Member, Wu Tsai Human Performance Alliance
Doctor of Philosophy, McMaster University (2021)
Master of Science, McMaster University (2015)
Bachelor of Science, McMaster University (2013)
Akshay Chaudhari, Postdoctoral Faculty Sponsor
Anthony Gatti. "United States Patent 11080857 Systems and methods for segmenting an image", NeuralSeg Ltd., Aug 3, 2021
Investigating acute changes in osteoarthritic cartilage by integrating biomechanics and statistical shape models of bone: data from the osteoarthritis initiative.
Magma (New York, N.Y.)
This proof-of-principle study integrates joint reaction forces (JRFs) and bone shape to assess acute cartilage changes from walking and cycling.Sixteen women with symptomatic knee osteoarthritis were recruited. Biomechanical assessment estimated JRFs during walking and cycling. Subsamples had magnetic resonance imaging (MRI) performed before and after a 25-min walking (n = 7) and/or cycling (n = 9) activity. MRI scans were obtained to assess cartilage shape and composition (T2 relaxation time). Bone shape was quantified using a statistical shape model built from 13 local participants and 100 MRI scans from the Osteoarthritis Initiative. Statistical parametric mapping quantified cartilage change and correlations between cartilage change with JRFs and statistical shape model features.Cartilage thickness (interior lateral, Δ - 0.10 mm) and T2 (medial, Δ - 4 ms) decreased on the tibial plateau. On the femur, T2 change depended on the activity. Greater tibiofemoral JRF was associated with more cartilage deformation on the lateral femoral trochlea after walking (r - 0.56). Knees more consistent with osteoarthritis showed smaller decreases in tibial cartilage thickness.Walking and cycling caused distinct patterns of cartilage deformation, which depended on knee JRFs and bone morphology. For the first time, these results show that cartilage deformation is dependent on bone shapes and JRFs in vivo.
View details for DOI 10.1007/s10334-022-01004-8
View details for PubMedID 35286512
View details for PubMedCentralID PMC8919909
- Thoracic imaging radiomics for staging lung cancer: a systematic review and radiomic quality assessment CLINICAL AND TRANSLATIONAL IMAGING 2021
An Artificial Intelligence Algorithm to Predict Nodal Metastasis in Lung Cancer.
The Annals of thoracic surgery
Endobronchial Ultrasound (EBUS) features have high accuracy for predicting lymph node (LN) malignancy. However, their clinical application remains limited due to high operator dependency. We hypothesized that an Artificial Intelligence algorithm (NeuralSeg) is capable of accurately identifying and predicting LN malignancy based on EBUS images.In the derivation phase, EBUS images were segmented twice by an endosonographer and used as controls in 5-fold cross-validation training of NeuralSeg. In the validation phase, the algorithm was tested on new images it had not seen before. Logistic regression and receiver operator characteristic curves were used to determine NeuralSeg's capability of discrimination between benign and malignant LNs, using pathologic specimens as gold standard.In total, 298 LNs from 140 patients were used for derivation and 108 LNs from 47 patients for validation. In the derivation cohort, NeuralSeg was able to predict malignant LNs with an accuracy of 73.8% (95% CI: 68.4% to 78.7%). In the validation cohort, NeuralSeg had an accuracy of 72.9% (95% CI: 63.5% to 81.0%), a specificity of 90.8% (95% CI: 81.9% to 96.2%) and negative predictive value (NPV) of 75.9% (95% CI: 71.5% to 79.9%). NeuralSeg showed higher diagnostic discrimination during validation compared to derivation (c-statistic= 0.75 [95% CI: 0.65-0.85] vs c-statistic=0.63 [95% CI: 0.54-0.72]).NeuralSeg is able to accurately rule out nodal metastasis and can possibly be used as an adjunct to EBUS when nodal biopsy is not possible or inconclusive. Future work to evaluate the algorithm in a clinical trial will be required.
View details for DOI 10.1016/j.athoracsur.2021.06.082
View details for PubMedID 34370986
Association of Machine Learning-Based Predictions of Medial Knee Contact Force With Cartilage Loss Over 2.5 Years in Knee Osteoarthritis
ARTHRITIS & RHEUMATOLOGY
2021; 73 (9): 1638-1645
The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA).In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm3 /year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force-measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined.The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R2 = 0.35, β = -0.119, P < 0.001).Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA.
