Quality of life of prosthetic and orthotic users in South India: a cross-sectional study

Factors influencing access to and participation in rehabilitation for people with lower limb amputation in East, South, and Southeast Asian developing countries: the perspective of rehabilitation professionals - a qualitative study

PURPOSE

To identify barriers and enablers for access to and participation in rehabilitation for people with LLA in East, South, and Southeast Asian developing countries from the perspective of rehabilitation professionals.

MATERIAL AND METHODS

A mixed-method study involving an anonymous cross-sectional screening survey followed by in-depth interviews of rehabilitation professionals in these regions following the COREQ guidelines. Participants were surveyed online using convenience and snowball sampling techniques to inform a purposive heterogenic sample for semi-structured online interviews, between September 2021 to February 2022. Interview transcripts were analysed and thematically coded using the modified Health Care Delivery System Approach (HCDSA) framework.

RESULTS

A total of 201 quantitative survey responses shaped the interview questions and participation of 28 participants from 13 countries for the qualitative investigation. Important factors at the patient level were sex, economics, health issues, language differences, and lack of awareness; at the care team level, peer and/or family support, referrals, and the gender of the professional; at the organizational level, service availability, resources, and quality; and at the environmental level, policies, supports, and physical and/or social accessibility.

CONCLUSIONS

Identified interlinked factors at multiple levels of the HCDSA underpin the need for a systems approach to develop and address regional rehabilitation service provision but requires contextually adapted policy.

Traumatic peripheral nerve injuries: a classification proposal

BACKGROUND

Peripheral nerve injuries (PNIs) include several conditions in which one or more peripheral nerves are damaged. Trauma is one of the most common causes of PNIs and young people are particularly affected. They have a significant impact on patients' quality of life and on the healthcare system, while timing and type of surgical treatment are of the utmost importance to guarantee the most favorable functional recovery. To date, several different classifications of PNIs have been proposed, most of them focusing on just one or few aspects of these complex conditions, such as type of injury, anatomic situation, or prognostic factors. Current classifications do not enable us to have a complete view of this pathology, which includes diagnosis, treatment choice, and possible outcomes. This fragmentation sometimes leads to an ambiguous definition of PNIs and the impossibility of exchanging crucial information between different physicians and healthcare structures, which can create confusion in the choice of therapeutic strategies and timing of surgery.

MATERIALS

The authors retrospectively analyzed a group of 24 patients treated in their center and applied a new classification for PNI injuries. They chose (a) five injury-related factors, namely nerve involved, lesion site, nerve type (whether motor, sensory or mixed), surrounding tissues (whether soft tissues were involved or not), and lesion type-whether partial/in continuity or complete. An alphanumeric code was applied to each of these classes, and (b) four prognostic codes, related to age, timing, techniques, and comorbidities.

RESULTS

An alphanumeric code was produced, similar to that used in the AO classification of fractures.

CONCLUSIONS

The authors propose this novel classification for PNIs, with the main advantage to allow physicians to easily understand the characteristics of nerve lesions, severity, possibility of spontaneous recovery, onset of early complications, need for surgical treatment, and the best surgical approach.

LEVEL OF EVIDENCE

according to the Oxford 2011 level of evidence, level 2.

3D shape reconstruction of the femur from planar X-ray images using statistical shape and appearance models

Major trauma is a condition that can result in severe bone damage. Customised orthopaedic reconstruction allows for limb salvage surgery and helps to restore joint alignment. For the best possible outcome three dimensional (3D) medical imaging is necessary, but its availability and access, especially in developing countries, can be challenging. In this study, 3D bone shapes of the femur reconstructed from planar radiographs representing bone defects were evaluated for use in orthopaedic surgery. Statistical shape and appearance models generated from 40 cadaveric X-ray computed tomography (CT) images were used to reconstruct 3D bone shapes. The reconstruction simulated bone defects of between 0% and 50% of the whole bone, and the prediction accuracy using anterior-posterior (AP) and anterior-posterior/medial-lateral (AP/ML) X-rays were compared. As error metrics for the comparison, measures evaluating the distance between contour lines of the projections as well as a measure comparing similarities in image intensities were used. The results were evaluated using the root-mean-square distance for surface error as well as differences in commonly used anatomical measures, including bow, femoral neck, diaphyseal-condylar and version angles between reconstructed surfaces from the shape model and the intact shape reconstructed from the CT image. The reconstructions had average surface errors between 1.59 and 3.59 mm with reconstructions using the contour error metric from the AP/ML directions being the most accurate. Predictions of bow and femoral neck angles were well below the clinical threshold accuracy of 3°, diaphyseal-condylar angles were around the threshold of 3° and only version angle predictions of between 5.3° and 9.3° were above the clinical threshold, but below the range reported in clinical practice using computer navigation (i.e., 17° internal to 15° external rotation). This study shows that the reconstructions from partly available planar images using statistical shape and appearance models had an accuracy which would support their potential use in orthopaedic reconstruction.

