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Research

CAN RUNNING BE RELIABLY QUANTIFIED IN PATIENT ACTIVITY MONITORING?

European Orthopaedic Research Society (EORS) 2015, Annual Conference, 2–4 September 2015. Part 2.



Abstract

Background

To complement subjective patient-reported outcome measures, objective assessments are needed. Activity is an objective clinical outcome which can be measured with wearable activity monitors (AM). AM's have been validated and used in joint arthroplasty patients to count postures, walking or transfers. However, for demanding patients such as after sports injury, running is an important activity to quantify. A new AM algorithm to distinguish walking from running is trialed in this validation study.

Methods

Test subjects (n=9) performed walking and running bouts of 30s duration on a treadmill at fixed speeds (walking: 3, 4, 5, 7km/h, running: 5, 7, 9, 12, 15km/h) and individually preferred speeds (slow, normal, fast, maximum, walk/run transition). Flat and inclined surfaces (8%, 16%), different footwear (soft, hard, barefoot) and running styles (hind/fore-foot) were tested. An AM (3D accelerometer) was worn on the lateral thigh. Previously validated algorithms to classify all gait as walking were adapted to differentiate running from walking, the main criterium being vertical acceleration peaks exceeding 2g within each subsequent 2s-interval. Independently annotated video observation served as reference.

Results

A total of 312 events had to be classified. Walking bouts (162) were correctly identified in 158 cases resulting in 97.5% detection accuracy. Running bouts (150) were correctly identified in 146 cases (97.3%). In 8 walking bouts (5.0%), an additional running event was falsely detected. These happened at 7km/h and maximum (>8.6km/h) walking speed and during continuous walk/run transitions at individual transition speeds. In 12 running bouts (8.2%), an additional walking event was falsely detected. These happened during slow running (<7km/h). Timing event duration and step counts were >95% accurate.

Conclusions

Thigh-worn AM and a simple algorithm can distinguish walking from running at high accuracy and thus can serve doctors, therapists or coaches to objectify outcomes, decisions about effective and safe exercise intensities or return-to-play.