View details for DOI 10.1002/art.41735
View details for Web of Science ID 000681703500001
View details for PubMedID 33760390
Daily cumulative load and body mass index alter knee cartilage response to running in women
GAIT & POSTURE
2021; 88: 192-197
It is unknown whether a greater accumulation of knee load over a typical day is related to how cartilage responds to an acute bout of loading. This information may clarify the role of habitual activity on cartilage function.Is there a relationship between change in tibial and femoral cartilage thickness, volume, and T2 relaxation time following running with daily cumulative knee load in women? Secondarily, is there a relationship between cartilage change following running and the statistical interaction of body mass index (BMI) and daily steps?Participants (n = 15) completed gait analyses and wore an accelerometer over a week. Daily cumulative knee load was the statistical interaction between tibial compressive joint reaction force (JRF) impulse with the average number of daily steps measured using accelerometry. Magnetic resonance imaging scans were acquired before and immediately after 15-min of treadmill running. Changes in tibial and femoral cartilage thickness, volume, and T2 relaxation time were calculated. Multiple linear regressions tested the associations of cartilage change outcomes with: baseline (thickness, volume, T2), JRF impulse, steps, and the interaction JRF impulse*steps. Secondarily, BMI was substituted for JRF impulse.Tibial volume change was explained by baseline volume, JRF impulse, steps, and JRF impulse*steps (R2 = 0.50, p = 0.013). Additionally, tibial volume change was explained by baseline volume, BMI, steps, and BMI*steps (R2 = 0.43, p = 0.002). Those who were more physically active with lower JRF impulse (or lower BMI) showed less change in tibial cartilage after a running exposure. This may suggest cartilage conditioning.
View details for DOI 10.1016/j.gaitpost.2021.05.030
View details for Web of Science ID 000677708600008
View details for PubMedID 34111696
Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation.Stage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest: 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using six fold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively.On the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11 s per knee.The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.
View details for DOI 10.1007/s10334-021-00934-z
View details for Web of Science ID 000658972800001
View details for PubMedID 34101071
Equations to Prescribe Bicycle Saddle Height based on Desired Joint Kinematics and Bicycle Geometry
EUROPEAN JOURNAL OF SPORT SCIENCE
Forty healthy adults (17 women, 23 men; mean (SD): 28.6 (7.2) years; 24.2 (2.6) kg/m2) participated. Kinematic analyses were conducted for 18 three-minute bicycling bouts including all combinations of 3 horizontal and 3 vertical saddle positions, and 2 crank arm lengths. For both minimum and maximum knee flexion, predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and final models were fit using linear regression. Secondary analyses determined if saddle height equations were sex dependent.The equation to predict saddle position from minimum knee flexion angle (R2=0.97; root mean squared error (RMSE) = 1.15 cm) was: Saddle height (cm) = 7.41 + 0.82(inseam cm) - 0.1(minimum knee flexion °) + 0.003(inseam cm)(seat tube angle °). The maximum knee flexion equation (R2=0.97; RMSE=1.15 cm) was: Saddle height (cm) = 41.63 + 0.78(inseam cm) - 0.25(maximum knee flexion °) + 0.002(inseam cm)(seat tube angle °). The saddle height equations were not dependent on sex.These equations provide a novel, practical strategy for bicycle-fit that accounts for rider anthropometrics, bicycle geometry and user-defined kinematics. HighlightsThis work developed simple equations to prescribed bicycle saddle height that elicits desired knee kinematics.Separate equations are presented for prescribing minimum or maximum knee flexion angle.Equations can be generalized to riders of both sexes, and a breadth of anthropometrics and ages.
