Extended reality used in the treatment of phantom limb pain: a multicenter, double-blind, randomized controlled trial.

ABSTRACT: Phantom limb pain (PLP) represents a significant challenge after amputation. This study investigated the use of phantom motor execution (PME) and phantom motor imagery (PMI) facilitated by extended reality (XR) for the treatment of PLP. Both treatments used XR, but PME involved overt execution of phantom movements, relying on the decoding of motor intent using machine learning to enable real-time control in XR. In contrast, PMI involved mental rehearsal of phantom movements guided by XR. The study hypothesized that PME would be superior to PMI. A multicenter, double-blind, randomized controlled trial was conducted in 9 outpatient clinics across 7 countries. Eighty-one participants with PLP were randomly assigned to PME or PMI training. The primary outcome was the change in PLP, measured by the Pain Rating Index, from baseline to treatment cessation. Secondary outcomes included various aspects related to PLP, such as the rate of clinically meaningful reduction in pain (CMRP; >50% pain decrease). No evidence was found for superiority of overt execution (PME) over imagery (PMI) using XR. PLP decreased by 64.5% and 68.2% in PME and PMI groups, respectively. Thirty-seven PME participants (71%) and 19 PMI participants (68%) experienced CMRP. Positive changes were recorded in all other outcomes, without group differences. Pain reduction for PME was larger than previously reported. Despite our initial hypothesis not being confirmed, PME and PMI, aided by XR, are likely to offer meaningful PLP relief to most patients. These findings merit consideration of these therapies as viable treatment options and alternatives to pharmacological treatments.

Balance confidence classification in people with a lower limb amputation using six minute walk test smartphone sensor signals.

The activities-specific balance confidence scale (ABC) assesses balance confidence during common activities. While low balance confidence can result in activity avoidance, excess confidence can increase fall risk. People with lower limb amputations can present with inconsistent gait, adversely affecting their balance confidence. Previous research demonstrated that clinical outcomes in this population (e.g., stride parameters, fall risk) can be determined from smartphone signals collected during walk tests, but this has not been evaluated for balance confidence. Fifty-eight (58) individuals with lower limb amputation completed a six-minute walk test (6MWT) while a smartphone at the posterior pelvis was used for signal collection. Participant ABC scores were categorized as low confidence or high confidence. A random forest classified ABC groups using features from each step, calculated from smartphone signals. The random forest correctly classified the confidence level of 47 of 58 participants (accuracy 81.0%, sensitivity 63.2%, specificity 89.7%). This research demonstrated that smartphone signal data can classify people with lower limb amputations into balance confidence groups after completing a 6MWT. Integration of this model into the TOHRC Walk Test app would provide balance confidence classification, in addition to previously demonstrated clinical outcomes, after completing a single assessment and could inform individualized rehabilitation programs to improve confidence and prevent activity avoidance.

L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm.

Functional mobility tests, such as the L test of functional mobility, are recommended to provide clinicians with information regarding the mobility progress of lower-limb amputees. Smartphone inertial sensors have been used to perform subtask segmentation on functional mobility tests, providing further clinically useful measures such as fall risk. However, L test subtask segmentation rule-based algorithms developed for able-bodied individuals have not produced sufficiently acceptable results when tested with lower-limb amputee data. In this paper, a random forest machine learning model was trained to segment subtasks of the L test for application to lower-limb amputees. The model was trained with 105 trials completed by able-bodied participants and 25 trials completed by lower-limb amputee participants and tested using a leave-one-out method with lower-limb amputees. This algorithm successfully classified subtasks within a one-foot strike for most lower-limb amputee participants. The algorithm produced acceptable results to enhance clinician understanding of a person's mobility status (>85% accuracy, >75% sensitivity, >95% specificity).

Evidence-based position paper on Physical and Rehabilitation Medicine (PRM) professional practice on telerehabilitation. The European PRM position (UEMS PRM Section).

