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Anterior cruciate ligament (ACL) tears are prevalent career-ending sports injuries. A barrier to successful return to activity is fear of re-injury. Evaluating psychological readiness is however limited to insufficient self-reported assessments. We developed machine learning models using biomechanical data from standardized rebound side hops (SRSH) to objectively classify fear levels post-ACL reconstruction (ACLR) and identify key biomechanical variables. Sixty individuals with ACLR and 47 controls performed up to 10 side hops per leg. Kinematic and kinetic data were collected using motion capture and force platforms. ACLR participants were classified (Tampa Scale for Kinesiophobia-17) as HIGH-FEAR (n = 32) or LOW-FEAR (n = 28). Analyses involved 1D convolutional neural networks (1D CNN) and logistic regression. Integrated gradients identified influential movement variables. The 1-D CNN distinguished HIGH-FEAR versus LOW-FEAR ACLR individuals in agreement with Tampa Scale scores, achieving a mean accuracy of 75.6% (F₁ Score = 0.76, Matthews Correlation Coefficient = 0.52), which was 8.6% better than logistic regression. Influential variables included trunk tilt, hip flexion/extension, and ankle supination/pronation. Machine learning from biomechanics can identify movement linked to fear of re-injury post-ACLR, potentially informing personalised rehabilitation to mitigate fear and enhance recovery.

Jaw-head movement coordination develops during adolescence. However, functional adjustments during this period remain poorly understood. This study aimed to characterize jaw and head movement adjustments in early adolescents and compare this to adults. Three-dimensional optical cameras captured jaw and head movements during maximum jaw opening-closing and chewing. Twenty (8 females, 12 males) adolescents (mean 13.5 yr, standard deviation [SD] 8 months) and 20 (9 females, 11 males) adults (mean 28.2 yr, SD 80 months) participated. Outcomes included jaw and head movement magnitudes, movement cycle time, time to first peak value, and initial phase. Functional data analysis and Wilcoxon rank-sum tests were employed. Adolescents showed larger head magnitude in jaw opening-closing and smaller jaw magnitude than did adults during chewing in the first movement cycle. Adolescents exhibited longer time to peak and time of first movement cycle during jaw opening-closing. During chewing, adolescents showed a longer initial phase, time to peak for consecutive cycles, and movement cycle time. For both age groups, the first cycle differed from consecutive cycles in jaw and head movement magnitudes and cycle times. Compared to adults, adolescents displayed pronounced spatiotemporal initial jaw-head movement adjustments during jaw function, particularly in the first movement cycle. Jaw-head coordination refines from early adolescence into adulthood.

This study provides guidance for researchers who work with functional (curve) data and aim to perform a prior sample size estimation. Focusing on the two-population framework, we test mean differences between two populations — a scenario common in fields such as human movement science. Through simulations, we examine how standard deviation and smoothness influence sample size requirements to achieve 0.80 statistical power, using four methods with control the family-wise error rate: interval-wise testing (IWT), threshold-wise testing (TWT), F-max, and Extreme Rank Length (ERL) global envelope. For instance, increasing the standard deviation from 5 to 10 can raise the sample size from approximately 10 to over 30. Adjusting the smoothness parameter from 5 to 45 can lead to varied outcomes: the required sample size may increase to over 50, remain near 10, or even decrease, depending on the method and data characteristics. Three key findings are: (1) higher noise levels require larger sample sizes, (2) smoother data necessitate more samples when mean differences span larger domains, and (3) TWT and IWT are more efficient for large-domain differences, while ERL and F-max performbetter for differences on narrower domains.

Thigh muscle weakness prevails following anterior cruciate ligament (ACL) injury, as usually evaluated by peak concentric quadriceps strength. Assessment throughout the range of motion (ROM), and for antagonists may provide more comprehensive information. We evaluated angle-specific torque profiles and ratios of isokinetic thigh muscle strength in 70 individuals 23 ± 2 years post-ACL injury (44males, 46.9 ± 5.4 years); 33 treated with ACL-reconstruction (ACL-R), and 37 treated only with physiotherapy (ACL-PT), and 33 controls. Quadriceps and hamstrings torques for concentric/eccentric contractions (90°/s) and ratios between hamstrings/quadriceps strength (HQ) were compared between and within groups using inferential functional data methods. The injured ACL-R leg had lower concentric and eccentric quadriceps strength compared to non-injured leg throughout the ROM, and lower concentric (interval 70–79°) and eccentric (64–67°) quadriceps strength compared to controls. The injured ACL-PT leg showed lower eccentric quadriceps strength (53–77°) than non-injured leg and lower concentric (41–79°) and eccentric (52–81°) quadriceps and eccentric hamstrings (30–77°) strength than controls. There were no group differences for HQ-ratios. The injured ACL-R leg had higher HQ-ratio (34–37°) than non-injured leg. Angle-specific torque profiles revealed strength deficits, masked if using only peak values, and seem valuable for ACL-injury rehabilitation.     