View details for DOI 10.1080/17461391.2021.1902570
View details for Web of Science ID 000636090900001
View details for PubMedID 33691592
A new technique to evaluate the impact of running on knee cartilage deformation by region
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
2021; 34 (4): 593-603
When measuring changes in knee cartilage thickness in vivo after loading, mean values may not reflect local changes. The objectives of this investigation were: (1) use statistical parametric mapping (SPM) to determine regional deformation patterns of tibiofemoral cartilage in response to running; (2) quantify regional differences in cartilage thickness between males and females; and (3) explore the influence of sex on deformation.Asymptomatic males (n = 15) and females (n = 15) had MRI imaging of their right knee before and after 15 min of treadmill running. Medial and lateral tibial, and medial and lateral weight-bearing femoral cartilage were segmented. SPM was completed on cartilage thickness maps to test the main effects of Running and Sex, and their interaction. F-statistic maps were thresholded; clusters above this threshold indicated significant differences.Deformation was observed in all four compartments; the lateral tibia had the largest area of deformation (p < 0.0001). Thickness differences between sexes were observed in all four compartments, showing females have thinner cartilage (p ≤ 0.009). The lateral tibia had small clusters indicating an interaction of sex on deformation (p ≤ 0.012).SPM identified detailed spatial information on tibiofemoral cartilage thickness differences observed after running, and between sexes and their interaction.
View details for DOI 10.1007/s10334-020-00896-8
View details for Web of Science ID 000604216700001
View details for PubMedID 33387105
Hip and ankle kinematics are the most important predictors of knee joint loading during bicycling
JOURNAL OF SCIENCE AND MEDICINE IN SPORT
2021; 24 (1): 98-104
To assess the effect of ankle, knee, and hip kinematics on patellofemoral and tibiofemoral joint reaction forces (JRF) during bicycling. Secondarily, to assess if sex, horizontal saddle position, or crank arm length were related to JRFs, after accounting for kinematics.Experimental cross-sectional study.Forty healthy adults (mean (SD); 28.6 (7.2) years, 24.2 (2.6)kg/m2, 17 women) bicycled under 18 bicycling positions. One position used commercial guidelines and 17 randomly deviated from commercial. Resultant patellofemoral as well as compressive and shear tibiofemoral JRFs were calculated. Linear mixed-effects models with a random intercept of leg-nested-in-participant were used.Patellofemoral resultant forces were most sensitive to all joint kinematics (i.e., sensitivity was defined as the slope of single predictor models); all JRFs were least sensitive to minimum knee flexion. Tibiofemoral compression was predicted by minimum hip flexion and maximum ankle dorsiflexion (R2=0.90). Tibiofemoral shear (R2=0.86) and the resultant patellofemoral JRF (R2=0.90) were predicted by minimum hip flexion, maximum ankle dorsiflexion, minimum knee flexion, and the interaction between minimum hip flexion and minimum knee flexion. Adding sex as a factor improved fit of all models. This sex-effect was driven by differences in cycling intensity, reflected by the tangential crank arm force. Horizontal saddle position and crank arm length were not related to JRFs.Optimizing joint kinematics should be the primary goal of bicycle-fit. JRFs were least sensitive to the current gold standard for assessing bicycle-fit, minimum knee flexion. Bicycle-fit is of particular importance for those working at high intensities.
View details for DOI 10.1016/j.jsams.2020.07.001
View details for Web of Science ID 000602698800017
View details for PubMedID 32948450
How to Optimize Measurement Protocols: An Example of Assessing Measurement Reliability Using Generalizability Theory
2020; 72 (2): 112-121
Purpose: This article identifies how to assess multiple sources of measurement error and identify optimal measurement strategies for obtaining clinical outcomes. Method: Obtaining, interpreting, and using information gained from measurements is instrumental in physiotherapy. To be useful, measurements must have a sufficiently small measurement error. Traditional expressions of reliability include relative reliability in the form of an intra-class correlation coefficient and absolute reliability in the form of the standard error of measurement. Traditional metrics are limited to assessing one source of error; however, real-world measurements consist of many sources of error. The measurement framework generalizability theory (GT) allows researchers to partition measurement errors into multiple sources. GT further allows them to calculate the relative and absolute reliability of any measurement strategy, thereby allowing them to identify the optimal strategy. We provide a brief comparison of classical test theory and GT, followed by an overview of the terminology and methodology used in GT, and then an example showing how GT can be used to minimize error associated with measuring knee extension power. Conclusion: The methodology described provides tools for researchers and clinicians that enable detailed interpretation and understanding of the error associated with their measurements.