INTRODUCTION: The evidence on the utility and effectiveness of rehabilitation interventions delivered via telerehabilitation is growing rapidly. Telerehabilitation is expected to have a key role in rehabilitation in the future. AIM: The aim of this evidence-based position paper (EBPP) is to improve PRM physicians' professional practice in telerehabilitation to be delivered to improve functioning and to reduce activity limitations and/or participation restrictions in individuals with a variety of disabling health conditions. METHODS: To produce recommendations for PRM physicians on telerehabilitation, a systematic review of the literature and a consensus procedure by means of a Delphi process have been performed involving the delegates of all European countries represented in the UEMS PRM Section. RESULTS: The systematic literature review is reported together with the 32 recommendations resulting from the Delphi procedure. CONCLUSIONS: It is recommended that PRM physicians deliver rehabilitation services remotely, via digital means or using communication technologies to eligible individuals, whenever required and feasible in a variety of health conditions in favor of the patient and his/her family, based on evidence of effectiveness and in compliance with relevant regulations. This EBPP represents the official position of the European Union through the UEMS PRM Section and designates the professional role of PRM physicians in telerehabilitation.

Detecting Subtle Cognitive Impairment in Patients with Parkinson's Disease and Normal Cognition: A Novel Cognitive Control Challenge Task (C3T).

Patients with Parkinson's disease (PD) often show early deficits in cognitive control, with primary difficulties in flexibility and relatively intact stable representations. The aim of our study was to assess executive function using an ecologically valid approach that combines measures of stability and flexibility. Fourteen patients without cognitive deficits and sixteen comparable control subjects completed a standardized neuropsychological test battery and a newly developed cognitive control challenge task (C3T). We found that the accuracy of C3T performance decreased with age in healthy participants and remained impaired in PD patients regardless of age. In addition, PD patients showed significantly lower overall performance for cognitive control tasks than healthy controls, even when they scored in the normal range on standardized neuropsychological tests. PD Patients responded significantly faster than healthy control subjects regarding flexible cognitive control tasks due to their impulsivity. Correlations showed that the C3T task targets multiple cognitive systems, including working memory, inhibition, and task switching, providing a reliable measure of complex cognitive control. C3T could be a valuable tool for characterizing cognitive deficits associated with PD and appears to be a more sensitive measure than standardized neuropsychological tests. A different assessment approach could potentially detect early signs of the disease and identify opportunities for early intervention with neuroprotective therapies.

Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests.

The 6-min walk test (6MWT) is commonly used to assess a person's physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, <50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees.

Automated step detection with 6-minute walk test smartphone sensors signals for fall risk classification in lower limb amputees.

Predictive models for fall risk classification are valuable for early identification and intervention. However, lower limb amputees are often neglected in fall risk research despite having increased fall risk compared to age-matched able-bodied individuals. A random forest model was previously shown to be effective for fall risk classification of lower limb amputees, however manual labelling of foot strikes was required. In this paper, fall risk classification is evaluated using the random forest model, using a recently developed automated foot strike detection approach. 80 participants (27 fallers, 53 non-fallers) with lower limb amputations completed a six-minute walk test (6MWT) with a smartphone at the posterior pelvis. Smartphone signals were collected with The Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app. Automated foot strike detection was completed using a novel Long Short-Term Memory (LSTM) approach. Step-based features were calculated using manually labelled or automated foot strikes. Manually labelled foot strikes correctly classified fall risk for 64 of 80 participants (accuracy 80%, sensitivity 55.6%, specificity 92.5%). Automated foot strikes correctly classified 58 of 80 participants (accuracy 72.5%, sensitivity 55.6%, specificity 81.1%). Both approaches had equivalent fall risk classification results, but automated foot strikes had 6 more false positives. This research demonstrates that automated foot strikes from a 6MWT can be used to calculate step-based features for fall risk classification in lower limb amputees. Automated foot strike detection and fall risk classification could be integrated into a smartphone app to provide clinical assessment immediately after a 6MWT.

Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations.

Foot strike detection is important when evaluating a person's gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

Dynamic balancing responses in unilateral transtibial amputees following outward-directed perturbations during slow treadmill walking differ considerably for amputated and non-amputated side.

