Economic analysis of digital motor rehabilitation technologies: a systematic review.

Rehabilitation technologies offer promising opportunities for interventions for patients with motor disabilities. However, their use in routine care remains limited due to their high cost and persistent doubts about their cost-effectiveness. Providing solid evidence of the economic efficiency of rehabilitation technologies would help dispel these doubts in order to better take advantage of these technologies. In this context, this systematic review aimed to examine the cost-effectiveness of rehabilitation interventions based on the use of digital technologies. In total, 660 articles published between 2011 and 2021 were identified, of which eleven studies met all the inclusion criteria. Of these eleven studies, seven proved to be cost-effective, while four were not. Four studies used cost-utility analyses (CUAs) and seven used cost-minimization analyses (CMAs). The majority (ten studies) focused on the rehabilitation of the upper and/or lower limbs after a stroke, while only one study examined the rehabilitation of the lower limbs after knee arthroplasty. Regarding the evaluated devices, seven studies analyzed the cost-effectiveness of robotic rehabilitation and four analyzed rehabilitation with virtual reality.The assessment of the quality of the included studies using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) suggested that the quality was related to the economic analysis method: all studies that adopted a cost-utility analysis obtained a high quality score (above 80%), while the quality scores of the cost-minimization analyses were average, with the highest score obtained by a CMA being 72%. The average quality score of all the articles was 75%, ranging between 52 and 100. Of the four studies with a considering score, two concluded that there was equivalence between the intervention and conventional care in terms of cost-effectiveness, one concluded that the intervention dominated, while the last one concluded that usual care dominated. This suggests that even considering the quality of the included studies, rehabilitation interventions based on digital technologies remain cost-effective, they improved health outcomes and quality of life for patients with motor disorders while also allowing cost savings.

Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke.

BACKGROUND: Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics. METHODS: This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ). RESULTS: At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (rSpearman=0.64) and to a lesser extent to changes in movement duration (rSpearman=0.51) while TDSM were very responsive to changes in movement duration (rSpearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (rSpearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations). CONCLUSIONS: Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke. TRIAL REGISTRATION: NCT01383512, https://clinicaltrials.gov/ , June 27, 2011.

Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning.

Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provides highly relevant clinical information. This information is used to guide therapeutic choices; however, it is underused in diagnostic processes, probably because of the lack of standardization of data collection methods. Therefore, 3D gait analysis is currently used as an assessment rather than a diagnostic tool. In this work, we aimed to determine if deep learning could be combined with 3D gait analysis data to diagnose gait disorders in children. We tested the diagnostic accuracy of deep learning methods combined with 3D gait analysis data from 371 children (148 with unilateral cerebral palsy, 60 with neuromuscular disease, 19 toe walkers, 60 with bilateral cerebral palsy, 25 stroke, and 59 typically developing children), with a total of 6400 gait cycles. We evaluated the accuracy, sensitivity, specificity, F1 score, Area Under the Curve (AUC) score, and confusion matrix of the predictions by ResNet, LSTM, and InceptionTime deep learning architectures for time series data. The deep learning-based models had good to excellent diagnostic accuracy (ranging from 0.77 to 0.99) for discrimination between healthy and pathological gait, discrimination between different etiologies of pathological gait (binary and multi-classification); and determining stroke onset time. LSTM performed best overall. This study revealed that the gait pattern contains specific, pathology-related information. These results open the way for an extension of 3D gait analysis from evaluation to diagnosis. Furthermore, the method we propose is a data-driven diagnostic model that can be trained and used without human intervention or expert knowledge. Furthermore, the method could be used to distinguish gait-related pathologies and their onset times beyond those studied in this research.

Lack of Proprioceptive Strategy Modulation Leads to At-Risk Biomechanics for Anterior Cruciate Ligament in Healthy Athletes.

INTRODUCTION: Anterior cruciate ligament (ACL) injuries are frequent in handball, and altered sensory integration may contribute to increased injury risk. Recent evidence showed that proprioceptive postural control strategies differ among athletes. The aim of this study was to evaluate the relationship between proprioceptive strategy and biomechanics during side-cutting maneuvers. METHODS: A total of 47 handball players performed anticipated and unanticipated cutting tasks. Their postural proprioceptive strategy was then characterized according to the perturbation of the center of pressure displacement generated by the muscle vibration on a firm and foam surface. Individuals able to reweight proprioception from ankle to lumbar signals according to the stability of the support were defined as flexible. Conversely, athletes maintaining an ankle-steered strategy on foam surface were characterized as rigid. Statistical parametric mapping analysis was used to compare pelvic and lower limb side-cutting kinematics, kinetics, and EMG activity from seven muscles 200 ms before and after initial contact (IC) using a two-way ANOVA (group-condition). RESULTS: Twenty athletes (11 females and 9 males, 18.5 yr) were characterized as flexible and 20 athletes (12 females and 8 males, 18.9 yr) as rigid. No interaction between condition and proprioceptive profile was observed. More ipsilateral pelvic tilt before IC and lower vastus lateralis (VL) activity immediately after IC was observed during CUT ant . When comparing proprioceptive strategy, rigid individuals exhibited less preactivity of the semitendinosus ( P < 0.001) and higher VL activity ( P = 0.032). Conversely, rigid showed higher gluteus medius preactivity ( P < 0.05) and higher VL activity 100 ms after IC ( P < 0.001). Ankle was also more internally rotated before and during the stance phase ( P < 0.05) among rigid athletes. CONCLUSIONS: Rigid handball players exhibited at-risk determinants for anterior cruciate ligament injuries during side-cutting maneuvers.

Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders.

Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.