Five models developed by supervised machine learning algorithms, including decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machine (SVM), and random forest (RF) were compared by constructing a classification system based on the same dataset. Several parameters including classification accuracy, area under the curve (AUC), and F1 score (a weighted average of precision and recall) were used for model evaluation. RESULTS: The k-NN model had the best classification accuracy (84.0%) with a moderate AUC (0.88) and F1 score (87.8). The SVM model also showed high classification accuracy (82.6%) along with the highest AUC (0.91), sensitivity (94.4), negative predictive value (87.5), and negative likelihood ratio (0.088). The DT, LDA, and RF models had high classification accuracies (≥ 79.9%) with moderate AUCs (≥ 0.84) and F1 scores (≥ 83.8). CONCLUSIONS: Regarding model performance, the k-NN and SVM seemed the best candidate algorithms for classifying the possibility of home discharge in stroke patients.

Prediction of Independent Gait in Acute Stroke Patients with Hemiplegia Using the Ability for Basic Movement Scale II Score.

INTRODUCTION: Since independent gait is an important factor for home discharge, early prediction of independent gait after stroke is essential. The revised version of the Ability for Basic Movement Scale II (ABMS II) has been developed and validated for assessment of basic movements poststroke. OBJECTIVE: The purpose of this study was to investigate the predictive value of the ABMS II score for independent gait in acute stroke patients with hemiplegia. METHODS: We included 67 patients with first stroke and a unilateral lesion who were admitted to the stroke care unit. We evaluated the gait on the 14th and 90th days from admission. RESULTS: The ABMS II score was significantly higher in patients with independent gait on both the 14th and 90th days from admission. On receiver operating characteristic curve analysis, a minimum score of 26 points was predictive of independent gait on the 14th day from admission. Similarly, a score of 15 points was predictive of independent gait on the 90th day from admission. CONCLUSIONS: The ABMS II score is a useful predictor of independent gait in acute stroke patients with hemiplegia.

The Effects of Vestibular Rehabilitation on Gait Performance in Patients with Stroke: A Systematic Review of Randomized Controlled Trials.

BACKGROUND: Patients with post-stroke hemiparesis have poor postural stability; nevertheless, it is unclear whether vestibular rehabilitation affects gait performance after a stroke or not. We performed a systematic review of randomized controlled trials to investigate the effects of vestibular rehabilitation on gait performance in patients with post stroke. METHODS: The Medline, Cochrane Central Register of Controlled Trials, Physiotherapy Evidence Database, and Cumulative Index to Nursing and Allied Health Literature databases were comprehensively searched. All literature published from each source's earliest date to June 2019 was included. Study selection and data extraction were performed independently by paired reviewers. Outcomes of gait performance were the 10-Meter Walking Test, Timed Up and Go Test, and Dynamic Gait Index. We applied the Physiotherapy Evidence Database scale to evaluate the risk of bias and the Grading of Recommendations Assessment, Development and Evaluation system to evaluate the quality of a body of evidence. RESULTS: Three studies were included, and two out of three trials showed beneficial effects of vestibular rehabilitation in post-stroke patients. Quality assessment using the Grading of Recommendations Assessment, Development and Evaluation criteria found very low-quality evidence of all included studies due to inadequate allocation concealment, low participant numbers, and lack of blinding. CONCLUSION: This review found beneficial effects of vestibular rehabilitation on gait performance in patients with stroke. However, due to the very low-quality evidence of previous randomized controlled trials as assessed by the Grading of Recommendations Assessment, Development and Evaluation criteria, definitive conclusions on the effectiveness of vestibular rehabilitation cannot be made. Hence, more high-quality and large-scale randomized controlled trials of vestibular rehabilitation after stroke are needed.

Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis.

BACKGROUND AND PURPOSE: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stroke severity and patients age cannot be modified with rehabilitative treatment. There are limited clinical prediction rules (CPRs) that have been established using only changeable variables to predict the activities of daily living (ADL) dependence of stroke patients. This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients. METHODS: In total, 1125 stroke patients were investigated. We used a maintained database of all stroke patients who were admitted to the convalescence rehabilitation ward of our facility. The classification and regression tree (CART) methodology with only the FIM subscores was used to predict the ADL dependence. RESULTS: The CART method identified FIM transfer (bed, chair, and wheelchair) (score ≤ 4.0 or > 4.0) as the best single discriminator for ADL dependence. Among those with FIM transfer (bed, chair, and wheelchair) score > 4.0, the next best predictor was FIM bathing (score ≤ 2.0 or > 2.0). Among those with FIM transfer (bed, chair, and wheelchair) score ≤ 4.0, the next predictor was FIM transfer toilet (score ≤ 3 or > 3). The accuracy of the CART model was 0.830 (95% confidence interval, 0.804-0.856). CONCLUSION: Machine learning-based CPRs with moderate predictive ability for the identification of ADL dependence in the stroke patients were developed.