Investigations of kinetic asymmetries during bilateral squats following anterior cruciate ligament reconstruction (ACLR) are limited to mainly cross-sectional studies and discrete value data extracted at specific knee angles. We assessed loading asymmetries during squats longitudinally throughout rehabilitation using curve analysis and compared patient-reported outcome measures (PROMs) between those with and without asymmetry. Bodyweight squats were performed by 24 individuals (13 females) post-ACLR on three occasions: (1) Early 2.9 (1.1) months; (2) Mid 8.8 (3.1) months; (3) at Return to Sport (RTS) 13.1 (3.6) months; and 29 asymptomatic controls (22 females) once. Time-normalized between-leg asymmetry curves of sagittal plane hip, knee, and ankle moments and vertical ground reaction forces were compared using functional data analysis methods. Individual asymmetrical loading for ACLR was classified when exceeding the 95% confidence interval of controls during ≥50% of the squat. At Early, ACLR had greater asymmetry than controls for knee (15%–100% eccentric phase; 0%–100% concentric) and ankle flexion moments (56%–65% concentric). At Mid, ACLR had greater asymmetry for knee (41%–72% eccentric) and ankle flexion moments (56%–69% concentric). No between-group differences were found at RTS. From Early to RTS, ACLR reduced asymmetry for hip (21%–46% eccentric), knee (27%–58% concentric), and ankle flexion moments (21%–57% eccentric). At Early, 11/24 underloaded their ACLR knee and 1 overloaded compared with controls. At RTS, 4 underloaded and 6 overloaded. No differences in PROMs were found based on loading asymmetry. Beyond the early phase of rehabilitation from ACLR, individual-level analyses are required to reveal differing loading strategies during bilateral squats.

This article presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect (FATE) and develop an outcome regression estimator. We show how to obtain valid inference on the FATE using simultaneous confidence bands, which cover the FATE with a given probability over the entire domain. Simulation experiments illustrate how the simultaneous confidence bands take the multiple comparison problem into account. Finally, we use the methods to infer the effect of early adult location on subsequent income development for one Swedish birth cohort.

Evaluating test–retest reliability is crucial in biomechanical research, as it validates experimental results. While methods for reliability of scalar outcome variables are well-established, methods to assess reliability of continuous curve data (such as joint angle trajectories during gait) remain less explored. This study investigates methods for constructing confidence sets for curve-level intraclass correlation coefficients (ICC), which can be expressed as either an ICC curve or an integrated ICC. Currently, no standardised guidelines exist in biomechanics for reporting curve-level ICC uncertainty. Nonparametric bootstrapping techniques are proposed for both the ICC curve's confidence bands and the integrated ICC's confidence intervals, and these methods are validated through Monte Carlo simulations, covering various effect sizes and curve characteristics. Additionally, these methods are applied to assess the test–retest reliability of knee kinematics in three different planes during landing of one-leg hops, where less uncertainty is observed for the ICC curve and integrated ICC in the frontal plane compared to other planes. When the entire time domain is of primary empirical interest, we recommend using a rank-based bootstrap confidence band to express ICC uncertainty, as it yields increasingly precise and valid results as the number of individuals increases, with the coverage rate approaching the correct level of 95%. When a single summary metric is of primary interest, we recommend using the integrated ICC along with a typical bootstrap confidence interval based on the normal distribution, as the coverage rate remains adequately accurate and stable at around the correct level of 95% across varying number of individuals.

Inom de flesta utbildningar förväntas studenter lära sig behärska såväl akademiskt som yrkesmässigt skrivande, så även inom Programmet i statistik och data science, 180 hp. Som lärare på detta program har vi upplevt att många studenter har svårt med alla former av skrivande. Vår egen kunskap om skrivande baseras enbart på erfarenheter från vårt eget skrivande i ämnet, både inom utbildning och forskning. Vi har därför i vissa avseenden känt oss begränsade i vårt arbete kopplat till studenters skrivande. Under våren 2023 deltog vi därför i projektet SKRIVUM vid Umeå universitet. SKRIVUM har som syfte att stärka universitetslärares skrivdidaktiska ämneskompetens genom att utforska och utveckla det ämnesintegrerade skrivandet inom programmen, med akademisk litteracitet (Wingate, 2018) som teoretisk utgångspunkt.  

