The Mobile-Based 6-Minute Walk Test: Usability Study and Algorithm Development and Validation

Abstract

Background: The 6-min walk test (6MWT) is a convenient method for assessing functional capacity in patients with cardiopulmonary conditions. It is usually performed in the context of a hospital clinic and thus requires the involvement of hospital staff and facilities, with their associated costs.

Objective: This study aimed to develop a mobile phone-based system that allows patients to perform the 6MWT in the community.

Methods: We developed 2 algorithms to compute the distance walked during a 6MWT using sensors embedded in a mobile phone. One algorithm makes use of the global positioning system to track the location of the phone when outdoors and hence computes the distance travelled. The other algorithm is meant to be used indoors and exploits the inertial sensors built into the phone to detect U-turns when patients walk back and forth along a corridor of fixed length. We included these algorithms in a mobile phone app, integrated with wireless pulse oximeters and a back-end server. We performed Bland-Altman analysis of the difference between the distances estimated by the phone and by a reference trundle wheel on 49 indoor tests and 30 outdoor tests, with 11 different mobile phones (both Apple iOS and Google Android operating systems). We also assessed usability aspects related to the app in a discussion group with patients and clinicians using a technology acceptance model to guide discussion.

Results: The mean difference between the mobile phone-estimated distances and the reference values was -2.013 m (SD 7.84 m) for the indoor algorithm and -0.80 m (SD 18.56 m) for the outdoor algorithm. The absolute maximum difference was, in both cases, below the clinically significant threshold. A total of 2 pulmonary hypertension patients, 1 cardiologist, 2 physiologists, and 1 nurse took part in the discussion group, where issues arising from the use of the 6MWT in hospital were identified. The app was demonstrated to be usable, and the 2 patients were keen to use it in the long term.

Conclusions: The system described in this paper allows patients to perform the 6MWT at a place of their convenience. In addition, the use of pulse oximetry allows more information to be generated about the patient's health status and, possibly, be more relevant to the real-life impact of their condition. Preliminary assessment has shown that the developed 6MWT app is highly accurate and well accepted by its users. Further tests are needed to assess its clinical value.

Keywords: cardiology; digital signal processing; exercise test; global positioning system; mobile apps; pulmonary hypertension.

Conflict of interest statement

Conflicts of Interest: None declared.

©Dario Salvi, Emma Poffley, Elizabeth Orchard, Lionel Tarassenko. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 03.01.2020.

Figures

Figure 1

Architecture of the 6-min walk test (6MWT) system. It includes 4 scenarios: (a) 6MWT at home, where patients perform a 6MWT in their home setting using their mobile phone with the app and a wireless pulse oximeter; (b) 6MWT in the hospital, where patients perform the test while being observed by a physician and with pulse oximetry data being collected through a tablet app; (c) activity tracking data retrieved by Google Fit or HealthKit transmitted for subsequent analysis; (d) data review performed by a physician through a Web interface.

Figure 2

Example of mobile phone azimuth signal. The first seconds show the calibration phase, after which U-turns are detected when the difference between near angles becomes greater than the set threshold within a short time window.

Figure 3

Example of a positioning trace (in red) retrieved from the mobile phone. The walking man figure indicates the starting point of the test; the flag indicates its end. Comparing the trace with the underlying picture shows that the position is sometimes affected by an error, for example, near tall buildings which reflect the signal or because of trees obscuring the global positioning system satellite’s signal.

Figure 4

Maximum and mean error of the distance estimation versus the sampling period of the localization signal selection_period computed on all available tests. The 5 seconds value minimizes both mean and maximum error.

Figure 5

Screenshots of the patients’ app. (a) Home page, (b) instructions about how to perform the test, (c) connection to the pulse oximeter and baseline measurements at rest, (d) estimation of the distance during walk, (e) total distance estimation and recovery at rest, (f) Borg scale questionnaire.

Figure 6

Screenshots of the server Web interface. (a) The form physiologists fill in when observing a 6-min walk test (6MWT), (b) an example of an outdoor 6MWT results (heart rate and oxygen saturation charts are omitted).

Figure 7

Bland-Altman plot of the difference between the estimated distance walked and the absolute distance. The Shapiro-Wilk test confirms the normality of the data (0.91).

Figure 8

Bland-Altman plot of the difference between the estimated distance walked and the ground truth. The Shapiro-Wilk test confirms the normality of the data (0.97).