Effects of Exoskeletal Lower Limb Robot Training on the Activities of Daily Living in Stroke Patients: Retrospective Pre-Post Comparison Using Propensity Score Matched Analysis.

PURPOSE: There is limited evidence of gait training using newly developed exoskeletal lower limb robot called Hybrid Assistive Limb (HAL) on the function and ability to perform ADL in stroke patients. In clinical settings, we frequently find it challenging to conduct a randomized controlled trial; thus, a large-scale observational study using propensity score analysis methods is a feasible alternative. The present study aimed to determine whether exoskeletal lower limb robot training improved the ability to perform ADL in stroke patients. MATERIALS AND METHODS: Acute stroke patients who were admitted to our facility from April 2016 to March 2017 were evaluated in the conventional rehabilitation period (CRP) and those admitted from April 2017 to June 2019 were evaluated in the HAL rehabilitation period (HRP). We started a new gait rehabilitation program using HAL at the midpoint of these two periods. The functional outcomes or ADL ability outcomes of the patients in the CRP and the subsequent HRP were compared using propensity score matched analyses. RESULTS: Propensity score matching analysis was performed for 108 stroke patients (63 from the CRP and 45 from the HRP), and 36 pairs were matched. The ADL ability, defined by the FIM scores and FIM score change, was significantly higher in patients admitted during the HRP. In addition, more stroke patients obtained practical walking ability during hospitalization in the HRP. CONCLUSION: Gait training using HAL affects the ADL ability and obtaining of practical walking ability of stroke patients.

Combination of Exoskeletal Upper Limb Robot and Occupational Therapy Improve Activities of Daily Living Function in Acute Stroke Patients.

PURPOSE: Previous studies have suggested that upper limb rehabilitation using therapeutic robots improves motor function of stroke patients. However, the effect of upper limb robotic rehabilitation on improving functioning in activities of daily living (ADL) remains unclear. The present study aimed to determine whether upper limb rehabilitation using single joint Hybrid Assistive Limb (HAL-SJ) affects ADL function and the use of a hemiparetic arm in ADLs of acute stroke patients. MATERIALS AND METHODS: Twelve acute stroke patients participated in the study and were randomly divided into group A or group B. The patients in group A followed an A-B-A-B design and those in group B followed a B-A-B-A design. The patients received combination HAL-SJ and occupational therapy during A and conventional occupational therapy during B. RESULTS: Upper limb motor function and ADLs, in particular, dressing the upper body, were improved during combination HAL-SJ and occupational therapy. Interestingly, the use of a hemiparetic arm in daily life evaluated using the motor activity log was also significantly improved during A in group A. CONCLUSIONS: Combination HAL-SJ and occupational therapy affects ADL function and real use of a hemiparetic arm in the daily life of acute stroke patients.

Analysis of scapular kinematics during active and passive arm elevation.

[Purpose] Early postoperative passive motion exercise after arthroscopic rotator cuff repair remains controversial. To better understand this issue, this study was aimed at evaluating scapular kinematics and muscle activities during passive arm elevation in healthy subjects. [Subjects and Methods] The dominant shoulders of 27 healthy subjects were examined. Electromagnetic sensors attached to the scapula, thorax, and humerus were used to determine three-dimensional scapular kinematics during active arm elevation with or without external loads and passive arm elevation. Simultaneously, the activities of seven shoulder muscles were recorded with surface and intramuscular fine-wire electrodes. [Results] Compared with active arm elevation, passive elevation between 30° and 100° significantly decreased the scapular upward rotation and increased the glenohumeral elevation angle. However, no significant differences in scapular posterior tilt and external rotation were observed between active and passive arm elevation, and scapular plane kinematics were not affected by muscle activity. [Conclusion] Unlike active motion with or without an external load, passive arm elevation significantly decreased the scapular upward rotation and significantly increased the mid-range glenohumeral elevation. These data, which suggest that passive arm elevation should be avoided during the early postoperative period, may expand the understanding of rehabilitation after arthroscopic rotator cuff repair.

Prediction of motor outcomes and activities of daily living function using diffusion tensor tractography in acute hemiparetic stroke patients.

[Purpose] The efficacy of diffusion tensor imaging in the prediction of motor outcomes and activities of daily living function remains unclear. We evaluated the most appropriate diffusion tensor parameters and methodology to determine whether the region of interest- or tractography-based method was more useful for predicting motor outcomes and activities of daily living function in stroke patients. [Subjects and Methods] Diffusion tensor imaging data within 10 days after stroke onset were collected and analyzed for 25 patients. The corticospinal tract was analyzed. Fractional anisotropy, number of fibers, and apparent diffusion coefficient were used as diffusion tensor parameters. Motor outcomes and activities of daily living function were evaluated on the same day as diffusion tensor imaging and at 1 month post-onset. [Results] The fractional anisotropy value of the affected corticospinal tract significantly correlated with the motor outcome and activities of daily living function within 10 days post-onset and at 1 month post-onset. Tthere were no significant correlations between other diffusion tensor parameters and motor outcomes or activities of daily living function. [Conclusion] The fractional anisotropy value of the affected corticospinal tract obtained using the tractography-based method was useful for predicting motor outcomes and activities of daily living function in stroke patients.

