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Integrin complexes facilitate cell communication, playing a role in ligament homeostasis. ITGB2 rs2230528 (C/T) wasimplicated in anteriorcruciate ligament rupture (ACL) risk in a South African cohort. Identifying biologically significant DNA signatures in the predisposition to ACL rupture risk remains important towards understanding mechanisms of ACL ruptures. ITGB2 is essential for the activation of important biological pathways regulated by structural components such as collagens and biomechanical components such as vasculo-endothelial growth factors. This study tested the association of (i) ITGB2 rs2230528 and (ii) allele-allele combinations of ITGB2’s network partners (COL5A1 rs12722 C/T, VEGFA rs699947 C/A and VEGFA rs2010963 G/C) with ACL rupture risk. The genetic study was conducted in a combined cohort [n=1279: uninjured controls (CON), n=548; ACL ruptures (ACL), n=731; subgroup with non-contact mechanism of ACL ruptures (NON, n=425)] recruited from Australia, Poland, Sweden and South Africa. The combined cohort, rs2230528 TT (best fit model) was significantly over-represented in the ACL (p=8.00×10⁻⁸; OR:3.21; 95% CI:2.10–4.89, AIC=1549) and NON (p=1.59×10⁻⁶; OR:3.11; 95% CI:1.97–4.91, AIC=1191) groups compared to CON. ITGB2 rs2230528-COL5A1 rs12722-VEGFA rs699947-VEGFA rs2010963, the C-C-A-G and C-T-C-G combinations were significantly associated with reduced ACL risk. This study provided additional evidence highlighting ITGB2 as potentially being associated with ACL ruptures even though the gene-gene combinations had a small effect size. Integrins containing the b2 subunit together with its key extracellular matrix components (type V collagen and VEGFA) are potential therapeutic targets for ACL ruptures and potentially other connective tissue-related conditions.

Background: Regular physical activity is a crucial and an important modifiable lifestyle factor reducing the risk of recurrent incidents after stroke or transient ischemic attack (TIA). Mobile health (mHealth) has emerged as a promising approach for providing long-term support for physical activity. However, little is known about how individuals poststroke or TIA adhere to and engage with mHealth interventions.

Objective: This study aimed to (1) describe adherence to supervised sessions in an mHealth intervention targeting physical activity, (2) describe engagement with self-managed mHealth support for physical activity during and after the intervention, (3) compare characteristics of participants with high and low adherence and app engagement, and (4) examine whether high adherence and app engagement were associated with maintained physical activity after having completed the intervention and at a 12-month follow-up.

Methods: In this study, a secondary analysis of data from the experimental arm of a feasibility randomized controlled trial was conducted. The experimental group received a 6-month mHealth version of the i-REBOUND intervention, which included supervised mHealth support for physical activity and behavior change, followed by a 6-month postintervention period with access to self-managed mHealth support. The control group received mHealth consultations via video conferencing. Adherence measures included attendance at supervised exercise and counseling sessions, while app engagement was measured by weekly interactions with self-managed mHealth support during and after the intervention. Participants' level of physical activity (steps per day) was measured using accelerometers at baseline, and at 6- and 12-month postbaseline. Logistic regression analysis examined the associations between high adherence and app engagement during the intervention and postintervention period and maintained physical activity (ie, >7000 steps/day) across the 12-month study period.

Results: Of the 57 participants enrolled, 51 (89%) completed the intervention; the average age was 71 years, 34/51 (67%) were female, and 47/51 (92%) had mild stroke symptoms. Adherence to supervised mHealth support was high (supervised exercise sessions: 79%, counseling sessions: 98%), while engagement with self-managed mHealth support was high during the intervention (83%) but declined postintervention (38%). A larger proportion of females (24/31, 77%) demonstrated high adherence to the intervention compared to males (7/31, 23%, χ²1=4.1; P=.04). High adherence (≥80%) during the intervention was associated with maintained physical activity between baseline and the 6-month follow-up (OR 12.07, 95% CI 2-72.76; P=.01), while high app engagement (≥80%) during postintervention was associated with maintained physical activity between the 6- and 12-month follow-up (OR 5.10, 95% CI 1.02-25.52; P=.05).

Conclusions: Supervised mHealth support was well received with high adherence, while modules for self-management of physical activity faced challenges in engaging the participants. Future studies could benefit from qualitative and cocreative approaches to better understand and refine self-managed mHealth support for individuals poststroke or TIA.

