Backgound: Metrics for movement smoothness include the number of zero-crossings on the acceleration profile (N0C), the log dimensionless jerk (LDLJ), the normalized averaged rectified jerk (NARJ) and the spectral arc length (SPARC). Sensitivity to the handedness and movement type of these four metrics was compared and correlations with other kinematic parameters were explored in healthy subjects. Methods: Thirty-two healthy participants underwent 3D upper limb motion analysis during two sets of pointing movements on each side. They performed forward- and backward-pointing movements at a self-selected speed to a target located ahead at shoulder height and at 90% arm length, with and without a three-second pause between forward and backward movements. Kinematics were collected, and smoothness metrics were computed. Results: LDLJ, NARJ and N0C found backward movements to be smoother, while SPARC found the opposite. Inter- and intra-subject coefficients of variation were lowest for SPARC. LDLJ, NARJ and N0C were correlated with each other and with movement time, unlike SPARC. Conclusion: There are major differences between smoothness metrics measured in the temporal domain (N0C, LDLJ, NARJ), which depend on movement time, and those measured in the frequency domain, the SPARC, which gave results opposite to the other metrics when comparing backward and forward movements.

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 (r_Spearman=0.64) and to a lesser extent to changes in movement duration (r_Spearman=0.51) while TDSM were very responsive to changes in movement duration (r_Spearman>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 (r_Spearman<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.

Background: Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the validity of available smoothness metrics has not been determined in this group. We aimed to measure the responsiveness to change, reliability, convergent and criterion (concurrent and predictive) 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, movements were significantly shorter in duration and trajectory, straighter, faster and smoother (highest effect size for smoothness change: SPARC, 0.76). Intra-subject coefficients of variation were 30% for nSUB and NARJ. SPARC was strongly correlated with all TDSM, and the TDSM were very strongly correlated with each other. Concurrent validity at D0 was higher for SPARC than TDSM in terms of correlation with proximal UE-FMA, ARAT and index of curvature (r: 0.56, 0.68 and 0.87 respectively, all p<0.01). At D30, concurrent validity was similar between all smoothness metrics. TDSM were very strongly correlated with movement duration at D0 and D30. Finally, SPARC had the highest predictive validity among the four smoothness metrics. Conclusions: Of the four smoothness metrics, the SPARC had the highest sensitivity to change, reliability, construct and criterion validity for the evaluation of the UE in people with moderate to severe stroke in the subacute phase. Among the TDSM, LDLJ was the most reliable. Trial Registration: NCT01383512, https://clinicaltrials.gov/, June 27, 2011

Background: Digital technologies for rehabilitation (DT4R), such as robotics and treadmill systems (RobTS), virtual reality and active video gaming (VR-AVG), and telehealth and apps (T&Apps), are promising tools for pediatric motor rehabilitation. Identifying acceptance factors is essential for effective clinical adoption. Objective: This study aimed to analyze the use of 3 different technologies for rehabilitation—RobTS, VR-AVG, and T&Apps—through a causal model based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Methods: This study was part of RehaTech4child, a cross-sectional survey (2022) supported by the European Academy of Childhood-onset Disability, aimed at professionals working in pediatric motor rehabilitation across Europe. It assessed DT4R use, intention to use, and UTAUT concepts (performance expectancy, effort expectancy, social influence, and barriers). Structural equation modeling was performed to analyze the data and understand relationships between observed and latent variables. Results: A total of 1397 responses were received, and 635 fulfilled the eligibility criteria. The fitness indices suggested a satisfactory fit between the data and the model. The model explained 67% of the variance in the use of RobTS, 62% in VR-AVG, and 57% in T&Apps. Among all studied determinants, access had the strongest impact on use for all 3 categories of DT4R (RobTS: β=0.78, VR-AVG: β=0.73, and T&Apps: β=0.70; P <.001). Intention to use significantly impacted use behavior for all technologies; it was the second determinant after access for VR-AVG (β=0.18, P <.001) and T&Apps (β=0.21, P <.001), with a lower weight for RobTS (β=0.06, P=.007; P <.001). In the subgroup analysis of respondents reporting easy access, intention to use was the strongest determinant of use. The model explained 61% of the variance in intention to use for RobTS, 67% for VR-AVG, and 68% for T&Apps. Performance expectancy had the strongest effect on intention to use for the 3 technologies (RobTS: β=0.81, VR-AVG: β=0.84, and T&Apps: β=0.90; P <.001). For this concept, the items with the highest weights were significantly related to the effectiveness of DT4R on rehabilitation. Social influence and effort expectancy had a slight impact on intention to use. Conclusions: These results underscore the need to ensure easy access as a prerequisite for assessing relevant determinants of acceptance. Developing the evidence base for DT4R effectiveness and ensuring the availability of existing evidence may facilitate DT4R implementation. In our study, within the framework of the UTAUT model, no acceptance barrier was linked to the use of DT4R with children. Gathering families’ views may be useful for the implementation of RobTS. T&Apps may be useful for involving parents in their child’s rehabilitation. Further studies should focus on children’s and families’ points of view.

Background: A 2019 French national online survey explored activity limitations and participation restrictions among children and youth with disabilities for which innovative solutions could help. Objective: The primary aim of this study was to use text mining analysis to complement previous human-led qualitative research and to uncover actionable additional knowledge related to difficult life situations that innovative solutions could help address and to compare the perspectives of children and youth with disabilities, relatives, and professionals. Methods: This study was part of Innovation for Participation , a cross-sectional online survey, including (1) closed questions about sociodemographic characteristics, (2) closed questions about life situations that induced difficulties, and (3) open questions to explore these situations in greater depth. This study used a text mining approach designed for French data to analyze answers to open-ended questions about difficulties and desired innovative solutions. The analysis process included (1) data cleansing; (2) computing and analyzing the word frequency in the answers and their cooccurrence; (3) clustering answers using K-means algorithms; (4) analyzing the cluster words to define a title for each one and identify answers that are consistent with cluster titles, thereafter defined as well-classified; and (5) evaluating internal consistency defined as the ratio of well-classified answers to the total number of answers. Results: In total, 1055 responses were received, and 962 (91.2%) were included, representing 2018 open-ended answers to questions about difficult life situations for which innovative solutions could be useful. Frequency analysis highlighted the desire for action and autonomy by children and youth with disabilities and their relatives (“do” in first position, closely followed by “by themselves” in third position). “Disability” was frequently used by professionals (third position) but did not figure among the 20 most frequently used terms by other respondents. Cooccurrences with “do” revealed a deep interest in “doing sports.” Answers were gathered into 14 clusters, ranging in size from 9 to 681 answers; 5 underwent a second clustering step. Internal consistency ranged from 42% to 100%. Finally, 8 clusters were related to the difficulties linked to a desire for autonomy, and 4 highlighted difficulties related to actions within a group or community. Some clusters reinforced domains that emerged in the human-led analysis. Conclusions: Analysis of 2018 open-ended answers using a text mining approach designed for French data revealed that solutions are required to facilitate autonomy and the ability to “do it by myself (or themselves),” as well as integration into group and community life. Complementary to previous human-led analysis, the results reinforced the need for solutions to facilitate mobility, transport, and sports. They additionally highlighted differences in vocabulary used between the 3 respondent groups, suggesting that professionals may need to consider the language they use when communicating with individuals and relatives.