A multi-arm cluster randomized clinical trial of the use of knee kinesiography in the management of osteoarthritis patients in a primary care setting

Cagnin, A.; Choinière, Manon; Bureau, Nathalie J.; Durand, Madéleine; Mezghani, Neila; Gaudreault, Nathaly; Hagemeister, Nicola

doi:10.1080/00325481.2019.1665457

Cited by 13 publications

(15 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of all the participants with knee osteoarthritis who took part in this RCT [18], 221 patients completed a 6-month personalized home-based exercise program and were included in this study. The demographic characteristics (age, sex, and BMI) and one clinical feature, the radiographic OA severity grade measured by the Kellgren-Lawrence scale (KL; grade 2: mild; grade 3: moderate; grade 4: severe) [19] were collected for all participants.…”

Section: Participants and Exercise Programmentioning

confidence: 99%

See 1 more Smart Citation

3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

et al. 2021

Self Cite

View full text Add to dashboard Cite

Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients’ response to a treatment consisting of a physical exercise program.

show abstract

Section: Participants and Exercise Programmentioning

confidence: 99%

“…Patients were simply encouraged to "do them regularly" and were asked if they followed their exercise program after 3 months and at the 6-month follow-up. Further details about the program (therapist training, examples of exercises...) are published elsewhere [18].…”

Section: Participants and Exercise Programmentioning

confidence: 99%

3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have conducted a four‐arm trial of different levels of incentives for small businesses to follow a healthy workplace initiative that included both continuous and binary outcomes measured pre‐ and post‐intervention 13 . Other examples include a three‐arm cRCT of different clinical management tools for knee osteoarthritis that enrolled 87 clusters, with a continuous outcome score measured at baseline and follow up, 14 and an adaptive cluster trial of different levels of incentives for HIV self‐testing in Malawi that started with six arms and six clusters per arm and that measured a dichotomous outcome only in the post‐intervention period 15 . There are evidently many possible variations to the design of multi‐arm cRCTs, but little guidance around their design and analysis.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of three‐arm parallel cluster randomized trials with small numbers of clusters

Watson

Girling

Hemming

2020

Statistics in Medicine

View full text Add to dashboard Cite

In this article, we review and evaluate a number of methods used in the design and analysis of small three‐arm parallel cluster randomized trials. We conduct a simulation‐based study to evaluate restricted randomization methods including covariate‐constrained randomization and a novel method for matched‐group cluster randomization. We also evaluate the appropriate modelling of the data and small sample inferential methods for a variety of treatment effects relevant to three‐arm trials. Our results indicate that small‐sample corrections are required for high (0.05) but not low (0.001) values of the intraclass correlation coefficient and their performance can depend on trial design, number of clusters, and the nature of the hypothesis being tested. The Satterthwaite correction generally performed best at an ICC of 0.05 with a nominal type I error rate for single‐period trials, and in trials with repeated measures type I error rates were between 0.04 and 0.06. Restricted randomization methods produce little benefit in trials with repeated measures but in trials with single post‐intervention design can provide relatively large gains in power when compared to the most unbalanced possible allocations. Matched‐group randomization improves power but is not as effective as covariate‐constrained randomization. For model‐based analysis, adjusting for fewer covariates than were used in a restricted randomization process under any design can produce non‐nominal type I error rates and reductions in power. Where comparisons to two‐arm cluster trials are possible, the performance of the methods is qualitatively very similar.

show abstract

“…3 ) could be address in priority to impact specific patient-reported outcomes. Clinical implications of the use of biomechanical markers in conservative care are detailed in the original RCT [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…This study was a secondary analysis of baseline data from a randomized controlled trial (RCT; ISRCTN16152290) evaluating the clinical utility of a knee kinesiography assessment as part of knee OA patients’ care pathway. Details on participant recruitment and enrollment are documented elsewhere [ 31 ]. Briefly, patients seeking care for knee pain were recruited in primary care clinics in the Province of Quebec (Canada).…”

Section: Methodsmentioning

confidence: 99%

Biomechanical markers associations with pain, symptoms, and disability compared to radiographic severity in knee osteoarthritis patients: a secondary analysis from a cluster randomized controlled trial

Bensalma

Hagemeister

Cagnin

et al. 2022

BMC Musculoskelet Disord

Self Cite

View full text Add to dashboard Cite

Background Conventional radiography is commonly used to diagnose knee osteoarthritis (OA), but also to guide clinical decision-making, despite a well-established discordance between radiographic severity and patient symptoms. The incidence and progression of OA is driven, in part, by biomechanical markers. Therefore, these dynamic markers may be a good metric of functional status and actionable targets for clinicians when developing conservative treatment plans. The aim of this study was to assess the associations between biomechanical markers and self-reported knee function compared to radiographic severity. Methods This was a secondary analysis of data from a randomized controlled trial (RCT) conducted in primary care clinics with knee OA participants. Correlation coefficients (canonical (ρ) and structural (Corr)) were assessed between the Knee Injury and Osteoarthritis Outcome Score (KOOS) and both, radiographic OA severity using the Kellgren-Lawrence grade, and three-dimensional biomechanical markers quantified by a knee kinesiography exam. Significant differences between coefficients were assessed using Fischer’s z-transformation method to compare correlations from dependent samples. Results KOOS and biomechanical data were significantly more associated than KOOS and X-ray grading (ρ: 0.41 vs 0.20; p < 0.001). Structural correlation (Corr) between KOOS and X-ray grade was 0.202 (4% of variance explained), while individual biomechanical markers, such as the flexion during loading, explained up to 14% of KOOS variance (i.e., Corr2). Biomechanical markers showed the strongest associations with Pain and Activity of Daily Living KOOS subscales (both > 36% variance explained), while X-ray grading was most associated with Symptoms subscale (21% explained; all p ≤ 0.001). Conclusions Knee biomechanical markers are associated with patient-reported knee function to a greater extent than X-ray grading, but both provide complementary information in the assessment of OA patients. Understanding how dynamic markers relate to function compared to radiographic severity is a valuable step towards precision medicine, allowing clinicians to refine and tailor therapeutic measures by prioritizing and targeting modifiable biomechanical markers linked to pain and function. Trial registration Original RCT was approved by the Research Ethics Boards of École de technologie supérieure (H20150505) and Centre hospitalier de l’Université de Montréal (CHUM-CE.14.339), first registered at https://www.isrctn.com/ (ID-ISRCTN16152290) on May 27, 2015.

show abstract

A multi-arm cluster randomized clinical trial of the use of knee kinesiography in the management of osteoarthritis patients in a primary care setting

Cited by 13 publications

References 44 publications

3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

Design and analysis of three‐arm parallel cluster randomized trials with small numbers of clusters

Biomechanical markers associations with pain, symptoms, and disability compared to radiographic severity in knee osteoarthritis patients: a secondary analysis from a cluster randomized controlled trial

Contact Info

Product

Resources

About