BACKGROUND: Due to disrupted motor and proprioceptive function, lower limb amputation imposes considerable challenges associated with balance and greatly increases risk of falling in presence of perturbations during walking. The aim of this study was to investigate dynamic balancing responses in unilateral transtibial amputees when they were subjected to perturbing pushes to the pelvis in outward direction at the time of foot strike on their non-amputated and amputated side during slow walking. METHODS: Fourteen subjects with unilateral transtibial amputation and nine control subjects participated in the study. They were subjected to perturbations that were delivered to the pelvis at the time of foot strike of either the left or right leg. We recorded trajectories of center of pressure and center of mass, durations of in-stance and stepping periods as well as ground reaction forces. Statistical analysis was performed to determine significant differences in dynamic balancing responses between control subjects and subjects with amputation when subjected to outward-directed perturbation upon entering stance phases on their non-amputated or amputated sides. RESULTS: When outward-directed perturbations were delivered at the time of foot strike of the non-amputated leg, subjects with amputation were able to modulate center of pressure and ground reaction force similarly as control subjects which indicates application of in-stance balancing strategies. On the other hand, there was a complete lack of in-stance response when perturbations were delivered when the amputated leg entered the stance phase. Subjects with amputations instead used the stepping strategy and adjusted placement of the non-amputated leg in the ensuing stance phase to make a cross-step. Such response resulted in significantly larger displacement of center of mass. CONCLUSIONS: Results of this study suggest that due to the absence of the COP modulation mechanism, which is normally supplied by ankle motor function, people with unilateral transtibial amputation are compelled to choose the stepping strategy over in-stance strategy when they are subjected to outward-directed perturbation on the amputated side. However, the stepping response is less efficient than in-stance response.

Measurement properties of the L Test with fast walking speed in patients after lower limb amputation in initial prosthetic training phase.

The L Test is a clinical mobility test used in patients after lower limb amputation. To assess dynamic balance, it should be performed with fast walking speed. Its measurement properties in the initial prosthetic training phase are not known yet. The objective of the study was to establish intra- and interrater reliability, concurrent and discriminant validity, minimal detectable change, effect size between the rehabilitation time points and ceiling effect of the L Test with fast walking speed in patients after lower limb amputation in initial prosthetic training phase. The study included 36 inpatients aged 19-86 years who were provided with a prosthesis for the first time. They were assessed repeatedly with the L Test, Ten-meter Walk Test and 6-min Walk Test. The intra- (ICC3, k = 0.94) and interrater reliability (ICC2, k = 0.96) of the L Test were excellent. Correlations with the walking tests were very good (r = 0.75-0.86). Regression analysis with respect to the level of lower limb amputation showed a linear relationship with other variables (R2 = 0.55). Influences of age, cause of lower limb amputation and walking aid were statistically significant. The L Test was responsive to change after two weeks of prosthetic training (Cohen's d = 1.21). No ceiling effect was identified. The L Test with fast walking speed is a feasible, reliable, valid, and responsive measure of basic mobility skills in patients after lower limb amputation in the initial prosthetic training phase.

Rasch validation and comparison of the German versions of the Locomotor Capabilities Index-5 and Prosthetic Mobility Questionnaire 2.0 in lower-limb prosthesis users.

This study aimed to compare, through Rasch analysis, the psychometric properties of the Locomotor Capabilities Index (LCI-5) and Prosthetic Mobility Questionnaire (PMQ 2.0) in German lower-limb prosthesis users. The questionnaires were concurrently administered to a convenience sample of 98 consecutively recruited individuals with lower limb amputation (LLA) (male/female = 61/37; mean age 57 ± 14 years). LCI-5 showed disordered rating scale thresholds (one response option in three items required collapsing); local dependence between two items (resolved by creating a testlet); underfit of one item ('Get up from the floor'); and presence of a second weak dimension. PMQ 2.0 showed a correctly functioning rating scale; good fit of the data to the model (apart from some overfit); local dependence between two items (absorbed by creating a testlet); and essential unidimensionality. At scale co-calibration onto a common interval-scaled metric, PMQ 2.0 was better targeted than LCI-5 (i.e. the extent of item difficulty was more appropriate for the sample) and its operational range allowed a more precise measurement of higher locomotor abilities. The correlation between LCI-5 and PMQ 2.0 scores was rho = 0.78. In conclusion, LCI-5 revealed some drawbacks, confirming a previous Rasch study; refinement of its rating scale and item selection seems therefore warranted. The PMQ 2.0 demonstrated good overall measurement quality, in line with previous Italian and Slovene studies. The operational range of the PMQ 2.0 makes it more suitable than LCI-5 for assessing people with high locomotor abilities.

