Functional capacity assessment plays a core role in the preoperative evaluation. The Duke Activity Status Index (DASI) and the 6-minute walk test (6MWT) are 2 methods that have demonstrated the ability to evaluate functional capacity and predict perioperative outcomes. Smartphones offer a novel method to facilitate functional capacity assessment as they can easily administer a survey and accelerometers can track patient activity during a 6MWT. We developed a smartphone application to administer a 6MWT and DASI survey and performed a pilot study to evaluate the accuracy of a smartphone-based functional capacity tool in our Anesthesia and Perioperative Medicine Clinic.
Using the Apple ResearchKit software platform, we developed an application that administers a DASI survey and 6MWT on an iOS smartphone. The DASI was presented to the patient 1 question on the screen at a time and the application calculated the DASI score and estimated peak oxygen uptake (Vo2). The 6MWT used the CMPedometer class from Apple’s core motion facility to retrieve accelerometer data collected from the device’s motion coprocessor to estimate steps walked. Smartphone estimated steps were compared to a research-grade pedometer using the intraclass correlation coefficient (ICC). Distance walked was directly measured during the 6MWT and we performed a multivariable linear regression with biometric variables to create a distance estimation algorithm to estimate distance walked from the number of steps recorded by the application.
Seventy-eight patients were enrolled in the study and completed the protocol. Steps measured by the smartphone application as compared to the pedometer demonstrated moderate agreement with an ICC (95% CI) of 0.87 (0.79–0.92; P = .0001). The variables in the distance estimation algorithm included (β coefficient [slope], 95% CI) steps walked (0.43, 0.29–0.57; P < .001), stride length (0.38, 0.22–0.53; P < .001), age in years (−1.90, −3.06 to −0.75; P = .002), and body mass index (−2.59, −5.13 to −0.06; P = .045). The overall model fit was R2 = 0.72, which indicates a moderate level of goodness of fit and explains 72% of the variation of distance walked during a 6MWT.
Our pilot study demonstrated that a smartphone-based functional capacity assessment is feasible using the DASI and 6MWT. The DASI was easily completed by patients and the application clearly presented the results of the DASI to providers. Our application measured steps walked during a 6MWT moderately well in a preoperative patient population; however, future studies are needed to improve the smartphone application’s step-counting accuracy and distance estimation algorithm.