Quality of Life of People with Mobility-Related Disabilities in Sweden: A Comparative Cross-Sectional Study

Little is known about the Quality of Life (QoL) and how QoL is related to the social and economic situation of people with mobility-related disabilities in Sweden. QoL and well-being do not only relate to the absence of impairments but also to the level of social inclusion and the economic situation. The objective of this study was to explore if there were differences in QoL between a group with and a group without mobility-related disabilities in Sweden. Cross-sectional data were collected through self-reported questionnaires. WHOQOL-BREF was used to assess QoL. Recruitment was conducted through social media platforms. Comparisons were made between and within groups using the Welch t-test. Generalized linear models were used to predict score change for the WHOQOL-BREF items and domains accounting for sex, age, education, social inclusion, economic situation, and presence of additional or other disability. Included in the analysis was data from 381 participants, 143 with mobility-related disabilities and 238 without. Participants in the mobility-related disability group scored significantly lower than those without on General Health, General QoL, Health Satisfaction, and the four WHOQOL-BREF domains. The group with mobility-related disabilities also reported a lower Social Inclusion Score (SIS) and a higher proportion of people without a cash margin. An increased SIS indicated higher QoL in the generalized linear model, whereas the absence of cash margin and mobility-related disability negatively influenced the QoL scores. This study indicated that a person with mobility-related disabilities has lower QoL than those without mobility-related disabilities. A lower QoL was also related to a lack of cash margin, a lower social inclusion score, and whether there were additional or other disabilities present.

Mobility impairment and life satisfaction in the Northern Region of Malawi

Background

There exist many psychosocial sequelae associated with mobility impairment, especially in low-resource settings where access to mobility assistive devices is limited.

Objectives

This study aims to (1) define the burden and presenting aetiologies of mobility impairment in the rural Northern Region of Malawi and (2) assess the relationship between physical disability, life satisfaction and access to mobility aids.

Methods

At mobility device donation clinics throughout the Northern Region of Malawi, adults living with mobility impairment were surveyed with a demographic questionnaire and a series of validated surveys to assess their physical activity levels (Global Physical Activity Questionnaire [GPAQ]), degree of mobility impairment (Washington Group Extended Set Questions on Disability) and life satisfaction (patient-reported outcomes measurement information systems satisfaction with participation in social roles and general life satisfaction).

Results

There were 251 participants who qualified for inclusion, of which 193 completed all surveys. Higher physical activity scores were positively correlated with increased life satisfaction: (1) satisfaction with participation in social roles (0.481, p < 0.0001) and (2) general life satisfaction (0.230, p < 0.001). Respondents who had previously used a formal mobility device reported 235.5% higher physical activity levels ([139.0%, 333.0%], p = 0.006), significantly higher satisfaction with participation in social roles ([0.21, 6.67], p = 0.037) and equivocally higher general life satisfaction ([-1.77, 3.84], p = 0.470).

Conclusion

Disability and mental health do not exist in isolation from one another. Given the positive correlations between formal mobility device usage and both physical activity and life satisfaction, interventions that increase access to mobility-assistive devices in undertreated populations are imperative.

Contribution

This study contributes to the understanding of the complex relationship between physical disability, access to mobility aids, and life satisfaction. Results from this study suggest the potential benefit that increasing access to mobility aids may have in improving the quality of life of mobility impaired persons in resource-limited settings, such as the Northern Region of Malawi.

A Position Sensing Glove to Aid Ankle-Foot Orthosis Diagnosis and Treatment

A position sensing glove called SmartScan, which creates a 3D virtual model of a real object, is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, having a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees

BACKGROUND AND OBJECTIVE

Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions.

METHODS

The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition.

RESULTS

The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space.

CONCLUSION

This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.