View details for DOI 10.3138/ptc-2018-0110
View details for Web of Science ID 000534596900002
View details for PubMedID 32494095
View details for PubMedCentralID PMC7238938
Association of Pain and Steps Per Day in Persons With Mild-to-Moderate, Symptomatic Knee Osteoarthritis: A Mixed-Effects Models Analysis of Multiple Measurements Over Three Years
ARTHRITIS CARE & RESEARCH
2020; 72 (1): 114-121
Pain is a consistently reported barrier to physical activity by persons with knee osteoarthritis (OA). Nonetheless, few studies of knee OA have investigated the association of pain with daily walking levels. The current study assessed the relationship of 2 distinct measures of knee pain with objectively measured physical activity in adults with knee OA.This was a longitudinal, observational investigation of 59 individuals (48 women; mean ± SD age 61.1 ± 6.4 years, mean ± SD body mass index 28.1 ± 5.6 kg/m2 ) with clinical knee OA. Data were collected every 3 months for up to 3 years. Physical activity was characterized as the average steps per day taken over at least 3 days, mea-sured by accelerometry. Pain was measured using 2 patient-administered questionnaires: the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS-pain) and the P4 pain scale (P4-pain). Mixed-effects models determined the association between pain and physical activity levels (over covariates) among adults with knee OA (α = 0.05).All covariates (age [β = -3.65, P < 0.001], body mass index [β = -3.06, P < 0.001], season [spring/fall β = -6.91, P = 0.002; winter β = -14.92, P < 0.001]) were predictors of physical activity. Neither the inverted KOOS-pain (β = 0.04, P = 0.717) nor P4-pain (β = -0.37, P = 0.264) was associated with physical activity.Knee pain is not associated with daily walking levels in persons with mild-to-moderate, symptomatic knee OA. While pain management remains an important target of interventions, strategies to increase steps per day in this population should focus on overcoming potentially more crucial barriers to activity participation.
View details for DOI 10.1002/acr.23842
View details for Web of Science ID 000505281300013
View details for PubMedID 30838814
Accuracy of estimates of cumulative load during a confined activity: bicycling.
2019; 6 (1): 66-74
Cumulative load reflects the total accumulated load across a loading exposure. Estimated cumulative load can identify individuals with or at risk for pathology. However, there is no research into the accuracy of the estimated cumulative load. This study determined: (1) which impulses, from a 500 revolution bicycling activity, accurately estimate cumulative pedal reaction force; and (2) how many impulses are required to accurately estimate cumulative pedal reaction force over 500 revolutions. Twenty-four healthy adults (mean 23.4 [SD 3.1] years; 11 men) participated. Participants performed three bicycling bouts of 10-min in duration and were randomized to one of two groups (group 1 = self-selected power and prescribed cadence of 80 revolutions per minute; group 2 = prescribed power of 100 W and self-selected cadence). The first 10 revolutions (2%) of the normal pedal reaction force (PRFN) and resultant pedal reaction force (PRFR), and the first five revolutions (1%) of the anterior-posterior reaction force (PRFAP) over-estimated cumulative load. The PRFN, PRFAP, and PRFR required 80 revolutions (16%), 320 revolutions (64%) and 65 revolutions (13%), respectively, to accurately estimate cumulative load across 500 cycles. These findings highlight that the context and amount of data collected are important in producing accurate estimates of cumulative load.
