Fatigue, a common symptom, together with the characteristic of performance fatigability, are well-documented features of SMA that impact quality of life and function. Importantly, establishing associations between multidimensional self-reported fatigue scales and patient performance has proven difficult. This review was conducted to evaluate the various patient-reported fatigue scales applied in SMA, with the objective of considering the limitations and advantages of each measure. Variable use of fatigue-related nomenclature, including conflicting terminology interpretation, has affected assessment of physical fatigue attributes, specifically perceived fatigability. This review encourages the development of original patient-reported scales to enable perceived fatigability assessment, providing a potential complementary method of evaluating treatment response.

Measuring center-of-pressure (COP) trajectories in out-of-the-lab environments may provide valuable information about changes in gait and balance function related to natural disease progression or treatment in neurological disorders. Traditional equipment to acquire COP trajectories includes stationary force plates, instrumented treadmills, electronic walkways, and insoles featuring high-density force sensing arrays, all of which are expensive and not widely accessible. This study introduces novel deep recurrent neural networks that can accurately estimate dynamic COP trajectories by fusing data from affordable and heterogeneous insole-embedded sensors (namely, an eight-cell array of force sensitive resistors (FSRs) and an inertial measurement unit (IMU)). The method was validated against gold-standard equipment during out-of-the-lab ambulatory tasks that simulated real-world walking. Root-mean-square errors (RMSE) in the mediolateral (ML) and anteroposterior (AP) directions obtained from healthy individuals (ML: 0.51 cm, AP: 1.44 cm) and individuals with neuromuscular conditions (ML: 0.59 cm, AP: 1.53 cm) indicated technical validity. In individuals with neuromuscular conditions, COP-derived metrics showed significant correlations with validated clinical measures of ambulatory function and lower-extremity muscle strength, providing proof-of-concept evidence of the convergent validity of the proposed method for clinical applications.

Continuous gait monitoring may aid the early diagnosis of neurological and musculoskeletal conditions and help validate the effectiveness of new treatments. However, consumer-grade activity trackers can only capture summary gait metrics, whereas most of the research-grade devices capable of estimating fine-grained gait parameters are too cumbersome for extended-time use in real-life environments. Instrumented footwear may offer a promising alternative tool owing to their good accuracy and relatively small form factor, but their ability to detect stride-by-stride spatial gait parameters in free-living conditions has not been well explored to date. This work describes machine learning (ML) inference models for an insole system capable of accurately estimating stride time (ST), length (SL), and velocity (SV) in real-life environments. Functional validity was assessed through unstructured tests including straight-line walking, curve walking, and turns. Ecological validity was examined in free-living conditions. The ML models demonstrated better accuracy than conventional data processing methods (mean absolute errors in unstructured conditions were 3.55% for SL and 3.59% for SV). Real-life gait parameters estimated with the ML models showed stronger associations with a standardized walking test compared with the same parameters obtained with conventional methods. These results indicate proof-of-concept feasibility of using instrumented insoles and ML inference models for free-living spatiotemporal gait analysis.

Background: Physical activity (PA) provides many substantial benefits to help reduce risk for cardiometabolic disease, improve cognitive function, and improve quality of life. Individuals with neuromuscular disorders (NMDs), such as spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are characterized by muscular weakness and fatigue, which limits the capacity to reach the recommended guidelines of PA. Measuring PA in these populations can provide insight to participation in daily activities, track disease progression, and monitor efficacy of drug treatments. Objective: The objective of this study was to identify how PA is measured in SMA and DMD using instrumented and self-report methods, and how these methods are employed in ambulatory and non-ambulatory groups. Methods: A scoping review was performed to identify studies that reported PA in these neuromuscular disorders. Inclusion was determined after a multi-stage review process by several reviewers, followed by an in-depth analysis of metrics reported by each tool that was used. Results: A total of nineteen studies were identified and included in this review. Sixteen studies included instrumented measures and four studies utilized self-reported measures, with eleven studies also reporting PA information from a non-ambulatory group. A variety of metrics have been reported using both classes of measurement tools. Conclusion: Although a wide variety of research exists that details both instrumented and self-reported measurement tools, feasibility, cost, and study aims are important factors to consider in addition to testing methodology when selecting which type of tool to use. We recommend using a combination of instrumented and self-report measures to provide context to the PA measured in these populations. Improvements in both instrumented and self-report methodologies will add valuable knowledge about the disease burden and efficacy of treatment and disease management methods in SMA and DMD.

Background: Individuals with spinal muscular atrophy (SMA) are at risk for low bone mass (LBM). The objectives of this study were to compare bone mineral density (BMD) in ambulatory SMA and control participants, identify LBM, and evaluate the associations of function and physical activity (PA) with LBM. Methods: Thirty-five children and adults, nineteen SMA and sixteen healthy controls, participated. Dual-energy absorptiometry determined BMD, T-scores, and Z-scores. The six-minute walk test (6MWT) and Timed Up and Go (TUG) assessed function. The International Physical Activity Questionnaire Short Form (IPAQ-SF) evaluated PA. Results: Group comparisons and factors associated with BMD were analyzed. Area under the receiver operating characteristic (ROC) curve (AUC) assessed the ability to identify individuals with LBM. SMA participants had lower BMD (p < 0.001) and increased odds of having LBM relative to controls (OR = 16.7; 95%CI: 1.8–152.8; p = 0.004). Conclusions: Ten SMA and one control had LBM. Z-score was associated with 6MWT (r_s = 0.65; p < 0.001) and TUG (r_s = −0.61; p < 0.001). IPAQ-SF and Z-score were weakly associated (r_s = 0.36, p < 0.03). 6MWT (AUC: 0.80; 95% CI: 0.65–0.94; p = 0.006) and TUG (AUC: 0.85; 95% CI: 0.71–0.98; p = 0.002) identified individuals with LBM. Function, assessed by the 6MWT and TUG, is associated with BMD and shows promise for use in identifying individuals with LBM.