Fall risk classification for people with lower extremity amputations using random forests and smartphone sensor features from a 6-minute walk test.

Fall-risk classification is a challenging but necessary task to enable the recommendation of preventative programs for individuals identified at risk for falling. Existing research has primarily focused on older adults, with no predictive fall-risk models for lower limb amputees, despite their greater likelihood of fall-risk than older adults. In this study, 89 amputees with varying degrees of lower limb amputation were asked if they had fallen in the past 6 months. Those who reported at least one fall were considered a fall risk. Each participant performed a 6 minute walk test (6MWT) with an Android smartphone placed in a holder located on the back of the pelvis. A fall-risk classification method was developed using data from sensors within the smartphone. The Ottawa Hospital Rehabilitation Center Walk Test app captured accelerometer and gyroscope data during the 6MWT. From this data, foot strikes were identified, and 248 features were extracted from the collection of steps. Steps were segmented into turn and straight walking, and four different data sets were created: turn steps, straightaway steps, straightaway and turn steps, and all steps. From these, three feature selection techniques (correlation-based feature selection, relief F, and extra trees classifier ensemble) were used to eliminate redundant or ineffective features. Each feature subset was tested with a random forest classifier and optimized for the best number of trees. The best model used turn data, with three features selected by Correlation-based feature selection (CFS), and used 500 trees in a random forest classifier. The resulting metrics were 81.3% accuracy, 57.2% sensitivity, 94.9% specificity, a Matthews correlation coefficient of 0.587, and an F1 score of 0.83. Since the outcomes are comparable to metrics achieved by existing clinical tests, the classifier may be viable for use in clinical practice.

A new valid Walking Aid Scale better predicts distance walked by prosthesis users than Prosthetic Mobility Questionnaire 2.0 and Activities-Specific Balance Confidence Scale.

The availability of psychometrically-sound and parsimonious outcome measures is key for optimizing decision-making about prosthetic fitting and rehabilitation in lower limb prosthesis users. Despite the increasing clinical use of observational and self-reported scales for assessing mobility and balance, there is currently no scale that accounts for the use of assistive devices while walking under conditions of increasing difficulty. Therefore, the purpose of this study was to develop and validate a Walking Aid Scale (WAS) in a cross-sectional sample of 144 prosthesis users. Specifically, we examined internal consistency and concurrent validity of WAS against two commonly used self-report measures of prosthetic mobility and balance confidence - the Prosthetic Mobility Questionnaire 2.0 (PMQ 2.0) and Activities-Specific Balance Confidence Scale (ABC-5). The predictive value of WAS, in comparison to PMQ 2.0 and ABC-5, was assessed using a 6-Minute Walk Test (6MWT) and participants' characteristics. The WAS showed significant moderate-to-good correlations with PMQ 2.0 and ABC-5, and all scales correlated well with age and 6MWT. Participants who relied less on walking aids reported higher mobility levels, greater balance confidence, and walked longer distances. Age was associated with greater use of walking aids and lower mobility and balance confidence. In the stepwise linear regression analysis, age, amputation level, time since amputation, and WAS predicted about two-thirds of the variability in 6MWT with no significant contribution of PMQ 2.0 and ABC-5. These findings indicate that WAS is a valid instrument and a better predictor of walking distance than PMQ 2.0 and ABC-5 in the lower limb prosthesis users.

Validation of the Activities-Specific Balance Confidence Scale With 5-Option Response Format in Slovene Lower-Limb Prosthetic Users.

