Investigating concurrent validity of inertial sensors to evaluate multiplanar spine movement.

Inertial measurement units (IMUs) offer a portable and inexpensive alternative to traditional optical motion capture systems, and have potential to support clinical diagnosis and treatment of low back pain; however, due to a lack of confidence regarding the validity of IMU-derived metrics, their uptake and acceptance remain a challenge. The objective of this work was to assess the concurrent validity of the Xsens DOT IMUs for tracking multiplanar spine movement, and to evaluate concurrent validity and reliability for estimating clinically relevant metrics relative to gold-standard optical motion capture equipment. Ten healthy controls performed spine range of motion (ROM) tasks, while data were simultaneously tracked from IMUs and optical marker clusters placed over the C7, T12, and S1 vertebrae. Root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficients (ICC2,1) were calculated to assess validity and reliability of absolute (abs; C7, T12, and S1 sensors) and relative joint (rel; intersegmental thoracic, lumbar, and total) motion. Overall RMSEabs = 1.33°, MAEabs = 0.74° ± 0.69, and ICC2,1,abs = 0.953 across all movements, sensors, and planes. Results were slightly better for uniplanar movements when evaluating the primary rotation axis (prim) absolute ROM (MAEabs,prim = 0.56° ± 0.49; ICC2,1,abs,prim = 0.999). Similarly, when evaluating relative intersegmental motion, overall RMSErel = 2.39°, MAErel = 1.10° ± 0.96, and ICC2,1,rel = 0.950, and relative primary rotation axis achieved MAErel,prim = 0.87° ± 0.77, and ICC2,1,rel,prim = 0.994. Findings from this study suggest that these IMUs can be considered valid for tracking multiplanar spine movement, and may be used to objectively assess spine movement and neuromuscular control in clinics.

Validation of a Novel Perceptual Body Image Assessment Method Using Mobile Digital Imaging Analysis: A Cross-Sectional Multicenter Evaluation in a Multiethnic Sample.

Given that mobile digital imaging analyses (DIA) are equipped to automate body composition and subsequently alter one's appearance at a given objective body fat percent (BF%), the purpose of this study was to validate the use of this tool for assessments of body image. Participants (f = 134, m = 89) from two separate centers underwent body composition scans using a mobile DIA and completed the Multidimensional Body Self-Relations Questionnaire-Appearance Scale (MBSRQ-AS). Using a DIA-generated avatar, participants altered their figure so that it represented their perceived body, ideal body, and what a partner would find attractive. Distortion was calculated as perceived minus actual BF% and dissatisfaction was calculated as either ideal or partner minus perceived BF%. The total sample and females (p < 0.050), but not males, had significantly lower perceived BF% compared to their actual. Ideal and partner BF% was significantly lower than the perceived BF% for all groups (all p < 0.050). Ideal and partner BF% mean differences (MD) from perceived were positively associated with appearance evaluation (AE) and body area satisfaction (BAS) and negatively associated with overweight preoccupation and self-classified weight for the total sample (all p < 0.050). PerceivedMD demonstrated negative associations with AE and BAS (p < 0.050), but only for females when separated by sex. Perceptual body image measured by DIA is significantly associated with attitudinal body image and may allow practitioners to better quantify this growing issue.

Dynamic assessment of spine movement patterns using an RGB-D camera and deep learning.

In clinical practice, functional limitations in patients with low back pain are subjectively assessed, potentially leading to misdiagnosis and prolonged pain. This paper proposes an objective deep learning (DL) markerless motion capture system that uses a red-green-blue-depth (RGB-D) camera to measure the kinematics of the spine during flexion-extension (FE) through: 1) the development and validation of a DL semantic segmentation algorithm that segments the back into four anatomical classes and 2) the development and validation of a framework that uses these segmentations to measure spine kinematics during FE. Twenty participants performed ten cycles of FE with drawn-on point markers while being recorded with an RGB-D camera. Five of these participants also performed an additional trial where they were recorded with an optical motion capture (OPT) system. The DL algorithm was trained to segment the back and pelvis into four anatomical classes: upper back, lower back, spine, and pelvis. A kinematic framework was then developed to refine these segmentations into upper spine, lower spine, and pelvis masks, which were used to measure spine kinematics after obtaining 3D global coordinates of the mask corners. The segmentation algorithm achieved high accuracy, and the root mean square error (RMSE) between ground truth and predicted lumbar kinematics was < 4°. When comparing markerless and OPT kinematics, RMSE values were < 6°. This work demonstrates the feasibility of using markerless motion capture to assess FE spine movement in clinical settings. Future work will expand the studied movement directions and test on different demographics.

Minimal clinically important differences in six-minute walking distance in late-onset Pompe disease.

BACKGROUND: The minimal clinically important difference (MCID) is the smallest change in outcome that physicians or patients would consider meaningful and is relevant when evaluating disease progression or the efficacy of interventions. Studies of patients with late-onset Pompe disease (LOPD) have used the 6-min walk distance (6MWD) as an endpoint to assess motor function. However, an MCID for 6MWD (% predicted and meters) has yet to be established in LOPD. The objective of the study was to derive 6MWD MCID (% predicted and meters) with different analysis methods and for subgroups of different disease severity for LOPD. METHODS: Data from the PROPEL trial were used to calculate 6MWD MCID in the overall PROPEL population and subgroups of baseline severity as assessed by walking distance and body mass index (BMI), using anchor- and distribution-based approaches. RESULTS: The 6MWD MCIDs varied widely, depending on the method and subgroup, ranging from 2.27%-8.11% predicted for the overall LOPD population (23.7 m-57.2 m). For patients with baseline 6MWD < 150 m, MCIDs ranged from -0.74%-3.37% (-2.1 m-11.3 m). MCIDs increased with distance walked at baseline until a plateau was reached. For BMI subgroups, the MCIDs were generally lowest in obese patients. CONCLUSION: Our analysis shows that MCID depends on the chosen method and disease severity. The findings suggest that applying a single MCID to all patients can be misleading; consequently, a range of possible MCIDs should be considered. This may also be highly relevant for other neuromuscular diseases. This study provides a range of 6MWD MCIDs for LOPD, with lower MCIDs for more severe patients.