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Currently used clinical assessments for physical function do not objectivelyquantify daily activities in routine living. Wearable activity monitors enableobjective measurement of routine daily activities, but do not map to clinicallymeasured physical performance measures. We represent physical function as adaily activity profile derived from minute-level activity data obtained via awearable activity monitor. We construct daily activity profiles representingaverage time spent in a set of activity classes over consecutive days using theOsteoarthritis Initiative (OAI) data. Using the daily activity profile asinput, we trained statistical models that classify subjects into quartiles ofobjective measurements of physical function as measured via the 400m walk test,the 20m walk test and 5 times sit stand test. We evaluated model performance onheld out data from the same calendar year as that used to train the models aswell as on activity data two years into the future. The daily activity profilepredicts physical performance as measured via clinical assessments. Using heldout data, the AUC obtained in classifying performance values in the 1stquartile was 0.79, 0.78 and 0.72, for the 400m walk, the 20m walk and 5 timessit stand tests. For classifying performance values in the 4th quartile, theAUC obtained was 0.77, 0.66 and 0.73 respectively. Evaluated on data from twoyears into the future, for the 20m pace test and the 5 times sit stand tests,the highest AUC obtained was 0.77 and 0.68 for the 1st quartile and 0.75 and0.70 for the 4th quartile respectively. We can construct activity profilesrepresenting actual physical function as demonstrated by the relationshipbetween the activity profiles and the clinically measured physical performancemeasures. Measurement of physical performance via the activity profile asdescribed can enable remote functional monitoring of patients.

Monitoring physical function in patients with knee osteoarthritis using data from wearable activity monitors