OBJECTIVE: To examine the psychometric properties of the Activities-specific Balance Confidence (ABC) scale administered in the Slovene version with a simplified 5-option response format (ABC-5/SLO) using Rasch analysis. DESIGN: Methodological research on data gathered in a cross-sectional study. SETTING: Outpatient university rehabilitation clinic. PARTICIPANTS: A convenience sample of adults with unilateral lower-limb amputation (N=138; 75% men) longer than 6 months who regularly wear a prosthesis. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: We evaluated functioning of rating scale categories, internal construct validity, reliability indices, and dimensionality using the ABC-5/SLO (0=no confidence to 4=complete confidence). RESULTS: The ABC-5/SLO rating scale fulfilled the category functioning criteria. All items fit the underlying scale construct (balance confidence) except item 8 ("walk outside the house to a car parked in the driveway"), which was overfitting. The person abilities-item difficulty matching (targeting) was good. The person separation reliability was .92, and the item separation reliability was .99. Analysis of the standardized Rasch residuals showed the scale's unidimensionality and absence of high item dependency (residual correlations, <.30). The correlation between the ABC-5/SLO and the Prosthetic Mobility Questionnaire (Rasch measures) was high (ρ=.84), as expected. Minor signs of item redundancy were found. CONCLUSIONS: The simplified ABC-5/SLO scale is a valid and reliable measure of balance confidence for individuals with lower-limb amputation. It is possible to transform the ordinal summed raw scores of the ABC-5/SLO into interval-level measurements using a nomogram.

Fall-Risk Classification in Amputees Using Smartphone Sensor Based Features in Turns.

Identifying people at risk of falling can prevent life altering injury. Existing research has demonstrated fall-risk classifier effectiveness in older adults from accelerometer-based data. The amputee population should similarly benefit from these classification techniques; however, validation is still required. 83 individuals with varying levels of lower limb amputation performed a six-minute walk test while wearing an Android smartphone on their posterior belt, with TOHRC Walk Test app to capture accelerometer and gyroscope data. A random forest classifier was applied to feature subsets found using three feature selection techniques. The feature subset with the greatest accuracy (78.3%), sensitivity (62.1%), and Matthews Correlation Coefficient (0.51) was selected by Correlation-based Feature Selection. The peak distinction feature was chosen by all feature selectors. Classification outcomes with this lower extremity amputee group were similar to results from elderly faller classification research. The 62.1% sensitivity and 87.0% specificity would make this approach viable in practice, but further research is needed to improve faller classification results.

Adjustment and Satisfaction with Prosthesis Among People after Upper Limb Amputation in Slovenia.

BACKGROUND: There is a lack of studies on adjustment to upper limb prosthesis with large representative samples that would compare different prosthesis types and use standardised outcome measures. Hence, we wanted to assess satisfaction with, and level of adjustment to, an upper-limb prosthesis among people after an upper limb amputation in our country. MATERIAL AND METHODS: We conducted a cross-sectional descriptive study. The TAPES-R questionnaire was mailed to 431 patients identified from electronic health records at national specialist outpatient clinics for rehabilitation of people after upper limb amputation. RESULTS: 191 patients (44%) responded and were subsequently ascertained to be a representative sample of the population of upper limb amputees in our country. Univariate analyses and multiple regression models indicated that, on average, overall satisfaction is lower among those who have received their current prosthesis more recently, women might be more satisfied with prosthesis than men, above-elbow amputees experience more activity restrictions than those with amputation at a lower level, patients with amputated fingers or palm are more satisfied with the prosthesis than others, and so are those who had amputation following an accident as compared to other reasons. CONCLUSION: We reliably identified some systematic factors, but it is individual factors and experience that largely determine adjustment to and satisfaction with a prosthesis following an upper limb amputation.

Information and communication technology used by people with lower limb loss in Slovenia.

