Mindful Walking in Knee Osteoarthritis: A Feasibility Randomized Controlled Trial

Kumar, Deepak; Costello, K.E.; Shah, Nina; Neogi, Tuhina; Lee, S.; Rose, Michael; Borrelli, Belinda; Messier, Stephen P.; LaValley, Michael P.

doi:10.1016/j.joca.2022.02.087

Cited by 4 publications

(2 citation statements)

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“…Lower gait complexity indicates reduced adaptability of neuromotor control to external perturbations and may reflect increased attention to pain and has been found in persons with OA who have knee buckling episodes 39,40 . Interventions to increase gait adaptability or to reduce attention to pain (e.g., mindfulness meditation) could increase gait complexity in people with knee OA [41][42][43] .…”

Section: Discussionmentioning

confidence: 99%

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Felson

Jafarzadeh

Kolachalama

et al. 2022

Sci Rep

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Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64–1.00) and aOR 0.79 (0.66–0.95), respectively].

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Section: Discussionmentioning

confidence: 99%

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Felson

Jafarzadeh

Kolachalama

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Both greater variability and lower complexity are considered detrimental gait adaptations and are associated with worse mobility outcomes including greater fall risk [25,26,34] .Lower gait complexity indicates reduced adaptability of neuromotor control to external perturbations and may re ect increased attention to pain and has been found in persons with OA who have knee buckling episodes [35] [36] . Interventions to increase gait adaptability or to reduce attention to pain (e.g., mindfulness meditation) could increase gait complexity in people with knee OA [37][38][39] .…”

Section: Discussionmentioning

confidence: 99%

Gait Alterations Associated With Mild Unilateral Knee Pain During Walking: a Machine Learning Approach Using Wearable Sensors

Felson

Jafarzadeh

Kolachalama

et al. 2022

Preprint

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Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis(OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-meter walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain.Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR=0.80 (0.64-1.00) and aOR=0.79(0.66-0.95), respectively].

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Exercise for osteoarthritis of the knee

Lawford,

Hall,

Hinman

et al. 2024

Cochrane Database of Systematic Reviews

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Mindful Walking in Knee Osteoarthritis: A Feasibility Randomized Controlled Trial

Cited by 4 publications

References 0 publications

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Gait Alterations Associated With Mild Unilateral Knee Pain During Walking: a Machine Learning Approach Using Wearable Sensors

Exercise for osteoarthritis of the knee

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