Characteristics of MicroRNA Expression Depending on the Presence or Absence of Meningioma in Patients with Neurofibromatosis Type 2: A Secondary Analysis.

Meningioma is the second most frequent tumor in patients with neurofibromatosis type 2 (NF2). The presence of meningioma is believed to be a negative prognostic marker in these patients. However, the molecular mechanisms involved in the tumorigenesis of NF2-associated meningioma are not well characterized. Epigenetic regulation, including microRNAs (miRNAs), may be involved in the development of different tumor types in patients with NF2. The objective of this study is to explore the different characteristics of serum miRNA expression depending on the presence or absence of meningioma in patients with NF2. Nine patients with NF2 who were treated at the Department of Neurosurgery, Hiroshima University Hospital, were included. Total RNA (including small RNAs) was extracted from serum samples for the preparation of a small RNA library for next-generation sequencing analysis. Differentially expressed miRNAs (DEMs) were analyzed using the DESeq2 package to compare the characteristic miRNA expression profiles of patients with and without meningioma. In small RNA sequencing analysis, out of a total of 1,879 miRNAs registered in the database, the expressions of 657 miRNAs were observed. In DEM analysis, the expressions of four miRNAs, namely, hsa-miR-664b, hsa-miR-7706, hsa-miR-590, and hsa-miR-6513, were downregulated in patients with NF2 with meningioma compared with patients with NF2 without meningioma. Hsa-miR-193a was identified as the only upregulated miRNA in patients with NF2 with meningioma. In conclusion, we identified different circulating miRNA expression characteristics depending on the presence or absence of meningioma in patients with NF2.

Clinically significant effects of gait modification on knee pain: A systematic review and meta-analysis.

BACKGROUND: Knee pain is the main symptom of knee osteoarthritis. Walking is effective against knee pain, and some studies have shown that gait modification can also relieve this condition. However, the quality of evidence for the clinically significant effects of gait modification on knee pain has not been examined. OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the level of evidence for the clinically significant effects of gait modification on knee pain and determine if the effects are greater than the minimal clinically important difference (MCID). METHODS: We comprehensively searched electronic databases such as MEDLINE, Cochrane Central Register of Controlled Trials, Physiotherapy Evidence Database, and Cumulative Index to Nursing and Allied Health Literature. Intervention studies with experimental groups who received gait modification and control groups who did not were evaluated. The Grading of Recommendations Assessment, Development and Evaluation system was used to assess the level of evidence. RESULTS: Nine studies met the inclusion criteria. All were included in the systematic review and two in the meta-analysis. Results showed that gait modification have significant effects (p= 0.02), and the quality of evidence was very low. However, several studies have revealed that the effects of gait modification, when used as a foot-focused intervention, were greater than the MCID. CONCLUSIONS: We concluded that there is a lack of high-quality evidence that supports the general efficacy of gait modification. Although based on low-quality evidence, when applied to the foot, it may have clinically significant effects.

The effects of nonpharmacological sleep hygiene on sleep quality in nonelderly individuals: A systematic review and network meta-analysis of randomized controlled trials.

The prevalence of locomotive syndrome naturally increases with age, but approximately half of nonelderly individuals also meet the criteria for locomotive syndrome, suggesting that even younger people need to pay attention to their own health status. Sleep is important for physical, cognitive, and psychological health. Some individuals with poor sleep quality may be at risk of developing negative health status. Although the effects of sleep hygiene strategies for elderly individuals have been well investigated, optimal nonpharmacological sleep hygiene strategies for improving sleep quality in nonelderly individuals has not been identified. We conducted a systematic review and network meta-analysis (NMA) of randomized controlled trials aimed to elucidate the effects of various nonpharmacological interventions on sleep quality in nonelderly individuals and to identify the optimal intervention. Cochrane Central Register of Controlled Trials, Medline, Cumulative Index to Nursing and Allied Health Literature, Physiotherapy Evidence Database, and Scopus were comprehensively searched. We identified 27 studies focusing on the effects of various nonpharmacological sleep hygiene strategies in nonelderly individuals, and 24 studies were applied into NMA. The present results showed that resistance training was the most effective intervention for improving sleep quality in nonelderly individuals. In addition, this study revealed the effects of nonpharmacological interventions, such as physical activity, nutritional intervention, as well as exercise interventions. This is the first report that utilized NMA to compare the effects of various nonpharmacological interventions on sleep quality in nonelderly individuals.