View details for DOI 10.1080/23335432.2019.1642141
View details for PubMedID 34042006
View details for PubMedCentralID PMC7864546
Cartilage recovery in runners with and without knee osteoarthritis: A pilot study
2019; 26 (5): 1049-1057
Running is an easy way of meeting physical activity recommendations for individuals with knee osteoarthritis (KOA); however, it remains unknown how their cartilage reacts to running. The objective of this pilot study was to compare the effects of 30 min of running on T2 and T1ρ relaxation times of tibiofemoral cartilage in female runners with and without KOA.Ten female runners with symptomatic KOA (mean age 52.6 ± 7.6 years) and 10 without KOA (mean age 52.5 ± 7.8 years) ran for 30 min on a treadmill. Tibiofemoral cartilage T2 and T1ρ relaxation times were measured using magnetic resonance imaging prior to and immediately after the bout of running. Repeated-measures analyses of covariance (ANCOVA) were conducted to examine between-group differences across scanning times.No Group × Time interactions were found for T2 (P ≥ 0.076) or T1ρ (P ≥ 0.288) relaxation times. However, runners with KOA showed increased T2 values compared with pre-running in the medial and lateral femur 55 min post-running (5.4 to 5.5%, P < 0.022) and in all four tibiofemoral compartments 90 min post-running (6.9 to 11.1%, P < 0.01). A significant group effect was found for T1ρ in the medial femur, with greater values in those with KOA compared with controls.While Group × Time interactions in T2 and T1ρ relaxation times remained statistically insignificant, the observed significant increases in T2 in runners with tibiofemoral osteoarthritis TFOA may suggest slower and continuing changes in the cartilage and thus a need for longer recovery after running. Future research should investigate the effects of repeated exposure to running.
View details for DOI 10.1016/j.knee.2019.07.011
View details for Web of Science ID 000498286200013
View details for PubMedID 31434630
Investigating the Test-Retest Reliability and Validity of Hand-Held Dynamometry for Measuring Knee Strength in Older Women with Knee Osteoarthritis
2019; 71 (3): 231-238
Purpose: Hand-held dynamometry (HHD) can be used to evaluate strength when gold-standard isokinetic dynamometry (IKD) is not feasible. HHD is useful for measuring lower limb strength in a healthy population; however, its reliability and validity in individuals with knee osteoarthritis (OA) has received little attention. In this research, we examined the test-retest reliability and validity of HHD in older women with knee OA. We also examined the associations between reliability and symptom and disease severity. Method: A total of 28 older women with knee OA completed knee extension and flexion exertions measured using HHD and IKD. Intra-class correlation coefficients (ICC2,3), standard error of measurement, and minimal detectable change were calculated. Correlation coefficients and regressions evaluated the relationships between inter-trial differences and symptom and disease severity. Results: High test-retest reliability was demonstrated for both exertions with each device (ICC2,3 = 0.83-0.96). Variance between trials was not correlated with OA symptoms. Criterion validity was good (ICC2,3 = 0.76), but extension yielded lower agreement than flexion. Regression analysis demonstrated that true strength can be predicted from HHD measurements. Conclusions: HHD is a reliable tool for capturing knee extension and flexion in individuals with OA. Because of lower agreement, HHD might be best suited for evaluating within-subject strength changes rather than true strength scores. However, gold-standard extension strength magnitudes may reasonably be predicted from regression equations (r 2 = 0.82).
View details for DOI 10.3138/ptc-2018-0051
View details for Web of Science ID 000478990400006
View details for PubMedID 31719719
View details for PubMedCentralID PMC6830419
Self-efficacy, pain, and quadriceps capacity at baseline predict changes in mobility performance over 2 years in women with knee osteoarthritis
2018; 37 (2): 495-504
This study examined the extent to which baseline measures of quadriceps strength, quadriceps power, knee pain and self-efficacy for functional tasks, and their interactions, predicted 2-year changes in mobility performance (walking, stair ascent, stair descent) in women with knee osteoarthritis. We hypothesized that lesser strength, power and self-efficacy, and higher pain at baseline would each be independently associated with reduced mobility over 2 years, and each of pain and self-efficacy would interact with strength and power in predicting 2-year change in stair-climbing performance. This was a longitudinal, observational study of women with clinical knee osteoarthritis. At baseline and follow-up, mobility was assessed with the Six-Minute Walk Test, and stair ascent and descent tasks. Quadriceps strength and power, knee pain, and self-efficacy for functional tasks were also collected at baseline. Multiple linear regression examined the extent to which 2-year changes in mobility performances were predicted by baseline strength, power, pain, and self-efficacy, after adjusting for covariates. Data were analyzed for 37 women with knee osteoarthritis over 2 years. Lower baseline self-efficacy predicted decreased walking (β = 1.783; p = 0.030) and stair ascent (β = -0.054; p < 0.001) performances over 2 years. Higher baseline pain intensity/frequency predicted decreased walking performance (β = 1.526; p = 0.002). Lower quadriceps strength (β = 0.051; p = 0.015) and power (β = 0.022; p = 0.022) interacted with lesser self-efficacy to predict worsening stair ascent performance. Strategies to sustain or improve mobility in women with knee osteoarthritis must focus on controlling pain and boosting self-efficacy. In those with worse self-efficacy, developing knee muscle capacity is an important target.