Under åtta verkstäder arbetade vi kollegialt med kartläggning av akademiskt och yrkesmässigt skrivande inom Programmet i statistik och data science. Vi fokuserade på kursernas förväntade studieresultat (lärandemål), examinationsuppgifter samt stöd och stöttning kopplade till skrivande. Med kartläggningen som utgångspunkt identifierade vi två mål för vårt fortsatta arbete. Det primära målet är att utveckla och implementera konkreta ”verktyg” som stödjer studenternas akademiska och yrkesmässiga skrivande. Det sekundära målet är att öka samsynen inom kollegiet kring hur vi kan och bör arbeta med detta. De förändringar vi föreslår ska i huvudsak rymmas inom ramen för verksamhetens befintliga resurstilldelning. 

Ett första konkret ”verktyg” för att stödja studenterna har redan utvecklats och implementerats. Verktyget är en modelltext för dataanalysrapporter. Modelltexten användes av studenter för första gången höstterminen 2023 som en förebild för deras rapportskrivande. Modelltexten introducerades till studenterna genom en verkstad där vi tillsammans identifierade modelltextens struktur, stil, innehåll samt dess ämnesspecifika kännetecken. En reviderad modelltext baserad på både lärarnas och studenternas utvärdering av modelltexten planeras att användas under vårterminen 2024.  

För att uppnå samsyn inom kollegiet planeras en studiecirkel kring boken Communicating with data: The art of writing for data science (Nolan och Stoudt, 2021) under vårterminen 2024. Dessutom presenteras och tillgängliggörs de ”verktyg” som utarbetas, så att de kan användas av hela kollegiet.  

Vi kommer att delge våra erfarenheter från tiden efter SKRIVUM, med huvudfokus på det kollegiala arbetet med modelltexten samt hur den har implementerats och använts för att stödja studenternas akademiska och yrkesmässiga skrivande.  

Referenser: 

Nolan, D., & Stoudt, S. (2021). Communicating with data: The art of writing for data science. Oxford University Press.  

Wingate, U. (2018). Academic literacy across the curriculum: Towards a collaborative instructional approach. Language Teaching, 51(3), 349-364.  

Background: Kinematic studies suggest that injury of the anterior cruciate ligament (ACL) leads to long-lasting movement deficits or compensations to unload the injured knee. This study evaluated lower body kinematics during squats in individuals who suffered unilateral ACL-injury more than 20 years ago.

Method: Using motion capture, we compared maximum squat depth, time to complete the squat task, detailed kinematics, estimated kinetic-chain joint moments 0- 80° knee flexion, and weight distribution between legs across three groups with (ACL_R, n = 27) and without ACL-reconstructive surgery (ACL_PT, physiotherapy only, n = 28), and age-matched non-injured asymptomatic Controls (n = 31, average age across groups 47 years).

Results: ACL_PT demonstrated significantly reduced squat depth compared to Controls (p = 0.004), whereas ACL_R performed similarly to Controls (p = 1.000). Other outcome variables were comparable between groups. All participants nevertheless demonstrated asymmetric weight distribution between legs but without systematic unloading of the injured side in the ACLgroups.

Conclusion: Expected compensatory strategies were not found in the ACL-groups, while poorer squat performance in the ACL-deficient group may depend on pure knee-joint mechanics, or lifestyle factors attributed to a less stable knee decades after ACL-injury.

Functional data are smooth, often continuous, random curves, which can be seen as an extreme case of multivariate data with infinite dimensionality. Just as component-wise inference for multivariate data naturally performs feature selection, subset-wise inference for functional data performs domain selection. In this paper, we present a unified testing framework for domain selection on populations of functional data. In detail, p-values of hypothesis tests performed on point-wise evaluations of functional data are suitably adjusted for providing a control of the family-wise error rate (FWER) over a family of subsets of the domain. We show that several state-of-the-art domain selection methods fit within this framework and differ from each other by the choice of the family over which the control of the FWER is provided. In the existing literature, these families are always defined a priori. In this work, we also propose a novel approach, coined threshold-wise testing, in which the family of subsets is instead built in a data-driven fashion. The method seamlessly generalizes to multidimensional domains in contrast to methods based on a-priori defined families. We provide theoretical results with respect to consistency and control of the FWER for the methods within the unified framework. We illustrate the performance of the methods within the unified framework on simulated and real data examples, and compare their performance with other existing methods.