In order to successfully implement individualized patient rehabilitation and home-based rehabilitation programs, the rehabilitation process should be objectifiable, monitorable and comprehensible. For this purpose, objective measurements are required in addition to subjective measurement tools. Thus, the aim of this prospective, single-center clinical trial is the clinical validation of an objective, digital medical device (DMD) during the rehabilitation after anterior cruciate ligament reconstruction (ACLR) with regards to an internationally accepted measurement tool. Sixty-seven patients planned for primary ACLR (70:30% male–female, aged 25 years [21–32], IKDC-SKF 47 [31–60], Tegner Activity Scale 6 [4–7], Lysholm Score 57 [42–72]) were included and received physical therapy and the DMD after surgery. For clinical validation, combined measures of range of motion (ROM), coordination, strength and agility were assessed using the DMD in addition to patient-reported outcome measures (PROMs) at three and six months after ACLR. Significant correlations were detected for ROM (rs = 0.36–0.46, p < 0.025) and strength/agility via the single-leg vertical jump (rs = 0.43, p = 0.011) and side hop test (rs = 0.37, p = 0.042), as well as for coordination via the Y-Balance test (rs = 0.58, p ≤ 0.0001) regarding the IKDC-SKF at three months. Additionally, DMD test results for coordination, strength and agility (Y-Balance test (rs = 0.50, p = 0.008), side hop test (rs = 0.54, p = 0.004) and single-leg vertical jump (rs = 0.44, p = 0.018)) correlate significantly with the IKDC-SKF at six months. No adverse events related to the use of the sensor-based application were reported. These findings confirm the clinical validity of a DMD to objectively quantify knee joint function for the first time. This will have further implications for clinical and therapeutic decision making, quality control and monitoring of rehabilitation measures as well as scientific research.

The single-leg squat (SLS) represents a functional movement task for determining leg function. Objective movement analysis is required to evaluate inter-limb symmetry and movement coordination. Therefore, this study aimed to investigate inter-limb symmetry of SLS kinematics and movement coordination using the modified vector coding technique. A 3D motion capture system and electromyography were used to assess SLS execution and muscle activation of hip ab- and adductors of 17 healthy, recreational athletes. Coordination patterns of hip, knee, and ankle joint movement were assessed by the modified vector coding technique. Statistical parametric mapping revealed no significant differences between both legs (p > 0.05). Inter-limb symmetry also appeared in movement coordination (p > 0.05). Additionally, the analysis of movement coordination indicates knee-dominant, in-phase coordination. However, coordination patterns were different between downward movement, change of direction, and upward movement (p < 0.001). Since perturbations during SLS execution, such as moments of imbalance, occur as anti-phase coordination patterns, the analysis of coordination patterns can be used as a new evaluation method for SLS performance. Furthermore, the modified vector coding technique might be helpful to analyze different compensation strategies during the SLS in symptomatic individuals.

In high-intensity interval training the interval duration can be adjusted to optimize training results in oxygen uptake, cardiac output, and local oxygen supply. This study aimed to compare these variables in two interval trainings (long intervals HIIT3m: 3 min work, 3 min active rest vs. short intervals HIIT30s: 30 s work, 30 s active rest) at the same overall work rate and training duration. 24 participants accomplished both protocols, (work: 80% power output at VO₂peak, relief: 85% power output at gas exchange threshold) in randomized order. Spirometry, impedance cardiography, and near-infrared spectroscopy were used to analyze the physiological stress of the cardiopulmonary system and muscle tissue. Although times above gas exchange threshold were shorter in HIIT3m (HIIT3m 1669.9 ± 310.9 s vs. HIIT30s 1769.5 ± 189.0 s, p = 0.034), both protocols evoked similar average fractional utilization of VO₂peak (HIIT3m 65.23 ± 4.68% VO₂peak vs. HIIT30s 64.39 ± 6.78% VO₂peak, p = 0.261). However, HIIT3m resulted in higher cardiovascular responses during the loaded phases (VO₂p < 0.001, cardiac output p < 0.001). Local hemodynamics were not different between both protocols. Average physiological responses were not different in both protocols owning to incomplete rests in HIIT30s and large response amplitudes in HIIT3m. Despite lower acute cardiovascular stress in HIIT30s, short submaximal intervals may also trigger microvascular and metabolic adaptions similar to HIIT3m. Therefore, the adaption of interval duration is an important tool to adjust the goals of interval training to the needs of the athlete or patient.

Gait analysis is a valuable tool for a wide range of clinical applications. Until now, the standard for gait analysis has been marker-based 3D optical systems. Recently, markerless gait analysis systems that utilize pose estimation models based on Convolutional Neural Networks (CNNs) and computer vision have gained importance. In this pilot study, we validated the performance of a CNN-based, markerless pose estimation algorithm (Orthelligent^® VISION; OV) compared to a standard marker-based 3D motion capture system in 16 healthy adults. Standard gait metrics were analyzed by calculating concordance correlation coefficients (CCCs) and coefficients of variation. With regard to gait event detection, we found good overlaps for both systems. Compared to the marker-based motion analysis, OV achieved a strong to almost complete concordance regarding the sagittal measurement of cadence, gait variability, step time, stance time, step length, and double support (CCC ≥ 0.624), as well as regarding the frontal plane parameters of cadence, step time, stance time, and step width (CCC ≥ 0.805). For gait symmetry only, we found a moderate to weak correlation. These results support the CNN-based, markerless gait analysis system OV as an alternative to marker-based 3D motion capture systems for a broad variety of clinical applications.