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.

Fear of re-injury after anterior cruciate ligament (ACL) rupture often hinders return-to-sport and has been linked to movement patterns associated with increased injury risk. Identifying fearful individuals is thus critical. Self-reported questionnaires are commonly used but may be affected by underreporting in sporting and clinical contexts. We aimed to classify individuals with ACL reconstruction (ACLR) into HIGH-FEAR and LOW-FEAR groups, using machine learning (ML) models trained on unimodal or multimodal time-series movement data from side hop landings. Seventy-two participants (median: 13.0 months post-ACLR; interquartile range: 15.8 months) were dichotomized into HIGH-FEAR/LOW-FEAR groups using a single item (statement 9) from the Tampa Scale for Kinesiophobia. Participants performed one-leg standardized rebound side hops while kinematics and kinetics were recorded using 3D motion capture and force plates, respectively, and electromyography (EMG) was registered from flexor and extensor thigh muscles. We extracted time-series features from each modality and ten ML algorithms were trained and evaluated using leave-one-participant-out and grouped 3-fold cross-validation. Integrating kinematic/kinetic and EMG data improved classification accuracy compared to single modality datasets. The extreme gradient boosting model achieved the highest accuracy for fused data (86%) using the top 40 ranked features, including trunk tilt, pelvic obliquity, knee rotation, and flexor and extensor muscle activations. Kinematic/kinetic data alone achieved 83% accuracy per participant, while EMG data alone yielded 85%. This study demonstrates the potential of integrating different movement-related data to enhance the accuracy of ML models in classifying fear of re-injury post-ACLR, supporting identification of patterns associated with fear and guiding treatment.

Background and aims: Although many gaming systems for early stroke rehabilitation capture kinematic data, studies on upper limb (UL) movement capacity analysis after virtual reality (VR)-based gaming interventions remain limited. This study is a secondary analysis of the EnteRtain randomized clinical trial and aims to examine the effects of gamified rehabilitation using a low-cost arm rehabilitation system compared to task-based training on UL movement capacity in individuals with acute or subacute stroke.

Methods: This secondary analysis used data from a randomized, multicenter, single-blind clinical trial involving 120 participants (91 males) with unilateral stroke and an UL Brunnstrom motor recovery stage ⩾1 to ⩽5, recruited from four centers across India. Participants received either gamified training with the ArmAbleTM device (experimental group; n = 64) or task-based training (control group; n = 56), alongside conventional therapy for 2 h/day, 6 days/week, over 2 weeks, followed by 4 weeks of home-based UL rehabilitation. Movement capacity outcomes (reach distance, time, and movement velocity) were assessed by blinded evaluators at 2 and 6 weeks and analyzed using a linear mixed-effects regression model.

Results: At 6 weeks, the experimental group demonstrated significantly greater improvements compared to control group in movement velocity for reaches to both near (mean difference (95% confidence interval (CI)): −2.8 (−5.0, −0.75); p = 0.008) and far targets (−2.7 (−4.9, −0.51); p = 0.016). No significant differences were, however, observed at 2 weeks. Changes in reach distance and movement time were not statistically significant between the groups at any time point.

Conclusion: Gamified rehabilitation with the ArmAbletm device enhanced UL movement velocity at 6 weeks compared to task-based training in individuals recovering from acute/subacute stroke. These findings support the use of ArmAbletm both as an engaging therapeutic tool and as a quantitative assessment platform for evaluating UL function post-stroke. Clinical trials registry number: CTRI/2020/09/027651

Background: Repeated-measures datasets are common in biomechanics and digital health, where each participant contributes multiple correlated trials. If cross-validation (CV) ignores this structure, information can leak from training to test folds, inflating performance and undermining clinical credibility.

Objective: This study evaluates the impact of participant-aware validation strategies on model reliability in repeated-measures classification tasks, using fear of reinjury prediction following anterior cruciate ligament reconstruction (ACLR) as a case study.

Methods: We analyzed 623 hop trials from 72 individuals after ACLR to classify fear of reinjury based on biomechanical features. Four CV strategies were compared: stratified 10-fold CV, leave-one-participant-out cross-validation (LOPOCV), group 3-fold CV, and a nested framework combining LOPOCV (outer loop) with group 3-fold CV (inner loop). Ten supervised classifiers were benchmarked across classification accuracy, train-test generalization gap, model ranking consistency, and computational efficiency.

