Got Knee Pain? A Personalized Walking Tweak Could Take the Pressure Off
Key Takeaways
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A small clinical trial tested personalized gait retraining in 68 adults with ongoing inner-knee discomfort.
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Researchers used motion analysis to identify whether a slight toe-in or toe-out adjustment reduced knee loading for each person.
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After one year, the personalized group reported greater improvement in knee comfort than a sham-training group.
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MRI results also suggested slower change in one marker linked to cartilage quality.
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This is not a DIY tip: the helpful adjustment differed from person to person, and the wrong change may increase knee stress.
The Way You Walk Shapes Knee Load
Every step sends force through the knee. For many people, more of that load passes through the inner part of the joint, and over years, repeated mechanical stress can affect comfort and mobility.
Researchers have long known that changing foot angle can redistribute some of that pressure. The problem is that there is no universally “correct” way to point your toes. A position that lowers load for one person may do little—or work against them—for someone else.
That is why this trial focused on personalization rather than prescribing the same walking change to everyone.
First, Find the Right Adjustment
Participants tested modest toe-in and toe-out positions while researchers measured their movement and knee loading. Only people for whom at least one adjustment lowered inner-knee load continued into the trial.
The 68 participants were then assigned either personalized gait retraining or a sham version that preserved their usual foot angle. Both groups received six weekly training visits, using a wearable device that provided vibration feedback when a walker drifted away from their assigned position.
Afterward, participants practiced the new pattern during daily walking until it became more automatic.
A Small Change, Repeated Often
At the one-year mark, people in the personalized-training group reported larger gains in knee comfort than those in the comparison group. Their average improvement was 2.5 points on a 0-to-10 scale, compared with 1.3 points in the sham group.
Advanced MRI scans also found slower change in one marker associated with cartilage composition, although a second imaging measure did not differ significantly. This does not show that cartilage was rebuilt—or that the approach can preserve every knee over time—but it supports the underlying idea: reducing excess mechanical load may matter.
Why “Personalized” Is the Point
The promising part of this research is not simply “walk with your toes turned in” or “walk with your toes turned out.” It is the recognition that movement mechanics are individual.
The original study used specialized treadmills, motion-capture cameras, and imaging to choose each person’s pattern, so it cannot yet be translated into a universal at-home method. Researchers are exploring whether smartphone video, wearable sensors, and smart-shoe feedback could eventually make this type of assessment more accessible.
For now, persistent knee discomfort is best approached with a qualified physical therapist or clinician who can assess gait, strength, range of motion, footwear, and the broader movement pattern. The larger lesson is encouraging: sometimes improving comfort and resilience is less about doing more and more about finding the movement strategy that fits your body.
References:
- Scott D Uhlrich, Valentina Mazzoli, Amy Silder, Andrea K Finlay, Feliks Kogan, Garry E Gold, Scott L Delp, Gary S Beaupre, Julie A Kolesar. Personalised gait retraining for medial compartment knee OA: a randomised controlled trial. The Lancet Rheumatology, 2025; 7 (10): e708 DOI: 10.1016/S2665-9913(25)00151-1
- Yi Wan, Polly McGuigan, James Bilzon, Logan Wade. Six-week biofeedback gait retraining programme for people with knee OA: A randomised controlled trial. Clinical Biomechanics, 2026; 134: 106776 DOI: 10.1016/j.clinbiomech.2026.106776
- Azza Tayari, Taysir Rezgui, Sami Bennour, Imen Safra. Prediction of knee adduction moment using foot acceleration: Toward personalized gait retraining for knee OA management. Machine Learning with Applications, 2026; 25: 100945 DOI: 10.1016/j.mlwa.2026.100945