View details for DOI 10.1007/s10067-017-3903-3
View details for Web of Science ID 000423034600025
View details for PubMedID 29127543
Acute changes in knee cartilage transverse relaxation time after running and bicycling
JOURNAL OF BIOMECHANICS
2017; 53: 171-177
To compare the acute effect of running and bicycling of an equivalent cumulative load on knee cartilage composition and morphometry in healthy young men. A secondary analysis investigated the relationship between activity history and the change in cartilage composition after activity.In fifteen men (25.8±4.2 years), the vertical ground reaction force was measured to determine the cumulative load exposure of a 15-min run. The vertical pedal reaction force was recorded during bicycling to define the bicycling duration of an equivalent cumulative load. On separate visits that were spaced on average 17 days apart, participants completed these running and bicycling bouts. Mean cartilage transverse relaxation times (T2) were determined for cartilage on the tibia and weight-bearing femur before and after each exercise. T2 was measured using a multi-echo spin-echo sequence and 3T MRI. Cartilage of the weight bearing femur and tibia was segmented using a highly-automated segmentation algorithm. Activity history was captured using the International Physical Activity Questionnaire.The response of T2 to bicycling and running was different (p=0.019; mean T2: pre-running=34.27ms, pre-bicycling=32.93ms, post-running=31.82ms, post-bicycling=32.36ms). While bicycling produced no change (-1.7%, p=0.300), running shortened T2 (-7.1%, p<0.001). Greater activity history predicted smaller changes in tibial, but not femoral, T2.Changes in knee cartilage vary based on activity type, independent of total load exposure, in healthy young men. Smaller changes in T2 were observed after bicycling relative to running. Activity history was inversely related to tibial T2, suggesting cartilage conditioning.
View details for DOI 10.1016/j.jbiomech.2017.01.017
View details for Web of Science ID 000396970100024
View details for PubMedID 28148412
GT3X+accelerometer placement affects the reliability of step-counts measured during running and pedal-revolution counts measured during bicycling
JOURNAL OF SPORTS SCIENCES
2016; 34 (12): 1168-1175
Accelerometers provide a measure of step-count. Reliability and validity of step-count and pedal-revolution count measurements by the GT3X+ accelerometer, placed at different anatomical locations, is absent in the literature. The purpose of this study was to investigate the reliability and validity of step and pedal-revolution counts produced by the GT3X+ placed at different anatomical locations during running and bicycling. Twenty-two healthy adults (14 men and 8 women) completed running and bicycling activity bouts (5 minutes each) while wearing 6 accelerometers: 2 each at the waist, thigh and shank. Accelerometer and video data were collected during activity. Excellent reliability and validity were found for measurements taken from accelerometers mounted at the waist and shank during running (Reliability: intraclass correlation (ICC) ≥ 0.99; standard error of measurement (SEM) ≤1.0 steps;Pearson ≥ 0.99) and at the thigh and shank during bicycling (Reliability: ICC ≥ 0.99; SEM ≤1.0 revolutions;Pearson ≥ 0.99). Excellent reliability was found between measurements taken at the waist and shank during running (ICC ≥ 0.98; SEM ≤1.6 steps) and between measurements taken at the thigh and shank during bicycling (ICC ≥ 0.99; SEM ≤1.0 revolutions). These data suggest that the GT3X+ can be used for measuring step-count during running and pedal-revolution count during bicycling. Only shank placement is recommended for both activities.
View details for DOI 10.1080/02640414.2015.1096018
View details for Web of Science ID 000372026600010
View details for PubMedID 26487374