Results: Stratified 10-fold CV systematically overestimated model performance (eg, extra trees accuracy of 0.91 vs 0.66 under LOPOCV) due to participant-level data leakage. Group and nested CV strategies yielded more conservative and stable estimates. The nested LOPOCV + group CV framework achieved a good balance between generalization and participant-aware separation, with reduced bias and overfitting compared with nonnested alternatives.

Conclusions: Participant-aware validation strategies are essential for trustworthy machine learning (ML) evaluation in repeated-measures settings. Nested CV designs improve reproducibility, reduce selection bias, and align with regulatory expectations for clinical ML tools. These findings support best practices in model validation for biomechanics and digital health applications.

Background: The term kinesiophobia originates in the context of the cognitive fear-avoidance model of pain. The Tampa Scale of Kinesiophobia (TSK) is frequently used to assess this construct, notably among populations for which it was not designed, including athletes with anterior cruciate ligament (ACL) injury, for whom pain is not a major concern. The objective of this study was to determine the suitability of the TSK for evaluating re-injury worries in ACL-injured persons with and without pain, by assessing key psychometric properties.

Methods: Ninety-two individuals post-ACL reconstruction (ACLR) were included and divided into PAIN and NO-PAIN subgroups, based on a 90% cutoff on the pain subscale of the Knee Injury and Osteoarthritis Outcome Score. Correlation analyses were employed to assess the contribution of pain-specific TSK items to the total score. Criterion validity (Cohen’s-kappa) was evaluated between an established TSKtotal cutoff and classification based on the re-injury fear-specific TSKQ9, with an optimal cutoff further explored via receiver operating characteristic (ROC) analysis. The TSK’s internal consistency was tested on subgroup level, using Chronbach’s-α. Finally, in a subset of participants, the TSK’s discriminant validity was assessed through correlation with the ACL Return-to-Sport-after-Injury survey (ACL-RSI) of psychological readiness.

Results: Pain-specific TSK items correlated strongly with TSKtotal (rs=0.85). Classification based on a previously recommended TSKtotal cutoff (38-point) demonstrated fair agreement with TSKQ9 (K = 0.31), with low sensitivity and high specificity. An optimal cutoff of 33.5 for TSKtotal had a sensitivity of 70.6% and specificity of 87.8%. The TSK’s internal consistency was poor (α = 0.65) for NO-PAIN and acceptable (α = 0.77) for the PAIN subgroup. The TSKtotal and ACL-RSI scores were not correlated.

Conclusion: The TSK may have limited suitability for individuals after ACLR due to poor internal consistency when pain is not a concern and limited relationship to re-injury fear, regardless of selected cutoff, and psychological readiness. The construct of kinesiophobia is likely less relevant in this population while other more suitable constructs could provide more meaningful assessment of psychological aspects affecting athletic recovery. Clinicians should consider prioritising more efficient, population-specific tools for detecting re-injury worries, over commonly-used but less-fitting tools like the TSK.

Long-term balance impairments are prevalent after anterior cruciate ligament (ACL) injury and are possibly linked to an overreliance on visual information and related cortical processing. We therefore aimed to explore characteristics of functional brain networks related to postural control with and without vision following ACL reconstruction (ACLR). Twenty-seven individuals after ACLR and 24 non-injured controls performed single-leg balance tasks under eyes-open/eyes-closed conditions. Graph-theoretical measures of functional network segregation (clustering coefficient, CC) and integration (path length, PL) were derived from mobile electroencephalography. Sway characteristics were calculated based on centre of pressure (CoP; area and velocity) and the mean distance between CoP and centre of mass (CoM). Knee antero-posterior kinematics were also explored. Group effects were analysed using permutation-based ANCOVA. During eyes-open only, the ACLR group exhibited greater cortical network segregation (higher CC; p = 0.025) in the alpha-1 band (8–10 Hz). While sway characteristics were similar between groups, the ACLR leg demonstrated greater knee flexion compared to their contralateral leg (p = 0.036). Individuals post-ACLR showed more efficient functional brain connectivity during eyes-open, combined with kinematic adaptations in their injured leg. These findings suggest post-ACLR neural adaptations of postural control mechanisms, particularly when visual information is available.

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.