Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels

Abstract

Objectives: Objective measurements using built-in smartphone sensors that can measure physical activity/inactivity in daily working life have the potential to provide a new approach to assessing workers' health effects. The aim of this study was to elucidate the characteristics and reliability of built-in step counting sensors on smartphones for development of an easy-to-use objective measurement tool that can be applied in ergonomics or epidemiological research. Methods: To evaluate the reliability of step counting sensors embedded in seven major smartphone models, the 6-minute walk test was conducted and the following analyses of sensor precision and accuracy were performed: 1) relationship between actual step count and step count detected by sensors, 2) reliability between smartphones of the same model, and 3) false detection rates when sitting during office work, while riding the subway, and driving. Results: On five of the seven models, the inter-class correlations coefficient (ICC (3,1)) showed high reliability with a range of 0.956-0.993. The other two models, however, had ranges of 0.443-0.504 and the relative error ratios of the sensor-detected step count to the actual step count were ±48.7%-49.4%. The level of agreement between the same models was ICC (3,1): 0.992-0.998. The false detection rates differed between the sitting conditions. Conclusions: These results suggest the need for appropriate regulation of step counts measured by sensors, through means such as correction or calibration with a predictive model formula, in order to obtain the highly reliable measurement results that are sought in scientific investigation.

Can self-monitoring mobile health apps reduce sedentary behavior? A randomized controlled trial

“…The reliability of the data obtained from the app was confirmed by the test of the false detection rate during various sitting situations. 11 The app also provides useful tips on sedentary-related issues, a questionnaire for self-reported sedentary time, and real-time feedback of users' current activity levels.…”

“…Although some studies examined the availability of smartphone measurements for academic research, the data compatibility with respect to the measurement of SB is not guaranteed between a smartphone and the specific wearable devices. 11,12 Finally, our participants were all adult office-workers. The intervention could be varied by selecting more heterogenous participants by changing, for example, selection criteria related to age and working environment.…”

“…It is important to note here that the smartphones used in everyday life around the world can track users' daily activity (even if users are not aware of it) using multiple built-in sensors, such as an accelerometer, a gyro sensor, and a GPS, and can also collect self-reported data using questionnaires. [9][10][11][12] However, the objective measurements taken by smartphones also have particular biases that must be solved. Because the technology assumes that subjects place their phones in a chest pocket, for example, placing the phone on a charger or in a bag may cause it to overestimate sedentary time.…”

“…Because the technology assumes that subjects place their phones in a chest pocket, for example, placing the phone on a charger or in a bag may cause it to overestimate sedentary time. 11 With subjective and objective measurements demonstrating many merits and demerits, it is better to perform composite measurements when possible.…”

Identifying characteristics of indicators of sedentary behavior using objective measurements

“…The reliability of the data obtained from the app was confirmed by the test of the false detection rate during various sitting situations. 20…”