World demography is changing as the population ages and there are more people with disabilities having problems to stay independently at home. Innovative technologies could help extend the independence of older people living at home. As part of a collaborative project, we investigated ownership and use of information and communication technologies (ICT) among older people with lower limb loss (LLL) using questionnaires and retrospective analysis. Our aim was to analyse factors associated with ICT use among people with LLL. We identified age as the main factor that limits ownership and use of ICT among older people with LLL in Slovenia. Cause of amputation also appears to be relevant, whereby those who had amputation because of peripheral vascular disease are more likely to use a personal or tablet computer, social networks, messaging apps, email and internet than those who had amputation because of diabetes. In addition, those living in the suburbs are more likely to use a health monitoring device than those living in the countryside.

Establishing K-levels and prescribing transtibial prostheses using six-minute walk test and one-leg standing test on prosthesis: a retrospective audit.

Rehabilitation programs after amputation often include fitting a prosthesis, but prescriptions vary under similar circumstances. The US Medicare Functional Classification Level (K-level) is a scale for describing functional abilities of persons after lower-limb amputation (from 0 = no ability or potential to ambulate, to 4 = prosthetic demands of a child/active adult/athlete). Different outcome measures are used to assess K-level, including six-minute walk test (6MWT). We attempted to predict the assigned K-level of unilateral transtibial prosthesis users from their results of 6MWT and one-leg standing test on prosthesis (OLSTP). Outpatients who had been rehabilitated and fitted with transtibial prosthesis at the University Rehabilitation Institute in Ljubljana in 2014 were included in a retrospective audit. The data were analysed using receiver-operating-characteristics curves, linear discriminant analysis, classification trees and ordinal logistic regression. Among the 120 patients (aged 39-90, mean 67 years; 79% men), eight belonged to K1 level, 94 to K2, and 18 to K3 or K4; 61 could not stand on the prosthesis, eight stood on it for 1 s, and 51 stood on it for 2 s or more. With a simple classification rule based only on 6MWT (130 m threshold for K2/K3/K4 vs. K1, 340 m for K3/K4 vs. K1/K2), we observed sensitivity and specificity close to 90%. The more sophisticated statistical approaches yielded substantially similar and comparably accurate results. 6MWT and OLST could therefore be used as predictors for transtibial prosthesis prescription in clinical practice.

The minimal clinically-important difference of the Prosthesis Evaluation Questionnaire - Mobility Scale in subjects undergoing lower limb prosthetic rehabilitation training.

BACKGROUND: There is increasing interest in psychometrically sound outcome measures of mobility for people with lower limb amputation (LLA), in order to accurately monitor the impact of the prosthetic training during and after rehabilitation. AIM: To determine the minimum detectable change (MDC) and minimal clinically important difference (MCID) for the Prosthesis Evaluation Questionnaire-Mobility Scale (PEQ-MS) in people with LLA. DESIGN: Prospective single-group observational study. SETTING: Two free-standing Rehabilitation Hospitals. POPULATION: Eighty-seven adult inpatients with LLA undergoing prosthetic rehabilitation. METHODS: Patients completed the self-report PEQ-MS twice, immediately before and after prosthetic rehabilitation training. We administered a 7-point Global Rating of Change scale at the end of training as external anchor, to quantify the effect (improvement/deterioration) of the intervention. RESULTS: Test-retest reliability of the PEQ-MS (N.=24) was high (ICC2,1=0.90). The MDC at the 95% confidence level was 5.5 points. This value, together with those of the mean-change approach and receiver-operating characteristic-curve analysis (AUC>0.89), suggested the selection of a MCID for PEQ-MS of eight points of change, i.e. 16.7% of the maximum possible score (95% CI: 6.5-9.5). CONCLUSIONS: The PEQ-MS showed a high ability to detect change over time (responsiveness).The above MCID value - derived from a triangulation of distribution (MDC) and anchor-based methods - represents a minimal level of change (perceived as important by the patient) in mobility of people with LLA undergoing prosthetic rehabilitation training. CLINICAL REHABILITATION IMPACT: The PEQ-MS is a widely used and analyzed outcome measure. The present study calculated - in a sample of people with LLA undergoing prosthetic training - both the MDC and MCID of the PEQ-MS, showing the high responsiveness of this tool. These values increase confidence in interpreting change in PEQ-MS values, and can help in clinical decision making.